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Sample records for brain classification reveals

  1. Brain classification reveals the right cerebellum as the best biomarker of dyslexia

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    Demonet Jean

    2009-06-01

    Full Text Available Abstract Background Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel, because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique. Results The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38 falling outside of the control group (N = 39 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD grey matter volumes than controls and subjects with higher cerebellar declive (HCD grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic

  2. Classification of Cortical Brain Malformations

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    J Gordon Millichap

    2008-03-01

    Full Text Available Clinical, radiological, and genetic classifications of 113 cases of malformations of cortical development (MCD were evaluated at the Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands.

  3. Transcriptome classification reveals molecular subtypes in psoriasis

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    Ainali Chrysanthi

    2012-09-01

    Full Text Available Abstract Background Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. Results We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. Conclusion Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.

  4. Local Kernel for Brains Classification in Schizophrenia

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    Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

    In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

  5. Simple Fully Automated Group Classification on Brain fMRI

    International Nuclear Information System (INIS)

    Honorio, J.; Goldstein, R.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-01-01

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  6. Simple Fully Automated Group Classification on Brain fMRI

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    Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-04-14

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  7. Brain tumor classification using Probabilistic Neural Network

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Baghdad, Iraq. 1sami.hasan@coie.nahrainuniv.edu.iq ... The Human brain is the most amazing and complex thing known in the world [1]. ... achieved using gray level co-occurrence matrix (GLCM). This work is aimed to ...

  8. A Dirichlet process mixture model for brain MRI tissue classification.

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    Ferreira da Silva, Adelino R

    2007-04-01

    Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.

  9. Comparison of four classification methods for brain-computer interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.

    2011-01-01

    Roč. 21, č. 2 (2011), s. 101-115 ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

  10. The brain MRI classification problem from wavelets perspective

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    Bendib, Mohamed M.; Merouani, Hayet F.; Diaba, Fatma

    2015-02-01

    Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

  11. Unsupervised classification of operator workload from brain signals

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    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  12. Modern radiosurgical and endovascular classification schemes for brain arteriovenous malformations.

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    Tayebi Meybodi, Ali; Lawton, Michael T

    2018-05-04

    Stereotactic radiosurgery (SRS) and endovascular techniques are commonly used for treating brain arteriovenous malformations (bAVMs). They are usually used as ancillary techniques to microsurgery but may also be used as solitary treatment options. Careful patient selection requires a clear estimate of the treatment efficacy and complication rates for the individual patient. As such, classification schemes are an essential part of patient selection paradigm for each treatment modality. While the Spetzler-Martin grading system and its subsequent modifications are commonly used for microsurgical outcome prediction for bAVMs, the same system(s) may not be easily applicable to SRS and endovascular therapy. Several radiosurgical- and endovascular-based grading scales have been proposed for bAVMs. However, a comprehensive review of these systems including a discussion on their relative advantages and disadvantages is missing. This paper is dedicated to modern classification schemes designed for SRS and endovascular techniques.

  13. Application of machine learning on brain cancer multiclass classification

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    Panca, V.; Rustam, Z.

    2017-07-01

    Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

  14. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

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    Richard eBeare

    2016-03-01

    Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

  15. Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging

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    Schwedt, Todd J.; Chong, Catherine D.; Wu, Teresa; Gaw, Nathan; Fu, Yinlin; Li, Jing

    2015-01-01

    Background The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: 1) that accurately identify individuals as having chronic vs. episodic migraine vs. being a healthy control; and 2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. Methods Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Ten-fold cross validation assessed classification accuracy within each of the ten runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5–15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. Results Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/− 11 years. Average classifier accuracies were: a) 68% for migraine (episodic + chronic) vs. healthy controls; b) 67.2% for episodic migraine vs. healthy controls; c) 86.3% for chronic

  16. Classification of types of stuttering symptoms based on brain activity.

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    Jing Jiang

    Full Text Available Among the non-fluencies seen in speech, some are more typical (MT of stuttering speakers, whereas others are less typical (LT and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT whole-word repetitions (WWR should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type.

  17. Classification of Types of Stuttering Symptoms Based on Brain Activity

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    Jiang, Jing; Lu, Chunming; Peng, Danling; Zhu, Chaozhe; Howell, Peter

    2012-01-01

    Among the non-fluencies seen in speech, some are more typical (MT) of stuttering speakers, whereas others are less typical (LT) and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT) whole-word repetitions (WWR) should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type. PMID:22761887

  18. Diffusion Tensor Tractography Reveals Disrupted Structural Connectivity during Brain Aging

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    Lin, Lan; Tian, Miao; Wang, Qi; Wu, Shuicai

    2017-10-01

    Brain aging is one of the most crucial biological processes that entail many physical, biological, chemical, and psychological changes, and also a major risk factor for most common neurodegenerative diseases. To improve the quality of life for the elderly, it is important to understand how the brain is changed during the normal aging process. We compared diffusion tensor imaging (DTI)-based brain networks in a cohort of 75 healthy old subjects by using graph theory metrics to describe the anatomical networks and connectivity patterns, and network-based statistic (NBS) analysis was used to identify pairs of regions with altered structural connectivity. The NBS analysis revealed a significant network comprising nine distinct fiber bundles linking 10 different brain regions showed altered white matter structures in young-old group compare with middle-aged group (p < .05, family-wise error-corrected). Our results might guide future studies and help to gain a better understanding of brain aging.

  19. Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

    NARCIS (Netherlands)

    Deco, Gustavo; Cabral, Joana; Saenger, Victor M; Boly, Melanie; Tagliazucchi, Enzo; Laufs, Helmut; Van Someren, Eus; Jobst, Beatrice; Stevner, Angus; Kringelbach, Morten L

    2017-01-01

    Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the

  20. Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

    NARCIS (Netherlands)

    Deco, Gustavo; Cabral, Joana; Saenger, Victor M; Boly, Melanie; Tagliazucchi, Enzo; Laufs, Helmut; Van Someren, Eus; Jobst, Beatrice M; Stevner, Angus B A; Kringelbach, Morten L

    2018-01-01

    Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the

  1. Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty

    NARCIS (Netherlands)

    Beekhuizen, J.; Clarke, K.C.

    2010-01-01

    The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by

  2. Classification of CT brain images based on deep learning networks.

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    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  3. Embedding filtering criteria into a wrapper marker selection method for brain tumor classification: an application on metabolic peak area ratios

    International Nuclear Information System (INIS)

    Kounelakis, M G; Zervakis, M E; Giakos, G C; Postma, G J; Buydens, L M C; Kotsiakis, X

    2011-01-01

    The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fisher's criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors

  4. Real-time fMRI using brain-state classification.

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    LaConte, Stephen M; Peltier, Scott J; Hu, Xiaoping P

    2007-10-01

    We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed approximately 80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness. Wiley-Liss, Inc.

  5. Digital tissue and what it may reveal about the brain.

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    Morgan, Josh L; Lichtman, Jeff W

    2017-10-30

    Imaging as a means of scientific data storage has evolved rapidly over the past century from hand drawings, to photography, to digital images. Only recently can sufficiently large datasets be acquired, stored, and processed such that tissue digitization can actually reveal more than direct observation of tissue. One field where this transformation is occurring is connectomics: the mapping of neural connections in large volumes of digitized brain tissue.

  6. Symbolic joint entropy reveals the coupling of various brain regions

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    Ma, Xiaofei; Huang, Xiaolin; Du, Sidan; Liu, Hongxing; Ning, Xinbao

    2018-01-01

    The convergence and divergence of oscillatory behavior of different brain regions are very important for the procedure of information processing. Measurements of coupling or correlation are very useful to study the difference of brain activities. In this study, EEG signals were collected from ten subjects under two conditions, i.e. eyes closed state and idle with eyes open. We propose a nonlinear algorithm, symbolic joint entropy, to compare the coupling strength among the frontal, temporal, parietal and occipital lobes and between two different states. Instead of decomposing the EEG into different frequency bands (theta, alpha, beta, gamma etc.), the novel algorithm is to investigate the coupling from the entire spectrum of brain wave activities above 4Hz. The coupling coefficients in two states with different time delay steps are compared and the group statistics are presented as well. We find that the coupling coefficient of eyes open state with delay consistently lower than that of eyes close state across the group except for one subject, whereas the results without delay are not consistent. The differences between two brain states with non-zero delay can reveal the intrinsic inter-region coupling better. We also use the well-known Hénon map data to validate the algorithm proposed in this paper. The result shows that the method is robust and has a great potential for other physiologic time series.

  7. The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

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    Banan, Rouzbeh; Hartmann, Christian

    2017-03-01

    The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only

  8. ELUCIDATING BRAIN CONNECTIVITY NETWORKS IN MAJOR DEPRESSIVE DISORDER USING CLASSIFICATION-BASED SCORING.

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    Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

    2014-04-01

    Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness , a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.

  9. Behavioral state classification in epileptic brain using intracranial electrophysiology

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    Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

    2017-04-01

    Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

  10. Classification of MR brain images by combination of multi-CNNs for AD diagnosis

    Science.gov (United States)

    Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

    2017-07-01

    Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

  11. Model sparsity and brain pattern interpretation of classification models in neuroimaging

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Churchill, Nathan W

    2012-01-01

    Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model. In this ...

  12. Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

    Full Text Available High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

  13. EEG classification of emotions using emotion-specific brain functional network.

    Science.gov (United States)

    Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

    2015-08-01

    The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

  14. Recursive partitioning analysis (RPA) classification predicts survival in patients with brain metastases from sarcoma.

    Science.gov (United States)

    Grossman, Rachel; Ram, Zvi

    2014-12-01

    Sarcoma rarely metastasizes to the brain, and there are no specific treatment guidelines for these tumors. The recursive partitioning analysis (RPA) classification is a well-established prognostic scale used in many malignancies. In this study we assessed the clinical characteristics of metastatic sarcoma to the brain and the validity of the RPA classification system in a subset of 21 patients who underwent surgical resection of metastatic sarcoma to the brain We retrospectively analyzed the medical, radiological, surgical, pathological, and follow-up clinical records of 21 patients who were operated for metastatic sarcoma to the brain between 1996 and 2012. Gliosarcomas, sarcomas of the head and neck with local extension into the brain, and metastatic sarcomas to the spine were excluded from this reported series. The patients' mean age was 49.6 ± 14.2 years (range, 25-75 years) at the time of diagnosis. Sixteen patients had a known history of systemic sarcoma, mostly in the extremities, and had previously received systemic chemotherapy and radiation therapy for their primary tumor. The mean maximal tumor diameter in the brain was 4.9 ± 1.7 cm (range 1.7-7.2 cm). The group's median preoperative Karnofsky Performance Scale was 80, with 14 patients presenting with Karnofsky Performance Scale of 70 or greater. The median overall survival was 7 months (range 0.2-204 months). The median survival time stratified by the Radiation Therapy Oncology Group RPA classes were 31, 7, and 2 months for RPA class I, II, and III, respectively (P = 0.0001). This analysis is the first to support the prognostic utility of the Radiation Therapy Oncology Group RPA classification for sarcoma brain metastases and may be used as a treatment guideline tool in this rare disease. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  16. Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

    Science.gov (United States)

    Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

    2015-09-01

    The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

  17. An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

    Directory of Open Access Journals (Sweden)

    Muhammad Faisal Siddiqui

    Full Text Available A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT, principal component analysis (PCA, and least squares support vector machine (LS-SVM are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%. Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities

  18. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

    DEFF Research Database (Denmark)

    Hoadley, Katherine A; Yau, Christina; Wolf, Denise M

    2014-01-01

    Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform...... on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset...

  19. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

    Science.gov (United States)

    Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

    2015-11-01

    One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    Science.gov (United States)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  1. Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

    Science.gov (United States)

    Damayanti, A.; Werdiningsih, I.

    2018-03-01

    The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

  2. Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans.

    Science.gov (United States)

    Smirnov, Dmitry; Lachat, Fanny; Peltola, Tomi; Lahnakoski, Juha M; Koistinen, Olli-Pekka; Glerean, Enrico; Vehtari, Aki; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

    2017-01-01

    Seeing an action may activate the corresponding action motor code in the observer. It remains unresolved whether seeing and performing an action activates similar action-specific motor codes in the observer and the actor. We used novel hyperclassification approach to reveal shared brain activation signatures of action execution and observation in interacting human subjects. In the first experiment, two "actors" performed four types of hand actions while their haemodynamic brain activations were measured with 3-T functional magnetic resonance imaging (fMRI). The actions were videotaped and shown to 15 "observers" during a second fMRI experiment. Eleven observers saw the videos of one actor, and the remaining four observers saw the videos of the other actor. In a control fMRI experiment, one of the actors performed actions with closed eyes, and five new observers viewed these actions. Bayesian canonical correlation analysis was applied to functionally realign observers' and actors' fMRI data. Hyperclassification of the seen actions was performed with Bayesian logistic regression trained on actors' data and tested with observers' data. Without the functional realignment, between-subjects accuracy was at chance level. With the realignment, the accuracy increased on average by 15 percentage points, exceeding both the chance level and the accuracy without functional realignment. The highest accuracies were observed in occipital, parietal and premotor cortices. Hyperclassification exceeded chance level also when the actor did not see her own actions. We conclude that the functional brain activation signatures underlying action execution and observation are partly shared, yet these activation signatures may be anatomically misaligned across individuals.

  3. Classification of brain compartments and head injury lesions by neural networks applied to MRI

    International Nuclear Information System (INIS)

    Kischell, E.R.; Kehtarnavaz, N.; Hillman, G.R.; Levin, H.; Lilly, M.; Kent, T.A.

    1995-01-01

    An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and 'unknown'. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network. (orig.)

  4. Classification of brain compartments and head injury lesions by neural networks applied to MRI

    Energy Technology Data Exchange (ETDEWEB)

    Kischell, E R [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Kehtarnavaz, N [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Hillman, G R [Dept. of Pharmacology, Univ. of Texas Medical Branch, Galveston, TX (United States); Levin, H [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Lilly, M [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Kent, T A [Dept. of Neurology and Psychiatry, Univ. of Texas Medical Branch, Galveston, TX (United States)

    1995-10-01

    An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and `unknown`. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician`s report used to train the neural network. (orig.)

  5. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  6. The musical brain: brain waves reveal the neurophysiological basis of musicality in human subjects.

    Science.gov (United States)

    Tervaniemi, M; Ilvonen, T; Karma, K; Alho, K; Näätänen, R

    1997-04-18

    To reveal neurophysiological prerequisites of musicality, auditory event-related potentials (ERPs) were recorded from musical and non-musical subjects, musicality being here defined as the ability to temporally structure auditory information. Instructed to read a book and to ignore sounds, subjects were presented with a repetitive sound pattern with occasional changes in its temporal structure. The mismatch negativity (MMN) component of ERPs, indexing the cortical preattentive detection of change in these stimulus patterns, was larger in amplitude in musical than non-musical subjects. This amplitude enhancement, indicating more accurate sensory memory function in musical subjects, suggests that even the cognitive component of musicality, traditionally regarded as depending on attention-related brain processes, in fact, is based on neural mechanisms present already at the preattentive level.

  7. A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

    Science.gov (United States)

    van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

    2017-05-01

    The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

    Directory of Open Access Journals (Sweden)

    Ying Mei

    2017-06-01

    Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

  9. Classification of brain tumors by means of proton nuclear magnetic resonance (NMR) spectroscopy

    International Nuclear Information System (INIS)

    Sottile, V.S.; Zanchi, D.E.

    2017-01-01

    In the present work, at the request of health professionals, a computer application named “ViDa” was developed. The aim of this study is to differentiate brain lesions according to whether or not they are tumors, and their subsequent classification into different tumor types using magnetic resonance spectroscopy (SVS) with an echo time of 30 milliseconds. For this development, different areas of knowledge were integrated, among which are Artificial intelligence, physics, programming, physiopathology, images in medicine, among others. Biomedical imaging can be divided into two stages: the pre-processing, performed by the resonator, and post-processing software, performed by ViDa, for the interpretation of the data. This application is included within the Medical Informatics area, as it provides assistance for clinical decision making. The role of the biomedical engineer is fulfilled by developing a health technology in response to a manifested real-life problem. The tool developed shows promising results achieving a 100% Sensitivity, 73% Specificity, 77% Positive Predictive Value and 100% Negative Predictive Value reported in 21 cases tested. The correct classifications of the tumor’s origin reach 70%, the classification of non-astrocytic lesions achieves 67% of correct classifications in that the gradation of astrocytomas achieves a 57% of gradations that agree with biopsies and 43% of slight errors. It was possible to develop an application of assistance to the diagnosis, which together with others medical tests, will make it possible to sharpen the diagnoses of brain tumors. (authors) [es

  10. Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

    Science.gov (United States)

    Kragel, Philip A; Labar, Kevin S

    2013-08-01

    Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  11. Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

    Science.gov (United States)

    Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

    2012-04-01

    A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

  12. Study Reveals Brain Biology behind Self-Control

    Science.gov (United States)

    Sparks, Sarah D.

    2011-01-01

    A new neuroscience twist on a classic psychology study offers some clues to what makes one student able to buckle down for hours of homework before a test while his classmates party. The study published in the September 2011 edition of "Proceedings of the National Academy of Science," suggests environmental cues may "hijack" the brain's mechanisms…

  13. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  14. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    Science.gov (United States)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  15. Fusing in vivo and ex vivo NMR sources of information for brain tumor classification

    International Nuclear Information System (INIS)

    Croitor-Sava, A R; Laudadio, T; Sima, D M; Van Huffel, S; Martinez-Bisbal, M C; Celda, B; Piquer, J; Heerschap, A

    2011-01-01

    In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results by means of extensive simulation and in vivo studies. Special attention is drawn to the possibility of considering HR-MAS data as a complementary dataset when dealing with a lack of MRSI data needed to build a classifier. Results show that HR-MAS information can have added value in the process of classifying MRSI data

  16. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  17. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune

    2014-01-01

    Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group......Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the latent...... sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model...

  18. Predict or classify: The deceptive role of time-locking in brain signal classification

    Science.gov (United States)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  19. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  20. Classification of brain tumours using short echo time 1H MR spectra

    Science.gov (United States)

    Devos, A.; Lukas, L.; Suykens, J. A. K.; Vanhamme, L.; Tate, A. R.; Howe, F. A.; Majós, C.; Moreno-Torres, A.; van der Graaf, M.; Arús, C.; Van Huffel, S.

    2004-09-01

    The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas ( n = 87), meningiomas ( n = 57), metastases ( n = 39), and astrocytomas grade II ( n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

  1. PET imaging reveals brain functional changes in internet gaming disorder

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Mei; Zhang, Ying; Du, Fenglei; Hou, Haifeng; Chao, Fangfang; Zhang, Hong [The Second Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou (China); Chen, Qiaozhen [The Second Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Psychiatry, Hangzhou (China)

    2014-07-15

    Internet gaming disorder is an increasing problem worldwide, resulting in critical academic, social, and occupational impairment. However, the neurobiological mechanism of internet gaming disorder remains unknown. The aim of this study is to assess brain dopamine D{sub 2} (D{sub 2})/Serotonin 2A (5-HT{sub 2A}) receptor function and glucose metabolism in the same subjects by positron emission tomography (PET) imaging approach, and investigate whether the correlation exists between D{sub 2} receptor and glucose metabolism. Twelve drug-naive adult males who met criteria for internet gaming disorder and 14 matched controls were studied with PET and {sup 11}C-N-methylspiperone ({sup 11}C-NMSP) to assess the availability of D{sub 2}/5-HT{sub 2A} receptors and with {sup 18}F-fluoro-D-glucose ({sup 18}F-FDG) to assess regional brain glucose metabolism, a marker of brain function. {sup 11}C-NMSP and {sup 18}F-FDG PET imaging data were acquired in the same individuals under both resting and internet gaming task states. In internet gaming disorder subjects, a significant decrease in glucose metabolism was observed in the prefrontal, temporal, and limbic systems. Dysregulation of D{sub 2} receptors was observed in the striatum, and was correlated to years of overuse. A low level of D{sub 2} receptors in the striatum was significantly associated with decreased glucose metabolism in the orbitofrontal cortex. For the first time, we report the evidence that D{sub 2} receptor level is significantly associated with glucose metabolism in the same individuals with internet gaming disorder, which indicates that D{sub 2}/5-HT{sub 2A} receptor-mediated dysregulation of the orbitofrontal cortex could underlie a mechanism for loss of control and compulsive behavior in internet gaming disorder subjects. (orig.)

  2. PET imaging reveals brain functional changes in internet gaming disorder

    International Nuclear Information System (INIS)

    Tian, Mei; Zhang, Ying; Du, Fenglei; Hou, Haifeng; Chao, Fangfang; Zhang, Hong; Chen, Qiaozhen

    2014-01-01

    Internet gaming disorder is an increasing problem worldwide, resulting in critical academic, social, and occupational impairment. However, the neurobiological mechanism of internet gaming disorder remains unknown. The aim of this study is to assess brain dopamine D 2 (D 2 )/Serotonin 2A (5-HT 2A ) receptor function and glucose metabolism in the same subjects by positron emission tomography (PET) imaging approach, and investigate whether the correlation exists between D 2 receptor and glucose metabolism. Twelve drug-naive adult males who met criteria for internet gaming disorder and 14 matched controls were studied with PET and 11 C-N-methylspiperone ( 11 C-NMSP) to assess the availability of D 2 /5-HT 2A receptors and with 18 F-fluoro-D-glucose ( 18 F-FDG) to assess regional brain glucose metabolism, a marker of brain function. 11 C-NMSP and 18 F-FDG PET imaging data were acquired in the same individuals under both resting and internet gaming task states. In internet gaming disorder subjects, a significant decrease in glucose metabolism was observed in the prefrontal, temporal, and limbic systems. Dysregulation of D 2 receptors was observed in the striatum, and was correlated to years of overuse. A low level of D 2 receptors in the striatum was significantly associated with decreased glucose metabolism in the orbitofrontal cortex. For the first time, we report the evidence that D 2 receptor level is significantly associated with glucose metabolism in the same individuals with internet gaming disorder, which indicates that D 2 /5-HT 2A receptor-mediated dysregulation of the orbitofrontal cortex could underlie a mechanism for loss of control and compulsive behavior in internet gaming disorder subjects. (orig.)

  3. Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

    Science.gov (United States)

    Dai, Mengxi; Liu, Shucong; Zhang, Pengju

    2018-01-01

    Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods. PMID:29743934

  4. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  5. Modern classification and outcome predictors of surgery in patients with brain arteriovenous malformations.

    Science.gov (United States)

    Tayebi Meybodi, Ali; Lawton, Michael T

    2018-02-23

    Brain arteriovenous malformations (bAVM) are challenging lesions. Part of this challenge stems from the infinite diversity of these lesions regarding shape, location, anatomy, and physiology. This diversity has called on a variety of treatment modalities for these lesions, of which microsurgical resection prevails as the mainstay of treatment. As such, outcome prediction and managing strategy mainly rely on unraveling the nature of these complex tangles and ways each lesion responds to various therapeutic modalities. This strategy needs the ability to decipher each lesion through accurate and efficient categorization. Therefore, classification schemes are essential parts of treatment planning and outcome prediction. This article summarizes different surgical classification schemes and outcome predictors proposed for bAVMs.

  6. Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

    Science.gov (United States)

    Yu, Pulan

    2012-01-01

    Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

  7. Neuropsychological assessment of individuals with brain tumor: Comparison of approaches used in the classification of impairment

    Directory of Open Access Journals (Sweden)

    Toni Maree Dwan

    2015-03-01

    Full Text Available Approaches to classifying neuropsychological impairment after brain tumor vary according to testing level (individual tests, domains or global index and source of reference (i.e., norms, controls and premorbid functioning. This study aimed to compare rates of impairment according to different classification approaches. Participants were 44 individuals (57% female with a primary brain tumor diagnosis (mean age = 45.6 years and 44 matched control participants (59% female, mean age = 44.5 years. All participants completed a test battery that assesses premorbid IQ (Wechsler Adult Reading Test, attention/processing speed (Digit Span, Trail Making Test A, memory (Hopkins Verbal Learning Test – Revised, Rey-Osterrieth Complex Figure-recall and executive function (Trail Making Test B, Rey-Osterrieth Complex Figure copy, Controlled Oral Word Association Test. Results indicated that across the different sources of reference, 86-93% of participants were classified as impaired at a test-specific level, 61-73% were classified as impaired at a domain-specific level, and 32-50% were classified as impaired at a global level. Rates of impairment did not significantly differ according to source of reference (p>.05; however, at the individual participant level, classification based on estimated premorbid IQ was often inconsistent with classification based on the norms or controls. Participants with brain tumor performed significantly poorer than matched controls on tests of neuropsychological functioning, including executive function (p=.001 and memory (p.05. These results highlight the need to examine individuals’ performance across a multi-faceted neuropsychological test battery to avoid over- or under-estimation of impairment.

  8. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

    algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

  9. Threshold selection for classification of MR brain images by clustering method

    Energy Technology Data Exchange (ETDEWEB)

    Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  10. Classification of brain MRI with big data and deep 3D convolutional neural networks

    Science.gov (United States)

    Wegmayr, Viktor; Aitharaju, Sai; Buhmann, Joachim

    2018-02-01

    Our ever-aging society faces the growing problem of neurodegenerative diseases, in particular dementia. Magnetic Resonance Imaging provides a unique tool for non-invasive investigation of these brain diseases. However, it is extremely difficult for neurologists to identify complex disease patterns from large amounts of three-dimensional images. In contrast, machine learning excels at automatic pattern recognition from large amounts of data. In particular, deep learning has achieved impressive results in image classification. Unfortunately, its application to medical image classification remains difficult. We consider two reasons for this difficulty: First, volumetric medical image data is considerably scarcer than natural images. Second, the complexity of 3D medical images is much higher compared to common 2D images. To address the problem of small data set size, we assemble the largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD). We use more than 20.000 images from subjects of these three classes, which is almost 9x the size of the previously largest data set. The problem of high dimensionality is addressed by using a deep 3D convolutional neural network, which is state-of-the-art in large-scale image classification. We exploit its ability to process the images directly, only with standard preprocessing, but without the need for elaborate feature engineering. Compared to other work, our workflow is considerably simpler, which increases clinical applicability. Accuracy is measured on the ADNI+AIBL data sets, and the independent CADDementia benchmark.

  11. Concussion classification via deep learning using whole-brain white matter fiber strains

    Science.gov (United States)

    Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang

    2018-01-01

    Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828–0.862 vs. 0.690–0.776, and .632+ error of 0.148–0.176 vs. 0.207–0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury. PMID:29795640

  12. Concussion classification via deep learning using whole-brain white matter fiber strains.

    Science.gov (United States)

    Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang; Ji, Songbai

    2018-01-01

    Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.

  13. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

    Full Text Available Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS, 24 Relapsing Remitting (RR, 24 Secondary Progressive (SP, and 17 Primary Progressive (PP along with 26 healthy controls (HC underwent MR examination. T1 and diffusion tensor imaging (DTI were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM combined with Radial Basic Function (RBF kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6% were obtained for binary (HC-CIS, CIS-RR, RR-PP and multi-class (CIS-RR-SP classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7% were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles.

  14. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-04-01

    Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

  15. Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

    Directory of Open Access Journals (Sweden)

    Jenessa Lancaster

    2018-02-01

    Full Text Available Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years we trained support vector machines to (i distinguish between young (<22 years and old (>50 years brains (classification and (ii predict chronological age (regression. We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years. Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm. For predicting chronological age, a mean absolute error (MAE of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm. This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian

  16. A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

    Science.gov (United States)

    Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

    2018-01-01

    The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

  17. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

  18. Educational games for brain health: revealing their unexplored potential through a neurocognitive approach

    Directory of Open Access Journals (Sweden)

    Patrick eFissler

    2015-07-01

    Full Text Available Educational games link the motivational nature of games with learning of knowledge and skills. Here, we go beyond effects on these learning outcomes. We review two lines of evidence which indicate the currently unexplored potential of educational games to promote brain health: First, gaming with specific neurocognitive demands (e.g., executive control, and second, educational learning experiences (e.g., studying foreign languages improve brain health markers. These markers include cognitive ability, brain function, and brain structure. As educational games allow the combination of specific neurocognitive demands with educational learning experiences, they seem to be optimally suited for promoting brain health. We propose a neurocognitive approach to reveal this unexplored potential of educational games in future research.

  19. Deep brain stimulation reveals emotional impact processing in ventromedial prefrontal cortex

    DEFF Research Database (Denmark)

    Gjedde, Albert; Geday, Jacob

    2009-01-01

    We tested the hypothesis that modulation of monoaminergic tone with deep-brain stimulation (DBS) of subthalamic nucleus would reveal a site of reactivity in the ventromedial prefrontal cortex that we previously identified by modulating serotonergic and noradrenergic mechanisms by blocking serotonin......-noradrenaline reuptake sites. We tested the hypothesis in patients with Parkinson's disease in whom we had measured the changes of blood flow everywhere in the brain associated with the deep brain stimulation of the subthalamic nucleus. We determined the emotional reactivity of the patients as the average impact...

  20. Amplitude-modulated stimuli reveal auditory-visual interactions in brain activity and brain connectivity.

    Science.gov (United States)

    Laing, Mark; Rees, Adrian; Vuong, Quoc C

    2015-01-01

    The temporal congruence between auditory and visual signals coming from the same source can be a powerful means by which the brain integrates information from different senses. To investigate how the brain uses temporal information to integrate auditory and visual information from continuous yet unfamiliar stimuli, we used amplitude-modulated tones and size-modulated shapes with which we could manipulate the temporal congruence between the sensory signals. These signals were independently modulated at a slow or a fast rate. Participants were presented with auditory-only, visual-only, or auditory-visual (AV) trials in the fMRI scanner. On AV trials, the auditory and visual signal could have the same (AV congruent) or different modulation rates (AV incongruent). Using psychophysiological interaction analyses, we found that auditory regions showed increased functional connectivity predominantly with frontal regions for AV incongruent relative to AV congruent stimuli. We further found that superior temporal regions, shown previously to integrate auditory and visual signals, showed increased connectivity with frontal and parietal regions for the same contrast. Our findings provide evidence that both activity in a network of brain regions and their connectivity are important for AV integration, and help to bridge the gap between transient and familiar AV stimuli used in previous studies.

  1. Amplitude-modulated stimuli reveal auditory-visual interactions in brain activity and brain connectivity

    Directory of Open Access Journals (Sweden)

    Mark eLaing

    2015-10-01

    Full Text Available The temporal congruence between auditory and visual signals coming from the same source can be a powerful means by which the brain integrates information from different senses. To investigate how the brain uses temporal information to integrate auditory and visual information from continuous yet unfamiliar stimuli, we use amplitude-modulated tones and size-modulated shapes with which we could manipulate the temporal congruence between the sensory signals. These signals were independently modulated at a slow or a fast rate. Participants were presented with auditory-only, visual-only or auditory-visual (AV trials in the scanner. On AV trials, the auditory and visual signal could have the same (AV congruent or different modulation rates (AV incongruent. Using psychophysiological interaction analyses, we found that auditory regions showed increased functional connectivity predominantly with frontal regions for AV incongruent relative to AV congruent stimuli. We further found that superior temporal regions, shown previously to integrate auditory and visual signals, showed increased connectivity with frontal and parietal regions for the same contrast. Our findings provide evidence that both activity in a network of brain regions and their connectivity are important for auditory-visual integration, and help to bridge the gap between transient and familiar AV stimuli used in previous studies.

  2. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI

    International Nuclear Information System (INIS)

    Magnin, Benoit; Mesrob, Lilia; Kinkingnehun, Serge; Pelegrini-Issac, Melanie; Colliot, Olivier; Sarazin, Marie; Dubois, Bruno; Lehericy, Stephane; Benali, Habib

    2009-01-01

    We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects. We studied 16 patients with AD [mean age ± standard deviation (SD)=74.1 ±5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3±5.0 years, MMSE=28.5± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD. (orig.)

  3. A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

    Directory of Open Access Journals (Sweden)

    Gwen A. Frishkoff

    2007-01-01

    Full Text Available This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG and magnetoencephalographic (MEG data. We describe recent progress on four goals: 1 specification of rules and concepts that capture expert knowledge of event-related potentials (ERP patterns in visual word recognition; 2 implementation of rules in an automated data processing and labeling stream; 3 data mining techniques that lead to refinement of rules; and 4 iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

  4. Pattern classification of brain activation during emotional processing in subclinical depression : psychosis proneness as potential confounding factor

    NARCIS (Netherlands)

    Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

    2013-01-01

    We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups

  5. CYSTIC LESIONS OF THE BRAIN - A CLASSIFICATION BASED ON PATHOGENESIS, WITH CONSIDERATION OF HISTOLOGICAL AND RADIOLOGICAL FEATURES

    NARCIS (Netherlands)

    GO, KG; HEW, JM; KAMMAN, RL; MOLENAAR, WM; PRUIM, J; BLAAUW, EH

    A classification of the existing multitude of cystic lesions of the brain is proposed, which allows an understanding of their genesis and consequent therapeutic implications, as well as their diagnostic characteristics. Essentially, cerebral cystic lesions may be classified into the following

  6. Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

    Science.gov (United States)

    Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

    2017-07-04

    This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

  7. Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy

    Directory of Open Access Journals (Sweden)

    Yiming Fan

    2017-09-01

    Full Text Available Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challenge to design an integral metric to track connectome evolution patterns across the lifespan, which is to understand the principles of network organization in the human brain. In this study, we first defined a brain network eigen-entropy (NEE based on the energy probability (EP of each brain node. Next, we used the NEE to characterize the lifespan orderness trajectory of the whole-brain functional connectivity of 173 healthy individuals ranging in age from 7 to 85 years. The results revealed that during the lifespan, the whole-brain NEE exhibited a significant non-linear decrease and that the EP distribution shifted from concentration to wide dispersion, implying orderness enhancement of functional connectome over age. Furthermore, brain regions with significant EP changes from the flourishing (7–20 years to the youth period (23–38 years were mainly located in the right prefrontal cortex and basal ganglia, and were involved in emotion regulation and executive function in coordination with the action of the sensory system, implying that self-awareness and voluntary control performance significantly changed during neurodevelopment. However, the changes from the youth period to middle age (40–59 years were located in the mesial temporal lobe and caudate, which are associated with long-term memory, implying that the memory of the human brain begins to decline with age during this period. Overall, the findings suggested that the human connectome

  8. Deep 3D convolution neural network for CT brain hemorrhage classification

    Science.gov (United States)

    Jnawali, Kamal; Arbabshirani, Mohammad R.; Rao, Navalgund; Patel, Alpen A.

    2018-02-01

    Intracranial hemorrhage is a critical conditional with the high mortality rate that is typically diagnosed based on head computer tomography (CT) images. Deep learning algorithms, in particular, convolution neural networks (CNN), are becoming the methodology of choice in medical image analysis for a variety of applications such as computer-aided diagnosis, and segmentation. In this study, we propose a fully automated deep learning framework which learns to detect brain hemorrhage based on cross sectional CT images. The dataset for this work consists of 40,367 3D head CT studies (over 1.5 million 2D images) acquired retrospectively over a decade from multiple radiology facilities at Geisinger Health System. The proposed algorithm first extracts features using 3D CNN and then detects brain hemorrhage using the logistic function as the last layer of the network. Finally, we created an ensemble of three different 3D CNN architectures to improve the classification accuracy. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve of the ensemble of three architectures was 0.87. Their results are very promising considering the fact that the head CT studies were not controlled for slice thickness, scanner type, study protocol or any other settings. Moreover, the proposed algorithm reliably detected various types of hemorrhage within the skull. This work is one of the first applications of 3D CNN trained on a large dataset of cross sectional medical images for detection of a critical radiological condition

  9. Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

    Directory of Open Access Journals (Sweden)

    Ni Shu

    2015-01-01

    Full Text Available The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

  10. Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

    Science.gov (United States)

    Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

    2016-09-01

    The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

  11. In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins.

    Science.gov (United States)

    Fang, Pan; Wang, Xin-Jian; Xue, Yu; Liu, Ming-Qi; Zeng, Wen-Feng; Zhang, Yang; Zhang, Lei; Gao, Xing; Yan, Guo-Quan; Yao, Jun; Shen, Hua-Li; Yang, Peng-Yuan

    2016-06-21

    N-glycosylation is one of the most prominent and abundant posttranslational modifications of proteins. It is estimated that over 50% of mammalian proteins undergo glycosylation. However, the analysis of N-glycoproteins has been limited by the available analytical technology. In this study, we comprehensively mapped the N-glycosylation sites in the mouse brain proteome by combining complementary methods, which included seven protease treatments, four enrichment techniques and two fractionation strategies. Altogether, 13492 N-glycopeptides containing 8386 N-glycosylation sites on 3982 proteins were identified. After evaluating the performance of the above methods, we proposed a simple and efficient workflow for large-scale N-glycosylation site mapping. The optimized workflow yielded 80% of the initially identified N-glycosylation sites with considerably less effort. Analysis of the identified N-glycoproteins revealed that many of the mouse brain proteins are N-glycosylated, including those proteins in critical pathways for nervous system development and neurological disease. Additionally, several important biomarkers of various diseases were found to be N-glycosylated. These data confirm that N-glycosylation is important in both physiological and pathological processes in the brain, and provide useful details about numerous N-glycosylation sites in brain proteins.

  12. MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

    Science.gov (United States)

    Sheejakumari, V.; Sankara Gomathi, B.

    2015-01-01

    The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. PMID:25977706

  13. Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: Preoperative classification accuracy and histopathologic correlation

    International Nuclear Information System (INIS)

    Xyda, Argyro; Haberland, Ulrike; Klotz, Ernst; Jung, Klaus; Bock, Hans Christoph; Schramm, Ramona; Knauth, Michael; Schramm, Peter

    2012-01-01

    Background: To evaluate the preoperative diagnostic power and classification accuracy of perfusion parameters derived from whole brain volume perfusion CT (VPCT) in patients with cerebral tumors. Methods: Sixty-three patients (31 male, 32 female; mean age 55.6 ± 13.9 years), with MRI findings suspected of cerebral lesions, underwent VPCT. Two readers independently evaluated VPCT data. Volumes of interest (VOIs) were marked circumscript around the tumor according to maximum intensity projection volumes, and then mapped automatically onto the cerebral blood volume (CBV), flow (CBF) and permeability Ktrans perfusion datasets. A second VOI was placed in the contra lateral cortex, as control. Correlations among perfusion values, tumor grade, cerebral hemisphere and VOIs were evaluated. Moreover, the diagnostic power of VPCT parameters, by means of positive and negative predictive value, was analyzed. Results: Our cohort included 32 high-grade gliomas WHO III/IV, 18 low-grade I/II, 6 primary cerebral lymphomas, 4 metastases and 3 tumor-like lesions. Ktrans demonstrated the highest sensitivity, specificity and positive predictive value, with a cut-off point of 2.21 mL/100 mL/min, for both the comparisons between high-grade versus low-grade and low-grade versus primary cerebral lymphomas. However, for the differentiation between high-grade and primary cerebral lymphomas, CBF and CBV proved to have 100% specificity and 100% positive predictive value, identifying preoperatively all the histopathologically proven high-grade gliomas. Conclusion: Volumetric perfusion data enable the hemodynamic assessment of the entire tumor extent and provide a method of preoperative differentiation among intra-axial cerebral tumors with promising diagnostic accuracy.

  14. Error-related brain activity and error awareness in an error classification paradigm.

    Science.gov (United States)

    Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E

    2016-10-01

    Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Validation of the RTOG recursive partitioning analysis (RPA) classification for brain metastases

    International Nuclear Information System (INIS)

    Gaspar, Laurie E.; Scott, Charles; Murray, Kevin; Curran, Walter

    2000-01-01

    Purpose: The Radiation Therapy Oncology Group (RTOG) previously developed three prognostic classes for brain metastases using recursive partitioning analysis (RPA) of a large database. These classes were based on Karnofsky performance status (KPS), primary tumor status, presence of extracranial system metastases, and age. An analysis of RTOG 91-04, a randomized study comparing two dose-fractionation schemes with a comparison to the established RTOG database, was considered important to validate the RPA classes. Methods and Materials: A total of 445 patients were randomized on RTOG 91-04, a Phase III study of accelerated hyperfractionation versus accelerated fractionation. No difference was observed between the two treatment arms with respect to survival. Four hundred thirty-two patients were included in this analysis. The majority of the patients were under age 65, had KPS 70-80, primary tumor controlled, and brain-only metastases. The initial RPA had three classes, but only patients in RPA Classes I and II were eligible for RTOG 91-04. Results: For RPA Class I, the median survival time was 6.2 months and 7.1 months for 91-04 and the database, respectively. The 1-year survival was 29% for 91-04 versus 32% for the database. There was no significant difference in the two survival distributions (p = 0.72). For RPA Class II, the median survival time was 3.8 months for 91-04 versus 4.2 months for the database. The 1-year survival was 12% and 16% for 91-04 and the database, respectively (p = 0.22). Conclusion: This analysis indicates that the RPA classes are valid and reliable for historical comparisons. Both the RTOG and other clinical trial organizers should currently utilize this RPA classification as a stratification factor for clinical trials

  16. The power of projectomes: genetic mosaic labeling in the larval zebrafish brain reveals organizing principles of sensory circuits.

    Science.gov (United States)

    Robles, Estuardo

    2017-09-01

    In no vertebrate species do we possess an accurate, comprehensive tally of neuron types in the brain. This is in no small part due to the vast diversity of neuronal types that comprise complex vertebrate nervous systems. A fundamental goal of neuroscience is to construct comprehensive catalogs of cell types defined by structure, connectivity, and physiological response properties. This type of information will be invaluable for generating models of how assemblies of neurons encode and distribute sensory information and correspondingly alter behavior. This review summarizes recent efforts in the larval zebrafish to construct sensory projectomes, comprehensive analyses of axonal morphologies in sensory axon tracts. Focusing on the olfactory and optic tract, these studies revealed principles of sensory information processing in the olfactory and visual systems that could not have been directly quantified by other methods. In essence, these studies reconstructed the optic and olfactory tract in a virtual manner, providing insights into patterns of neuronal growth that underlie the formation of sensory axon tracts. Quantitative analysis of neuronal diversity revealed organizing principles that determine information flow through sensory systems in the zebrafish that are likely to be conserved across vertebrate species. The generation of comprehensive cell type classifications based on structural, physiological, and molecular features will lead to testable hypotheses on the functional role of individual sensory neuron subtypes in controlling specific sensory-evoked behaviors.

  17. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

    Science.gov (United States)

    Portugues, Ruben; Feierstein, Claudia E; Engert, Florian; Orger, Michael B

    2014-03-19

    Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate but ordered pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments systematically reveal the functional architecture of neural circuits underlying a sensorimotor behavior in a vertebrate brain. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

    Science.gov (United States)

    Hong, Keum-Shik; Khan, Muhammad Jawad

    2017-01-01

    In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

  19. Automatic segmentation of MR brain images of preterm infants using supervised classification.

    Science.gov (United States)

    Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

    2015-09-01

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

  20. Tensor-Based Morphometry Reveals Volumetric Deficits in Moderate=Severe Pediatric Traumatic Brain Injury.

    Science.gov (United States)

    Dennis, Emily L; Hua, Xue; Villalon-Reina, Julio; Moran, Lisa M; Kernan, Claudia; Babikian, Talin; Mink, Richard; Babbitt, Christopher; Johnson, Jeffrey; Giza, Christopher C; Thompson, Paul M; Asarnow, Robert F

    2016-05-01

    Traumatic brain injury (TBI) can cause widespread and prolonged brain degeneration. TBI can affect cognitive function and brain integrity for many years after injury, often with lasting effects in children, whose brains are still immature. Although TBI varies in how it affects different individuals, image analysis methods such as tensor-based morphometry (TBM) can reveal common areas of brain atrophy on magnetic resonance imaging (MRI), secondary effects of the initial injury, which will differ between subjects. Here we studied 36 pediatric moderate to severe TBI (msTBI) participants in the post-acute phase (1-6 months post-injury) and 18 msTBI participants who returned for their chronic assessment, along with well-matched controls at both time-points. Participants completed a battery of cognitive tests that we used to create a global cognitive performance score. Using TBM, we created three-dimensional (3D) maps of individual and group differences in regional brain volumes. At both the post-acute and chronic time-points, the greatest group differences were expansion of the lateral ventricles and reduction of the lingual gyrus in the TBI group. We found a number of smaller clusters of volume reduction in the cingulate gyrus, thalamus, and fusiform gyrus, and throughout the frontal, temporal, and parietal cortices. Additionally, we found extensive associations between our cognitive performance measure and regional brain volume. Our results indicate a pattern of atrophy still detectable 1-year post-injury, which may partially underlie the cognitive deficits frequently found in TBI.

  1. Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

    Science.gov (United States)

    Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong

    2012-01-01

    The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

  2. Tensor-Based Morphometry Reveals Volumetric Deficits in Moderate=Severe Pediatric Traumatic Brain Injury

    Science.gov (United States)

    Hua, Xue; Villalon-Reina, Julio; Moran, Lisa M.; Kernan, Claudia; Babikian, Talin; Mink, Richard; Babbitt, Christopher; Johnson, Jeffrey; Giza, Christopher C.; Thompson, Paul M.; Asarnow, Robert F.

    2016-01-01

    Abstract Traumatic brain injury (TBI) can cause widespread and prolonged brain degeneration. TBI can affect cognitive function and brain integrity for many years after injury, often with lasting effects in children, whose brains are still immature. Although TBI varies in how it affects different individuals, image analysis methods such as tensor-based morphometry (TBM) can reveal common areas of brain atrophy on magnetic resonance imaging (MRI), secondary effects of the initial injury, which will differ between subjects. Here we studied 36 pediatric moderate to severe TBI (msTBI) participants in the post-acute phase (1–6 months post-injury) and 18 msTBI participants who returned for their chronic assessment, along with well-matched controls at both time-points. Participants completed a battery of cognitive tests that we used to create a global cognitive performance score. Using TBM, we created three-dimensional (3D) maps of individual and group differences in regional brain volumes. At both the post-acute and chronic time-points, the greatest group differences were expansion of the lateral ventricles and reduction of the lingual gyrus in the TBI group. We found a number of smaller clusters of volume reduction in the cingulate gyrus, thalamus, and fusiform gyrus, and throughout the frontal, temporal, and parietal cortices. Additionally, we found extensive associations between our cognitive performance measure and regional brain volume. Our results indicate a pattern of atrophy still detectable 1-year post-injury, which may partially underlie the cognitive deficits frequently found in TBI. PMID:26393494

  3. Metabolomics reveals metabolic alterations by intrauterine growth restriction in the fetal rabbit brain.

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    Erwin van Vliet

    Full Text Available Intrauterine Growth Restriction (IUGR due to placental insufficiency occurs in 5-10% of pregnancies and is a major risk factor for abnormal neurodevelopment. The perinatal diagnosis of IUGR related abnormal neurodevelopment represents a major challenge in fetal medicine. The development of clinical biomarkers is considered a promising approach, but requires the identification of biochemical/molecular alterations by IUGR in the fetal brain. This targeted metabolomics study in a rabbit IUGR model aimed to obtain mechanistic insight into the effects of IUGR on the fetal brain and identify metabolite candidates for biomarker development.At gestation day 25, IUGR was induced in two New Zealand rabbits by 40-50% uteroplacental vessel ligation in one horn and the contralateral horn was used as control. At day 30, fetuses were delivered by Cesarian section, weighed and brains collected for metabolomics analysis. Results showed that IUGR fetuses had a significantly lower birth and brain weight compared to controls. Metabolomics analysis using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS and database matching identified 78 metabolites. Comparison of metabolite intensities using a t-test demonstrated that 18 metabolites were significantly different between control and IUGR brain tissue, including neurotransmitters/peptides, amino acids, fatty acids, energy metabolism intermediates and oxidative stress metabolites. Principle component and hierarchical cluster analysis showed cluster formations that clearly separated control from IUGR brain tissue samples, revealing the potential to develop predictive biomarkers. Moreover birth weight and metabolite intensity correlations indicated that the extent of alterations was dependent on the severity of IUGR.IUGR leads to metabolic alterations in the fetal rabbit brain, involving neuronal viability, energy metabolism, amino acid levels, fatty acid profiles and oxidative stress

  4. Inter-species activity correlations reveal functional correspondences between monkey and human brain areas

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    Mantini, Dante; Hasson, Uri; Betti, Viviana; Perrucci, Mauro G.; Romani, Gian Luca; Corbetta, Maurizio; Orban, Guy A.; Vanduffel, Wim

    2012-01-01

    Evolution-driven functional changes in the primate brain are typically assessed by aligning monkey and human activation maps using cortical surface expansion models. These models use putative homologous areas as registration landmarks, assuming they are functionally correspondent. In cases where functional changes have occurred in an area, this assumption prohibits to reveal whether other areas may have assumed lost functions. Here we describe a method to examine functional correspondences across species. Without making spatial assumptions, we assess similarities in sensory-driven functional magnetic resonance imaging responses between monkey (Macaca mulatta) and human brain areas by means of temporal correlation. Using natural vision data, we reveal regions for which functional processing has shifted to topologically divergent locations during evolution. We conclude that substantial evolution-driven functional reorganizations have occurred, not always consistent with cortical expansion processes. This novel framework for evaluating changes in functional architecture is crucial to building more accurate evolutionary models. PMID:22306809

  5. Metabolic connectivity mapping reveals effective connectivity in the resting human brain.

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    Riedl, Valentin; Utz, Lukas; Castrillón, Gabriel; Grimmer, Timo; Rauschecker, Josef P; Ploner, Markus; Friston, Karl J; Drzezga, Alexander; Sorg, Christian

    2016-01-12

    Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using "eyes open" versus "eyes closed" conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.

  6. Species-Specific Mechanisms of Neuron Subtype Specification Reveal Evolutionary Plasticity of Amniote Brain Development

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    Tadashi Nomura

    2018-03-01

    Full Text Available Summary: Highly ordered brain architectures in vertebrates consist of multiple neuron subtypes with specific neuronal connections. However, the origin of and evolutionary changes in neuron specification mechanisms remain unclear. Here, we report that regulatory mechanisms of neuron subtype specification are divergent in developing amniote brains. In the mammalian neocortex, the transcription factors (TFs Ctip2 and Satb2 are differentially expressed in layer-specific neurons. In contrast, these TFs are co-localized in reptilian and avian dorsal pallial neurons. Multi-potential progenitors that produce distinct neuronal subtypes commonly exist in the reptilian and avian dorsal pallium, whereas a cis-regulatory element of avian Ctip2 exhibits attenuated transcription suppressive activity. Furthermore, the neuronal subtypes distinguished by these TFs are not tightly associated with conserved neuronal connections among amniotes. Our findings reveal the evolutionary plasticity of regulatory gene functions that contribute to species differences in neuronal heterogeneity and connectivity in developing amniote brains. : Neuronal heterogeneity is essential for assembling intricate neuronal circuits. Nomura et al. find that species-specific transcriptional mechanisms underlie diversities of excitatory neuron subtypes in mammalian and non-mammalian brains. Species differences in neuronal subtypes and connections suggest functional plasticity of regulatory genes for neuronal specification during amniote brain evolution. Keywords: Ctip2, Satb2, multi-potential progenitors, transcriptional regulation, neuronal connectivity

  7. Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

    Science.gov (United States)

    Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

    2018-04-16

    Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

  8. Validation of the RTOG recursive partitioning analysis (RPA) classification for small-cell lung cancer-only brain metastases

    International Nuclear Information System (INIS)

    Videtic, Gregory M.M.; Adelstein, David J.; Mekhail, Tarek M.; Rice, Thomas W.; Stevens, Glen H.J.; Lee, S.-Y.; Suh, John H.

    2007-01-01

    Purpose: Radiation Therapy Oncology Group (RTOG) developed a prognostic classification based on a recursive partitioning analysis (RPA) of patient pretreatment characteristics from three completed brain metastases randomized trials. Clinical trials for patients with brain metastases generally exclude small-cell lung cancer (SCLC) cases. We hypothesize that the RPA classes are valid in the setting of SCLC brain metastases. Methods and Materials: A retrospective review of 154 SCLC patients with brain metastases treated between April 1983 and May 2005 was performed. RPA criteria used for class assignment were Karnofsky performance status (KPS), primary tumor status (PT), presence of extracranial metastases (ED), and age. Results: Median survival was 4.9 months, with 4 patients (2.6%) alive at analysis. Median follow-up was 4.7 months (range, 0.3-40.3 months). Median age was 65 (range, 42-85 years). Median KPS was 70 (range, 40-100). Number of patients with controlled PT and no ED was 20 (13%) and with ED, 27 (18%); without controlled PT and ED, 34 (22%) and with ED, 73 (47%). RPA class distribution was: Class I: 8 (5%); Class II: 96 (62%); Class III: 51 (33%). Median survivals (in months) by RPA class were: Class I: 8.6; Class II: 4.2; Class III: 2.3 (p = 0.0023). Conclusions: Survivals for SCLC-only brain metastases replicate the results from the RTOG RPA classification. These classes are therefore valid for brain metastases from SCLC, support the inclusion of SCLC patients in future brain metastases trials, and may also serve as a basis for historical comparisons

  9. Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach.

    Science.gov (United States)

    Ecker, Christine; Marquand, Andre; Mourão-Miranda, Janaina; Johnston, Patrick; Daly, Eileen M; Brammer, Michael J; Maltezos, Stefanos; Murphy, Clodagh M; Robertson, Dene; Williams, Steven C; Murphy, Declan G M

    2010-08-11

    Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating patterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e.g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.

  10. Resting-state brain networks revealed by granger causal connectivity in frogs.

    Science.gov (United States)

    Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

    2016-10-15

    Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  11. Classification of frequency response areas in the inferior colliculus reveals continua not discrete classes.

    Science.gov (United States)

    Palmer, Alan R; Shackleton, Trevor M; Sumner, Christian J; Zobay, Oliver; Rees, Adrian

    2013-08-15

    A differential response to sound frequency is a fundamental property of auditory neurons. Frequency analysis in the cochlea gives rise to V-shaped tuning functions in auditory nerve fibres, but by the level of the inferior colliculus (IC), the midbrain nucleus of the auditory pathway, neuronal receptive fields display diverse shapes that reflect the interplay of excitation and inhibition. The origin and nature of these frequency receptive field types is still open to question. One proposed hypothesis is that the frequency response class of any given neuron in the IC is predominantly inherited from one of three major afferent pathways projecting to the IC, giving rise to three distinct receptive field classes. Here, we applied subjective classification, principal component analysis, cluster analysis, and other objective statistical measures, to a large population (2826) of frequency response areas from single neurons recorded in the IC of the anaesthetised guinea pig. Subjectively, we recognised seven frequency response classes (V-shaped, non-monotonic Vs, narrow, closed, tilt down, tilt up and double-peaked), that were represented at all frequencies. We could identify similar classes using our objective classification tools. Importantly, however, many neurons exhibited properties intermediate between these classes, and none of the objective methods used here showed evidence of discrete response classes. Thus receptive field shapes in the IC form continua rather than discrete classes, a finding consistent with the integration of afferent inputs in the generation of frequency response areas. The frequency disposition of inhibition in the response areas of some neurons suggests that across-frequency inputs originating at or below the level of the IC are involved in their generation.

  12. Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

    Science.gov (United States)

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

    2017-01-01

    Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

  13. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

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    Noman Naseer

    2016-01-01

    Full Text Available We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest using functional near-infrared spectroscopy (fNIRS signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA, quadratic discriminant analysis (QDA, k-nearest neighbour (kNN, the Naïve Bayes approach, support vector machine (SVM, and artificial neural networks (ANN, were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005 using HbO signals.

  14. Revealing the cerebello-ponto-hypothalamic pathway in the human brain.

    Science.gov (United States)

    Kamali, Arash; Karbasian, Niloofar; Rabiei, Pejman; Cano, Andres; Riascos, Roy F; Tandon, Nitin; Arevalo, Octavio; Ocasio, Laura; Younes, Kyan; Khayat-Khoei, Mahsa; Mirbagheri, Saeedeh; Hasan, Khader M

    2018-04-16

    The cerebellum is shown to be involved in some limbic functions of the human brain such as emotion and affect. The major connection of the cerebellum with the limbic system is known to be through the cerebello-hypothalamic pathways. The consensus is that the projections from the cerebellar nuclei to the limbic system, and particularly the hypothalamus, or from the hypothalamus to the cerebellar nuclei, are through multisynaptic pathways in the bulbar reticular formation. The detailed anatomy of the pathways responsible for mediating these responses, however, is yet to be determined. Diffusion tensor imaging may be helpful in better visualizing the surgical anatomy of the cerebello-ponto-hypothalamic (CPH) pathway. This study aimed to investigate the utility of high-spatial-resolution diffusion tensor tractography for mapping the trajectory of the CPH tract in the human brain. Fifteen healthy adults were studied. We delineated, for the first time, the detailed trajectory of the CPH tract of the human brain in fifteen normal adult subjects using high-spatial-resolution diffusion tensor tractography. We further revealed the close relationship of the CPH tract with the optic tract, temporo-pontine tract, amygdalofugal tract and the fornix in the human brain. Copyright © 2018. Published by Elsevier B.V.

  15. Multifrequency magnetic resonance elastography of the brain reveals tissue degeneration in neuromyelitis optica spectrum disorder

    International Nuclear Information System (INIS)

    Streitberger, Kaspar-Josche; Fehlner, Andreas; Sack, Ingolf; Pache, Florence; Lacheta, Anna; Papazoglou, Sebastian; Brandt, Alexander; Bellmann-Strobl, Judith; Ruprecht, Klemens; Braun, Juergen; Paul, Friedemann; Wuerfel, Jens

    2017-01-01

    Application of multifrequency magnetic resonance elastography (MMRE) of the brain parenchyma in patients with neuromyelitis optica spectrum disorder (NMOSD) compared to age matched healthy controls (HC). 15 NMOSD patients and 17 age- and gender-matched HC were examined using MMRE. Two three-dimensional viscoelastic parameter maps, the magnitude G* and phase angle φ of the complex shear modulus were reconstructed by simultaneous inversion of full wave-field data in 1.9-mm isotropic resolution at 7 harmonic drive frequencies from 30 to 60 Hz. In NMOSD patients, a significant reduction of G* was observed within the white matter fraction (p = 0.017), predominantly within the thalamic regions (p = 0.003), compared to HC. These parameters exceeded the reduction in brain volume measured in patients versus HC (p = 0.02 whole-brain volume reduction). Volumetric differences in white matter fraction and the thalami were not detectable between patients and HC. However, phase angle φ was decreased in patients within the white matter (p = 0.03) and both thalamic regions (p = 0.044). MMRE reveals global tissue degeneration with accelerated softening of the brain parenchyma in patients with NMOSD. The predominant reduction of stiffness is found within the thalamic region and related white matter tracts, presumably reflecting Wallerian degeneration. (orig.)

  16. Multifrequency magnetic resonance elastography of the brain reveals tissue degeneration in neuromyelitis optica spectrum disorder

    Energy Technology Data Exchange (ETDEWEB)

    Streitberger, Kaspar-Josche [Charite - Universitaetsmedizin Berlin, Department of Radiology, Berlin (Germany); Charite - Universitaetsmedizin Berlin, Department of Neurology with Experimental Neurology, Berlin (Germany); Fehlner, Andreas; Sack, Ingolf [Charite - Universitaetsmedizin Berlin, Department of Radiology, Berlin (Germany); Pache, Florence [Charite - Universitaetsmedizin Berlin, Department of Neurology with Experimental Neurology, Berlin (Germany); Charite - Universitaetsmedizin Berlin, NeuroCure Clinical Research Center, Berlin (Germany); Lacheta, Anna; Papazoglou, Sebastian; Brandt, Alexander [Charite - Universitaetsmedizin Berlin, NeuroCure Clinical Research Center, Berlin (Germany); Bellmann-Strobl, Judith [Max Delbrueck Center for Molecular Medicine and Charite - Universitaetsmedizin Berlin, Experimental and Clinical Research Center, Berlin (Germany); Ruprecht, Klemens [Charite - Universitaetsmedizin Berlin, Department of Neurology with Experimental Neurology, Berlin (Germany); Braun, Juergen [Charite - Universitaetsmedizin Berlin, Institute of Medical Informatics, Berlin (Germany); Paul, Friedemann [Charite - Universitaetsmedizin Berlin, Department of Neurology with Experimental Neurology, Berlin (Germany); Charite - Universitaetsmedizin Berlin, NeuroCure Clinical Research Center, Berlin (Germany); Max Delbrueck Center for Molecular Medicine and Charite - Universitaetsmedizin Berlin, Experimental and Clinical Research Center, Berlin (Germany); Wuerfel, Jens [Charite - Universitaetsmedizin Berlin, NeuroCure Clinical Research Center, Berlin (Germany); Max Delbrueck Center for Molecular Medicine and Charite - Universitaetsmedizin Berlin, Experimental and Clinical Research Center, Berlin (Germany); Medical Image Analysis Center (MIAC AG), Basel (Switzerland)

    2017-05-15

    Application of multifrequency magnetic resonance elastography (MMRE) of the brain parenchyma in patients with neuromyelitis optica spectrum disorder (NMOSD) compared to age matched healthy controls (HC). 15 NMOSD patients and 17 age- and gender-matched HC were examined using MMRE. Two three-dimensional viscoelastic parameter maps, the magnitude G* and phase angle φ of the complex shear modulus were reconstructed by simultaneous inversion of full wave-field data in 1.9-mm isotropic resolution at 7 harmonic drive frequencies from 30 to 60 Hz. In NMOSD patients, a significant reduction of G* was observed within the white matter fraction (p = 0.017), predominantly within the thalamic regions (p = 0.003), compared to HC. These parameters exceeded the reduction in brain volume measured in patients versus HC (p = 0.02 whole-brain volume reduction). Volumetric differences in white matter fraction and the thalami were not detectable between patients and HC. However, phase angle φ was decreased in patients within the white matter (p = 0.03) and both thalamic regions (p = 0.044). MMRE reveals global tissue degeneration with accelerated softening of the brain parenchyma in patients with NMOSD. The predominant reduction of stiffness is found within the thalamic region and related white matter tracts, presumably reflecting Wallerian degeneration. (orig.)

  17. Common brain regions underlying different arithmetic operations as revealed by conjunct fMRI-BOLD activation.

    Science.gov (United States)

    Fehr, Thorsten; Code, Chris; Herrmann, Manfred

    2007-10-03

    The issue of how and where arithmetic operations are represented in the brain has been addressed in numerous studies. Lesion studies suggest that a network of different brain areas are involved in mental calculation. Neuroimaging studies have reported inferior parietal and lateral frontal activations during mental arithmetic using tasks of different complexities and using different operators (addition, subtraction, etc.). Indeed, it has been difficult to compare brain activation across studies because of the variety of different operators and different presentation modalities used. The present experiment examined fMRI-BOLD activity in participants during calculation tasks entailing different arithmetic operations -- addition, subtraction, multiplication and division -- of different complexities. Functional imaging data revealed a common activation pattern comprising right precuneus, left and right middle and superior frontal regions during all arithmetic operations. All other regional activations were operation specific and distributed in prominently frontal, parietal and central regions when contrasting complex and simple calculation tasks. The present results largely confirm former studies suggesting that activation patterns due to mental arithmetic appear to reflect a basic anatomical substrate of working memory, numerical knowledge and processing based on finger counting, and derived from a network originally related to finger movement. We emphasize that in mental arithmetic research different arithmetic operations should always be examined and discussed independently of each other in order to avoid invalid generalizations on arithmetics and involved brain areas.

  18. Deep sequencing analysis of the developing mouse brain reveals a novel microRNA

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    Piltz Sandra

    2011-04-01

    Full Text Available Abstract Background MicroRNAs (miRNAs are small non-coding RNAs that can exert multilevel inhibition/repression at a post-transcriptional or protein synthesis level during disease or development. Characterisation of miRNAs in adult mammalian brains by deep sequencing has been reported previously. However, to date, no small RNA profiling of the developing brain has been undertaken using this method. We have performed deep sequencing and small RNA analysis of a developing (E15.5 mouse brain. Results We identified the expression of 294 known miRNAs in the E15.5 developing mouse brain, which were mostly represented by let-7 family and other brain-specific miRNAs such as miR-9 and miR-124. We also discovered 4 putative 22-23 nt miRNAs: mm_br_e15_1181, mm_br_e15_279920, mm_br_e15_96719 and mm_br_e15_294354 each with a 70-76 nt predicted pre-miRNA. We validated the 4 putative miRNAs and further characterised one of them, mm_br_e15_1181, throughout embryogenesis. Mm_br_e15_1181 biogenesis was Dicer1-dependent and was expressed in E3.5 blastocysts and E7 whole embryos. Embryo-wide expression patterns were observed at E9.5 and E11.5 followed by a near complete loss of expression by E13.5, with expression restricted to a specialised layer of cells within the developing and early postnatal brain. Mm_br_e15_1181 was upregulated during neurodifferentiation of P19 teratocarcinoma cells. This novel miRNA has been identified as miR-3099. Conclusions We have generated and analysed the first deep sequencing dataset of small RNA sequences of the developing mouse brain. The analysis revealed a novel miRNA, miR-3099, with potential regulatory effects on early embryogenesis, and involvement in neuronal cell differentiation/function in the brain during late embryonic and early neonatal development.

  19. Classification tree analyses reveal limited potential for early targeted prevention against childhood overweight.

    Science.gov (United States)

    Beyerlein, Andreas; Kusian, Dennis; Ziegler, Anette-Gabriele; Schaffrath-Rosario, Angelika; von Kries, Rüdiger

    2014-02-01

    Whether specific combinations of risk factors in very early life might allow identification of high-risk target groups for overweight prevention programs was examined. Data of n = 8981 children from the German KiGGS study were analyzed. Using a classification tree approach, predictive risk factor combinations were assessed for overweight in 3-6, 7-10, and 11-17-year-old children. In preschool children, the subgroup with the highest overweight risk were migrant children with at least one obese parent, with a prevalence of 36.6 (95% confidence interval or CI: 22.9, 50.4)%, compared to an overall prevalence of 10.0 (8.9, 11.2)%. The prevalence of overweight increased from 18.3 (16.8, 19.8)% to 57.9 (46.6, 69.3)% in 7-10-year-old children, if at least one parent was obese and the child had been born large-for-gestational-age. In 11-17-year-olds, the overweight risk increased from 20.1 (18.9, 21.3)% to 63.0 (46.4, 79.7)% in the highest risk group. However, high prevalence ratios were found only in small subgroups, containing <10% of all overweight cases in the respective age group. Our results indicate only a limited potential for early targeted preventions against overweight in children and adolescents. Copyright © 2013 The Obesity Society.

  20. Molecular phylogeny of Pasiphaeidae (Crustacea, Decapoda, Caridea) reveals systematic incongruence of the current classification.

    Science.gov (United States)

    Liao, Yunshi; De Grave, Sammy; Ho, Tsz Wai; Ip, Brian H Y; Tsang, Ling Ming; Chan, Tin-Yam; Chu, Ka Hou

    2017-10-01

    Caridean shrimps constitute one of the most diverse groups of decapod crustaceans, notwithstanding their poorly resolved infraordinal relationships. One of the systematically controversial families in Caridea is the predominantly pelagic Pasiphaeidae, comprises 101 species in seven genera. Pasiphaeidae species exhibit high morphological disparity, as well as ecological niche width, inhabiting shallow to very deep waters (>4000m). The present work presents the first molecular phylogeny of the family, based on a combined dataset of six mitochondrial and nuclear gene markers (12S rDNA, 16S rDNA, histone 3, sodium-potassium ATPase α-subunit, enolase and ATP synthase β-subunit) from 33 species belonged to six genera of Pasiphaeidae with 19 species from 12 other caridean families as outgroup taxa. Maximum likelihood and Bayesian inference analyses conducted on the concatenated dataset of 2265bp suggest the family Pasiphaeidae is not monophyletic, with Psathyrocaris more closely related to other carideans than to the other five pasiphaeid genera included in this analysis. Leptochela occupies a sister position to the remaining genera and is genetically quite distant from them. At the generic level, the analysis supports the monophyly of Pasiphaea, Leptochela and Psathyrocaris, while Eupasiphae is shown to be paraphyletic, closely related to Parapasiphae and Glyphus. The present molecular result strongly implies that certain morphological characters used in the present systematic delineation within Pasiphaeidae may not be synapomorphies and the classification within the family needs to be urgently revised. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Lithological control on gas hydrate saturation as revealed by signal classification of NMR logging data

    Science.gov (United States)

    Bauer, Klaus; Kulenkampff, Johannes; Henninges, Jan; Spangenberg, Erik

    2015-09-01

    In this paper, nuclear magnetic resonance (NMR) downhole logging data are analyzed with a new strategy to study gas hydrate-bearing sediments in the Mackenzie Delta (NW Canada). In NMR logging, transverse relaxation time (T2) distribution curves are usually used to determine single-valued parameters such as apparent total porosity or hydrocarbon saturation. Our approach analyzes the entire T2 distribution curves as quasi-continuous signals to characterize the rock formation. We apply self-organizing maps, a neural network clustering technique, to subdivide the data set of NMR curves into classes with a similar and distinctive signal shape. The method includes (1) preparation of data vectors, (2) unsupervised learning, (3) cluster definition, and (4) classification and depth mapping of all NMR signals. Each signal class thus represents a specific pore size distribution which can be interpreted in terms of distinct lithologies and reservoir types. A key step in the interpretation strategy is to reconcile the NMR classes with other log data not considered in the clustering analysis, such as gamma ray, hydrate saturation, and other logs. Our results defined six main lithologies within the target zone. Gas hydrate layers were recognized by their low signal amplitudes for all relaxation times. Most importantly, two subtypes of hydrate-bearing shaly sands were identified. They show distinct NMR signals and differ in hydrate saturation and gamma ray values. An inverse linear relationship between hydrate saturation and clay content was concluded. Finally, we infer that the gas hydrate is not grain coating, but rather, pore filling with matrix support is the preferred growth habit model for the studied formation.

  2. Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor

    Directory of Open Access Journals (Sweden)

    Gemma Modinos

    2013-02-01

    Full Text Available We used Support Vector Machine (SVM to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II. Two groups were subsequently formed: (i subclinical (mild mood disturbance (n = 17 and (ii no mood disturbance (n = 17. Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE positive subscale. The functional magnetic resonance imaging (fMRI paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002, within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006. Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

  3. Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

    Science.gov (United States)

    Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

    2013-01-01

    We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

  4. Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism.

    Science.gov (United States)

    Mohammed, Akram; Guda, Chittibabu

    2015-01-01

    Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and

  5. Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism

    Science.gov (United States)

    2015-01-01

    Background Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. Results ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids

  6. Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

    Directory of Open Access Journals (Sweden)

    Bo Peng

    2017-05-01

    Full Text Available Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR images using multilevel-features-based classification method.Method: The multilevel region of interest (ROI features consist of two types of features: (i ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs with appropriate weighting factor.Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions.Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

  7. Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility.

    Science.gov (United States)

    Bagot, Rosemary C; Cates, Hannah M; Purushothaman, Immanuel; Lorsch, Zachary S; Walker, Deena M; Wang, Junshi; Huang, Xiaojie; Schlüter, Oliver M; Maze, Ian; Peña, Catherine J; Heller, Elizabeth A; Issler, Orna; Wang, Minghui; Song, Won-Min; Stein, Jason L; Liu, Xiaochuan; Doyle, Marie A; Scobie, Kimberly N; Sun, Hao Sheng; Neve, Rachael L; Geschwind, Daniel; Dong, Yan; Shen, Li; Zhang, Bin; Nestler, Eric J

    2016-06-01

    Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex.

    Science.gov (United States)

    Guadalupe, Tulio; Mathias, Samuel R; vanErp, Theo G M; Whelan, Christopher D; Zwiers, Marcel P; Abe, Yoshinari; Abramovic, Lucija; Agartz, Ingrid; Andreassen, Ole A; Arias-Vásquez, Alejandro; Aribisala, Benjamin S; Armstrong, Nicola J; Arolt, Volker; Artiges, Eric; Ayesa-Arriola, Rosa; Baboyan, Vatche G; Banaschewski, Tobias; Barker, Gareth; Bastin, Mark E; Baune, Bernhard T; Blangero, John; Bokde, Arun L W; Boedhoe, Premika S W; Bose, Anushree; Brem, Silvia; Brodaty, Henry; Bromberg, Uli; Brooks, Samantha; Büchel, Christian; Buitelaar, Jan; Calhoun, Vince D; Cannon, Dara M; Cattrell, Anna; Cheng, Yuqi; Conrod, Patricia J; Conzelmann, Annette; Corvin, Aiden; Crespo-Facorro, Benedicto; Crivello, Fabrice; Dannlowski, Udo; de Zubicaray, Greig I; de Zwarte, Sonja M C; Deary, Ian J; Desrivières, Sylvane; Doan, Nhat Trung; Donohoe, Gary; Dørum, Erlend S; Ehrlich, Stefan; Espeseth, Thomas; Fernández, Guillén; Flor, Herta; Fouche, Jean-Paul; Frouin, Vincent; Fukunaga, Masaki; Gallinat, Jürgen; Garavan, Hugh; Gill, Michael; Suarez, Andrea Gonzalez; Gowland, Penny; Grabe, Hans J; Grotegerd, Dominik; Gruber, Oliver; Hagenaars, Saskia; Hashimoto, Ryota; Hauser, Tobias U; Heinz, Andreas; Hibar, Derrek P; Hoekstra, Pieter J; Hoogman, Martine; Howells, Fleur M; Hu, Hao; Hulshoff Pol, Hilleke E; Huyser, Chaim; Ittermann, Bernd; Jahanshad, Neda; Jönsson, Erik G; Jurk, Sarah; Kahn, Rene S; Kelly, Sinead; Kraemer, Bernd; Kugel, Harald; Kwon, Jun Soo; Lemaitre, Herve; Lesch, Klaus-Peter; Lochner, Christine; Luciano, Michelle; Marquand, Andre F; Martin, Nicholas G; Martínez-Zalacaín, Ignacio; Martinot, Jean-Luc; Mataix-Cols, David; Mather, Karen; McDonald, Colm; McMahon, Katie L; Medland, Sarah E; Menchón, José M; Morris, Derek W; Mothersill, Omar; Maniega, Susana Munoz; Mwangi, Benson; Nakamae, Takashi; Nakao, Tomohiro; Narayanaswaamy, Janardhanan C; Nees, Frauke; Nordvik, Jan E; Onnink, A Marten H; Opel, Nils; Ophoff, Roel; Paillère Martinot, Marie-Laure; Papadopoulos Orfanos, Dimitri; Pauli, Paul; Paus, Tomáš; Poustka, Luise; Reddy, Janardhan Yc; Renteria, Miguel E; Roiz-Santiáñez, Roberto; Roos, Annerine; Royle, Natalie A; Sachdev, Perminder; Sánchez-Juan, Pascual; Schmaal, Lianne; Schumann, Gunter; Shumskaya, Elena; Smolka, Michael N; Soares, Jair C; Soriano-Mas, Carles; Stein, Dan J; Strike, Lachlan T; Toro, Roberto; Turner, Jessica A; Tzourio-Mazoyer, Nathalie; Uhlmann, Anne; Hernández, Maria Valdés; van den Heuvel, Odile A; van der Meer, Dennis; van Haren, Neeltje E M; Veltman, Dick J; Venkatasubramanian, Ganesan; Vetter, Nora C; Vuletic, Daniella; Walitza, Susanne; Walter, Henrik; Walton, Esther; Wang, Zhen; Wardlaw, Joanna; Wen, Wei; Westlye, Lars T; Whelan, Robert; Wittfeld, Katharina; Wolfers, Thomas; Wright, Margaret J; Xu, Jian; Xu, Xiufeng; Yun, Je-Yeon; Zhao, JingJing; Franke, Barbara; Thompson, Paul M; Glahn, David C; Mazoyer, Bernard; Fisher, Simon E; Francks, Clyde

    2017-10-01

    The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

  9. Single-nanotube tracking reveals the nanoscale organization of the extracellular space in the live brain

    Science.gov (United States)

    Godin, Antoine G.; Varela, Juan A.; Gao, Zhenghong; Danné, Noémie; Dupuis, Julien P.; Lounis, Brahim; Groc, Laurent; Cognet, Laurent

    2017-03-01

    The brain is a dynamic structure with the extracellular space (ECS) taking up almost a quarter of its volume. Signalling molecules, neurotransmitters and nutrients transit via the ECS, which constitutes a key microenvironment for cellular communication and the clearance of toxic metabolites. The spatial organization of the ECS varies during sleep, development and aging and is probably altered in neuropsychiatric and degenerative diseases, as inferred from electron microscopy and macroscopic biophysical investigations. Here we show an approach to directly observe the local ECS structures and rheology in brain tissue using super-resolution imaging. We inject single-walled carbon nanotubes into rat cerebroventricles and follow the near-infrared emission of individual nanotubes as they diffuse inside the ECS for tens of minutes in acute slices. Because of the interplay between the nanotube geometry and the ECS local environment, we can extract information about the dimensions and local viscosity of the ECS. We find a striking diversity of ECS dimensions down to 40 nm, and as well as of local viscosity values. Moreover, by chemically altering the extracellular matrix of the brains of live animals before nanotube injection, we reveal that the rheological properties of the ECS are affected, but these alterations are local and inhomogeneous at the nanoscale.

  10. Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

    Science.gov (United States)

    Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

  11. A functional genomics screen in planarians reveals regulators of whole-brain regeneration

    Science.gov (United States)

    Roberts-Galbraith, Rachel H; Brubacher, John L; Newmark, Phillip A

    2016-01-01

    Planarians regenerate all body parts after injury, including the central nervous system (CNS). We capitalized on this distinctive trait and completed a gene expression-guided functional screen to identify factors that regulate diverse aspects of neural regeneration in Schmidtea mediterranea. Our screen revealed molecules that influence neural cell fates, support the formation of a major connective hub, and promote reestablishment of chemosensory behavior. We also identified genes that encode signaling molecules with roles in head regeneration, including some that are produced in a previously uncharacterized parenchymal population of cells. Finally, we explored genes downregulated during planarian regeneration and characterized, for the first time, glial cells in the planarian CNS that respond to injury by repressing several transcripts. Collectively, our studies revealed diverse molecules and cell types that underlie an animal’s ability to regenerate its brain. DOI: http://dx.doi.org/10.7554/eLife.17002.001 PMID:27612384

  12. AUTOMATED CLASSIFICATION AND SEGREGATION OF BRAIN MRI IMAGES INTO IMAGES CAPTURED WITH RESPECT TO VENTRICULAR REGION AND EYE-BALL REGION

    Directory of Open Access Journals (Sweden)

    C. Arunkumar

    2014-05-01

    Full Text Available Magnetic Resonance Imaging (MRI images of the brain are used for detection of various brain diseases including tumor. In such cases, classification of MRI images captured with respect to ventricular and eye ball regions helps in automated location and classification of such diseases. The methods employed in the paper can segregate the given MRI images of brain into images of brain captured with respect to ventricular region and images of brain captured with respect to eye ball region. First, the given MRI image of brain is segmented using Particle Swarm Optimization (PSO algorithm, which is an optimized algorithm for MRI image segmentation. The algorithm proposed in the paper is then applied on the segmented image. The algorithm detects whether the image consist of a ventricular region or an eye ball region and classifies it accordingly.

  13. Which method of posttraumatic stress disorder classification best predicts psychosocial function in children with traumatic brain injury?

    Science.gov (United States)

    Iselin, Greg; Le Brocque, Robyne; Kenardy, Justin; Anderson, Vicki; McKinlay, Lynne

    2010-10-01

    Controversy surrounds the classification of posttraumatic stress disorder (PTSD), particularly in children and adolescents with traumatic brain injury (TBI). In these populations, it is difficult to differentiate TBI-related organic memory loss from dissociative amnesia. Several alternative PTSD classification algorithms have been proposed for use with children. This paper investigates DSM-IV-TR and alternative PTSD classification algorithms, including and excluding the dissociative amnesia item, in terms of their ability to predict psychosocial function following pediatric TBI. A sample of 184 children aged 6-14 years were recruited following emergency department presentation and/or hospital admission for TBI. PTSD was assessed via semi-structured clinical interview (CAPS-CA) with the child at 3 months post-injury. Psychosocial function was assessed using the parent report CHQ-PF50. Two alternative classification algorithms, the PTSD-AA and 2 of 3 algorithms, reached statistical significance. While the inclusion of the dissociative amnesia item increased prevalence rates across algorithms, it generally resulted in weaker associations with psychosocial function. The PTSD-AA algorithm appears to have the strongest association with psychosocial function following TBI in children and adolescents. Removing the dissociative amnesia item from the diagnostic algorithm generally results in improved validity. Copyright 2010 Elsevier Ltd. All rights reserved.

  14. Defining pediatric traumatic brain injury using International Classification of Diseases Version 10 Codes: a systematic review.

    Science.gov (United States)

    Chan, Vincy; Thurairajah, Pravheen; Colantonio, Angela

    2015-02-04

    Although healthcare administrative data are commonly used for traumatic brain injury (TBI) research, there is currently no consensus or consistency on the International Classification of Diseases Version 10 (ICD-10) codes used to define TBI among children and youth internationally. This study systematically reviewed the literature to explore the range of ICD-10 codes that are used to define TBI in this population. The identification of the range of ICD-10 codes to define this population in administrative data is crucial, as it has implications for policy, resource allocation, planning of healthcare services, and prevention strategies. The databases MEDLINE, MEDLINE In-Process, Embase, PsychINFO, CINAHL, SPORTDiscus, and Cochrane Database of Systematic Reviews were systematically searched. Grey literature was searched using Grey Matters and Google. Reference lists of included articles were also searched for relevant studies. Two reviewers independently screened all titles and abstracts using pre-defined inclusion and exclusion criteria. A full text screen was conducted on articles that met the first screen inclusion criteria. All full text articles that met the pre-defined inclusion criteria were included for analysis in this systematic review. A total of 1,326 publications were identified through the predetermined search strategy and 32 articles/reports met all eligibility criteria for inclusion in this review. Five articles specifically examined children and youth aged 19 years or under with TBI. ICD-10 case definitions ranged from the broad injuries to the head codes (ICD-10 S00 to S09) to concussion only (S06.0). There was overwhelming consensus on the inclusion of ICD-10 code S06, intracranial injury, while codes S00 (superficial injury of the head), S03 (dislocation, sprain, and strain of joints and ligaments of head), and S05 (injury of eye and orbit) were only used by articles that examined head injury, none of which specifically examined children and

  15. Recent adaptive events in human brain revealed by meta-analysis of positively selected genes.

    Directory of Open Access Journals (Sweden)

    Yue Huang

    Full Text Available BACKGROUND AND OBJECTIVES: Analysis of positively-selected genes can help us understand how human evolved, especially the evolution of highly developed cognitive functions. However, previous works have reached conflicting conclusions regarding whether human neuronal genes are over-represented among genes under positive selection. METHODS AND RESULTS: We divided positively-selected genes into four groups according to the identification approaches, compiling a comprehensive list from 27 previous studies. We showed that genes that are highly expressed in the central nervous system are enriched in recent positive selection events in human history identified by intra-species genomic scan, especially in brain regions related to cognitive functions. This pattern holds when different datasets, parameters and analysis pipelines were used. Functional category enrichment analysis supported these findings, showing that synapse-related functions are enriched in genes under recent positive selection. In contrast, immune-related functions, for instance, are enriched in genes under ancient positive selection revealed by inter-species coding region comparison. We further demonstrated that most of these patterns still hold even after controlling for genomic characteristics that might bias genome-wide identification of positively-selected genes including gene length, gene density, GC composition, and intensity of negative selection. CONCLUSION: Our rigorous analysis resolved previous conflicting conclusions and revealed recent adaptation of human brain functions.

  16. Revealing the cerebral regions and networks mediating vulnerability to depression: oxidative metabolism mapping of rat brain.

    Science.gov (United States)

    Harro, Jaanus; Kanarik, Margus; Kaart, Tanel; Matrov, Denis; Kõiv, Kadri; Mällo, Tanel; Del Río, Joaquin; Tordera, Rosa M; Ramirez, Maria J

    2014-07-01

    The large variety of available animal models has revealed much on the neurobiology of depression, but each model appears as specific to a significant extent, and distinction between stress response, pathogenesis of depression and underlying vulnerability is difficult to make. Evidence from epidemiological studies suggests that depression occurs in biologically predisposed subjects under impact of adverse life events. We applied the diathesis-stress concept to reveal brain regions and functional networks that mediate vulnerability to depression and response to chronic stress by collapsing data on cerebral long term neuronal activity as measured by cytochrome c oxidase histochemistry in distinct animal models. Rats were rendered vulnerable to depression either by partial serotonergic lesion or by maternal deprivation, or selected for a vulnerable phenotype (low positive affect, low novelty-related activity or high hedonic response). Environmental adversity was brought about by applying chronic variable stress or chronic social defeat. Several brain regions, most significantly median raphe, habenula, retrosplenial cortex and reticular thalamus, were universally implicated in long-term metabolic stress response, vulnerability to depression, or both. Vulnerability was associated with higher oxidative metabolism levels as compared to resilience to chronic stress. Chronic stress, in contrast, had three distinct patterns of effect on oxidative metabolism in vulnerable vs. resilient animals. In general, associations between regional activities in several brain circuits were strongest in vulnerable animals, and chronic stress disrupted this interrelatedness. These findings highlight networks that underlie resilience to stress, and the distinct response to stress that occurs in vulnerable subjects. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Classification of Error Related Brain Activity in an Auditory Identification Task with Conditions of Varying Complexity

    Science.gov (United States)

    Kakkos, I.; Gkiatis, K.; Bromis, K.; Asvestas, P. A.; Karanasiou, I. S.; Ventouras, E. M.; Matsopoulos, G. K.

    2017-11-01

    The detection of an error is the cognitive evaluation of an action outcome that is considered undesired or mismatches an expected response. Brain activity during monitoring of correct and incorrect responses elicits Event Related Potentials (ERPs) revealing complex cerebral responses to deviant sensory stimuli. Development of accurate error detection systems is of great importance both concerning practical applications and in investigating the complex neural mechanisms of decision making. In this study, data are used from an audio identification experiment that was implemented with two levels of complexity in order to investigate neurophysiological error processing mechanisms in actors and observers. To examine and analyse the variations of the processing of erroneous sensory information for each level of complexity we employ Support Vector Machines (SVM) classifiers with various learning methods and kernels using characteristic ERP time-windowed features. For dimensionality reduction and to remove redundant features we implement a feature selection framework based on Sequential Forward Selection (SFS). The proposed method provided high accuracy in identifying correct and incorrect responses both for actors and for observers with mean accuracy of 93% and 91% respectively. Additionally, computational time was reduced and the effects of the nesting problem usually occurring in SFS of large feature sets were alleviated.

  18. Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain.

    Science.gov (United States)

    Kotrschal, Alexander; Rogell, Björn; Bundsen, Andreas; Svensson, Beatrice; Zajitschek, Susanne; Brännström, Ioana; Immler, Simone; Maklakov, Alexei A; Kolm, Niclas

    2013-01-21

    The large variation in brain size that exists in the animal kingdom has been suggested to have evolved through the balance between selective advantages of greater cognitive ability and the prohibitively high energy demands of a larger brain (the "expensive-tissue hypothesis"). Despite over a century of research on the evolution of brain size, empirical support for the trade-off between cognitive ability and energetic costs is based exclusively on correlative evidence, and the theory remains controversial. Here we provide experimental evidence for costs and benefits of increased brain size. We used artificial selection for large and small brain size relative to body size in a live-bearing fish, the guppy (Poecilia reticulata), and found that relative brain size evolved rapidly in response to divergent selection in both sexes. Large-brained females outperformed small-brained females in a numerical learning assay designed to test cognitive ability. Moreover, large-brained lines, especially males, developed smaller guts, as predicted by the expensive-tissue hypothesis, and produced fewer offspring. We propose that the evolution of brain size is mediated by a functional trade-off between increased cognitive ability and reproductive performance and discuss the implications of these findings for vertebrate brain evolution. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. K -shell decomposition reveals hierarchical cortical organization of the human brain

    International Nuclear Information System (INIS)

    Lahav, Nir; Ksherim, Baruch; Havlin, Shlomo; Ben-Simon, Eti; Maron-Katz, Adi; Cohen, Reuven

    2016-01-01

    In recent years numerous attempts to understand the human brain were undertaken from a network point of view. A network framework takes into account the relationships between the different parts of the system and enables to examine how global and complex functions might emerge from network topology. Previous work revealed that the human brain features ‘small world’ characteristics and that cortical hubs tend to interconnect among themselves. However, in order to fully understand the topological structure of hubs, and how their profile reflect the brain’s global functional organization, one needs to go beyond the properties of a specific hub and examine the various structural layers that make up the network. To address this topic further, we applied an analysis known in statistical physics and network theory as k-shell decomposition analysis. The analysis was applied on a human cortical network, derived from MRI/DSI data of six participants. Such analysis enables us to portray a detailed account of cortical connectivity focusing on different neighborhoods of inter-connected layers across the cortex. Our findings reveal that the human cortex is highly connected and efficient, and unlike the internet network contains no isolated nodes. The cortical network is comprised of a nucleus alongside shells of increasing connectivity that formed one connected giant component, revealing the human brain’s global functional organization. All these components were further categorized into three hierarchies in accordance with their connectivity profile, with each hierarchy reflecting different functional roles. Such a model may explain an efficient flow of information from the lowest hierarchy to the highest one, with each step enabling increased data integration. At the top, the highest hierarchy (the nucleus) serves as a global interconnected collective and demonstrates high correlation with consciousness related regions, suggesting that the nucleus might serve as a

  20. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.

    Science.gov (United States)

    Lotte, F; Bougrain, L; Cichocki, A; Clerc, M; Congedo, M; Rakotomamonjy, A; Yger, F

    2018-06-01

    Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

  1. Altered spontaneous brain activity in patients with acute spinal cord injury revealed by resting-state functional MRI.

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    Ling Zhu

    Full Text Available Previous neuroimaging studies have provided evidence of structural and functional reorganization of brain in patients with chronic spinal cord injury (SCI. However, it remains unknown whether the spontaneous brain activity changes in acute SCI. In this study, we investigated intrinsic brain activity in acute SCI patients using a regional homogeneity (ReHo analysis based on resting-state functional magnetic resonance imaging.A total of 15 patients with acute SCI and 16 healthy controls participated in the study. The ReHo value was used to evaluate spontaneous brain activity, and voxel-wise comparisons of ReHo were performed to identify brain regions with altered spontaneous brain activity between groups. We also assessed the associations between ReHo and the clinical scores in brain regions showing changed spontaneous brain activity.Compared with the controls, the acute SCI patients showed decreased ReHo in the bilateral primary motor cortex/primary somatosensory cortex, bilateral supplementary motor area/dorsal lateral prefrontal cortex, right inferior frontal gyrus, bilateral dorsal anterior cingulate cortex and bilateral caudate; and increased ReHo in bilateral precuneus, the left inferior parietal lobe, the left brainstem/hippocampus, the left cingulate motor area, bilateral insula, bilateral thalamus and bilateral cerebellum. The average ReHo values of the left thalamus and right insula were negatively correlated with the international standards for the neurological classification of spinal cord injury motor scores.Our findings indicate that acute distant neuronal damage has an immediate impact on spontaneous brain activity. In acute SCI patients, the ReHo was prominently altered in brain regions involved in motor execution and cognitive control, default mode network, and which are associated with sensorimotor compensatory reorganization. Abnormal ReHo values in the left thalamus and right insula could serve as potential biomarkers for

  2. Toward FRP-Based Brain-Machine Interfaces-Single-Trial Classification of Fixation-Related Potentials.

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    Andrea Finke

    Full Text Available The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant's body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction.

  3. Transcriptomic analyses reveal novel genes with sexually dimorphic expression in the zebrafish gonad and brain.

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    Rajini Sreenivasan

    Full Text Available BACKGROUND: Our knowledge on zebrafish reproduction is very limited. We generated a gonad-derived cDNA microarray from zebrafish and used it to analyze large-scale gene expression profiles in adult gonads and other organs. METHODOLOGY/PRINCIPAL FINDINGS: We have identified 116638 gonad-derived zebrafish expressed sequence tags (ESTs, 21% of which were isolated in our lab. Following in silico normalization, we constructed a gonad-derived microarray comprising 6370 unique, full-length cDNAs from differentiating and adult gonads. Labeled targets from adult gonad, brain, kidney and 'rest-of-body' from both sexes were hybridized onto the microarray. Our analyses revealed 1366, 881 and 656 differentially expressed transcripts (34.7% novel that showed highest expression in ovary, testis and both gonads respectively. Hierarchical clustering showed correlation of the two gonadal transcriptomes and their similarities to those of the brains. In addition, we have identified 276 genes showing sexually dimorphic expression both between the brains and between the gonads. By in situ hybridization, we showed that the gonadal transcripts with the strongest array signal intensities were germline-expressed. We found that five members of the GTP-binding septin gene family, from which only one member (septin 4 has previously been implicated in reproduction in mice, were all strongly expressed in the gonads. CONCLUSIONS/SIGNIFICANCE: We have generated a gonad-derived zebrafish cDNA microarray and demonstrated its usefulness in identifying genes with sexually dimorphic co-expression in both the gonads and the brains. We have also provided the first evidence of large-scale differential gene expression between female and male brains of a teleost. Our microarray would be useful for studying gonad development, differentiation and function not only in zebrafish but also in related teleosts via cross-species hybridizations. Since several genes have been shown to play similar

  4. Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

    Science.gov (United States)

    Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

    2010-06-01

    The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

  5. Rhythmic EEG patterns in extremely preterm infants : Classification and association with brain injury and outcome

    NARCIS (Netherlands)

    Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C.; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

    2017-01-01

    OBJECTIVE: Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. METHODS: Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure

  6. Automatic segmentation of MR brain images of preterm infants using supervised classification

    NARCIS (Netherlands)

    Moeskops, Pim; Benders, Manon J N L; Chiţă, Sabina M.; Kersbergen, Karina J.; Groenendaal, Floris; de Vries, Linda S.; Viergever, Max A.; Isgum, Ivana

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images.This paper

  7. Automatic segmentation of MR brain images of preterm infants using supervised classification

    NARCIS (Netherlands)

    Moeskops, P.; Benders, M.J.N.L.; Chiţ, S.M.; Kersbergen, K.J.; Groenendaal, F.; de Vries, L.S.; Viergever, M.A.; Išgum, I.

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper

  8. On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

    Science.gov (United States)

    Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

    2017-12-01

    Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017

  9. Electrical brain responses in language-impaired children reveal grammar-specific deficits.

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    Elisabeth Fonteneau

    2008-03-01

    Full Text Available Scientific and public fascination with human language have included intensive scrutiny of language disorders as a new window onto the biological foundations of language and its evolutionary origins. Specific language impairment (SLI, which affects over 7% of children, is one such disorder. SLI has received robust scientific attention, in part because of its recent linkage to a specific gene and loci on chromosomes and in part because of the prevailing question regarding the scope of its language impairment: Does the disorder impact the general ability to segment and process language or a specific ability to compute grammar? Here we provide novel electrophysiological data showing a domain-specific deficit within the grammar of language that has been hitherto undetectable through behavioural data alone.We presented participants with Grammatical(G-SLI, age-matched controls, and younger child and adult controls, with questions containing syntactic violations and sentences containing semantic violations. Electrophysiological brain responses revealed a selective impairment to only neural circuitry that is specific to grammatical processing in G-SLI. Furthermore, the participants with G-SLI appeared to be partially compensating for their syntactic deficit by using neural circuitry associated with semantic processing and all non-grammar-specific and low-level auditory neural responses were normal.The findings indicate that grammatical neural circuitry underlying language is a developmentally unique system in the functional architecture of the brain, and this complex higher cognitive system can be selectively impaired. The findings advance fundamental understanding about how cognitive systems develop and all human language is represented and processed in the brain.

  10. Proteomic Profiling in the Brain of CLN1 Disease Model Reveals Affected Functional Modules.

    Science.gov (United States)

    Tikka, Saara; Monogioudi, Evanthia; Gotsopoulos, Athanasios; Soliymani, Rabah; Pezzini, Francesco; Scifo, Enzo; Uusi-Rauva, Kristiina; Tyynelä, Jaana; Baumann, Marc; Jalanko, Anu; Simonati, Alessandro; Lalowski, Maciej

    2016-03-01

    Neuronal ceroid lipofuscinoses (NCL) are the most commonly inherited progressive encephalopathies of childhood. Pathologically, they are characterized by endolysosomal storage with different ultrastructural features and biochemical compositions. The molecular mechanisms causing progressive neurodegeneration and common molecular pathways linking expression of different NCL genes are largely unknown. We analyzed proteome alterations in the brains of a mouse model of human infantile CLN1 disease-palmitoyl-protein thioesterase 1 (Ppt1) gene knockout and its wild-type age-matched counterpart at different stages: pre-symptomatic, symptomatic and advanced. For this purpose, we utilized a combination of laser capture microdissection-based quantitative liquid chromatography tandem mass spectrometry (MS) and matrix-assisted laser desorption/ionization time-of-flight MS imaging to quantify/visualize the changes in protein expression in disease-affected brain thalamus and cerebral cortex tissue slices, respectively. Proteomic profiling of the pre-symptomatic stage thalamus revealed alterations mostly in metabolic processes and inhibition of various neuronal functions, i.e., neuritogenesis. Down-regulation in dynamics associated with growth of plasma projections and cellular protrusions was further corroborated by findings from RNA sequencing of CLN1 patients' fibroblasts. Changes detected at the symptomatic stage included: mitochondrial functions, synaptic vesicle transport, myelin proteome and signaling cascades, such as RhoA signaling. Considerable dysregulation of processes related to mitochondrial cell death, RhoA/Huntington's disease signaling and myelin sheath breakdown were observed at the advanced stage of the disease. The identified changes in protein levels were further substantiated by bioinformatics and network approaches, immunohistochemistry on brain tissues and literature knowledge, thus identifying various functional modules affected in the CLN1 childhood

  11. Lost for emotion words: What motor and limbic brain activity reveals about autism and semantic theory

    Science.gov (United States)

    Moseley, Rachel L.; Shtyrov, Yury; Mohr, Bettina; Lombardo, Michael V.; Baron-Cohen, Simon; Pulvermüller, Friedemann

    2015-01-01

    Autism spectrum conditions (ASC) are characterised by deficits in understanding and expressing emotions and are frequently accompanied by alexithymia, a difficulty in understanding and expressing emotion words. Words are differentially represented in the brain according to their semantic category and these difficulties in ASC predict reduced activation to emotion-related words in limbic structures crucial for affective processing. Semantic theories view ‘emotion actions’ as critical for learning the semantic relationship between a word and the emotion it describes, such that emotion words typically activate the cortical motor systems involved in expressing emotion actions such as facial expressions. As ASC are also characterised by motor deficits and atypical brain structure and function in these regions, motor structures would also be expected to show reduced activation during emotion-semantic processing. Here we used event-related fMRI to compare passive processing of emotion words in comparison to abstract verbs and animal names in typically-developing controls and individuals with ASC. Relatively reduced brain activation in ASC for emotion words, but not matched control words, was found in motor areas and cingulate cortex specifically. The degree of activation evoked by emotion words in the motor system was also associated with the extent of autistic traits as revealed by the Autism Spectrum Quotient. We suggest that hypoactivation of motor and limbic regions for emotion word processing may underlie difficulties in processing emotional language in ASC. The role that sensorimotor systems and their connections might play in the affective and social-communication difficulties in ASC is discussed. PMID:25278250

  12. The Brain-to-Pancreatic Islet Neuronal Map Reveals Differential Glucose Regulation From Distinct Hypothalamic Regions.

    Science.gov (United States)

    Rosario, Wilfredo; Singh, Inderroop; Wautlet, Arnaud; Patterson, Christa; Flak, Jonathan; Becker, Thomas C; Ali, Almas; Tamarina, Natalia; Philipson, Louis H; Enquist, Lynn W; Myers, Martin G; Rhodes, Christopher J

    2016-09-01

    The brain influences glucose homeostasis, partly by supplemental control over insulin and glucagon secretion. Without this central regulation, diabetes and its complications can ensue. Yet, the neuronal network linking to pancreatic islets has never been fully mapped. Here, we refine this map using pseudorabies virus (PRV) retrograde tracing, indicating that the pancreatic islets are innervated by efferent circuits that emanate from the hypothalamus. We found that the hypothalamic arcuate nucleus (ARC), ventromedial nucleus (VMN), and lateral hypothalamic area (LHA) significantly overlap PRV and the physiological glucose-sensing enzyme glucokinase. Then, experimentally lowering glucose sensing, specifically in the ARC, resulted in glucose intolerance due to deficient insulin secretion and no significant effect in the VMN, but in the LHA it resulted in a lowering of the glucose threshold that improved glucose tolerance and/or improved insulin sensitivity, with an exaggerated counter-regulatory response for glucagon secretion. No significant effect on insulin sensitivity or metabolic homeostasis was noted. Thus, these data reveal novel direct neuronal effects on pancreatic islets and also render a functional validation of the brain-to-islet neuronal map. They also demonstrate that distinct regions of the hypothalamus differentially control insulin and glucagon secretion, potentially in partnership to help maintain glucose homeostasis and guard against hypoglycemia. © 2016 by the American Diabetes Association.

  13. PET Imaging Reveals Brain Metabolic Changes in Adolescent Rats Following Chronic Escalating Morphine Administration.

    Science.gov (United States)

    Chen, Qing; Hou, Haifeng; Feng, Jin; Zhang, Xiaohui; Chen, Yao; Wang, Jing; Ji, Jianfeng; He, Xiao; Wu, Hao; Zhang, Hong

    2018-04-10

    Non-medical use of prescription opioids, especially among adolescents, has been substantially increased in recent years. However, the neuromechanism remains largely unexplored. In the present study, we aimed to investigate the brain metabolic changes in adolescent rats following chronic escalating morphine administration using positron emission tomography (PET). 2-Deoxy-2-[ 18 F]Fluoro-D-glucose ([ 18 F]FDG) microPET imaging was performed, and statistical parametric mapping (SPM) was used for image analysis. Glucose transporter 3 (Glut-3), dopamine D 2 receptor (D 2 R), and Mμ-opioid receptor (μ-OR) were used for immunostaining analysis. Cerebral glucose metabolism was increased in the corpus callosum (CC) and right retrosplenial dysgranular cortex (rRSD), while it was decreased in the right ventral pallidum (rVP). The expressions of Glut-3, D 2 R, and μ-OR were increased in CC and rRSD, while they were decreased in rVP. Furthermore, glucose metabolism and Glut-3 expression were positively correlated with the expressions of D 2 R or μ-OR in CC, rRSD, and rVP. [ 18 F]FDG microPET brain imaging study in combination with immunohistological investigation revealed that CC, rRSD, and rVP were specifically involved in opioid dependence in adolescents. Our findings provided valuable insights into the neuromechanism of adolescent addiction of prescription opioids and might have important implications for the development of prevention and intervention approaches.

  14. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

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    Liu, Yaou, E-mail: asiaeurope80@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Duan, Yunyun, E-mail: xiaoyun81.love@163.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); He, Yong, E-mail: yong.h.he@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Yu, Chunshui, E-mail: csyuster@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Wang, Jun, E-mail: jun_wang@bnu.edu.cn [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Huang, Jing, E-mail: sainthj@126.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Ye, Jing, E-mail: jingye.2007@yahoo.com.cn [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Parizel, Paul M., E-mail: paul.parizel@ua.ac.be [Department of Radiology, Antwerp University Hospital and University of Antwerp, Wilrijkstraat 10, 2650 Edegem, 8 Belgium (Belgium); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shu, Ni, E-mail: nshu55@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China)

    2012-10-15

    Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS.

  15. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

    International Nuclear Information System (INIS)

    Liu, Yaou; Duan, Yunyun; He, Yong; Yu, Chunshui; Wang, Jun; Huang, Jing; Ye, Jing; Parizel, Paul M.; Li, Kuncheng; Shu, Ni

    2012-01-01

    Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS

  16. Water diffusion closely reveals neural activity status in rat brain loci affected by anesthesia.

    Directory of Open Access Journals (Sweden)

    Yoshifumi Abe

    2017-04-01

    Full Text Available Diffusion functional MRI (DfMRI reveals neuronal activation even when neurovascular coupling is abolished, contrary to blood oxygenation level-dependent (BOLD functional MRI (fMRI. Here, we show that the water apparent diffusion coefficient (ADC derived from DfMRI increased in specific rat brain regions under anesthetic conditions, reflecting the decreased neuronal activity observed with local field potentials (LFPs, especially in regions involved in wakefulness. In contrast, BOLD signals showed nonspecific changes, reflecting systemic effects of the anesthesia on overall brain hemodynamics status. Electrical stimulation of the central medial thalamus nucleus (CM exhibiting this anesthesia-induced ADC increase led the animals to transiently wake up. Infusion in the CM of furosemide, a specific neuronal swelling blocker, led the ADC to increase further locally, although LFP activity remained unchanged, and increased the current threshold awakening the animals under CM electrical stimulation. Oppositely, induction of cell swelling in the CM through infusion of a hypotonic solution (-80 milliosmole [mOsm] artificial cerebrospinal fluid [aCSF] led to a local ADC decrease and a lower current threshold to wake up the animals. Strikingly, the local ADC changes produced by blocking or enhancing cell swelling in the CM were also mirrored remotely in areas functionally connected to the CM, such as the cingulate and somatosensory cortex. Together, those results strongly suggest that neuronal swelling is a significant mechanism underlying DfMRI.

  17. Glycogen distribution in the microwave-fixed mouse brain reveals heterogeneous astrocytic patterns.

    Science.gov (United States)

    Oe, Yuki; Baba, Otto; Ashida, Hitoshi; Nakamura, Kouichi C; Hirase, Hajime

    2016-09-01

    In the brain, glycogen metabolism has been implied in synaptic plasticity and learning, yet the distribution of this molecule has not been fully described. We investigated cerebral glycogen of the mouse by immunohistochemistry (IHC) using two monoclonal antibodies that have different affinities depending on the glycogen size. The use of focused microwave irradiation yielded well-defined glycogen immunoreactive signals compared with the conventional periodic acid-Schiff method. The IHC signals displayed a punctate distribution localized predominantly in astrocytic processes. Glycogen immunoreactivity (IR) was high in the hippocampus, striatum, cortex, and cerebellar molecular layer, whereas it was low in the white matter and most of the subcortical structures. Additionally, glycogen distribution in the hippocampal CA3-CA1 and striatum had a 'patchy' appearance with glycogen-rich and glycogen-poor astrocytes appearing in alternation. The glycogen patches were more evident with large-molecule glycogen in young adult mice but they were hardly observable in aged mice (1-2 years old). Our results reveal brain region-dependent glycogen accumulation and possibly metabolic heterogeneity of astrocytes. GLIA 2016;64:1532-1545. © 2016 The Authors. Glia Published by Wiley Periodicals, Inc.

  18. Glycogen distribution in the microwave‐fixed mouse brain reveals heterogeneous astrocytic patterns

    Science.gov (United States)

    Baba, Otto; Ashida, Hitoshi; Nakamura, Kouichi C.

    2016-01-01

    In the brain, glycogen metabolism has been implied in synaptic plasticity and learning, yet the distribution of this molecule has not been fully described. We investigated cerebral glycogen of the mouse by immunohistochemistry (IHC) using two monoclonal antibodies that have different affinities depending on the glycogen size. The use of focused microwave irradiation yielded well‐defined glycogen immunoreactive signals compared with the conventional periodic acid‐Schiff method. The IHC signals displayed a punctate distribution localized predominantly in astrocytic processes. Glycogen immunoreactivity (IR) was high in the hippocampus, striatum, cortex, and cerebellar molecular layer, whereas it was low in the white matter and most of the subcortical structures. Additionally, glycogen distribution in the hippocampal CA3‐CA1 and striatum had a ‘patchy’ appearance with glycogen‐rich and glycogen‐poor astrocytes appearing in alternation. The glycogen patches were more evident with large‐molecule glycogen in young adult mice but they were hardly observable in aged mice (1–2 years old). Our results reveal brain region‐dependent glycogen accumulation and possibly metabolic heterogeneity of astrocytes. GLIA 2016;64:1532–1545 PMID:27353480

  19. Diversity of sharp-wave–ripple LFP signatures reveals differentiated brain-wide dynamical events

    Science.gov (United States)

    Ramirez-Villegas, Juan F.; Logothetis, Nikos K.; Besserve, Michel

    2015-01-01

    Sharp-wave–ripple (SPW-R) complexes are believed to mediate memory reactivation, transfer, and consolidation. However, their underlying neuronal dynamics at multiple scales remains poorly understood. Using concurrent hippocampal local field potential (LFP) recordings and functional MRI (fMRI), we study local changes in neuronal activity during SPW-R episodes and their brain-wide correlates. Analysis of the temporal alignment between SPW and ripple components reveals well-differentiated SPW-R subtypes in the CA1 LFP. SPW-R–triggered fMRI maps show that ripples aligned to the positive peak of their SPWs have enhanced neocortical metabolic up-regulation. In contrast, ripples occurring at the trough of their SPWs relate to weaker neocortical up-regulation and absent subcortical down-regulation, indicating differentiated involvement of neuromodulatory pathways in the ripple phenomenon mediated by long-range interactions. To our knowledge, this study provides the first evidence for the existence of SPW-R subtypes with differentiated CA1 activity and metabolic correlates in related brain areas, possibly serving different memory functions. PMID:26540729

  20. Diversity of sharp-wave-ripple LFP signatures reveals differentiated brain-wide dynamical events.

    Science.gov (United States)

    Ramirez-Villegas, Juan F; Logothetis, Nikos K; Besserve, Michel

    2015-11-17

    Sharp-wave-ripple (SPW-R) complexes are believed to mediate memory reactivation, transfer, and consolidation. However, their underlying neuronal dynamics at multiple scales remains poorly understood. Using concurrent hippocampal local field potential (LFP) recordings and functional MRI (fMRI), we study local changes in neuronal activity during SPW-R episodes and their brain-wide correlates. Analysis of the temporal alignment between SPW and ripple components reveals well-differentiated SPW-R subtypes in the CA1 LFP. SPW-R-triggered fMRI maps show that ripples aligned to the positive peak of their SPWs have enhanced neocortical metabolic up-regulation. In contrast, ripples occurring at the trough of their SPWs relate to weaker neocortical up-regulation and absent subcortical down-regulation, indicating differentiated involvement of neuromodulatory pathways in the ripple phenomenon mediated by long-range interactions. To our knowledge, this study provides the first evidence for the existence of SPW-R subtypes with differentiated CA1 activity and metabolic correlates in related brain areas, possibly serving different memory functions.

  1. Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets.

    Science.gov (United States)

    Agarwal, Rahul; Narayan, Jitendra; Bhattacharyya, Amitava; Saraswat, Mayank; Tomar, Anil Kumar

    2017-10-01

    A very low 5-year survival rate among hepatocellular carcinoma (HCC) patients is mainly due to lack of early stage diagnosis, distant metastasis and high risk of postoperative recurrence. Hence ascertaining novel biomarkers for early diagnosis and patient specific therapeutics is crucial and urgent. Here, we have performed a comprehensive analysis of the expression data of 423 HCC patients (373 tumors and 50 controls) downloaded from The Cancer Genome Atlas (TCGA) followed by pathway enrichment by gene ontology annotations, subtype classification and overall survival analysis. The differential gene expression analysis using non-parametric Wilcoxon test revealed a total of 479 up-regulated and 91 down-regulated genes in HCC compared to controls. The list of top differentially expressed genes mainly consists of tumor/cancer associated genes, such as AFP, THBS4, LCN2, GPC3, NUF2, etc. The genes over-expressed in HCC were mainly associated with cell cycle pathways. In total, 59 kinases associated genes were found over-expressed in HCC, including TTK, MELK, BUB1, NEK2, BUB1B, AURKB, PLK1, CDK1, PKMYT1, PBK, etc. Overall four distinct HCC subtypes were predicted using consensus clustering method. Each subtype was unique in terms of gene expression, pathway enrichment and median survival. Conclusively, this study has exposed a number of interesting genes which can be exploited in future as potential markers of HCC, diagnostic as well as prognostic and subtype classification may guide for improved and specific therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Split-brain reveals separate but equal self-recognition in the two cerebral hemispheres.

    Science.gov (United States)

    Uddin, Lucina Q; Rayman, Jan; Zaidel, Eran

    2005-09-01

    To assess the ability of the disconnected cerebral hemispheres to recognize images of the self, a split-brain patient (an individual who underwent complete cerebral commissurotomy to relieve intractable epilepsy) was tested using morphed self-face images presented to one visual hemifield (projecting to one hemisphere) at a time while making "self/other" judgments. The performance of the right and left hemispheres of this patient as assessed by a signal detection method was not significantly different, though a measure of bias did reveal hemispheric differences. The right and left hemispheres of this patient independently and equally possessed the ability to self-recognize, but only the right hemisphere could successfully recognize familiar others. This supports a modular concept of self-recognition and other-recognition, separately present in each cerebral hemisphere.

  3. Discovery of methylfarnesoate as the annelid brain hormone reveals an ancient role of sesquiterpenoids in reproduction.

    Science.gov (United States)

    Schenk, Sven; Krauditsch, Christian; Frühauf, Peter; Gerner, Christopher; Raible, Florian

    2016-11-29

    Animals require molecular signals to determine when to divert resources from somatic functions to reproduction. This decision is vital in animals that reproduce in an all-or-nothing mode, such as bristle worms: females committed to reproduction spend roughly half their body mass for yolk and egg production; following mass spawning, the parents die. An enigmatic brain hormone activity suppresses reproduction. We now identify this hormone as the sesquiterpenoid methylfarnesoate. Methylfarnesoate suppresses transcript levels of the yolk precursor Vitellogenin both in cell culture and in vivo , directly inhibiting a central energy-costly step of reproductive maturation. We reveal that contrary to common assumptions, sesquiterpenoids are ancient animal hormones present in marine and terrestrial lophotrochozoans. In turn, insecticides targeting this pathway suppress vitellogenesis in cultured worm cells. These findings challenge current views of animal hormone evolution, and indicate that non-target species and marine ecosystems are susceptible to commonly used insect larvicides.

  4. Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

    Science.gov (United States)

    Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

    2017-04-01

    Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. Distinguishing Adolescents With Conduct Disorder From Typically Developing Youngsters Based on Pattern Classification of Brain Structural MRI

    Directory of Open Access Journals (Sweden)

    Jianing Zhang

    2018-04-01

    Full Text Available Background: Conduct disorder (CD is a mental disorder diagnosed in childhood or adolescence that presents antisocial behaviors, and is associated with structural alterations in brain. However, whether these structural alterations can distinguish CD from healthy controls (HCs remains unknown. Here, we quantified these structural differences and explored the classification ability of these quantitative features based on machine learning (ML.Materials and Methods: High-resolution 3D structural magnetic resonance imaging (sMRI was acquired from 60 CD subjects and 60 age-matched HCs. Voxel-based morphometry (VBM was used to assess the regional gray matter (GM volume difference. The significantly different regional GM volumes were then extracted as features, and input into three ML classifiers: logistic regression, random forest and support vector machine (SVM. We trained and tested these ML models for classifying CD from HCs by using fivefold cross-validation (CV.Results: Eight brain regions with abnormal GM volumes were detected, which mainly distributed in the frontal lobe, parietal lobe, anterior cingulate, cerebellum posterior lobe, lingual gyrus, and insula areas. We found that these ML models achieved comparable classification performance, with accuracy of 77.9 ∼ 80.4%, specificity of 73.3 ∼ 80.4%, sensitivity of 75.4 ∼ 87.5%, and area under the receiver operating characteristic curve (AUC of 0.76 ∼ 0.80.Conclusion: Based on sMRI and ML, the regional GM volumes may be used as potential imaging biomarkers for stable and accurate classification of CD.

  6. Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

    Science.gov (United States)

    Kim, Yong-Ku; Na, Kyoung-Sae

    2018-01-03

    Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright

  7. [Nordic accident classification system used in the Danish National Hospital Registration System to register causes of severe traumatic brain injury].

    Science.gov (United States)

    Engberg, Aase Worsaa; Penninga, Elisabeth Irene; Teasdale, Thomas William

    2007-11-05

    The purpose was to illustrate the use of the accident classification system worked out by the Nordic Medico-Statistical Committee (NOMESCO). In particular, registration of causes of severe traumatic brain injury according to the system as part of the Danish National Hospital Registration System was studied. The study comprised 117 patients with very severe traumatic brain injury (TBI) admitted to the Brain Injury Unit of the University Hospital in Hvidovre, Copenhagen, from 1 October 2000 to 30 September 2002. Prospective NOMESCO coding at discharge was compared to independent retrospective coding based on hospital records, and to coding from other wards in the Danish National Hospital Registration System. Furthermore, sets of codes in the Danish National Hospital Registration System for consecutive admissions after a particular accident were compared. Identical results of prospective and independent retrospective coding were found for 65% of 588 single codes, and complete sets of codes for the same accident were identical only in 28% of cases. Sets of codes for the first admission in a hospital course corresponded to retrospective coding at the end of the course in only 17% of cases. Accident code sets from different wards, based on the same injury, were identical in only 7% of cases. Prospective coding by the NOMESCO accident classification system proved problematic, both with regard to correctness and completeness. The system--although logical--seems too complicated compared to the resources invested in the coding. The results of this investigation stress the need for better management and for better instruction to those who carry out the registration.

  8. Magnetic resonance imaging in classification of congenital muscular dystrophies with brain abnormalities

    NARCIS (Netherlands)

    vanderKnaap, MS; Smit, LME; Barth, PG; CatsmanBerrevoets, CE; Brouwer, OF; Begeer, JH; deCoo, IFM; Valk, J.

    A survey was performed of magnetic resonance imaging (MRI) findings in 21 patients with congenital muscular dystrophy (QID) with cerebral abnormalities to evaluate the contribution of MRI to the classification of CMD patients. In 5 patients with Walker-Warburg syndrome (WWS), MRI showed

  9. Altered spontaneous brain activity in adolescent boys with pure conduct disorder revealed by regional homogeneity analysis.

    Science.gov (United States)

    Wu, Qiong; Zhang, Xiaocui; Dong, Daifeng; Wang, Xiang; Yao, Shuqiao

    2017-07-01

    Functional magnetic resonance imaging (fMRI) studies have revealed abnormal neural activity in several brain regions of adolescents with conduct disorder (CD) performing various tasks. However, little is known about the spontaneous neural activity in people with CD in a resting state. The aims of this study were to investigate CD-associated regional activity abnormalities and to explore the relationship between behavioral impulsivity and regional activity abnormalities. Resting-state fMRI (rs-fMRI) scans were administered to 28 adolescents with CD and 28 age-, gender-, and IQ-matched healthy controls (HCs). The rs-fMRI data were subjected to regional homogeneity (ReHo) analysis. ReHo can demonstrate the temporal synchrony of regional blood oxygen level-dependent signals and reflect the coordination of local neuronal activity facilitating similar goals or representations. Compared to HCs, the CD group showed increased ReHo bilaterally in the insula as well as decreased ReHo in the right inferior parietal lobule, right middle temporal gyrus and right fusiform gyrus, left anterior cerebellum anterior, and right posterior cerebellum. In the CD group, mean ReHo values in the left and the right insula correlated positively with Barratt Impulsivity Scale (BIS) total scores. The results suggest that CD is associated with abnormal intrinsic brain activity, mainly in the cerebellum and temporal-parietal-limbic cortices, regions that are related to emotional and cognitive processing. BIS scores in adolescents with CD may reflect severity of abnormal neuronal synchronization in the insula.

  10. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

    NARCIS (Netherlands)

    Guadalupe, Tulio; Mathias, Samuel R.; vanErp, Theo G.M.; Whelan, Christopher D.; Zwiers, Marcel P.; Abe, Yoshinari; Abramovic, Lucija; Agartz, Ingrid; Andreassen, Ole A.; Arias-Vásquez, Alejandro; Aribisala, Benjamin S.; Armstrong, Nicola J.; Arolt, Volker; Artiges, Eric; Ayesa-Arriola, Rosa; Baboyan, Vatche G.; Banaschewski, Tobias; Barker, Gareth; Bastin, Mark E.; Baune, Bernhard T.; Blangero, John; Bokde, Arun L.W.; Boedhoe, Premika S.W.; Bose, Anushree; Brem, Silvia; Brodaty, Henry; Bromberg, Uli; Brooks, Samantha; Büchel, Christian; Buitelaar, Jan; Calhoun, Vince D.; Cannon, Dara M.; Cattrell, Anna; Cheng, Yuqi; Conrod, Patricia J.; Conzelmann, Annette; Corvin, Aiden; Crespo-Facorro, Benedicto; Crivello, Fabrice; Dannlowski, Udo; de Zubicaray, Greig I.; de Zwarte, Sonja M.C.; Deary, Ian J.; Desrivières, Sylvane; Doan, Nhat Trung; Donohoe, Gary; Dørum, Erlend S.; Ehrlich, Stefan; Espeseth, Thomas; Fernández, Guillén; Flor, Herta; Fouche, Jean Paul; Frouin, Vincent; Fukunaga, Masaki; Gallinat, Jürgen; Garavan, Hugh; Gill, Michael; Suarez, Andrea Gonzalez; Gowland, Penny; Grabe, Hans J.; Grotegerd, Dominik; Gruber, Oliver; Hagenaars, Saskia; Hashimoto, Ryota; Hauser, Tobias U.; Heinz, Andreas; Hibar, Derrek P.; Hoekstra, Pieter J.; Hoogman, Martine; Howells, Fleur M.; Hu, Hao; Hulshoff Pol, Hilleke E.; Huyser, Chaim; Ittermann, Bernd; Jahanshad, Neda; Jönsson, Erik G.; Jurk, Sarah; Kahn, Rene S.; Kelly, Sinead; Kraemer, Bernd; Kugel, Harald; Kwon, Jun Soo; Lemaitre, Herve; Lesch, Klaus Peter; Lochner, Christine; Luciano, Michelle; Marquand, Andre F.; Martin, Nicholas G.; Martínez-Zalacaín, Ignacio; Martinot, Jean Luc; Mataix-Cols, David; Mather, Karen; McDonald, Colm; McMahon, Katie L.; Medland, Sarah E.; Menchón, José M.; Morris, Derek W.; Mothersill, Omar; Maniega, Susana Munoz; Mwangi, Benson; Nakamae, Takashi; Nakao, Tomohiro; Narayanaswaamy, Janardhanan C.; Nees, Frauke; Nordvik, Jan E.; Onnink, A. Marten H.; Opel, Nils; Ophoff, Roel; Paillère Martinot, Marie Laure; Papadopoulos Orfanos, Dimitri; Pauli, Paul; Paus, Tomáš; Poustka, Luise; Reddy, Janardhan Yc; Renteria, Miguel E.; Roiz-Santiáñez, Roberto; Roos, Annerine; Royle, Natalie A.; Sachdev, Perminder; Sánchez-Juan, Pascual; Schmaal, Lianne; Schumann, Gunter; Shumskaya, Elena; Smolka, Michael N.; Soares, Jair C.; Soriano-Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Toro, Roberto; Turner, Jessica A.; Tzourio-Mazoyer, Nathalie; Uhlmann, Anne; Hernández, Maria Valdés; van den Heuvel, Odile A.; van der Meer, Dennis; van Haren, Neeltje E.M.; Veltman, Dick J.; Venkatasubramanian, Ganesan; Vetter, Nora C.; Vuletic, Daniella; Walitza, Susanne; Walter, Henrik; Walton, Esther; Wang, Zhen; Wardlaw, Joanna; Wen, Wei; Westlye, Lars T.; Whelan, Robert; Wittfeld, Katharina; Wolfers, Thomas; Wright, Margaret J.; Xu, Jian; Xu, Xiufeng; Yun, Je Yeon; Zhao, Jing Jing; Franke, Barbara; Thompson, Paul M.; Glahn, David C.; Mazoyer, Bernard; Fisher, Simon E.; Francks, Clyde

    2017-01-01

    The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain

  11. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  12. Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging

    Science.gov (United States)

    Pohling, Christoph; Bocklitz, Thomas; Duarte, Alex S.; Emmanuello, Cinzia; Ishikawa, Mariana S.; Dietzeck, Benjamin; Buckup, Tiago; Uckermann, Ortrud; Schackert, Gabriele; Kirsch, Matthias; Schmitt, Michael; Popp, Jürgen; Motzkus, Marcus

    2017-06-01

    Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

  13. WAIS Digit Span-Based Indicators of Malingered Neurocognitive Dysfunction: Classification Accuracy in Traumatic Brain Injury

    Science.gov (United States)

    Heinly, Matthew T.; Greve, Kevin W.; Bianchini, Kevin J.; Love, Jeffrey M.; Brennan, Adrianne

    2005-01-01

    The present study determined specificity and sensitivity to malingered neurocognitive dysfunction (MND) in traumatic brain injury (TBI) for several Wechsler Adult Intelligence Scale (WAIS) Digit Span scores. TBI patients (n = 344) were categorized into one of five groups: no incentive, incentive only, suspect, probable MND, and definite MND.…

  14. 77 FR 16925 - Medical Devices; Neurological Devices; Classification of the Near Infrared Brain Hematoma Detector

    Science.gov (United States)

    2012-03-23

    ... notification, prior to marketing the device, which contains information about the NIR Brain Hematoma Detector...), and the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-4). Executive Orders 12866 and 13563 direct..., environmental, public health and safety, and other advantages; distributive impacts; and equity). The Agency...

  15. Safety and reliability of the insertable Reveal XT recorder in patients undergoing 3 Tesla brain magnetic resonance imaging.

    Science.gov (United States)

    Haeusler, Karl Georg; Koch, Lydia; Ueberreiter, Juliane; Coban, Nalan; Safak, Erdal; Kunze, Claudia; Villringer, Kersten; Endres, Matthias; Schultheiss, Heinz-Peter; Fiebach, Jochen B; Schirdewan, Alexander

    2011-03-01

    Up to now there is little evidence about the safety and reliability of insertable cardiac monitors (ICMs) in patients undergoing magnetic resonance imaging (MRI). The purpose of this prospective single-center study (MACPAF; clinicaltrials.govNCT01061931), which we are currently performing, was to evaluate these issues for the ICM Reveal XT at a 3 Tesla MRI scanner in patients undergoing serial brain MRI. We present an interim analysis including 62 brain MRI examinations in 24 patients with paroxysmal atrial fibrillation bearing the Reveal XT. All patients were interviewed for potential ICM-associated clinical symptoms during and after MRI examination. According to the study protocol, data from the Reveal XT were transmitted before and after the MRI examination. All patients were clinically asymptomatic during the MRI procedure. Moreover, the reliability (ability to detect signals, battery status) of the Reveal XT was unaffected, except for one MRI-induced artifact that was recorded by the ICM, mimicking a narrow complex tachycardia, as similarly recorded in a further study patient bearing the forerunner ICM Reveal DX. No loss of ICM data was observed after the MRI examination. The 3 Tesla brain MRI scanning is safe for patients bearing the ICM Reveal XT and does not alloy reliability of the Reveal XT itself. MRI-induced artifacts occur rarely but have to be taken into account. Copyright © 2011 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

  16. Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD.

    Science.gov (United States)

    Atasoy, Selen; Roseman, Leor; Kaelen, Mendel; Kringelbach, Morten L; Deco, Gustavo; Carhart-Harris, Robin L

    2017-12-15

    Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used 'connectome-harmonic decomposition', a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

  17. Support vector machine classification and characterization of age-related reorganization of functional brain networks.

    Science.gov (United States)

    Meier, Timothy B; Desphande, Alok S; Vergun, Svyatoslav; Nair, Veena A; Song, Jie; Biswal, Bharat B; Meyerand, Mary E; Birn, Rasmus M; Prabhakaran, Vivek

    2012-03-01

    Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18-35 yrs) and 26 older adults (55-85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5mm(3) radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in

  18. Protein profiling in serum after traumatic brain injury in rats reveals potential injury markers.

    Science.gov (United States)

    Thelin, Eric Peter; Just, David; Frostell, Arvid; Häggmark-Månberg, Anna; Risling, Mårten; Svensson, Mikael; Nilsson, Peter; Bellander, Bo-Michael

    2018-03-15

    The serum proteome following traumatic brain injury (TBI) could provide information for outcome prediction and injury monitoring. The aim with this affinity proteomic study was to identify serum proteins over time and between normoxic and hypoxic conditions in focal TBI. Sprague Dawley rats (n=73) received a 3mm deep controlled cortical impact ("severe injury"). Following injury, the rats inhaled either a normoxic (22% O 2 ) or hypoxic (11% O 2 ) air mixture for 30min before resuscitation. The rats were sacrificed at day 1, 3, 7, 14 and 28 after trauma. A total of 204 antibodies targeting 143 unique proteins of interest in TBI research, were selected. The sample proteome was analyzed in a suspension bead array set-up. Comparative statistics and factor analysis were used to detect differences as well as variance in the data. We found that complement factor 9 (C9), complement factor B (CFB) and aldolase c (ALDOC) were detected at higher levels the first days after trauma. In contrast, hypoxia inducing factor (HIF)1α, amyloid precursor protein (APP) and WBSCR17 increased over the subsequent weeks. S100A9 levels were higher in hypoxic-compared to normoxic rats, together with a majority of the analyzed proteins, albeit few reached statistical significance. The principal component analysis revealed a variance in the data, highlighting clusters of proteins. Protein profiling of serum following TBI using an antibody based microarray revealed temporal changes of several proteins over an extended period of up to four weeks. Further studies are warranted to confirm our findings. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

  19. Recovered neuronal viability revealed by Iodine-123-iomazenil SPECT following traumatic brain injury.

    Science.gov (United States)

    Koizumi, Hiroyasu; Fujisawa, Hirosuke; Kurokawa, Tetsu; Suehiro, Eiichi; Iwanaga, Hideyuki; Nakagawara, Jyoji; Suzuki, Michiyasu

    2010-10-01

    We evaluated cortical damages following traumatic brain injury (TBI) in the acute phase with [(123)I] iomazenil (IMZ) single photon emission computed tomography (SPECT). In all, 12 patients with cerebral contusion following TBI were recruited. All patients underwent IMZ SPECT within 1 week after TBI. To investigate the changes in distribution of IMZ in the cortex in the chronic phase, after conventional treatment, patients underwent IMZ SPECT again. A decrease in the accumulation of radioligand for the central benzodiazepine receptor in the cortex corresponding to the contusion revealed with computed tomography (CT) scans and magnetic resonance imaging (MRI) were shown on IMZ SPECT in the acute phase in all patients. In 9 of 12 patients (75%), images of IMZ SPECT obtained in the chronic phase of TBI showed that areas with a decreased distribution of IMZ were remarkably reduced in comparison with those obtained in the acute phase. Both CT scans and MRI showed a normal appearance of the cortex morphologically, where the binding potential of IMZ recovered in the chronic phase. Reduced binding potential of radioligand for the central benzodiazepine receptor is considered to be an irreversible reaction; however, in this study, IMZ accumulation in the cortex following TBI was recovered in the chronic phase in several patients. [(123)I] iomazenil SPECT may have a potential to disclose a reversible vulnerability of neurons following TBI.

  20. Brain Signals of Face Processing as Revealed by Event-Related Potentials

    Directory of Open Access Journals (Sweden)

    Ela I. Olivares

    2015-01-01

    Full Text Available We analyze the functional significance of different event-related potentials (ERPs as electrophysiological indices of face perception and face recognition, according to cognitive and neurofunctional models of face processing. Initially, the processing of faces seems to be supported by early extrastriate occipital cortices and revealed by modulations of the occipital P1. This early response is thought to reflect the detection of certain primary structural aspects indicating the presence grosso modo of a face within the visual field. The posterior-temporal N170 is more sensitive to the detection of faces as complex-structured stimuli and, therefore, to the presence of its distinctive organizational characteristics prior to within-category identification. In turn, the relatively late and probably more rostrally generated N250r and N400-like responses might respectively indicate processes of access and retrieval of face-related information, which is stored in long-term memory (LTM. New methods of analysis of electrophysiological and neuroanatomical data, namely, dynamic causal modeling, single-trial and time-frequency analyses, are highly recommended to advance in the knowledge of those brain mechanisms concerning face processing.

  1. Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface

    Science.gov (United States)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

    Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

  2. Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity.

    Science.gov (United States)

    Liu, Meijie; Zeng, Ling-Li; Shen, Hui; Liu, Zhening; Hu, Dewen

    2012-03-28

    Recent resting-state functional connectivity MRI studies using group-level statistical analysis have demonstrated the inheritable characters of schizophrenia. The objective of the present study was to use pattern classification as a means to investigate schizophrenia inheritance based on the whole-brain resting-state functional connectivity at the individual subject level. One-against-one pattern classifications were made amongst three groups (i.e. patients diagnosed with schizophrenia, healthy siblings, and healthy controls after preprocessing), resulting in an 80.4% separation between patients with schizophrenia and healthy controls, a 77.6% separation between schizophrenia patients and their healthy siblings, and a 78.7% separation between healthy siblings and healthy controls, respectively. These results suggest that the healthy siblings of schizophrenia patients have an altered resting-state functional connectivity pattern compared with healthy controls. Thus, healthy siblings may have a potential higher risk for developing schizophrenia compared with the general population. Moreover, this pattern differed from that of schizophrenia patients and may contribute to the normal behavior exhibition of healthy siblings in daily life.

  3. Intra-axial brain tumors in adults. On the basis of the 2016 WHO classification

    International Nuclear Information System (INIS)

    Peitgen, N.; Papanagiotou, P.

    2017-01-01

    The influence of the World Health Organization (WHO) classification from 2016 on the radiological diagnosis for tumors of the central nervous system (CNS) in adults. Computed tomography (CT), magnetic resonance imaging (MRI) and MR spectroscopy. In order to come as close as possible to the correct diagnosis of CNS tumors, MRI is the long-standing accepted method of choice that can in some cases be supported by the use of CT to demonstrate calcification or bone destruction. In individual cases MRI spectroscopy can be helpful for the differentiation between neoplasms and inflammatory lesions or surveillance of tumor therapy, just as perfusion, which is not discussed in this article. (orig.) [de

  4. USE OF DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING FOR REVEALING HYPOXIC-ISCHEMIC BRAIN LESIONS IN NEONATES

    Directory of Open Access Journals (Sweden)

    E. V. Shimchenko

    2014-01-01

    Full Text Available The article presents advantages of use of diffusion-weighted magnetic resonance imaging (DW MRI for revealing hypoxic-ischemic brain lesions in neonates. The trial included 97 neonates with perinatal brain lesion who had been undergoing treatment at a resuscitation department or neonatal pathology department in the first month of life. The article shows high information value of diffusion-weighted images (DWI for diagnostics of hypoxic-ischemic lesions in comparison with regular standard modes. In the event of no structural brain lesions of neonates, pronounced increase in signal characteristics revealed by DWI indicated considerable pathophysiological alterations. Subsequently, children developed structural alterations in the form of cystic encephalomalacia with expansion of cerebrospinal fluid spaces manifested with pronounced neurological deficit. DW MRI has been offered as a method of prognosticating further neurological development of children on early stages. 

  5. Atlas-based segmentation and classification of magnetic resonance brain images

    OpenAIRE

    Bach Cuadra, Meritxell; Thiran, Jean-Philippe

    2005-01-01

    A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among...

  6. Brain-based decoding of mentally imagined film clips and sounds reveals experience-based information patterns in film professionals.

    Science.gov (United States)

    de Borst, Aline W; Valente, Giancarlo; Jääskeläinen, Iiro P; Tikka, Pia

    2016-04-01

    In the perceptual domain, it has been shown that the human brain is strongly shaped through experience, leading to expertise in highly-skilled professionals. What has remained unclear is whether specialization also shapes brain networks underlying mental imagery. In our fMRI study, we aimed to uncover modality-specific mental imagery specialization of film experts. Using multi-voxel pattern analysis we decoded from brain activity of professional cinematographers and sound designers whether they were imagining sounds or images of particular film clips. In each expert group distinct multi-voxel patterns, specific for the modality of their expertise, were found during classification of imagery modality. These patterns were mainly localized in the occipito-temporal and parietal cortex for cinematographers and in the auditory cortex for sound designers. We also found generalized patterns across perception and imagery that were distinct for the two expert groups: they involved frontal cortex for the cinematographers and temporal cortex for the sound designers. Notably, the mental representations of film clips and sounds of cinematographers contained information that went beyond modality-specificity. We were able to successfully decode the implicit presence of film genre from brain activity during mental imagery in cinematographers. The results extend existing neuroimaging literature on expertise into the domain of mental imagery and show that experience in visual versus auditory imagery can alter the representation of information in modality-specific association cortices. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Diagnostic and prognostic value of asphyxia, Sarnat's clinical classification, and CT-scan in perinatal brain damage

    Energy Technology Data Exchange (ETDEWEB)

    Kubo, Toshihide; Wakita, Yoshiharu; Kubonishi, Sakae; Yoshikawa, Seishi (Kochi Prefectural Central Hospital (Japan)); Ito, Toshiyuki; Okada, Yasusuke

    1990-11-01

    A retrospective review was made of 145 babies, excluding those with congenital heart disease or chromosome aberration, admitted for CT scanning. The study was done to determine the diagnostic and prognostic value of CT findings, as well as the presence of asphyxia and the clinical stage based on the Sarnat's classification, in perinatal brain damage. The patients had a minimum follow up of 2 years for the evaluation of neurologic manifestations, such as cerebral palsy, epilepsy and mental retardation. Among babies weighing 2,000 g or more at birth, neonatal asphyxia was significantly correlated with neurologic prognosis. In addition, both clinical stages and CT findings were significantly correlated with neurologic prognosis, irrespective of birth weight. The correlation between clinical stages and CT findings was significant, irrespective of body weight, however, a significant correlation between clinical stages and neonatal asphyxia was restricted to those weighing 2,000 g or more. These findings suggest that the presence of asphyxia, clinical stages and CT findings are complementary in the diagnosis and prognosis evaluation of perinatal brain damage. (N.K.).

  8. Transcriptional profiling of human brain endothelial cells reveals key properties crucial for predictive in vitro blood-brain barrier models.

    Directory of Open Access Journals (Sweden)

    Eduard Urich

    Full Text Available Brain microvascular endothelial cells (BEC constitute the blood-brain barrier (BBB which forms a dynamic interface between the blood and the central nervous system (CNS. This highly specialized interface restricts paracellular diffusion of fluids and solutes including chemicals, toxins and drugs from entering the brain. In this study we compared the transcriptome profiles of the human immortalized brain endothelial cell line hCMEC/D3 and human primary BEC. We identified transcriptional differences in immune response genes which are directly related to the immortalization procedure of the hCMEC/D3 cells. Interestingly, astrocytic co-culturing reduced cell adhesion and migration molecules in both BECs, which possibly could be related to regulation of immune surveillance of the CNS controlled by astrocytic cells within the neurovascular unit. By matching the transcriptome data from these two cell lines with published transcriptional data from freshly isolated mouse BECs, we discovered striking differences that could explain some of the limitations of using cultured BECs to study BBB properties. Key protein classes such as tight junction proteins, transporters and cell surface receptors show differing expression profiles. For example, the claudin-5, occludin and JAM2 expression is dramatically reduced in the two human BEC lines, which likely explains their low transcellular electric resistance and paracellular leakiness. In addition, the human BEC lines express low levels of unique brain endothelial transporters such as Glut1 and Pgp. Cell surface receptors such as LRP1, RAGE and the insulin receptor that are involved in receptor-mediated transport are also expressed at very low levels. Taken together, these data illustrate that BECs lose their unique protein expression pattern outside of their native environment and display a more generic endothelial cell phenotype. A collection of key genes that seems to be highly regulated by the local

  9. Comparison of Dolphins' Body and Brain Measurements with Four Other Groups of Cetaceans Reveals Great Diversity.

    Science.gov (United States)

    Ridgway, Sam H; Carlin, Kevin P; Van Alstyne, Kaitlin R; Hanson, Alicia C; Tarpley, Raymond J

    2016-01-01

    We compared mature dolphins with 4 other groupings of mature cetaceans. With a large data set, we found great brain diversity among 5 different taxonomic groupings. The dolphins in our data set ranged in body mass from about 40 to 6,750 kg and in brain mass from 0.4 to 9.3 kg. Dolphin body length ranged from 1.3 to 7.6 m. In our combined data set from the 4 other groups of cetaceans, body mass ranged from about 20 to 120,000 kg and brain mass from about 0.2 to 9.2 kg, while body length varied from 1.21 to 26.8 m. Not all cetaceans have large brains relative to their body size. A few dolphins near human body size have human-sized brains. On the other hand, the absolute brain mass of some other cetaceans is only one-sixth as large. We found that brain volume relative to body mass decreases from Delphinidae to a group of Phocoenidae and Monodontidae, to a group of other odontocetes, to Balaenopteroidea, and finally to Balaenidae. We also found the same general trend when we compared brain volume relative to body length, except that the Delphinidae and Phocoenidae-Monodontidae groups do not differ significantly. The Balaenidae have the smallest relative brain mass and the lowest cerebral cortex surface area. Brain parts also vary. Relative to body mass and to body length, dolphins also have the largest cerebellums. Cortex surface area is isometric with brain size when we exclude the Balaenidae. Our data show that the brains of Balaenidae are less convoluted than those of the other cetaceans measured. Large vascular networks inside the cranial vault may help to maintain brain temperature, and these nonbrain tissues increase in volume with body mass and with body length ranging from 8 to 65% of the endocranial volume. Because endocranial vascular networks and other adnexa, such as the tentorium cerebelli, vary so much in different species, brain size measures from endocasts of some extinct cetaceans may be overestimates. Our regression of body length on endocranial

  10. Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

    2012-12-01

    We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Water diffusion reveals networks that modulate multiregional morphological plasticity after repetitive brain stimulation.

    Science.gov (United States)

    Abe, Mitsunari; Fukuyama, Hidenao; Mima, Tatsuya

    2014-03-25

    Repetitive brain stimulation protocols induce plasticity in the stimulated site in brain slice models. Recent evidence from network models has indicated that additional plasticity-related changes occur in nonstimulated remote regions. Despite increasing use of brain stimulation protocols in experimental and clinical settings, the neural substrates underlying the additional effects in remote regions are unknown. Diffusion-weighted MRI (DWI) probes water diffusion and can be used to estimate morphological changes in cortical tissue that occur with the induction of plasticity. Using DWI techniques, we estimated morphological changes induced by application of repetitive transcranial magnetic stimulation (rTMS) over the left primary motor cortex (M1). We found that rTMS altered water diffusion in multiple regions including the left M1. Notably, the change in water diffusion was retained longest in the left M1 and remote regions that had a correlation of baseline fluctuations in water diffusion before rTMS. We conclude that synchronization of water diffusion at rest between stimulated and remote regions ensures retention of rTMS-induced changes in water diffusion in remote regions. Synchronized fluctuations in the morphology of cortical microstructures between stimulated and remote regions might identify networks that allow retention of plasticity-related morphological changes in multiple regions after brain stimulation protocols. These results increase our understanding of the effects of brain stimulation-induced plasticity on multiregional brain networks. DWI techniques could provide a tool to evaluate treatment effects of brain stimulation protocols in patients with brain disorders.

  12. Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience.

    Directory of Open Access Journals (Sweden)

    Yudan Ren

    Full Text Available Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA, sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.

  13. Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism.

    Science.gov (United States)

    Kim, Yongsoo; Yang, Guangyu Robert; Pradhan, Kith; Venkataraju, Kannan Umadevi; Bota, Mihail; García Del Molino, Luis Carlos; Fitzgerald, Greg; Ram, Keerthi; He, Miao; Levine, Jesse Maurica; Mitra, Partha; Huang, Z Josh; Wang, Xiao-Jing; Osten, Pavel

    2017-10-05

    The stereotyped features of neuronal circuits are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors, yet it has not been possible to comprehensively quantify neuronal distributions across animals or genders due to the size and complexity of the mammalian brain. Here we apply our quantitative brain-wide (qBrain) mapping platform to document the stereotyped distributions of mainly inhibitory cell types. We discover an unexpected cortical organizing principle: sensory-motor areas are dominated by output-modulating parvalbumin-positive interneurons, whereas association, including frontal, areas are dominated by input-modulating somatostatin-positive interneurons. Furthermore, we identify local cell type distributions with more cells in the female brain in 10 out of 11 sexually dimorphic subcortical areas, in contrast to the overall larger brains in males. The qBrain resource can be further mined to link stereotyped aspects of neuronal distributions to known and unknown functions of diverse brain regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome.

    Science.gov (United States)

    Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

    2017-12-01

    Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born Rhythmic patterns were observed in 62.3% (ictal 1.3%, PEDs 44%, other waveforms 86.3%) with multiple patterns in 36.4%. Ictal discharges were only observed in one and excluded from further analyses. The EEG location of the other waveforms (pRhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  15. Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

    Science.gov (United States)

    Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

    2018-01-01

    Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

  16. Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's.

    Directory of Open Access Journals (Sweden)

    Balázs Szalkai

    Full Text Available Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google's PageRank and the subsequent rise of the most popular search engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections

  17. Exploratory Metabolomic Analyses Reveal Compounds Correlated with Lutein Concentration in Frontal Cortex, Hippocampus, and Occipital Cortex of Human Infant Brain.

    Directory of Open Access Journals (Sweden)

    Jacqueline C Lieblein-Boff

    Full Text Available Lutein is a dietary carotenoid well known for its role as an antioxidant in the macula, and recent reports implicate a role for lutein in cognitive function. Lutein is the dominant carotenoid in both pediatric and geriatric brain tissue. In addition, cognitive function in older adults correlated with macular and postmortem brain lutein concentrations. Furthermore, lutein was found to preferentially accumulate in the infant brain in comparison to other carotenoids that are predominant in diet. While lutein is consistently related to cognitive function, the mechanisms by which lutein may influence cognition are not clear. In an effort to identify potential mechanisms through which lutein might influence neurodevelopment, an exploratory study relating metabolite signatures and lutein was completed. Post-mortem metabolomic analyses were performed on human infant brain tissues in three regions important for learning and memory: the frontal cortex, hippocampus, and occipital cortex. Metabolomic profiles were compared to lutein concentration, and correlations were identified and reported here. A total of 1276 correlations were carried out across all brain regions. Of 427 metabolites analyzed, 257 were metabolites of known identity. Unidentified metabolite correlations (510 were excluded. In addition, moderate correlations with xenobiotic relationships (2 or those driven by single outliers (3 were excluded from further study. Lutein concentrations correlated with lipid pathway metabolites, energy pathway metabolites, brain osmolytes, amino acid neurotransmitters, and the antioxidant homocarnosine. These correlations were often brain region-specific. Revealing relationships between lutein and metabolic pathways may help identify potential candidates on which to complete further analyses and may shed light on important roles of lutein in the human brain during development.

  18. Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity

    Directory of Open Access Journals (Sweden)

    Rosaleena Mohanty

    2018-05-01

    Full Text Available Interventional therapy using brain-computer interface (BCI technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke

  19. A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

    International Nuclear Information System (INIS)

    Wels, Michael; Hornegger, Joachim; Zheng Yefeng; Comaniciu, Dorin; Huber, Martin

    2011-01-01

    We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average

  20. A Knowledge Discovery Approach to Diagnosing Intracranial Hematomas on Brain CT: Recognition, Measurement and Classification

    Science.gov (United States)

    Liao, Chun-Chih; Xiao, Furen; Wong, Jau-Min; Chiang, I.-Jen

    Computed tomography (CT) of the brain is preferred study on neurological emergencies. Physicians use CT to diagnose various types of intracranial hematomas, including epidural, subdural and intracerebral hematomas according to their locations and shapes. We propose a novel method that can automatically diagnose intracranial hematomas by combining machine vision and knowledge discovery techniques. The skull on the CT slice is located and the depth of each intracranial pixel is labeled. After normalization of the pixel intensities by their depth, the hyperdense area of intracranial hematoma is segmented with multi-resolution thresholding and region-growing. We then apply C4.5 algorithm to construct a decision tree using the features of the segmented hematoma and the diagnoses made by physicians. The algorithm was evaluated on 48 pathological images treated in a single institute. The two discovered rules closely resemble those used by human experts, and are able to make correct diagnoses in all cases.

  1. Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic–Ischemic Brain Injury

    Directory of Open Access Journals (Sweden)

    Thanh G. Phan

    2018-03-01

    Full Text Available BackgroundPrognostication following hypoxic ischemic encephalopathy (brain injury is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data.MethodThe inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003–2011. Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS, features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume associates of severe disability and death. We used the area under the ROC (auROC to determine accuracy of model. There were 41 (63.7% males patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82–1.00. At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86–1.00.ConclusionOur findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

  2. Brain damages in ketamine addicts as revealed by magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Chunmei eWang

    2013-07-01

    Full Text Available Ketamine, a known antagonist of N-methyl-D-aspartic (NMDA glutamate receptors, had been used as an anesthetic particularly for pediatric or for cardiac patients. Unfortunately, ketamine has become an abusive drug in many parts of the world while chronic and prolonged usage led to damages of many organs including the brain. However, no studies on possible damages in the brains induced by chronic ketamine abuse have been documented in the human via neuroimaging. This paper described for the first time via employing magnetic resonance imaging (MRI the changes in ketamine addicts of 0.5 to 12 years and illustrated the possible brain regions susceptible to ketamine abuse. Twenty-one ketamine addicts were recruited and the results showed that the lesions in the brains of ketamine addicts were located in many regions which appeared 2-4 years after ketamine addiction. Cortical atrophy was usually evident in the frontal, parietal or occipital cortices of addicts. Such study confirmed that many brain regions in the human were susceptible to chronic ketamine injury and presented a diffuse effect of ketamine on the brain which might differ from other central nervous system (CNS drugs, such as cocaine, heroin and methamphetamine.

  3. Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

    Science.gov (United States)

    Horikawa, Tomoyasu; Kamitani, Yukiyasu

    2017-01-01

    Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

  4. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients.

    Science.gov (United States)

    Qiu, Xiangzhe; Zhang, Yanjun; Feng, Hongbo; Jiang, Donglang

    2016-01-01

    Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized characteristic path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing functional evidence for the abnormalities of brain networks in DM.

  5. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients

    Directory of Open Access Journals (Sweden)

    Qiu eXiangzhe

    2016-05-01

    Full Text Available Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM. However, the DM-related changes in the topological properties in functional brain networks are almost unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs, followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized shortest path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing the functional evidence for the abnormalities of brain networks in DM.

  6. Structural brain abnormalities in women with subclinical depression, as revealed by voxel-based morphometry and diffusion tensor imaging.

    Science.gov (United States)

    Hayakawa, Yayoi K; Sasaki, Hiroki; Takao, Hidemasa; Mori, Harushi; Hayashi, Naoto; Kunimatsu, Akira; Aoki, Shigeki; Ohtomo, Kuni

    2013-01-25

    Brain structural changes accompany major depressive disorder, but whether subclinical depression is accompanied by similar changes in brain volume and white matter integrity is unknown. By using voxel-based morphometry (VBM) of the gray matter and tract-specific analysis based on diffusion tensor imaging (DTI) of the white matter, we explored the extent to which abnormalities could be identified in specific brain structures of healthy adults with subclinical depression. The subjects were 21 community-dwelling adults with subclinical depression, as measured by their Center for Epidemiologic Studies Depression Scale (CES-D) scores. They were not demented and had no neurological or psychiatric history. We collected brain magnetic resonance images of the patients and of 21 matched control subjects, and we used VBM to analyze the differences in regional gray matter volume between the two groups. Moreover, we examined the white matter integrity by using tract-specific analysis based on the gray matter volume changes revealed by VBM. VBM revealed that the volumes of both anterior cingulate gyri and the right rectal gyrus were smaller in subclinically depressed women than in control women. Calculation of DTI measures in the anterior cingulum bundle revealed a positive correlation between CES-D scale score and radial diffusivity in the right anterior cingulum in subclinically depressed women. The small sample size limits the stability of the reported findings. Gray matter volume reduction and white matter integrity change in specific frontal brain regions may be associated with depressive symptoms in women, even at a subclinical level. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. Molecular and metabolic pattern classification for detection of brain glioma progression

    Energy Technology Data Exchange (ETDEWEB)

    Imani, Farzin, E-mail: imanif@upmc.edu [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Boada, Fernando E. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Lieberman, Frank S. [Department of Neurology, University of Pittsburgh Medical Center, PA (United States); Davis, Denise K.; Mountz, James M. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States)

    2014-02-15

    Objectives: The ability to differentiate between brain tumor progression and radiation therapy induced necrosis is critical for appropriate patient management. In order to improve the differential diagnosis, we combined fluorine-18 2-fluoro-deoxyglucose positron emission tomography ({sup 18}F-FDG PET), proton magnetic resonance spectroscopy ({sup 1}H MRS) and histological data to develop a multi-parametric machine-learning model. Methods: We enrolled twelve post-therapy patients with grade 2 and 3 gliomas that were suspicious of tumor progression. All patients underwent {sup 18}F-FDG PET and {sup 1}H MRS. Maximal standardized uptake value (SUVmax) of the tumors and reference regions were obtained. Multiple 2D maps of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector machine (SVM) learning model was established to take imaging biomarkers and histological data as input vectors. A combination of clinical follow-up and multiple sequential MRI studies served as the basis for assessing the clinical outcome. All vector combinations were evaluated for diagnostic accuracy and cross validation. The optimal cutoff value of individual parameters was calculated using Receiver operating characteristic (ROC) plots. Results: The SVM and ROC analyses both demonstrated that SUVmax of the lesion was the most significant single diagnostic parameter (75% accuracy) followed by Cho concentration (67% accuracy). SVM analysis of all paired parameters showed SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax of the lesion paired with SUVmax of the white matter as well as the tumor Cho paired with the tumor Cr both showed 83% accuracy. These were the most significant paired diagnostic parameters of either modality. Combining all four parameters did not improve the results. However, addition of two more parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased the accuracy to 92

  8. 18F-FDG PET brain images as features for Alzheimer classification

    Science.gov (United States)

    Azmi, M. H.; Saripan, M. I.; Nordin, A. J.; Ahmad Saad, F. F.; Abdul Aziz, S. A.; Wan Adnan, W. A.

    2017-08-01

    2-Deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) Positron Emission Tomography (PET) imaging offers meaningful information for various types of diseases diagnosis. In Alzheimer's disease (AD), the hypometabolism of glucose which observed on the low intensity voxel in PET image may relate to the onset of the disease. The importance of early detection of AD is inevitable because the resultant brain damage is irreversible. Several statistical analysis and machine learning algorithm have been proposed to investigate the rate and the pattern of the hypometabolism. This study focus on the same aim with further investigation was performed on several hypometabolism pattern. Some pre-processing steps were implemented to standardize the data in order to minimize the effect of resolution and anatomical differences. The features used are the mean voxel intensity within the AD pattern mask, which derived from several z-score and FDR threshold values. The global mean voxel (GMV) and slice-based mean voxel (SbMV) intensity were observed and used as input to the neural network. Several neural network architectures were tested and compared to the nearest neighbour method. The highest accuracy equals to 0.9 and recorded at z-score ≤-1.3 with 1 node neural network architecture (sensitivity=0.81 and specificity=0.95) and at z-score ≤-0.7 with 10 nodes neural network (sensitivity=0.83 and specificity=0.94).

  9. In vivo NAD assay reveals the intracellular NAD contents and redox state in healthy human brain and their age dependences

    Science.gov (United States)

    Zhu, Xiao-Hong; Lu, Ming; Lee, Byeong-Yeul; Ugurbil, Kamil; Chen, Wei

    2015-01-01

    NAD is an essential metabolite that exists in NAD+ or NADH form in all living cells. Despite its critical roles in regulating mitochondrial energy production through the NAD+/NADH redox state and modulating cellular signaling processes through the activity of the NAD+-dependent enzymes, the method for quantifying intracellular NAD contents and redox state is limited to a few in vitro or ex vivo assays, which are not suitable for studying a living brain or organ. Here, we present a magnetic resonance (MR) -based in vivo NAD assay that uses the high-field MR scanner and is capable of noninvasively assessing NAD+ and NADH contents and the NAD+/NADH redox state in intact human brain. The results of this study provide the first insight, to our knowledge, into the cellular NAD concentrations and redox state in the brains of healthy volunteers. Furthermore, an age-dependent increase of intracellular NADH and age-dependent reductions in NAD+, total NAD contents, and NAD+/NADH redox potential of the healthy human brain were revealed in this study. The overall findings not only provide direct evidence of declined mitochondrial functions and altered NAD homeostasis that accompany the normal aging process but also, elucidate the merits and potentials of this new NAD assay for noninvasively studying the intracellular NAD metabolism and redox state in normal and diseased human brain or other organs in situ. PMID:25730862

  10. Effects of anesthetic agents on brain blood oxygenation level revealed with ultra-high field MRI.

    Directory of Open Access Journals (Sweden)

    Luisa Ciobanu

    Full Text Available During general anesthesia it is crucial to control systemic hemodynamics and oxygenation levels. However, anesthetic agents can affect cerebral hemodynamics and metabolism in a drug-dependent manner, while systemic hemodynamics is stable. Brain-wide monitoring of this effect remains highly challenging. Because T(2*-weighted imaging at ultra-high magnetic field strengths benefits from a dramatic increase in contrast to noise ratio, we hypothesized that it could monitor anesthesia effects on brain blood oxygenation. We scanned rat brains at 7T and 17.2T under general anesthesia using different anesthetics (isoflurane, ketamine-xylazine, medetomidine. We showed that the brain/vessels contrast in T(2*-weighted images at 17.2T varied directly according to the applied pharmacological anesthetic agent, a phenomenon that was visible, but to a much smaller extent at 7T. This variation is in agreement with the mechanism of action of these agents. These data demonstrate that preclinical ultra-high field MRI can monitor the effects of a given drug on brain blood oxygenation level in the absence of systemic blood oxygenation changes and of any neural stimulation.

  11. Human Brain Organoids on a Chip Reveal the Physics of Folding.

    Science.gov (United States)

    Karzbrun, Eyal; Kshirsagar, Aditya; Cohen, Sidney R; Hanna, Jacob H; Reiner, Orly

    2018-05-01

    Human brain wrinkling has been implicated in neurodevelopmental disorders and yet its origins remain unknown. Polymer gel models suggest that wrinkling emerges spontaneously due to compression forces arising during differential swelling, but these ideas have not been tested in a living system. Here, we report the appearance of surface wrinkles during the in vitro development and self-organization of human brain organoids in a micro-fabricated compartment that supports in situ imaging over a timescale of weeks. We observe the emergence of convolutions at a critical cell density and maximal nuclear strain, which are indicative of a mechanical instability. We identify two opposing forces contributing to differential growth: cytoskeletal contraction at the organoid core and cell-cycle-dependent nuclear expansion at the organoid perimeter. The wrinkling wavelength exhibits linear scaling with tissue thickness, consistent with balanced bending and stretching energies. Lissencephalic (smooth brain) organoids display reduced convolutions, modified scaling and a reduced elastic modulus. Although the mechanism here does not include the neuronal migration seen in in vivo , it models the physics of the folding brain remarkably well. Our on-chip approach offers a means for studying the emergent properties of organoid development, with implications for the embryonic human brain.

  12. Tensor-based morphometry and stereology reveal brain pathology in the complexin1 knockout mouse.

    Science.gov (United States)

    Kielar, Catherine; Sawiak, Stephen J; Navarro Negredo, Paloma; Tse, Desmond H Y; Morton, A Jennifer

    2012-01-01

    Complexins (Cplxs) are small, soluble, regulatory proteins that bind reversibly to the SNARE complex and modulate synaptic vesicle release. Cplx1 knockout mice (Cplx1(-/-)) have the earliest known onset of ataxia seen in a mouse model, although hitherto no histopathology has been described in these mice. Nevertheless, the profound neurological phenotype displayed by Cplx1(-/-) mutants suggests that significant functional abnormalities must be present in these animals. In this study, MRI was used to automatically detect regions where structural differences were not obvious when using a traditional histological approach. Tensor-based morphometry of Cplx1(-/-) mouse brains showed selective volume loss from the thalamus and cerebellum. Stereological analysis of Cplx1(-/-) and Cplx1(+/+) mice brain slices confirmed the volume loss in the thalamus as well as loss in some lobules of the cerebellum. Finally, stereology was used to show that there was loss of cerebellar granule cells in Cplx1(-/-) mice when compared to Cplx1(+/+) animals. Our study is the first to describe pathological changes in Cplx1(-/-) mouse brain. We suggest that the ataxia in Cplx1(-/-) mice is likely to be due to pathological changes in both cerebellum and thalamus. Reduced levels of Cplx proteins have been reported in brains of patients with neurodegenerative diseases. Therefore, understanding the effects of Cplx depletion in brains from Cplx1(-/-) mice may also shed light on the mechanisms underlying pathophysiology in disorders in which loss of Cplx1 occurs.

  13. Clinical study on eating disorders. Brain atrophy revealed by cranial computed tomography scans

    Energy Technology Data Exchange (ETDEWEB)

    Nishiwaki, Shinichi

    1988-06-01

    Cranial computed tomography (CT) scans were reviewed in 34 patients with anorexia nervosa (Group I) and 22 with bulimia (Group II) to elucidate the cause and pathological significance of morphological brain alterations. The findings were compared with those from 47 normal women. The incidence of brain atrophy was significantly higher in Group I (17/34, 50%) and Group II (11/22, 50%) than the control group (3/47, 6%). In Group I, there was a significant increase in the left septum-caudate distance, the maximum width of interhemispheric fissure, the width of the both-side Sylvian fissures adjacent to the skull, and the maximum width of the third ventricle. A significant increase in the maximum width of interhemispheric fissure and the width of the left-side Sylvian fissure adjacent to the skull were noted as well in Group II. Ventricular brain ratios were significantly higher in Groups I and II than the control group (6.76 and 7.29 vs 4.55). Brain atrophy did not correlate with age, body weight, malnutrition, eating behavior, depression, thyroid function, EEG findings, or intelligence scale. In Group I, serum cortisol levels after the administration of dexamethasone were correlated with ventricular brain ratio. (Namekawa, K) 51 refs.

  14. Human brain organoids on a chip reveal the physics of folding

    Science.gov (United States)

    Karzbrun, Eyal; Kshirsagar, Aditya; Cohen, Sidney R.; Hanna, Jacob H.; Reiner, Orly

    2018-05-01

    Human brain wrinkling has been implicated in neurodevelopmental disorders and yet its origins remain unknown. Polymer gel models suggest that wrinkling emerges spontaneously due to compression forces arising during differential swelling, but these ideas have not been tested in a living system. Here, we report the appearance of surface wrinkles during the in vitro development and self-organization of human brain organoids in a microfabricated compartment that supports in situ imaging over a timescale of weeks. We observe the emergence of convolutions at a critical cell density and maximal nuclear strain, which are indicative of a mechanical instability. We identify two opposing forces contributing to differential growth: cytoskeletal contraction at the organoid core and cell-cycle-dependent nuclear expansion at the organoid perimeter. The wrinkling wavelength exhibits linear scaling with tissue thickness, consistent with balanced bending and stretching energies. Lissencephalic (smooth brain) organoids display reduced convolutions, modified scaling and a reduced elastic modulus. Although the mechanism here does not include the neuronal migration seen in vivo, it models the physics of the folding brain remarkably well. Our on-chip approach offers a means for studying the emergent properties of organoid development, with implications for the embryonic human brain.

  15. Tensor-based morphometry and stereology reveal brain pathology in the complexin1 knockout mouse.

    Directory of Open Access Journals (Sweden)

    Catherine Kielar

    Full Text Available Complexins (Cplxs are small, soluble, regulatory proteins that bind reversibly to the SNARE complex and modulate synaptic vesicle release. Cplx1 knockout mice (Cplx1(-/- have the earliest known onset of ataxia seen in a mouse model, although hitherto no histopathology has been described in these mice. Nevertheless, the profound neurological phenotype displayed by Cplx1(-/- mutants suggests that significant functional abnormalities must be present in these animals. In this study, MRI was used to automatically detect regions where structural differences were not obvious when using a traditional histological approach. Tensor-based morphometry of Cplx1(-/- mouse brains showed selective volume loss from the thalamus and cerebellum. Stereological analysis of Cplx1(-/- and Cplx1(+/+ mice brain slices confirmed the volume loss in the thalamus as well as loss in some lobules of the cerebellum. Finally, stereology was used to show that there was loss of cerebellar granule cells in Cplx1(-/- mice when compared to Cplx1(+/+ animals. Our study is the first to describe pathological changes in Cplx1(-/- mouse brain. We suggest that the ataxia in Cplx1(-/- mice is likely to be due to pathological changes in both cerebellum and thalamus. Reduced levels of Cplx proteins have been reported in brains of patients with neurodegenerative diseases. Therefore, understanding the effects of Cplx depletion in brains from Cplx1(-/- mice may also shed light on the mechanisms underlying pathophysiology in disorders in which loss of Cplx1 occurs.

  16. Effects of anesthetic agents on brain blood oxygenation level revealed with ultra-high field MRI

    International Nuclear Information System (INIS)

    Ciobanu, Luisa; Reynaud, Olivier; Le Bihan, Denis; Uhrig, Lynn; Jarraya, Bechir

    2012-01-01

    During general anesthesia it is crucial to control systemic hemodynamics and oxygenation levels. However, anesthetic agents can affect cerebral hemodynamics and metabolism in a drug-dependent manner, while systemic hemodynamics is stable. Brain-wide monitoring of this effect remains highly challenging. Because T2'*-weighted imaging at ultra-high magnetic field strengths benefits from a dramatic increase in contrast to noise ratio, we hypothesized that it could monitor anesthesia effects on brain blood oxygenation. We scanned rat brains at 7 T and 17.2 T under general anesthesia using different anesthetics (isoflurane, ketamine-xylazine, medetomidine). We showed that the brain/vessels contrast in T2'*- weighted images at 17.2 T varied directly according to the applied pharmacological anesthetic agent, a phenomenon that was visible, but to a much smaller extent at 7 T. This variation is in agreement with the mechanism of action of these agents. These data demonstrate that preclinical ultra-high field MRI can monitor the effects of a given drug on brain blood oxygenation level in the absence of systemic blood oxygenation changes and of any neural stimulation. (authors)

  17. Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

    Science.gov (United States)

    Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

    2014-09-01

    To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

  18. Cell proliferation in the Drosophila adult brain revealed by clonal analysis and bromodeoxyuridine labelling

    Directory of Open Access Journals (Sweden)

    Brand Andrea H

    2009-03-01

    Full Text Available Abstract Background The production of new neurons during adulthood and their subsequent integration into a mature central nervous system have been shown to occur in all vertebrate species examined to date. However, the situation in insects is less clear and, in particular, it has been reported that there is no proliferation in the Drosophila adult brain. Results We report here, using clonal analysis and 5'-bromo-2'-deoxyuridine (BrdU labelling, that cell proliferation does occur in the Drosophila adult brain. The majority of clones cluster on the ventrolateral side of the antennal lobes, as do the BrdU-positive cells. Of the BrdU-labelled cells, 86% express the glial gene reversed polarity (repo, and 14% are repo negative. Conclusion We have observed cell proliferation in the Drosophila adult brain. The dividing cells may be adult stem cells, generating glial and/or non-glial cell types.

  19. Perceptual Shift in Bilingualism: Brain Potentials Reveal Plasticity in Pre-Attentive Colour Perception

    Science.gov (United States)

    Athanasopoulos, Panos; Dering, Benjamin; Wiggett, Alison; Kuipers, Jan-Rouke; Thierry, Guillaume

    2010-01-01

    The validity of the linguistic relativity principle continues to stimulate vigorous debate and research. The debate has recently shifted from the behavioural investigation arena to a more biologically grounded field, in which tangible physiological evidence for language effects on perception can be obtained. Using brain potentials in a colour…

  20. Three-dimensional textural analysis of brain images reveals distributed grey-matter abnormalities in schizophrenia

    Energy Technology Data Exchange (ETDEWEB)

    Ganeshan, Balaji [University of Sussex, Falmer, Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton (United Kingdom); University of Sussex, Falmer, Department of Engineering and Design, Brighton (United Kingdom); Miles, Kenneth A.; Critchley, Hugo D. [University of Sussex, Falmer, Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton (United Kingdom); Young, Rupert C.D.; Chatwin, Christopher R. [University of Sussex, Falmer, Department of Engineering and Design, Brighton (United Kingdom); Gurling, Hugh M.D. [University College London, Department of Mental Health Sciences, London (United Kingdom)

    2010-04-15

    Three-dimensional (3-D) selective- and relative-scale texture analysis (TA) was applied to structural magnetic resonance (MR) brain images to quantify the presence of grey-matter (GM) and white-matter (WM) textural abnormalities associated with schizophrenia. Brain TA comprised volume filtration using the Laplacian of Gaussian filter to highlight fine, medium and coarse textures within GM and WM, followed by texture quantification. Relative TA (e.g. ratio of fine to medium) was also computed. T1-weighted MR whole-brain images from 32 participants with diagnosis of schizophrenia (n = 10) and healthy controls (n = 22) were examined. Five patients possessed marker alleles (SZ8) associated with schizophrenia on chromosome 8 in the pericentriolar material 1 gene while the remaining five had not inherited any of the alleles (SZ0). Filtered fine GM texture (mean grey-level intensity; MGI) most significantly differentiated schizophrenic patients from controls (P = 0.0058; area under the receiver-operating characteristic curve = 0.809, sensitivity = 90%, specificity = 70%). WM measurements did not distinguish the two groups. Filtered GM and WM textures (MGI) correlated with total GM and WM volume respectively. Medium-to-coarse GM entropy distinguished SZ0 from controls (P = 0.0069) while measures from SZ8 were intermediate between the two. 3-D TA of brain MR enables detection of subtle distributed morphological features associated with schizophrenia, determined partly by susceptibility genes. (orig.)

  1. Three-dimensional textural analysis of brain images reveals distributed grey-matter abnormalities in schizophrenia

    International Nuclear Information System (INIS)

    Ganeshan, Balaji; Miles, Kenneth A.; Critchley, Hugo D.; Young, Rupert C.D.; Chatwin, Christopher R.; Gurling, Hugh M.D.

    2010-01-01

    Three-dimensional (3-D) selective- and relative-scale texture analysis (TA) was applied to structural magnetic resonance (MR) brain images to quantify the presence of grey-matter (GM) and white-matter (WM) textural abnormalities associated with schizophrenia. Brain TA comprised volume filtration using the Laplacian of Gaussian filter to highlight fine, medium and coarse textures within GM and WM, followed by texture quantification. Relative TA (e.g. ratio of fine to medium) was also computed. T1-weighted MR whole-brain images from 32 participants with diagnosis of schizophrenia (n = 10) and healthy controls (n = 22) were examined. Five patients possessed marker alleles (SZ8) associated with schizophrenia on chromosome 8 in the pericentriolar material 1 gene while the remaining five had not inherited any of the alleles (SZ0). Filtered fine GM texture (mean grey-level intensity; MGI) most significantly differentiated schizophrenic patients from controls (P = 0.0058; area under the receiver-operating characteristic curve = 0.809, sensitivity = 90%, specificity = 70%). WM measurements did not distinguish the two groups. Filtered GM and WM textures (MGI) correlated with total GM and WM volume respectively. Medium-to-coarse GM entropy distinguished SZ0 from controls (P = 0.0069) while measures from SZ8 were intermediate between the two. 3-D TA of brain MR enables detection of subtle distributed morphological features associated with schizophrenia, determined partly by susceptibility genes. (orig.)

  2. Speech processing asymmetry revealed by dichotic listening and functional brain imaging.

    Science.gov (United States)

    Hugdahl, Kenneth; Westerhausen, René

    2016-12-01

    In this article, we review research in our laboratory from the last 25 to 30 years on the neuronal basis for laterality of speech perception focusing on the upper, posterior parts of the temporal lobes, and its functional and structural connections to other brain regions. We review both behavioral and brain imaging data, with a focus on dichotic listening experiments, and using a variety of imaging modalities. The data have come in most parts from healthy individuals and from studies on normally functioning brain, although we also review a few selected clinical examples. We first review and discuss the structural model for the explanation of the right-ear advantage (REA) and left hemisphere asymmetry for auditory language processing. A common theme across many studies have been our interest in the interaction between bottom-up, stimulus-driven, and top-down, instruction-driven, aspects of hemispheric asymmetry, and how perceptual factors interact with cognitive factors to shape asymmetry of auditory language information processing. In summary, our research have shown laterality for the initial processing of consonant-vowel syllables, first observed as a behavioral REA when subjects are required to report which syllable of a dichotic syllable-pair they perceive. In subsequent work we have corroborated the REA with brain imaging, and have shown that the REA is modulated through both bottom-up manipulations of stimulus properties, like sound intensity, and top-down manipulations of cognitive properties, like attention focus. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour.

    Science.gov (United States)

    Vatansever, Deniz; Bzdok, Danilo; Wang, Hao-Ting; Mollo, Giovanna; Sormaz, Mladen; Murphy, Charlotte; Karapanagiotidis, Theodoros; Smallwood, Jonathan; Jefferies, Elizabeth

    2017-09-01

    Contemporary theories assume that semantic cognition emerges from a neural architecture in which different component processes are combined to produce aspects of conceptual thought and behaviour. In addition to the state-level, momentary variation in brain connectivity, individuals may also differ in their propensity to generate particular configurations of such components, and these trait-level differences may relate to individual differences in semantic cognition. We tested this view by exploring how variation in intrinsic brain functional connectivity between semantic nodes in fMRI was related to performance on a battery of semantic tasks in 154 healthy participants. Through simultaneous decomposition of brain functional connectivity and semantic task performance, we identified distinct components of semantic cognition at rest. In a subsequent validation step, these data-driven components demonstrated explanatory power for neural responses in an fMRI-based semantic localiser task and variation in self-generated thoughts during the resting-state scan. Our findings showed that good performance on harder semantic tasks was associated with relative segregation at rest between frontal brain regions implicated in controlled semantic retrieval and the default mode network. Poor performance on easier tasks was linked to greater coupling between the same frontal regions and the anterior temporal lobe; a pattern associated with deliberate, verbal thematic thoughts at rest. We also identified components that related to qualities of semantic cognition: relatively good performance on pictorial semantic tasks was associated with greater separation of angular gyrus from frontal control sites and greater integration with posterior cingulate and anterior temporal cortex. In contrast, good speech production was linked to the separation of angular gyrus, posterior cingulate and temporal lobe regions. Together these data show that quantitative and qualitative variation in semantic

  4. Gender, Race, and Survival: A Study in Non-Small-Cell Lung Cancer Brain Metastases Patients Utilizing the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification

    International Nuclear Information System (INIS)

    Videtic, Gregory M.M.; Reddy, Chandana A.; Chao, Samuel T.; Rice, Thomas W.; Adelstein, David J.; Barnett, Gene H.; Mekhail, Tarek M.; Vogelbaum, Michael A.; Suh, John H.

    2009-01-01

    Purpose: To explore whether gender and race influence survival in non-small-cell lung cancer (NSCLC) in patients with brain metastases, using our large single-institution brain tumor database and the Radiation Therapy Oncology Group recursive partitioning analysis (RPA) brain metastases classification. Methods and materials: A retrospective review of a single-institution brain metastasis database for the interval January 1982 to September 2004 yielded 835 NSCLC patients with brain metastases for analysis. Patient subsets based on combinations of gender, race, and RPA class were then analyzed for survival differences. Results: Median follow-up was 5.4 months (range, 0-122.9 months). There were 485 male patients (M) (58.4%) and 346 female patients (F) (41.6%). Of the 828 evaluable patients (99%), 143 (17%) were black/African American (B) and 685 (83%) were white/Caucasian (W). Median survival time (MST) from time of brain metastasis diagnosis for all patients was 5.8 months. Median survival time by gender (F vs. M) and race (W vs. B) was 6.3 months vs. 5.5 months (p = 0.013) and 6.0 months vs. 5.2 months (p = 0.08), respectively. For patients stratified by RPA class, gender, and race, MST significantly favored BFs over BMs in Class II: 11.2 months vs. 4.6 months (p = 0.021). On multivariable analysis, significant variables were gender (p = 0.041, relative risk [RR] 0.83) and RPA class (p < 0.0001, RR 0.28 for I vs. III; p < 0.0001, RR 0.51 for II vs. III) but not race. Conclusions: Gender significantly influences NSCLC brain metastasis survival. Race trended to significance in overall survival but was not significant on multivariable analysis. Multivariable analysis identified gender and RPA classification as significant variables with respect to survival.

  5. Near-infrared spectroscopy can reveal increases in brain activity related to animal-assisted therapy

    OpenAIRE

    Morita, Yuka; Ebara, Fumio; Morita, Yoshimitsu; Horikawa, Etsuo

    2017-01-01

    [Purpose] Previous studies have indicated that animal-assisted therapy can promote recovery of psychological, social, and physiological function in mental disorders. This study was designed as a pilot evaluation of the use of near-infrared spectroscopy to objectively identify changes in brain activity that could mediate the effect of animal-assisted therapy. [Subjects and Methods] The participants were 20 healthy students (10 males and 10 females; age 19?21 years) of the Faculty of Agricultur...

  6. Recovered neuronal viability revealed by Iodine-123-iomazenil SPECT following traumatic brain injury

    OpenAIRE

    Koizumi, Hiroyasu; Fujisawa, Hirosuke; Kurokawa, Tetsu; Suehiro, Eiichi; Iwanaga, Hideyuki; Nakagawara, Jyoji; Suzuki, Michiyasu

    2010-01-01

    We evaluated cortical damages following traumatic brain injury (TBI) in the acute phase with [123I] iomazenil (IMZ) single photon emission computed tomography (SPECT). In all, 12 patients with cerebral contusion following TBI were recruited. All patients underwent IMZ SPECT within 1 week after TBI. To investigate the changes in distribution of IMZ in the cortex in the chronic phase, after conventional treatment, patients underwent IMZ SPECT again. A decrease in the accumulation of radioligand...

  7. Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy.

    Science.gov (United States)

    Wang, Junjing; Qiu, Shijun; Xu, Yong; Liu, Zhenyin; Wen, Xue; Hu, Xiangshu; Zhang, Ruibin; Li, Meng; Wang, Wensheng; Huang, Ruiwang

    2014-09-01

    Temporal lobe epilepsy (TLE) is one of the most common forms of drug-resistant epilepsy. Previous studies have indicated that the TLE-related impairments existed in extensive local functional networks. However, little is known about the alterations in the topological properties of whole brain functional networks. In this study, we acquired resting-state BOLD-fMRI (rsfMRI) data from 26 TLE patients and 25 healthy controls, constructed their whole brain functional networks, compared the differences in topological parameters between the TLE patients and the controls, and analyzed the correlation between the altered topological properties and the epilepsy duration. The TLE patients showed significant increases in clustering coefficient and characteristic path length, but significant decrease in global efficiency compared to the controls. We also found altered nodal parameters in several regions in the TLE patients, such as the bilateral angular gyri, left middle temporal gyrus, right hippocampus, triangular part of left inferior frontal gyrus, left inferior parietal but supramarginal and angular gyri, and left parahippocampus gyrus. Further correlation analysis showed that the local efficiency of the TLE patients correlated positively with the epilepsy duration. Our results indicated the disrupted topological properties of whole brain functional networks in TLE patients. Our findings indicated the TLE-related impairments in the whole brain functional networks, which may help us to understand the clinical symptoms of TLE patients and offer a clue for the diagnosis and treatment of the TLE patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Fluorescent nanodiamond tracking reveals intraneuronal transport abnormalities induced by brain-disease-related genetic risk factors

    Science.gov (United States)

    Haziza, Simon; Mohan, Nitin; Loe-Mie, Yann; Lepagnol-Bestel, Aude-Marie; Massou, Sophie; Adam, Marie-Pierre; Le, Xuan Loc; Viard, Julia; Plancon, Christine; Daudin, Rachel; Koebel, Pascale; Dorard, Emilie; Rose, Christiane; Hsieh, Feng-Jen; Wu, Chih-Che; Potier, Brigitte; Herault, Yann; Sala, Carlo; Corvin, Aiden; Allinquant, Bernadette; Chang, Huan-Cheng; Treussart, François; Simonneau, Michel

    2017-05-01

    Brain diseases such as autism and Alzheimer's disease (each inflicting >1% of the world population) involve a large network of genes displaying subtle changes in their expression. Abnormalities in intraneuronal transport have been linked to genetic risk factors found in patients, suggesting the relevance of measuring this key biological process. However, current techniques are not sensitive enough to detect minor abnormalities. Here we report a sensitive method to measure the changes in intraneuronal transport induced by brain-disease-related genetic risk factors using fluorescent nanodiamonds (FNDs). We show that the high brightness, photostability and absence of cytotoxicity allow FNDs to be tracked inside the branches of dissociated neurons with a spatial resolution of 12 nm and a temporal resolution of 50 ms. As proof of principle, we applied the FND tracking assay on two transgenic mouse lines that mimic the slight changes in protein concentration (∼30%) found in the brains of patients. In both cases, we show that the FND assay is sufficiently sensitive to detect these changes.

  9. Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans.

    Science.gov (United States)

    Rebollo, Ignacio; Devauchelle, Anne-Dominique; Béranger, Benoît; Tallon-Baudry, Catherine

    2018-03-21

    Resting-state networks offer a unique window into the brain's functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with partial overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics. © 2018, Rebollo et al.

  10. Statistical language learning in neonates revealed by event-related brain potentials

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    Näätänen Risto

    2009-03-01

    Full Text Available Abstract Background Statistical learning is a candidate for one of the basic prerequisites underlying the expeditious acquisition of spoken language. Infants from 8 months of age exhibit this form of learning to segment fluent speech into distinct words. To test the statistical learning skills at birth, we recorded event-related brain responses of sleeping neonates while they were listening to a stream of syllables containing statistical cues to word boundaries. Results We found evidence that sleeping neonates are able to automatically extract statistical properties of the speech input and thus detect the word boundaries in a continuous stream of syllables containing no morphological cues. Syllable-specific event-related brain responses found in two separate studies demonstrated that the neonatal brain treated the syllables differently according to their position within pseudowords. Conclusion These results demonstrate that neonates can efficiently learn transitional probabilities or frequencies of co-occurrence between different syllables, enabling them to detect word boundaries and in this way isolate single words out of fluent natural speech. The ability to adopt statistical structures from speech may play a fundamental role as one of the earliest prerequisites of language acquisition.

  11. Expression weighted cell type enrichments reveal genetic and cellular nature of major brain disorders

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    Nathan Gerald Skene

    2016-01-01

    Full Text Available The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer’s disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer’s and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesised that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer’s disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.

  12. Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

    Science.gov (United States)

    Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

    2018-02-01

    Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

  13. Temporal gene expression profiling reveals CEBPD as a candidate regulator of brain disease in prosaposin deficient mice

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    Ran Huimin

    2008-08-01

    Full Text Available Abstract Background Prosaposin encodes, in tandem, four small acidic activator proteins (saposins with specificities for glycosphingolipid (GSL hydrolases in lysosomes. Extensive GSL storage occurs in various central nervous system regions in mammalian prosaposin deficiencies. Results Our hypomorphic prosaposin deficient mouse, PS-NA, exhibited 45% WT levels of brain saposins and showed neuropathology that included neuronal GSL storage and Purkinje cell loss. Impairment of neuronal function was observed as early as 6 wks as demonstrated by the narrow bridges tests. Temporal transcriptome microarray analyses of brain tissues were conducted with mRNA from three prosaposin deficient mouse models: PS-NA, prosaposin null (PS-/- and a V394L/V394L glucocerebrosidase mutation combined with PS-NA (4L/PS-NA. Gene expression alterations in cerebrum and cerebellum were detectable at birth preceding the neuronal deficits. Differentially expressed genes encompassed a broad spectrum of cellular functions. The number of down-regulated genes was constant, but up-regulated gene numbers increased with age. CCAAT/enhancer-binding protein delta (CEBPD was the only up-regulated transcription factor in these two brain regions of all three models. Network analyses revealed that CEBPD has functional relationships with genes in transcription, pro-inflammation, cell death, binding, myelin and transport. Conclusion These results show that: 1 Regionally specific gene expression abnormalities precede the brain histological and neuronal function changes, 2 Temporal gene expression profiles provide insights into the molecular mechanism during the GSL storage disease course, and 3 CEBPD is a candidate regulator of brain disease in prosaposin deficiency to participate in modulating disease acceleration or progression.

  14. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

    Science.gov (United States)

    Wirsich, Jonathan; Perry, Alistair; Ridley, Ben; Proix, Timothée; Golos, Mathieu; Bénar, Christian; Ranjeva, Jean-Philippe; Bartolomei, Fabrice; Breakspear, Michael; Jirsa, Viktor; Guye, Maxime

    2016-01-01

    The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

  15. Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging.

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    Chunxiang Jiang

    Full Text Available Biorhythm is a fundamental property of human physiology. Changes in the extracellular space induced by cell swelling in response to the neural activity enable the in vivo characterization of cerebral microstructure by measuring the water diffusivity using diffusion tensor imaging (DTI. To study the diurnal microstructural alterations of human brain, fifteen right-handed healthy adult subjects were recruited for DTI studies in two repeated sessions (8∶30 AM and 8∶30 PM within a 24-hour interval. Fractional anisotropy (FA, apparent diffusion coefficient (ADC, axial (λ// and radial diffusivity (λ⊥ were compared pixel by pixel between the sessions for each subject. Significant increased morning measurements in FA, ADC, λ// and λ⊥ were seen in a wide range of brain areas involving frontal, parietal, temporal and occipital lobes. Prominent evening dominant λ⊥ (18.58% was detected in the right inferior temporal and ventral fusiform gyri. AM-PM variation of λ⊥ was substantially left side hemisphere dominant (p<0.05, while no hemispheric preference was observed for the same analysis for ADC (p = 0.77, λ// (p = 0.08 or FA (p = 0.25. The percentage change of ADC, λ//, λ⊥, and FA were 1.59%, 2.15%, 1.20% and 2.84%, respectively, for brain areas without diurnal diffusivity contrast. Microstructural variations may function as the substrates of the phasic neural activities in correspondence to the environment adaptation in a light-dark cycle. This research provided a baseline for researches in neuroscience, sleep medicine, psychological and psychiatric disorders, and necessitates that diurnal effect should be taken into account in following up studies using diffusion tensor quantities.

  16. Ex-vivo diffusion MRI reveals microstructural alterations in stress-sensitive brain regions: A chronic mild stress recovery study

    DEFF Research Database (Denmark)

    Khan, Ahmad Raza; Hansen, Brian; Wiborg, Ove

    Depression is a leading cause of disability worldwide and causes significant microstructural alterations in stress-sensitive brain regions. However, the potential recovery of these microstructural alterations has not previously been investigated, which we, therefore, set out to do using diffusion...... MRI (d-MRI) in the chronic mild stress (CMS) rat model of depression. This study reveals significant microstructural alterations after 8 weeks of recovery, in the opposite direction to change induced by stress in the acute phase of the experiment. Such findings may be useful in the prognosis...... of depression or for monitoring treatment response....

  17. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Jonathan Wirsich

    2016-01-01

    In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

  18. Quantitative Susceptibility Mapping Reveals an Association between Brain Iron Load and Depression Severity

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    Shun Yao

    2017-08-01

    Full Text Available Previous studies have detected abnormal serum ferritin levels in patients with depression; however, the results have been inconsistent. This study used quantitative susceptibility mapping (QSM for the first time to examine brain iron concentration in depressed patients and evaluated whether it is related to severity. We included three groups of age- and gender-matched participants: 30 patients with mild-moderate depression (MD, 14 patients with major depression disorder (MDD and 20 control subjects. All participants underwent MR scans with a 3D gradient-echo sequence reconstructing for QSM and performed the 17-item Hamilton Depression Rating Scale (HDRS test. In MDD, the susceptibility value in the bilateral putamen was significantly increased compared with MD or control subjects. In addition, a significant difference was also observed in the left thalamus in MDD patients compared with controls. However, the susceptibility values did not differ between MD patients and controls. The susceptibility values positively correlated with the severity of depression as indicated by the HDRS scores. Our results provide evidence that brain iron deposition may be associated with depression and may even be a biomarker for investigating the pathophysiological mechanism of depression.

  19. Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

    Science.gov (United States)

    López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2015-04-15

    Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Local Anesthesia at ST36 to Reveal Responding Brain Areas to deqi

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    Ling-min Jin

    2014-01-01

    Full Text Available Background. Development of non-deqi control is still a challenge. This study aims to set up a potential approach to non-deqi control by using lidocaine anesthesia at ST36. Methods. Forty healthy volunteers were recruited and they received two fMRI scans. One was accompanied with manual acupuncture at ST36 (DQ group, and another was associated with both local anesthesia and manual acupuncture at the same acupoint (LA group. Results. Comparing to DQ group, more than 90 percent deqi sensations were reduced by local anesthesia in LA group. The mainly activated regions in DQ group were bilateral IFG, S1, primary motor cortex, IPL, thalamus, insula, claustrum, cingulate gyrus, putamen, superior temporal gyrus, and cerebellum. Surprisingly only cerebellum showed significant activation in LA group. Compared to the two groups, bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC were remarkably activated. Conclusions. Local anesthesia at ST36 is able to block most of the deqi feelings and inhibit brain responses to deqi, which would be developed into a potential approach for non-deqi control. Bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC might be the key brain regions responding to deqi.

  1. Near-infrared spectroscopy can reveal increases in brain activity related to animal-assisted therapy.

    Science.gov (United States)

    Morita, Yuka; Ebara, Fumio; Morita, Yoshimitsu; Horikawa, Etsuo

    2017-08-01

    [Purpose] Previous studies have indicated that animal-assisted therapy can promote recovery of psychological, social, and physiological function in mental disorders. This study was designed as a pilot evaluation of the use of near-infrared spectroscopy to objectively identify changes in brain activity that could mediate the effect of animal-assisted therapy. [Subjects and Methods] The participants were 20 healthy students (10 males and 10 females; age 19-21 years) of the Faculty of Agriculture, Saga University. Participants were shown a picture of a Tokara goat or shack (control) while prefrontal cortical oxygenated haemoglobin levels (representing neural activity) were measured by near-infrared spectroscopy. [Results] The prefrontal cortical near-infrared spectroscopy signal was significantly higher during viewing of the animal picture than during a rest condition or during viewing of the control picture. [Conclusion] Our results suggest that near-infrared spectroscopy can be used to objectively identify brain activity changes during human mentation regarding animals; furthermore, these preliminary results suggest the efficacy of animal-assisted therapy could be related to increased activation of the prefrontal cortex.

  2. Bilingualism at the core of the brain. Structural differences between bilinguals and monolinguals revealed by subcortical shape analysis.

    Science.gov (United States)

    Burgaleta, Miguel; Sanjuán, Ana; Ventura-Campos, Noelia; Sebastian-Galles, Núria; Ávila, César

    2016-01-15

    Naturally acquiring a language shapes the human brain through a long-lasting learning and practice process. This is supported by previous studies showing that managing more than one language from early childhood has an impact on brain structure and function. However, to what extent bilingual individuals present neuroanatomical peculiarities at the subcortical level with respect to monolinguals is yet not well understood, despite the key role of subcortical gray matter for a number of language functions, including monitoring of speech production and language control - two processes especially solicited by bilinguals. Here we addressed this issue by performing a subcortical surface-based analysis in a sample of monolinguals and simultaneous bilinguals (N=88) that only differed in their language experience from birth. This analysis allowed us to study with great anatomical precision the potential differences in morphology of key subcortical structures, namely, the caudate, accumbens, putamen, globus pallidus and thalamus. Vertexwise analyses revealed significantly expanded subcortical structures for bilinguals compared to monolinguals, localized in bilateral putamen and thalamus, as well as in the left globus pallidus and right caudate nucleus. A topographical interpretation of our results suggests that a more complex phonological system in bilinguals may lead to a greater development of a subcortical brain network involved in monitoring articulatory processes. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases

    DEFF Research Database (Denmark)

    Mahmood, Faisal; Johannesen, Helle H; Geertsen, Poul

    2017-01-01

    An imaging biomarker for early prediction of treatment response potentially provides a non-invasive tool for better prognostics and individualized management of the disease. Radiotherapy (RT) response is generally related to changes in gross tumor volume manifesting months later. In this prospect......An imaging biomarker for early prediction of treatment response potentially provides a non-invasive tool for better prognostics and individualized management of the disease. Radiotherapy (RT) response is generally related to changes in gross tumor volume manifesting months later....... In this prospective study we investigated the apparent diffusion coefficient (ADC), perfusion fraction and pseudo diffusion coefficient derived from diffusion weighted MRI as potential early biomarkers for radiotherapy response of brain metastases. It was a particular aim to assess the optimal time point...

  4. Factor analysis of regional brain activation in bipolar and healthy individuals reveals a consistent modular structure.

    Science.gov (United States)

    Fleck, David E; Welge, Jeffrey A; Eliassen, James C; Adler, Caleb M; DelBello, Melissa P; Strakowski, Stephen M

    2018-07-01

    The neurophysiological substrates of cognition and emotion, as seen with fMRI, are generally explained using modular structures. The present study was designed to probe the modular structure of cognitive-emotional processing in bipolar and healthy individuals using factor analysis and compare the results with current conceptions of the neurophysiology of bipolar disorder. Exploratory factor analysis was used to assess patterns of covariation among brain regions-of-interest activated during the Continuous Performance Task with Emotional and Neutral Distractors in healthy and bipolar individuals without a priori constraints on the number or composition of latent factors. Results indicated a common cognitive-emotional network consisting of prefrontal, medial temporal, limbic, parietal, anterior cingulate and posterior cingulate modules. However, reduced brain activation to emotional stimuli in the frontal, medial temporal and limbic modules was apparent in the bipolar relative to the healthy group, potentially accounting for emotional dysregulation in bipolar disorder. This study is limited by a relatively small sample size recruited at a single site. The results have yet to be validated on a larger independent sample. Although the modular structure of cognitive-emotional processing is similar in bipolar and healthy individuals, activation in response to emotional/neutral cues varies. These findings are not only consistent with recent conceptions of mood regulation in bipolar disorder, but also suggest that regional activation can be considered within tighter modular structures without compromising data interpretation. This demonstration may serve as a template for data reduction in future region-of-interest analyses to increase statistical power. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

    Science.gov (United States)

    So, Nina; Franks, Becca; Lim, Sean; Curley, James P.

    2015-01-01

    Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive

  6. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression.

    Directory of Open Access Journals (Sweden)

    Nina So

    Full Text Available Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David's Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg's Hub Centrality and Bonacich's Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing

  7. Exploring Deep Space - Uncovering the Anatomy of Periventricular Structures to Reveal the Lateral Ventricles of the Human Brain.

    Science.gov (United States)

    Colibaba, Alexandru S; Calma, Aicee Dawn B; Webb, Alexandra L; Valter, Krisztina

    2017-10-22

    Anatomy students are typically provided with two-dimensional (2D) sections and images when studying cerebral ventricular anatomy and students find this challenging. Because the ventricles are negative spaces located deep within the brain, the only way to understand their anatomy is by appreciating their boundaries formed by related structures. Looking at a 2D representation of these spaces, in any of the cardinal planes, will not enable visualisation of all of the structures that form the boundaries of the ventricles. Thus, using 2D sections alone requires students to compute their own mental image of the 3D ventricular spaces. The aim of this study was to develop a reproducible method for dissecting the human brain to create an educational resource to enhance student understanding of the intricate relationships between the ventricles and periventricular structures. To achieve this, we created a video resource that features a step-by-step guide using a fiber dissection method to reveal the lateral and third ventricles together with the closely related limbic system and basal ganglia structures. One of the advantages of this method is that it enables delineation of the white matter tracts that are difficult to distinguish using other dissection techniques. This video is accompanied by a written protocol that provides a systematic description of the process to aid in the reproduction of the brain dissection. This package offers a valuable anatomy teaching resource for educators and students alike. By following these instructions educators can create teaching resources and students can be guided to produce their own brain dissection as a hands-on practical activity. We recommend that this video guide be incorporated into neuroanatomy teaching to enhance student understanding of the morphology and clinical relevance of the ventricles.

  8. Biodiversity among Lactobacillus helveticus Strains Isolated from Different Natural Whey Starter Cultures as Revealed by Classification Trees

    Science.gov (United States)

    Gatti, Monica; Trivisano, Carlo; Fabrizi, Enrico; Neviani, Erasmo; Gardini, Fausto

    2004-01-01

    Lactobacillus helveticus is a homofermentative thermophilic lactic acid bacterium used extensively for manufacturing Swiss type and aged Italian cheese. In this study, the phenotypic and genotypic diversity of strains isolated from different natural dairy starter cultures used for Grana Padano, Parmigiano Reggiano, and Provolone cheeses was investigated by a classification tree technique. A data set was used that consists of 119 L. helveticus strains, each of which was studied for its physiological characters, as well as surface protein profiles and hybridization with a species-specific DNA probe. The methodology employed in this work allowed the strains to be grouped into terminal nodes without difficult and subjective interpretation. In particular, good discrimination was obtained between L. helveticus strains isolated, respectively, from Grana Padano and from Provolone natural whey starter cultures. The method used in this work allowed identification of the main characteristics that permit discrimination of biotypes. In order to understand what kind of genes could code for phenotypes of technological relevance, evidence that specific DNA sequences are present only in particular biotypes may be of great interest. PMID:14711641

  9. Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

    Science.gov (United States)

    Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

    2017-04-15

    Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pcoding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pcoding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Microarray analysis of a salamander hopeful monster reveals transcriptional signatures of paedomorphic brain development

    Science.gov (United States)

    2010-01-01

    Background The Mexican axolotl (Ambystoma mexicanum) is considered a hopeful monster because it exhibits an adaptive and derived mode of development - paedomorphosis - that has evolved rapidly and independently among tiger salamanders. Unlike related tiger salamanders that undergo metamorphosis, axolotls retain larval morphological traits into adulthood and thus present an adult body plan that differs dramatically from the ancestral (metamorphic) form. The basis of paedomorphic development was investigated by comparing temporal patterns of gene transcription between axolotl and tiger salamander larvae (Ambystoma tigrinum tigrinum) that typically undergo a metamorphosis. Results Transcript abundances from whole brain and pituitary were estimated via microarray analysis on four different days post hatching (42, 56, 70, 84 dph) and regression modeling was used to independently identify genes that were differentially expressed as a function of time in both species. Collectively, more differentially expressed genes (DEGs) were identified as unique to the axolotl (n = 76) and tiger salamander (n = 292) than were identified as shared (n = 108). All but two of the shared DEGs exhibited the same temporal pattern of expression and the unique genes tended to show greater changes later in the larval period when tiger salamander larvae were undergoing anatomical metamorphosis. A second, complementary analysis that directly compared the expression of 1320 genes between the species identified 409 genes that differed as a function of species or the interaction between time and species. Of these 409 DEGs, 84% exhibited higher abundances in tiger salamander larvae at all sampling times. Conclusions Many of the unique tiger salamander transcriptional responses are probably associated with metamorphic biological processes. However, the axolotl also showed unique patterns of transcription early in development. In particular, the axolotl showed a genome-wide reduction in mRNA abundance

  11. Microarray analysis of a salamander hopeful monster reveals transcriptional signatures of paedomorphic brain development

    Directory of Open Access Journals (Sweden)

    Putta Srikrishna

    2010-06-01

    Full Text Available Abstract Background The Mexican axolotl (Ambystoma mexicanum is considered a hopeful monster because it exhibits an adaptive and derived mode of development - paedomorphosis - that has evolved rapidly and independently among tiger salamanders. Unlike related tiger salamanders that undergo metamorphosis, axolotls retain larval morphological traits into adulthood and thus present an adult body plan that differs dramatically from the ancestral (metamorphic form. The basis of paedomorphic development was investigated by comparing temporal patterns of gene transcription between axolotl and tiger salamander larvae (Ambystoma tigrinum tigrinum that typically undergo a metamorphosis. Results Transcript abundances from whole brain and pituitary were estimated via microarray analysis on four different days post hatching (42, 56, 70, 84 dph and regression modeling was used to independently identify genes that were differentially expressed as a function of time in both species. Collectively, more differentially expressed genes (DEGs were identified as unique to the axolotl (n = 76 and tiger salamander (n = 292 than were identified as shared (n = 108. All but two of the shared DEGs exhibited the same temporal pattern of expression and the unique genes tended to show greater changes later in the larval period when tiger salamander larvae were undergoing anatomical metamorphosis. A second, complementary analysis that directly compared the expression of 1320 genes between the species identified 409 genes that differed as a function of species or the interaction between time and species. Of these 409 DEGs, 84% exhibited higher abundances in tiger salamander larvae at all sampling times. Conclusions Many of the unique tiger salamander transcriptional responses are probably associated with metamorphic biological processes. However, the axolotl also showed unique patterns of transcription early in development. In particular, the axolotl showed a genome

  12. Functional Magnetic Resonance Imaging of Rats with Experimental Autoimmune Encephalomyelitis Reveals Brain Cortex Remodeling

    Science.gov (United States)

    Tambalo, Stefano; Peruzzotti-Jametti, Luca; Rigolio, Roberta; Fiorini, Silvia; Bontempi, Pietro; Mallucci, Giulia; Balzarotti, Beatrice; Marmiroli, Paola; Sbarbati, Andrea; Cavaletti, Guido

    2015-01-01

    Cortical reorganization occurring in multiple sclerosis (MS) patients is thought to play a key role in limiting the effect of structural tissue damage. Conversely, its exhaustion may contribute to the irreversible disability that accumulates with disease progression. Several aspects of MS-related cortical reorganization, including the overall functional effect and likely modulation by therapies, still remain to be elucidated. The aim of this work was to assess the extent of functional cortical reorganization and its brain structural/pathological correlates in Dark Agouti rats with experimental autoimmune encephalomyelitis (EAE), a widely accepted preclinical model of chronic MS. Morphological and functional MRI (fMRI) were performed before disease induction and during the relapsing and chronic phases of EAE. During somatosensory stimulation of the right forepaw, fMRI demonstrated that cortical reorganization occurs in both relapsing and chronic phases of EAE with increased activated volume and decreased laterality index versus baseline values. Voxel-based morphometry demonstrated gray matter (GM) atrophy in the cerebral cortex, and both GM and white matter atrophy were assessed by ex vivo pathology of the sensorimotor cortex and corpus callosum. Neuroinflammation persisted in the relapsing and chronic phases, with dendritic spine density in the layer IV sensory neurons inversely correlating with the number of cluster of differentiation 45-positive inflammatory lesions. Our work provides an innovative experimental platform that may be pivotal for the comprehension of key mechanisms responsible for the accumulation of irreversible brain damage and for the development of innovative therapies to reduce disability in EAE/MS. SIGNIFICANCE STATEMENT Since the early 2000s, functional MRI (fMRI) has demonstrated profound modifications in the recruitment of cortical areas during motor, cognitive, and sensory tasks in multiple sclerosis (MS) patients. Experimental autoimmune

  13. A network analysis of ¹⁵O-H₂O PET reveals deep brain stimulation effects on brain network of Parkinson's disease.

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    Park, Hae-Jeong; Park, Bumhee; Kim, Hae Yu; Oh, Maeng-Keun; Kim, Joong Il; Yoon, Misun; Lee, Jong Doo; Chang, Jin Woo

    2015-05-01

    As Parkinson's disease (PD) can be considered a network abnormality, the effects of deep brain stimulation (DBS) need to be investigated in the aspect of networks. This study aimed to examine how DBS of the bilateral subthalamic nucleus (STN) affects the motor networks of patients with idiopathic PD during motor performance and to show the feasibility of the network analysis using cross-sectional positron emission tomography (PET) images in DBS studies. We obtained [¹⁵O]H₂O PET images from ten patients with PD during a sequential finger-to-thumb opposition task and during the resting state, with DBS-On and DBS-Off at STN. To identify the alteration of motor networks in PD and their changes due to STN-DBS, we applied independent component analysis (ICA) to all the cross-sectional PET images. We analysed the strength of each component according to DBS effects, task effects and interaction effects. ICA blindly decomposed components of functionally associated distributed clusters, which were comparable to the results of univariate statistical parametric mapping. ICA further revealed that STN-DBS modifies usage-strengths of components corresponding to the basal ganglia-thalamo-cortical circuits in PD patients by increasing the hypoactive basal ganglia and by suppressing the hyperactive cortical motor areas, ventrolateral thalamus and cerebellum. Our results suggest that STN-DBS may affect not only the abnormal local activity, but also alter brain networks in patients with PD. This study also demonstrated the usefulness of ICA for cross-sectional PET data to reveal network modifications due to DBS, which was not observable using the subtraction method.

  14. A classification scheme for alternative oxidases reveals the taxonomic distribution and evolutionary history of the enzyme in angiosperms.

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    Costa, José Hélio; McDonald, Allison E; Arnholdt-Schmitt, Birgit; Fernandes de Melo, Dirce

    2014-11-01

    A classification scheme based on protein phylogenies and sequence harmony method was used to clarify the taxonomic distribution and evolutionary history of the alternative oxidase (AOX) in angiosperms. A large data set analyses showed that AOX1 and AOX2 subfamilies were distributed into 4 phylogenetic clades: AOX1a-c/1e, AOX1d, AOX2a-c and AOX2d. High diversity in AOX family compositions was found. While the AOX2 subfamily was not detected in monocots, the AOX1 subfamily has expanded (AOX1a-e) in the large majority of these plants. In addition, Poales AOX1b and 1d were orthologous to eudicots AOX1d and then renamed as AOX1d1 and 1d2. AOX1 or AOX2 losses were detected in some eudicot plants. Several AOX2 duplications (AOX2a-c) were identified in eudicot species, mainly in the asterids. The AOX2b originally identified in eudicots in the Fabales order (soybean, cowpea) was divergent from AOX2a-c showing some specific amino acids with AOX1d and then it was renamed as AOX2d. AOX1d and AOX2d seem to be stress-responsive, facultative and mutually exclusive among species suggesting a complementary role with an AOX1(a) in stress conditions. Based on the data collected, we present a model for the evolutionary history of AOX in angiosperms and highlight specific areas where further research would be most beneficial. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Novel effects of edaravone on human brain microvascular endothelial cells revealed by a proteomic approach.

    Science.gov (United States)

    Onodera, Hidetaka; Arito, Mitsumi; Sato, Toshiyuki; Ito, Hidemichi; Hashimoto, Takuo; Tanaka, Yuichiro; Kurokawa, Manae S; Okamoto, Kazuki; Suematsu, Naoya; Kato, Tomohiro

    2013-10-09

    Edaravone (3-methyl-1-phenyl-2-pyrazolin-5-one) is a free radical scavenger used for acute ischemic stroke. However, it is not known whether edaravone works only as a free radical scavenger or possess other pharmacological actions. Therefore, we elucidated the effects of edaravone on human brain microvascular endothelial cells (HBMECs) by 2 dimensional fluorescence difference gel electrophoresis (2D-DIGE). We found 38 protein spots the intensity of which was significantly altered 1.3 fold on average (pedaravone treatment and successfully identified 17 proteins of those. Four of those 17 proteins were cytoskeleton proteins or cytoskeleton-regulating proteins. Therefore, we subsequently investigated the change of size and shape of the cells, the actin network, and the tight junction of HBMEC by immunocytochemistry. As a result, most edaravone-treated HBMECs became larger and rounder compared with those that were not treated. Furthermore, edaravone-treated HBMECs formed gathering zona occludens (ZO)-1, a tight junction protein, along the junction of the cells. In addition, we found that edaravone suppressed interleukin (IL)-1β-induced secretion of monocyte chemoattractant protein-1 (MCP-1), which was reported to increase cell permeability. We found a novel function of edaravone is the promotion of tight junction formations of vascular endothelial cells partly via the down-regulation of MCP-1 secretion. These data provide fundamental and useful information in the clinical use of edaravone in patients with cerebral vascular diseases. © 2013 Elsevier B.V. All rights reserved.

  16. Whole brain resting-state analysis reveals decreased functional connectivity in major depression

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    Ilya M. Veer

    2010-09-01

    Full Text Available Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within six months before inclusion and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxelwise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: 1 decreased bilateral amygdala and left anterior insula connectivity in an affective network, 2 reduced connectivity of the left frontal pole in a network associated with attention and working memory, and 3 decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or grey matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

  17. Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury.

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    Sorenson Donna J

    2009-12-01

    Full Text Available Abstract Background Patients with traumatic brain injury (TBI often present with significant cognitive deficits without corresponding evidence of cortical damage on neuroradiological examinations. One explanation for this puzzling observation is that the diffuse cortical abnormalities that characterize TBI are difficult to detect with standard imaging procedures. Here we investigated a patient with severe TBI-related cognitive impairments whose scan was interpreted as normal by a board-certified radiologist in order to determine if quantitative neuroimaging could detect cortical abnormalities not evident with standard neuroimaging procedures. Methods Cortical abnormalities were quantified using multimodal surfaced-based morphometry (MSBM that statistically combined information from high-resolution structural MRI and diffusion tensor imaging (DTI. Normal values of cortical anatomy and cortical and pericortical DTI properties were quantified in a population of 43 healthy control subjects. Corresponding measures from the patient were obtained in two independent imaging sessions. These data were quantified using both the average values for each lobe and the measurements from each point on the cortical surface. The results were statistically analyzed as z-scores from the mean with a p Results The TBI patient showed significant regional abnormalities in cortical thickness, gray matter diffusivity and pericortical white matter integrity that replicated across imaging sessions. Consistent with the patient's impaired performance on neuropsychological tests of executive function, cortical abnormalities were most pronounced in the frontal lobes. Conclusions MSBM is a promising tool for detecting subtle cortical abnormalities with high sensitivity and selectivity. MSBM may be particularly useful in evaluating cortical structure in TBI and other neurological conditions that produce diffuse abnormalities in both cortical structure and tissue properties.

  18. Integrated genomic and immunophenotypic classification of pancreatic cancer reveals three distinct subtypes with prognostic/predictive significance.

    Science.gov (United States)

    Wartenberg, Martin; Cibin, Silvia; Zlobec, Inti; Vassella, Erik; Eppenberger-Castori, Serenella M M; Terracciano, Luigi; Eichmann, Micha; Worni, Mathias; Gloor, Beat; Perren, Aurel; Karamitopoulou, Eva

    2018-04-16

    Current clinical classification of pancreatic ductal adenocarcinoma (PDAC) is unable to predict prognosis or response to chemo- or immunotherapy and does not take into account the host reaction to PDAC-cells. Our aim is to classify PDAC according to host- and tumor-related factors into clinically/biologically relevant subtypes by integrating molecular and microenvironmental findings. A well-characterized PDAC-cohort (n=110) underwent next-generation sequencing with a hotspot cancer panel, while Next-generation Tissue-Microarrays were immunostained for CD3, CD4, CD8, CD20, PD-L1, p63, hyaluronan-mediated motility receptor (RHAMM) and DNA mismatch-repair proteins. Previous data on FOXP3 were integrated. Immune-cell counts and protein expression were correlated with tumor-derived driver mutations, clinicopathologic features (TNM 8. 2017), survival and epithelial-mesenchymal-transition (EMT)-like tumor budding.  Results: Three PDAC-subtypes were identified: the "immune-escape" (54%), poor in T- and B-cells and enriched in FOXP3+Tregs, with high-grade budding, frequent CDKN2A- , SMAD4- and PIK3CA-mutations and poor outcome; the "immune-rich" (35%), rich in T- and B-cells and poorer in FOXP3+Tregs, with infrequent budding, lower CDKN2A- and PIK3CA-mutation rate and better outcome and a subpopulation with tertiary lymphoid tissue (TLT), mutations in DNA damage response genes (STK11, ATM) and the best outcome; and the "immune-exhausted" (11%) with immunogenic microenvironment and two subpopulations: one with PD-L1-expression and high PIK3CA-mutation rate and a microsatellite-unstable subpopulation with high prevalence of JAK3-mutations. The combination of low budding, low stromal FOXP3-counts, presence of TLTs and absence of CDKN2A-mutations confers significant survival advantage in PDAC-patients. Immune host responses correlate with tumor characteristics leading to morphologically recognizable PDAC-subtypes with prognostic/predictive significance. Copyright ©2018

  19. Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker.

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    Carlton Chu

    Full Text Available Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1-weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y. The inclusion criteria for those born preterm were birth weight ≤ 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment of MR images in two different ways. First, all the individuals were used for training and classification was performed by the leave-one-out method, yielding 93% correct classification (sensitivity = 0.905, specificity = 0.942. Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86% and 91% accurate classifications. Both gestational age (R = -0.24, p<0.04 and birth weight (R = -0.51, p < 0.001 correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = -0.30, p < 0.001 and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long-term goal is to automatically and non-invasively predict the outcome of preterm-born individuals on an individual basis using as early a scan as possible.

  20. Multiplex Brain Proteomic Analysis Revealed the Molecular Therapeutic Effects of Buyang Huanwu Decoction on Cerebral Ischemic Stroke Mice.

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    Hong-Jhang Chen

    Full Text Available Stroke is the second-leading cause of death worldwide, and tissue plasminogen activator (TPA is the only drug used for a limited group of stroke patients in the acute phase. Buyang Huanwu Decoction (BHD, a traditional Chinese medicine prescription, has long been used for improving neurological functional recovery in stroke. In this study, we characterized the therapeutic effect of TPA and BHD in a cerebral ischemia/reperfusion (CIR injury mouse model using multiplex proteomics approach. After the iTRAQ-based proteomics analysis, 1310 proteins were identified from the mouse brain with <1% false discovery rate. Among them, 877 quantitative proteins, 10.26% (90/877, 1.71% (15/877, and 2.62% (23/877 of the proteins was significantly changed in the CIR, BHD treatment, and TPA treatment, respectively. Functional categorization analysis showed that BHD treatment preserved the integrity of the blood-brain barrier (BBB (Alb, Fga, and Trf, suppressed excitotoxicity (Grm5, Gnai, and Gdi, and enhanced energy metabolism (Bdh, thereby revealing its multiple effects on ischemic stroke mice. Moreover, the neurogenesis marker doublecortin was upregulated, and the activity of glycogen synthase kinase 3 (GSK-3 and Tau was inhibited, which represented the neuroprotective effects. However, TPA treatment deteriorated BBB breakdown. This study highlights the potential of BHD in clinical applications for ischemic stroke.

  1. Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

    Science.gov (United States)

    Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

    2013-01-01

    Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

  2. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

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    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  3. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  4. Genome-wide identification of Bcl11b gene targets reveals role in brain-derived neurotrophic factor signaling.

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    Bin Tang

    Full Text Available B-cell leukemia/lymphoma 11B (Bcl11b is a transcription factor showing predominant expression in the striatum. To date, there are no known gene targets of Bcl11b in the nervous system. Here, we define targets for Bcl11b in striatal cells by performing chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq in combination with genome-wide expression profiling. Transcriptome-wide analysis revealed that 694 genes were significantly altered in striatal cells over-expressing Bcl11b, including genes showing striatal-enriched expression similar to Bcl11b. ChIP-seq analysis demonstrated that Bcl11b bound a mixture of coding and non-coding sequences that were within 10 kb of the transcription start site of an annotated gene. Integrating all ChIP-seq hits with the microarray expression data, 248 direct targets of Bcl11b were identified. Functional analysis on the integrated gene target list identified several zinc-finger encoding genes as Bcl11b targets, and further revealed a significant association of Bcl11b to brain-derived neurotrophic factor/neurotrophin signaling. Analysis of ChIP-seq binding regions revealed significant consensus DNA binding motifs for Bcl11b. These data implicate Bcl11b as a novel regulator of the BDNF signaling pathway, which is disrupted in many neurological disorders. Specific targeting of the Bcl11b-DNA interaction could represent a novel therapeutic approach to lowering BDNF signaling specifically in striatal cells.

  5. Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning.

    Science.gov (United States)

    Reindl, Vanessa; Gerloff, Christian; Scharke, Wolfgang; Konrad, Kerstin

    2018-05-25

    Parent-child synchrony, the coupling of behavioral and biological signals during social contact, may fine-tune the child's brain circuitries associated with emotional bond formation and the child's development of emotion regulation. Here, we examined the neurobiological underpinnings of these processes by measuring parent's and child's prefrontal neural activity concurrently with functional near-infrared spectroscopy hyperscanning. Each child played both a cooperative and a competitive game with the parent, mostly the mother, as well as an adult stranger. During cooperation, parent's and child's brain activities synchronized in the dorsolateral prefrontal and frontopolar cortex (FPC), which was predictive for their cooperative performance in subsequent trials. No significant brain-to-brain synchrony was observed in the conditions parent-child competition, stranger-child cooperation and stranger-child competition. Furthermore, parent-child compared to stranger-child brain-to-brain synchrony during cooperation in the FPC mediated the association between the parent's and the child's emotion regulation, as assessed by questionnaires. Thus, we conclude that brain-to-brain synchrony may represent an underlying neural mechanism of the emotional connection between parent and child, which is linked to the child's development of adaptive emotion regulation. Future studies may uncover whether brain-to-brain synchrony can serve as a neurobiological marker of the dyad's socio-emotional interaction, which is sensitive to risk conditions, and can be modified by interventions. Copyright © 2018. Published by Elsevier Inc.

  6. PrP-C1 fragment in cattle brains reveals features of the transmissible spongiform encephalopathy associated PrPsc.

    Science.gov (United States)

    Serra, Fabienne; Müller, Joachim; Gray, John; Lüthi, Ramona; Dudas, Sandor; Czub, Stefanie; Seuberlich, Torsten

    2017-03-15

    Three different types of bovine spongiform encephalopathy (BSE) are known and supposedly caused by distinct prion strains: the classical (C-) BSE type that was typically found during the BSE epidemic, and two relatively rare atypical BSE types, termed H-BSE and L-BSE. The three BSE types differ in the molecular phenotype of the disease associated prion protein, namely the N-terminally truncated proteinase K (PK) resistant prion protein fragment (PrP res ). In this study, we report and analyze yet another PrP res type (PrP res-2011 ), which was found in severely autolytic brain samples of two cows in the framework of disease surveillance in Switzerland in 2011. Analysis of brain tissues from these animals by PK titration and PK inhibitor assays ruled out the process of autolysis as the cause for the aberrant PrP res profile. Immunochemical characterization of the PrP fragments present in the 2011 cases by epitope mapping indicated that PrP res-2011 corresponds in its primary sequence to the physiologically occurring PrP-C1 fragment. However, high speed centrifugation, sucrose gradient assay and NaPTA precipitation revealed biochemical similarities between PrP res-2011 and the disease-associated prion protein found in BSE affected cattle in terms of detergent insolubility, PK resistance and PrP aggregation. Although it remains to be established whether PrP res-2011 is associated with a transmissible disease, our results point out the need of further research on the role the PrP-C1 aggregation and misfolding in health and disease. Copyright © 2017. Published by Elsevier B.V.

  7. Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings

    NARCIS (Netherlands)

    Bralten, J.; Greven, C.U.; Franke, B.; Mennes, M.J.J.; Zwiers, M.P.; Rommelse, N.N.J.; Hartman, C.A.; Meer, D. van der; O'Dwyer, L.G.; Oosterlaan, J.; Hoekstra, P.J.; Heslenfeld, D.; Arias-Vasquez, A.; Buitelaar, J.K.

    2016-01-01

    BACKGROUND: Data on structural brain alterations in patients with attention-deficit/hyperactivity disorder (ADHD) have been inconsistent. Both ADHD and brain volumes have a strong genetic loading, but whether brain alterations in patients with ADHD are familial has been underexplored. We aimed to

  8. Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings

    NARCIS (Netherlands)

    Bralten, Janita; Greven, Corina U.; Franke, Barbara; Mennes, Maarten; Zwiers, Marcel P.; Rommelse, Nanda N. J.; Hartman, Catharina; van der Meer, Dennis; O'Dwyer, Laurence; Oosterlaan, Jaap; Hoekstra, Pieter J.; Heslenfeld, Dirk; Arias-Vasquez, Alejandro; Buitelaar, Jan K.

    Background: Data on structural brain alterations in patients with attention-deficit/hyperactivity disorder (ADHD) have been inconsistent. Both ADHD and brain volumes have a strong genetic loading, but whether brain alterations in patients with ADHD are familial has been underexplored. We aimed to

  9. Brain transcriptional responses to high-fat diet in Acads-deficient mice reveal energy sensing pathways.

    Directory of Open Access Journals (Sweden)

    Claudia Kruger

    Full Text Available How signals from fatty acid metabolism are translated into changes in food intake remains unclear. Previously we reported that mice with a genetic inactivation of Acads (acyl-coenzyme A dehydrogenase, short-chain, the enzyme responsible for mitochondrial beta-oxidation of C4-C6 short-chain fatty acids (SCFAs, shift consumption away from fat and toward carbohydrate when offered a choice between diets. In the current study, we sought to indentify candidate genes and pathways underlying the effects of SCFA oxidation deficiency on food intake in Acads-/- mice.We performed a transcriptional analysis of gene expression in brain tissue of Acads-/- and Acads+/+ mice fed either a high-fat (HF or low-fat (LF diet for 2 d. Ingenuity Pathway Analysis revealed three top-scoring pathways significantly modified by genotype or diet: oxidative phosphorylation, mitochondrial dysfunction, and CREB signaling in neurons. A comparison of statistically significant responses in HF Acads-/- vs. HF Acads+/+ (3917 and Acads+/+ HF vs. LF Acads+/+ (3879 revealed 2551 genes or approximately 65% in common between the two experimental comparisons. All but one of these genes were expressed in opposite direction with similar magnitude, demonstrating that HF-fed Acads-deficient mice display transcriptional responses that strongly resemble those of Acads+/+ mice fed LF diet. Intriguingly, genes involved in both AMP-kinase regulation and the neural control of food intake followed this pattern. Quantitative RT-PCR in hypothalamus confirmed the dysregulation of genes in these pathways. Western blotting showed an increase in hypothalamic AMP-kinase in Acads-/- mice and HF diet increased, a key protein in an energy-sensing cascade that responds to depletion of ATP.Our results suggest that the decreased beta-oxidation of short-chain fatty acids in Acads-deficient mice fed HF diet produces a state of energy deficiency in the brain and that AMP-kinase may be the cellular energy

  10. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

    Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

    2012-01-01

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related...

  11. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

    Science.gov (United States)

    Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

    2015-07-30

    Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Diminished social reward anticipation in the broad autism phenotype as revealed by event-related brain potentials.

    Science.gov (United States)

    Cox, Anthony; Kohls, Gregor; Naples, Adam J; Mukerji, Cora E; Coffman, Marika C; Rutherford, Helena J V; Mayes, Linda C; McPartland, James C

    2015-10-01

    Diminished responsivity to reward incentives is a key contributor to the social-communication problems seen in autism spectrum disorders (ASDs). Social motivation theories suggest that individuals with ASD do not experience social interactions as rewarding, leading to negative consequences for the development of brain circuitry subserving social information. In this study, we examined neural responses to social and non-social reward anticipation in 35 typically developing young adults, examining modulation of reward sensitivity by level of autistic traits. Using an Event-related potential incentive-delay task incorporating novel, more ecologically valid forms of reward, higher expression of autistic traits was associated with an attenuated P3 response to the anticipation of social (simulated real-time video feedback from an observer), but not non-social (candy), rewards. Exploratory analyses revealed that this was unrelated to mentalizing ability. The P3 component reflects motivated attention to reward signals, suggesting attenuated motivation allocation specific to social incentives. The study extends prior findings of atypical reward anticipation in ASD, demonstrating that attenuated social reward responsiveness extends to autistic traits in the range of typical functioning. Results support the development of innovative paradigms for investigating social and non-social reward responsiveness. Insight into vulnerabilities in reward processing is critical for understanding social function in ASD. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  13. Phonological abilities in literacy-impaired children: Brain potentials reveal deficient phoneme discrimination, but intact prosodic processing

    Directory of Open Access Journals (Sweden)

    Claudia Männel

    2017-02-01

    Full Text Available Intact phonological processing is crucial for successful literacy acquisition. While individuals with difficulties in reading and spelling (i.e., developmental dyslexia are known to experience deficient phoneme discrimination (i.e., segmental phonology, findings concerning their prosodic processing (i.e., suprasegmental phonology are controversial. Because there are no behavior-independent studies on the underlying neural correlates of prosodic processing in dyslexia, these controversial findings might be explained by different task demands. To provide an objective behavior-independent picture of segmental and suprasegmental phonological processing in impaired literacy acquisition, we investigated event-related brain potentials during passive listening in typically and poor-spelling German school children. For segmental phonology, we analyzed the Mismatch Negativity (MMN during vowel length discrimination, capturing automatic auditory deviancy detection in repetitive contexts. For suprasegmental phonology, we analyzed the Closure Positive Shift (CPS that automatically occurs in response to prosodic boundaries. Our results revealed spelling group differences for the MMN, but not for the CPS, indicating deficient segmental, but intact suprasegmental phonological processing in poor spellers. The present findings point towards a differential role of segmental and suprasegmental phonology in literacy disorders and call for interventions that invigorate impaired literacy by utilizing intact prosody in addition to training deficient phonemic awareness.

  14. Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson's Disease.

    Science.gov (United States)

    Herz, Damian M; Little, Simon; Pedrosa, David J; Tinkhauser, Gerd; Cheeran, Binith; Foltynie, Tom; Bogacz, Rafal; Brown, Peter

    2018-04-23

    To optimally balance opposing demands of speed and accuracy during decision-making, we must flexibly adapt how much evidence we require before making a choice. Such adjustments in decision thresholds have been linked to the subthalamic nucleus (STN), and therapeutic STN deep-brain stimulation (DBS) has been shown to interfere with this function. Here, we performed continuous as well as closed-loop DBS of the STN while Parkinson's disease patients performed a perceptual decision-making task. Closed-loop STN DBS allowed temporally patterned STN stimulation and simultaneous recordings of STN activity. This revealed that DBS only affected patients' ability to adjust decision thresholds if applied in a specific temporally confined time window during deliberation. Only stimulation in that window diminished the normal slowing of response times that occurred on difficult trials when DBS was turned off. Furthermore, DBS eliminated a relative, time-specific increase in STN beta oscillations and compromised its functional relationship with trial-by-trial adjustments in decision thresholds. Together, these results provide causal evidence that the STN is involved in adjusting decision thresholds in distinct, time-limited processing windows during deliberation. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. Tactile object familiarity in the blind brain reveals the supramodal perceptual-mnemonic nature of the perirhinal cortex

    Directory of Open Access Journals (Sweden)

    Laura eCacciamani

    2016-04-01

    Full Text Available This study is the first to investigate the neural underpinnings of tactile object familiarity in the blind during both perception and memory. In the sighted, the perirhinal cortex (PRC has been implicated in the assessment of visual object familiarity—a crucial everyday task—as evidenced by reduced activation when an object becomes familiar. Here, to examine the PRC’s role in tactile object familiarity in the absence of vision, we trained blind subjects on a unique memory-guided drawing technique and measured brain activity while they perceptually explored raised-line drawings, drew them from tactile memory, and scribbled (control. FMRI before and after a week of training revealed a significant decrease in PRC activation from pre- to post-training (i.e., from unfamiliar to familiar during perceptual exploration as well as memory-guided drawing, but not scribbling. This familiarity-based reduction is the first evidence that the PRC represents tactile object familiarity in the blind. Furthermore, the finding of this effect during both tactile perception and tactile memory provides the critical link in establishing the PRC as a structure whose representations are supramodal for both perception and memory.

  16. Multichannel brain recordings in behaving Drosophila reveal oscillatory activity and local coherence in response to sensory stimulation and circuit activation.

    Science.gov (United States)

    Paulk, Angelique C; Zhou, Yanqiong; Stratton, Peter; Liu, Li; van Swinderen, Bruno

    2013-10-01

    Neural networks in vertebrates exhibit endogenous oscillations that have been associated with functions ranging from sensory processing to locomotion. It remains unclear whether oscillations may play a similar role in the insect brain. We describe a novel "whole brain" readout for Drosophila melanogaster using a simple multichannel recording preparation to study electrical activity across the brain of flies exposed to different sensory stimuli. We recorded local field potential (LFP) activity from >2,000 registered recording sites across the fly brain in >200 wild-type and transgenic animals to uncover specific LFP frequency bands that correlate with: 1) brain region; 2) sensory modality (olfactory, visual, or mechanosensory); and 3) activity in specific neural circuits. We found endogenous and stimulus-specific oscillations throughout the fly brain. Central (higher-order) brain regions exhibited sensory modality-specific increases in power within narrow frequency bands. Conversely, in sensory brain regions such as the optic or antennal lobes, LFP coherence, rather than power, best defined sensory responses across modalities. By transiently activating specific circuits via expression of TrpA1, we found that several circuits in the fly brain modulate LFP power and coherence across brain regions and frequency domains. However, activation of a neuromodulatory octopaminergic circuit specifically increased neuronal coherence in the optic lobes during visual stimulation while decreasing coherence in central brain regions. Our multichannel recording and brain registration approach provides an effective way to track activity simultaneously across the fly brain in vivo, allowing investigation of functional roles for oscillations in processing sensory stimuli and modulating behavior.

  17. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2013-10-11

    This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10s task period. Moreover, when the analysis time was confined to the 2-7s span within the overall 10s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  18. Multichannel brain recordings in behaving Drosophila reveal oscillatory activity and local coherence in response to sensory stimulation and circuit activation

    Science.gov (United States)

    Paulk, Angelique C.; Zhou, Yanqiong; Stratton, Peter; Liu, Li

    2013-01-01

    Neural networks in vertebrates exhibit endogenous oscillations that have been associated with functions ranging from sensory processing to locomotion. It remains unclear whether oscillations may play a similar role in the insect brain. We describe a novel “whole brain” readout for Drosophila melanogaster using a simple multichannel recording preparation to study electrical activity across the brain of flies exposed to different sensory stimuli. We recorded local field potential (LFP) activity from >2,000 registered recording sites across the fly brain in >200 wild-type and transgenic animals to uncover specific LFP frequency bands that correlate with: 1) brain region; 2) sensory modality (olfactory, visual, or mechanosensory); and 3) activity in specific neural circuits. We found endogenous and stimulus-specific oscillations throughout the fly brain. Central (higher-order) brain regions exhibited sensory modality-specific increases in power within narrow frequency bands. Conversely, in sensory brain regions such as the optic or antennal lobes, LFP coherence, rather than power, best defined sensory responses across modalities. By transiently activating specific circuits via expression of TrpA1, we found that several circuits in the fly brain modulate LFP power and coherence across brain regions and frequency domains. However, activation of a neuromodulatory octopaminergic circuit specifically increased neuronal coherence in the optic lobes during visual stimulation while decreasing coherence in central brain regions. Our multichannel recording and brain registration approach provides an effective way to track activity simultaneously across the fly brain in vivo, allowing investigation of functional roles for oscillations in processing sensory stimuli and modulating behavior. PMID:23864378

  19. Novel Middle-Type Kenyon Cells in the Honeybee Brain Revealed by Area-Preferential Gene Expression Analysis

    OpenAIRE

    Kaneko, Kumi; Ikeda, Tsubomi; Nagai, Mirai; Hori, Sayaka; Umatani, Chie; Tadano, Hiroto; Ugajin, Atsushi; Nakaoka, Takayoshi; Paul, Rajib Kumar; Fujiyuki, Tomoko; Shirai, Kenichi; Kunieda, Takekazu; Takeuchi, Hideaki; Kubo, Takeo

    2013-01-01

    The mushroom bodies (a higher center) of the honeybee (Apis mellifera L) brain were considered to comprise three types of intrinsic neurons, including large- and small-type Kenyon cells that have distinct gene expression profiles. Although previous neural activity mapping using the immediate early gene kakusei suggested that small-type Kenyon cells are mainly active in forager brains, the precise Kenyon cell types that are active in the forager brain remain to be elucidated. We searched for n...

  20. Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

    Science.gov (United States)

    Balbekova, Anna; Lohninger, Hans; van Tilborg, Geralda A F; Dijkhuizen, Rick M; Bonta, Maximilian; Limbeck, Andreas; Lendl, Bernhard; Al-Saad, Khalid A; Ali, Mohamed; Celikic, Minja; Ofner, Johannes

    2018-02-01

    Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

  1. Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.

    Science.gov (United States)

    Chen, Qiu-Feng; Chen, Hua-Jun; Liu, Jun; Sun, Tao; Shen, Qun-Tai

    2016-01-01

    Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.

  2. Comparison of subjective and objective assessments of outcome after traumatic brain injury using the International Classification of Functioning, Disability and Health (ICF).

    Science.gov (United States)

    Koskinen, Sanna; Hokkinen, Eeva-Maija; Wilson, Lindsay; Sarajuuri, Jaana; Von Steinbüchel, Nicole; Truelle, Jean-Luc

    2011-01-01

    The aim is to examine two aspects of outcome after traumatic brain injury (TBI). Functional outcome was assessed by the Glasgow Outcome Scale - Extended (GOSE) and by clinician ratings, while health-related quality of life (HRQoL) was assessed by the Quality of Life after Brain Injury (QOLIBRI). The GOSE and the QOLIBRI were linked to the International Classification of Functioning, Disability and Health (ICF) to analyse their content. Functional outcome on ICF categories was assessed by rehabilitation clinicians in 55 participants with TBI and was compared to the participants' own judgements of their HRQoL. The QOLIBRI was linked to 42 and the GOSE to 57 two-level ICF categories covering 78% of the categories on the ICF brief core set for TBI. The closest agreement in the views of the professionals and the participants was found on the Physical Problems and Cognition scales of the QOLIBRI. The problems encountered after TBI are well covered by the QOLIBRI and the GOSE. They capture important domains that are not traditionally sufficiently documented, especially in the domains of interpersonal relationships, social and leisure activities, self and the environment. The findings indicate that they are useful and complementary outcome measures for TBI. In rehabilitation, they can serve as tools in assessment, setting meaningful goals and creating therapeutic alliance.

  3. Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

    Science.gov (United States)

    Amanpour, Behzad; Erfanian, Abbas

    2013-01-01

    An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

  4. Inter-subject Functional Correlation Reveal a Hierarchical Organization of Extrinsic and Intrinsic Systems in the Brain.

    Science.gov (United States)

    Ren, Yudan; Nguyen, Vinh Thai; Guo, Lei; Guo, Christine Cong

    2017-09-07

    The brain is constantly monitoring and integrating both cues from the external world and signals generated intrinsically. These extrinsically and intrinsically-driven neural processes are thought to engage anatomically distinct regions, which are thought to constitute the extrinsic and intrinsic systems of the brain. While the specialization of extrinsic and intrinsic system is evident in primary and secondary sensory cortices, a systematic mapping of the whole brain remains elusive. Here, we characterized the extrinsic and intrinsic functional activities in the brain during naturalistic movie-viewing. Using a novel inter-subject functional correlation (ISFC) analysis, we found that the strength of ISFC shifts along the hierarchical organization of the brain. Primary sensory cortices appear to have strong inter-subject functional correlation, consistent with their role in processing exogenous information, while heteromodal regions that attend to endogenous processes have low inter-subject functional correlation. Those brain systems with higher intrinsic tendency show greater inter-individual variability, likely reflecting the aspects of brain connectivity architecture unique to individuals. Our study presents a novel framework for dissecting extrinsically- and intrinsically-driven processes, as well as examining individual differences in brain function during naturalistic stimulation.

  5. Proton Magnetic Resonance Spectroscopy and MRI Reveal No Evidence for Brain Mitochondrial Dysfunction in Children with Autism Spectrum Disorder

    Science.gov (United States)

    Corrigan, Neva M.; Shaw, Dennis. W. W.; Richards, Todd L.; Estes, Annette M.; Friedman, Seth D.; Petropoulos, Helen; Artru, Alan A.; Dager, Stephen R.

    2012-01-01

    Brain mitochondrial dysfunction has been proposed as an etiologic factor in autism spectrum disorder (ASD). Proton magnetic resonance spectroscopic imaging ([superscript 1]HMRS) and MRI were used to assess for evidence of brain mitochondrial dysfunction in longitudinal samples of children with ASD or developmental delay (DD), and cross-sectionally…

  6. Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

    Science.gov (United States)

    Bouts, Mark J R J; Westmoreland, Susan V; de Crespigny, Alex J; Liu, Yutong; Vangel, Mark; Dijkhuizen, Rick M; Wu, Ona; D'Arceuil, Helen E

    2015-12-15

    Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

  7. Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-11-01

    Full Text Available Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging, we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging.

  8. Noninvasive monitoring of treatment response in a rabbit cyanide toxicity model reveals differences in brain and muscle metabolism

    Science.gov (United States)

    Kim, Jae G.; Lee, Jangwoen; Mahon, Sari B.; Mukai, David; Patterson, Steven E.; Boss, Gerry R.; Tromberg, Bruce J.; Brenner, Matthew

    2012-10-01

    Noninvasive near infrared spectroscopy measurements were performed to monitor cyanide (CN) poisoning and recovery in the brain region and in foreleg muscle simultaneously, and the effects of a novel CN antidote, sulfanegen sodium, on tissue hemoglobin oxygenation changes were compared using a sub-lethal rabbit model. The results demonstrated that the brain region is more susceptible to CN poisoning and slower in endogenous CN detoxification following exposure than peripheral muscles. However, sulfanegen sodium rapidly reversed CN toxicity, with brain region effects reversing more quickly than muscle. In vivo monitoring of multiple organs may provide important clinical information regarding the extent of CN toxicity and subsequent recovery, and facilitate antidote drug development.

  9. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

    Science.gov (United States)

    Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-01-01

    To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

  10. Fixel-based analysis reveals alterations is brain microstructure and macrostructure of preterm-born infants at term equivalent age

    Directory of Open Access Journals (Sweden)

    Kerstin Pannek

    Full Text Available Preterm birth causes significant disruption in ongoing brain development, frequently resulting in adverse neurodevelopmental outcomes. Brain imaging using diffusion MRI may provide valuable insight into microstructural properties of the developing brain. The aim of this study was to establish whether the recently introduced fixel-based analysis method, with its associated measures of fibre density (FD, fibre bundle cross-section (FC, and fibre density and bundle cross-section (FDC, is suitable for the investigation of the preterm infant brain at term equivalent age. High-angular resolution diffusion weighted images (HARDI of 55 preterm-born infants and 20 term-born infants, scanned around term-equivalent age, were included in this study (3 T, 64 directions, b = 2000 s/mm2. Postmenstrual age at the time of MRI, and intracranial volume (FC and FDC only, were identified as confounding variables. Gestational age at birth was correlated with all fixel measures in the splenium of the corpus callosum. Compared to term-born infants, preterm infants showed reduced FD, FC, and FDC in a number of regions, including the corpus callosum, anterior commissure, cortico-spinal tract, optic radiations, and cingulum. Preterm infants with minimal macroscopic brain abnormality showed more extensive reductions than preterm infants without any macroscopic brain abnormality; however, little differences were observed between preterm infants with no and with minimal brain abnormality. FC showed significant reductions in preterm versus term infants outside regions identified with FD and FDC, highlighting the complementary role of these measures. Fixel-based analysis identified both microstructural and macrostructural abnormalities in preterm born infants, providing a more complete picture of early brain development than previous diffusion tensor imaging (DTI based approaches. Keywords: Fixel-based analysis, Diffusion, Prematurity, Neonate

  11. Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI

    Directory of Open Access Journals (Sweden)

    Yeu-Sheng Tyan

    2017-01-01

    Full Text Available The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD, Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females age- and education-matched subjects (age range: 13 to 14 years. The structural connectome was obtained by graph theoretical and network-based statistical (NBS analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

  12. The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI

    Science.gov (United States)

    Thompson, William H.; Fransson, Peter

    2015-01-01

    When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed. PMID:26236216

  13. Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI.

    Science.gov (United States)

    Tyan, Yeu-Sheng; Liao, Jan-Ray; Shen, Chao-Yu; Lin, Yu-Chieh; Weng, Jun-Cheng

    2017-01-01

    The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI) provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI) has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females) age- and education-matched subjects (age range: 13 to 14 years). The structural connectome was obtained by graph theoretical and network-based statistical (NBS) analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

  14. The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI.

    Science.gov (United States)

    Thompson, William H; Fransson, Peter

    2015-01-01

    When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed.

  15. Our Faces in the Dog's Brain: Functional Imaging Reveals Temporal Cortex Activation during Perception of Human Faces.

    Directory of Open Access Journals (Sweden)

    Laura V Cuaya

    Full Text Available Dogs have a rich social relationship with humans. One fundamental aspect of it is how dogs pay close attention to human faces in order to guide their behavior, for example, by recognizing their owner and his/her emotional state using visual cues. It is well known that humans have specific brain regions for the processing of other human faces, yet it is unclear how dogs' brains process human faces. For this reason, our study focuses on describing the brain correlates of perception of human faces in dogs using functional magnetic resonance imaging (fMRI. We trained seven domestic dogs to remain awake, still and unrestrained inside an MRI scanner. We used a visual stimulation paradigm with block design to compare activity elicited by human faces against everyday objects. Brain activity related to the perception of faces changed significantly in several brain regions, but mainly in the bilateral temporal cortex. The opposite contrast (i.e., everyday objects against human faces showed no significant brain activity change. The temporal cortex is part of the ventral visual pathway, and our results are consistent with reports in other species like primates and sheep, that suggest a high degree of evolutionary conservation of this pathway for face processing. This study introduces the temporal cortex as candidate to process human faces, a pillar of social cognition in dogs.

  16. Intracellular Redox State Revealed by In Vivo 31P MRS Measurement of NAD+ and NADH Contents in Brains

    Science.gov (United States)

    Lu, Ming; Zhu, Xiao-Hong; Zhang, Yi; Chen, Wei

    2015-01-01

    Purpose Nicotinamide adenine dinucleotide (NAD), in oxidized (NAD+) or reduced (NADH) form, plays key roles in cellular metabolism. Intracellular NAD+/NADH ratio represents the cellular redox state; however, it is difficult to measure in vivo. We report here a novel in vivo 31P MRS method for noninvasive measurement of intracellular NAD concentrations and NAD+/NADH ratio in the brain. Methods It uses a theoretical model to describe the NAD spectral patterns at a given field for quantification. Standard NAD solutions and independent cat brain measurements at 9.4 T and 16.4 T were used to evaluate this method. We also measured T1 values of brain NAD. Results Model simulation and studies of solutions and brains indicate that the proposed method can quantify submillimolar NAD concentrations with reasonable accuracy if adequate 31P MRS signal-to-noise ratio and linewidth were obtained. The NAD concentrations and NAD+/NADH ratio of cat brains measured at 16.4 T and 9.4 T were consistent despite the significantly different T1 values and NAD spectra patterns at two fields. Conclusion This newly established 31P MRS method makes it possible for the first time to noninvasively study the intracellular redox state and its roles in brain functions and diseases, and it can potentially be applied to other organs. PMID:23843330

  17. Brain source localization: A new method based on MUltiple SIgnal Classification algorithm and spatial sparsity of the field signal for electroencephalogram measurements

    Science.gov (United States)

    Vergallo, P.; Lay-Ekuakille, A.

    2013-08-01

    Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to

  18. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

    Directory of Open Access Journals (Sweden)

    Justin Ronald Estepp

    2015-03-01

    Full Text Available The passive brain-computer interface (pBCI framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neurophysiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors.

  19. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload.

    Science.gov (United States)

    Estepp, Justin R; Christensen, James C

    2015-01-01

    The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface) on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral) may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of) effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors.

  20. A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.

    Science.gov (United States)

    Amaral, Carlos P; Simões, Marco A; Mouga, Susana; Andrade, João; Castelo-Branco, Miguel

    2017-10-01

    We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Naïve-Bayes classifier. We compared: 1 - g.Mobilab+ (active dry-electrodes, wireless transmission); 2 - g.Nautilus (active electrodes, wireless transmission); 3 - V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD. This work provides a unique combination of 'easy-to-use' BCI systems with new technologies such as VR to train joint-attention skills in autism. Our P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings

    Science.gov (United States)

    Bralten, Janita; Greven, Corina U.; Franke, Barbara; Mennes, Maarten; Zwiers, Marcel P.; Rommelse, Nanda N.J.; Hartman, Catharina; van der Meer, Dennis; O’Dwyer, Laurence; Oosterlaan, Jaap; Hoekstra, Pieter J.; Heslenfeld, Dirk; Arias-Vasquez, Alejandro; Buitelaar, Jan K.

    2016-01-01

    Background Data on structural brain alterations in patients with attention-deficit/hyperactivity disorder (ADHD) have been inconsistent. Both ADHD and brain volumes have a strong genetic loading, but whether brain alterations in patients with ADHD are familial has been underexplored. We aimed to detect structural brain alterations in adolescents and young adults with ADHD compared with healthy controls. We examined whether these alterations were also found in their unaffected siblings, using a uniquely large sample. Methods We performed voxel-based morphometry analyses on MRI scans of patients with ADHD, their unaffected siblings and typically developing controls. We identified brain areas that differed between participants with ADHD and controls and investigated whether these areas were different in unaffected siblings. Influences of medication use, age, sex and IQ were considered. Results Our sample included 307 patients with ADHD, 169 unaffected siblings and 196 typically developing controls (mean age 17.2 [range 8–30] yr). Compared with controls, participants with ADHD had significantly smaller grey matter volume in 5 clusters located in the precentral gyrus, medial and orbitofrontal cortex, and (para)cingulate cortices. Unaffected siblings showed intermediate volumes significantly different from controls in 4 of these clusters (all except the precentral gyrus). Medication use, age, sex and IQ did not have an undue influence on the results. Limitations Our sample was heterogeneous, most participants with ADHD were taking medication, and the comparison was cross-sectional. Conclusion Brain areas involved in decision making, motivation, cognitive control and motor functioning were smaller in participants with ADHD than in controls. Investigation of unaffected siblings indicated familiality of 4 of the structural brain differences, supporting their potential in molecular genetic analyses in ADHD research. PMID:26679925

  2. Single Cell Immuno-Laser Microdissection Coupled to Label-Free Proteomics to Reveal the Proteotypes of Human Brain Cells After Ischemia.

    Science.gov (United States)

    García-Berrocoso, Teresa; Llombart, Víctor; Colàs-Campàs, Laura; Hainard, Alexandre; Licker, Virginie; Penalba, Anna; Ramiro, Laura; Simats, Alba; Bustamante, Alejandro; Martínez-Saez, Elena; Canals, Francesc; Sanchez, Jean-Charles; Montaner, Joan

    2018-01-01

    Cerebral ischemia entails rapid tissue damage in the affected brain area causing devastating neurological dysfunction. How each component of the neurovascular unit contributes or responds to the ischemic insult in the context of the human brain has not been solved yet. Thus, the analysis of the proteome is a straightforward approach to unraveling these cell proteotypes. In this study, post-mortem brain slices from ischemic stroke patients were obtained corresponding to infarcted (IC) and contralateral (CL) areas. By means of laser microdissection, neurons and blood brain barrier structures (BBB) were isolated and analyzed using label-free quantification. MS data are available via ProteomeXchange with identifier PXD003519. Ninety proteins were identified only in neurons, 260 proteins only in the BBB and 261 proteins in both cell types. Bioinformatics analyses revealed that repair processes, mainly related to synaptic plasticity, are outlined in microdissected neurons, with nonexclusive important functions found in the BBB. A total of 30 proteins showing p 2 between IC and CL areas were considered meaningful in this study: 13 in neurons, 14 in the BBB and 3 in both cell types. Twelve of these proteins were selected as candidates and analyzed by immunohistofluorescence in independent brains. The MS findings were completely verified for neuronal SAHH2 and SRSF1 whereas the presence in both cell types of GABT and EAA2 was only validated in neurons. In addition, SAHH2 showed its potential as a prognostic biomarker of neurological improvement when analyzed early in the plasma of ischemic stroke patients. Therefore, the quantitative proteomes of neurons and the BBB (or proteotypes) after human brain ischemia presented here contribute to increasing the knowledge regarding the molecular mechanisms of ischemic stroke pathology and highlight new proteins that might represent putative biomarkers of brain ischemia or therapeutic targets. © 2018 by The American Society for

  3. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

    Science.gov (United States)

    Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

    2016-02-01

    Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the

  4. Headache in military service members with a history of mild traumatic brain injury: A cohort study of diagnosis and classification.

    Science.gov (United States)

    Finkel, Alan G; Yerry, Juanita A; Klaric, John S; Ivins, Brian J; Scher, Ann; Choi, Young S

    2017-05-01

    Introduction Headaches after concussion are highly prevalent, relatively persistent and are being treated like primary headaches, especially migraine. Methods We studied all new patients seen between August 2008 and December 2009 assessed by a civilian headache specialist at the TBI Center at Womack Army Medical Center, Fort Bragg, NC. We report sample demographics, injuries and headache characteristics, including time from injury to headache onset, detailed descriptions and International Classification of Headache Disorders second edition primary headache diagnosis type. Results A total of 95 soldiers reported 166 headaches. The most common injury cited was a blast (53.7%). Most subjects (76.8%) recalled the onset of any headache within 7 days of injury. The most commonly diagnosed headache was a continuous type with migraine features ( n = 31 (18.7%)), followed by chronic migraine (type 1.5.1, n = 14 (8.4%)), migraine with aura (type 1.2.1, n = 10 (6.0%)), hemicrania continua (type 4.7, n = 12 (7.2%)), chronic cluster (type 3.1.2, n = 6 (3.6%)) and headaches not otherwise classifiable (type 14.1, n = 5 (3.0%)) also present. The most clinically important was a continuous headache with migraine features. Conclusion We present a series of patients seen in a military treatment facility for headache diagnosis after concussion in whom we found migraine, as well as uncommon primary headache types, at frequencies that were much higher than expected.

  5. Signs of long-term adaptation to permanent brain damage as revealed by prehension studies of children with spastic hemiparesis

    NARCIS (Netherlands)

    Steenbergen, B.; Meulenbroek, R.G.J.; Latash, M.L.; Levin, M.

    2003-01-01

    This chapter focusses on signs of long-term adaptation to permanent brain damage in children with spastic hemiparesis. First, we recognize that adaptation processes may occur at various time scales. Then, we formulate a tentative strategy to infer signs of adaptation from behavioral data.

  6. In vivo proton magnetic resonance spectroscopy reveals region specific metabolic responses to SIV infection in the macaque brain

    Directory of Open Access Journals (Sweden)

    Joo Chan-Gyu

    2009-06-01

    Full Text Available Abstract Background In vivo proton magnetic resonance spectroscopy (1H-MRS studies of HIV-infected humans have demonstrated significant metabolic abnormalities that vary by brain region, but the causes are poorly understood. Metabolic changes in the frontal cortex, basal ganglia and white matter in 18 SIV-infected macaques were investigated using MRS during the first month of infection. Results Changes in the N-acetylaspartate (NAA, choline (Cho, myo-inositol (MI, creatine (Cr and glutamine/glutamate (Glx resonances were quantified both in absolute terms and relative to the creatine resonance. Most abnormalities were observed at the time of peak viremia, 2 weeks post infection (wpi. At that time point, significant decreases in NAA and NAA/Cr, reflecting neuronal injury, were observed only in the frontal cortex. Cr was significantly elevated only in the white matter. Changes in Cho and Cho/Cr were similar across the brain regions, increasing at 2 wpi, and falling below baseline levels at 4 wpi. MI and MI/Cr levels were increased across all brain regions. Conclusion These data best support the hypothesis that different brain regions have variable intrinsic vulnerabilities to neuronal injury caused by the AIDS virus.

  7. Potassium-selective microelectrode revealed difference in threshold potassium concentration for cortical spreading depression in female and male rat brain

    Czech Academy of Sciences Publication Activity Database

    Adámek, S.; Vyskočil, František

    2011-01-01

    Roč. 1370, - (2011), s. 215-219 ISSN 0006-8993 R&D Projects: GA AV ČR(CZ) IAA500110905 Institutional research plan: CEZ:AV0Z50110509 Keywords : rat cortex * potassium in brain Subject RIV: ED - Physiology Impact factor: 2.728, year: 2011

  8. Exploratory metabolomic analyses reveal compounds correlated with lutein concentration in frontal cortex, hippocampus, and occipital cortex of human infant brain

    Science.gov (United States)

    Lutein is a dietary carotenoid well known for its role as an antioxidant in the macula and recent reports implicate a role for lutein in cognitive function. Lutein is the dominant carotenoid in both pediatric and geriatric brain tissue. In addition, cognitive function in older adults correlated with...

  9. Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

    Science.gov (United States)

    Kim, Heejung; Hahm, Jarang; Lee, Hyekyoung; Kang, Eunjoo; Kang, Hyejin; Lee, Dong Soo

    2015-05-01

    The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

  10. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  11. Effects of Perfluorooctanoic Acid on Metabolic Profiles in Brain and Liver of Mouse Revealed by a High-throughput Targeted Metabolomics Approach

    Science.gov (United States)

    Yu, Nanyang; Wei, Si; Li, Meiying; Yang, Jingping; Li, Kan; Jin, Ling; Xie, Yuwei; Giesy, John P.; Zhang, Xiaowei; Yu, Hongxia

    2016-04-01

    Perfluorooctanoic acid (PFOA), a perfluoroalkyl acid, can result in hepatotoxicity and neurobehavioral effects in animals. The metabolome, which serves as a connection among transcriptome, proteome and toxic effects, provides pathway-based insights into effects of PFOA. Since understanding of changes in the metabolic profile during hepatotoxicity and neurotoxicity were still incomplete, a high-throughput targeted metabolomics approach (278 metabolites) was used to investigate effects of exposure to PFOA for 28 d on brain and liver of male Balb/c mice. Results of multivariate statistical analysis indicated that PFOA caused alterations in metabolic pathways in exposed individuals. Pathway analysis suggested that PFOA affected metabolism of amino acids, lipids, carbohydrates and energetics. Ten and 18 metabolites were identified as potential unique biomarkers of exposure to PFOA in brain and liver, respectively. In brain, PFOA affected concentrations of neurotransmitters, including serotonin, dopamine, norepinephrine, and glutamate in brain, which provides novel insights into mechanisms of PFOA-induced neurobehavioral effects. In liver, profiles of lipids revealed involvement of β-oxidation and biosynthesis of saturated and unsaturated fatty acids in PFOA-induced hepatotoxicity, while alterations in metabolism of arachidonic acid suggesting potential of PFOA to cause inflammation response in liver. These results provide insight into the mechanism and biomarkers for PFOA-induced effects.

  12. Violence-related content in video game may lead to functional connectivity changes in brain networks as revealed by fMRI-ICA in young men.

    Science.gov (United States)

    Zvyagintsev, M; Klasen, M; Weber, R; Sarkheil, P; Esposito, F; Mathiak, K A; Schwenzer, M; Mathiak, K

    2016-04-21

    In violent video games, players engage in virtual aggressive behaviors. Exposure to virtual aggressive behavior induces short-term changes in players' behavior. In a previous study, a violence-related version of the racing game "Carmageddon TDR2000" increased aggressive affects, cognitions, and behaviors compared to its non-violence-related version. This study investigates the differences in neural network activity during the playing of both versions of the video game. Functional magnetic resonance imaging (fMRI) recorded ongoing brain activity of 18 young men playing the violence-related and the non-violence-related version of the video game Carmageddon. Image time series were decomposed into functional connectivity (FC) patterns using independent component analysis (ICA) and template-matching yielded a mapping to established functional brain networks. The FC patterns revealed a decrease in connectivity within 6 brain networks during the violence-related compared to the non-violence-related condition: three sensory-motor networks, the reward network, the default mode network (DMN), and the right-lateralized frontoparietal network. Playing violent racing games may change functional brain connectivity, in particular and even after controlling for event frequency, in the reward network and the DMN. These changes may underlie the short-term increase of aggressive affects, cognitions, and behaviors as observed after playing violent video games. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  13. Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

    Science.gov (United States)

    Cheng, Lin; Zhu, Yang; Sun, Junfeng; Deng, Lifu; He, Naying; Yang, Yang; Ling, Huawei; Ayaz, Hasan; Fu, Yi; Tong, Shanbao

    2018-01-25

    Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique

  14. The fast detection of rare auditory feature conjunctions in the human brain as revealed by cortical gamma-band electroencephalogram.

    Science.gov (United States)

    Ruusuvirta, T; Huotilainen, M

    2005-01-01

    Natural environments typically contain temporal scatters of sounds emitted from multiple sources. The sounds may often physically stand out from one another in their conjoined rather than simple features. This poses a particular challenge for the brain to detect which of these sounds are rare and, therefore, potentially important for survival. We recorded gamma-band (32-40 Hz) electroencephalographic (EEG) oscillations from the scalp of adult humans who passively listened to a repeated tone carrying frequent and rare conjunctions of its frequency and intensity. EEG oscillations that this tone induced, rather than evoked, differed in amplitude between the two conjunction types within the 56-ms analysis window from tone onset. Our finding suggests that, perhaps with the support of its non-phase-locked synchrony in the gamma band, the human brain is able to detect rare sounds as feature conjunctions very rapidly.

  15. Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

    Directory of Open Access Journals (Sweden)

    Satoru Hiwa

    2016-01-01

    Full Text Available Functional near-infrared spectroscopy (fNIRS is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

  16. Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry.

    Directory of Open Access Journals (Sweden)

    Tianhao Zhang

    Full Text Available The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI provides an opportunity to evaluate how menopausal hormone therapy (HT affects the structure of older women's brains. Our earlier work based on region of interest (ROI analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes.We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA. ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods.Women assigned to HT treatments had significant Gray Matter (GM losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes.HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects.

  17. Protein expression profiling of the drosophila fragile X mutant brain reveals up-regulation of monoamine synthesis.

    Science.gov (United States)

    Zhang, Yong Q; Friedman, David B; Wang, Zhe; Woodruff, Elvin; Pan, Luyuan; O'donnell, Janis; Broadie, Kendal

    2005-03-01

    Fragile X syndrome is the most common form of inherited mental retardation, associated with both cognitive and behavioral anomalies. The disease is caused by silencing of the fragile X mental retardation 1 (fmr1) gene, which encodes the mRNA-binding, translational regulator FMRP. Previously we established a disease model through mutation of Drosophila fmr1 (dfmr1) and showed that loss of dFMRP causes defects in neuronal structure, function, and behavioral output similar to the human disease state. To uncover molecular targets of dFMRP in the brain, we use here a proteomic approach involving two-dimensional difference gel electrophoresis analyses followed by mass spectrometry identification of proteins with significantly altered expression in dfmr1 null mutants. We then focus on two misregulated enzymes, phenylalanine hydroxylase (Henna) and GTP cyclohydrolase (Punch), both of which mediate in concert the synthetic pathways of two key monoamine neuromodulators, dopamine and serotonin. Brain enzymatic assays show a nearly 2-fold elevation of Punch activity in dfmr1 null mutants. Consistently brain neurochemical assays show that both dopamine and serotonin are significantly increased in dfmr1 null mutants. At a cellular level, dfmr1 null mutant neurons display a highly significant elevation of the dense core vesicles that package these monoamine neuromodulators for secretion. Taken together, these data indicate that dFMRP normally down-regulates the monoamine pathway, which is consequently up-regulated in the mutant condition. Elevated brain levels of dopamine and serotonin provide a plausible mechanistic explanation for aspects of cognitive and behavioral deficits in human patients.

  18. Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury.

    Science.gov (United States)

    Llorens-Bobadilla, Enric; Zhao, Sheng; Baser, Avni; Saiz-Castro, Gonzalo; Zwadlo, Klara; Martin-Villalba, Ana

    2015-09-03

    Heterogeneous pools of adult neural stem cells (NSCs) contribute to brain maintenance and regeneration after injury. The balance of NSC activation and quiescence, as well as the induction of lineage-specific transcription factors, may contribute to diversity of neuronal and glial fates. To identify molecular hallmarks governing these characteristics, we performed single-cell sequencing of an unbiased pool of adult subventricular zone NSCs. This analysis identified a discrete, dormant NSC subpopulation that already expresses distinct combinations of lineage-specific transcription factors during homeostasis. Dormant NSCs enter a primed-quiescent state before activation, which is accompanied by downregulation of glycolytic metabolism, Notch, and BMP signaling and a concomitant upregulation of lineage-specific transcription factors and protein synthesis. In response to brain ischemia, interferon gamma signaling induces dormant NSC subpopulations to enter the primed-quiescent state. This study unveils general principles underlying NSC activation and lineage priming and opens potential avenues for regenerative medicine in the brain. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

    International Nuclear Information System (INIS)

    Wang, Jieqiong; Li, Ting; Xian, Junfang; Wang, Ningli; He, Huiguang

    2016-01-01

    Most previous glaucoma studies with resting-state fMRI have focused on the neuronal activity in the individual structure of the brain, yet ignored the functional communication of anatomically separated structures. The purpose of this study is to investigate the efficiency of the functional communication change or not in glaucoma patients. We applied the resting-state fMRI data to construct the connectivity network of 25 normal controls and 25 age-gender-matched primary open angle glaucoma patients. Graph theoretical analysis was performed to assess brain network pattern differences between the two groups. No significant differences of the global network measures were found between the two groups. However, the local measures were radically reorganized in glaucoma patients. Comparing with the hub regions in normal controls' network, we found that six hub regions disappeared and nine hub regions appeared in the network of patients. In addition, the betweenness centralities of two altered hub regions, right fusiform gyrus and right lingual gyrus, were significantly correlated with the visual field mean deviation. Although the efficiency of functional communication is preserved in the brain network of the glaucoma at the global level, the efficiency of functional communication is altered in some specialized regions of the glaucoma. (orig.)

  20. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jieqiong [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Li, Ting; Xian, Junfang [Capital Medical University, Department of Radiology, Beijing Tongren Hospital, Beijing (China); Wang, Ningli [Capital Medical University, Department of Ophthalmology, Beijing Tongren Hospital, Beijing (China); He, Huiguang [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Chinese Academy of Sciences, Research Center for Brain-Inspired Intelligence, Institute of Automation, Beijing (China)

    2016-11-15

    Most previous glaucoma studies with resting-state fMRI have focused on the neuronal activity in the individual structure of the brain, yet ignored the functional communication of anatomically separated structures. The purpose of this study is to investigate the efficiency of the functional communication change or not in glaucoma patients. We applied the resting-state fMRI data to construct the connectivity network of 25 normal controls and 25 age-gender-matched primary open angle glaucoma patients. Graph theoretical analysis was performed to assess brain network pattern differences between the two groups. No significant differences of the global network measures were found between the two groups. However, the local measures were radically reorganized in glaucoma patients. Comparing with the hub regions in normal controls' network, we found that six hub regions disappeared and nine hub regions appeared in the network of patients. In addition, the betweenness centralities of two altered hub regions, right fusiform gyrus and right lingual gyrus, were significantly correlated with the visual field mean deviation. Although the efficiency of functional communication is preserved in the brain network of the glaucoma at the global level, the efficiency of functional communication is altered in some specialized regions of the glaucoma. (orig.)

  1. A mouse model for creatine transporter deficiency reveals early onset cognitive impairment and neuropathology associated with brain aging.

    Science.gov (United States)

    Baroncelli, Laura; Molinaro, Angelo; Cacciante, Francesco; Alessandrì, Maria Grazia; Napoli, Debora; Putignano, Elena; Tola, Jonida; Leuzzi, Vincenzo; Cioni, Giovanni; Pizzorusso, Tommaso

    2016-10-01

    Mutations in the creatine (Cr) transporter (CrT) gene lead to cerebral creatine deficiency syndrome-1 (CCDS1), an X-linked metabolic disorder characterized by cerebral Cr deficiency causing intellectual disability, seizures, movement and autistic-like behavioural disturbances, language and speech impairment. Since no data are available about the neural and molecular underpinnings of this disease, we performed a longitudinal analysis of behavioural and pathological alterations associated with CrT deficiency in a CCDS1 mouse model. We found precocious cognitive and autistic-like defects, mimicking the early key features of human CCDS1. Moreover, mutant mice displayed a progressive impairment of short and long-term declarative memory denoting an early brain aging. Pathological examination showed a prominent loss of GABAergic synapses, marked activation of microglia, reduction of hippocampal neurogenesis and the accumulation of autofluorescent lipofuscin. Our data suggest that brain Cr depletion causes both early intellectual disability and late progressive cognitive decline, and identify novel targets to design intervention strategies aimed at overcoming brain CCDS1 alterations. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. A comparative antibody analysis of Pannexin1 expression in four rat brain regions reveals varying subcellular localizations

    Directory of Open Access Journals (Sweden)

    Angela C Cone

    2013-02-01

    Full Text Available Pannexin1 (Panx1 channels release cytosolic ATP in response to signaling pathways. Panx1 is highly expressed in the central nervous system. We used four antibodies with different Panx1 anti-peptide epitopes to analyze four regions of rat brain. These antibodies labeled the same bands in Western blots and had highly similar patterns of immunofluorescence in tissue culture cells expressing Panx1, but Western blots of brain lysates from Panx1 knockout and control mice showed different banding patterns. Localizations of Panx1 in brain slices were generated using automated wide-field mosaic confocal microscopy for imaging large regions of interest while retaining maximum resolution for examining cell populations and compartments. We compared Panx1 expression over the cerebellum, hippocampus with adjacent cortex, thalamus and olfactory bulb. While Panx1 localizes to the same neuronal cell types, subcellular localizations differ. Two antibodies with epitopes against the intracellular loop and one against the carboxy terminus preferentially labeled cell bodies, while an antibody raised against an N-terminal peptide highlighted neuronal processes more than cell bodies. These labeling patterns may be a reflection of different cellular and subcellular localizations of full-length and/or modified Panx1 channels where each antibody is highlighting unique or differentially accessible Panx1 populations. However, we cannot rule out that one or more of these antibodies have specificity issues. All data associated with experiments from these four antibodies are presented in a manner that allows them to be compared and our claims thoroughly evaluated, rather than eliminating results that were questionable. Each antibody is given a unique identifier through the NIF Antibody Registry that can be used to track usage of individual antibodies across papers and all image and metadata are made available in the public repository, the Cell Centered Database, for on

  3. Regional variation of white matter development in the cat brain revealed by ex vivo diffusion MR tractography.

    Science.gov (United States)

    Dai, Guangping; Das, Avilash; Hayashi, Emiko; Chen, Qin; Takahashi, Emi

    2016-11-01

    Three-dimensional reconstruction of developing fiber pathways is essential to assessing the developmental course of fiber pathways in the whole brain. We applied diffusion spectrum imaging (DSI) tractography to five juvenile ex vivo cat brains at postnatal day (P) 35, when the degree of myelination varies across brain regions. We quantified diffusion properties (fractional anisotropy [FA] and apparent diffusion coefficient [ADC]) and other measurements (number, volume, and voxel count) on reconstructed pathways for projection (cortico-spinal and thalamo-cortical), corpus callosal, limbic (cingulum and fornix), and association (cortico-cortical) pathways, and characterized regional differences in maturation patterns by assessing diffusion properties. FA values were significantly higher in cortico-cortical pathways within the right hemisphere compared to those within the left hemisphere, while the other measurements for the cortico-cortical pathways within the hemisphere did not show asymmetry. ADC values were not asymmetric in both types of pathways. Interestingly, tract count and volume were significantly larger in the left thalamo-cortical pathways compared to the right thalamo-cortical pathways. The bilateral thalamo-cortical pathways showed high FA values compared to the other fiber pathways. On the other hand, ADC values did not show any differences across pathways studied. These results demonstrate that DSI tractography successfully depicted regional variations of white matter tracts during development when myelination is incomplete. Low FA and high ADC values in the cingulum bundle suggest that the cingulum bundle is less mature than the others at this developmental stage. Copyright © 2016 ISDN. Published by Elsevier Ltd. All rights reserved.

  4. Integration of miRNA and protein profiling reveals coordinated neuroadaptations in the alcohol-dependent mouse brain.

    Directory of Open Access Journals (Sweden)

    Giorgio Gorini

    Full Text Available The molecular mechanisms underlying alcohol dependence involve different neurochemical systems and are brain region-dependent. Chronic Intermittent Ethanol (CIE procedure, combined with a Two-Bottle Choice voluntary drinking paradigm, represents one of the best available animal models for alcohol dependence and relapse drinking. MicroRNAs, master regulators of the cellular transcriptome and proteome, can regulate their targets in a cooperative, combinatorial fashion, ensuring fine tuning and control over a large number of cellular functions. We analyzed cortex and midbrain microRNA expression levels using an integrative approach to combine and relate data to previous protein profiling from the same CIE-subjected samples, and examined the significance of the data in terms of relative contribution to alcohol consumption and dependence. MicroRNA levels were significantly altered in CIE-exposed dependent mice compared with their non-dependent controls. More importantly, our integrative analysis identified modules of coexpressed microRNAs that were highly correlated with CIE effects and predicted target genes encoding differentially expressed proteins. Coexpressed CIE-relevant proteins, in turn, were often negatively correlated with specific microRNA modules. Our results provide evidence that microRNA-orchestrated translational imbalances are driving the behavioral transition from alcohol consumption to dependence. This study represents the first attempt to combine ex vivo microRNA and protein expression on a global scale from the same mammalian brain samples. The integrative systems approach used here will improve our understanding of brain adaptive changes in response to drug abuse and suggests the potential therapeutic use of microRNAs as tools to prevent or compensate multiple neuroadaptations underlying addictive behavior.

  5. Do event-related potentials reveal the mechanism of the auditory sensory memory in the human brain?

    Science.gov (United States)

    Näätänen, R; Paavilainen, P; Alho, K; Reinikainen, K; Sams, M

    1989-03-27

    Event-related brain potentials (ERP) to task-irrelevant tone pips presented at short intervals were recorded from the scalp of normal human subjects. Infrequent decrements in stimulus intensity elicited the mismatch negativity (MMN) which was larger in amplitude and shorter in latency the softer the deviant stimulus was. The results obtained imply memory representations which develop automatically and accurately represent the physical features of the repetitive stimulus. These memory traces appear to be those of the acoustic sensory memory, the 'echoic' memory. When an input does not match with such a trace the MMN is generated.

  6. Changes in Male Rat Sexual Behavior and Brain Activity Revealed by Functional Magnetic Resonance Imaging in Response to Chronic Mild Stress.

    Science.gov (United States)

    Chen, Guotao; Yang, Baibing; Chen, Jianhuai; Zhu, Leilei; Jiang, Hesong; Yu, Wen; Zang, Fengchao; Chen, Yun; Dai, Yutian

    2018-02-01

    G, Yang B, Chen J, et al. Changes in Male Rat Sexual Behavior and Brain Activity Revealed by Functional Magnetic Resonance Imaging in Response to Chronic Mild Stress. J Sex Med 2018;15:136-147. Copyright © 2017 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

  7. FTIR Imaging of Brain Tissue Reveals Crystalline Creatine Deposits Are an ex Vivo Marker of Localized Ischemia during Murine Cerebral Malaria: General Implications for Disease Neurochemistry

    Science.gov (United States)

    2012-01-01

    Phosphocreatine is a major cellular source of high energy phosphates, which is crucial to maintain cell viability under conditions of impaired metabolic states, such as decreased oxygen and energy availability (i.e., ischemia). Many methods exist for the bulk analysis of phosphocreatine and its dephosphorylated product creatine; however, no method exists to image the distribution of creatine or phosphocreatine at the cellular level. In this study, Fourier transform infrared (FTIR) spectroscopic imaging has revealed the ex vivo development of creatine microdeposits in situ in the brain region most affected by the disease, the cerebellum of cerebral malaria (CM) diseased mice; however, such deposits were also observed at significantly lower levels in the brains of control mice and mice with severe malaria. In addition, the number of deposits was observed to increase in a time-dependent manner during dehydration post tissue cutting. This challenges the hypotheses in recent reports of FTIR spectroscopic imaging where creatine microdeposits found in situ within thin sections from epileptic, Alzheimer’s (AD), and amlyoid lateral sclerosis (ALS) diseased brains were proposed to be disease specific markers and/or postulated to contribute to the brain pathogenesis. As such, a detailed investigation was undertaken, which has established that the creatine microdeposits exist as the highly soluble HCl salt or zwitterion and are an ex-vivo tissue processing artifact and, hence, have no effect on disease pathogenesis. They occur as a result of creatine crystallization during dehydration (i.e., air-drying) of thin sections of brain tissue. As ischemia and decreased aerobic (oxidative metabolism) are common to many brain disorders, regions of elevated creatine-to-phosphocreatine ratio are likely to promote crystal formation during tissue dehydration (due to the lower water solubility of creatine relative to phosphocreatine). The results of this study have demonstrated that

  8. Classification of binary intentions for individuals with impaired oculomotor function: ‘eyes-closed’ SSVEP-based brain-computer interface (BCI)

    Science.gov (United States)

    Lim, Jeong-Hwan; Hwang, Han-Jeong; Han, Chang-Hee; Jung, Ki-Young; Im, Chang-Hwan

    2013-04-01

    Objective. Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. Approach. The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). Main results. Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min-1. A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. Significance. The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our ‘eyes-closed’ SSVEP-based BCI system can be potentially used for communication of

  9. Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates

    NARCIS (Netherlands)

    Bouts, Mark. J. R. J.; Westmoreland, Susan. V.; de Crespigny, Alex J.; Liu, Yutong; Vangel, Mark; Dijkhuizen, Rick M.; Wu, Ona; D'Arceuil, Helen E.

    2015-01-01

    Background: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by

  10. Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A gamma band analysis.

    Science.gov (United States)

    Balconi, Michela; Lucchiari, Claudio

    2008-01-01

    It remains an open question whether it is possible to assign a single brain operation or psychological function for facial emotion decoding to a certain type of oscillatory activity. Gamma band activity (GBA) offers an adequate tool for studying cortical activation patterns during emotional face information processing. In the present study brain oscillations were analyzed in response to facial expression of emotions. Specifically, GBA modulation was measured when twenty subjects looked at emotional (angry, fearful, happy, and sad faces) or neutral faces in two different conditions: supraliminal (10 ms) vs subliminal (150 ms) stimulation (100 target-mask pairs for each condition). The results showed that both consciousness and significance of the stimulus in terms of arousal can modulate the power synchronization (ERD decrease) during 150-350 time range: an early oscillatory event showed its peak at about 200 ms post-stimulus. GBA was enhanced by supraliminal more than subliminal elaboration, as well as more by high arousal (anger and fear) than low arousal (happiness and sadness) emotions. Finally a left-posterior dominance for conscious elaboration was found, whereas right hemisphere was discriminant in emotional processing of face in comparison with neutral face.

  11. Body representations in the human brain revealed by kinesthetic illusions and their essential contributions to motor control and corporeal awareness.

    Science.gov (United States)

    Naito, Eiichi; Morita, Tomoyo; Amemiya, Kaoru

    2016-03-01

    The human brain can generate a continuously changing postural model of our body. Somatic (proprioceptive) signals from skeletal muscles and joints contribute to the formation of the body representation. Recent neuroimaging studies of proprioceptive bodily illusions have elucidated the importance of three brain systems (motor network, specialized parietal systems, right inferior fronto-parietal network) in the formation of the human body representation. The motor network, especially the primary motor cortex, processes afferent input from skeletal muscles. Such information may contribute to the formation of kinematic/dynamic postural models of limbs, thereby enabling fast online feedback control. Distinct parietal regions appear to play specialized roles in the transformation/integration of information across different coordinate systems, which may subserve the adaptability and flexibility of the body representation. Finally, the right inferior fronto-parietal network, connected by the inferior branch of the superior longitudinal fasciculus, is consistently recruited when an individual experiences various types of bodily illusions and its possible roles relate to corporeal awareness, which is likely elicited through a series of neuronal processes of monitoring and accumulating bodily information and updating the body representation. Because this network is also recruited when identifying one's own features, the network activity could be a neuronal basis for self-consciousness. Copyright © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  12. Similarities and differences between the brain networks underlying allocentric and egocentric spatial learning in rat revealed by cytochrome oxidase histochemistry.

    Science.gov (United States)

    Rubio, S; Begega, A; Méndez, M; Méndez-López, M; Arias, J L

    2012-10-25

    The involvement of different brain regions in place- and response-learning was examined using a water cross-maze. Rats were trained to find the goal from the initial arm by turning left at the choice point (egocentric strategy) or by using environmental cues (allocentric strategy). Although different strategies were required, the same maze and learning conditions were used. Using cytochrome oxidase histochemistry as a marker of cellular activity, the function of the 13 diverse cortical and subcortical regions was assessed in rats performing these two tasks. Our results show that allocentric learning depends on the recruitment of a large functional network, which includes the hippocampal CA3, dentate gyrus, medial mammillary nucleus and supramammillary nucleus. Along with the striatum, these last three structures are also related to egocentric spatial learning. The present study provides evidence for the contribution of these regions to spatial navigation and supports a possible functional interaction between the two memory systems, as their structural convergence may facilitate functional cooperation in the behaviours guided by more than one strategy. In summary, it can be argued that spatial learning is based on dynamic functional systems in which the interaction of brain regions is modulated by task requirements. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  13. Baseline brain activity changes in patients with clinically isolated syndrome revealed by resting-state functional MRI

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou; Duan, Yunyun; Liang, Peipeng; Jia, Xiuqin; Yu, Chunshui [Dept. of Radiology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Ye, Jing [Dept. of Neurology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Butzkueven, Helmut [Dept. of Medicine, Univ. of Melbourne, Melbourne (Australia); Dong, Huiqing [Dept. of Neurology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Li, Kuncheng [Dept. of Radiology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Beijing Key Laboratory of MRI and Brain Informatics, Beijing (China)], E-mail: likuncheng1955@yahoo.com.cn

    2012-11-15

    Background A clinically isolated syndrome (CIS) is the first manifestation of multiple sclerosis (MS). Previous task-related functional MRI studies demonstrate functional reorganization in patients with CIS. Purpose To assess baseline brain activity changes in patients with CIS by using the technique of regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI. Material and Methods Resting-state fMRIs data acquired from 37 patients with CIS and 37 age- and sex-matched normal controls were compared to investigate ALFF differences. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration, and T2 lesion volume (T2LV) were further explored. Results Patients with CIS had significantly decreased ALFF in the right anterior cingulate cortex, right caudate, right lingual gyrus, and right cuneus (P < 0.05 corrected for multiple comparisons using Monte Carlo simulation) compared to normal controls, while no significantly increased ALFF were observed in CIS. No significant correlation was found between the EDSS, disease duration, T2LV, and ALFF in regions with significant group differences. Conclusion In patients with CIS, resting-state fMRI demonstrates decreased activity in several brain regions. These results are in contrast to patients with established MS, in whom ALFF demonstrates several regions of increased activity. It is possible that this shift from decreased activity in CIS to increased activity in MS could reflect the dynamics of cortical reorganization.

  14. Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

    Science.gov (United States)

    Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

    2008-01-01

    Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

  15. Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism

    Science.gov (United States)

    2013-01-01

    Background A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest. Results The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression. Conclusions The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the

  16. Royal jelly-like protein localization reveals differences in hypopharyngeal glands buildup and conserved expression pattern in brains of bumblebees and honeybees

    Directory of Open Access Journals (Sweden)

    Štefan Albert

    2014-03-01

    Full Text Available Royal jelly proteins (MRJPs of the honeybee bear several open questions. One of them is their expression in tissues other than the hypopharyngeal glands (HGs, the site of royal jelly production. The sole MRJP-like gene of the bumblebee, Bombus terrestris (BtRJPL, represents a pre-diversification stage of the MRJP gene evolution in bees. Here we investigate the expression of BtRJPL in the HGs and the brain of bumblebees. Comparison of the HGs of bumblebees and honeybees revealed striking differences in their morphology with respect to sex- and caste-specific appearance, number of cells per acinus, and filamentous actin (F-actin rings. At the cellular level, we found a temporary F-actin-covered meshwork in the secretory cells, which suggests a role for actin in the biogenesis of the end apparatus in HGs. Using immunohistochemical localization, we show that BtRJPL is expressed in the bumblebee brain, predominantly in the Kenyon cells of the mushroom bodies, the site of sensory integration in insects, and in the optic lobes. Our data suggest that a dual gland-brain function preceded the multiplication of MRJPs in the honeybee lineage. In the course of the honeybee evolution, HGs dramatically changed their morphology in order to serve a food-producing function.

  17. Brain regions involved in voluntary movements as revealed by radioisotopic mapping of CBF or CMR-glucose changes

    DEFF Research Database (Denmark)

    Lassen, N A; Ingvar, D H

    1990-01-01

    Mapping of cortical and subcortical grey matter active during voluntary movements by means of measurements of local increases of CBF or CMR-Glucose is reviewed. Most of the studies concern observations in man during hand movements using the intracarotid Xenon-133 injection technique, an approach...... that only allows to image the cortex of the hemisphere on one side (the injected side) of the brain. The results show that simple static or repetitive movements mainly activate the contralateral primary hand area (MI and SI); complex preprogrammed or spontaneous purposeful movements the supplementary motor...... area SMA on both sides increase in CBF/CMR-glucose and even internally ("mentally") going through the trained movements, causes such changes; complex purposeful movements also activate the premotor cortex, a response that is bilateral with greatest response contralaterally. Studies in patients...

  18. Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

    Science.gov (United States)

    Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

    2016-01-01

    The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

  19. Task-Related Edge Density (TED-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

    Directory of Open Access Journals (Sweden)

    Gabriele Lohmann

    Full Text Available The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED. TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

  20. Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

    Science.gov (United States)

    Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

    2016-01-01

    The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

  1. Brain structural abnormalities in behavior therapy-resistant obsessive-compulsive disorder revealed by voxel-based morphometry

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    Hashimoto N

    2014-10-01

    Full Text Available Nobuhiko Hashimoto,1 Shutaro Nakaaki,2 Akiko Kawaguchi,1 Junko Sato,1 Harumasa Kasai,3 Takashi Nakamae,4 Jin Narumoto,4 Jun Miyata,5 Toshi A Furukawa,6,7 Masaru Mimura2 1Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; 2Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; 3Department of Central Radiology, Nagoya City University Hospital, Nagoya, Japan; 4Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan; 5Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan; 6Department of Health Promotion and Human Behavior, 7Department of Clinical Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan Background: Although several functional imaging studies have demonstrated that behavior therapy (BT modifies the neural circuits involved in the pathogenesis of obsessive-compulsive disorder (OCD, the structural abnormalities underlying BT-resistant OCD remain unknown. Methods: In this study, we examined the existence of regional structural abnormalities in both the gray matter and the white matter of patients with OCD at baseline using voxel-based morphometry in responders (n=24 and nonresponders (n=15 to subsequent BT. Three-dimensional T1-weighted magnetic resonance imaging was performed before the completion of 12 weeks of BT. Results: Relative to the responders, the nonresponders exhibited significantly smaller gray matter volumes in the right ventromedial prefrontal cortex, the right orbitofrontal cortex, the right precentral gyrus, and the left anterior cingulate cortex. In addition, relative to the responders, the nonresponders exhibited significantly smaller white matter volumes in the left cingulate bundle and the left superior frontal white matter. Conclusion: These results suggest that the brain

  2. Gene expression profiling in brain of mice exposed to the marine neurotoxin ciguatoxin reveals an acute anti-inflammatory, neuroprotective response

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    Ryan James C

    2010-08-01

    Full Text Available Abstract Background Ciguatoxins (CTXs are polyether marine neurotoxins and potent activators of voltage-gated sodium channels. This toxin is carried by multiple reef-fish species and human consumption of ciguatoxins can result in an explosive gastrointestinal/neurologic illness. This study characterizes the global transcriptional response in mouse brain to a symptomatic dose of the highly toxic Pacific ciguatoxin P-CTX-1 and additionally compares this data to transcriptional profiles from liver and whole blood examined previously. Adult male C57/BL6 mice were injected with 0.26 ng/g P-CTX-1 while controls received only vehicle. Animals were sacrificed at 1, 4 and 24 hrs and transcriptional profiling was performed on brain RNA with Agilent whole genome microarrays. RT-PCR was used to independently validate gene expression and the web tool DAVID was used to analyze gene ontology (GO and molecular pathway enrichment of the gene expression data. Results A pronounced 4°C hypothermic response was recorded in these mice, reaching a minimum at 1 hr and lasting for 8 hrs post toxin exposure. Ratio expression data were filtered by intensity, fold change and p-value, with the resulting data used for time course analysis, K-means clustering, ontology classification and KEGG pathway enrichment. Top GO hits for this gene set included acute phase response and mono-oxygenase activity. Molecular pathway analysis showed enrichment for complement/coagulation cascades and metabolism of xenobiotics. Many immediate early genes such as Fos, Jun and Early Growth Response isoforms were down-regulated although others associated with stress such as glucocorticoid responsive genes were up-regulated. Real time PCR confirmation was performed on 22 differentially expressed genes with a correlation of 0.9 (Spearman's Rho, p Conclusions Many of the genes differentially expressed in this study, in parallel with the hypothermia, figure prominently in protection against

  3. Gene expression profiling in brain of mice exposed to the marine neurotoxin ciguatoxin reveals an acute anti-inflammatory, neuroprotective response.

    Science.gov (United States)

    Ryan, James C; Morey, Jeanine S; Bottein, Marie-Yasmine Dechraoui; Ramsdell, John S; Van Dolah, Frances M

    2010-08-26

    Ciguatoxins (CTXs) are polyether marine neurotoxins and potent activators of voltage-gated sodium channels. This toxin is carried by multiple reef-fish species and human consumption of ciguatoxins can result in an explosive gastrointestinal/neurologic illness. This study characterizes the global transcriptional response in mouse brain to a symptomatic dose of the highly toxic Pacific ciguatoxin P-CTX-1 and additionally compares this data to transcriptional profiles from liver and whole blood examined previously. Adult male C57/BL6 mice were injected with 0.26 ng/g P-CTX-1 while controls received only vehicle. Animals were sacrificed at 1, 4 and 24 hrs and transcriptional profiling was performed on brain RNA with Agilent whole genome microarrays. RT-PCR was used to independently validate gene expression and the web tool DAVID was used to analyze gene ontology (GO) and molecular pathway enrichment of the gene expression data. A pronounced 4°C hypothermic response was recorded in these mice, reaching a minimum at 1 hr and lasting for 8 hrs post toxin exposure. Ratio expression data were filtered by intensity, fold change and p-value, with the resulting data used for time course analysis, K-means clustering, ontology classification and KEGG pathway enrichment. Top GO hits for this gene set included acute phase response and mono-oxygenase activity. Molecular pathway analysis showed enrichment for complement/coagulation cascades and metabolism of xenobiotics. Many immediate early genes such as Fos, Jun and Early Growth Response isoforms were down-regulated although others associated with stress such as glucocorticoid responsive genes were up-regulated. Real time PCR confirmation was performed on 22 differentially expressed genes with a correlation of 0.9 (Spearman's Rho, p < 0.0001) with microarray results. Many of the genes differentially expressed in this study, in parallel with the hypothermia, figure prominently in protection against neuroinflammation. Pathologic

  4. NIRS-Based Hyperscanning Reveals Inter-brain Neural Synchronization during Cooperative Jenga Game with Face-to-Face Communication.

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    Liu, Ning; Mok, Charis; Witt, Emily E; Pradhan, Anjali H; Chen, Jingyuan E; Reiss, Allan L

    2016-01-01

    Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying social cognition. In particular, fNIRS permits simultaneous measurement of hemodynamic activity in two or more individuals interacting in a naturalistic setting. Here, we used fNIRS hyperscanning to study social cognition and communication in human dyads engaged in cooperative and obstructive interaction while they played the game of Jenga™. Novel methods were developed to identify synchronized channels for each dyad and a structural node-based spatial registration approach was utilized for inter-dyad analyses. Strong inter-brain neural synchrony (INS) was observed in the posterior region of the right middle and superior frontal gyrus, in particular Brodmann area 8 (BA8), during cooperative and obstructive interaction. This synchrony was not observed during the parallel game play condition and the dialog section, suggesting that BA8 was involved in goal-oriented social interaction such as complex interactive movements and social decision-making. INS was also observed in the dorsomedial prefrontal cortex (dmPFC), in particular Brodmann 9, during cooperative interaction only. These additional findings suggest that BA9 may be particularly engaged when theory-of-mind (ToM) is required for cooperative social interaction. The new methods described here have the potential to significantly extend fNIRS applications to social cognitive research.

  5. MEG Working Memory N-Back Task Reveals Functional Deficits in Combat-Related Mild Traumatic Brain Injury.

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    Huang, Ming-Xiong; Nichols, Sharon; Robb-Swan, Ashley; Angeles-Quinto, Annemarie; Harrington, Deborah L; Drake, Angela; Huang, Charles W; Song, Tao; Diwakar, Mithun; Risbrough, Victoria B; Matthews, Scott; Clifford, Royce; Cheng, Chung-Kuan; Huang, Jeffrey W; Sinha, Anusha; Yurgil, Kate A; Ji, Zhengwei; Lerman, Imanuel; Lee, Roland R; Baker, Dewleen G

    2018-04-13

    Combat-related mild traumatic brain injury (mTBI) is a leading cause of sustained cognitive impairment in military service members and Veterans. However, the mechanism of persistent cognitive deficits including working memory (WM) dysfunction is not fully understood in mTBI. Few studies of WM deficits in mTBI have taken advantage of the temporal and frequency resolution afforded by electromagnetic measurements. Using magnetoencephalography (MEG) and an N-back WM task, we investigated functional abnormalities in combat-related mTBI. Study participants included 25 symptomatic active-duty service members or Veterans with combat-related mTBI and 20 healthy controls with similar combat experiences. MEG source-magnitude images were obtained for alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and low-frequency (1-7 Hz) bands. Compared with healthy combat controls, mTBI participants showed increased MEG signals across frequency bands in frontal pole (FP), ventromedial prefrontal cortex, orbitofrontal cortex (OFC), and anterior dorsolateral prefrontal cortex (dlPFC), but decreased MEG signals in anterior cingulate cortex. Hyperactivations in FP, OFC, and anterior dlPFC were associated with slower reaction times. MEG activations in lateral FP also negatively correlated with performance on tests of letter sequencing, verbal fluency, and digit symbol coding. The profound hyperactivations from FP suggest that FP is particularly vulnerable to combat-related mTBI.

  6. First- and second-order contrast sensitivity functions reveal disrupted visual processing following mild traumatic brain injury.

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    Spiegel, Daniel P; Reynaud, Alexandre; Ruiz, Tatiana; Laguë-Beauvais, Maude; Hess, Robert; Farivar, Reza

    2016-05-01

    Vision is disrupted by traumatic brain injury (TBI), with vision-related complaints being amongst the most common in this population. Based on the neural responses of early visual cortical areas, injury to the visual cortex would be predicted to affect both 1(st) order and 2(nd) order contrast sensitivity functions (CSFs)-the height and/or the cut-off of the CSF are expected to be affected by TBI. Previous studies have reported disruptions only in 2(nd) order contrast sensitivity, but using a narrow range of parameters and divergent methodologies-no study has characterized the effect of TBI on the full CSF for both 1(st) and 2(nd) order stimuli. Such information is needed to properly understand the effect of TBI on contrast perception, which underlies all visual processing. Using a unified framework based on the quick contrast sensitivity function, we measured full CSFs for static and dynamic 1(st) and 2(nd) order stimuli. Our results provide a unique dataset showing alterations in sensitivity for both 1(st) and 2(nd) order visual stimuli. In particular, we show that TBI patients have increased sensitivity for 1(st) order motion stimuli and decreased sensitivity to orientation-defined and contrast-defined 2(nd) order stimuli. In addition, our data suggest that TBI patients' sensitivity for both 1(st) order stimuli and 2(nd) order contrast-defined stimuli is shifted towards higher spatial frequencies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. The winner takes it all: Event-related brain potentials reveal enhanced motivated attention toward athletes' nonverbal signals of leading.

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    Furley, Philip; Schnuerch, Robert; Gibbons, Henning

    2017-08-01

    Observers of sports can reliably estimate who is leading or trailing based on nonverbal cues. Most likely, this is due to an adaptive mechanism of detecting motivationally relevant signals such as high status, superiority, and dominance. We reasoned that the relevance of leading athletes should lead to a sustained attentional prioritization. To test this idea, we recorded electroencephalography while 45 participants saw brief stills of athletes and estimated whether they were leading or trailing. Based on these recordings, we assessed event-related potentials and focused on the late positive complex (LPC), a well-established signature of controlled attention to motivationally relevant visual stimuli. Confirming our expectation, we found that LPC amplitude was significantly enhanced for leading as compared to trailing athletes. Moreover, this modulation was significantly related to behavioral performance on the score-estimation task. The present data suggest that subtle cues related to athletic supremacy are reliably differentiated in the human brain, involving a strong attentional orienting toward leading athletes. This mechanism might be part of an adaptive cognitive strategy that guides human social behavior.

  8. Profiling of G protein-coupled receptors in vagal afferents reveals novel gut-to-brain sensing mechanisms.

    Science.gov (United States)

    Egerod, Kristoffer L; Petersen, Natalia; Timshel, Pascal N; Rekling, Jens C; Wang, Yibing; Liu, Qinghua; Schwartz, Thue W; Gautron, Laurent

    2018-06-01

    G protein-coupled receptors (GPCRs) act as transmembrane molecular sensors of neurotransmitters, hormones, nutrients, and metabolites. Because unmyelinated vagal afferents richly innervate the gastrointestinal mucosa, gut-derived molecules may directly modulate the activity of vagal afferents through GPCRs. However, the types of GPCRs expressed in vagal afferents are largely unknown. Here, we determined the expression profile of all GPCRs expressed in vagal afferents of the mouse, with a special emphasis on those innervating the gastrointestinal tract. Using a combination of high-throughput quantitative PCR, RNA sequencing, and in situ hybridization, we systematically quantified GPCRs expressed in vagal unmyelinated Na v 1.8-expressing afferents. GPCRs for gut hormones that were the most enriched in Na v 1.8-expressing vagal unmyelinated afferents included NTSR1, NPY2R, CCK1R, and to a lesser extent, GLP1R, but not GHSR and GIPR. Interestingly, both GLP1R and NPY2R were coexpressed with CCK1R. In contrast, NTSR1 was coexpressed with GPR65, a marker preferentially enriched in intestinal mucosal afferents. Only few microbiome-derived metabolite sensors such as GPR35 and, to a lesser extent, GPR119 and CaSR were identified in the Na v 1.8-expressing vagal afferents. GPCRs involved in lipid sensing and inflammation (e.g. CB1R, CYSLTR2, PTGER4), and neurotransmitters signaling (CHRM4, DRD2, CRHR2) were also highly enriched in Na v 1.8-expressing neurons. Finally, we identified 21 orphan GPCRs with unknown functions in vagal afferents. Overall, this study provides a comprehensive description of GPCR-dependent sensing mechanisms in vagal afferents, including novel coexpression patterns, and conceivably coaction of key receptors for gut-derived molecules involved in gut-brain communication. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

  9. Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis) Revealed by Brain Transcriptome Analysis.

    Science.gov (United States)

    Wang, Pingping; Zheng, Min; Liu, Jian; Liu, Yongzhuang; Lu, Jianguo; Sun, Xiaowen

    2016-08-26

    In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female's highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and reproduction that might be the candidate factors affecting sexual dimorphism of tongue sole, laying the foundation to understand the complex process of sex determination of this economic valuable species.

  10. Microspectroscopy (μFTIR) reveals co-localization of lipid oxidation and amyloid plaques in human Alzheimer disease brains.

    Science.gov (United States)

    Benseny-Cases, Núria; Klementieva, Oxana; Cotte, Marine; Ferrer, Isidre; Cladera, Josep

    2014-12-16

    Amyloid peptides are the main component of one of the characteristic pathological hallmarks of Alzheimer's disease (AD): senile plaques. According to the amyloid cascade hypothesis, amyloid peptides may play a central role in the sequence of events that leads to neurodegeneration. However, there are other factors, such as oxidative stress, that may be crucial for the development of the disease. In the present paper, we show that it is possible, by using Fourier tranform infrared (FTIR) microscopy, to co-localize amyloid deposits and lipid peroxidation in tissue slides from patients affected by Alzheimer's disease. Plaques and lipids can be analyzed in the same sample, making use of the characteristic infrared bands for peptide aggregation and lipid oxidation. The results show that, in samples from patients diagnosed with AD, the plaques and their immediate surroundings are always characterized by the presence of oxidized lipids. As for samples from non-AD individuals, those without amyloid plaques show a lower level of lipid oxidation than AD individuals. However, it is known that plaques can be detected in the brains of some non-AD individuals. Our results show that, in such cases, the lipid in the plaques and their surroundings display oxidation levels that are similar to those of tissues with no plaques. These results point to lipid oxidation as a possible key factor in the path that goes from showing the typical neurophatological hallmarks to suffering from dementia. In this process, the oxidative power of the amyloid peptide, possibly in the form of nonfibrillar aggregates, could play a central role.

  11. Removal of reproductive suppression reveals latent sex differences in brain steroid hormone receptors in naked mole-rats, Heterocephalus glaber.

    Science.gov (United States)

    Swift-Gallant, Ashlyn; Mo, Kaiguo; Peragine, Deane E; Monks, D Ashley; Holmes, Melissa M

    2015-01-01

    Naked mole-rats are eusocial mammals, living in large colonies with a single breeding female and 1-3 breeding males. Breeders are socially dominant, and only the breeders exhibit traditional sex differences in circulating gonadal steroid hormones and reproductive behaviors. Non-reproductive subordinates also fail to show sex differences in overall body size, external genital morphology, and non-reproductive behaviors. However, subordinates can transition to breeding status if removed from their colony and housed with an opposite-sex conspecific, suggesting the presence of latent sex differences. Here, we assessed the expression of steroid hormone receptor and aromatase messenger RNA (mRNA) in the brains of males and females as they transitioned in social and reproductive status. We compared in-colony subordinates to opposite-sex subordinate pairs that were removed from their colony for either 1 day, 1 week, 1 month, or until they became breeders (i.e., produced a litter). Diencephalic tissue was collected and mRNA of androgen receptor (Ar), estrogen receptor alpha (Esr1), progesterone receptor (Pgr), and aromatase (Cyp19a1) was measured using qPCR. Testosterone, 17β-estradiol, and progesterone from serum were also measured. As early as 1 week post-removal, males exhibited increased diencephalic Ar mRNA and circulating testosterone, whereas females had increased Cyp19a1 mRNA in the diencephalon. At 1 month post-removal, females exhibited increased 17β-estradiol and progesterone. The largest changes in steroid hormone receptors were observed in breeders. Breeding females had a threefold increase in Cyp19a1 and fivefold increases in Esr1 and Pgr, whereas breeding males had reduced Pgr and increased Ar. These data demonstrate that sex differences in circulating gonadal steroids and hypothalamic gene expression emerge weeks to months after subordinate animals are removed from reproductive suppression in their home colony.

  12. Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis Revealed by Brain Transcriptome Analysis

    Directory of Open Access Journals (Sweden)

    Pingping Wang

    2016-08-01

    Full Text Available In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female’s highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and reproduction that might be the candidate factors affecting sexual dimorphism of tongue sole, laying the foundation to understand the complex process of sex determination of this economic valuable species.

  13. Presynaptic selectivity of a ligand for serotonin 1A receptors revealed by in vivo PET assays of rat brain.

    Directory of Open Access Journals (Sweden)

    Takeaki Saijo

    Full Text Available A novel investigational antidepressant with high affinity for the serotonin transporter and the serotonin 1A (5-HT(1A receptor, called Wf-516 (structural formula: (2S-1-[4-(3,4-dichlorophenylpiperidin-1-yl]-3-[2-(5-methyl-1,3,4-oxadiazol-2-ylbenzo[b]furan-4-yloxy]propan-2-ol monohydrochloride, has been found to exert a rapid therapeutic effect, although the mechanistic basis for this potential advantage remains undetermined. We comparatively investigated the pharmacokinetics and pharmacodynamics of Wf-516 and pindolol by positron emission tomographic (PET and autoradiographic assays of rat brains in order to elucidate their molecular interactions with presynaptic and postsynaptic 5-HT(1A receptors. In contrast to the full receptor occupancy by pindolol in PET measurements, the binding of Wf-516 to 5-HT(1A receptors displayed limited capacity, with relatively high receptor occupancy being achieved in regions predominantly containing presynaptic receptors. This selectivity was further proven by PET scans of neurotoxicant-treated rats deficient in presynaptic 5-HT(1A receptors. In addition, [(35S]guanosine 5'-O-[γ-thio]triphosphate autoradiography indicated a partial agonistic ability of Wf-516 for 5-HT(1A receptors. This finding has lent support to reports that diverse partial agonists for 5-HT(1A receptors exert high sensitivity for presynaptic components. Thus, the present PET data suggest a relatively high capacity of presynaptic binding sites for partial agonists. Since our in vitro and ex vivo autoradiographies failed to illustrate these distinct features of Wf-516, in vivo PET imaging is considered to be, thus far, the sole method capable of pharmacokinetically demonstrating the unique actions of Wf-516 and similar new-generation antidepressants.

  14. Presynaptic selectivity of a ligand for serotonin 1A receptors revealed by in vivo PET assays of rat brain.

    Science.gov (United States)

    Saijo, Takeaki; Maeda, Jun; Okauchi, Takashi; Maeda, Jun-ichi; Morio, Yasunori; Kuwahara, Yasuhiro; Suzuki, Masayuki; Goto, Nobuharu; Fukumura, Toshimitsu; Suhara, Tetsuya; Higuchi, Makoto

    2012-01-01

    A novel investigational antidepressant with high affinity for the serotonin transporter and the serotonin 1A (5-HT(1A)) receptor, called Wf-516 (structural formula: (2S)-1-[4-(3,4-dichlorophenyl)piperidin-1-yl]-3-[2-(5-methyl-1,3,4-oxadiazol-2-yl)benzo[b]furan-4-yloxy]propan-2-ol monohydrochloride), has been found to exert a rapid therapeutic effect, although the mechanistic basis for this potential advantage remains undetermined. We comparatively investigated the pharmacokinetics and pharmacodynamics of Wf-516 and pindolol by positron emission tomographic (PET) and autoradiographic assays of rat brains in order to elucidate their molecular interactions with presynaptic and postsynaptic 5-HT(1A) receptors. In contrast to the full receptor occupancy by pindolol in PET measurements, the binding of Wf-516 to 5-HT(1A) receptors displayed limited capacity, with relatively high receptor occupancy being achieved in regions predominantly containing presynaptic receptors. This selectivity was further proven by PET scans of neurotoxicant-treated rats deficient in presynaptic 5-HT(1A) receptors. In addition, [(35)S]guanosine 5'-O-[γ-thio]triphosphate autoradiography indicated a partial agonistic ability of Wf-516 for 5-HT(1A) receptors. This finding has lent support to reports that diverse partial agonists for 5-HT(1A) receptors exert high sensitivity for presynaptic components. Thus, the present PET data suggest a relatively high capacity of presynaptic binding sites for partial agonists. Since our in vitro and ex vivo autoradiographies failed to illustrate these distinct features of Wf-516, in vivo PET imaging is considered to be, thus far, the sole method capable of pharmacokinetically demonstrating the unique actions of Wf-516 and similar new-generation antidepressants.

  15. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  16. The human brain and face: mechanisms of cranial, neurological and facial development revealed through malformations of holoprosencephaly, cyclopia and aberrations in chromosome 18.

    Science.gov (United States)

    Gondré-Lewis, Marjorie C; Gboluaje, Temitayo; Reid, Shaina N; Lin, Stephen; Wang, Paul; Green, William; Diogo, Rui; Fidélia-Lambert, Marie N; Herman, Mary M

    2015-09-01

    The study of inborn genetic errors can lend insight into mechanisms of normal human development and congenital malformations. Here, we present the first detailed comparison of cranial and neuro pathology in two exceedingly rare human individuals with cyclopia and alobar holoprosencephaly (HPE) in the presence and absence of aberrant chromosome 18 (aCh18). The aCh18 fetus contained one normal Ch18 and one with a pseudo-isodicentric duplication of chromosome 18q and partial deletion of 18p from 18p11.31 where the HPE gene, TGIF, resides, to the p terminus. In addition to synophthalmia, the aCh18 cyclopic malformations included a failure of induction of most of the telencephalon - closely approximating anencephaly, unchecked development of brain stem structures, near absence of the sphenoid bone and a malformed neurocranium and viscerocranium that constitute the median face. Although there was complete erasure of the olfactory and superior nasal structures, rudiments of nasal structures derived from the maxillary bone were evident, but with absent pharyngeal structures. The second non-aCh18 cyclopic fetus was initially classified as a true Cyclops, as it appeared to have a proboscis and one median eye with a single iris, but further analysis revealed two eye globes as expected for synophthalmic cyclopia. Furthermore, the proboscis was associated with the medial ethmoid ridge, consistent with an incomplete induction of these nasal structures, even as the nasal septum and paranasal sinuses were apparently developed. An important conclusion of this study is that it is the brain that predicts the overall configuration of the face, due to its influence on the development of surrounding skeletal structures. The present data using a combination of macroscopic, computed tomography (CT) and magnetic resonance imaging (MRI) techniques provide an unparalleled analysis on the extent of the effects of median defects, and insight into normal development and patterning of the brain

  17. Multivariate imaging-genetics study of MRI gray matter volume and SNPs reveals biological pathways correlated with brain structural differences in Attention Deficit Hyperactivity Disorder

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    Sabin Khadka

    2016-07-01

    Full Text Available Background: Attention Deficit Hyperactivity Disorder (ADHD is a prevalent neurodevelopmental disorder affecting children, adolescents, and adults. Its etiology is not well-understood, but it is increasingly believed to result from diverse pathophysiologies that affect the structure and function of specific brain circuits. Although one of the best-studied neurobiological abnormalities in ADHD is reduced fronto-striatal-cerebellar gray matter volume, its specific genetic correlates are largely unknown. Methods: In this study, T1-weighted MR images of brain structure were collected from 198 adolescents (63 ADHD-diagnosed. A multivariate parallel independent component analysis technique (Para-ICA identified imaging-genetic relationships between regional gray matter volume and single nucleotide polymorphism data. Results: Para-ICA analyses extracted 14 components from genetic data and 9 from MR data. An iterative cross-validation using randomly-chosen sub-samples indicated acceptable stability of these ICA solutions. A series of partial correlation analyses controlling for age, sex, and ethnicity revealed two genotype-phenotype component pairs significantly differed between ADHD and non-ADHD groups, after a Bonferroni correction for multiple comparisons. The brain phenotype component not only included structures frequently found to have abnormally low volume in previous ADHD studies, but was also significantly associated with ADHD differences in symptom severity and performance on cognitive tests frequently found to be impaired in patients diagnosed with the disorder. Pathway analysis of the genotype component identified several different biological pathways linked to these structural abnormalities in ADHD. Conclusions: Some of these pathways implicate well-known dopaminergic neurotransmission and neurodevelopment hypothesized to be abnormal in ADHD. Other more recently implicated pathways included glutamatergic and GABA-eric physiological systems

  18. Network-Based Logistic Classification with an Enhanced L1/2 Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer

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    Hai-Hui Huang

    2015-01-01

    Full Text Available Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.

  19. Brain microstructural abnormalities revealed by diffusion tensor images in patients with treatment-resistant depression compared with major depressive disorder before treatment

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Yan, E-mail: clare1475@hotmail.com [Department of Radiology, Ren-Ji Hospital, Jiao Tong University Medical School, Shanghai 200127 (China); Qin Lingdi, E-mail: flyfool318@hotmail.com [Department of Radiology, Ren-Ji Hospital, Jiao Tong University Medical School, Shanghai 200127 (China); Chen Jun, E-mail: doctor_cj@msn.com [Shanghai Mental Health Center, Jiao Tong University Medical School, Shanghai, 200030 (China); Qian Lijun, E-mail: dearqlj@hotmail.com [Department of Radiology, Ren-Ji Hospital, Jiao Tong University Medical School, Shanghai 200127 (China); Tao Jing, E-mail: jing318@hotmail.com [Department of Radiology, Ren-Ji Hospital, Jiao Tong University Medical School, Shanghai 200127 (China); Fang Yiru, E-mail: fangyr@sina.com [Shanghai Mental Health Center, Jiao Tong University Medical School, Shanghai, 200030 (China); Xu Jianrong, E-mail: xujianr@hotmail.com [Department of Radiology, Ren-Ji Hospital, Jiao Tong University Medical School, Shanghai 200127 (China)

    2011-11-15

    Treatment-resistant depression (TRD) is a therapeutic challenge for clinicians. Despite a growing interest in this area, an understanding of the pathophysiology of depression, particularly TRD, remains lacking. This study aims to detect the white matter abnormalities of whole brain fractional anisotropy (FA) in patients with TRD compared with major depressive disorder (MDD) before treatment by voxel-based analysis using diffusion tensor imaging. A total of 100 patients first diagnosed with untreated MDD underwent diffusion tensor imaging scans. 8 weeks after the first treatment, 54 patients showed response to the medication, whereas 46 did not. Finally, 20 patients were diagnosed with TRD after undergoing another treatment. A total of 20 patients with TRD and another 20 with MDD before treatment matched in gender, age, and education was enrolled in the research. For every subject, an FA map was generated and analyzed using SPM5. Subsequently, t-test was conducted to compare the FA values voxel to voxel between the two groups (p < 0.001 [FDR corrected], t > 7.57, voxel size > 30). Voxel-based morphometric (VBM) analysis was performed using T1W images. Significant reductions in FA were found in the white matter located in the bilateral of the hippocampus (left hippocampus: t = 7.63, voxel size = 50; right hippocampus: t = 7.82, voxel size = 48). VBM analysis revealed no morphological abnormalities between the two groups. Investigation of brain anisotropy revealed significantly decreased FA in both sides of the hippocampus. Although preliminary, our findings suggest that microstructural abnormalities in the hippocampus indicate vulnerability to treatment resistance.

  20. Optogenetic activation of CA1 pyramidal neurons at the dorsal and ventral hippocampus evokes distinct brain-wide responses revealed by mouse fMRI.

    Directory of Open Access Journals (Sweden)

    Norio Takata

    Full Text Available The dorsal and ventral hippocampal regions (dHP and vHP are proposed to have distinct functions. Electrophysiological studies have revealed intra-hippocampal variances along the dorsoventral axis. Nevertheless, the extra-hippocampal influences of dHP and vHP activities remain unclear. In this study, we compared the spatial distribution of brain-wide responses upon dHP or vHP activation and further estimate connection strengths between the dHP and the vHP with corresponding extra-hippocampal areas. To achieve this, we first investigated responses of local field potential (LFP and multi unit activities (MUA upon light stimulation in the hippocampus of an anesthetized transgenic mouse, whose CA1 pyramidal neurons expressed a step-function opsin variant of channelrhodopsin-2 (ChR2. Optogenetic stimulation increased hippocampal LFP power at theta, gamma, and ultra-fast frequency bands, and augmented MUA, indicating light-induced activation of CA1 pyramidal neurons. Brain-wide responses examined using fMRI revealed that optogenetic activation at the dHP or vHP caused blood oxygenation level-dependent (BOLD fMRI signals in situ. Although activation at the dHP induced BOLD responses at the vHP, the opposite was not observed. Outside the hippocampal formation, activation at the dHP, but not the vHP, evoked BOLD responses at the retrosplenial cortex (RSP, which is in line with anatomical evidence. In contrast, BOLD responses at the lateral septum (LS were induced only upon vHP activation, even though both dHP and vHP send axonal fibers to the LS. Our findings suggest that the primary targets of dHP and vHP activation are distinct, which concurs with attributed functions of the dHP and RSP in spatial memory, as well as of the vHP and LS in emotional responses.

  1. Brain microstructural abnormalities revealed by diffusion tensor images in patients with treatment-resistant depression compared with major depressive disorder before treatment

    International Nuclear Information System (INIS)

    Zhou Yan; Qin Lingdi; Chen Jun; Qian Lijun; Tao Jing; Fang Yiru; Xu Jianrong

    2011-01-01

    Treatment-resistant depression (TRD) is a therapeutic challenge for clinicians. Despite a growing interest in this area, an understanding of the pathophysiology of depression, particularly TRD, remains lacking. This study aims to detect the white matter abnormalities of whole brain fractional anisotropy (FA) in patients with TRD compared with major depressive disorder (MDD) before treatment by voxel-based analysis using diffusion tensor imaging. A total of 100 patients first diagnosed with untreated MDD underwent diffusion tensor imaging scans. 8 weeks after the first treatment, 54 patients showed response to the medication, whereas 46 did not. Finally, 20 patients were diagnosed with TRD after undergoing another treatment. A total of 20 patients with TRD and another 20 with MDD before treatment matched in gender, age, and education was enrolled in the research. For every subject, an FA map was generated and analyzed using SPM5. Subsequently, t-test was conducted to compare the FA values voxel to voxel between the two groups (p 7.57, voxel size > 30). Voxel-based morphometric (VBM) analysis was performed using T1W images. Significant reductions in FA were found in the white matter located in the bilateral of the hippocampus (left hippocampus: t = 7.63, voxel size = 50; right hippocampus: t = 7.82, voxel size = 48). VBM analysis revealed no morphological abnormalities between the two groups. Investigation of brain anisotropy revealed significantly decreased FA in both sides of the hippocampus. Although preliminary, our findings suggest that microstructural abnormalities in the hippocampus indicate vulnerability to treatment resistance.

  2. Unsupervised Analysis of Array Comparative Genomic Hybridization Data from Early-Onset Colorectal Cancer Reveals Equivalence with Molecular Classification and Phenotypes

    Directory of Open Access Journals (Sweden)

    María Arriba

    2017-01-01

    Full Text Available AIM: To investigate whether chromosomal instability (CIN is associated with tumor phenotypes and/or with global genomic status based on MSI (microsatellite instability and CIMP (CpG island methylator phenotype in early-onset colorectal cancer (EOCRC. METHODS: Taking as a starting point our previous work in which tumors from 60 EOCRC cases (≤45 years at the time of diagnosis were analyzed by array comparative genomic hybridization (aCGH, in the present study we performed an unsupervised hierarchical clustering analysis of those aCGH data in order to unveil possible associations between the CIN profile and the clinical features of the tumors. In addition, we evaluated the MSI and the CIMP statuses of the samples with the aim of investigating a possible relationship between copy number alterations (CNAs and the MSI/CIMP condition in EOCRC. RESULTS: Based on the similarity of the CNAs detected, the unsupervised analysis stratified samples into two main clusters (A, B and four secondary clusters (A1, A2, B3, B4. The different subgroups showed a certain correspondence with the molecular classification of colorectal cancer (CRC, which enabled us to outline an algorithm to categorize tumors according to their CIMP status. Interestingly, each subcluster showed some distinctive clinicopathological features. But more interestingly, the CIN of each subcluster mainly affected particular chromosomes, allowing us to define chromosomal regions more specifically affected depending on the CIMP/MSI status of the samples. CONCLUSIONS: Our findings may provide a basis for a new form of classifying EOCRC according to the genomic status of the tumors.

  3. Orchestrating Proactive and Reactive Mechanisms for Filtering Distracting Information: Brain-Behavior Relationships Revealed by a Mixed-Design fMRI Study

    Science.gov (United States)

    Marini, Francesco; Demeter, Elise; Roberts, Kenneth C.; Chelazzi, Leonardo

    2016-01-01

    Given the information overload often imparted to human cognitive-processing systems, suppression of irrelevant and distracting information is essential for successful behavior. Using a hybrid block/event-related fMRI design, we characterized proactive and reactive brain mechanisms for filtering distracting stimuli. Participants performed a flanker task, discriminating the direction of a target arrow in the presence versus absence of congruent or incongruent flanking distracting arrows during either Pure blocks (distracters always absent) or Mixed blocks (distracters on 80% of trials). Each Mixed block had either 20% or 60% incongruent trials. Activations in the dorsal frontoparietal attention network during Mixed versus Pure blocks evidenced proactive (blockwise) recruitment of a distraction-filtering mechanism. Sustained activations in right middle frontal gyrus during 60% Incongruent blocks correlated positively with behavioral indices of distraction-filtering (slowing when distracters might occur) and negatively with distraction-related behavioral costs (incongruent vs congruent trials), suggesting a role in coordinating proactive filtering of potential distracters. Event-related analyses showed that incongruent trials elicited greater reactive activations in 20% (vs 60%) Incongruent blocks for counteracting distraction and conflict, including in the insula and anterior cingulate. Context-related effects in occipitoparietal cortex consisted of greater target-evoked activations for distracter-absent trials (central-target-only) in Mixed versus Pure blocks, suggesting enhanced attentional engagement. Functional-localizer analyses in V1/V2/V3 revealed less distracter-processing activity in 60% (vs 20%) Incongruent blocks, presumably reflecting tonic suppression by proactive filtering mechanisms. These results delineate brain mechanisms underlying proactive and reactive filtering of distraction and conflict, and how they are orchestrated depending on distraction

  4. Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

    Science.gov (United States)

    Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

    2018-01-01

    The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

  5. Sorghum root-system classification in contrasting P environments reveals three main rooting types and root-architecture-related marker-trait associations.

    Science.gov (United States)

    Parra-Londono, Sebastian; Kavka, Mareike; Samans, Birgit; Snowdon, Rod; Wieckhorst, Silke; Uptmoor, Ralf

    2018-02-12

    Roots facilitate acquisition of macro- and micronutrients, which are crucial for plant productivity and anchorage in the soil. Phosphorus (P) is rapidly immobilized in the soil and hardly available for plants. Adaptation to P scarcity relies on changes in root morphology towards rooting systems well suited for topsoil foraging. Root-system architecture (RSA) defines the spatial organization of the network comprising primary, lateral and stem-derived roots and is important for adaptation to stress conditions. RSA phenotyping is a challenging task and essential for understanding root development. In this study, 19 traits describing RSA were analysed in a diversity panel comprising 194 sorghum genotypes, fingerprinted with a 90-k single-nucleotide polymorphism (SNP) array and grown under low and high P availability. Multivariate analysis was conducted and revealed three different RSA types: (1) a small root system; (2) a compact and bushy rooting type; and (3) an exploratory root system, which might benefit plant growth and development if water, nitrogen (N) or P availability is limited. While several genotypes displayed similar rooting types in different environments, others responded to P scarcity positively by developing more exploratory root systems, or negatively with root growth suppression. Genome-wide association studies revealed significant quantitative trait loci (P root-system development on chromosomes SBI-02 and SBI-03. Sorghum genotypes with a compact, bushy and shallow root system provide potential adaptation to P scarcity in the field by allowing thorough topsoil foraging, while genotypes with an exploratory root system may be advantageous if N or water is the limiting factor, although such genotypes showed highest P uptake levels under the artificial conditions of the present study. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  6. Manganese-enhanced magnetic resonance imaging (MEMRI) reveals brain circuitry involved in responding to an acute novel stress in rats with a history of repeated social stress.

    Science.gov (United States)

    Bangasser, Debra A; Lee, Catherine S; Cook, Philip A; Gee, James C; Bhatnagar, Seema; Valentino, Rita J

    2013-10-02

    Responses to acute stressors are determined in part by stress history. For example, a history of chronic stress results in facilitated responses to a novel stressor and this facilitation is considered to be adaptive. We previously demonstrated that repeated exposure of rats to the resident-intruder model of social stress results in the emergence of two subpopulations that are characterized by different coping responses to stress. The submissive subpopulation failed to show facilitation to a novel stressor and developed a passive strategy in the Porsolt forced swim test. Because a passive stress coping response has been implicated in the propensity to develop certain psychiatric disorders, understanding the unique circuitry engaged by exposure to a novel stressor in these subpopulations would advance our understanding of the etiology of stress-related pathology. An ex vivo functional imaging technique, manganese-enhanced magnetic resonance imaging (MEMRI), was used to identify and distinguish brain regions that are differentially activated by an acute swim stress (15 min) in rats with a history of social stress compared to controls. Specifically, Mn(2+) was administered intracerebroventricularly prior to swim stress and brains were later imaged ex vivo to reveal activated structures. When compared to controls, all rats with a history of social stress showed greater activation in specific striatal, hippocampal, hypothalamic, and midbrain regions. The submissive subpopulation of rats was further distinguished by significantly greater activation in amygdala, bed nucleus of the stria terminalis, and septum, suggesting that these regions may form a circuit mediating responses to novel stress in individuals that adopt passive coping strategies. The finding that different circuits are engaged by a novel stressor in the two subpopulations of rats exposed to social stress implicates a role for these circuits in determining individual strategies for responding to stressors

  7. Language learning and brain reorganization in a 3.5-year-old child with left perinatal stroke revealed using structural and functional connectivity.

    Science.gov (United States)

    François, Clément; Ripollés, Pablo; Bosch, Laura; Garcia-Alix, Alfredo; Muchart, Jordi; Sierpowska, Joanna; Fons, Carme; Solé, Jorgina; Rebollo, Monica; Gaitán, Helena; Rodriguez-Fornells, Antoni

    2016-04-01

    Brain imaging methods have contributed to shed light on the possible mechanisms of recovery and cortical reorganization after early brain insult. The idea that a functional left hemisphere is crucial for achieving a normalized pattern of language development after left perinatal stroke is still under debate. We report the case of a 3.5-year-old boy born at term with a perinatal ischemic stroke of the left middle cerebral artery, affecting mainly the supramarginal gyrus, superior parietal and insular cortex extending to the precentral and postcentral gyri. Neurocognitive development was assessed at 25 and 42 months of age. Language outcomes were more extensively evaluated at the latter age with measures on receptive vocabulary, phonological whole-word production and linguistic complexity in spontaneous speech. Word learning abilities were assessed using a fast-mapping task to assess immediate and delayed recall of newly mapped words. Functional and structural imaging data as well as a measure of intrinsic connectivity were also acquired. While cognitive, motor and language levels from the Bayley Scales fell within the average range at 25 months, language scores were below at 42 months. Receptive vocabulary fell within normal limits but whole word production was delayed and the child had limited spontaneous speech. Critically, the child showed clear difficulties in both the immediate and delayed recall of the novel words, significantly differing from an age-matched control group. Neuroimaging data revealed spared classical cortical language areas but an affected left dorsal white-matter pathway together with right lateralized functional activations. In the framework of the model for Social Communication and Language Development, these data confirm the important role of the left arcuate fasciculus in understanding and producing morpho-syntactic elements in sentences beyond two word combinations and, most importantly, in learning novel word-referent associations, a

  8. Biosensor Technology Reveals the Disruption of the Endothelial Barrier Function and the Subsequent Death of Blood Brain Barrier Endothelial Cells to Sodium Azide and Its Gaseous Products.

    Science.gov (United States)

    Kho, Dan T; Johnson, Rebecca H; O'Carroll, Simon J; Angel, Catherine E; Graham, E Scott

    2017-09-21

    Herein we demonstrate the sensitive nature of human blood-brain barrier (BBB) endothelial cells to sodium azide and its gaseous product. Sodium azide is known to be acutely cytotoxic at low millimolar concentrations, hence its use as a biological preservative (e.g., in antibodies). Loss of barrier integrity was noticed in experiments using Electric Cell-substrate Impedance Sensing (ECIS) biosensor technology, to measure endothelial barrier integrity continuously in real-time. Initially the effect of sodium azide was observed as an artefact where it was present in antibodies being employed in neutralisation experiments. This was confirmed where antibody clones that were azide-free did not mediate loss of barrier function. A delayed loss of barrier function in neighbouring wells implied the influence of a liberated gaseous product. ECIS technology demonstrated that the BBB endothelial cells had a lower level of direct sensitivity to sodium azide of ~3 µM. Evidence of gaseous toxicity was consistently observed at 30 µM and above, with disrupted barrier function and cell death in neighbouring wells. We highlight the ability of this cellular biosensor technology to reveal both the direct and gaseous toxicity mediated by sodium azide. The sensitivity and temporal dimension of ECIS technology was instrumental in these observations. These findings have substantial implications for the wide use of sodium azide in biological reagents, raising issues of their application in live-cell assays and with regard to the protection of the user. This research also has wider relevance highlighting the sensitivity of brain endothelial cells to a known mitochondrial disruptor. It is logical to hypothesise that BBB endothelial dysfunction due to mitochondrial dys-regulation could have an important but underappreciated role in a range of neurological diseases.

  9. Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: Investigation of nicotinic acid receptor agonists

    International Nuclear Information System (INIS)

    Zgoda-Pols, Joanna R.; Chowdhury, Swapan; Wirth, Mark; Milburn, Michael V.; Alexander, Danny C.; Alton, Kevin B.

    2011-01-01

    An investigative renal toxicity study using metabolomics was conducted with a potent nicotinic acid receptor (NAR) agonist, SCH 900424. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) techniques were used to identify small molecule biomarkers of acute kidney injury (AKI) that could aid in a better mechanistic understanding of SCH 900424-induced AKI in mice. The metabolomics study revealed 3-indoxyl sulfate (3IS) as a more sensitive marker of SCH 900424-induced renal toxicity than creatinine or urea. An LC-MS assay for quantitative determination of 3IS in mouse matrices was also developed. Following treatment with SCH 900424, 3IS levels were markedly increased in murine plasma and brain, thereby potentially contributing to renal- and central nervous system (CNS)-related rapid onset of toxicities. Furthermore, significant decrease in urinary excretion of 3IS in those animals due to compromised renal function may be associated with the elevation of 3IS in plasma and brain. These data suggest that 3IS has a potential to be a marker of renal and CNS toxicities during chemically-induced AKI in mice. In addition, based on the metabolomic analysis other statistically significant plasma markers including p-cresol-sulfate and tryptophan catabolites (kynurenate, kynurenine, 3-indole-lactate) might be of toxicological importance but have not been studied in detail. This comprehensive approach that includes untargeted metabolomic and targeted bioanalytical sample analyses could be used to investigate toxicity of other compounds that pose preclinical or clinical development challenges in a pharmaceutical discovery and development. - Research highlights: → Nicotinic acid receptor agonist, SCH 900424, caused acute kidney injury in mice. → MS-based metabolomics was conducted to identify potential small molecule markers of renal toxicity. → 3-indoxyl-sulfate was found to be as a more sensitive marker of renal toxicity than

  10. Whole-brain functional connectivity during emotional word classification in medication-free Major Depressive Disorder: Abnormal salience circuitry and relations to positive emotionality

    NARCIS (Netherlands)

    van Tol, Marie-José; Veer, Ilya M.; van der Wee, Nic J. A.; Aleman, André; van Buchem, Mark A.; Rombouts, Serge A. R. B.; Zitman, Frans G.; Veltman, Dick J.; Johnstone, Tom

    2013-01-01

    Major Depressive Disorder (MDD) has been associated with biased processing and abnormal regulation of negative and positive information, which may result from compromised coordinated activity of prefrontal and subcortical brain regions involved in evaluating emotional information. We tested whether

  11. Whole-brain functional connectivity during emotional word classification in medication-free Major Depressive Disorder : Abnormal salience circuitry and relations to positive emotionality

    NARCIS (Netherlands)

    van Tol, Marie-Jose; Veer, Ilya M.; van der Wee, Nic J. A.; Aleman, Andre; van Buchem, Mark A.; Rombouts, Serge A. R. B.; Zitman, Frans G.; Veltman, Dick J.; Johnstone, Tom

    2013-01-01

    Major Depressive Disorder (MDD) has been associated with biased processing and abnormal regulation of negative and positive information, which may result from compromised coordinated activity of prefrontal and subcortical brain regions involved in evaluating emotional information. We tested whether

  12. Dynamic genome wide expression profiling of Drosophila head development reveals a novel role of Hunchback in retinal glia cell development and blood-brain barrier integrity.

    Directory of Open Access Journals (Sweden)

    Montserrat Torres-Oliva

    2018-01-01

    Full Text Available Drosophila melanogaster head development represents a valuable process to study the developmental control of various organs, such as the antennae, the dorsal ocelli and the compound eyes from a common precursor, the eye-antennal imaginal disc. While the gene regulatory network underlying compound eye development has been extensively studied, the key transcription factors regulating the formation of other head structures from the same imaginal disc are largely unknown. We obtained the developmental transcriptome of the eye-antennal discs covering late patterning processes at the late 2nd larval instar stage to the onset and progression of differentiation at the end of larval development. We revealed the expression profiles of all genes expressed during eye-antennal disc development and we determined temporally co-expressed genes by hierarchical clustering. Since co-expressed genes may be regulated by common transcriptional regulators, we combined our transcriptome dataset with publicly available ChIP-seq data to identify central transcription factors that co-regulate genes during head development. Besides the identification of already known and well-described transcription factors, we show that the transcription factor Hunchback (Hb regulates a significant number of genes that are expressed during late differentiation stages. We confirm that hb is expressed in two polyploid subperineurial glia cells (carpet cells and a thorough functional analysis shows that loss of Hb function results in a loss of carpet cells in the eye-antennal disc. Additionally, we provide for the first time functional data indicating that carpet cells are an integral part of the blood-brain barrier. Eventually, we combined our expression data with a de novo Hb motif search to reveal stage specific putative target genes of which we find a significant number indeed expressed in carpet cells.

  13. Dynamic genome wide expression profiling of Drosophila head development reveals a novel role of Hunchback in retinal glia cell development and blood-brain barrier integrity

    Science.gov (United States)

    Torres-Oliva, Montserrat; Schneider, Julia; Wiegleb, Gordon

    2018-01-01

    Drosophila melanogaster head development represents a valuable process to study the developmental control of various organs, such as the antennae, the dorsal ocelli and the compound eyes from a common precursor, the eye-antennal imaginal disc. While the gene regulatory network underlying compound eye development has been extensively studied, the key transcription factors regulating the formation of other head structures from the same imaginal disc are largely unknown. We obtained the developmental transcriptome of the eye-antennal discs covering late patterning processes at the late 2nd larval instar stage to the onset and progression of differentiation at the end of larval development. We revealed the expression profiles of all genes expressed during eye-antennal disc development and we determined temporally co-expressed genes by hierarchical clustering. Since co-expressed genes may be regulated by common transcriptional regulators, we combined our transcriptome dataset with publicly available ChIP-seq data to identify central transcription factors that co-regulate genes during head development. Besides the identification of already known and well-described transcription factors, we show that the transcription factor Hunchback (Hb) regulates a significant number of genes that are expressed during late differentiation stages. We confirm that hb is expressed in two polyploid subperineurial glia cells (carpet cells) and a thorough functional analysis shows that loss of Hb function results in a loss of carpet cells in the eye-antennal disc. Additionally, we provide for the first time functional data indicating that carpet cells are an integral part of the blood-brain barrier. Eventually, we combined our expression data with a de novo Hb motif search to reveal stage specific putative target genes of which we find a significant number indeed expressed in carpet cells. PMID:29360820

  14. Diverse Brain Myeloid Expression Profiles Reveal Distinct Microglial Activation States and Aspects of Alzheimer’s Disease Not Evident in Mouse Models

    Directory of Open Access Journals (Sweden)

    Brad A. Friedman

    2018-01-01

    Full Text Available Microglia, the CNS-resident immune cells, play important roles in disease, but the spectrum of their possible activation states is not well understood. We derived co-regulated gene modules from transcriptional profiles of CNS myeloid cells of diverse mouse models, including new tauopathy model datasets. Using these modules to interpret single-cell data from an Alzheimer’s disease (AD model, we identified microglial subsets—distinct from previously reported “disease-associated microglia”—expressing interferon-related or proliferation modules. We then analyzed whole-tissue RNA profiles from human neurodegenerative diseases, including a new AD dataset. Correcting for altered cellular composition of AD tissue, we observed elevated expression of the neurodegeneration-related modules, but also modules not implicated using expression profiles from mouse models alone. We provide a searchable, interactive database for exploring gene expression in all these datasets (http://research-pub.gene.com/BrainMyeloidLandscape. Understanding the dimensions of CNS myeloid cell activation in human disease may reveal opportunities for therapeutic intervention.

  15. Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).

    Science.gov (United States)

    Cascianelli, S; Tranfaglia, C; Fravolini, M L; Bianconi, F; Minestrini, M; Nuvoli, S; Tambasco, N; Dottorini, M E; Palumbo, B

    2017-01-01

    The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. 123 I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using 123 I-FP-CIT brain SPET data, a classification tree (CIT), was applied. CIT is an automatic classifier composed of a set of logical rules, organized as a decision tree to produce an optimised threshold based classification of data to provide discriminative cut-off values. We applied a CIT to 123 I-FP-CIT brain SPET semiquantitave data, to obtain cut-off values of radiopharmaceutical uptake ratios in caudate nuclei and putamina with the aim to diagnose PD versus other conditions. We retrospectively investigated 187 patients undergoing 123 I-FP-CIT brain SPET (Millenium VG, G.E.M.S.) with semiquantitative analysis performed with Basal Ganglia (BasGan) V2 software according to EANM guidelines; among them 113 resulted affected by PD (PD group) and 74 (N group) by other non parkinsonian conditions, such as Essential Tremor and drug-induced PD. PD group included 113 subjects (60M and 53F of age: 60-81yrs) having Hoehn and Yahr score (HY): 0.5-1.5; Unified Parkinson Disease Rating Scale (UPDRS) score: 6-38; N group included 74 subjects (36M and 38 F range of age 60-80 yrs). All subjects were clinically followed for at least 6-18 months to confirm the diagnosis. To examinate data obtained by using CIT, for each of the 1,000 experiments carried out, 10% of patients were randomly selected as the CIT training set, while the remaining 90% validated the trained CIT, and the percentage of the validation data correctly classified in the two groups of patients was computed. The expected performance of an "average

  16. Distinct BOLD fMRI Responses of Capsaicin-Induced Thermal Sensation Reveal Pain-Related Brain Activation in Nonhuman Primates.

    Directory of Open Access Journals (Sweden)

    Abu Bakar Ali Asad

    Full Text Available Approximately 20% of the adult population suffer from chronic pain that is not adequately treated by current therapies, highlighting a great need for improved treatment options. To develop effective analgesics, experimental human and animal models of pain are critical. Topically/intra-dermally applied capsaicin induces hyperalgesia and allodynia to thermal and tactile stimuli that mimics chronic pain and is a useful translation from preclinical research to clinical investigation. Many behavioral and self-report studies of pain have exploited the use of the capsaicin pain model, but objective biomarker correlates of the capsaicin augmented nociceptive response in nonhuman primates remains to be explored.Here we establish an aversive capsaicin-induced fMRI model using non-noxious heat stimuli in Cynomolgus monkeys (n = 8. BOLD fMRI data were collected during thermal challenge (ON:20 s/42°C; OFF:40 s/35°C, 4-cycle at baseline and 30 min post-capsaicin (0.1 mg, topical, forearm application. Tail withdrawal behavioral studies were also conducted in the same animals using 42°C or 48°C water bath pre- and post- capsaicin application (0.1 mg, subcutaneous, tail.Group comparisons between pre- and post-capsaicin application revealed significant BOLD signal increases in brain regions associated with the 'pain matrix', including somatosensory, frontal, and cingulate cortices, as well as the cerebellum (paired t-test, p<0.02, n = 8, while no significant change was found after the vehicle application. The tail withdrawal behavioral study demonstrated a significant main effect of temperature and a trend towards capsaicin induced reduction of latency at both temperatures.These findings provide insights into the specific brain regions involved with aversive, 'pain-like', responses in a nonhuman primate model. Future studies may employ both behavioral and fMRI measures as translational biomarkers to gain deeper understanding of pain processing and evaluate

  17. Effect of aging on phosphate metabolites of rat brain as revealed by the in vivo and in vitro 31P NMR measurements

    International Nuclear Information System (INIS)

    Liu, Hsiuchih; Chi, Chinwen; Liu, Tsungyun; Liu, Lianghui; Luh, Wenming; Hsieh, Changhuain; Wu, Wenguey

    1991-01-01

    Changes of phosphate metabolism in brains of neonate, weaning and adult rats were compared using both in vivo and in vitro nuclear magnetic resonance spectra. Ratios of phosphocreatine/nucleoside triphosphate (PCr/NTP) were the same in neonatal brain in both in vivo and in vitro studies, but not in weaning and adult brains. This discrepancy may have resulted from extended cerebral hypoxia due to slowed freezing of the brain by the increased skull thickness and brain mass in the weaning and adult rats. Variations of in vitro extraction condition for this age-related study may lead to systematic errors in the adult rats. Nevertheless, the phosphomonoester/nucleoside triphosphate (PME/NTP) ratios in extracts of brain from neonatal rats were higher than those obtained in vivo. In addition, the glycerophosphorylethanolamine plus glycerophosphorylcholine/nucleoside triphosphate (GPE+GPC/NTP) ratios, which were not measurable in vivo, showed age-dependent increase in extracts of rat brain. Some of the phosphomonoester and phosphodiester molecules in rat brain may be undetectable in in vivo NMR analysis because of their interaction with cellular components. The total in vitro GPE and GPC concentration in brain from neonatal rat was estimated to be 0.34 mmole/g wet tissue

  18. Deep brain stimulation reveals a dissociation of consummatory and motivated behaviour in the medial and lateral nucleus accumbens shell of the rat.

    Science.gov (United States)

    van der Plasse, Geoffrey; Schrama, Regina; van Seters, Sebastiaan P; Vanderschuren, Louk J M J; Westenberg, Herman G M

    2012-01-01

    Following the successful application of deep brain stimulation (DBS) in the treatment of Parkinson's disease and promising results in clinical trials for obsessive compulsive disorder and major depression, DBS is currently being tested in small patient-populations with eating disorders and addiction. However, in spite of its potential use in a broad spectrum of disorders, the mechanisms of action of DBS remain largely unclear and optimal neural targets for stimulation in several disorders have yet to be established. Thus, there is a great need to examine site-specific effects of DBS on a behavioural level and to understand how DBS may modulate pathological behaviour. In view of the possible application of DBS in the treatment of disorders characterized by impaired processing of reward and motivation, like addiction and eating disorders, we examined the effect of DBS of the nucleus accumbens (NAcc) on food-directed behavior. Rats were implanted with bilateral stimulation electrodes in one of three anatomically and functionally distinct sub-areas of the NAcc: the core, lateral shell (lShell) and medial shell (mShell). Subsequently, we studied the effects of DBS on food consumption, and the motivational and appetitive properties of food. The data revealed a functional dissociation between the lShell and mShell. DBS of the lShell reduced motivation to respond for sucrose under a progressive ratio schedule of reinforcement, mShell DBS, however, profoundly and selectively increased the intake of chow. DBS of the NAcc core did not alter any form of food-directed behavior studied. DBS of neither structure affected sucrose preference. These data indicate that the intake of chow and the motivation to work for palatable food can independently be modulated by DBS of subregions of the NAcc shell. As such, these findings provide important leads for the possible future application of DBS as a treatment for eating disorders such as anorexia nervosa.

  19. Deep brain stimulation reveals a dissociation of consummatory and motivated behaviour in the medial and lateral nucleus accumbens shell of the rat.

    Directory of Open Access Journals (Sweden)

    Geoffrey van der Plasse

    Full Text Available Following the successful application of deep brain stimulation (DBS in the treatment of Parkinson's disease and promising results in clinical trials for obsessive compulsive disorder and major depression, DBS is currently being tested in small patient-populations with eating disorders and addiction. However, in spite of its potential use in a broad spectrum of disorders, the mechanisms of action of DBS remain largely unclear and optimal neural targets for stimulation in several disorders have yet to be established. Thus, there is a great need to examine site-specific effects of DBS on a behavioural level and to understand how DBS may modulate pathological behaviour. In view of the possible application of DBS in the treatment of disorders characterized by impaired processing of reward and motivation, like addiction and eating disorders, we examined the effect of DBS of the nucleus accumbens (NAcc on food-directed behavior. Rats were implanted with bilateral stimulation electrodes in one of three anatomically and functionally distinct sub-areas of the NAcc: the core, lateral shell (lShell and medial shell (mShell. Subsequently, we studied the effects of DBS on food consumption, and the motivational and appetitive properties of food. The data revealed a functional dissociation between the lShell and mShell. DBS of the lShell reduced motivation to respond for sucrose under a progressive ratio schedule of reinforcement, mShell DBS, however, profoundly and selectively increased the intake of chow. DBS of the NAcc core did not alter any form of food-directed behavior studied. DBS of neither structure affected sucrose preference. These data indicate that the intake of chow and the motivation to work for palatable food can independently be modulated by DBS of subregions of the NAcc shell. As such, these findings provide important leads for the possible future application of DBS as a treatment for eating disorders such as anorexia nervosa.

  20. Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein

    Directory of Open Access Journals (Sweden)

    Deussing Jan M

    2010-10-01

    Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

  1. History of epilepsy: nosological concepts and classification.

    Science.gov (United States)

    Wolf, Peter

    2014-09-01

    The purpose of this review is to provide insight into the development of the nosological views of the epilepsies, from prehistoric times to the present, and highlight how these views are reflected by terminology and classification. Even the earliest written documents reveal awareness that there are multiple forms of epilepsy, and it is surprising that they should be included under the same disease concept, perhaps because the generalised tonic-clonic seizure served as a common denominator. The Hippocratic doctrine that the seat of epilepsy is in the brain may be rooted in earlier knowledge of traumatic seizures. Galenus differentiated cases where the brain was the primary site of origin from others where epilepsy was concomitant with illness in other parts of the body. This laid the fundament for the distinction between idiopathic and symptomatic epilepsies, the definition of which changed considerably over time. The description of the multiple seizure types as they are known at present started in the late 18th century. Attempts to classify seizure types began in the late 19th century, when Jackson formulated a comprehensive pathophysiological definition of epilepsy. Electroencephalography supported a second dichotomy, between seizures with localised onset and others with immediate involvement of both hemispheres which became known as "generalised". In recent years, advanced methods of studying brain function in vivo, including the generation of both spontaneous and reflex epileptic seizures, have revolutionised our understanding of focal and "generalised" human ictogenesis. Both involve complex neuronal networks which are currently being investigated.

  2. Removing the effect of response time on brain activity reveals developmental differences in conflict processing in the posterior medial prefrontal cortex.

    Science.gov (United States)

    Carp, Joshua; Fitzgerald, Kate Dimond; Taylor, Stephan F; Weissman, Daniel H

    2012-01-02

    In functional magnetic resonance imaging (fMRI) studies, researchers often attempt to ensure that group differences in brain activity are not confounded with group differences in mean reaction time (RT). However, even when groups are matched for performance, they may differ in terms of the RT-BOLD relationship: the degree to which brain activity varies with RT on a trial-by-trial basis. Group activation differences might therefore be influenced by group differences in the relationship between brain activity and time on task. Here, we investigated whether correcting for this potential confound alters group differences in brain activity. Specifically, we reanalyzed data from a functional MRI study of response conflict in children and adults, in which conventional analyses indicated that conflict-related activity did not differ between groups. We found that the RT-BOLD relationship was weaker in children than in adults. Consequently, after removing the effect of RT on brain activity, children exhibited greater conflict-related activity than adults in both the posterior medial prefrontal cortex and the right dorsolateral prefrontal cortex. These results identify the RT-BOLD relationship as an important potential confound in fMRI studies of group differences. They also suggest that the magnitude of the RT-BOLD relationship may be a useful biomarker of brain maturity. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination.

    Directory of Open Access Journals (Sweden)

    Xiaomin Dong

    2015-12-01

    Full Text Available Long non-coding RNAs (lncRNAs (> 200 bp play crucial roles in transcriptional regulation during numerous biological processes. However, it is challenging to comprehensively identify lncRNAs, because they are often expressed at low levels and with more cell-type specificity than are protein-coding genes. In the present study, we performed ab initio transcriptome reconstruction using eight purified cell populations from mouse cortex and detected more than 5000 lncRNAs. Predicting the functions of lncRNAs using cell-type specific data revealed their potential functional roles in Central Nervous System (CNS development. We performed motif searches in ENCODE DNase I digital footprint data and Mouse ENCODE promoters to infer transcription factor (TF occupancy. By integrating TF binding and cell-type specific transcriptomic data, we constructed a novel framework that is useful for systematically identifying lncRNAs that are potentially essential for brain cell fate determination. Based on this integrative analysis, we identified lncRNAs that are regulated during Oligodendrocyte Precursor Cell (OPC differentiation from Neural Stem Cells (NSCs and that are likely to be involved in oligodendrogenesis. The top candidate, lnc-OPC, shows highly specific expression in OPCs and remarkable sequence conservation among placental mammals. Interestingly, lnc-OPC is significantly up-regulated in glial progenitors from experimental autoimmune encephalomyelitis (EAE mouse models compared to wild-type mice. OLIG2-binding sites in the upstream regulatory region of lnc-OPC were identified by ChIP (chromatin immunoprecipitation-Sequencing and validated by luciferase assays. Loss-of-function experiments confirmed that lnc-OPC plays a functional role in OPC genesis. Overall, our results substantiated the role of lncRNA in OPC fate determination and provided an unprecedented data source for future functional investigations in CNS cell types. We present our datasets and

  4. NMDA receptor antagonism by repetitive MK801 administration induces schizophrenia-like structural changes in the rat brain as revealed by voxel-based morphometry and diffusion tensor imaging.

    Science.gov (United States)

    Wu, H; Wang, X; Gao, Y; Lin, F; Song, T; Zou, Y; Xu, L; Lei, H

    2016-05-13

    Animal models of N-methyl-d-aspartate receptor (NMDAR) antagonism have been widely used for schizophrenia research. Less is known whether these models are associated with macroscopic brain structural changes that resemble those in clinical schizophrenia. Magnetic resonance imaging (MRI) was used to measure brain structural changes in rats subjected to repeated administration of MK801 in a regimen (daily dose of 0.2mg/kg for 14 consecutive days) known to be able to induce schizophrenia-like cognitive impairments. Voxel-based morphometry (VBM) revealed significant gray matter (GM) atrophy in the hippocampus, ventral striatum (vStr) and cortex. Diffusion tensor imaging (DTI) demonstrated microstructural impairments in the corpus callosum (cc). Histopathological results corroborated the MRI findings. Treatment-induced behavioral abnormalities were not measured such that correlation between the brain structural changes observed and schizophrenia-like behaviors could not be established. Chronic MK801 administration induces MRI-observable brain structural changes that are comparable to those observed in schizophrenia patients, supporting the notion that NMDAR hypofunction contributes to the pathology of schizophrenia. Imaging-derived brain structural changes in animal models of NMDAR antagonism may be useful measurements for studying the effects of treatments and interventions targeting schizophrenia. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  5. Structural imaging of the brain reveals decreased total brain and total gray matter volumes in obese but not in lean women with polycystic ovary syndrome compared to body mass index-matched counterparts.

    Science.gov (United States)

    Ozgen Saydam, Basak; Has, Arzu Ceylan; Bozdag, Gurkan; Oguz, Kader Karli; Yildiz, Bulent Okan

    2017-07-01

    To detect differences in global brain volumes and identify relations between brain volume and appetite-related hormones in women with polycystic ovary syndrome (PCOS) compared to body mass index-matched controls. Forty subjects participated in this study. Cranial magnetic resonance imaging and measurements of fasting ghrelin, leptin and glucagon-like peptide 1 (GLP-1), as well as GLP-1 levels during mixed-meal tolerance test (MTT), were performed. Total brain volume and total gray matter volume (GMV) were decreased in obese PCOS compared to obese controls (p lean PCOS and controls did not show a significant difference. Secondary analyses of regional brain volumes showed decreases in GMV of the caudate nucleus, ventral diencephalon and hippocampus in obese PCOS compared to obese controls (p lean patients with PCOS had lower GMV in the amygdala than lean controls (p PCOS, suggests volumetric reductions in global brain areas in obese women with PCOS. Functional studies with larger sample size are needed to determine physiopathological roles of these changes and potential effects of long-term medical management on brain structure of PCOS.

  6. Neuroimaging meta-analysis of cannabis use studies reveals convergent functional alterations in brain regions supporting cognitive control and reward processing.

    Science.gov (United States)

    Yanes, Julio A; Riedel, Michael C; Ray, Kimberly L; Kirkland, Anna E; Bird, Ryan T; Boeving, Emily R; Reid, Meredith A; Gonzalez, Raul; Robinson, Jennifer L; Laird, Angela R; Sutherland, Matthew T

    2018-03-01

    Lagging behind rapid changes to state laws, societal views, and medical practice is the scientific investigation of cannabis's impact on the human brain. While several brain imaging studies have contributed important insight into neurobiological alterations linked with cannabis use, our understanding remains limited. Here, we sought to delineate those brain regions that consistently demonstrate functional alterations among cannabis users versus non-users across neuroimaging studies using the activation likelihood estimation meta-analysis framework. In ancillary analyses, we characterized task-related brain networks that co-activate with cannabis-affected regions using data archived in a large neuroimaging repository, and then determined which psychological processes may be disrupted via functional decoding techniques. When considering convergent alterations among users, decreased activation was observed in the anterior cingulate cortex, which co-activated with frontal, parietal, and limbic areas and was linked with cognitive control processes. Similarly, decreased activation was observed in the dorsolateral prefrontal cortex, which co-activated with frontal and occipital areas and linked with attention-related processes. Conversely, increased activation among users was observed in the striatum, which co-activated with frontal, parietal, and other limbic areas and linked with reward processing. These meta-analytic outcomes indicate that cannabis use is linked with differential, region-specific effects across the brain.

  7. Revealing Rembrandt

    Directory of Open Access Journals (Sweden)

    Andrew J Parker

    2014-04-01

    Full Text Available The power and significance of artwork in shaping human cognition is self-evident. The starting point for our empirical investigations is the view that the task of neuroscience is to integrate itself with other forms of knowledge, rather than to seek to supplant them. In our recent work, we examined a particular aspect of the appreciation of artwork using present-day functional magnetic resonance imaging (fMRI. Our results emphasised the continuity between viewing artwork and other human cognitive activities. We also showed that appreciation of a particular aspect of artwork, namely authenticity, depends upon the co-ordinated activity between the brain regions involved in multiple decision making and those responsible for processing visual information. The findings about brain function probably have no specific consequences for understanding how people respond to the art of Rembrandt in comparison with their response to other artworks. However, the use of images of Rembrandt’s portraits, his most intimate and personal works, clearly had a significant impact upon our viewers, even though they have been spatially confined to the interior of an MRI scanner at the time of viewing. Neuroscientific studies of humans viewing artwork have the capacity to reveal the diversity of human cognitive responses that may be induced by external advice or context as people view artwork in a variety of frameworks and settings.

  8. Intra-axial brain tumors in adults. On the basis of the 2016 WHO classification; Intraaxiale Hirntumoren des Erwachsenenalters. Auf der Basis der WHO-Klassifizierung 2016

    Energy Technology Data Exchange (ETDEWEB)

    Peitgen, N.; Papanagiotou, P. [Klinikum Bremen-Mitte/Bremen-Ost, Klinik fuer Diagnostische und Interventionelle Neuroradiologie, Bremen (Germany)

    2017-09-15

    The influence of the World Health Organization (WHO) classification from 2016 on the radiological diagnosis for tumors of the central nervous system (CNS) in adults. Computed tomography (CT), magnetic resonance imaging (MRI) and MR spectroscopy. In order to come as close as possible to the correct diagnosis of CNS tumors, MRI is the long-standing accepted method of choice that can in some cases be supported by the use of CT to demonstrate calcification or bone destruction. In individual cases MRI spectroscopy can be helpful for the differentiation between neoplasms and inflammatory lesions or surveillance of tumor therapy, just as perfusion, which is not discussed in this article. (orig.) [German] Einfluss der WHO-Klassifikation von 2016 fuer ZNS-Tumoren auf die radiologische Befunderstellung fuer ZNS-Tumoren des Erwachsenenalters. CT, MRT, MR-Spektroskopie. Um sich der korrekten Diagnose bzgl. eines ZNS-Tumors bestmoeglich zu naehern, ist die MRT das akzeptierte Standardverfahren, die in Einzelfaellen durch eine CT ergaenzt werden kann, wenn Verkalkungen oder knoecherne Arrosionen dargestellt werden sollen. Eine MR-Spektroskopie kann in Einzelfaellen zur Differenzierung von Neoplasien und entzuendlichen Veraenderungen oder Tumorverlaufskontrollen unter Therapie hilfreich sein, ebenso wie die Perfusion, auf die in diesem Artikel nicht eingegangen wird. (orig.)

  9. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain

    Science.gov (United States)

    Nguyen, AnhThu; Rauch, Tibor A.; Pfeifer, Gerd P.; Hu, Valerie W.

    2010-01-01

    Autism is currently considered a multigene disorder with epigenetic influences. To investigate the contribution of DNA methylation to autism spectrum disorders, we have recently completed large-scale methylation profiling by CpG island microarray analysis of lymphoblastoid cell lines derived from monozygotic twins discordant for diagnosis of autism and their nonautistic siblings. Methylation profiling revealed many candidate genes differentially methylated between discordant MZ twins as well as between both twins and nonautistic siblings. Bioinformatics analysis of the differentially methylated genes demonstrated enrichment for high-level functions including gene transcription, nervous system development, cell death/survival, and other biological processes implicated in autism. The methylation status of 2 of these candidate genes, BCL-2 and retinoic acid-related orphan receptor alpha (RORA), was further confirmed by bisulfite sequencing and methylation-specific PCR, respectively. Immunohistochemical analyses of tissue arrays containing slices of the cerebellum and frontal cortex of autistic and age- and sex-matched control subjects revealed decreased expression of RORA and BCL-2 proteins in the autistic brain. Our data thus confirm the role of epigenetic regulation of gene expression via differential DNA methylation in idiopathic autism, and furthermore link molecular changes in a peripheral cell model with brain pathobiology in autism.—Nguyen, A., Rauch, T. A., Pfeifer, G. P., Hu, V. W. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain. PMID:20375269

  10. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  11. Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

    Science.gov (United States)

    Zwemmer, J N P; Berkhof, J; Castelijns, J A; Barkhof, F; Polman, C H; Uitdehaag, B M J

    2006-10-01

    Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

  12. Resting-state fMRI revealed different brain activities responding to valproic acid and levetiracetam in benign epilepsy with central-temporal spikes

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Qirui; Zhang, Zhiqiang; Xu, Qiang; Wu, Han; Li, Zhipeng; Lu, Guangming [Nanjing University School of Medicine, Department of Medical Imaging, Jinling Hospital, Nanjing (China); Yang, Fang; Li, Qian [Nanjing University School of Medicine, Department of Neurology, Jinling Hospital, Nanjing (China); Hu, Zheng [Nanjing Children' s Hospital, Department of Neurology, Nanjing (China); Dante, Mantini [Faculty of Kinesiology and Rehabilitation Sciences, KU Leuven (Belgium); Li, Kai [Suzhou University, Laboratory of Molecular Medicine, Suzhou (China)

    2017-05-15

    Our aim was to investigate regional difference in brain activities in response to antiepileptic drug (AED) medications in benign epilepsy with central-temporal spikes (BECTS) using resting-state functional magnetic resonance imaging (fMRI). Fifty-seven patients with BECTS underwent resting-state fMRI scans after receiving either valproic acid (VPA) (n = 15), levetiracetam (LEV) (n = 21), or no medication (n = 21). fMRI regional homogeneity (ReHo) parameter among the three groups of patients were compared and were correlated with total doses of AED in the two medicated groups. Compared with patients on no-medication, patients receiving either VPA or LEV showed decreased ReHo in the central-temporal region, frontal cortex, and thalamus. In particular, the VPA group showed greater ReHo decrease in the thalamus and milder in cortices and caudate heads compared with the LEV group. In addition, the VPA group demonstrated a negative correlation between ReHo values in the central-temporal region and medication dose. Both VPA and LEV inhibit resting-state neural activity in the central-temporal region, which is the main epileptogenic focus of BECTS. VPA reduced brain activity in the cortical epileptogenic regions and thalamus evenly, whereas LEV reduced brain activity predominantly in the cortices. Interestingly, VPA showed a cumulative effect on inhibiting brain activity in the epileptogenic regions in BECTS. (orig.)

  13. An optimized method for measuring hypocretin-1 peptide in the mouse brain reveals differential circadian regulation of hypocretin-1 levels rostral and caudal to the hypothalamus.

    Science.gov (United States)

    Justinussen, J L; Holm, A; Kornum, B R

    2015-12-03

    The hypocretin/orexin system regulates, among other things, sleep and energy homeostasis. The system is likely regulated by both homeostatic and circadian mechanisms. Little is known about local differences in the regulation of hypocretin activity. The aim of this study was to establish an optimized peptide quantification method for hypocretin-1 extracted from different mouse brain areas and use this method for investigating circadian fluctuations of hypocretin-1 levels in these areas. The results show that hypocretin-1 peptide can be extracted from small pieces of intact tissue, with sufficient yield for measurements in a standard radioimmunoassay. Utilizing the optimized method, it was found that prepro-hypocretin mRNA and peptide show circadian fluctuations in the mouse brain. This study further demonstrates that the hypocretin-1 peptide level in the frontal brain peaks during dark as does prepro-hypocretin mRNA in the hypothalamus. However, in midbrain and brainstem tissue caudal to the hypothalamus, there was less circadian fluctuation and a tendency for higher levels during the light phase. These data suggest that regulation of the hypocretin system differs between brain areas. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. Recombinant Adeno-Associated Virus-Mediated microRNA Delivery into the Postnatal Mouse Brain Reveals a Role for miR-134 in Dendritogenesis in Vivo

    DEFF Research Database (Denmark)

    Christensen, Mette; Larsen, Lars A; Kauppinen, Sakari

    2010-01-01

    delivery of microRNAs in vivo by use of recombinant adeno-associated virus (rAAV). rAAV-mediated overexpression of miR-134 in neurons of the postnatal mouse brain provided evidence for a negative role of miR-134 in dendritic arborization of cortical layer V pyramidal neurons in vivo, thereby confirming...

  15. Resting-state fMRI revealed different brain activities responding to valproic acid and levetiracetam in benign epilepsy with central-temporal spikes

    International Nuclear Information System (INIS)

    Zhang, Qirui; Zhang, Zhiqiang; Xu, Qiang; Wu, Han; Li, Zhipeng; Lu, Guangming; Yang, Fang; Li, Qian; Hu, Zheng; Dante, Mantini; Li, Kai

    2017-01-01

    Our aim was to investigate regional difference in brain activities in response to antiepileptic drug (AED) medications in benign epilepsy with central-temporal spikes (BECTS) using resting-state functional magnetic resonance imaging (fMRI). Fifty-seven patients with BECTS underwent resting-state fMRI scans after receiving either valproic acid (VPA) (n = 15), levetiracetam (LEV) (n = 21), or no medication (n = 21). fMRI regional homogeneity (ReHo) parameter among the three groups of patients were compared and were correlated with total doses of AED in the two medicated groups. Compared with patients on no-medication, patients receiving either VPA or LEV showed decreased ReHo in the central-temporal region, frontal cortex, and thalamus. In particular, the VPA group showed greater ReHo decrease in the thalamus and milder in cortices and caudate heads compared with the LEV group. In addition, the VPA group demonstrated a negative correlation between ReHo values in the central-temporal region and medication dose. Both VPA and LEV inhibit resting-state neural activity in the central-temporal region, which is the main epileptogenic focus of BECTS. VPA reduced brain activity in the cortical epileptogenic regions and thalamus evenly, whereas LEV reduced brain activity predominantly in the cortices. Interestingly, VPA showed a cumulative effect on inhibiting brain activity in the epileptogenic regions in BECTS. (orig.)

  16. Expression of progerin in aging mouse brains reveals structural nuclear abnormalities without detectible significant alterations in gene expression, hippocampal stem cells or behavior

    DEFF Research Database (Denmark)

    Baek, Jean-Ha; Schmidt, Eva; Viceconte, Nikenza

    2015-01-01

    , the HGPS mutation results in organ-specific defects. For example, bone and skin are strongly affected by HGPS, while the brain appears to be unaffected. There are no definite explanations as to the variable sensitivity to progeria disease among different organs. In addition, low levels of progerin have...

  17. Genome-wide DNA methylation analyses in the brain reveal four differentially methylated regions between humans and non-human primates

    Directory of Open Access Journals (Sweden)

    Wang Jinkai

    2012-08-01

    Full Text Available Abstract Background The highly improved cognitive function is the most significant change in human evolutionary history. Recently, several large-scale studies reported the evolutionary roles of DNA methylation; however, the role of DNA methylation on brain evolution is largely unknown. Results To test if DNA methylation has contributed to the evolution of human brain, with the use of MeDIP-Chip and SEQUENOM MassARRAY, we conducted a genome-wide analysis to identify differentially methylated regions (DMRs in the brain between humans and rhesus macaques. We first identified a total of 150 candidate DMRs by the MeDIP-Chip method, among which 4 DMRs were confirmed by the MassARRAY analysis. All 4 DMRs are within or close to the CpG islands, and a MIR3 repeat element was identified in one DMR, but no repeat sequence was observed in the other 3 DMRs. For the 4 DMR genes, their proteins tend to be conserved and two genes have neural related functions. Bisulfite sequencing and phylogenetic comparison among human, chimpanzee, rhesus macaque and rat suggested several regions of lineage specific DNA methylation, including a human specific hypomethylated region in the promoter of K6IRS2 gene. Conclusions Our study provides a new angle of studying human brain evolution and understanding the evolutionary role of DNA methylation in the central nervous system. The results suggest that the patterns of DNA methylation in the brain are in general similar between humans and non-human primates, and only a few DMRs were identified.

  18. Voxel-Based Morphometry and fMRI Revealed Differences in Brain Gray Matter in Breastfed and Milk Formula-Fed Children.

    Science.gov (United States)

    Ou, X; Andres, A; Pivik, R T; Cleves, M A; Snow, J H; Ding, Z; Badger, T M

    2016-04-01

    Infant diets may have significant impact on brain development in children. The aim of this study was to evaluate brain gray matter structure and function in 8-year-old children who were predominantly breastfed or fed cow's milk formula as infants. Forty-two healthy children (breastfed: n = 22, 10 boys and 12 girls; cow's milk formula: n = 20, 10 boys and 10 girls) were studied by using structural MR imaging (3D T1-weighted imaging) and blood oxygen level-dependent fMRI (while performing tasks involving visual perception and language functions). They were also administered standardized tests evaluating intelligence (Reynolds Intellectual Assessment Scales) and language skills (Clinical Evaluation of Language Fundamentals). Total brain gray matter volume did not differ between the breastfed and cow's milk formula groups. However, breastfed children had significantly higher (P left inferior temporal lobe and left superior parietal lobe compared with cow's milk formula-fed children. Breastfed children showed significantly more brain activation in the right frontal and left/right temporal lobes on fMRI when processing the perception task and in the left temporal/occipital lobe when processing the visual language task than cow's milk formula-fed children. The imaging findings were associated with significantly better performance for breastfed than cow's milk formula-fed children on both tasks. Our findings indicated greater regional gray matter development and better regional gray matter function in breastfed than cow's milk formula-fed children at 8 years of age and suggested that infant diets may have long-term influences on brain development in children. © 2016 by American Journal of Neuroradiology.

  19. Novel MeCP2 isoform-specific antibody reveals the endogenous MeCP2E1 expression in murine brain, primary neurons and astrocytes.

    Directory of Open Access Journals (Sweden)

    Robby M Zachariah

    Full Text Available Rett Syndrome (RTT is a severe neurological disorder in young females, and is caused by mutations in the X-linked MECP2 gene. MECP2/Mecp2 gene encodes for two protein isoforms; MeCP2E1 and MeCP2E2 that are identical except for the N-terminus region of the protein. In brain, MECP2E1 transcripts are 10X higher, and MeCP2E1 is suggested to be the relevant isoform for RTT. However, due to the unavailability of MeCP2 isoform-specific antibodies, the endogenous expression pattern of MeCP2E1 is unknown. To gain insight into the expression of MeCP2E1 in brain, we have developed an anti-MeCP2E1 antibody and validated its specificity in cells exogenously expressing individual MeCP2 isoforms. This antibody does not show any cross-reactivity with MeCP2E2 and detects endogenous MeCP2E1 in mice brain, with no signal in Mecp2(tm1.1Bird y/- null mice. Additionally, we show the endogenous MeCP2E1 expression throughout different brain regions in adult mice, and demonstrate its highest expression in the brain cortex. Our results also indicate that MeCP2E1 is highly expressed in primary neurons, as compared to primary astrocytes. This is the first report of the endogenous MeCP2E1 expression at the protein levels, providing novel avenues for understanding different aspects of MeCP2 function.

  20. In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation.

    Science.gov (United States)

    Zhang, Xiuqing; Liu, Ting; Fan, Xiaohui; Ai, Ni

    2017-08-01

    In silico modeling of blood-brain barrier (BBB) permeability plays an important role in early discovery of central nervous system (CNS) drugs due to its high-throughput and cost-effectiveness. Natural products (NP) have demonstrated considerable therapeutic efficacy against several CNS diseases. However, BBB permeation property of NP is scarcely evaluated both experimentally and computationally. It is well accepted that significant difference in chemical spaces exists between NP and synthetic drugs, which calls into doubt on suitability of available synthetic chemical based BBB permeability models for the evaluation of NP. Herein poor discriminative performance on BBB permeability of NP are first confirmed using internal constructed and previously published drug-derived computational models, which warrants the need for NP-oriented modeling. Then a quantitative structure-property relationship (QSPR) study on a NP dataset was carried out using four different machine learning methods including support vector machine, random forest, Naïve Bayes and probabilistic neural network with 67 selected features. The final consensus model was obtained with approximate 90% overall accuracy for the cross-validation study, which is further taken to predict passive BBB permeability of a large dataset consisting of over 10,000 compounds from traditional Chinese medicine (TCM). For 32 selected TCM molecules, their predicted BBB permeability were evaluated by in vitro parallel artificial membrane permeability assay and overall accuracy for in vitro experimental validation is around 81%. Interestingly, our in silico model successfully predicted different BBB permeation potentials of parent molecules and their known in vivo metabolites. Finally, we found that the lipophilicity, the number of hydrogen bonds and molecular polarity were important molecular determinants for BBB permeability of NP. Our results suggest that the consensus model proposed in current work is a reliable tool for

  1. An optimized method for measuring hypocretin-1 peptide in the mouse brain reveals differential circadian regulation of hypocretin-1 levels rostral and caudal to the hypothalamus

    DEFF Research Database (Denmark)

    Justinussen, J L; Holm, A; Kornum, B R

    2015-01-01

    an optimized peptide quantification method for hypocretin-1 extracted from different mouse brain areas and use this method for investigating circadian fluctuations of hypocretin-1 levels in these areas. The results show that hypocretin-1 peptide can be extracted from small pieces of intact tissue...... as does prepro-hypocretin mRNA in the hypothalamus. However, in midbrain and brainstem tissue caudal to the hypothalamus, there was less circadian fluctuation and a tendency for higher levels during the light phase. These data suggest that regulation of the hypocretin system differs between brain areas.......The hypocretin/orexin system regulates, among other things, sleep and energy homeostasis. The system is likely regulated by both homeostatic and circadian mechanisms. Little is known about local differences in the regulation of hypocretin activity. The aim of this study was to establish...

  2. Association of Protein Distribution and Gene Expression Revealed by PET and Post-Mortem Quantification in the Serotonergic System of the Human Brain.

    Science.gov (United States)

    Komorowski, A; James, G M; Philippe, C; Gryglewski, G; Bauer, A; Hienert, M; Spies, M; Kautzky, A; Vanicek, T; Hahn, A; Traub-Weidinger, T; Winkler, D; Wadsak, W; Mitterhauser, M; Hacker, M; Kasper, S; Lanzenberger, R

    2017-01-01

    Regional differences in posttranscriptional mechanisms may influence in vivo protein densities. The association of positron emission tomography (PET) imaging data from 112 healthy controls and gene expression values from the Allen Human Brain Atlas, based on post-mortem brains, was investigated for key serotonergic proteins. PET binding values and gene expression intensities were correlated for the main inhibitory (5-HT1A) and excitatory (5-HT2A) serotonin receptor, the serotonin transporter (SERT) as well as monoamine oxidase-A (MAO-A), using Spearman's correlation coefficients (rs) in a voxel-wise and region-wise analysis. Correlations indicated a strong linear relationship between gene and protein expression for both the 5-HT1A (voxel-wise rs = 0.71; region-wise rs = 0.93) and the 5-HT2A receptor (rs = 0.66; 0.75), but only a weak association for MAO-A (rs = 0.26; 0.66) and no clear correlation for SERT (rs = 0.17; 0.29). Additionally, region-wise correlations were performed using mRNA expression from the HBT, yielding comparable results (5-HT1Ars = 0.82; 5-HT2Ars = 0.88; MAO-A rs = 0.50; SERT rs = -0.01). The SERT and MAO-A appear to be regulated in a region-specific manner across the whole brain. In contrast, the serotonin-1A and -2A receptors are presumably targeted by common posttranscriptional processes similar in all brain areas suggesting the applicability of mRNA expression as surrogate parameter for density of these proteins. © The Author 2016. Published by Oxford University Press.

  3. Genome-wide analysis of brain and gonad transcripts reveals changes of key sex reversal-related genes expression and signaling pathways in three stages of Monopterus albus.

    Directory of Open Access Journals (Sweden)

    Wei Chi

    Full Text Available The natural sex reversal severely affects the sex ratio and thus decreases the productivity of the rice field eel (Monopterus albus. How to understand and manipulate this process is one of the major issues for the rice field eel stocking. So far the genomics and transcriptomics data available for this species are still scarce. Here we provide a comprehensive study of transcriptomes of brain and gonad tissue in three sex stages (female, intersex and male from the rice field eel to investigate changes in transcriptional level during the sex reversal process.Approximately 195 thousand unigenes were generated and over 44.4 thousand were functionally annotated. Comparative study between stages provided multiple differentially expressed genes in brain and gonad tissue. Overall 4668 genes were found to be of unequal abundance between gonad tissues, far more than that of the brain tissues (59 genes. These genes were enriched in several different signaling pathways. A number of 231 genes were found with different levels in gonad in each stage, with several reproduction-related genes included. A total of 19 candidate genes that could be most related to sex reversal were screened out, part of these genes' expression patterns were validated by RT-qPCR. The expression of spef2, maats1, spag6 and dmc1 were abundant in testis, but was barely detected in females, while the 17β-hsd12, zpsbp3, gal3 and foxn5 were only expressed in ovary.This study investigated the complexity of brain and gonad transcriptomes in three sex stages of the rice field eel. Integrated analysis of different gene expression and changes in signaling pathways, such as PI3K-Akt pathway, provided crucial data for further study of sex transformation mechanisms.

  4. The chemopreventive properties of chlorogenic acid reveal a potential new role for the microsomal glucose-6-phosphate translocase in brain tumor progression

    Directory of Open Access Journals (Sweden)

    Desgagnés Julie

    2006-03-01

    Full Text Available Abstract Background Chlorogenic acid (CHL, the most potent functional inhibitor of the microsomal glucose-6-phosphate translocase (G6PT, is thought to possess cancer chemopreventive properties. It is not known, however, whether any G6PT functions are involved in tumorigenesis. We investigated the effects of CHL and the potential role of G6PT in regulating the invasive phenotype of brain tumor-derived glioma cells. Results RT-PCR was used to show that, among the adult and pediatric brain tumor-derived cells tested, U-87 glioma cells expressed the highest levels of G6PT mRNA. U-87 cells lacked the microsomal catalytic subunit glucose-6-phosphatase (G6Pase-α but expressed G6Pase-β which, when coupled to G6PT, allows G6P hydrolysis into glucose to occur in non-glyconeogenic tissues such as brain. CHL inhibited U-87 cell migration and matrix metalloproteinase (MMP-2 secretion, two prerequisites for tumor cell invasion. Moreover, CHL also inhibited cell migration induced by sphingosine-1-phosphate (S1P, a potent mitogen for glioblastoma multiform cells, as well as the rapid, S1P-induced extracellular signal-regulated protein kinase phosphorylation potentially mediated through intracellular calcium mobilization, suggesting that G6PT may also perform crucial functions in regulating intracellular signalling. Overexpression of the recombinant G6PT protein induced U-87 glioma cell migration that was, in turn, antagonized by CHL. MMP-2 secretion was also inhibited by the adenosine triphosphate (ATP-depleting agents 2-deoxyglucose and 5-thioglucose, a mechanism that may inhibit ATP-mediated calcium sequestration by G6PT. Conclusion We illustrate a new G6PT function in glioma cells that could regulate the intracellular signalling and invasive phenotype of brain tumor cells, and that can be targeted by the anticancer properties of CHL.

  5. Adenoviral vectors for highly selective gene expression in central serotonergic neurons reveal quantal characteristics of serotonin release in the rat brain

    Directory of Open Access Journals (Sweden)

    Teschemacher Anja G

    2009-03-01

    Full Text Available Abstract Background 5-hydroxytryptamine (5 HT, serotonin is one of the key neuromodulators in mammalian brain, but many fundamental properties of serotonergic neurones and 5 HT release remain unknown. The objective of this study was to generate an adenoviral vector system for selective targeting of serotonergic neurones and apply it to study quantal characteristics of 5 HT release in the rat brain. Results We have generated adenoviral vectors which incorporate a 3.6 kb fragment of the rat tryptophan hydroxylase-2 (TPH-2 gene which selectively (97% co-localisation with TPH-2 target raphe serotonergic neurones. In order to enhance the level of expression a two-step transcriptional amplification strategy was employed. This allowed direct visualization of serotonergic neurones by EGFP fluorescence. Using these vectors we have performed initial characterization of EGFP-expressing serotonergic neurones in rat organotypic brain slice cultures. Fluorescent serotonergic neurones were identified and studied using patch clamp and confocal Ca2+ imaging and had features consistent with those previously reported using post-hoc identification approaches. Fine processes of serotonergic neurones could also be visualized in un-fixed tissue and morphometric analysis suggested two putative types of axonal varicosities. We used micro-amperometry to analyse the quantal characteristics of 5 HT release and found that central 5 HT exocytosis occurs predominantly in quanta of ~28000 molecules from varicosities and ~34000 molecules from cell bodies. In addition, in somata, we observed a minority of large release events discharging on average ~800000 molecules. Conclusion For the first time quantal release of 5 HT from somato-dendritic compartments and axonal varicosities in mammalian brain has been demonstrated directly and characterised. Release from somato-dendritic and axonal compartments might have different physiological functions. Novel vectors generated in this

  6. The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?

    Science.gov (United States)

    Bao, Yue; Wang, Yong; Wang, Wei; Wang, Yibao

    2017-01-01

    The existence of the superior fronto-occipital fasciculus (SFOF) in the human brain remains controversial. The aim of the present study was to clarify the existence, course, and terminations of the SFOF. High angular diffusion spectrum imaging (DSI) analysis was performed on six healthy adults and on a template of 842 subjects from the Human Connectome Project. To verify tractography results, we performed fiber microdissections of four post-mortem human brains. Based on DSI tractography, we reconstructed the SFOF in the subjects and the template from the Human Connectome Project that originated from the rostral and medial parts of the superior and middle frontal gyri. By tractography, we found that the fibers formed a compact fascicle at the level of the anterior horn of the lateral ventricle coursing above the head of caudate nucleus, medial to the corona radiate and under the corpus callosum (CC), and terminated at the parietal region via the lower part of the caudate nucleus. We consider that this fiber bundle observed by tractography is the SFOF, although it terminates mainly at the parietal region, rather than occipital lobe. By contrast, we were unable to identify a fiber bundle corresponding to the SFOF in our fiber dissection study. Although we did not provide definite evidence of the SFOF in the human brain, these findings may be useful for future studies in this field. PMID:29321729

  7. The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?

    Directory of Open Access Journals (Sweden)

    Yue Bao

    2017-12-01

    Full Text Available The existence of the superior fronto-occipital fasciculus (SFOF in the human brain remains controversial. The aim of the present study was to clarify the existence, course, and terminations of the SFOF. High angular diffusion spectrum imaging (DSI analysis was performed on six healthy adults and on a template of 842 subjects from the Human Connectome Project. To verify tractography results, we performed fiber microdissections of four post-mortem human brains. Based on DSI tractography, we reconstructed the SFOF in the subjects and the template from the Human Connectome Project that originated from the rostral and medial parts of the superior and middle frontal gyri. By tractography, we found that the fibers formed a compact fascicle at the level of the anterior horn of the lateral ventricle coursing above the head of caudate nucleus, medial to the corona radiate and under the corpus callosum (CC, and terminated at the parietal region via the lower part of the caudate nucleus. We consider that this fiber bundle observed by tractography is the SFOF, although it terminates mainly at the parietal region, rather than occipital lobe. By contrast, we were unable to identify a fiber bundle corresponding to the SFOF in our fiber dissection study. Although we did not provide definite evidence of the SFOF in the human brain, these findings may be useful for future studies in this field.

  8. Dynamic changes in glucose metabolism of living rat brain slices induced by hypoxia and neurotoxic chemical-loading revealed by positron autoradiography

    International Nuclear Information System (INIS)

    Omata, N.; Fujibayashi, Y.; Waki, A.; Sadato, N.; Yano, R.; Yoshimoto, M.; Yonekura, Y.; Murata, T.; Yoshida, S.

    1999-01-01

    Fresh rat brain slices were incubated with 2-deoxy-2-[ 18 F]-fluoro-D-glucose ([ 18 F]FDG) in oxygenated Krebs-Ringer solution at 36 degree C, and serial two-dimensional time-resolved images of [ 18 F]FDG uptake were obtained from these specimens on imaging plates. The fractional rate constant (= k3*) of [ 18 F]FDG proportional to the cerebral glucose metabolic rate (CMRglc) was evaluated by applying the Gjedde-Patlak graphical method to the image data. With hypoxia loading (oxygen deprivation) or glucose metabolism inhibitors acting on oxidative phosphorylation, the k3* value increased dramatically suggesting enhanced glycolysis. After relieving hypoxia ≤10-min, the k3* value returned to the pre-loading level. In contrast, with ≥20-min hypoxia only partial or no recovery was observed, indicating that irreversible neuronal damage had been induced. However, after loading with tetrodotoxin (TTX), the k3* value also decreased but returned to the pre-loading level even after 70-min TTX-loading, reflecting a transient inhibition of neuronal activity. This technique provides a new means of quantifying dynamic changes in the regional CMRglc in living brain slices in response to various interventions such as hypoxia and neurotoxic chemical-loading as well as determining the viability and prognosis of brain tissues. (author)

  9. Acute toxicity profile of cadmium revealed by proteomics in brain tissue of Paralichthys olivaceus: Potential role of transferrin in cadmium toxicity

    International Nuclear Information System (INIS)

    Zhu Jinyong; Huang Heqing; Bao Xiaodong; Lin Qingmei; Cai Zongwei

    2006-01-01

    An analytical approach using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) separated proteins from the brain tissue of the fish Paralichthys olivaceus. Approximately 600 protein spots were detected from the brain sample when applying 600 μg protein to a 2D-PAGE gel in the pH range 3.5-10.0. Compared to a control sample, significant changes of 24 protein spots were observed in the fish tissue exposed to acute toxicity of seawater cadmium (SCAT) at 10 ppm for 24 h. Among these spots, nine were down-regulated, nine were up-regulated, two showed high expression, and four showed low expression. The collected spots were identified by peptide mass fingerprinting (PMF) and database search, and they were further classified by LOCtree, a hierarchical system of support vector machines which predict their sub-cellular localization. The amount of transferrin expression in brain cells decreased linearly with the increase of SCAT concentration in seawater. Among the 24 proteins identified on a 2D-PAGE gel, 9 demonstrated a synchronous response to acute cadmium, suggesting that they might represent a biomarker profile. Based on their variable levels and trends on the 2D-PAGE gel this protein (likely to be transferrin) suggesting they might be utilized as biomarkers to investigate cadmium pollution levels in seawater and halobios survival, as well as to evaluate the degree of risk of human fatalities. The results indicate that the application of multiple biomarkers has an advantage over a single biomarker for monitoring levels of environmental contamination

  10. Differential ontogenetic patterns of levocabastine-sensitive neurotensin NT2 receptors and of NT1 receptors in the rat brain revealed by in situ hybridization.

    Science.gov (United States)

    Lépée-Lorgeoux, I; Betancur, C; Rostène, W; Pélaprat, D

    1999-03-12

    The postnatal ontogeny of the levocabastine-sensitive neurotensin receptor (NT2) mRNA was studied by in situ hybridization in the rat brain and compared with the distribution of the levocabastine-insensitive NT1 receptor. NT2 receptor mRNA was absent at birth from all brain structures except the ependymal cell layer lining the ventricles. The development of NT2 receptor mRNA followed three ontogenetic patterns. The first pattern, involving the majority of the cerebral gray matter, was characterized by a continuous increase from postnatal day 5 (P5) to P30. The second one, involving regions rich in myelinated fibers such as the corpus callosum and lacunosum moleculare layer of the hippocampus, exhibited a pronounced increase between P5 and P10, peaked at P15 and was followed by a plateau or a slight decrease. The third pattern was observed in the ependymal cell layer lining the olfactory and lateral ventricles, where the high labeling already present at birth continued to increase during development. These different developmental patterns could reflect the variety of cells expressing NT2 receptor mRNA, including neurons, protoplasmic astrocytes in gray matter, fibrous astrocytes present in myelinated fibers tracts, and ependymal cells. In contrast, NT1 receptor mRNA, which seems to be associated only with neurons, was highly and transiently expressed during the perinatal period in the cerebral cortex, hippocampus and striatal neuroepithelium. Other regions, notably the ventral tegmental area and substantia nigra compacta, exhibited a gradual increase in NT1 receptor signal, reaching adult levels by P21. Both the differential localization and ontogenetic profiles of NT1 and NT2 receptor mRNAs suggest different involvement of these two receptors in brain functions and development. Copyright 1999 Elsevier Science B.V.

  11. Metabolomic Analyses of Brain Tissue in Sepsis Induced by Cecal Ligation Reveal Specific Redox Alterations-Protective Effects of the Oxygen Radical Scavenger Edaravone

    DEFF Research Database (Denmark)

    Hara, Naomi; Chijiiwa, Miyuki; Yara, Miki

    2015-01-01

    at analyzing the preventive effect of the free radical scavenger edaravone on sepsis-induced brain alterations. Sepsis was induced by cecal ligation and puncture (CLP) and the mice were divided into three groups-CLP vehicle (CLPV), CLP and edaravone (MCI-186, 3-methyl-1-phenyl-2-pyrazolin-5-one) (CLPE......), and sham-operated (Sham). Mice in CLPV and CLPE were injected with saline or edaravone intraperitoneally at a dose of 10 mg/kg twice daily. The treatments were initiated 4 days prior to the surgical procedure. Mortality, histological changes, electron microscopy (EM), and expression of Bcl-2 family genes...

  12. PREDICTING APHASIA TYPE FROM BRAIN DAMAGE MEASURED WITH STRUCTURAL MRI

    Science.gov (United States)

    Yourganov, Grigori; Smith, Kimberly G.; Fridriksson, Julius; Rorden, Chris

    2015-01-01

    Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca’s, Wernicke’s, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery. Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients’ aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. PMID:26465238

  13. Predicting aphasia type from brain damage measured with structural MRI.

    Science.gov (United States)

    Yourganov, Grigori; Smith, Kimberly G; Fridriksson, Julius; Rorden, Chris

    2015-12-01

    Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Maxillectomy defects: a suggested classification scheme.

    Science.gov (United States)

    Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

    2013-06-01

    The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

  15. Triazolam-induced modulation of muscarinic acetylcholine receptor in living brain slices as revealed by a new positron-based imaging technique

    International Nuclear Information System (INIS)

    Murata, T.; Matsumura, K.; Onoe, H.; Watanabe, Y.; Sihver, S.; Sihver, W.; Langstroem, B.; Bergstroem, M.; Yonekura, Y.

    1997-01-01

    The effect of triazolam, a potent benzodiazepine (BZ) agonist, on muscarinic acetylcholinergic receptor (mAChR) binding was investigated in living brain slices by use of a novel positron-based imaging technique. Fresh rat brain slices were incubated with [ 11 C]N-methyl-4-piperidylbenzilate ([ 11 C]NMPB), a mAChR antagonist, in oxygenated Krebs-Ringer solution at 37 degree C. During incubation, time-resolved imaging of [ 11 C]NMPB binding in the slices was constructed on the storage phosphor screens. Addition of triazolam (1 μM) plus muscimol (30 μM), a GABA A receptor agonist, to the incubation mixture decreased the specific binding of [ 11 C]NMPB. Ro15-1788, a BZ receptor antagonist, prevented this effect, indicating that the effect was exerted through the GABA A /BZ receptor complex. These results demonstrated that stimulation of the GABA A /BZ receptor lowers the affinity of the mAChR for its ligand, which may underlie the BZ-induced amnesia, a serious clinical side effect of BZ. No such effect in the P2-fraction instead implies that the integrity of the neuronal cells and/or their environment is prerequisite for the modulation of mAChR by GABA A /BZ stimulation. (author)

  16. Metabolomic Profiling of Post-Mortem Brain Reveals Changes in Amino Acid and Glucose Metabolism in Mental Illness Compared with Controls

    Directory of Open Access Journals (Sweden)

    Rong Zhang

    2016-01-01

    Full Text Available Metabolomic profiling was carried out on 53 post-mortem brain samples from subjects diagnosed with schizophrenia, depression, bipolar disorder (SDB, diabetes, and controls. Chromatography on a ZICpHILIC column was used with detection by Orbitrap mass spectrometry. Data extraction was carried out with m/z Mine 2.14 with metabolite searching against an in-house database. There was no clear discrimination between the controls and the SDB samples on the basis of a principal components analysis (PCA model of 755 identified or putatively identified metabolites. Orthogonal partial least square discriminant analysis (OPLSDA produced clear separation between 17 of the controls and 19 of the SDB samples (R2CUM 0.976, Q2 0.671, p-value of the cross-validated ANOVA score 0.0024. The most important metabolites producing discrimination were the lipophilic amino acids leucine/isoleucine, proline, methionine, phenylalanine, and tyrosine; the neurotransmitters GABA and NAAG and sugar metabolites sorbitol, gluconic acid, xylitol, ribitol, arabinotol, and erythritol. Eight samples from diabetic brains were analysed, six of which grouped with the SDB samples without compromising the model (R2 CUM 0.850, Q2 CUM 0.534, p-value for cross-validated ANOVA score 0.00087. There appears on the basis of this small sample set to be some commonality between metabolic perturbations resulting from diabetes and from SDB.

  17. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  18. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  19. Delivery of an anti-TTR Nanobody to the brain through intranasal administration reveals TTR expression and secretion by Motor Neurons.

    Science.gov (United States)

    Gomes, João R; Cabrito, Inês; Soares, Hugo R; Costelha, Susete; Teixeira, Anabela; Wittelsberger, Angela; Stortelers, Catelijne; Vanlandschoot, Peter; Saraiva, Maria J

    2018-03-12

    Transthyretin (TTR) is a transport protein of retinol and thyroxine in serum and cerebrospinal fluid (CSF), which is mainly secreted in liver and choroid plexus, and in smaller amounts in other cells throughout the body. The exact role of TTR and its specific expression in Central Nervous System (CNS) remains understudied. We investigated TTR expression and metabolism in CNS, through the intranasal and intracerebroventricular delivery of a specific anti-TTR Nanobody to the brain, unveiling Nanobody pharmacokinetics to the CNS. In TTR deficient mice, we observed that anti-TTR Nanobody was successfully distributed throughout all brain areas, and also reaching the spinal cord. In wild type (WT) mice, a similar distribution pattern was observed. However, in areas known to be rich in TTR, reduced levels of Nanobody were found, suggesting potential target-mediated effects. Indeed, in WT mice, the anti-TTR Nanobody was specifically internalized in a receptor-mediated process, by neuronal-like cells, which were identified as motor neurons. Whereas in KO TTR mice Nanobody was internalized by all cells, for late lysosomal degradation. Moreover, we demonstrate that in-vivo motor neurons also actively synthesize TTR. Finally, in-vitro cultured primary motor neurons were also found to synthesize and secrete TTR into culture media. Thus, through a novel intranasal CNS distribution study with an anti-TTR Nanobody, we disclose a new cell type capable of synthesizing TTR, which might be important for the understanding of the physiological role of TTR, as well as in pathological conditions where TTR levels are altered in CSF, such as amyotrophic lateral sclerosis. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  20. Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia.

    Science.gov (United States)

    Hess, Jonathan L; Tylee, Daniel S; Barve, Rahul; de Jong, Simone; Ophoff, Roel A; Kumarasinghe, Nishantha; Tooney, Paul; Schall, Ulrich; Gardiner, Erin; Beveridge, Natalie Jane; Scott, Rodney J; Yasawardene, Surangi; Perera, Antionette; Mendis, Jayan; Carr, Vaughan; Kelly, Brian; Cairns, Murray; Tsuang, Ming T; Glatt, Stephen J

    2016-10-01

    The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia. Published by Elsevier B.V.

  1. Meeting the Challenge: Evoking Some Hope; From Personal Advocacy to Public Activism; Seeing Yourself Sitting There; Letting Kids' Gifts Shine Through; Revealing the Secrets of the Brain.

    Science.gov (United States)

    Sherman, Lee; Lewis, Bryan; Ramsey, Betsy; Tibbetts, Daniel; Kaplan, Kay; Berninger, Virginia

    2003-01-01

    Interviews with a learning disabled student, parent activist, teacher, tutor, and researcher reveal that learning disabilities are neurologically caused, not the result of low motivation or dysfunctional families. A variety of educational practices are explained that accommodate different learning styles of children with learning disabilities. It…

  2. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.

    1996-01-01

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  3. 99mTc-MIBI-SPECT-studies in the evaluation of brain tumors

    International Nuclear Information System (INIS)

    Ambrus, E.; Pavics, L.; Gruenwald, F.; Barath, B.; Tiszlavicz, L.; Bender, H.; Menzel, C.; Almasi, L.; Lang, J.; Bodosi, M.; Biersack, H.J.; Csernay, L.

    1994-01-01

    Brain SPECT studies were performed 5 and 60 minutes after 99m Tc-MIBI administration in 41 patients with brain tumors confirmed by CT and surgical removal (13 meningeomas, 8 astrocytomas grades I-III, 10 glioblastomas, 10 metastases). 99m Tc-MIBI uptake was found in 32 out of 41 brain tumors. According to the semiquantitative SPECT analysis, the tumor/non tumor radios revealed a statistically significant difference in the early tracer uptake between meningeomas and astrocytomas (+4.73±2.91 vs -1.75±0.75, p 99m Tc-MIBI uptake and its changes with time. We concluded that the combination of an early and late 99m Tc-MIBI brain SPECT may be helpful in the non invasive histological classification of brain tumors and the determination of the grade of theirs malignancy. (orig.) [de

  4. Vigilance task-related change in brain functional connectivity as revealed by wavelet phase coherence analysis of near-infrared spectroscopy signals

    Directory of Open Access Journals (Sweden)

    Wang Wei

    2016-08-01

    Full Text Available This study aims to assess the vigilance task-related change in connectivity in healthy adults using wavelet phase coherence (WPCO analysis of near-infrared spectroscopy signals (NIRS. NIRS is a non-invasive neuroimaging technique for assessing brain activity. Continuous recordings of the NIRS signals were obtained from the prefrontal cortex (PFC and sensorimotor cortical areas of 20 young healthy adults (24.9±3.3 years during a 10-min resting state and a 20-min vigilance task state. The vigilance task was used to simulate driving mental load by judging three random numbers (i.e., whether odd numbers. The task was divided into two sessions: the first 10 minutes (Task t1 and the second 10 minutes (Task t2. The WPCO of six channel pairs were calculated in five frequency intervals: 0.6–2 Hz (I, 0.145–0.6 Hz (II, 0.052–0.145 Hz (III, 0.021–0.052 Hz (IV, and 0.0095–0.021 Hz (V. The significant WPCO formed global connectivity (GC maps in intervals I and II and functional connectivity (FC maps in intervals III to V. Results show that the GC levels in interval I and FC levels in interval III were significantly lower in the Task t2 than in the resting state (p < 0.05, particularly between the left PFC and bilateral sensorimotor regions. Also, the reaction time shows an increase in Task t2 compared with that in Task t1. However, no significant difference in WPCO was found between Task t1 and resting state. The results showed that the change in FC at the range of 0.6-2 Hz was not attributed to the vigilance task pe se, but the interaction effect of vigilance task and time factors. The findings suggest that the decreased attention level might be partly attributed to the reduced GC levels between the left prefrontal region and sensorimotor area. The present results provide a new insight into the vigilance task-related brain activity.

  5. Externalizing psychopathology and gain-loss feedback in a simulated gambling task: dissociable components of brain response revealed by time-frequency analysis.

    Science.gov (United States)

    Bernat, Edward M; Nelson, Lindsay D; Steele, Vaughn R; Gehring, William J; Patrick, Christopher J

    2011-05-01

    Externalizing is a broad construct that reflects propensity toward a variety of impulse control problems, including antisocial personality disorder and substance use disorders. Two event-related potential responses known to be reduced among individuals high in externalizing proneness are the P300, which reflects postperceptual processing of a stimulus, and the error-related negativity (ERN), which indexes performance monitoring based on endogenous representations. In the current study, the authors used a simulated gambling task to examine the relation between externalizing proneness and the feedback-related negativity (FRN), a brain response that indexes performance monitoring related to exogenous cues, which is thought to be highly related to the ERN. Time-frequency (TF) analysis was used to disentangle the FRN from the accompanying P300 response to feedback cues by parsing the overall feedback-locked potential into distinctive theta (4-7 Hz) and delta (<3 Hz) TF components. Whereas delta-P300 amplitude was reduced among individuals high in externalizing proneness, theta-FRN response was unrelated to externalizing. These findings suggest that in contrast with previously reported deficits in endogenously based performance monitoring (as indexed by the ERN), individuals prone to externalizing problems show intact monitoring of exogenous cues (as indexed by the FRN). The results also contribute to a growing body of evidence indicating that the P300 is attenuated across a broad range of task conditions in high-externalizing individuals.

  6. A polymeric micelle magnetic resonance imaging (MRI) contrast agent reveals blood-brain barrier (BBB) permeability for macromolecules in cerebral ischemia-reperfusion injury.

    Science.gov (United States)

    Shiraishi, Kouichi; Wang, Zuojun; Kokuryo, Daisuke; Aoki, Ichio; Yokoyama, Masayuki

    2017-05-10

    Blood-brain barrier (BBB) opening is a key phenomenon for understanding ischemia-reperfusion injuries that are directly linked to hemorrhagic transformation. The recombinant human tissue-type plasminogen activator (rtPA) increases the risk of symptomatic intracranial hemorrhages. Recent imaging technologies have advanced our understanding of pathological BBB disorders; however, an ongoing challenge in the pre-"rtPA treatment" stage is the task of developing a rigorous method for hemorrhage-risk assessments. Therefore, we examined a novel method for assessment of rtPA-extravasation through a hyper-permeable BBB. To examine the image diagnosis of rtPA-extravasation for a rat transient occlusion-reperfusion model, in this study we used a polymeric micelle MRI contrast-agent (Gd-micelles). Specifically, we used two MRI contrast agents at 1h after reperfusion. Gd-micelles provided very clear contrast images in 15.5±10.3% of the ischemic hemisphere at 30min after i.v. injection, whereas a classic gadolinium chelate MRI contrast agent provided no satisfactorily clear images. The obtained images indicate both the hyper-permeable BBB area for macromolecules and the distribution area of macromolecules in the ischemic hemisphere. Owing to their large molecular weight, Gd-micelles remained in the ischemic hemisphere through the hyper-permeable BBB. Our results indicate the feasibility of a novel clinical diagnosis for evaluating rtPA-related hemorrhage risks. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Schmah, Tanya; Madsen, Kristoffer Hougaard

    2012-01-01

    Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting...... the underlying neural encoding of an experiment defining multiple brain states. In this relation there is a great desire for the researcher to generate brain maps, that highlight brain locations of importance to the classifiers decisions. Based on sensitivity analysis, we develop further procedures for model...... direction the individual locations influence the classification. We illustrate the visualization procedure on a real data from a simple functional magnetic resonance imaging experiment....

  8. Predicting Battle Outcomes with Classification Trees

    National Research Council Canada - National Science Library

    Coban, Muzaffer

    2001-01-01

    ... from the actual battlefield, The models built by using classification trees reveal that the objective variables alone cannot explain the outcome of battles, Relative factors, such as leadership, have deep...

  9. Phylogenetic classification of bony fishes.

    Science.gov (United States)

    Betancur-R, Ricardo; Wiley, Edward O; Arratia, Gloria; Acero, Arturo; Bailly, Nicolas; Miya, Masaki; Lecointre, Guillaume; Ortí, Guillermo

    2017-07-06

    Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results. The updated classification presented here is based on phylogenies inferred using molecular and genomic data for nearly 2000 fishes. A total of 72 orders (and 79 suborders) are recognized in this version, compared with 66 orders in version 1. The phylogeny resolves placement of 410 families, or ~80% of the total of 514 families of bony fishes currently recognized. The ordinal status of 30 percomorph families included in this study, however, remains uncertain (incertae sedis in the series Carangaria, Ovalentaria, or Eupercaria). Comments to support taxonomic decisions and comparisons with conflicting taxonomic groups proposed by others are presented. We also highlight cases were morphological support exist for the groups being classified. This version of the phylogenetic classification of bony fishes is substantially improved, providing resolution

  10. Lineage analysis of quiescent regenerative stem cells in the adult brain by genetic labelling reveals spatially restricted neurogenic niches in the olfactory bulb.

    Science.gov (United States)

    Giachino, Claudio; Taylor, Verdon

    2009-07-01

    The subventricular zone (SVZ) of the lateral ventricles is the major neurogenic region in the adult mammalian brain, harbouring neural stem cells within defined niches. The identity of these stem cells and the factors regulating their fate are poorly understood. We have genetically mapped a population of Nestin-expressing cells during postnatal development to study their potential and fate in vivo. Taking advantage of the recombination characteristics of a nestin::CreER(T2) allele, we followed a subpopulation of neural stem cells and traced their fate in a largely unrecombined neurogenic niche. Perinatal nestin::CreER(T2)-expressing cells give rise to multiple glial cell types and neurons, as well as to stem cells of the adult SVZ. In the adult SVZ nestin::CreER(T2)-expressing neural stem cells give rise to several neuronal subtypes in the olfactory bulb (OB). We addressed whether the same population of neural stem cells play a role in SVZ regeneration. Following anti-mitotic treatment to eliminate rapidly dividing progenitors, relatively quiescent nestin::CreER(T2)-targeted cells are spared and contribute to SVZ regeneration, generating new proliferating precursors and neuroblasts. Finally, we have identified neurogenic progenitors clustered in ependymal-like niches within the rostral migratory stream (RMS) of the OB. These OB-RMS progenitors generate neuroblasts that, upon transplantation, graft, migrate and differentiate into granule and glomerular neurons. In summary, using conditional lineage tracing we have identified neonatal cells that are the source of neurogenic and regenerative neural stem cells in the adult SVZ and occupy a novel neurogenic niche in the OB.

  11. Heterogeneity of ductular reactions in adult rat and human liver revealed by novel expression of deleted in malignant brain tumor 1

    DEFF Research Database (Denmark)

    Bisgaard, H.C.; Holmskov, U.; Santoni-Rugiu, E.

    2002-01-01

    The regenerative capacity of mammalian adult liver reflects the ability of a number of cell populations within the hepatic lineage to take action. Limited information is available regarding factors and mechanisms that determine the specific lineage level at which liver cells contribute to liver......), were specifically associated with the emergence of ductular (oval) cell populations in injured liver. Subsequent cloning and characterization of the rat DMBT1 homologue revealed a highly inducible expression in ductular reactions composed of transit-amplifying ductular (oval) cells, but not in ductular...... reactions after ligation of the common bile duct. In human liver diseases, DMBT1 was expressed in ductular reactions after infection with hepatitis B and acetaminophen intoxication, but not in primary biliary cirrhosis, primary sclerosing cholangitis, and obstruction of the large bile duct. The expression...

  12. Difference in trafficking of brain-derived neurotrophic factor between axons and dendrites of cortical neurons, revealed by live-cell imaging

    Directory of Open Access Journals (Sweden)

    Kohara Keigo

    2005-06-01

    Full Text Available Abstract Background Brain-derived neurotrophic factor (BDNF, which is sorted into a regulated secretory pathway of neurons, is supposed to act retrogradely through dendrites on presynaptic neurons or anterogradely through axons on postsynaptic neurons. Depending on which is the case, the pattern and direction of trafficking of BDNF in dendrites and axons are expected to be different. To address this issue, we analyzed movements of green fluorescent protein (GFP-tagged BDNF in axons and dendrites of living cortical neurons by time-lapse imaging. In part of the experiments, the expression of BDNF tagged with cyan fluorescent protein (CFP was compared with that of nerve growth factor (NGF tagged with yellow fluorescent protein (YFP, to see whether fluorescent protein-tagged BDNF is expressed in a manner specific to this neurotrophin. Results We found that BDNF tagged with GFP or CFP was expressed in a punctated manner in dendrites and axons in about two-thirds of neurons into which plasmid cDNAs had been injected, while NGF tagged with GFP or YFP was diffusely expressed even in dendrites in about 70% of the plasmid-injected neurons. In neurons in which BDNF-GFP was expressed as vesicular puncta in axons, 59 and 23% of the puncta were moving rapidly in the anterograde and retrograde directions, respectively. On the other hand, 64% of BDNF-GFP puncta in dendrites did not move at all or fluttered back and forth within a short distance. The rest of the puncta in dendrites were moving relatively smoothly in either direction, but their mean velocity of transport, 0.47 ± 0.23 (SD μm/s, was slower than that of the moving puncta in axons (0.73 ± 0.26 μm/s. Conclusion The present results show that the pattern and velocity of the trafficking of fluorescence protein-tagged BDNF are different between axons and dendrites, and suggest that the anterograde transport in axons may be the dominant stream of BDNF to release sites.

  13. Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.

    Science.gov (United States)

    Silvoni, S; Konicar, L; Prats-Sedano, M A; Garcia-Cossio, E; Genna, C; Volpato, C; Cavinato, M; Paggiaro, A; Veser, S; De Massari, D; Birbaumer, N

    2016-01-01

    We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  14. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    Energy Technology Data Exchange (ETDEWEB)

    Jürgens, Björn, E-mail: bjurgens@agenciaidea.es [Agency of Innovation and Development of Andalusia, CITPIA PATLIB Centre (Spain); Herrero-Solana, Victor, E-mail: victorhs@ugr.es [University of Granada, SCImago-UGR (SEJ036) (Spain)

    2017-04-15

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  15. Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

    International Nuclear Information System (INIS)

    Jürgens, Björn; Herrero-Solana, Victor

    2017-01-01

    Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

  16. Icariin reverses corticosterone-induced depression-like behavior, decrease in hippocampal brain-derived neurotrophic factor (BDNF) and metabolic network disturbances revealed by NMR-based metabonomics in rats.

    Science.gov (United States)

    Gong, Meng-Juan; Han, Bin; Wang, Shu-mei; Liang, Sheng-wang; Zou, Zhong-jie

    2016-05-10

    Previously published reports have revealed the antidepressant-like effects of icariin in a chronic mild stress model of depression and in a social defeat stress model in mice. However, the therapeutic effect of icariin in an animal model of glucocorticoid-induced depression remains unclear. This study aimed to investigate antidepressant-like effect and the possible mechanisms of icariin in a rat model of corticosterone (CORT)-induced depression by using a combination of behavioral and biochemical assessments and NMR-based metabonomics. The depression model was established by subcutaneous injections of CORT for 21 consecutive days in rats, as evidenced by reduced sucrose intake and hippocampal brain-derived neurotrophic factor (BDNF) levels, together with an increase in immobility time in a forced swim test (FST). Icariin significantly increased sucrose intake and hippocampal BDNF level and decreased the immobility time in FST in CORT-induced depressive rats, suggesting its potent antidepressant activity. Moreover, metabonomic analysis identified eight, five and three potential biomarkers associated with depression in serum, urine and brain tissue extract, respectively. These biomarkers are primarily involved in energy metabolism, lipid metabolism, amino acid metabolism and gut microbe metabolism. Icariin reversed the pathological process of CORT-induced depression, partially via regulation of the disturbed metabolic pathways. These results provide important mechanistic insights into the protective effects of icariin against CORT-induced depression and metabolic dysfunction. Copyright © 201