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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 Medical Brain Images

    Institute of Scientific and Technical Information of China (English)

    Pan Haiwei(潘海为); Li Jianzhong; Zhang Wei

    2003-01-01

    Since brain tumors endanger people's living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with those datasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy. For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This paper shows that the algorithm works well for real CT data.

  3. GLCM textural features for Brain Tumor Classification

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    N S Zulpe

    2012-05-01

    Full Text Available Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT, Magnetic Resonance Imaging (MRI, etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to two-layered Feed forward Neural Network, which gives 97.5% classification rate.

  4. Tissue tracking: applications for brain MRI classification

    Science.gov (United States)

    Melonakos, John; Gao, Yi; Tannenbaum, Allen

    2007-03-01

    Bayesian classification methods have been extensively used in a variety of image processing applications, including medical image analysis. The basic procedure is to combine data-driven knowledge in the likelihood terms with clinical knowledge in the prior terms to classify an image into a pre-determined number of classes. In many applications, it is difficult to construct meaningful priors and, hence, homogeneous priors are assumed. In this paper, we show how expectation-maximization weights and neighboring posterior probabilities may be combined to make intuitive use of the Bayesian priors. Drawing upon insights from computer vision tracking algorithms, we cast the problem in a tissue tracking framework. We show results of our algorithm on the classification of gray and white matter along with surrounding cerebral spinal fluid in brain MRI scans. We show results of our algorithm on 20 brain MRI datasets along with validation against expert manual segmentations.

  5. 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.

  6. Deep learning for brain tumor classification

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    Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

    2017-03-01

    Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

  7. Adaptive multiclass classification for brain computer interfaces.

    Science.gov (United States)

    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

    We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.

  8. 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.

  9. Brain tumour classification using Gaussian decomposition and neural networks.

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    Arizmendi, Carlos; Sierra, Daniel A; Vellido, Alfredo; Romero, Enrique

    2011-01-01

    The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

  10. Simple Fully Automated Group Classification on Brain fMRI

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. Brain tumor classification of microscopy images using deep residual learning

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    Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

    2016-12-01

    The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

  12. Pattern classification of EEG signals reveals perceptual and attentional states.

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    List, Alexandra; Rosenberg, Monica D; Sherman, Aleksandra; Esterman, Michael

    2017-01-01

    Pattern classification techniques have been widely used to differentiate neural activity associated with different perceptual, attentional, or other cognitive states, often using fMRI, but more recently with EEG as well. Although these methods have identified EEG patterns (i.e., scalp topographies of EEG signals occurring at certain latencies) that decode perceptual and attentional states on a trial-by-trial basis, they have yet to be applied to the spatial scope of attention toward global or local features of the display. Here, we initially used pattern classification to replicate and extend the findings that perceptual states could be reliably decoded from EEG. We found that visual perceptual states, including stimulus location and object category, could be decoded with high accuracy peaking between 125-250 ms, and that the discriminative spatiotemporal patterns mirrored and extended our (and other well-established) ERP results. Next, we used pattern classification to investigate whether spatiotemporal EEG signals could reliably predict attentional states, and particularly, the scope of attention. The EEG data were reliably differentiated for local versus global attention on a trial-by-trial basis, emerging as a specific spatiotemporal activation pattern over posterior electrode sites during the 250-750 ms interval after stimulus onset. In sum, we demonstrate that multivariate pattern analysis of EEG, which reveals unique spatiotemporal patterns of neural activity distinguishing between behavioral states, is a sensitive tool for characterizing the neural correlates of perception and attention.

  13. Classification of Brain Tumor Using Support Vector Machine Classfiers

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    Dr.D. J. Pete

    2014-03-01

    Full Text Available Magnetic resonance imagi ng (MRI is an imaging technique that has played an important role in neuro science research for studying brain images. Classification is an important part in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. The proposed method consists of two stages: feature extraction and classification. In first stage features are extracted from images using GLCM. In the next stage, extracted features are fed as input to Kernel-Based SVM classifier. It classifies the images between normal and abnormal along with Grade of tumor depending upon features. For Brain MRI images; features extracted with GLCM gives 98% accuracy with Kernel-Based SVM Classifiesr. Software used is MATLAB R2011a.

  14. 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.

  15. Leveraging Human Brain Activity to Improve Object Classification

    OpenAIRE

    Fong, Ruth Catherine

    2015-01-01

    Today, most object detection algorithms differ drastically from how humans tackle visual problems. In this thesis, I present a new paradigm for improving machine vision algorithms by designing them to better mimic how humans approach these tasks. Specifically, I demonstrate how human brain activity from functional magnetic resonance imaging (fMRI) can be leveraged to improve object classification. Inspired by the graduated manner in which humans learn, I present a novel algorithm that sim...

  16. Brain rhythms reveal a hierarchical network organization.

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    G Karl Steinke

    2011-10-01

    Full Text Available Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic, in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states

  17. Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

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    Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

    2015-01-01

    Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

  18. Improved Classification Methods for Brain Computer Interface System

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    YI Fang

    2012-03-01

    Full Text Available Brain computer interface (BCI aims at providing a new communication way without brain’s normal output through nerve and muscle. The electroencephalography (EEG has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization (ERD and event-related synchronization(ERS are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, the performance are also stable with the balance of two misclassifications.

  19. 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.

  20. EMOTION INTERACTION WITH VIRTUAL REALITY USING HYBRID EMOTION CLASSIFICATION TECHNIQUE TOWARD BRAIN SIGNALS

    National Research Council Canada - National Science Library

    Faris A. Abuhashish; Jamal Zraqou; Wesam Alkhodour; Mohd S. Sunar; Hoshang Kolivand

    2015-01-01

    .... Last decade many researchers focused on emotion classification in order to employ emotion in interaction with virtual reality, the classification will be done based on Electroencephalogram (EEG) brain signals...

  1. 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

  2. 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.

  3. 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.

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

    Science.gov (United States)

    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

  5. Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

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    Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

    2016-08-01

    Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

  6. Retrieving binary answers using whole-brain activity pattern classification

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    Norberto Eiji Nawa

    2015-12-01

    Full Text Available Multivariate pattern analysis (MVPA has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., the month you were born is an odd number, and were told to countdown numbers if the statement was true (yes or recall positive events otherwise (no. The previously trained classifiers were then used to assign labels (yes/no to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6% to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.

  7. Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

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    Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

    2017-08-01

    This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

  8. Diffusion tensor imaging reveals evolution of primate brain architectures.

    Science.gov (United States)

    Zhang, Degang; Guo, Lei; Zhu, Dajiang; Li, Kaiming; Li, Longchuan; Chen, Hanbo; Zhao, Qun; Hu, Xiaoping; Liu, Tianming

    2013-11-01

    Evolution of the brain has been an inherently interesting problem for centuries. Recent studies have indicated that neuroimaging is a powerful technique for studying brain evolution. In particular, a variety of reports have demonstrated that consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) tractography reveal common brain architecture and are predictive of brain functions. In this paper, based on our recently discovered 358 dense individualized and common connectivity-based cortical landmarks (DICCCOL) defined by consistent fiber connection patterns in DTI datasets of human brains, we derived 65 DICCCOLs that are common in macaque monkey, chimpanzee and human brains and 175 DICCCOLs that exhibit significant discrepancies amongst these three primate species. Qualitative and quantitative evaluations not only demonstrated the consistencies of anatomical locations and structural fiber connection patterns of these 65 common DICCCOLs across three primates, suggesting an evolutionarily preserved common brain architecture but also revealed regional patterns of evolutionarily induced complexity and variability of those 175 discrepant DICCCOLs across the three species.

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

    Science.gov (United States)

    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

  10. Reducing Dataset Size in Frequency Domain for Brain Computer Interface Motor Imagery Classification

    Directory of Open Access Journals (Sweden)

    Ch.Aparna

    2010-12-01

    Full Text Available Brain computer interface is an emerging area of research where the BCI system is able to detect and interpret the mental activity into computer interpretable signals opening a wide area of applications where activities can be completed without using muscular movement. In Brain Computer Interface research, for classification of EEG signals the raw signals captured has to undergo some preprocessing, to obtain the right attributes for classification. In this paper, we present a system which allows for classification of mental tasks based on a statistical data obtained in frequency domain using Discrete cosine transform and extracting useful frequencies from the same with application of decision tree algorithms for classification.

  11. 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 cons

  12. 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

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

    Science.gov (United States)

    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.

  14. Resting-state brain organization revealed by functional covariance networks.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

    Full Text Available BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN and structural covariance network (SCN have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

  15. Epidemiology, severity classification, and outcome of moderate and severe traumatic brain injury: a prospective multicenter study

    NARCIS (Netherlands)

    Andriessen, T.M.J.C.; Horn, J.; Franschman, G.; Naalt, J. van der; Haitsma, I.; Jacobs, B.; Steyerberg, E.W.; Vos, P.E.

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  16. Epidemiology, Severity Classification, and Outcome of Moderate and Severe Traumatic Brain Injury: A Prospective Multicenter Study

    NARCIS (Netherlands)

    T.M.J.C. Andriessen; J. Horn; G. Franschman; J. van der Naalt; I. Haitsma; B. Jacobs; E.W. Steyerberg; P.E. Vos

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  17. Epidemiology, Severity Classification, and Outcome of Moderate and Severe Traumatic Brain Injury : A Prospective Multicenter Study

    NARCIS (Netherlands)

    Andriessen, Teuntje M. J. C.; Horn, Janneke; Franschman, Gaby; van der Naalt, Joukje; Haitsma, Iain; Jacobs, Bram; Steyerberg, Ewout W.; Vos, Pieter E.

    2011-01-01

    Changes in the demographics, approach, and treatment of traumatic brain injury (TBI) patients require regular evaluation of epidemiological profiles, injury severity classification, and outcomes. This prospective multicenter study provides detailed information on TBI-related variables of 508 moderat

  18. An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

    OpenAIRE

    Rajendran, P.; M.Madheswaran

    2010-01-01

    An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low-level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple ...

  19. Extracting salient brain patterns for imaging-based classification of neurodegenerative diseases.

    Science.gov (United States)

    Rueda, Andrea; González, Fabio A; Romero, Eduardo

    2014-06-01

    Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.

  20. An improved brain image classification technique with mining and shape prior segmentation procedure.

    Science.gov (United States)

    Rajendran, P; Madheswaran, M

    2012-04-01

    The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

  1. Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

    Science.gov (United States)

    Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

    2016-02-01

    The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7

  2. Word pair classification during imagined speech using direct brain recordings

    Science.gov (United States)

    Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José Del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

    2016-05-01

    People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58% p perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

  3. Behavioral state classification in epileptic brain using intracranial electrophysiology

    Science.gov (United States)

    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.

  4. An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

    Directory of Open Access Journals (Sweden)

    P. Rajendran

    2009-12-01

    Full Text Available An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low-level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on pre-diagnosed database of brain images showed 96% and 93% sensitivity and accuracy respectively.Keywords- Data mining; Image ming; Association rule mining; Medical Imaging; Medical image diagnosis; Classification;

  5. AN IMPROVED TECHNIQUE FOR IDENTIFICATION AND CLASSIFICATION OF BRAIN DISORDER FROM MRI BRAIN IMAGE

    Directory of Open Access Journals (Sweden)

    Finitha Joseph

    2015-11-01

    Full Text Available Medical image processing is developing recently due to its wide applications. An efficient MRI image segmentation is needed at present. In this paper, MRI brain segmentation is done by Semi supervised learning which does not require pathology modelling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks. We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. After this features are extracted by Gray-Level Co-occurrence Matrices (GLCM algorithm and those features are given to Particle Spam Optimization (PSO and finally classification is done by using Library Support Vector Machine (LIBSVM.Thus results are evaluated and proved its efficiency using accuracy.

  6. Motor imagery classification by means of source analysis for brain computer interface applications

    Science.gov (United States)

    Qin, Lei; Ding, Lei; He, Bin

    2004-09-01

    We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

  7. Automated Brain Image classification using Neural Network Approach and Abnormality Analysis

    Directory of Open Access Journals (Sweden)

    P.Muthu Krishnammal

    2015-06-01

    Full Text Available Image segmentation of surgical images plays an important role in diagnosis and analysis the anatomical structure of human body. Magnetic Resonance Imaging (MRI helps in obtaining a structural image of internal parts of the body. This paper aims at developing an automatic support system for stage classification using learning machine and to detect brain Tumor by fuzzy clustering methods to detect the brain Tumor in its early stages and to analyze anatomical structures. The three stages involved are: feature extraction using GLCM and the tumor classification using PNN-RBF network and segmentation using SFCM. Here fast discrete curvelet transformation is used to analyze texture of an image which be used as a base for a Computer Aided Diagnosis (CAD system .The Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. It classifies the stage of Brain Tumor that is benign, malignant or normal automatically. Then the segmentation of the brain abnormality using Spatial FCM and the severity of the tumor is analysed using the number of tumor cells in the detected abnormal region.The proposed method reports promising results in terms of training performance and classification accuracies.

  8. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    Science.gov (United States)

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

  9. Multiple instance learning for classification of dementia in brain MRI.

    Science.gov (United States)

    Tong, Tong; Wolz, Robin; Gao, Qinquan; Hajnal, Joseph V; Rueckert, Daniel

    2013-01-01

    Machine learning techniques have been widely used to support the diagnosis of neurological diseases such as dementia. Recent approaches utilize local intensity patterns within patches to derive voxelwise grading measures of disease. However, the relationships among these patches are usually ignored. In addition, there is some ambiguity in assigning disease labels to the extracted patches. Not all of the patches extracted from patients with dementia are characteristic of morphology associated with disease. In this paper, we propose to use a multiple instance learning method to address the problem of assigning training labels to the patches. In addition, a graph is built for each image to exploit the relationships among these patches, which aids the classification work. We illustrate the proposed approach in an application for the detection of Alzheimer's disease (AD): Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 88.8% between AD patients and healthy controls, and 69.6% between patients with stable Mild Cognitive Impairment (MCI) and progressive MCI. These results compare favourably with state-of-the-art classification methods.

  10. BRAIN TUMOR CLASSIFICATION BASED ON CLUSTERED DISCRETE COSINE TRANSFORM IN COMPRESSED DOMAIN

    Directory of Open Access Journals (Sweden)

    V. Anitha

    2014-01-01

    Full Text Available This study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any classification method is to increase the classification accuracy.The actual need is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this study is to implement Discrete Cosine Transform (DCT on the brain tumor images for various classes. Using DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined feature sets and classified using Support Vector Machine (SVM is proposed in this study. This method of using DCT helps to adjust and vary the performance of classification based on the count of the DCT coefficients taken into account. There exists a good demand for an automatic classification of brain tumors which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of 100% classification accuracy has been achieved. This research is basically aiming and opening a new way of classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile computing and internet based medical diagnosis.

  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...

  12. Classification of Brain Signals in Normal Subjects and Patients with Epilepsy Using Mixture of Experts

    Directory of Open Access Journals (Sweden)

    S. Amoozegar

    2013-06-01

    Full Text Available EEG is one of the most important and common sources for study of brain function and neurological disorders. Automated systems are under study for many years to detect EEG changes. Because of the importance of making correct decision, we are looking for better classification methods for EEG signals. In this paper a smart compound system is used for classifying EEG signals to different groups. Since in each classification the system accuracy of making decision is very important, in this study we look for some methods to improve the accuracy of EEG signals classification. In this paper the use of Mixture of Experts for improving the EEG signals classification of normal subjects and patients with epilepsy is shown and the classification accuracy is evaluated. Decision making was performed in two stages: 1 feature extractions with different methods of eigenvector and 2 Classification using the classifier trained by extracted features. This smart system inputs are formed from composites features that are selected appropriate with network structure. In this study tree methods based on eigenvectors (Minimum Norm, MUSIC, Pisarenko are chosen for the estimation of Power Spectral Density (PSD. After the implementation of ME and train it on composite features, we propose that this technique can reach high classification accuracy. Hence, EEG signals classification of epilepsy patients in different situations and control subjects is available. In this study, Mixture of Experts structure was used for EEG signals classification. Proper performance of Neural Network depends on the size of train and test data. Combination of multiple Neural Networks even without using the probable structure in obtaining weights in classification problem can produce high accuracy in less time, which is important and valuable in the classification point of view.

  13. Efficient multilevel brain tumor segmentation with integrated bayesian model classification.

    Science.gov (United States)

    Corso, J J; Sharon, E; Dube, S; El-Saden, S; Sinha, U; Yuille, A

    2008-05-01

    We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.

  14. 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.

  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. An efficient approach of EEG feature extraction and classification for brain computer interface

    Institute of Scientific and Technical Information of China (English)

    Wu Ting; Yan Guozheng; Yang Banghua

    2009-01-01

    In the study of brain-computer interfaces, a method of feature extraction and classification used for two kinds of imaginations is proposed. It considers Euclidean distance between mean traces recorded from the channels with two kinds of imaginations as a feature, and determines imagination classes using threshold value. It analyzed the background of experiment and theoretical foundation referring to the data sets of BCI 2003, and compared the classification precision with the best result of the competition. The result shows that the method has a high precision and is advantageous for being applied to practical systems.

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

    Science.gov (United States)

    Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

    2015-01-01

    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 from the

  19. Brain research reveals automatic musical memory functions in children.

    Science.gov (United States)

    Huotilainen, Minna; Putkinen, Vesa; Tervaniemi, Mari

    2009-07-01

    Even though music has special meanings and values compared to other sounds, it is nonetheless processed in the brain via partly the same neural networks that are built to process all kinds of sounds. The development of these brain areas depends on the input: on the sounds that a child is exposed to and chooses to attend to. We present two brain research paradigms that can be used to assess the specialization of the brain for musical sounds, and show promising results with these paradigms in a group of young children who have music as their hobby.

  20. Classification of EEG with structural feature dictionaries in a brain computer interface.

    Science.gov (United States)

    Göksu, Fikri; Ince, Nuri Firat; Tadipatri, Vijay Aditya; Tewfik, Ahmed H

    2008-01-01

    We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.

  1. 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.

  2. Feature Extraction from Subband Brain Signals and Its Classification

    Science.gov (United States)

    Mukul, Manoj Kumar; Matsuno, Fumitoshi

    This paper considers both the non-stationarity as well as independence/uncorrelated criteria along with the asymmetry ratio over the electroencephalogram (EEG) signals and proposes a hybrid approach of the signal preprocessing methods before the feature extraction. A filter bank approach of the discrete wavelet transform (DWT) is used to exploit the non-stationary characteristics of the EEG signals and it decomposes the raw EEG signals into the subbands of different center frequencies called as rhythm. A post processing of the selected subband by the AMUSE algorithm (a second order statistics based ICA/BSS algorithm) provides the separating matrix for each class of the movement imagery. In the subband domain the orthogonality as well as orthonormality criteria over the whitening matrix and separating matrix do not come respectively. The human brain has an asymmetrical structure. It has been observed that the ratio between the norms of the left and right class separating matrices should be different for better discrimination between these two classes. The alpha/beta band asymmetry ratio between the separating matrices of the left and right classes will provide the condition to select an appropriate multiplier. So we modify the estimated separating matrix by an appropriate multiplier in order to get the required asymmetry and extend the AMUSE algorithm in the subband domain. The desired subband is further subjected to the updated separating matrix to extract subband sub-components from each class. The extracted subband sub-components sources are further subjected to the feature extraction (power spectral density) step followed by the linear discriminant analysis (LDA).

  3. Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

    CERN Document Server

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

    2014-01-01

    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 ...

  4. Classification of normal and pathological aging processes based on brain MRI morphology measures

    Science.gov (United States)

    Perez-Gonzalez, J. L.; Yanez-Suarez, O.; Medina-Bañuelos, V.

    2014-03-01

    Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.

  5. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.

    Directory of Open Access Journals (Sweden)

    Jun Cheng

    Full Text Available Automatic classification of tissue types of region of interest (ROI plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor in T1-weighted contrast-enhanced MRI (CE-MRI images. Spatial pyramid matching (SPM, which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM, and bag-of-words (BoW model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

  6. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.

    Science.gov (United States)

    Cheng, Jun; Huang, Wei; Cao, Shuangliang; Yang, Ru; Yang, Wei; Yun, Zhaoqiang; Wang, Zhijian; Feng, Qianjin

    2015-01-01

    Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

  7. An Improved Brain Tumour Classification System using Wavelet Transform and Neural Network.

    Science.gov (United States)

    Dhas, DAS; Madheswaran, M

    2015-06-09

    An improved brain tumour classification system using wavelet transform and neural network is developed and presented in this paper. The anisotropic diffusion filter is used for image denoising and the performance of oriented rician noise reducing anisotropic diffusion (ORNRAD) filter is validated. The segmentation of the denoised image is carried out by Fuzzy C-means clustering. The features are extracted using Symlet and Coiflet Wavelet transform and Levenberg Marquardt algorithm based neural network is used to classify the magnetic resonance imaging (MRI) images. This MRI classification technique is tested and analysed with the existing methodologies and its performance is found to be satisfactory with a classification accuracy of 93.02%. The developed system can assist the physicians for classifying the MRI images for better decision-making.

  8. Extreme learning machine-based classification of ADHD using brain structural MRI data.

    Directory of Open Access Journals (Sweden)

    Xiaolong Peng

    Full Text Available BACKGROUND: Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1 Propose an ADHD classification model using the extreme learning machine (ELM algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2 Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM methods and analyze which brain segments are involved in ADHD. METHODS: High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc. were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. RESULTS: We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. CONCLUSION: Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  9. Masking reveals parallel form systems in the visual brain

    National Research Council Canada - National Science Library

    Lo, Yu Tung; Zeki, Semir

    2014-01-01

    ...-selective cells of V1. In this psychophysical study, we undertook to test another hypothesis, namely that the brain's visual form system consists of multiple parallel systems and that complex forms are other than the sum of their parts...

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

    Full Text Available 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.

  12. 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.

  13. 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.

  14. 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.

  15. Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database.

    NARCIS (Netherlands)

    Julia-Sape, M.; Acosta, D.M.; Majos, C.; Moreno-Torres, A.; Wesseling, P.; Acebes, J.J.; Griffiths, J.R.; Arus, C.

    2006-01-01

    OBJECT: The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy. METHODS: The authors retrospectively assessed the correlation between neuroimaging classifications a

  16. Supervised, Multivariate, Whole-brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

    Directory of Open Access Journals (Sweden)

    Eva Janousova

    2016-08-01

    Full Text Available We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or grey matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification in case there is no external validation set to avoid optimistically biasing the results of classification studies.

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

    Science.gov (United States)

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

    2011-11-01

    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.

  18. Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

    Science.gov (United States)

    Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

    2007-08-01

    Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

  19. 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…

  20. What Brain Sciences Reveal about Integrating Theory and Practice

    Science.gov (United States)

    Patton, Michael Quinn

    2014-01-01

    Theory and practice are integrated in the human brain. Situation recognition and response are key to this integration. Scholars of decision making and expertise have found that people with great expertise are more adept at situational recognition and intentional about their decision-making processes. Several interdisciplinary fields of inquiry…

  1. 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…

  2. 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…

  3. 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.

  4. Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

    Directory of Open Access Journals (Sweden)

    Yaki Stern

    2016-12-01

    Full Text Available The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject’s spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200 and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95. The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits. Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

  5. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    C. R. Hema

    2008-01-01

    Full Text Available Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.

  6. Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean

    Science.gov (United States)

    Maze, Guillaume; Mercier, Herlé; Fablet, Ronan; Tandeo, Pierre; Lopez Radcenco, Manuel; Lenca, Philippe; Feucher, Charlène; Le Goff, Clément

    2017-02-01

    A quantitative understanding of the integrated ocean heat content depends on our ability to determine how heat is distributed in the ocean and identify the associated coherent patterns. This study demonstrates how this can be achieved using unsupervised classification of Argo temperature profiles. The classification method used is a Gaussian Mixture Model (GMM) that decomposes the Probability Density Function of a dataset into a weighted sum of Gaussian modes. It is determined that the North Atlantic Argo dataset of temperature profiles contains 8 groups of vertically coherent heat patterns, or classes. Each of the temperature profile classes reveals unique and physically coherent heat distributions along the vertical axis. A key result of this study is that, when mapped in space, each of the 8 classes is found to define an oceanic region, even if no spatial information was used in the model determination. The classification result is independent of the location and time of the ARGO profiles. Two classes show cold anomalies throughout the water column with amplitude decreasing with depth. They are found to be localized in the subpolar gyre and along the poleward flank of the Gulf Stream and North Atlantic Current (NAC). One class has nearly zero anomalies and a large spread throughout the water column. It is found mostly along the NAC. One class has warm anomalies near the surface (50 m) and cold ones below 200 m. It is found in the tropical/equatorial region. The remaining four classes have warm anomalies throughout the water column, one without depth dependance (in the southeastern part of the subtropical gyre), the other three with clear maximums at different depths (100 m, 400 m and 1000 m). These are found along the southern flank of the North Equatorial Current, the western part of the subtropical gyre and over the West European Basin. These results are robust to both the seasonal variability and to method parameters such as the size of the analyzed domain.

  7. Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images

    Directory of Open Access Journals (Sweden)

    V.P. Gladis Pushpa Rathi

    2015-05-01

    Full Text Available Magnetic Resonance Imaging (MRI has become an effective tool for clinical research in recent years and has found itself in applications such as brain tumour detection. In this study, tumor classification using multiple kernel-based probabilistic clustering and deep learning classifier is proposed. The proposed technique consists of three modules, namely segmentation module, feature extraction module and classification module. Initially, the MRI image is pre-processed to make it fit for segmentation and de-noising process is carried out using median filter. Then, pre-processed image is segmented using Multiple Kernel based Probabilistic Clustering (MKPC. Subsequently, features are extracted for every segment based on the shape, texture and intensity. After features extraction, important features will be selected using Linear Discriminant Analysis (LDA for classification purpose. Finally, deep learning classifier is employed for classification into tumor or non-tumor. The proposed technique is evaluated using sensitivity, specificity and accuracy. The proposed technique results are also compared with existing technique which uses Feed-Forward Back Propagation Network (FFBN. The proposed technique achieved an average sensitivity, specificity and accuracy of 0.88, 0.80 and 0.83, respectively with the highest values as about 1, 0.85 and 0.94. Improved results show the efficiency of the proposed technique.

  8. Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data.

    Science.gov (United States)

    Li, Meng; Zhao, Fang; Lee, Jason; Wang, Dong; Kuang, Hui; Tsien, Joe Z

    2015-07-27

    The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed "inter-spike-interval classification-analysis" (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification for dopaminergic neurons or CA1 pyramidal cell subtypes regardless of the brain states from which spike data were collected. Moreover, we show that these ISICA-classified neuron subtypes underlie distinct physiological functions. We demonstrate that the uncovered dopaminergic neuron subtypes encoded distinct aspects of fearful experiences such as valence or value, whereas distinct hippocampal CA1 pyramidal cells responded differentially to ketamine-induced anesthesia. This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, leading to novel insights into circuit dynamics associated with cognitions.

  9. 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...

  10. What body parts reveal about the organization of the brain.

    Science.gov (United States)

    Peelen, Marius V; Caramazza, Alfonso

    2010-11-04

    In this issue of Neuron, Orlov et al. show that the human occipitotemporal cortex contains regions responding preferentially to body part categories, such as upper limbs (hand, elbow), torsos, or lower faces (mouth, chin). This organization may reflect differences in the connectivity of these regions with other brain regions, to support the efficient processing of the different types of information different body parts provide.

  11. 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.

  12. Verbal fluency indicators of malingering in traumatic brain injury: classification accuracy in known groups.

    Science.gov (United States)

    Curtis, Kelly L; Thompson, Laura K; Greve, Kevin W; Bianchini, Kevin J

    2008-09-01

    A known-groups design was used to determine the classification accuracy of verbal fluency variables in detecting Malingered Neurocognitive Dysfunction (MND) in traumatic brain injury (TBI). Participants were 204 TBI and 488 general clinical patients. The Slick et al. (1999) criteria were used to classify the TBI patients into non-MND and MND groups. An educationally corrected FAS Total Correct word T-score proved to be the most accurate of the several verbal fluency indicators examined. Classification accuracy of this variable at specific cutoffs is presented in a cumulative frequency table. This variable accurately differentiated non-MND from MND mild TBI patients but its accuracy was unacceptable in moderate/severe TBI. The clinical application of these findings is discussed.

  13. Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique

    Science.gov (United States)

    Jones, Timothy L.; Byrnes, Tiernan J.; Yang, Guang; Howe, Franklyn A.; Bell, B. Anthony; Barrick, Thomas R.

    2015-01-01

    Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. PMID:25121771

  14. 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

    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...... 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...

  15. 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.

  16. 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.)

  17. Single-trial EEG classification using in-phase average for brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250-550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.

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

    CERN Document Server

    Rusconi, Marco

    2016-01-01

    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...

  19. Brain potentials reveal unconscious translation during foreign-language comprehension.

    Science.gov (United States)

    Thierry, Guillaume; Wu, Yan Jing

    2007-07-24

    Whether the native language of bilingual individuals is active during second-language comprehension is the subject of lively debate. Studies of bilingualism have often used a mix of first- and second-language words, thereby creating an artificial "dual-language" context. Here, using event-related brain potentials, we demonstrate implicit access to the first language when bilinguals read words exclusively in their second language. Chinese-English bilinguals were required to decide whether English words presented in pairs were related in meaning or not; they were unaware of the fact that half of the words concealed a character repetition when translated into Chinese. Whereas the hidden factor failed to affect behavioral performance, it significantly modulated brain potentials in the expected direction, establishing that English words were automatically and unconsciously translated into Chinese. Critically, the same modulation was found in Chinese monolinguals reading the same words in Chinese, i.e., when Chinese character repetition was evident. Finally, we replicated this pattern of results in the auditory modality by using a listening comprehension task. These findings demonstrate that native-language activation is an unconscious correlate of second-language comprehension.

  20. 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…

  1. 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.

  2. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.

    Science.gov (United States)

    Rashid, Barnaly; Arbabshirani, Mohammad R; Damaraju, Eswar; Cetin, Mustafa S; Miller, Robyn; Pearlson, Godfrey D; Calhoun, Vince D

    2016-07-01

    Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.

  3. Classification of brain disease in magnetic resonance images using two-stage local feature fusion

    Science.gov (United States)

    Li, Tao; Li, Wu; Yang, Yehui

    2017-01-01

    Background Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. Methods Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. Results We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. Discussion This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls. PMID:28207873

  4. Boosting Brain Connectome Classification Accuracy in Alzheimer’s disease using Higher-Order Singular Value Decomposition

    Directory of Open Access Journals (Sweden)

    Liang eZhan

    2015-07-01

    Full Text Available Alzheimer's disease (AD is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI, are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer’s disease. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer’s Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer’s disease.

  5. A hypo-status in drug-dependent brain revealed by multi-modal MRI.

    Science.gov (United States)

    Wang, Ze; Suh, Jesse; Duan, Dingna; Darnley, Stefanie; Jing, Ying; Zhang, Jian; O'Brien, Charles; Childress, Anna Rose

    2016-09-22

    Drug addiction is a chronic brain disorder with no proven effective cure. Assessing both structural and functional brain alterations by using multi-modal, rather than purely unimodal imaging techniques, may provide a more comprehensive understanding of the brain mechanisms underlying addiction, which in turn may facilitate future treatment strategies. However, this type of research remains scarce in the literature. We acquired multi-modal magnetic resonance imaging from 20 cocaine-addicted individuals and 19 age-matched controls. Compared with controls, cocaine addicts showed a multi-modal hypo-status with (1) decreased brain tissue volume in the medial and lateral orbitofrontal cortex (OFC); (2) hypo-perfusion in the prefrontal cortex, anterior cingulate cortex, insula, right temporal cortex and dorsolateral prefrontal cortex and (3) reduced irregularity of resting state activity in the OFC and limbic areas, as well as the cingulate, visual and parietal cortices. In the cocaine-addicted brain, larger tissue volume in the medial OFC, anterior cingulate cortex and ventral striatum and smaller insular tissue volume were associated with higher cocaine dependence levels. Decreased perfusion in the amygdala and insula was also correlated with higher cocaine dependence levels. Tissue volume, perfusion, and brain entropy in the insula and prefrontal cortex, all showed a trend of negative correlation with drug craving scores. The three modalities showed voxel-wise correlation in various brain regions, and combining them improved patient versus control brain classification accuracy. These results, for the first time, demonstrate a comprehensive cocaine-dependence and craving-related hypo-status regarding the tissue volume, perfusion and resting brain irregularity in the cocaine-addicted brain. © 2016 Society for the Study of Addiction.

  6. Sleep Deprivation Reveals Altered Brain Perfusion Patterns in Somnambulism.

    Directory of Open Access Journals (Sweden)

    Thien Thanh Dang-Vu

    Full Text Available Despite its high prevalence, relatively little is known about the pathophysiology of somnambulism. Increasing evidence indicates that somnambulism is associated with functional abnormalities during wakefulness and that sleep deprivation constitutes an important drive that facilitates sleepwalking in predisposed patients. Here, we studied the neural mechanisms associated with somnambulism using Single Photon Emission Computed Tomography (SPECT with 99mTc-Ethylene Cysteinate Dimer (ECD, during wakefulness and after sleep deprivation.Ten adult sleepwalkers and twelve controls with normal sleep were scanned using 99mTc-ECD SPECT in morning wakefulness after a full night of sleep. Eight of the sleepwalkers and nine of the controls were also scanned during wakefulness after a night of total sleep deprivation. Between-group comparisons of regional cerebral blood flow (rCBF were performed to characterize brain activity patterns during wakefulness in sleepwalkers.During wakefulness following a night of total sleep deprivation, rCBF was decreased bilaterally in the inferior temporal gyrus in sleepwalkers compared to controls.Functional neural abnormalities can be observed during wakefulness in somnambulism, particularly after sleep deprivation and in the inferior temporal cortex. Sleep deprivation thus not only facilitates the occurrence of sleepwalking episodes, but also uncovers patterns of neural dysfunction that characterize sleepwalkers during wakefulness.

  7. 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

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

    Science.gov (United States)

    Kocevar, Gabriel; Stamile, Claudio; Hannoun, Salem; Cotton, François; Vukusic, Sandra; Durand-Dubief, Françoise; Sappey-Marinier, Dominique

    2016-01-01

    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.

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

    Science.gov (United States)

    Kocevar, Gabriel; Stamile, Claudio; Hannoun, Salem; Cotton, François; Vukusic, Sandra; Durand-Dubief, Françoise; Sappey-Marinier, Dominique

    2016-01-01

    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. PMID:27826224

  10. 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.

  11. Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling.

    Science.gov (United States)

    Boddy, A M; McGowen, M R; Sherwood, C C; Grossman, L I; Goodman, M; Wildman, D E

    2012-05-01

    There is a well-established allometric relationship between brain and body mass in mammals. Deviation of relatively increased brain size from this pattern appears to coincide with enhanced cognitive abilities. To examine whether there is a phylogenetic structure to such episodes of changes in encephalization across mammals, we used phylogenetic techniques to analyse brain mass, body mass and encephalization quotient (EQ) among 630 extant mammalian species. Among all mammals, anthropoid primates and odontocete cetaceans have significantly greater variance in EQ, suggesting that evolutionary constraints that result in a strict correlation between brain and body mass have independently become relaxed. Moreover, ancestral state reconstructions of absolute brain mass, body mass and EQ revealed patterns of increase and decrease in EQ within anthropoid primates and cetaceans. We propose both neutral drift and selective factors may have played a role in the evolution of brain-body allometry.

  12. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    Science.gov (United States)

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  13. Proteomic signatures reveal a dualistic and clinically relevant classification of anal canal carcinoma.

    Science.gov (United States)

    Herfs, Michael; Longuespée, Rémi; Quick, Charles M; Roncarati, Patrick; Suarez-Carmona, Meggy; Hubert, Pascale; Lebeau, Alizée; Bruyere, Diane; Mazzucchelli, Gabriel; Smargiasso, Nicolas; Baiwir, Dominique; Lai, Keith; Dunn, Andrew; Obregon, Fabiola; Yang, Eric J; Pauw, Edwin De; Crum, Christopher P; Delvenne, Philippe

    2017-03-01

    Aetiologically linked to HPV infection, malignancies of the anal canal have substantially increased in incidence over the last 20 years. Although most anal squamous cell carcinomas (SCCs) respond well to chemoradiotherapy, about 30% of patients experience a poor outcome, for undetermined reasons. Despite cumulative efforts for discovering independent predictors of overall survival, both nodal status and tumour size are still the only reliable factors predicting patient outcome. Recent efforts have revealed that the biology of HPV-related lesions in the cervix is strongly linked to the originally infected cell population. To address the hypothesis that topography also influences both gene expression profile and behaviour of anal (pre)neoplastic lesions, we correlated both proteomic signatures and clinicopathological features of tumours arising from two distinct portions of the anal canal: the lower part (squamous zone) and the more proximal anal transitional zone. Although microdissected cancer cells appeared indistinguishable by morphology (squamous phenotype), unsupervised clustering analysis of the whole proteome significantly highlighted the heterogeneity that exists within anal canal tumours. More importantly, two region-specific subtypes of SCC were revealed. The expression profile (sensitivity/specificity) of several selected biomarkers (keratin filaments) further confirmed the subclassification of anal (pre)cancers based on their cellular origin. Less commonly detected compared to their counterparts located in the squamous mucosa, SCCs originating in the transitional zone more frequently displayed a poor or basaloid differentiation, and were significantly correlated with reduced disease-free and overall survivals. Taken together, we present direct evidence that anal canal SCC comprises two distinct entities with different cells of origin, proteomic signatures, and survival rates. This study forms the basis for a dualistic classification of anal carcinoma

  14. Lateralized electrical brain activity reveals covert attention allocation during speaking.

    Science.gov (United States)

    Rommers, Joost; Meyer, Antje S; Praamstra, Peter

    2017-01-27

    Speakers usually begin to speak while only part of the utterance has been planned. Earlier work has shown that speech planning processes are reflected in speakers' eye movements as they describe visually presented objects. However, to-be-named objects can be processed to some extent before they have been fixated upon, presumably because attention can be allocated to objects covertly, without moving the eyes. The present study investigated whether EEG could track speakers' covert attention allocation as they produced short utterances to describe pairs of objects (e.g., "dog and chair"). The processing difficulty of each object was varied by presenting it in upright orientation (easy) or in upside down orientation (difficult). Background squares flickered at different frequencies in order to elicit steady-state visual evoked potentials (SSVEPs). The N2pc component, associated with the focusing of attention on an item, was detectable not only prior to speech onset, but also during speaking. The time course of the N2pc showed that attention shifted to each object in the order of mention prior to speech onset. Furthermore, greater processing difficulty increased the time speakers spent attending to each object. This demonstrates that the N2pc can track covert attention allocation in a naming task. In addition, an effect of processing difficulty at around 200-350ms after stimulus onset revealed early attention allocation to the second to-be-named object. The flickering backgrounds elicited SSVEPs, but SSVEP amplitude was not influenced by processing difficulty. These results help complete the picture of the coordination of visual information uptake and motor output during speaking. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

    Science.gov (United States)

    Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

    2016-03-01

    Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

  16. Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

    Directory of Open Access Journals (Sweden)

    Nikolay V. Manyakov

    2011-01-01

    Full Text Available We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI on a group of amyotrophic lateral sclerosis (ALS, middle cerebral artery (MCA stroke, and subarachnoid hemorrhage (SAH patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

  17. 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.

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

    Science.gov (United States)

    Fissler, Patrick; Kolassa, Iris-Tatjana; Schrader, Claudia

    2015-01-01

    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. Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

    Directory of Open Access Journals (Sweden)

    Lawrence A. Farwell

    2014-12-01

    Full Text Available A classification concealed information test (CIT used the brain fingerprinting method of applying P300 event-related potential (ERP in detecting information that is 1 acquired in real life and 2 unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to 3 types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified. We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP? versus comparison CIT (Does a probe produce a larger ERP than an irrelevant? using P300 plus the late negative component (LNP; together, P300-MERMER. Comparison CIT produced a significantly higher error rate (20% and lower statistical confidences -- mean 67%; information-absent mean was 28.9%, less than chance (50%. We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational

  20. An atlas-based fuzzy connectedness method for automatic tissue classification in brain MRI

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

    A framework incorporating a subject-registered atlas into the fuzzy connectedness (FC) method is proposed for the automatic tissue classification of 3D images of brain MRI. The pre-labeled atlas is first registered onto the subject to provide an initial approximate segmentation. The initial segmentation is used to estimate the intensity histograms of gray matter and white matter. Based on the estimated intensity histograms, multiple seed voxels are assigned to each tissue automatically. The normalized intensity histograms are utilized in the FC method as the intensity probability density function (PDF) directly. Relative fuzzy connectedness technique is adopted in the final classification of gray matter and white matter. Experimental results based on the 20 data sets from IBSR are included, as well as comparisons of the performance of our method with that of other published methods. This method is fully automatic and operator-independent. Therefore, it is expected to find wide applications, such as 3D visualization, radiation therapy planning, and medical database construction.

  1. Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data.

    Science.gov (United States)

    Chen, Wei; Wheeler, Krista K; Lin, Simon; Huang, Yungui; Xiang, Huiyun

    2016-04-01

    One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a "Learn-As-You-Go" machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. We examined the narrative text of traumatic brain injuries (TBIs) in the National Electronic Injury Surveillance System (NEISS) and classified TBIs into sport and non-sport categories. Our results suggest that the DUALIST "Learn-As-You-Go" program, which features a user-friendly online interface, is effective in injury narrative classification. In our study, the time frame to classify tens of thousands of narratives was reduced from a few days to minutes after approximately sixty minutes of training. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. 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.

  3. 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

    1993-01-01

    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 catego

  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 w

  5. 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

  6. Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

    Science.gov (United States)

    Zimmer, Veronika A; Glocker, Ben; Hahner, Nadine; Eixarch, Elisenda; Sanroma, Gerard; Gratacós, Eduard; Rueckert, Daniel; González Ballester, Miguel Ángel; Piella, Gemma

    2017-08-09

    It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defined distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. 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.

  8. The impact of image dynamic range on texture classification of brain white matter

    Directory of Open Access Journals (Sweden)

    de Certaines Jacques D

    2008-12-01

    Full Text Available Abstract Background The Greylevel Cooccurrence Matrix method (COM is one of the most promising methods used in Texture Analysis of Magnetic Resonance Images. This method provides statistical information about the spatial distribution of greylevels in the image which can be used for classification of different tissue regions. Optimizing the size and complexity of the COM has the potential to enhance the reliability of Texture Analysis results. In this paper we investigate the effect of matrix size and calculation approach on the ability of COM to discriminate between peritumoral white matter and other white matter regions. Method MR images were obtained from patients with histologically confirmed brain glioblastoma using MRI at 3-T giving isotropic resolution of 1 mm3. Three Regions of Interest (ROI were outlined in visually normal white matter on three image slices based on relative distance from the tumor: one peritumoral white matter region and two distant white matter regions on both hemispheres. Volumes of Interest (VOI were composed from the three slices. Two different calculation approaches for COM were used: i Classical approach (CCOM on each individual ROI, and ii Three Dimensional approach (3DCOM calculated on VOIs. For, each calculation approach five dynamic ranges (number of greylevels N were investigated (N = 16, 32, 64, 128, and 256. Results Classification showed that peritumoral white matter always represents a homogenous class, separate from other white matter, regardless of the value of N or the calculation approach used. The best test measures (sensitivity and specificity for average CCOM were obtained for N = 128. These measures were also optimal for 3DCOM with N = 128, which additionally showed a balanced tradeoff between the measures. Conclusion We conclude that the dynamic range used for COM calculation significantly influences the classification results for identical samples. In order to obtain more reliable classification

  9. 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

    2017-01-18

    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.

  10. 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

  11. 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...... of emotive images rated by the patients off the DBS. We then searched for sites in the brain that had significant correlation of the changes of blood flow with the emotional impact rated by the patients. The results indicate a significant link between the emotional impact when patients are not stimulated...

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

    Science.gov (United States)

    Shu, Ni; Liu, Yaou; Duan, Yunyun; Li, Kuncheng

    2015-01-01

    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.

  13. 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.

  14. A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals.

    Directory of Open Access Journals (Sweden)

    Mohit eRana

    2013-10-01

    Full Text Available There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.* M. Rana and N. Gupta are equally contributing authors.

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

    Energy Technology Data Exchange (ETDEWEB)

    Xyda, Argyro, E-mail: argyro.xyda@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Department of Radialogy, University Hospital of Heraklion, Voutes, 71110 Heraklion, Crete (Greece); Haberland, Ulrike, E-mail: ulrike.haberland@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Klotz, Ernst, E-mail: ernst.klotz@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Jung, Klaus, E-mail: kjung1@uni-goettingen.de [Department of Medical Statistics, Georg-August University, Humboldtallee 32, 37073 Goettingen (Germany); Bock, Hans Christoph, E-mail: cbock@gmx.de [Department of Neurosurgery, Johannes Gutenberg University Hospital of Mainz, Langenbeckstraße 1, 55101 Mainz (Germany); Schramm, Ramona, E-mail: ramona.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Knauth, Michael, E-mail: michael.knauth@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Schramm, Peter, E-mail: p.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany)

    2012-12-15

    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.

  16. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

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    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  17. Brain-heart interactions reveal consciousness in non-communicating patients.

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    Raimondo, Federico; Rohaut, Benjamin; Demertzi, Athena; Valente, Melanie; Engemann, Denis; Salti, Moti; Fernandez Slezak, Diego; Naccache, Lionel; Sitt, Jacobo D

    2017-09-11

    Objective We here aimed at characterizing heart-brain interactions in patients with disorders of consciousness. We tested how this information impacts data-driven classification between unresponsive and minimally conscious patients. Methods A cohort of 127 patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS, n=70) and minimally conscious state (MCS, n=57) were presented with the 'Local-Global' auditory oddball paradigm, which distinguishes two levels of processing: short-term deviation of local auditory regularities and global long-term rule violations. In addition to previously validated markers of consciousness extracted from electroencephalograms (EEG), we computed autonomic cardiac markers, such as heart rate and variability (HR, HRV), and cardiac cycle phase-shifts triggered by the processing of the auditory stimuli. Results HR and HRV were similar in patients across groups. The cardiac cycle was not sensitive to the processing of local regularities in either the VS/UWS or MCS patients. In contrast, global regularities induced a phase-shift of the cardiac cycle exclusively in the MCS group. The interval between the auditory stimulation and the following R-peak was significantly shortened in MCS when the auditory rule was violated. When the information of the cardiac cycle modulations and other consciousness-related EEG markers were combined, single-patient classification performance was enhanced compared to classification with solely EEG markers. Interpretation Our work shows a link between residual cognitive processing and the modulation of autonomic somatic markers. These results open a new window to evaluate patients with disorders of consciousness via the embodied paradigm, according to which body-brain functions contribute to a holistic approach to conscious processing. This article is protected by copyright. All rights reserved. © 2017 American Neurological Association.

  18. Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease.

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    Natalie A Twine

    Full Text Available Recent studies strongly indicate that aberrations in the control of gene expression might contribute to the initiation and progression of Alzheimer's disease (AD. In particular, alternative splicing has been suggested to play a role in spontaneous cases of AD. Previous transcriptome profiling of AD models and patient samples using microarrays delivered conflicting results. This study provides, for the first time, transcriptomic analysis for distinct regions of the AD brain using RNA-Seq next-generation sequencing technology. Illumina RNA-Seq analysis was used to survey transcriptome profiles from total brain, frontal and temporal lobe of healthy and AD post-mortem tissue. We quantified gene expression levels, splicing isoforms and alternative transcript start sites. Gene Ontology term enrichment analysis revealed an overrepresentation of genes associated with a neuron's cytological structure and synapse function in AD brain samples. Analysis of the temporal lobe with the Cufflinks tool revealed that transcriptional isoforms of the apolipoprotein E gene, APOE-001, -002 and -005, are under the control of different promoters in normal and AD brain tissue. We also observed differing expression levels of APOE-001 and -002 splice variants in the AD temporal lobe. Our results indicate that alternative splicing and promoter usage of the APOE gene in AD brain tissue might reflect the progression of neurodegeneration.

  19. Characterization of traumatic brain injury in human brains reveals distinct cellular and molecular changes in contusion and pericontusion.

    Science.gov (United States)

    Harish, Gangadharappa; Mahadevan, Anita; Pruthi, Nupur; Sreenivasamurthy, Sreelakshmi K; Puttamallesh, Vinuth N; Keshava Prasad, Thottethodi Subrahmanya; Shankar, Susarla Krishna; Srinivas Bharath, Muchukunte Mukunda

    2015-07-01

    Traumatic brain injury (TBI) contributes to fatalities and neurological disabilities worldwide. While primary injury causes immediate damage, secondary events contribute to long-term neurological defects. Contusions (Ct) are primary injuries correlated with poor clinical prognosis, and can expand leading to delayed neurological deterioration. Pericontusion (PC) (penumbra), the region surrounding Ct, can also expand with edema, increased intracranial pressure, ischemia, and poor clinical outcome. Analysis of Ct and PC can therefore assist in understanding the pathobiology of TBI and its management. This study on human TBI brains noted extensive neuronal, astroglial and inflammatory changes, alterations in mitochondrial, synaptic and oxidative markers, and associated proteomic profile, with distinct differences in Ct and PC. While Ct displayed petechial hemorrhages, thrombosis, inflammation, neuronal pyknosis, and astrogliosis, PC revealed edema, vacuolation of neuropil, axonal loss, and dystrophic changes. Proteomic analysis demonstrated altered immune response, synaptic, and mitochondrial dysfunction, among others, in Ct, while PC displayed altered regulation of neurogenesis and cytoskeletal architecture, among others. TBI brains displayed oxidative damage, glutathione depletion, mitochondrial dysfunction, and loss of synaptic proteins, with these changes being more profound in Ct. We suggest that analysis of markers specific to Ct and PC may be valuable in the evaluation of TBI pathobiology and therapeutics. We have characterized the primary injury in human traumatic brain injury (TBI). Contusions (Ct) - the injury core displayed hemorrhages, inflammation, and astrogliosis, while the surrounding pericontusion (PC) revealed edema, vacuolation, microglial activation, axonal loss, and dystrophy. Proteomic analysis demonstrated altered immune response, synaptic and mitochondrial dysfunction in Ct, and altered regulation of neurogenesis and cytoskeletal architecture in

  20. The retinal projectome reveals brain-area-specific visual representations generated by ganglion cell diversity.

    Science.gov (United States)

    Robles, Estuardo; Laurell, Eva; Baier, Herwig

    2014-09-22

    Visual information is transmitted to the vertebrate brain exclusively via the axons of retinal ganglion cells (RGCs). The functional diversity of RGCs generates multiple representations of the visual environment that are transmitted to several brain areas. However, in no vertebrate species has a complete wiring diagram of RGC axonal projections been constructed. We employed sparse genetic labeling and in vivo imaging of the larval zebrafish to generate a cellular-resolution map of projections from the retina to the brain. Our data define 20 stereotyped axonal projection patterns, the majority of which innervate multiple brain areas. Morphometric analysis of pre- and postsynaptic RGC structure revealed more than 50 structural RGC types with unique combinations of dendritic and axonal morphologies, exceeding current estimates of RGC diversity in vertebrates. These single-cell projection mapping data indicate that specific projection patterns are nonuniformly specified in the retina to generate retinotopically biased visual maps throughout the brain. The retinal projectome also successfully predicted a functional subdivision of the pretectum. Our data indicate that RGC projection patterns are precisely coordinated to generate brain-area-specific visual representations originating from RGCs with distinct dendritic morphologies and topographic distributions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain.

    Science.gov (United States)

    Chou, Shen-Ju; Wang, Chindi; Sintupisut, Nardnisa; Niou, Zhen-Xian; Lin, Chih-Hsu; Li, Ker-Chau; Yeang, Chen-Hsiang

    2016-01-20

    Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns. Second, standard tools of expression analysis such as matrix factorization can capture those patterns but do not explicitly incorporate spatial dependency. To overcome those limitations, we proposed a computational method to detect recurrent patterns in the spatial-temporal gene expression data of developing mouse brains. We demonstrated that regional distinction in brain development could be revealed by localized gene expression patterns. The patterns expressed in the forebrain, medullary and pontomedullary, and basal ganglia are enriched with genes involved in forebrain development, locomotory behavior, and dopamine metabolism respectively. In addition, the timing of global gene expression patterns reflects the general trends of molecular events in mouse brain development. Furthermore, we validated functional implications of the inferred patterns by showing genes sharing similar spatial-temporal expression patterns with Lhx2 exhibited differential expression in the embryonic forebrains of Lhx2 mutant mice. These analysis outcomes confirm the utility of recurrent expression patterns in studying brain development.

  2. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

    Directory of Open Access Journals (Sweden)

    N. Sauwen

    2016-01-01

    Full Text Available Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs, as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI, diffusion-weighted imaging (DWI and magnetic resonance spectroscopic imaging (MRSI have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

  3. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification.

    Science.gov (United States)

    Sun, Yu; Lim, Julian; Meng, Jianjun; Kwok, Kenneth; Thakor, Nitish; Bezerianos, Anastasios

    2014-10-01

    Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.

  4. 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.

  5. Evolving the stimulus to fit the brain: a genetic algorithm reveals the brain's feature priorities in visual search.

    Science.gov (United States)

    Van der Burg, Erik; Cass, John; Theeuwes, Jan; Alais, David

    2015-02-06

    How does the brain find objects in cluttered visual environments? For decades researchers have employed the classic visual search paradigm to answer this question using factorial designs. Although such approaches have yielded important information, they represent only a tiny fraction of the possible parametric space. Here we use a novel approach, by using a genetic algorithm (GA) to discover the way the brain solves visual search in complex environments, free from experimenter bias. Participants searched a series of complex displays, and those supporting fastest search were selected to reproduce (survival of the fittest). Their display properties (genes) were crossed and combined to create a new generation of "evolved" displays. Displays evolved quickly over generations towards a stable, efficiently searched array. Color properties evolved first, followed by orientation. The evolved displays also contained spatial patterns suggesting a coarse-to-fine search strategy. We argue that this behavioral performance-driven GA reveals the way the brain selects information during visual search in complex environments. We anticipate that our approach can be adapted to a variety of sensory and cognitive questions that have proven too intractable for factorial designs. © 2015 ARVO.

  6. Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.

    Science.gov (United States)

    Farquhar, J; Hill, N J

    2013-04-01

    Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.

  7. Metabolomics Reveals Metabolic Alterations by Intrauterine Growth Restriction in the Fetal Rabbit Brain

    Science.gov (United States)

    van Vliet, Erwin; Eixarch, Elisenda; Illa, Miriam; Arbat-Plana, Ariadna; González-Tendero, Anna; Hogberg, Helena T.; Zhao, Liang; Hartung, Thomas; Gratacos, Eduard

    2013-01-01

    Background 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. Methodology/Principal Findings 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. Conclusions IUGR leads to metabolic alterations in the fetal rabbit brain, involving neuronal viability, energy metabolism, amino acid levels, fatty

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

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    Erwin van Vliet

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS: IUGR leads to metabolic alterations in the fetal rabbit brain, involving neuronal viability, energy metabolism, amino

  9. Whole-brain circuit dissection in free-moving animals reveals cell-specific mesocorticolimbic networks

    Science.gov (United States)

    Michaelides, Michael; Anderson, Sarah Ann R.; Ananth, Mala; Smirnov, Denis; Thanos, Panayotis K.; Neumaier, John F.; Wang, Gene-Jack; Volkow, Nora D.; Hurd, Yasmin L.

    2013-01-01

    The ability to map the functional connectivity of discrete cell types in the intact mammalian brain during behavior is crucial for advancing our understanding of brain function in normal and disease states. We combined designer receptor exclusively activated by designer drug (DREADD) technology and behavioral imaging with μPET and [18F]fluorodeoxyglucose (FDG) to generate whole-brain metabolic maps of cell-specific functional circuits during the awake, freely moving state. We have termed this approach DREADD-assisted metabolic mapping (DREAMM) and documented its ability in rats to map whole-brain functional anatomy. We applied this strategy to evaluating changes in the brain associated with inhibition of prodynorphin-expressing (Pdyn-expressing) and of proenkephalin-expressing (Penk-expressing) medium spiny neurons (MSNs) of the nucleus accumbens shell (NAcSh), which have been implicated in neuropsychiatric disorders. DREAMM revealed discrete behavioral manifestations and concurrent engagement of distinct corticolimbic networks associated with dysregulation of Pdyn and Penk in MSNs of the NAcSh. Furthermore, distinct neuronal networks were recruited in awake versus anesthetized conditions. These data demonstrate that DREAMM is a highly sensitive, molecular, high-resolution quantitative imaging approach. PMID:24231358

  10. Interspecies activity correlations reveal functional correspondence 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

    2012-02-05

    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. For cases in which 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 assessed similarities in sensory-driven functional magnetic resonance imaging responses between monkey (Macaca mulatta) and human brain areas by temporal correlation. Using natural vision data, we revealed 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 framework for evaluating changes in functional architecture is crucial to building more accurate evolutionary models.

  11. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

    Science.gov (United States)

    Cuadra, Meritxell Bach; Cammoun, Leila; Butz, Torsten; Cuisenaire, Olivier; Thiran, Jean-Philippe

    2005-12-01

    This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

  12. 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.

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

    Science.gov (United States)

    Qureshi, Nauman Khalid; Noori, Farzan Majeed; Hong, Keum-Shik

    2016-01-01

    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. Matched signal detection on graphs: Theory and application to brain imaging data classification.

    Science.gov (United States)

    Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A; Fakhri, Georges E; Lu, Yue M; Li, Quanzheng

    2016-01-15

    Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data.

  15. Classification images reveal decision variables and strategies in forced choice tasks.

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    Pritchett, Lisa M; Murray, Richard F

    2015-06-09

    Despite decades of research, there is still uncertainty about how people make simple decisions about perceptual stimuli. Most theories assume that perceptual decisions are based on decision variables, which are internal variables that encode task-relevant information. However, decision variables are usually considered to be theoretical constructs that cannot be measured directly, and this often makes it difficult to test theories of perceptual decision making. Here we show how to measure decision variables on individual trials, and we use these measurements to test theories of perceptual decision making more directly than has previously been possible. We measure classification images, which are estimates of templates that observers use to extract information from stimuli. We then calculate the dot product of these classification images with the stimuli to estimate observers' decision variables. Finally, we reconstruct each observer's "decision space," a map that shows the probability of the observer's responses for all values of the decision variables. We use this method to examine decision strategies in two-alternative forced choice (2AFC) tasks, for which there are several competing models. In one experiment, the resulting decision spaces support the difference model, a classic theory of 2AFC decisions. In a second experiment, we find unexpected decision spaces that are not predicted by standard models of 2AFC decisions, and that suggest intrinsic uncertainty or soft thresholding. These experiments give new evidence regarding observers' strategies in 2AFC tasks, and they show how measuring decision variables can answer long-standing questions about perceptual decision making.

  16. Revealing pathologies in the liquid crystalline structures of the brain by polarimetric studies (Presentation Recording)

    Science.gov (United States)

    Bakhshetyan, Karen; Melkonyan, Gurgen G.; Galstian, Tigran V.; Saghatelyan, Armen

    2015-10-01

    Natural or "self" alignment of molecular complexes in living tissue represents many similarities with liquid crystals (LC), which are anisotropic liquids. The orientational characteristics of those complexes may be related to many important functional parameters and their study may reveal important pathologies. The know-how, accumulated thanks to the study of LC materials, may thus be used to this end. One of the traditionally used methods, to characterize those materials, is the polarized light imaging (PLI) that allows for label-free analysis of anisotropic structures in the brain tissue and can be used, for example, for the analysis of myelinated fiber bundles. In the current work, we first attempted to apply the PLI on the mouse histological brain sections to create a map of anisotropic structures using cross-polarizer transmission light. Then we implemented the PLI for comparative study of histological sections of human postmortem brain samples under normal and pathological conditions, such as Parkinson's disease (PD). Imaging the coronal, sagittal and horizontal sections of mouse brain allowed us to create a false color-coded fiber orientation map under polarized light. In human brain datasets for both control and PD groups we measured the pixel intensities in myelin-rich subregions of internal capsule and normalized these to non-myelinated background signal from putamen and caudate nucleus. Quantification of intensities revealed a statistically significant reduction of fiber intensity of PD compared to control subjects (2.801 +/- 0.303 and 3.724 +/- 0.07 respectively; *p < 0.05). Our study confirms the validity of PLI method for visualizing myelinated axonal fibers. This relatively simple technique can become a promising tool for study of neurodegenerative diseases where labeling-free imaging is an important benefit.

  17. Revealing time-unlocked brain activity from MEG measurements by common waveform estimation.

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    Yusuke Takeda

    Full Text Available Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus onsets. Recently, we proposed a method (Common Waveform Estimation, CWE that could extract such brain activities from magnetoencephalography (MEG or electroencephalography (EEG measurements. CWE estimates spatiotemporal MEG/EEG patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For this purpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE to the MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties, specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks, and calculated the powers of the extracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any hypothesis in advance.

  18. Integrated Classification of Prostate Cancer Reveals a Novel Luminal Subtype with Poor Outcome.

    Science.gov (United States)

    You, Sungyong; Knudsen, Beatrice S; Erho, Nicholas; Alshalalfa, Mohammed; Takhar, Mandeep; Al-Deen Ashab, Hussam; Davicioni, Elai; Karnes, R Jeffrey; Klein, Eric A; Den, Robert B; Ross, Ashley E; Schaeffer, Edward M; Garraway, Isla P; Kim, Jayoung; Freeman, Michael R

    2016-09-01

    Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1-3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948-58. ©2016 AACR.

  19. Traumatic brain injury reveals novel cell lineage relationships within the subventricular zone

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    Gretchen M. Thomsen

    2014-07-01

    Full Text Available The acute response of the rodent subventricular zone (SVZ to traumatic brain injury (TBI involves a physical expansion through increased cell proliferation. However, the cellular underpinnings of these changes are not well understood. Our analyses have revealed that there are two distinct transit-amplifying cell populations that respond in opposite ways to injury. Mash1+ transit-amplifying cells are the primary SVZ cell type that is stimulated to divide following TBI. In contrast, the EGFR+ population, which has been considered to be a functionally equivalent progenitor population to Mash1+ cells in the uninjured brain, becomes significantly less proliferative after injury. Although normally quiescent GFAP+ stem cells are stimulated to divide in SVZ ablation models, we found that the GFAP+ stem cells do not divide more after TBI. We found, instead, that TBI results in increased numbers of GFAP+/EGFR+ stem cells via non-proliferative means—potentially through the dedifferentiation of progenitor cells. EGFR+ progenitors from injured brains only were competent to revert to a stem cell state following brief exposure to growth factors. Thus, our results demonstrate previously unknown changes in lineage relationships that differ from conventional models and likely reflect an adaptive response of the SVZ to maintain endogenous brain repair after TBI.

  20. 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.)

  1. 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.

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

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    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. Insights into the metabolic response to traumatic brain injury as revealed by 13C NMR spectroscopy.

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    Brenda eBartnik-Olson

    2013-10-01

    Full Text Available The present review highlights critical issues related to cerebral metabolism following traumatic brain injury (TBI and the use of 13C labeled substrates and nuclear magnetic resonance (NMR spectroscopy to study these changes. First we address some pathophysiologic factors contributing to metabolic dysfunction following TBI. We then examine how 13C NMR spectroscopy strategies have been used to investigate energy metabolism, neurotransmission, the intracellular redox state, and neuroglial compartmentation following injury. 13C NMR spectroscopy studies of brain extracts from animal models of TBI have revealed enhanced glycolytic production of lactate, evidence of pentose phosphate pathway (PPP activation, and alterations in neuronal and astrocyte oxidative metabolism that are dependent on injury severity. Differential incorporation of label into glutamate and glutamine from 13C labeled glucose or acetate also suggest TBI-induced adaptations to the glutamate-glutamine cycle.

  5. BRAIN DYSFUNCTION OF PATIENTS WITH QIGONG INDUCED MENTAL DISORDER REVEALED BY EVOKED POTENTIALS RECORDING

    Institute of Scientific and Technical Information of China (English)

    LU Yingzhi; ZONG Wenbin; CHEN Xingshi

    2003-01-01

    Objective: In order to investigate the brain function of patients with Qigong induced mental disorder (QIMD), this study was carried out. Methods: Four kinds of evoked potentials, including contingent negative variation (CNV), auditory evoked potentials (AEP), visual evoked potentials (VEP), and somatosensory evoked potentials (SEP), were recorded from 12 patients with Qigong induced mental disorder.Comparison of their evoked potentials with the data from some normal controls was made. Results: The results revealed that there were 3 kinds of abnormal changes in evoked potentials of patients with QIMD that is latency prolongation, amplitude increase and amplitude decrease, as compared with normal controls. Conclusion: Brain dysfunction of patients with QIMD was confirmed. Its biological mechanism needs further studying.

  6. Source-based morphometry reveals distinct patterns of aberrant brain volume in delusional infestation.

    Science.gov (United States)

    Wolf, Robert Ch; Huber, Markus; Lepping, Peter; Sambataro, Fabio; Depping, Malte S; Karner, Martin; Freudenmann, Roland W

    2014-01-03

    Little is known about the neural correlates of delusional infestation (DI), the delusional belief to be infested with pathogens. So far, evidence comes mainly from case reports and case series. We investigated brain morphology in 16 DI patients and 16 healthy controls using structural magnetic resonance imaging and a multivariate data analysis technique, i.e. source-based morphometry (SBM). In addition, we explored differences in brain structure in patient subgroups based on disease aetiology. SBM revealed two patterns exhibiting significantly (pdisorder) and "organic" DI (DI due to a medical condition). In contrast, aberrant white matter volume was only confirmed for the "organic" DI patient subgroup. These results suggest prefrontal, temporal, parietal, insular, thalamic and striatal dysfunction underlying DI. Moreover, the data suggest that aetiologically distinct presentations of DI share similar patterns of abnormal grey matter volume, whereas aberrant white matter volume appears to be restricted to organic cases. © 2013.

  7. Computerized three-dimensional reconstruction reveals cerebrovascular regulatory subregions in rat brain stem.

    Science.gov (United States)

    Underwood, M D; Arango, V; Smith, R W; Bakalian, M J; Mann, J J

    1993-09-01

    Three-dimensional wireframe reconstructions were used to examine the relationship between the anatomical localization of electrode sites and the cerebrovascular response which was elicited by electrical stimulation of the dorsal raphe nucleus (DRN). Reconstructions of the rat brain and DRN were done from atlas plates and from Nissl-stained coronal sections (100-micron increments). Data points were entered and three-dimensional reconstructions were performed using commercially available software and a personal computer. Display of the entire brain yielded views which obscured visualization of the DRN. The data file was edited to reduce the number of contours without affecting the display resolution of the DRN. Selective display of the DRN and electronic rotation from the coronal to a sagittal view revealed a functional organization of the cerebral blood flow responses which was not apparent in two-dimensional coronal sections.

  8. The organization of thinking: what functional brain imaging reveals about the neuroarchitecture of complex cognition.

    Science.gov (United States)

    Just, Marcel Adam; Varma, Sashank

    2007-09-01

    Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network's critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.

  9. Intact-Brain Analyses Reveal Distinct Information Carried by SNc Dopamine Subcircuits.

    Science.gov (United States)

    Lerner, Talia N; Shilyansky, Carrie; Davidson, Thomas J; Evans, Kathryn E; Beier, Kevin T; Zalocusky, Kelly A; Crow, Ailey K; Malenka, Robert C; Luo, Liqun; Tomer, Raju; Deisseroth, Karl

    2015-07-30

    Recent progress in understanding the diversity of midbrain dopamine neurons has highlighted the importance--and the challenges--of defining mammalian neuronal cell types. Although neurons may be best categorized using inclusive criteria spanning biophysical properties, wiring of inputs, wiring of outputs, and activity during behavior, linking all of these measurements to cell types within the intact brains of living mammals has been difficult. Here, using an array of intact-brain circuit interrogation tools, including CLARITY, COLM, optogenetics, viral tracing, and fiber photometry, we explore the diversity of dopamine neurons within the substantia nigra pars compacta (SNc). We identify two parallel nigrostriatal dopamine neuron subpopulations differing in biophysical properties, input wiring, output wiring to dorsomedial striatum (DMS) versus dorsolateral striatum (DLS), and natural activity patterns during free behavior. Our results reveal independently operating nigrostriatal information streams, with implications for understanding the logic of dopaminergic feedback circuits and the diversity of mammalian neuronal cell types.

  10. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

    Science.gov (United States)

    Formisano, Elia; De Martino, Federico; Valente, Giancarlo

    2008-09-01

    Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

  11. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

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    D. Mudali

    2015-01-01

    Full Text Available 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 (fMRI data.

  12. Abnormality Segmentation and Classification of Brain MR Images using Combined Edge, Texture Region Features and Radial basics Function

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    B. Balakumar

    2013-09-01

    Full Text Available Magnetic Resonance Images (MRI are widely used in the diagnosis of Brain tumor. In this study we have developed a new approach for automatic classification of the normal and abnormal non-enhanced MRI images. The proposed method consists of four stages namely Preprocessing, feature extraction, feature reduction and classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting the features. In the second stage, Region growing base segmentation is used for partitioning the image into meaningful regions. In the third stage, combined edge and Texture based features are extracted using Histogram and Gray Level Co-occurrence Matrix (GLCM from the segmented image. In the next stage PCA is used to reduce the dimensionality of the Feature space which results in a more efficient and accurate classification. Finally, in the classification stage, a supervised Radial Basics Function (RBF classifier is used to classify the experimental images into normal and abnormal. The obtained experimental are evaluated using the metrics sensitivity, specificity and accuracy. For comparison, the performance of the proposed technique has significantly improved the tumor detection accuracy with other neural network based classifier SVM, FFNN and FSVM.

  13. A wavelet-based time frequency analysis approach for classification of motor imagery for brain computer interface applications

    Science.gov (United States)

    Qin, Lei; He, Bin

    2005-12-01

    Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

  14. 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.

  15. 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.

  16. 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

    2016-11-01

    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.

  17. Neural correlates of apathy revealed by lesion mapping in participants with traumatic brain injuries.

    Science.gov (United States)

    Knutson, Kristine M; Monte, Olga Dal; Raymont, Vanessa; Wassermann, Eric M; Krueger, Frank; Grafman, Jordan

    2014-03-01

    Apathy, common in neurological disorders, is defined as disinterest and loss of motivation, with a reduction in self-initiated activity. Research in diseased populations has shown that apathy is associated with variations in the volume of brain regions such as the anterior cingulate and the frontal lobes. The goal of this study was to determine the neural signatures of apathy in people with penetrating traumatic brain injuries (pTBIs), as to our knowledge, these have not been studied in this sample. We studied 176 male Vietnam War veterans with pTBIs using voxel-based lesion-symptom mapping (VLSM) and apathy scores from the UCLA Neuropsychiatric Inventory (NPI), a structured inventory of symptoms completed by a caregiver. Our results revealed that increased apathy symptoms were associated with brain damage in limbic and cortical areas of the left hemisphere including the anterior cingulate, inferior, middle, and superior frontal regions, insula, and supplementary motor area. Our results are consistent with the literature, and extend them to people with focal pTBI. Apathy is a significant symptom since it can reduce participation of the patient in family and other social interactions, and diminish affective decision-making.

  18. Gene set based integrated data analysis reveals phenotypic differences in a brain cancer model.

    Directory of Open Access Journals (Sweden)

    Kjell Petersen

    Full Text Available A key challenge in the data analysis of biological high-throughput experiments is to handle the often low number of samples in the experiments compared to the number of biomolecules that are simultaneously measured. Combining experimental data using independent technologies to illuminate the same biological trends, as well as complementing each other in a larger perspective, is one natural way to overcome this challenge. In this work we investigated if integrating proteomics and transcriptomics data from a brain cancer animal model using gene set based analysis methodology, could enhance the biological interpretation of the data relative to more traditional analysis of the two datasets individually. The brain cancer model used is based on serial passaging of transplanted human brain tumor material (glioblastoma--GBM through several generations in rats. These serial transplantations lead over time to genotypic and phenotypic changes in the tumors and represent a medically relevant model with a rare access to samples and where consequent analyses of individual datasets have revealed relatively few significant findings on their own. We found that the integrated analysis both performed better in terms of significance measure of its findings compared to individual analyses, as well as providing independent verification of the individual results. Thus a better context for overall biological interpretation of the data can be achieved.

  19. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

    Science.gov (United States)

    Lehmann, Christoph; Koenig, Thomas; Jelic, Vesna; Prichep, Leslie; John, Roy E; Wahlund, Lars-Olof; Dodge, Yadolah; Dierks, Thomas

    2007-04-15

    The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

  20. 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.

  1. Laterality of brain areas associated with arithmetic calculations revealed by functional magnetic resonance imaging

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yun-ting; ZHANG Quan; ZHANG Jing; LI Wei

    2005-01-01

    Background Asymmetry of bilateral cerebral function, i.e. laterality, is an important phenomenon in many brain actions: arithmetic calculation may be one of these phenomena. In this study, first, laterality of brain areas associated with arithmetic calculations was revealed by functional magnetic resonance imaging (fMRI). Second, the relationship among laterality, handedness, and types of arithmetic task was assessed. Third, we postulate possible reasons for laterality.Methods Using a block-designed experiment, twenty-five right-handed and seven left-handed healthy volunteers carried out simple calculations, complex calculations and proximity judgments. T1WI and GRE-EPI fMRI were performed with a GE 1.5T whole body MRI scanner. Statistical parametric mapping (SPM99) was used to process data and localize functional areas. Numbers of activated voxels were recorded to calculate laterality index for evaluating the laterality of functional brain areas.Results For both groups, the activation of functional areas in the frontal lobe showed a tendency towards the nonpredominant hand side, but the functional areas in the inferior parietal lobule had left laterality. During simple and complex calculations, the laterality indices of the prefrontal cortex and premotor area were higher in the right-handed group than that in the left-handed group, whereas the laterality of the inferior parietal lobule had no such significant difference. In both groups, when the difficulty of the task increased, the laterality of the prefrontal cortex, premotor area, and inferior parietal lobule decreased, but the laterality of posterior part of the inferior frontal gyrus increased.Conclusions The laterality of the functional brain areas associated with arithmetic calculations can be detected with fMRI. The laterality of the functional areas was related to handedness and task difficulty.

  2. Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

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    Jacob, Yael; Winetraub, Yonatan; Raz, Gal; Ben-Simon, Eti; Okon-Singer, Hadas; Rosenberg-Katz, Keren; Hendler, Talma; Ben-Jacob, Eshel

    2016-01-01

    Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network’s hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. PMID:27271458

  3. Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.

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    Liberati, Giulia; Dalboni da Rocha, Josué Luiz; van der Heiden, Linda; Raffone, Antonino; Birbaumer, Niels; Olivetti Belardinelli, Marta; Sitaram, Ranganatha

    2012-01-01

    Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.

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

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    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.

  5. 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

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

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

    2016-08-01

    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

  7. 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.

  8. 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

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

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    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

  10. Artificial selection on relative brain size reveals a positive genetic correlation between brain size and proactive personality in the guppy.

    Science.gov (United States)

    Kotrschal, Alexander; Lievens, Eva J P; Dahlbom, Josefin; Bundsen, Andreas; Semenova, Svetlana; Sundvik, Maria; Maklakov, Alexei A; Winberg, Svante; Panula, Pertti; Kolm, Niclas

    2014-04-01

    Animal personalities range from individuals that are shy, cautious, and easily stressed (a "reactive" personality type) to individuals that are bold, innovative, and quick to learn novel tasks, but also prone to routine formation (a "proactive" personality type). Although personality differences should have important consequences for fitness, their underlying mechanisms remain poorly understood. Here, we investigated how genetic variation in brain size affects personality. We put selection lines of large- and small-brained guppies (Poecilia reticulata), with known differences in cognitive ability, through three standard personality assays. First, we found that large-brained animals were faster to habituate to, and more exploratory in, open field tests. Large-brained females were also bolder. Second, large-brained animals excreted less cortisol in a stressful situation (confinement). Third, large-brained animals were slower to feed from a novel food source, which we interpret as being caused by reduced behavioral flexibility rather than lack of innovation in the large-brained lines. Overall, the results point toward a more proactive personality type in large-brained animals. Thus, this study provides the first experimental evidence linking brain size and personality, an interaction that may affect important fitness-related aspects of ecology such as dispersal and niche exploration.

  11. Pain facilitation brain regions activated by nalbuphine are revealed by pharmacological fMRI.

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    Robert Gear

    Full Text Available Nalbuphine, an agonist-antagonist kappa-opioid, produces brief analgesia followed by enhanced pain/hyperalgesia in male postsurgical patients. However, it produces profound analgesia without pain enhancement when co-administration with low dose naloxone. To examine the effect of nalbuphine or nalbuphine plus naloxone on activity in brain regions that may explain these differences, we employed pharmacological magnetic resonance imaging (phMRI in a double blind cross-over study with 13 healthy male volunteers. In separate imaging sessions subjects were administered nalbuphine (5 mg/70 kg preceded by either saline (Sal-Nalb or naloxone 0.4 mg (Nalox-Nalb. Blood oxygen level-dependent (BOLD activation maps followed by contrast and connectivity analyses revealed marked differences. Sal-Nalb produced significantly increased activity in 60 brain regions and decreased activity in 9; in contrast, Nalox-Nalb activated only 14 regions and deactivated only 3. Nalbuphine, like morphine in a previous study, attenuated activity in the inferior orbital cortex, and, like noxious stimulation, increased activity in temporal cortex, insula, pulvinar, caudate, and pons. Co-administration/pretreatment of naloxone selectively blocked activity in pulvinar, pons and posterior insula. Nalbuphine induced functional connectivity between caudate and regions in the frontal, occipital, temporal, insular, middle cingulate cortices, and putamen; naloxone co-admistration reduced all connectivity to non-significant levels, and, like phMRI measures of morphine, increased activation in other areas (e.g., putamen. Naloxone pretreatment to nalbuphine produced changes in brain activity possess characteristics of both analgesia and algesia; naloxone selectively blocks activity in areas associated with algesia. Given these findings, we suggest that nalbuphine interacts with a pain salience system, which can modulate perceived pain intensity.

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

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    Elisabeth Fonteneau

    Full Text Available BACKGROUND: 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. METHODS AND FINDINGS: 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. CONCLUSIONS: 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.

  13. 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.

  14. Research of signal classification for brain-computer interface%模拟阅读型脑-机接口信号分类研究

    Institute of Scientific and Technical Information of China (English)

    朱学才; 李梅; 邹思轩

    2011-01-01

    脑-机接口(BCI)研究中的一个关键问题是如何正确地对EEG信号进行模式分类,以输出控制命令.本文在对“模拟自然阅读”模式下非靶刺激和靶刺激诱发的EEG进行去均值、低通滤波、下采样和归一化等处理后,结合对视觉诱发事件相关电位时域特征分析,提取出最佳特征量,分别利用BP神经网络和线性感知器算法对这些特征模式进行了分类.最终平均识别正确率分别达到87%和84%以上.对比研究表明,BP神经网络算法的分类效果较好,推测这是由于大部分EEG模式线性可分,只有10%左右线性不可分但非线性可分造成的.为提高分类正确率,简化BCI设计,详细研究了信号时程、时段的选择以及通道的选取对模式分类精度的影响.结果表明,信号时程越长分类精度越高;信号时段的选择对分类精度亦有较大的影响.通过实验发现:32个通道中,选取第14(PO3)通道的EEG进行模式分类的精度最高.%In order to produce the output of command, a key issue in brain-computer interface (BCD is to classify EEG signals correctly. EEG signal preprocessing were discussed in this paper, which include the lowpass filtering, baseline removing, down-sampling and normalization, et al. The EEG signals were recorded in an "Imitating Nature Reading" modality. The optimal feature patterns were extracted basing on the analyses in temporal and frequency for the Visual Evoked Event Related Potentials, and then BP Neural Networks and the perceptron approach were used separately to classify these patterns. The best classification results on testing set revealed an accuracy of more than 87% and 84 % respectively. The effects of classification by BP Neural Networks were better than those by perceptron approach, which revealed that about 90% EEG patterns are linearly separable while other 10% are linearly inseparable but maybe non-linearly separable. In order to get a better accuracy of

  15. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.

    Science.gov (United States)

    Tohka, Jussi

    2014-11-28

    Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

  16. 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.

  17. 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

  18. The time course of word retrieval revealed by event-related brain potentials during overt speech.

    Science.gov (United States)

    Costa, Albert; Strijkers, Kristof; Martin, Clara; Thierry, Guillaume

    2009-12-15

    Speech production is one of the most fundamental activities of humans. A core cognitive operation involved in this skill is the retrieval of words from long-term memory, that is, from the mental lexicon. In this article, we establish the time course of lexical access by recording the brain electrical activity of participants while they named pictures aloud. By manipulating the ordinal position of pictures belonging to the same semantic categories, the cumulative semantic interference effect, we were able to measure the exact time at which lexical access takes place. We found significant correlations between naming latencies, ordinal position of pictures, and event-related potential mean amplitudes starting 200 ms after picture presentation and lasting for 180 ms. The study reveals that the brain engages extremely fast in the retrieval of words one wishes to utter and offers a clear time frame of how long it takes for the competitive process of activating and selecting words in the course of speech to be resolved.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  20. Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease.

    Science.gov (United States)

    Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Zu, Chen; Nie, Feiping; Shen, Dinggang; Wu, Guorong

    2016-10-01

    Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined subject-wise relationships, and (3) verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data.

  1. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

    Directory of Open Access Journals (Sweden)

    Mark Plitt

    2015-01-01

    Conclusions: While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

  2. 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

  3. 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.

    1997-01-01

    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 hydrocephalu

  4. Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data

    OpenAIRE

    Chen, Wei; Wheeler, Krista K.; Lin, Simon; Huang, Yungui; Xiang, Huiyun

    2016-01-01

    One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can be difficult to utilize by novice users. This study evaluates a “learn-as-you-go” machine-learning program. When using this program, the user trains classification models and interactively checks on accuracy until a desired threshold is reached. ...

  5. Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data

    OpenAIRE

    Meng Li; Fang Zhao; Jason Lee; Dong Wang; Hui Kuang; Joe Z Tsien

    2015-01-01

    The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed “inter-spike-interval classification-analysis” (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-cluste...

  6. 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...

  7. Classification of traumatic brain injury severity using informed data reduction in a series of binary classifier algorithms.

    Science.gov (United States)

    Prichep, Leslie S; Jacquin, Arnaud; Filipenko, Julie; Dastidar, Samanwoy Ghosh; Zabele, Stephen; Vodencarević, Asmir; Rothman, Neil S

    2012-11-01

    Assessment of medical disorders is often aided by objective diagnostic tests which can lead to early intervention and appropriate treatment. In the case of brain dysfunction caused by head injury, there is an urgent need for quantitative evaluation methods to aid in acute triage of those subjects who have sustained traumatic brain injury (TBI). Current clinical tools to detect mild TBI (mTBI/concussion) are limited to subjective reports of symptoms and short neurocognitive batteries, offering little objective evidence for clinical decisions; or computed tomography (CT) scans, with radiation-risk, that are most often negative in mTBI. This paper describes a novel methodology for the development of algorithms to provide multi-class classification in a substantial population of brain injured subjects, across a broad age range and representative subpopulations. The method is based on age-regressed quantitative features (linear and nonlinear) extracted from brain electrical activity recorded from a limited montage of scalp electrodes. These features are used as input to a unique "informed data reduction" method, maximizing confidence of prospective validation and minimizing over-fitting. A training set for supervised learning was used, including: "normal control," "concussed," and "structural injury/CT positive (CT+)." The classifier function separating CT+ from the other groups demonstrated a sensitivity of 96% and specificity of 78%; the classifier separating "normal controls" from the other groups demonstrated a sensitivity of 81% and specificity of 74%, suggesting high utility of such classifiers in acute clinical settings. The use of a sequence of classifiers where the desired risk can be stratified further supports clinical utility.

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

    Science.gov (United States)

    2007-10-01

    Spatial analysis of evoked po- tentials in man—a review,” Progress in Neurobiology, vol. 23, no. 3, pp. 227–250, 1984. [38] “ Cartool software...Functional Brain Mapping Laboratory, Geneva, Switzerland, http://brainmapping.unige.ch/ Cartool .htm. [39] T. Koenig, K. Kochi, and D. Lehmann, “Event

  9. 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).

  10. Radiation therapy for brain metastases from breast cancer by histological classification

    Energy Technology Data Exchange (ETDEWEB)

    Mizutani, Yoshihide [Showa Univ., Tokyo (Japan). School of Medicine; Yamashita, Takashi; Sakamoto, Goi

    2001-02-01

    One hundred thirteen patients with metastatic brain tumor from breast cancer who were treated with external irradiation between 1989 and 1997 at Cancer Institute Hospital were studied. The patients were all histopathologically proven to have invasive ductal carcinoma (scirrhous type 54 cases, papillotubular type 18, solid-tubular type 41). The patients were evaluated for efficacy and histopathological subtypes. The time interval between the diagnosis of breast cancer and brain metastases was 53.6 months for the scirrhous type, 75.0 months for the papillotubular type, and 35.5 months for the solid-tubular type. The time interval between the diagnosis of initial distant metastases and brain metastases was 14.3 months for the scirrhous type, 22.5 months for the papillotubular type, and 12.5 months for the solid-tubular type. Efficacy rates (CR+PR) for external irradiation of the brain metastases were 40.0% for the scirrhous type, 66.7% for the papillotubular type, and 36.6% for the solid-tubular type. The papillotubular type had a favorable efficacy rate compared with the other two types. Median survival time (MST) from the start of treatment for brain metastases and one-year survival rate were 5 months and 11.1% for the scirrhous type, 7 months and 41.5% for the papillotubular type, and 4 months and 28.3% for the solid-tubular type, respectively. No statistically significant difference between survival rates was observed among the histopathological types. Univariate analysis showed performance status, number of metastatic tumors, and existence of extracranial metastases without bony metastasis to be significantly related to prognosis. Multivariate analysis showed only extracranial metastases without bony metastases to be related to prognosis. (author)

  11. Brain network analysis reveals affected connectome structure in bipolar I disorder

    NARCIS (Netherlands)

    Collin, Guusje; van den Heuvel, Martijn P.; Abramovic, Lucija; Vreeker, Annabel; de Reus, Marcel A.; van Haren, Neeltje E M; Boks, Marco P M; Ophoff, Roel A.; Kahn, René S.

    The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections-i.e., the connectome-suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in

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

    NARCIS (Netherlands)

    Zwiers, M.P.; Buitelaar, J.K.; Fernandez, G.S.E.; Flor, H.; Fouche, J.P.; Frouin, V.; Wolfers, T.; Fisher, S.E.; Francks, C.

    2016-01-01

    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 asymm

  13. Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

    Science.gov (United States)

    Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert; Angstadt, Michael; Liberzon, Israel; Phan, K Luan; Scott, Clayton

    2013-11-01

    Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning.

  14. Brain Tumor Classification Using AFM in Combination with Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Marlene Huml

    2013-01-01

    Full Text Available Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

  15. Brain tumor classification using AFM in combination with data mining techniques.

    Science.gov (United States)

    Huml, Marlene; Silye, René; Zauner, Gerald; Hutterer, Stephan; Schilcher, Kurt

    2013-01-01

    Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

  16. Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation

    OpenAIRE

    Corso, Jason J.; Eitan Sharon; Alan Yuille

    2006-01-01

    We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumo...

  17. Binary Color Classification For Brain Computer Interface Using Neural Networks And Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Charmi Sunil Mehta

    2014-04-01

    Full Text Available As the power of modern computers grows alongside our understanding of the human brain, we move a step closer in transforming some pretty spectacular science fiction into reality. The advent of Brain Computer Interface (BCI is indeed leading us to a burgeoning era of complete automation empowering our interaction with computer not only with robustness but with also a gift of intelligence. For the fraction of our society suffering from severe motor disabilities BCI has offered a novel solution of overcoming the problems faced in communicating and environment control. Thus the purpose of our current research is to harness the brain‟s ability to generate Visually Evoked Potentials (VEPs by capturing the response of the brain to the transitions of color from grey to green and grey to red. Our prime focus is to explore EEG-based signal processing techniques in order to classify two colors; which can be further deployed in future by coupling the actuators so as to perform few basic tasks. The extracted EEG features are classified using Support Vector Machines (SVM and Artificial Neural Networks (ANN. We recorded 100% accuracy on testing the model after training and validation process. Moreover, we obtained 90% accuracy on re-testing the model with all samples acquired for the task using Quadratic SVM classifier.

  18. The categorization of natural scenes: brain attention networks revealed by dense sensor ERPs.

    Science.gov (United States)

    Codispoti, Maurizio; Ferrari, Vera; Junghöfer, Markus; Schupp, Harald T

    2006-08-15

    The present study examined cortical indicators of selective attention underlying categorization based on target features in natural scenes. The primary focus was to determine the neural sources associated with the processing of target stimuli containing animals compared to non-target control stimuli. Neural source estimation techniques [current source density (CSD) and L2-minimum norm estimate (L2-MNE)] were used to determine the sources of the potential fields measured from 58 sensor sites. Assuring an excellent signal-to-noise ratio, the categorization task consisted of 2400 trials. Replicating previous findings, target and non-target ERP activity diverged sharply around 150 ms after stimulus onset and the early differential ERP activity appeared as positive deflection over fronto-central sensor sites and as negative deflection over temporo-occipital regions. Both source estimation techniques (CSD and L2-MNE) suggested primary sources of the early differential ERP activity in posterior, visual-associative brain regions and, although less pronounced, revealed the contribution of additional anterior sources. These findings suggest that selective attention to category-relevant features reflects the interactions between prefrontal and inferior temporal cortex during visual processing of natural scenes.

  19. Microfiberoptic fluorescence photobleaching reveals size-dependent macromolecule diffusion in extracellular space deep in brain.

    Science.gov (United States)

    Zador, Zsolt; Magzoub, Mazin; Jin, Songwan; Manley, Geoffrey T; Papadopoulos, Marios C; Verkman, A S

    2008-03-01

    Diffusion in brain extracellular space (ECS) is important for nonsynaptic intercellular communication, extracellular ionic buffering, and delivery of drugs and metabolites. We measured macromolecular diffusion in normally light-inaccessible regions of mouse brain by microfiberoptic epifluorescence photobleaching, in which a fiberoptic with a micron-size tip is introduced deep in brain tissue. In brain cortex, the diffusion of a noninteracting molecule [fluorescein isothiocyanate (FITC)-dextran, 70 kDa] was slowed 4.5 +/- 0.5-fold compared with its diffusion in water (D(o)/D), and was depth-independent down to 800 microm from the brain surface. Diffusion was significantly accelerated (D(o)/D of 2.9+/-0.3) in mice lacking the glial water channel aquaporin-4. FITC-dextran diffusion varied greatly in different regions of brain, with D(o)/D of 3.5 +/- 0.3 in hippocampus and 7.4 +/- 0.3 in thalamus. Remarkably, D(o)/D in deep brain was strongly dependent on solute size, whereas diffusion in cortex changed little with solute size. Mathematical modeling of ECS diffusion required nonuniform ECS dimensions in deep brain, which we call "heterometricity," to account for the size-dependent diffusion. Our results provide the first data on molecular diffusion in ECS deep in brain in vivo and demonstrate previously unrecognized hindrance and heterometricity for diffusion of large macromolecules in deep brain.

  20. 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.

  1. Interspecies avian brain chimeras reveal that large brain size differences are influenced by cell-interdependent processes.

    Science.gov (United States)

    Chen, Chun-Chun; Balaban, Evan; Jarvis, Erich D

    2012-01-01

    Like humans, birds that exhibit vocal learning have relatively delayed telencephalon maturation, resulting in a disproportionately smaller brain prenatally but enlarged telencephalon in adulthood relative to vocal non-learning birds. To determine if this size difference results from evolutionary changes in cell-autonomous or cell-interdependent developmental processes, we transplanted telencephala from zebra finch donors (a vocal-learning species) into Japanese quail hosts (a vocal non-learning species) during the early neural tube stage (day 2 of incubation), and harvested the chimeras at later embryonic stages (between 9-12 days of incubation). The donor and host tissues fused well with each other, with known major fiber pathways connecting the zebra finch and quail parts of the brain. However, the overall sizes of chimeric finch telencephala were larger than non-transplanted finch telencephala at the same developmental stages, even though the proportional sizes of telencephalic subregions and fiber tracts were similar to normal finches. There were no significant changes in the size of chimeric quail host midbrains, even though they were innervated by the physically smaller zebra finch brain, including the smaller retinae of the finch eyes. Chimeric zebra finch telencephala had a decreased cell density relative to normal finches. However, cell nucleus size differences between each species were maintained as in normal birds. These results suggest that telencephalic size development is partially cell-interdependent, and that the mechanisms controlling the size of different brain regions may be functionally independent.

  2. Interspecies avian brain chimeras reveal that large brain size differences are influenced by cell-interdependent processes.

    Directory of Open Access Journals (Sweden)

    Chun-Chun Chen

    Full Text Available Like humans, birds that exhibit vocal learning have relatively delayed telencephalon maturation, resulting in a disproportionately smaller brain prenatally but enlarged telencephalon in adulthood relative to vocal non-learning birds. To determine if this size difference results from evolutionary changes in cell-autonomous or cell-interdependent developmental processes, we transplanted telencephala from zebra finch donors (a vocal-learning species into Japanese quail hosts (a vocal non-learning species during the early neural tube stage (day 2 of incubation, and harvested the chimeras at later embryonic stages (between 9-12 days of incubation. The donor and host tissues fused well with each other, with known major fiber pathways connecting the zebra finch and quail parts of the brain. However, the overall sizes of chimeric finch telencephala were larger than non-transplanted finch telencephala at the same developmental stages, even though the proportional sizes of telencephalic subregions and fiber tracts were similar to normal finches. There were no significant changes in the size of chimeric quail host midbrains, even though they were innervated by the physically smaller zebra finch brain, including the smaller retinae of the finch eyes. Chimeric zebra finch telencephala had a decreased cell density relative to normal finches. However, cell nucleus size differences between each species were maintained as in normal birds. These results suggest that telencephalic size development is partially cell-interdependent, and that the mechanisms controlling the size of different brain regions may be functionally independent.

  3. 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.

  4. 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

  5. High-field proton magnetic resonance spectroscopy reveals metabolic effects of normal brain aging.

    Science.gov (United States)

    Harris, Janna L; Yeh, Hung-Wen; Swerdlow, Russell H; Choi, In-Young; Lee, Phil; Brooks, William M

    2014-07-01

    Altered brain metabolism is likely to be an important contributor to normal cognitive decline and brain pathology in elderly individuals. To characterize the metabolic changes associated with normal brain aging, we used high-field proton magnetic resonance spectroscopy in vivo to quantify 20 neurochemicals in the hippocampus and sensorimotor cortex of young adult and aged rats. We found significant differences in the neurochemical profile of the aged brain when compared with younger adults, including lower aspartate, ascorbate, glutamate, and macromolecules, and higher glucose, myo-inositol, N-acetylaspartylglutamate, total choline, and glutamine. These neurochemical biomarkers point to specific cellular mechanisms that are altered in brain aging, such as bioenergetics, oxidative stress, inflammation, cell membrane turnover, and endogenous neuroprotection. Proton magnetic resonance spectroscopy may be a valuable translational approach for studying mechanisms of brain aging and pathology, and for investigating treatments to preserve or enhance cognitive function in aging.

  6. EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

  7. Brain network analysis reveals affected connectome structure in bipolar I disorder.

    Science.gov (United States)

    Collin, Guusje; van den Heuvel, Martijn P; Abramovic, Lucija; Vreeker, Annabel; de Reus, Marcel A; van Haren, Neeltje E M; Boks, Marco P M; Ophoff, Roel A; Kahn, René S

    2016-01-01

    The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections--i.e., the connectome--suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in schizophrenia are indicative of altered connectome architecture including reduced communication efficiency, disruptions of central brain hubs, and affected "rich club" organization. Whether similar deficits are present in bipolar disorder is currently unknown. This study examines structural connectome topology in 216 bipolar I disorder patients as compared to 144 healthy controls, focusing in particular on central regions (i.e., brain hubs) and connections (i.e., rich club connections, interhemispheric connections) of the brain's network. We find that bipolar I disorder patients exhibit reduced global efficiency (-4.4%, P =0.002) and that this deficit relates (r = 0.56, P brain hub connections in general, or of connections spanning brain hubs (i.e., "rich club" connections) in particular (all P > 0.1). These findings highlight a role for aberrant brain network architecture in bipolar I disorder with reduced global efficiency in association with disruptions in interhemispheric connectivity, while the central "rich club" system appears not to be particularly affected.

  8. Resting state functional magnetic resonance imaging reveals distinct brain activity in heavy cannabis users - a multi-voxel pattern analysis.

    Science.gov (United States)

    Cheng, H; Skosnik, P D; Pruce, B J; Brumbaugh, M S; Vollmer, J M; Fridberg, D J; O'Donnell, B F; Hetrick, W P; Newman, S D

    2014-11-01

    Chronic cannabis use can cause cognitive, perceptual and personality alterations, which are believed to be associated with regional brain changes and possible changes in connectivity between functional regions. This study aims to identify the changes from resting state functional magnetic resonance imaging scans. A two-level multi-voxel pattern analysis was proposed to classify male cannabis users from normal controls. The first level analysis works on a voxel basis and identifies clusters for the input of a second level analysis, which works on the functional connectivity between these regions. We found distinct clusters for male cannabis users in the middle frontal gyrus, precentral gyrus, superior frontal gyrus, posterior cingulate cortex, cerebellum and some other regions. Based on the functional connectivity of these clusters, a high overall accuracy rate of 84-88% in classification accuracy was achieved. High correlations were also found between the overall classification accuracy and Barrett Barrett Impulsiveness Scale factor scores of attention and motor. Our result suggests regional differences in the brains of male cannabis users that span from the cerebellum to the prefrontal cortex, which are associated with differences in functional connectivity. © The Author(s) 2014.

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

    Energy Technology Data Exchange (ETDEWEB)

    Wels, Michael; Hornegger, Joachim [Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen (Germany); Zheng Yefeng; Comaniciu, Dorin [Corporate Research and Technologies, Siemens Corporate Technology, 755 College Road East, Princeton, NJ 08540 (United States); Huber, Martin, E-mail: michael.wels@informatik.uni-erlangen.de [Corporate Research and Technologies, Siemens Corporate Technology, Guenther-Scharowsky-Str. 1, 91058 Erlangen (Germany)

    2011-06-07

    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

  10. Fossil skulls reveal that blood flow rate to the brain increased faster than brain volume during human evolution

    Science.gov (United States)

    Seymour, Roger S.; Bosiocic, Vanya; Snelling, Edward P.

    2016-08-01

    The evolution of human cognition has been inferred from anthropological discoveries and estimates of brain size from fossil skulls. A more direct measure of cognition would be cerebral metabolic rate, which is proportional to cerebral blood flow rate (perfusion). The hominin cerebrum is supplied almost exclusively by the internal carotid arteries. The sizes of the foramina that transmitted these vessels in life can be measured in hominin fossil skulls and used to calculate cerebral perfusion rate. Perfusion in 11 species of hominin ancestors, from Australopithecus to archaic Homo sapiens, increases disproportionately when scaled against brain volume (the allometric exponent is 1.41). The high exponent indicates an increase in the metabolic intensity of cerebral tissue in later Homo species, rather than remaining constant (1.0) as expected by a linear increase in neuron number, or decreasing according to Kleiber's Law (0.75). During 3 Myr of hominin evolution, cerebral tissue perfusion increased 1.7-fold, which, when multiplied by a 3.5-fold increase in brain size, indicates a 6.0-fold increase in total cerebral blood flow rate. This is probably associated with increased interneuron connectivity, synaptic activity and cognitive function, which all ultimately depend on cerebral metabolic rate.

  11. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

    Science.gov (United States)

    He, Yong; Chen, Zhang J; Evans, Alan C

    2007-10-01

    An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

  12. Aberrant spontaneous brain activity in chronic tinnitus patients revealed by resting-state functional MRI

    Directory of Open Access Journals (Sweden)

    Yu-Chen Chen

    2014-01-01

    Conclusions: The present study confirms that chronic tinnitus patients have aberrant ALFF in many brain regions, which is associated with specific clinical tinnitus characteristics. ALFF disturbance in specific brain regions might be used to identify the neuro-pathophysiological mechanisms in chronic tinnitus patients.

  13. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

    Science.gov (United States)

    Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

    2016-05-01

    Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

  14. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.

    Science.gov (United States)

    Combrisson, Etienne; Jerbi, Karim

    2015-07-30

    Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naïve Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

    Full Text Available An approach for nonsupervised segmentation of Computed Tomography (CT brain slices which is based on the use of Vector Quantization Networks (VQNs is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.

  16. Three-dimensional mouse brain cytoarchitecture revealed by laboratory-based x-ray phase-contrast tomography

    Science.gov (United States)

    Töpperwien, Mareike; Krenkel, Martin; Vincenz, Daniel; Stöber, Franziska; Oelschlegel, Anja M.; Goldschmidt, Jürgen; Salditt, Tim

    2017-02-01

    Studies of brain cytoarchitecture in mammals are routinely performed by serial sectioning of the specimen and staining of the sections. The procedure is labor-intensive and the 3D architecture can only be determined after aligning individual 2D sections, leading to a reconstructed volume with non-isotropic resolution. Propagation-based x-ray phase-contrast tomography offers a unique potential for high-resolution 3D imaging of intact biological specimen due to the high penetration depth and potential resolution. We here show that even compact laboratory CT at an optimized liquid-metal jet microfocus source combined with suitable phase-retrieval algorithms and a novel tissue preparation can provide cellular and subcellular resolution in millimeter sized samples of mouse brain. We removed water and lipids from entire mouse brains and measured the remaining dry tissue matrix in air, lowering absorption but increasing phase contrast. We present single-cell resolution images of mouse brain cytoarchitecture and show that axons can be revealed in myelinated fiber bundles. In contrast to optical 3D techniques our approach does neither require staining of cells nor tissue clearing, procedures that are increasingly difficult to apply with increasing sample and brain sizes. The approach thus opens a novel route for high-resolution high-throughput studies of brain architecture in mammals.

  17. 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

  18. 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.

  19. 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

  20. Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images.

    Science.gov (United States)

    Murakawa, Saki; Ikuta, Rie; Uchiyama, Yoshikazu; Shiraishi, Junji

    2016-02-01

    Hospital information systems (HISs) and picture archiving and communication systems (PACSs) are archiving large amounts of data (i.e., "big data") that are not being used. Therefore, many research projects in progress are trying to use "big data" for the development of early diagnosis, prediction of disease onset, and personalized therapies. In this study, we propose a new method for image data mining to identify regularities and abnormalities in the large image data sets. We used 70 archived magnetic resonance (MR) images that were acquired using three-dimensional magnetization-prepared rapid acquisition with gradient echo (3D MP-RAGE). These images were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database. For anatomical standardization of the data, we used the statistical parametric mapping (SPM) software. Using a similarity matrix based on cross-correlation coefficients (CCs) calculated from an anatomical region and a hierarchical clustering technique, we classified all the abnormal cases into five groups. The Z score map identified the difference between a standard normal brain and each of those from the Alzheimer's groups. In addition, the scatter plot obtained from two similarity matrixes visualized the regularities and abnormalities in the image data sets. Image features identified using our method could be useful for understanding of image findings associated with Alzheimer's disease.

  1. 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).

  2. Fine-tuning convolutional deep features for MRI based brain tumor classification

    Science.gov (United States)

    Ahmed, Kaoutar B.; Hall, Lawrence O.; Goldgof, Dmitry B.; Liu, Renhao; Gatenby, Robert A.

    2017-03-01

    Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient's prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN's, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

  3. 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.

  4. Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain

    Science.gov (United States)

    Sepulcre, Jorge; Sabuncu, Mert R.; Yeo, Thomas B.; Liu, Hesheng; Johnson, Keith A.

    2012-01-01

    How human beings integrate information from external sources and internal cognition to produce a coherent experience is still not well understood. During the past decades, anatomical, neurophysiological and neuroimaging research in multimodal integration have stood out in the effort to understand the perceptual binding properties of the brain. Areas in the human lateral occipito-temporal, prefrontal and posterior parietal cortices have been associated with sensory multimodal processing. Even though this, rather patchy, organization of brain regions gives us a glimpse of the perceptual convergence, the articulation of the flow of information from modality-related to the more parallel cognitive processing systems remains elusive. Using a method called Stepwise Functional Connectivity analysis, the present study analyzes the functional connectome and transitions from primary sensory cortices to higher-order brain systems. We identify the large-scale multimodal integration network and essential connectivity axes for perceptual integration in the human brain. PMID:22855814

  5. Differential brain activity states during the perception and nonperception of illusory motion as revealed by magnetoencephalography.

    Science.gov (United States)

    Crowe, David A; Leuthold, Arthur C; Georgopoulos, Apostolos P

    2010-12-28

    We studied visual perception using an annular random-dot motion stimulus called the racetrack. We recorded neural activity using magnetoencephalography while subjects viewed variants of this stimulus that contained no inherent motion or various degrees of embedded motion. Subjects reported seeing rotary motion during viewing of all stimuli. We found that, in the absence of any motion signals, patterns of brain activity differed between states of motion perception and nonperception. Furthermore, when subjects perceived motion, activity states within the brain did not differ across stimuli of different amounts of embedded motion. In contrast, we found that during periods of nonperception brain-activity states varied with the amount of motion signal embedded in the stimulus. Taken together, these results suggest that during perception the brain may lock into a stable state in which lower-level signals are suppressed.

  6. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

    Directory of Open Access Journals (Sweden)

    Hui Wang

    Full Text Available We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs in 29 individuals with autism spectrum disorders (ASD, and 29 individuals with typical development (TD while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.

  7. Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

    Science.gov (United States)

    Wang, Hui; Chen, Chen; Fushing, Hsieh

    2012-01-01

    We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.

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

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    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.

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

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    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.

  10. Competing visual flicker reveals attention-like rivalry in the fly brain

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    Bruno eVan Swinderen

    2012-10-01

    Full Text Available There is increasing evidence that invertebrates such as flies display selective attention (van Swinderen, 2011a, although parallel processing of simultaneous cues remains difficult to demonstrate in such tiny brains. Local field potential (LFP activity in the fly brain is associated with stimulus selection and suppression (van Swinderen and Greenspan, 2003;Tang and Juusola, 2010, like in other animals such as monkeys (Fries et al., 2001, suggesting that similar processes may be working to control attention in vastly different brains. To investigate selective attention to competing visual cues, I recorded brain activity from behaving flies while applying a method used in human attention studies: competing visual flicker, or frequency tags (Vialatte et al., 2010. Behavioral fixation in a closed-loop flight arena increased the response to visual flicker in the fly brain, and visual salience modulated responses to competing tags arranged in a center-surround pattern. Visual competition dynamics in the fly brain were dependent on the rate of pattern presentation, suggesting that attention-like switching in insects is tuned to the pace of visual changes in the environment rather than simply the passage of time.

  11. Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions.

    Science.gov (United States)

    Lin, Aijing; Liu, Kang K L; Bartsch, Ronny P; Ivanov, Plamen Ch

    2016-05-13

    Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

  12. Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions

    Science.gov (United States)

    Lin, Aijing; Liu, Kang K. L.; Bartsch, Ronny P.; Ivanov, Plamen Ch.

    2016-05-01

    Within the framework of `Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

  13. Monoamines tissue content analysis reveals restricted and site-specific correlations in brain regions involved in cognition.

    Science.gov (United States)

    Fitoussi, A; Dellu-Hagedorn, F; De Deurwaerdère, P

    2013-01-01

    The dopamine (DA), noradrenalin (NA) and serotonin (5-HT) monoaminergic systems are deeply involved in cognitive processes via their influence on cortical and subcortical regions. The widespread distribution of these monoaminergic networks is one of the main difficulties in analyzing their functions and interactions. To address this complexity, we assessed whether inter-individual differences in monoamine tissue contents of various brain areas could provide information about their functional relationships. We used a sensitive biochemical approach to map endogenous monoamine tissue content in 20 rat brain areas involved in cognition, including 10 cortical areas and examined correlations within and between the monoaminergic systems. Whereas DA content and its respective metabolite largely varied across brain regions, the NA and 5-HT contents were relatively homogenous. As expected, the tissue content varied among individuals. Our analyses revealed a few specific relationships (10%) between the tissue content of each monoamine in paired brain regions and even between monoamines in paired brain regions. The tissue contents of NA, 5-HT and DA were inter-correlated with a high incidence when looking at a specific brain region. Most correlations found between cortical areas were positive while some cortico-subcortical relationships regarding the DA, NA and 5-HT tissue contents were negative, in particular for DA content. In conclusion, this work provides a useful database of the monoamine tissue content in numerous brain regions. It suggests that the regulation of these neuromodulatory systems is achieved mainly at the terminals, and that each of these systems contributes to the regulation of the other two.

  14. Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces.

    Science.gov (United States)

    Darvishi, Sam; Ridding, Michael C; Abbott, Derek; Baumert, Mathias

    2013-01-01

    Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.

  15. 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

    2015-03-03

    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.

  16. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

    OpenAIRE

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accura...

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

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    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.

  18. An Anatomically Resolved Mouse Brain Proteome Reveals Parkinson Disease-relevant Pathways.

    Science.gov (United States)

    Jung, Sung Yun; Choi, Jong Min; Rousseaux, Maxime W C; Malovannaya, Anna; Kim, Jean J; Kutzera, Joachim; Wang, Yi; Huang, Yin; Zhu, Weimin; Maity, Suman; Zoghbi, Huda Yahya; Qin, Jun

    2017-04-01

    Here, we present a mouse brain protein atlas that covers 17 surgically distinct neuroanatomical regions of the adult mouse brain, each less than 1 mm(3) in size. The protein expression levels are determined for 6,500 to 7,500 gene protein products from each region and over 12,000 gene protein products for the entire brain, documenting the physiological repertoire of mouse brain proteins in an anatomically resolved and comprehensive manner. We explored the utility of our spatially defined protein profiling methods in a mouse model of Parkinson's disease. We compared the proteome from a vulnerable region (substantia nigra pars compacta) of wild type and parkinsonian mice with that of an adjacent, less vulnerable, region (ventral tegmental area) and identified several proteins that exhibited both spatiotemporal- and genotype-restricted changes. We validated the most robustly altered proteins using an alternative profiling method and found that these modifications may highlight potential new pathways for future studies. This proteomic atlas is a valuable resource that offers a practical framework for investigating the molecular intricacies of normal brain function as well as regional vulnerability in neurological diseases. All of the mouse regional proteome profiling data are published on line at http://mbpa.bprc.ac.cn/. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

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

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    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.

  20. scMRI reveals large-scale brain network abnormalities in autism.

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    Brandon A Zielinski

    Full Text Available Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

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

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    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.

  2. Separation methods that are capable of revealing blood-brain barrier permeability.

    Science.gov (United States)

    Dash, Alekha K; Elmquist, William F

    2003-11-25

    The objective of this review is to emphasize the application of separation science in evaluating the blood-brain barrier (BBB) permeability to drugs and bioactive agents. Several techniques have been utilized to quantitate the BBB permeability. These methods can be classified into two major categories: in vitro or in vivo. The in vivo methods used include brain homogenization, cerebrospinal fluid (CSF) sampling, voltametry, autoradiography, nuclear magnetic resonance (NMR) spectroscopy, positron emission tomography (PET), intracerebral microdialysis, and brain uptake index (BUI) determination. The in vitro methods include tissue culture and immobilized artificial membrane (IAM) technology. Separation methods have always played an important role as adjunct methods to the methods outlined above for the quantitation of BBB permeability and have been utilized the most with brain homogenization, in situ brain perfusion, CSF sampling, intracerebral microdialysis, in vitro tissue culture and IAM chromatography. However, the literature published to date indicates that the separation method has been used the most in conjunction with intracerebral microdialysis and CSF sampling methods. The major advantages of microdialysis sampling in BBB permeability studies is the possibility of online separation and quantitation as well as the need for only a small sample volume for such an analysis. Separation methods are preferred over non-separation methods in BBB permeability evaluation for two main reasons. First, when the selectivity of a determination method is insufficient, interfering substances must be separated from the analyte of interest prior to determination. Secondly, when large number of analytes is to be detected and quantitated by a single analytical procedure, the mixture must be separated to each individual component prior to determination. Chiral separation in particular can be essential to evaluate the stereo-selective permeation and distribution of agents into the

  3. Atypical Brain Responses to Reward Cues in Autism as Revealed by Event-Related Potentials

    Science.gov (United States)

    Kohls, Gregor; Peltzer, Judith; Schulte-Ruther, Martin; Kamp-Becker, Inge; Remschmidt, Helmut; Herpertz-Dahlmann, Beate; Konrad, Kerstin

    2011-01-01

    Social motivation deficit theories suggest that children with autism do not properly anticipate and appreciate the pleasure of social stimuli. In this study, we investigated event-related brain potentials evoked by cues that triggered social versus monetary reward anticipation in children with autism. Children with autism showed attenuated P3…

  4. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics.

    Science.gov (United States)

    Bruno, Jennifer Lynn; Hosseini, S M Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira Michelle; Reiss, Allan L

    2016-03-22

    Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.

  5. 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.

  6. Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI.

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    Ting Xue

    Full Text Available BACKGROUND: Previous studies have defined low-frequency, spatially consistent intrinsic connectivity networks (ICN in resting functional magnetic resonance imaging (fMRI data which reflect functional interactions among distinct brain areas. We sought to explore whether and how repeated migraine attacks influence intrinsic brain connectivity, as well as how activity in these networks correlates with clinical indicators of migraine. METHODS/PRINCIPAL FINDINGS: Resting-state fMRI data in twenty-three patients with migraines without aura (MwoA and 23 age- and gender-matched healthy controls (HC were analyzed using independent component analysis (ICA, in combination with a "dual-regression" technique to identify the group differences of three important pain-related networks [default mode network (DMN, bilateral central executive network (CEN, salience network (SN] between the MwoA patients and HC. Compared with the HC, MwoA patients showed aberrant intrinsic connectivity within the bilateral CEN and SN, and greater connectivity between both the DMN and right CEN (rCEN and the insula cortex - a critical region involving in pain processing. Furthermore, greater connectivity between both the DMN and rCEN and the insula correlated with duration of migraine. CONCLUSIONS: Our findings may provide new insights into the characterization of migraine as a condition affecting brain activity in intrinsic connectivity networks. Moreover, the abnormalities may be the consequence of a persistent central neural system dysfunction, reflecting cumulative brain insults due to frequent ongoing migraine attacks.

  7. Atypical Brain Responses to Reward Cues in Autism as Revealed by Event-Related Potentials

    Science.gov (United States)

    Kohls, Gregor; Peltzer, Judith; Schulte-Ruther, Martin; Kamp-Becker, Inge; Remschmidt, Helmut; Herpertz-Dahlmann, Beate; Konrad, Kerstin

    2011-01-01

    Social motivation deficit theories suggest that children with autism do not properly anticipate and appreciate the pleasure of social stimuli. In this study, we investigated event-related brain potentials evoked by cues that triggered social versus monetary reward anticipation in children with autism. Children with autism showed attenuated P3…

  8. Brain imaging reveals neuronal circuitry underlying the crow's perception of human faces.

    Science.gov (United States)

    Marzluff, John M; Miyaoka, Robert; Minoshima, Satoshi; Cross, Donna J

    2012-09-25

    Crows pay close attention to people and can remember specific faces for several years after a single encounter. In mammals, including humans, faces are evaluated by an integrated neural system involving the sensory cortex, limbic system, and striatum. Here we test the hypothesis that birds use a similar system by providing an imaging analysis of an awake, wild animal's brain as it performs an adaptive, complex cognitive task. We show that in vivo imaging of crow brain activity during exposure to familiar human faces previously associated with either capture (threatening) or caretaking (caring) activated several brain regions that allow birds to discriminate, associate, and remember visual stimuli, including the rostral hyperpallium, nidopallium, mesopallium, and lateral striatum. Perception of threatening faces activated circuitry including amygdalar, thalamic, and brainstem regions, known in humans and other vertebrates to be related to emotion, motivation, and conditioned fear learning. In contrast, perception of caring faces activated motivation and striatal regions. In our experiments and in nature, when perceiving a threatening face, crows froze and fixed their gaze (decreased blink rate), which was associated with activation of brain regions known in birds to regulate perception, attention, fear, and escape behavior. These findings indicate that, similar to humans, crows use sophisticated visual sensory systems to recognize faces and modulate behavioral responses by integrating visual information with expectation and emotion. Our approach has wide applicability and potential to improve our understanding of the neural basis for animal behavior.

  9. 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…

  10. Event-Related Brain Potentials Reveal Anomalies in Temporal Processing of Faces in Autism Spectrum Disorder

    Science.gov (United States)

    McPartland, James; Dawson, Geraldine; Webb, Sara J.; Panagiotides, Heracles; Carver, Leslie J.

    2004-01-01

    Background: Individuals with autism exhibit impairments in face recognition, and neuroimaging studies have shown that individuals with autism exhibit abnormal patterns of brain activity during face processing. The current study examined the temporal characteristics of face processing in autism and their relation to behavior. Method: High-density…

  11. Brain imaging reveals neuronal circuitry underlying the crow’s perception of human faces

    Science.gov (United States)

    Marzluff, John M.; Miyaoka, Robert; Minoshima, Satoshi; Cross, Donna J.

    2012-01-01

    Crows pay close attention to people and can remember specific faces for several years after a single encounter. In mammals, including humans, faces are evaluated by an integrated neural system involving the sensory cortex, limbic system, and striatum. Here we test the hypothesis that birds use a similar system by providing an imaging analysis of an awake, wild animal’s brain as it performs an adaptive, complex cognitive task. We show that in vivo imaging of crow brain activity during exposure to familiar human faces previously associated with either capture (threatening) or caretaking (caring) activated several brain regions that allow birds to discriminate, associate, and remember visual stimuli, including the rostral hyperpallium, nidopallium, mesopallium, and lateral striatum. Perception of threatening faces activated circuitry including amygdalar, thalamic, and brainstem regions, known in humans and other vertebrates to be related to emotion, motivation, and conditioned fear learning. In contrast, perception of caring faces activated motivation and striatal regions. In our experiments and in nature, when perceiving a threatening face, crows froze and fixed their gaze (decreased blink rate), which was associated with activation of brain regions known in birds to regulate perception, attention, fear, and escape behavior. These findings indicate that, similar to humans, crows use sophisticated visual sensory systems to recognize faces and modulate behavioral responses by integrating visual information with expectation and emotion. Our approach has wide applicability and potential to improve our understanding of the neural basis for animal behavior. PMID:22984177

  12. 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.)

  13. Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy.

    Science.gov (United States)

    Faress, Ahmed; Chau, Tom

    2013-08-15

    Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (paccuracy of future brain-computer interfaces. Copyright © 2013 Elsevier Inc. All rights reserved.

  14. Imaging studies in congenital anophthalmia reveal preservation of brain architecture in 'visual' cortex.

    Science.gov (United States)

    Bridge, Holly; Cowey, Alan; Ragge, Nicola; Watkins, Kate

    2009-12-01

    The functional specialization of the human brain means that many regions are dedicated to processing a single sensory modality. When a modality is absent, as in congenital total blindness, 'visual' regions can be reliably activated by non-visual stimuli. The connections underlying this functional adaptation, however, remain elusive. In this study, using structural and diffusion-weighted magnetic resonance imaging, we investigated the structural differences in the brains of six bilaterally anophthalmic subjects compared with sighted subjects. Surprisingly, the gross structural differences in the brains were small, even in the occipital lobe where only a small region of the primary visual cortex showed a bilateral reduction in grey matter volume in the anophthalmic subjects compared with controls. Regions of increased cortical thickness were apparent on the banks of the Calcarine sulcus, but not in the fundus. Subcortically, the white matter volume around the optic tract and internal capsule in anophthalmic subjects showed a large decrease, yet the optic radiation volume did not differ significantly. However, the white matter integrity, as measured with fractional anisotropy showed an extensive reduction throughout the brain in the anophthalmic subjects, with the greatest difference in the optic radiations. In apparent contradiction to the latter finding, the connectivity between the lateral geniculate nucleus and primary visual cortex measured with diffusion tractography did not differ between the two populations. However, these findings can be reconciled by a demonstration that at least some of the reduction in fractional anisotropy in the optic radiation is due to an increase in the strength of fibres crossing the radiations. In summary, the major changes in the 'visual' brain in anophthalmic subjects may be subcortical, although the evidence of decreased fractional anisotropy and increased crossing fibres could indicate considerable re-organization.

  15. Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.

    Directory of Open Access Journals (Sweden)

    Jaeyun Sung

    Full Text Available We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

  16. Is the shock index based classification of hypovolemic shock applicable in multiple injured patients with severe traumatic brain injury?—an analysis of the TraumaRegister DGU®

    OpenAIRE

    Fröhlich, Matthias; Driessen, Arne; Böhmer, Andreas; Nienaber, Ulrike; Igressa, Alhadi; Probst, Christian; Bouillon, Bertil; Maegele, Marc; Mutschler, Manuel; ,

    2016-01-01

    Background A new classification of hypovolemic shock based on the shock index (SI) was proposed in 2013. This classification contains four classes of shock and shows good correlation with acidosis, blood product need and mortality. Since their applicability was questioned, the aim of this study was to verify the validity of the new classification in multiple injured patients with traumatic brain injury. Methods Between 2002 and 2013, data from 40 888 patients from the TraumaRegister DGU® were...

  17. Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks

    OpenAIRE

    C. R. Hema; Paulraj, M.P.; Nagarajan, R.; Sazali Yaacob; Abdul Hamid Adom

    2008-01-01

    Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp ...

  18. Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

    Science.gov (United States)

    Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

    2016-10-01

    Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

  19. The action sites of propofol in the normal human brain revealed by functional magnetic resonance imaging.

    Science.gov (United States)

    Zhang, Hui; Wang, Wei; Zhao, Zhijing; Ge, Yali; Zhang, Jinsong; Yu, Daihua; Chai, Wei; Wu, Shengxi; Xu, Lixian

    2010-12-01

    Propofol has been used for many years but its functional target in the intact brain remains unclear. In the present study, we used functional magnetic resonance imaging to demonstrate blood oxygen level dependence signal changes in the normal human brain during propofol anesthesia and explored the possible action targets of propofol. Ten healthy subjects were enrolled in two experimental sessions. In session 1, the Observer's Assessment of Alertness/Sedation Scale was performed to evaluate asleep to awake/alert status. In session 2, images with blood oxygen level dependence contrast were obtained with echo-planar imaging on a 1.5-T Philips Gyroscan Magnetic Resonance System and analyzed. In both sessions, subjects were intravenously administered with saline (for 3 min) and then propofol (for 1.5 min) and saline again (for 10.5 min) with a constant speed infusion pump. Observer's Assessment of Alertness/Sedation Scale scoring showed that the subjects experienced conscious–sedative–unconscious–analepsia, which correlated well with the signal decreases in the anesthesia states. Propofol induced significant signal decreases in hypothalamus (18.2%±3.6%), frontal lobe (68.5%±11.2%), and temporal lobe (34.7%±6.1%). Additionally, the signals at these three sites were fulminant and changed synchronously. While in the thalamus, the signal decrease was observed in 5 of 10 of the subjects and the magnitude of decrease was 3.9%±1.6%. These results suggest that there is most significant inhibition in hypothalamus, frontal lobe, and temporal in propofol anesthesia and moderate inhibition in thalamus. These brain regions might be the targets of propofol anesthesia in human brain.

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

    Directory of Open Access Journals (Sweden)

    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.

  1. Exercise challenge in Gulf War Illness reveals two subgroups with altered brain structure and function.

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    Rakib U Rayhan

    Full Text Available Nearly 30% of the approximately 700,000 military personnel who served in Operation Desert Storm (1990-1991 have developed Gulf War Illness, a condition that presents with symptoms such as cognitive impairment, autonomic dysfunction, debilitating fatigue and chronic widespread pain that implicate the central nervous system. A hallmark complaint of subjects with Gulf War Illness is post-exertional malaise; defined as an exacerbation of symptoms following physical and/or mental effort. To study the causal relationship between exercise, the brain, and changes in symptoms, 28 Gulf War veterans and 10 controls completed an fMRI scan before and after two exercise stress tests to investigate serial changes in pain, autonomic function, and working memory. Exercise induced two clinical Gulf War Illness subgroups. One subgroup presented with orthostatic tachycardia (n = 10. This phenotype correlated with brainstem atrophy, baseline working memory compensation in the cerebellar vermis, and subsequent loss of compensation after exercise. The other subgroup developed exercise induced hyperalgesia (n = 18 that was associated with cortical atrophy and baseline working memory compensation in the basal ganglia. Alterations in cognition, brain structure, and symptoms were absent in controls. Our novel findings may provide an understanding of the relationship between the brain and post-exertional malaise in Gulf War Illness.

  2. Genetic depletion of brain 5HT reveals a common molecular pathway mediating compulsivity and impulsivity.

    Science.gov (United States)

    Angoa-Pérez, Mariana; Kane, Michael J; Briggs, Denise I; Sykes, Catherine E; Shah, Mrudang M; Francescutti, Dina M; Rosenberg, David R; Thomas, David M; Kuhn, Donald M

    2012-06-01

    Neuropsychiatric disorders characterized by behavioral disinhibition, including disorders of compulsivity (e.g. obsessive-compulsive disorder; OCD) and impulse-control (e.g. impulsive aggression), are severe, highly prevalent and chronically disabling. Treatment options for these diseases are extremely limited. The pathophysiological bases of disorders of behavioral disinhibition are poorly understood but it has been suggested that serotonin dysfunction may play a role. Mice lacking the gene encoding brain tryptophan hydroxylase 2 (Tph2-/-), the initial and rate-limiting enzyme in the synthesis of serotonin, were tested in numerous behavioral assays that are well known for their utility in modeling human neuropsychiatric diseases. Mice lacking Tph2 (and brain 5HT) show intense compulsive and impulsive behaviors to include extreme aggression. The impulsivity is motor in form and not cognitive because Tph2-/- mice show normal acquisition and reversal learning on a spatial learning task. Restoration of 5HT levels by treatment of Tph2-/- mice with its immediate precursor 5-hydroxytryptophan attenuated compulsive and impulsive-aggressive behaviors. Surprisingly, in Tph2-/- mice, the lack of 5HT was not associated with anxiety-like behaviors. The results indicate that 5HT mediates behavioral disinhibition in the mammalian brain independent of anxiogenesis.

  3. Exercise challenge in Gulf War Illness reveals two subgroups with altered brain structure and function.

    Science.gov (United States)

    Rayhan, Rakib U; Stevens, Benson W; Raksit, Megna P; Ripple, Joshua A; Timbol, Christian R; Adewuyi, Oluwatoyin; VanMeter, John W; Baraniuk, James N

    2013-01-01

    Nearly 30% of the approximately 700,000 military personnel who served in Operation Desert Storm (1990-1991) have developed Gulf War Illness, a condition that presents with symptoms such as cognitive impairment, autonomic dysfunction, debilitating fatigue and chronic widespread pain that implicate the central nervous system. A hallmark complaint of subjects with Gulf War Illness is post-exertional malaise; defined as an exacerbation of symptoms following physical and/or mental effort. To study the causal relationship between exercise, the brain, and changes in symptoms, 28 Gulf War veterans and 10 controls completed an fMRI scan before and after two exercise stress tests to investigate serial changes in pain, autonomic function, and working memory. Exercise induced two clinical Gulf War Illness subgroups. One subgroup presented with orthostatic tachycardia (n = 10). This phenotype correlated with brainstem atrophy, baseline working memory compensation in the cerebellar vermis, and subsequent loss of compensation after exercise. The other subgroup developed exercise induced hyperalgesia (n = 18) that was associated with cortical atrophy and baseline working memory compensation in the basal ganglia. Alterations in cognition, brain structure, and symptoms were absent in controls. Our novel findings may provide an understanding of the relationship between the brain and post-exertional malaise in Gulf War Illness.

  4. 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.

  5. Small RNA sequencing-microarray analyses in Parkinson leukocytes reveal deep brain stimulation-induced and splicing changes that classify brain region transcriptomes

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    Lilach eSoreq

    2013-05-01

    Full Text Available MicroRNAs (miRNAs are key post transcriptional regulators of their multiple target genes. However, the detailed profile of miRNA expression in Parkinson's disease, the second most common neurodegenerative disease worldwide and the first motor disorder has not been charted yet. Here, we report comprehensive miRNA profiling by next-generation small-RNA sequencing, combined with targets inspection by splice-junction and exon arrays interrogating leukocyte RNA in Parkinson’s disease patients before and after deep brain stimulation (DBS treatment and of matched healthy control volunteers (HC. RNA-Seq analysis identified 254 miRNAs and 79 passenger strand forms as expressed in blood leukocytes, 16 of which were modified in patients pre treatment as compared to HC. 11 miRNAs were modified following brain stimulation, 5 of which were changed inversely to the disease induced changes. Stimulation cessation further induced changes in 11 miRNAs. Transcript isoform abundance analysis yielded 332 changed isoforms in patients compared to HC, which classified brain transcriptomes of 47 PD and control independent microarrays. Functional enrichment analysis highlighted mitochondrion organization. DBS induced 155 splice changes, enriched in ubiquitin homeostasis. Cellular composition analysis revealed immune cell activity pre and post treatment. Overall, 217 disease and 74 treatment alternative isoforms were predictably targeted by modified miRNAs within both 3’ and 5’ untranslated ends and coding sequence sites. The stimulation-induced network sustained 4 miRNAs and 7 transcripts of the disease network. We believe that the presented dynamic networks provide a novel avenue for identifying disease and treatment-related therapeutic targets. Furthermore, the identification of these networks is a major step forward in the road for understanding the molecular basis for neurological and neurodegenerative diseases and assessment of the impact of brain stimulation

  6. 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.

  7. Novel middle-type Kenyon cells in the honeybee brain revealed by area-preferential gene expression analysis.

    Science.gov (United States)

    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 novel gene(s) that are expressed in an area-preferential manner in the honeybee brain. By identifying and analyzing expression of a gene that we termed mKast (middle-type Kenyon cell-preferential arrestin-related protein), we discovered novel 'middle-type Kenyon cells' that are sandwiched between large- and small-type Kenyon cells and have a gene expression profile almost complementary to those of large- and small-type Kenyon cells. Expression analysis of kakusei revealed that both small-type Kenyon cells and some middle-type Kenyon cells are active in the forager brains, suggesting their possible involvement in information processing during the foraging flight. mKast expression began after the differentiation of small- and large-type Kenyon cells during metamorphosis, suggesting that middle-type Kenyon cells differentiate by modifying some characteristics of large- and/or small-type Kenyon cells. Interestingly, CaMKII and mKast, marker genes for large- and middle-type Kenyon cells, respectively, were preferentially expressed in a distinct set of optic lobe (a visual center) neurons. Our findings suggested that it is not simply the Kenyon cell-preferential gene expression profiles, rather, a 'clustering' of neurons with similar gene expression profiles as particular Kenyon cell types that characterize the honeybee mushroom body structure.

  8. The TNM 8 M1b and M1c classification for non-small cell lung cancer in a cohort of patients with brain metastases.

    Science.gov (United States)

    Nieder, C; Hintz, M; Oehlke, O; Bilger, A; Grosu, A L

    2017-09-01

    According to the recent TNM 8 classification, patients with metastatic non-small cell lung cancer (NSCLC) and single extrathoracic metastasis should be classified as stage M1b, while those with 2 or more metastases comprise stage M1c. The purpose of this study was to analyze the impact of this classification in patients with brain metastases. This retrospective study included 172 patients treated with individualized approaches. Actuarial survival was calculated. Uni- and multivariate analyses were performed. Thirty patients (17%) were staged as M1b. Those with squamous cell cancer were more likely to harbor M1b disease (29%, adenocarcinoma 14%, other histology 17%, p = 0.16). Median survival was 5.4 months (8.0 months in case of M1b disease and 4.5 months in case of M1c disease, p = 0.001). Multivariate analysis confirmed the role of M1b stage. M1b patients managed with upfront surgery or radiosurgery had significantly longer median survival than those who received whole-brain irradiation (21.0 vs. 3.5 months, p = 0.0001) and the potential to survive beyond 5 years. We found the M1b classification to provide clinically relevant information. The multivariate analysis suggested that patients with M1b disease, better performance status and younger age have better survival.

  9. 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.

  10. Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance.

    Science.gov (United States)

    Minkova, Lora; Eickhoff, Simon B; Abdulkadir, Ahmed; Kaller, Christoph P; Peter, Jessica; Scheller, Elisa; Lahr, Jacob; Roos, Raymund A; Durr, Alexandra; Leavitt, Blair R; Tabrizi, Sarah J; Klöppel, Stefan

    2016-01-01

    Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.

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

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    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.

  12. Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms.

    Science.gov (United States)

    Sun, Daqiang; van Erp, Theo G M; Thompson, Paul M; Bearden, Carrie E; Daley, Melita; Kushan, Leila; Hardt, Molly E; Nuechterlein, Keith H; Toga, Arthur W; Cannon, Tyrone D

    2009-12-01

    No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data. Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects. Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation. These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.

  13. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

    Science.gov (United States)

    Naseer, Noman; Noori, Farzan M; Qureshi, Nauman K; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.

  14. Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2016-05-01

    Full Text Available In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS signals with the best accuracies for development of a two-class brain-computer interface (BCI. Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR features — mean, slope, variance, peak, skewness and kurtosis — are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic versus rest using linear discriminant analysis (LDA. It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05 classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic versus rest for a two-class BCI.

  15. 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.

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

    Science.gov (United States)

    Jin, Ling-Min; Qin, Cai-Juan; Lan, Lei; Sun, Jin-Bo; Zeng, Fang; Zhu, Yuan-Qiang; Yu, Shu-Guang; Yin, Hai-Yan; Tang, Yong

    2014-01-01

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  18. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia

    Science.gov (United States)

    Lee, Phil H.; Baker, Justin T.; Holmes, Avram J.; Jahanshad, Neda; Ge, Tian; Jung, Jae-Yoon; Cruz, Yanela; Manoach, Dara S.; Hibar, Derrek P.; Faskowitz, Joshua; McMahon, Katie L.; de Zubicaray, Greig I.; Martin, Nicolas H.; Wright, Margaret J.; Öngür, Dost; Buckner, Randy; Roffman, Joshua; Thompson, Paul M.; Smoller, Jordan W.

    2016-01-01

    Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide SNP and neuroimaging data from 1,750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (FDR=10%). In particular, intracranial volume (ICV) and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder. PMID:27725656

  19. 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.

  20. Proteomic analysis and functional characterization of mouse brain mitochondria during aging reveal alterations in energy metabolism.

    Science.gov (United States)

    Stauch, Kelly L; Purnell, Phillip R; Villeneuve, Lance M; Fox, Howard S

    2015-05-01

    Mitochondria are the main cellular source of reactive oxygen species and are recognized as key players in several age-associated disorders and neurodegeneration. Their dysfunction has also been linked to cellular aging. Additionally, mechanisms leading to the preservation of mitochondrial function promote longevity. In this study we investigated the proteomic and functional alterations in brain mitochondria isolated from mature (5 months old), old (12 months old), and aged (24 months old) mice as determinants of normal "healthy" aging. Here the global changes concomitant with aging in the mitochondrial proteome of mouse brain analyzed by quantitative mass-spectrometry based super-SILAC identified differentially expressed proteins involved in several metabolic pathways including glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation. Despite these changes, the bioenergetic function of these mitochondria was preserved. Overall, this data indicates that proteomic changes during aging may compensate for functional defects aiding in preservation of mitochondrial function. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD001370 (http://proteomecentral.proteomexchange.org/dataset/PXD001370).

  1. 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.

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    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.

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

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    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.

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

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    Mahmood, Faisal; Johannesen, Helle H.; Geertsen, Poul; Hansen, Rasmus H.

    2017-04-01

    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 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 for acquiring the DW-MRI scan during the course of treatment, since to our knowledge this important question has not been addressed directly in previous studies. Twenty-nine metastases (N  =  29) from twenty-one patients, treated with whole-brain fractionated external beam RT were analyzed. Patients were scanned with a 1 T MRI system to acquire DW-, T2*W-, T2W- and T1W scans, before start of RT, at each fraction and at follow up two to three months after RT. The DW-MRI parameters were derived using regions of interest based on high b-value images (b  =  800 s mm‑2). Both volumetric and RECIST criteria were applied for response evaluation. It was found that in non-responding metastases the mean ADC decreased and in responding metastases it increased. The volume based response proved to be far more consistently predictable by the ADC change found at fraction number 7 and later, compared to the linear response (RECIST). The perfusion fraction and pseudo diffusion coefficient did not show sufficient prognostic value with either response assessment criteria. In conclusion this study shows that the ADC derived using high b-values may be a reliable biomarker for early assessment of radiotherapy response for brain metastases patients. The earliest response stratification can be achieved using two DW-MRI scans, one pre-treatment and one at treatment day 7–9 (equivalent to 21

  4. [Lowe syndrome revealed by prenatal diagnosis of congenital cataract with brain abnormalities].

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    Zéphir, P; Decramer, S; Sartor, A; Vayssière, C

    2014-05-01

    Congenital cataract is a rare disease whose incidence is estimated to 0.5% of birth in France. A study of the literature shows that congenital cataract is idiopathic in 50% of cases, hereditary forms representing 25% of cases. Other causes of congenital cataract are represented by viral embryofoetopathies acquired during pregnancy, metabolic disorders and chromosomal aberrations within the scope of malformative syndromes. The authors report the case of a neonatal diagnosis of Lowe syndrome suspected by the discovery of bilateral cataract initially isolated. The morphological exploration was completed by secondary brain abnormalities (periventricular lesions). The etiological prenatal exploration was negative. Lowe syndrome is a rare cause of antenatal cataract, which so far only one case has been reported.

  5. Immunogold labeling reveals subcellular localisation of silica nanoparticles in a human blood-brain barrier model

    Science.gov (United States)

    Ye, Dong; Anguissola, Sergio; O'Neill, Tiina; Dawson, Kenneth A.

    2015-05-01

    Subcellular location of nanoparticles has been widely investigated with fluorescence microscopy, via fluorescently labeled antibodies to visualise target antigens in cells. However, fluorescence microscopy, such as confocal or live cell imaging, has generally limited 3D spatial resolution. Conventional electron microscopy can be useful in bridging resolution gap, but still not ideal in resolving subcellular organelle identities. Using the pre-embedding immunogold electron microscopic imaging, we performed accurate examination of the intracellular trafficking and gathered further evidence of transport mechanisms of silica nanoparticles across a human in vitro blood-brain barrier model. Our approach can effectively immunolocalise a variety of intracellular compartments and provide new insights into the uptake and subcellular transport of nanoparticles.Subcellular location of nanoparticles has been widely investigated with fluorescence microscopy, via fluorescently labeled antibodies to visualise target antigens in cells. However, fluorescence microscopy, such as confocal or live cell imaging, has generally limited 3D spatial resolution. Conventional electron microscopy can be useful in bridging resolution gap, but still not ideal in resolving subcellular organelle identities. Using the pre-embedding immunogold electron microscopic imaging, we performed accurate examination of the intracellular trafficking and gathered further evidence of transport mechanisms of silica nanoparticles across a human in vitro blood-brain barrier model. Our approach can effectively immunolocalise a variety of intracellular compartments and provide new insights into the uptake and subcellular transport of nanoparticles. Electronic supplementary information (ESI) available: Nanoparticle characterisation data, preservation of cellular structures, staining controls, optimisation of size amplification via the silver enhancement, and more imaging results from anti-clathrin and anti-caveolin 1

  6. Gene Regulatory Network Analysis Reveals Differences in Site-specific Cell Fate Determination in Mammalian Brain

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    Gokhan eErtaylan

    2014-12-01

    Full Text Available Neurogenesis - the generation of new neurons - is an ongoing process that persists in the adult mammalian brain of several species, including humans. In this work we analyze two discrete brain regions: the subventricular zone (SVZ lining the walls of the lateral ventricles; and the subgranular zone (SGZ of the dentate gyrus of the hippocampus in mice and shed light on the SVZ and SGZ specific neurogenesis. We propose a computational model that relies on the construction and analysis of region specific gene regulatory networks from the publicly available data on these two regions. Using this model a number of putative factors involved in neuronal stem cell (NSC identity and maintenance were identified. We also demonstrate potential gender and niche-derived differences based on cell surface and nuclear receptors via Ar, Hif1a and Nr3c1.We have also conducted cell fate determinant analysis for SVZ NSC populations to Olfactory Bulb interneurons and SGZ NSC populations to the granule cells of the Granular Cell Layer. We report thirty-one candidate cell fate determinant gene pairs, ready to be validated. We focus on Ar - Pax6 in SVZ and Sox2 - Ncor1 in SGZ. Both pairs are expressed and localized in the suggested anatomical structures as shown by in situ hybridization and found to physically interact.Finally, we conclude that there are fundamental differences between SGZ and SVZ neurogenesis. We argue that these regulatory mechanisms are linked to the observed differential neurogenic potential of these regions. The presence of nuclear and cell surface receptors in the region specific regulatory circuits indicate the significance of niche derived extracellular factors, hormones and region specific factors such as the oxygen sensitivity, dictating SGZ and SVZ specific neurogenesis.

  7. The Biology Of Physics: What The Brain Reveals About Our Understanding Of The Physical World

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    Dunbar, Kevin Niall

    2009-11-01

    Fundamental concepts in physics such as Newtonian mechanics are surprisingly difficult to learn and discover. Over the past decade have been using an educational neuroscience approach to science education using a combination of ecologically naturalistic situations, classroom settings, and neuroimaging methodologies to investigate the different ways that scientific concepts are invoked or activated in different contexts. In particular, we have sought to determine how networks of brain regions that are highly sensitive to features of the context in which they are used are involved in the use of scientific concepts. We have found that some concepts in physics that are highly tuned to perception are often inhibited in experts (with increased activations in error detection and inhibitory networks of the prefrontal cortex) rather than having undergone a wholesale conceptual reorganization. Other, concepts, such as those involved in perceptual causality can activate highly diverse brain regions, depending on task instructions. For example, when students are shown movies of balls colliding, we find increased activation in the right parietal lobe, yet when the students see the exact same movies and are told that these are positively charged particles repulsing we find increased activations in the temporal lobe that is consistent with the students retrieving semantic information. We also see similar see similar changes in activation patterns in students learning about phase shifts in chemistry classes. A key component of both students and scientists' discourse and reasoning is analogical thinking. Our recent fMRI work indicates that categorization is a key component of this type of reasoning that helps bind superficially different concepts together in the service of reasoning about the causes of unexpected findings. Taken together, these results are allowing us to make insights into the contextually relevant networks of knowledge that are activated during learning. This work

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

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    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

  9. Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion.

    Science.gov (United States)

    Savalia, Neil K; Agres, Phillip F; Chan, Micaela Y; Feczko, Eric J; Kennedy, Kristen M; Wig, Gagan S

    2017-01-01

    Motion-contaminated T1-weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion-induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20-89 years) were analyzed to reveal stable features of in-scanner head motion. The magnitude of head motion increased with age and exhibited within-participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to "flag" individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age- and gender-matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472-492, 2017. © 2016 Wiley Periodicals, Inc.

  10. Global deprivation of brain-derived neurotrophic factor in the CNS reveals an area-specific requirement for dendritic growth.

    Science.gov (United States)

    Rauskolb, Stefanie; Zagrebelsky, Marta; Dreznjak, Anita; Deogracias, Rubén; Matsumoto, Tomoya; Wiese, Stefan; Erne, Beat; Sendtner, Michael; Schaeren-Wiemers, Nicole; Korte, Martin; Barde, Yves-Alain

    2010-02-03

    Although brain-derived neurotrophic factor (BDNF) is linked with an increasing number of conditions causing brain dysfunction, its role in the postnatal CNS has remained difficult to assess. This is because the bdnf-null mutation causes the death of the animals before BDNF levels have reached adult levels. In addition, the anterograde axonal transport of BDNF complicates the interpretation of area-specific gene deletion. The present study describes the generation of a new conditional mouse mutant essentially lacking BDNF throughout the CNS. It shows that BDNF is not essential for prolonged postnatal survival, but that the behavior of such mutant animals is markedly altered. It also reveals that BDNF is not a major survival factor for most CNS neurons and for myelination of their axons. However, it is required for the postnatal growth of the striatum, and single-cell analyses revealed a marked decreased in dendritic complexity and spine density. In contrast, BDNF is dispensable for the growth of the hippocampus and only minimal changes were observed in the dendrites of CA1 pyramidal neurons in mutant animals. Spine density remained unchanged, whereas the proportion of the mushroom-type spine was moderately decreased. In line with these in vivo observations, we found that BDNF markedly promotes the growth of cultured striatal neurons and of their dendrites, but not of those of hippocampal neurons, suggesting that the differential responsiveness to BDNF is part of a neuron-intrinsic program.

  11. Dissecting the social brain: Introducing the EmpaToM to reveal distinct neural networks and brain-behavior relations for empathy and Theory of Mind.

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    Kanske, Philipp; Böckler, Anne; Trautwein, Fynn-Mathis; Singer, Tania

    2015-11-15

    Successful social interactions require both affect sharing (empathy) and understanding others' mental states (Theory of Mind, ToM). As these two functions have mostly been investigated in isolation, the specificity of the underlying neural networks and the relation of these networks to the respective behavioral indices could not be tested. Here, we present a novel fMRI paradigm (EmpaToM) that independently manipulates both empathy and ToM. Experiments 1a/b (N=90) validated the task with established empathy and ToM paradigms on a behavioral and neural level. Experiment 2 (N=178) employed the EmpaToM and revealed clearly separable neural networks including anterior insula for empathy and ventral temporoparietal junction for ToM. These distinct networks could be replicated in task-free resting state functional connectivity. Importantly, brain activity in these two networks specifically predicted the respective behavioral indices, that is, inter-individual differences in ToM related brain activity predicted inter-individual differences in ToM performance, but not empathic responding, and vice versa. Taken together, the validated EmpaToM allows separation of affective and cognitive routes to understanding others. It may thus benefit future clinical, developmental, and intervention studies on identifying selective impairments and improvement in specific components of social cognition.

  12. 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

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

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    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

  14. 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.

  15. Quantitative Proteomics of Sleep-Deprived Mouse Brains Reveals Global Changes in Mitochondrial Proteins

    Science.gov (United States)

    Li, Tie-Mei; Zhang, Ju-en; Lin, Rui; Chen, She; Luo, Minmin; Dong, Meng-Qiu

    2016-01-01

    Sleep is a ubiquitous, tightly regulated, and evolutionarily conserved behavior observed in almost all animals. Prolonged sleep deprivation can be fatal, indicating that sleep is a physiological necessity. However, little is known about its core function. To gain insight into this mystery, we used advanced quantitative proteomics technology to survey the global changes in brain protein abundance. Aiming to gain a comprehensive profile, our proteomics workflow included filter-aided sample preparation (FASP), which increased the coverage of membrane proteins; tandem mass tag (TMT) labeling, for relative quantitation; and high resolution, high mass accuracy, high throughput mass spectrometry (MS). In total, we obtained the relative abundance ratios of 9888 proteins encoded by 6070 genes. Interestingly, we observed significant enrichment for mitochondrial proteins among the differentially expressed proteins. This finding suggests that sleep deprivation strongly affects signaling pathways that govern either energy metabolism or responses to mitochondrial stress. Additionally, the differentially-expressed proteins are enriched in pathways implicated in age-dependent neurodegenerative diseases, including Parkinson’s, Huntington’s, and Alzheimer’s, hinting at possible connections between sleep loss, mitochondrial stress, and neurodegeneration. PMID:27684481

  16. Error-related negativity in the skilled brain of pianists reveals motor simulation.

    Science.gov (United States)

    Proverbio, Alice Mado; Cozzi, Matteo; Orlandi, Andrea; Carminati, Manuel

    2017-03-27

    Evidences have been provided of a crucial role of multimodal audio-visuomotor processing in subserving the musical ability. In this paper we investigated whether musical audiovisual stimulation might trigger the activation of motor information in the brain of professional pianists, due to the presence of permanent gestures/sound associations. At this aim EEG was recorded in 24 pianists and naive participants engaged in the detection of rare targets while watching hundreds of video clips showing a pair of hands in the act of playing, along with a compatible or incompatible piano soundtrack. Hands size and apparent distance allowed self-ownership and agency illusions, and therefore motor simulation. Event-related potentials (ERPs) and relative source reconstruction showed the presence of an Error-related negativity (ERN) to incongruent trials at anterior frontal scalp sites, only in pianists, with no difference in naïve participants. ERN was mostly explained by an anterior cingulate cortex (ACC) source. Other sources included "hands" IT regions, the superior temporal gyrus (STG) involved in conjoined auditory and visuomotor processing, SMA and cerebellum (representing and controlling motor subroutines), and regions involved in body parts representation (somatosensory cortex, uncus, cuneus and precuneus). The findings demonstrate that instrument-specific audiovisual stimulation is able to trigger error shooting and correction neural responses via motor resonance and mirroring, being a possible aid in learning and rehabilitation. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  17. The neural bases for devaluing radical political statements revealed by penetrating traumatic brain injury.

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    Cristofori, Irene; Viola, Vanda; Chau, Aileen; Zhong, Wanting; Krueger, Frank; Zamboni, Giovanna; Grafman, Jordan

    2015-08-01

    Given the determinant role of ventromedial prefrontal cortex (vmPFC) in valuation, we examined whether vmPFC lesions also modulate how people scale political beliefs. Patients with penetrating traumatic brain injury (pTBI; N = 102) and healthy controls (HCs; N = 31) were tested on the political belief task, where they rated 75 statements expressing political opinions concerned with welfare, economy, political involvement, civil rights, war and security. Each statement was rated for level of agreement and scaled along three dimensions: radicalism, individualism and conservatism. Voxel-based lesion-symptom mapping (VLSM) analysis showed that diminished scores for the radicalism dimension (i.e. statements were rated as less radical than the norms) were associated with lesions in bilateral vmPFC. After dividing the pTBI patients into three groups, according to lesion location (i.e. vmPFC, dorsolateral prefrontal cortex [dlPFC] and parietal cortex), we found that the vmPFC, but not the dlPFC, group had reduced radicalism scores compared with parietal and HC groups. These findings highlight the crucial role of the vmPFC in appropriately valuing political behaviors and may explain certain inappropriate social judgments observed in patients with vmPFC lesions.

  18. Neuroaffective processing in criminal psychopaths: brain event-related potentials reveal task-specific anomalies.

    Science.gov (United States)

    Howard, Rick; McCullagh, Paul

    2007-06-01

    This study aimed to confirm neuroaffective processing deficits in psychopaths by measuring late brain event-related potential (ERP) components and behavior in groups of psychopathic and nonpsychopathic inmates of a Singaporean prison while they performed two tasks. In a Categorization task, affective stimuli were task-relevant and required focused attention, while in a Vigilance task, affective pictures were presented in the background while participants discriminated vertical from oblique lines. Psychopaths showed differences in late positive ERPs that were sensitive to affective stimulus properties (valence and arousal) in the Categorization, but not in the Vigilance task, suggesting that only under conditions of focused attention did psychopaths show a neuroaffective processing deficit. In the Categorization task, psychopaths also showed a significantly larger prefrontal negative ERP (N350) whose amplitude correlated positively with the behavioral facet of psychopathy. In the Vigilance task, psychopaths both missed more targets and showed significantly smaller target-evoked parietal ERPs when viewing arousing pictures, suggesting their attentional focus was disrupted by the affective background.

  19. Differences in brain circuitry for appetitive and reactive aggression as revealed by realistic auditory scripts

    Directory of Open Access Journals (Sweden)

    James Kenneth Moran

    2014-12-01

    Full Text Available Aggressive behavior is thought to divide into two motivational elements: The first being a self-defensively motivated aggression against threat and a second, hedonically motivated ‘appetitive’ aggression. Appetitive aggression is the less understood of the two, often only researched within abnormal psychology. Our approach is to understand it as a universal and adaptive response, and examine the functional neural activity of ordinary men (N=50 presented with an imaginative listening task involving a murderer describing a kill. We manipulated motivational context in a between-subjects design to evoke appetitive or reactive aggression, against a neutral control, measuring activity with Magnetoencephalography (MEG. Results show differences in left frontal regions in delta (2-5 Hz and alpha band (8-12 Hz for aggressive conditions and right parietal delta activity differentiating appetitive and reactive aggression. These results validate the distinction of reward-driven appetitive aggression from reactive aggression in ordinary populations at the level of functional neural brain circuitry.

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

    Science.gov (United States)

    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.

  1. Longitudinal MRI reveals altered trajectory of brain development during childhood and adolescence in fetal alcohol spectrum disorders.

    Science.gov (United States)

    Treit, Sarah; Lebel, Catherine; Baugh, Lauren; Rasmussen, Carmen; Andrew, Gail; Beaulieu, Christian

    2013-06-12

    Diffusion tensor imaging (DTI) of brain development in fetal alcohol spectrum disorders (FASD) has revealed structural abnormalities, but studies have been limited by the use of cross-sectional designs. Longitudinal scans can provide key insights into trajectories of neurodevelopment within individuals with this common developmental disorder. Here we evaluate serial DTI and T1-weighted volumetric MRI in a human sample of 17 participants with FASD and 27 controls aged 5-15 years who underwent 2-3 scans each, ∼2-4 years apart (92 scans total). Increases of fractional anisotropy and decreases of mean diffusivity (MD) were observed between scans for both groups, in keeping with changes expected of typical development, but mixed-models analysis revealed significant age-by-group interactions for three major white matter tracts: superior longitudinal fasciculus and superior and inferior fronto-occipital fasciculus. These findings indicate altered developmental progression in these frontal-association tracts, with the FASD group notably showing greater reduction of MD between scans. ΔMD is shown to correlate with reading and receptive vocabulary in the FASD group, with steeper decreases of MD in the superior fronto-occipital fasciculus and superior longitudinal fasciculus between scans correlating with greater improvement in language scores. Volumetric analysis revealed reduced total brain, white, cortical gray, and deep gray matter volumes and fewer significant age-related volume increases in the FASD group, although age-by-group interactions were not significant. Longitudinal DTI indicates delayed white matter development during childhood and adolescence in FASD, which may underlie persistent or worsening behavioral and cognitive deficits during this critical period.

  2. Pathway analysis reveals common pro-survival mechanisms of metyrapone and carbenoxolone after traumatic brain injury.

    Directory of Open Access Journals (Sweden)

    Helen L Hellmich

    Full Text Available Developing new pharmacotherapies for traumatic brain injury (TBI requires elucidation of the neuroprotective mechanisms of many structurally and functionally diverse compounds. To test our hypothesis that diverse neuroprotective drugs similarly affect common gene targets after TBI, we compared the effects of two drugs, metyrapone (MT and carbenoxolone (CB, which, though used clinically for noncognitive conditions, improved learning and memory in rats and humans. Although structurally different, both MT and CB inhibit a common molecular target, 11β hydroxysteroid dehydrogenase type 1, which converts inactive cortisone to cortisol, thereby effectively reducing glucocorticoid levels. We examined injury-induced signaling pathways to determine how the effects of these two compounds correlate with pro-survival effects in surviving neurons of the injured rat hippocampus. We found that treatment of TBI rats with MT or CB acutely induced in hippocampal neurons transcriptional profiles that were remarkably similar (i.e., a coordinated attenuation of gene expression across multiple injury-induced cell signaling networks. We also found, to a lesser extent, a coordinated increase in cell survival signals. Analysis of injury-induced gene expression altered by MT and CB provided additional insight into the protective effects of each. Both drugs attenuated expression of genes in the apoptosis, death receptor and stress signaling pathways, as well as multiple genes in the oxidative phosphorylation pathway such as subunits of NADH dehydrogenase (Complex1, cytochrome c oxidase (Complex IV and ATP synthase (Complex V. This suggests an overall inhibition of mitochondrial function. Complex 1 is the primary source of reactive oxygen species in the mitochondrial oxidative phosphorylation pathway, thus linking the protective effects of these drugs to a reduction in oxidative stress. The net effect of the drug-induced transcriptional changes observed here indicates that

  3. Deficient orthographic and phonological representations in children with dyslexia revealed by brain activation patterns

    Science.gov (United States)

    Cao, Fan; Bitan, Tali; Chou, Tai-Li; Burman, Douglas D.

    2008-01-01

    Background The current study examined the neuro-cognitive network of visual word rhyming judgment in 14 children with dyslexia and 14 age-matched control children (8- to 14-year-olds) using functional magnetic resonance imaging (fMRI). Methods In order to manipulate the difficulty of mapping orthography to phonology, we used conflicting and non-conflicting trials. The words in conflicting trials either had similar orthography but different phonology (e.g., pint-mint) or similar phonology but different orthography (e.g., jazz-has). The words in non-conflicting trials had similar orthography and phonology (e.g., gate-hate) or different orthography and phonology (e.g., press-list). Results There were no differences in brain activation between the controls and children with dyslexia in the easier non-conflicting trials. However, the children with dyslexia showed less activation than the controls in left inferior frontal gyrus (BA 45/44/47/9), left inferior parietal lobule (BA 40), left inferior temporal gyrus/fusiform gyrus (BA 20/37) and left middle temporal gyrus (BA 21) for the more difficult conflicting trials. For the direct comparison of conflicting minus non-conflicting trials, controls showed greater activation than children with dyslexia in left inferior frontal gyrus (BA 9/45/46) and medial frontal gyrus (BA 8). Children with dyslexia did not show greater activation than controls for any comparison. Conclusions Reduced activation in these regions suggests that children with dyslexia have deficient orthographic representations in ventral temporal cortex as well as deficits in mapping between orthographic and phonological representations in inferior parietal cortex. The greater activation for the controls in inferior frontal gyrus could reflect more effective top-down modulation of posterior representations. PMID:17073983

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

    Directory of Open Access Journals (Sweden)

    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.

  5. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

    NARCIS (Netherlands)

    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 dise

  6. Sound Classification and Call Discrimination Are Decoded in Order as Revealed by Event-Related Potential Components in Frogs.

    Science.gov (United States)

    Fang, Guangzhan; Yang, Ping; Xue, Fei; Cui, Jianguo; Brauth, Steven E; Tang, Yezhong

    2015-01-01

    Species that use communication sounds to coordinate social and reproductive behavior must be able to distinguish vocalizations from nonvocal sounds as well as to identify individual vocalization types. In this study we sought to identify the neural localization of the processes involved and the temporal order in which they occur in an anuran species, the music frog Babina daunchina. To do this we measured telencephalic and mesencephalic event-related potentials (ERPs) elicited by synthesized white noise (WN), highly sexually attractive (HSA) calls produced by males from inside nests and male calls of low sexual attractiveness (LSA) produced outside of nests. Each stimulus possessed similar temporal structures. The results showed the following: (1) the amplitudes of the first negative ERP component (N1) at ∼ 100 ms differed significantly between WN and conspecific calls but not between HSA and LSA calls, indicating that discrimination between conspecific calls and nonvocal sounds occurs in ∼ 100 ms, (2) the amplitudes of the second positive ERP component (P2) at ∼ 200 ms in the difference waves between HSA calls and WN were significantly higher than between LSA calls and WN in the right telencephalon, implying that call characteristic identification occurs in ∼ 200 ms and (3) WN evoked a larger third positive ERP component (P3) at ∼ 300 ms than conspecific calls, suggesting the frogs had classified the conspecific calls into one category and perceived WN as novel. Thus, both the detection of sounds and the identification of call characteristics are accomplished quickly in a specific temporal order, as reflected by ERP components. In addition, the most dynamic ERP patterns appeared in the left mesencephalon and the right telencephalon, indicating the two brain regions might play key roles in anuran vocal communication. © 2015 S. Karger AG, Basel.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions

    Science.gov (United States)

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M.; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  9. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions.

    Science.gov (United States)

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  10. Exome sequence reveals mutations in CoA synthase as a cause of neurodegeneration with brain iron accumulation.

    Science.gov (United States)

    Dusi, Sabrina; Valletta, Lorella; Haack, Tobias B; Tsuchiya, Yugo; Venco, Paola; Pasqualato, Sebastiano; Goffrini, Paola; Tigano, Marco; Demchenko, Nikita; Wieland, Thomas; Schwarzmayr, Thomas; Strom, Tim M; Invernizzi, Federica; Garavaglia, Barbara; Gregory, Allison; Sanford, Lynn; Hamada, Jeffrey; Bettencourt, Conceição; Houlden, Henry; Chiapparini, Luisa; Zorzi, Giovanna; Kurian, Manju A; Nardocci, Nardo; Prokisch, Holger; Hayflick, Susan; Gout, Ivan; Tiranti, Valeria

    2014-01-02

    Neurodegeneration with brain iron accumulation (NBIA) comprises a clinically and genetically heterogeneous group of disorders with progressive extrapyramidal signs and neurological deterioration, characterized by iron accumulation in the basal ganglia. Exome sequencing revealed the presence of recessive missense mutations in COASY, encoding coenzyme A (CoA) synthase in one NBIA-affected subject. A second unrelated individual carrying mutations in COASY was identified by Sanger sequence analysis. CoA synthase is a bifunctional enzyme catalyzing the final steps of CoA biosynthesis by coupling phosphopantetheine with ATP to form dephospho-CoA and its subsequent phosphorylation to generate CoA. We demonstrate alterations in RNA and protein expression levels of CoA synthase, as well as CoA amount, in fibroblasts derived from the two clinical cases and in yeast. This is the second inborn error of coenzyme A biosynthesis to be implicated in NBIA.

  11. Analysis of tumor metabolism reveals mitochondrial glucose oxidation in genetically diverse, human glioblastomas in the mouse brain in vivo

    Science.gov (United States)

    Marin-Valencia, Isaac; Yang, Chendong; Mashimo, Tomoyuki; Cho, Steve; Baek, Hyeonman; Yang, Xiao-Li; Rajagopalan, Kartik N.; Maddie, Melissa; Vemireddy, Vamsidhara; Zhao, Zhenze; Cai, Ling; Good, Levi; Tu, Benjamin P.; Hatanpaa, Kimmo J.; Mickey, Bruce E.; Matés, José M.; Pascual, Juan M.; Maher, Elizabeth A.; Malloy, Craig R.; DeBerardinis, Ralph J.; Bachoo, Robert M.

    2012-01-01

    SUMMARY Dysregulated metabolism is a hallmark of cancer cell lines, but little is known about the fate of glucose and other nutrients in tumors growing in their native microenvironment. To study tumor metabolism in vivo, we used an orthotopic mouse model of primary human glioblastoma (GBM). We infused 13C-labeled nutrients into mice bearing three independent GBM lines, each with a distinct set of mutations. All three lines displayed glycolysis, as expected for aggressive tumors. They also displayed unexpected metabolic complexity, oxidizing glucose via pyruvate dehydrogenase and the citric acid cycle, and using glucose to supply anaplerosis and other biosynthetic activities. Comparing the tumors to surrounding brain revealed obvious metabolic differences, notably the accumulation of a large glutamine pool within the tumors. Many of these same activities were conserved in cells cultured ex vivo from the tumors. Thus GBM cells utilize mitochondrial glucose oxidation during aggressive tumor growth in vivo. PMID:22682223

  12. Comparative analysis of A-to-I editing in human and non-human primate brains reveals conserved patterns and context-dependent regulation of RNA editing.

    Science.gov (United States)

    O'Neil, Richard T; Wang, Xiaojing; Morabito, Michael V; Emeson, Ronald B

    2017-04-06

    A-to-I RNA editing is an important process for generating molecular diversity in the brain through modification of transcripts encoding several proteins important for neuronal signaling. We investigated the relationships between the extent of editing at multiple substrate transcripts (5HT2C, MGLUR4, CADPS, GLUR2, GLUR4, and GABRA3) in brain tissue obtained from adult humans and rhesus macaques. Several patterns emerged from these studies revealing conservation of editing across primate species. Additionally, variability in the human population allows us to make novel inferences about the co-regulation of editing at different editing sites and even across different brain regions.

  13. 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...

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

    Directory of Open Access Journals (Sweden)

    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.

  15. 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

  16. 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.; Zwiers, M.P.; Rommelse, N.N.J.; Hartman, C.A.; Meer, D. van der; O'Dwyer, L.; 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

  17. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

    Science.gov (United States)

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

  18. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.

    Science.gov (United States)

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

    2016-01-01

    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

  19. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis

    Science.gov (United States)

    Pan, Jie; Gao, Xiaorong; Duan, Fang; Yan, Zheng; Gao, Shangkai

    2011-06-01

    In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.

  20. A Subset of Cerebrospinal Fluid Proteins from a Multi-Analyte Panel Associated with Brain Atrophy, Disease Classification and Prediction in Alzheimer's Disease.

    Science.gov (United States)

    Khan, Wasim; Aguilar, Carlos; Kiddle, Steven J; Doyle, Orla; Thambisetty, Madhav; Muehlboeck, Sebastian; Sattlecker, Martina; Newhouse, Stephen; Lovestone, Simon; Dobson, Richard; Giampietro, Vincent; Westman, Eric; Simmons, Andrew

    2015-01-01

    In this exploratory neuroimaging-proteomic study, we aimed to identify CSF proteins associated with AD and test their prognostic ability for disease classification and MCI to AD conversion prediction. Our study sample consisted of 295 subjects with CSF multi-analyte panel data and MRI at baseline downloaded from ADNI. Firstly, we tested the statistical effects of CSF proteins (n = 83) to measures of brain atrophy, CSF biomarkers, ApoE genotype and cognitive decline. We found that several proteins (primarily CgA and FABP) were related to either brain atrophy or CSF biomarkers. In relation to ApoE genotype, a unique biochemical profile characterised by low CSF levels of Apo E was evident in ε4 carriers compared to ε3 carriers. In an exploratory analysis, 3/83 proteins (SGOT, MCP-1, IL6r) were also found to be mildly associated with cognitive decline in MCI subjects over a 4-year period. Future studies are warranted to establish the validity of these proteins as prognostic factors for cognitive decline. For disease classification, a subset of proteins (n = 24) combined with MRI measurements and CSF biomarkers achieved an accuracy of 95.1% (Sensitivity 87.7%; Specificity 94.3%; AUC 0.95) and accurately detected 94.1% of MCI subjects progressing to AD at 12 months. The subset of proteins included FABP, CgA, MMP-2, and PPP as strong predictors in the model. Our findings suggest that the marker of panel of proteins identified here may be important candidates for improving the earlier detection of AD. Further targeted proteomic and longitudinal studies would be required to validate these findings with more generalisability.

  1. Classification accuracy of the Millon Clinical Multiaxial Inventory-III modifier indices in the detection of malingering in traumatic brain injury.

    Science.gov (United States)

    Aguerrevere, Luis E; Greve, Kevin W; Bianchini, Kevin J; Ord, Jonathan S

    2011-06-01

    The present study used criterion groups validation to determine the ability of the Millon Clinical Multiaxial Inventory-III (MCMI-III) modifier indices to detect malingering in traumatic brain injury (TBI). Patients with TBI who met criteria for malingered neurocognitive dysfunction (MND) were compared to those who showed no indications of malingering. Data were collected from 108 TBI patients referred for neuropsychological evaluation. Base rate (BR) scores were used for MCMI-III modifier indices: Disclosure, Desirability, and Debasement. Malingering classification was based on the Slick, Sherman, and Iverson (1999) criteria for MND. TBI patients were placed in one of three groups: MND (n = 55), not-MND (n = 26), or Indeterminate (n = 26).The not-MND group had lower modifier index scores than the MND group. At scores associated with a 4% false-positive (FP) error rate, sensitivity was 47% for Disclosure, 51% for Desirability, and 55% for Debasement. Examination of joint classification analysis demonstrated 54% sensitivity at cutoffs associated with 0% FP error rate. Results suggested that scores from all MCMI-III modifier indices are useful for identifying intentional symptom exaggeration in TBI. Debasement was the most sensitive of the three indices. Clinical implications are discussed.

  2. 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.

  3. 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.

  4. Event-related brain potentials reveal correlates of the transformation of stimulus functions through derived relations in healthy humans.

    Science.gov (United States)

    O'Regan, L M; Farina, F R; Hussey, I; Roche, R A P

    2015-03-02

    This research aimed to explore the neural correlates of relational learning by recording high-density EEG during a behavioural task involving derivation levels of varying complexity. A total of 15 participants (5 male; age range 18-23 years; mean age=20.0 years) completed contextual cue training, relational learning, function training and a derivation task while 128-channel event-related potentials (ERPs) were recorded from the scalp (Background). Differences in response latencies were observed between the two derived (symmetry and equivalence) and directly trained relations, with longest latencies found for equivalence and shortest for the directly trained relations. This pattern failed to reach statistical significance. Importantly, ERPs revealed an early P3a positivity (from 230 to 350ms) over right posterior scalp sites. Significantly larger mean amplitudes were found at three channels (P6, E115 and E121) for the equivalence relations compared to the two other types (Results). We believe this may constitute a first demonstration of differences in brain electrophysiology in the transformation of stimulus functions through derived relations of hierarchical levels of complexity (Conclusions). Copyright © 2014 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Männel, Claudia; Schaadt, Gesa; Illner, Franziska K; van der Meer, Elke; Friederici, Angela D

    2017-02-01

    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. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Tactile Object Familiarity in the Blind Brain Reveals the Supramodal Perceptual-Mnemonic Nature of the Perirhinal Cortex

    Science.gov (United States)

    Cacciamani, Laura; Likova, Lora T.

    2016-01-01

    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 participants 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). Functional magnetic resonance imaging (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. PMID:27148002

  7. Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month.

    Directory of Open Access Journals (Sweden)

    Bing Chen

    Full Text Available Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.

  8. Impaired integrity of the brain parenchyma in non-geriatric patients with major depressive disorder revealed by diffusion tensor imaging.

    Science.gov (United States)

    Tha, Khin K; Terae, Satoshi; Nakagawa, Shin; Inoue, Takeshi; Kitagawa, Nobuki; Kako, Yuki; Nakato, Yasuya; Akter Popy, Kawser; Fujima, Noriyuki; Zaitsu, Yuri; Yoshida, Daisuke; Ito, Yoichi M; Miyamoto, Tamaki; Koyama, Tsukasa; Shirato, Hiroki

    2013-06-30

    Diffusion tensor imaging (DTI) is considered to be able to non-invasively quantify white matter integrity. This study aimed to use DTI to evaluate white matter integrity in non-geriatric patients with major depressive disorder (MDD) who were free of antidepressant medication. DTI was performed on 19 non-geriatric patients with MDD, free of antidepressant medication, and 19 age-matched healthy subjects. Voxel-based and histogram analyses were used to compare fractional anisotropy (FA) and mean diffusivity (MD) values between the two groups, using two-sample t tests. The abnormal DTI indices, if any, were tested for correlation with disease duration and severity, using Pearson product-moment correlation analysis. Voxel-based analysis showed clusters with FA decrease at the bilateral frontal white matter, anterior limbs of internal capsule, cerebellum, left putamen and right thalamus of the patients. Histogram analysis revealed lower peak position of FA histograms in the patients. FA values of the abnormal clusters and peak positions of FA histograms of the patients exhibited moderate correlation with disease duration and severity. These results suggest the implication of frontal-subcortical circuits and cerebellum in MDD, and the potential utility of FA in evaluation of brain parenchymal integrity. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  9. 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.

  10. Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Chanan S. Syan

    2010-01-01

    Full Text Available The P300 component of Event Related Brain Potentials (ERP is commonly used in Brain Computer Interfaces (BCI to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19% and multi-trial format (100%. It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general

  11. Lazarillo expression reveals a subset of neurons contributing to the primary axon scaffold of the embryonic brain of the grasshopper Schistocerca gregaria.

    Science.gov (United States)

    Graf, S; Ludwig, P; Boyan, G

    2000-04-10

    The authors studied the contribution of seven clusters of Lazarillo-expressing cells to the primary axon scaffold of the brain in the grasshopper Schistocerca gregaria from 26% to 43% of embryogenesis. Each cluster, which was numbered according to when Lazarillo expression first appeared, was uniquely identifiable on the basis of its stereotypic position in the brain and the number of Lazarillo-expressing cells it contained. At no time during embryogenesis was Lazarillo expression found in brain neuroblasts: It was found only in progeny. For ease of analysis, axogenesis was followed in a cell cluster that contained only a single Lazarillo-expressing cell (the lateral cell) in the dorsal median domain of the brain midline. Bromodeoxyuridine incorporation revealed the presence of only a single midline precursor cell in this region during embryogenesis. Intracellular injection of Lucifer yellow into the lateral cell at various ages showed that there was no dye coupling to the midline precursor or to the nearby term-1-expressing primary commissure pioneers. The lateral cell is not related lineally to these cells and most likely differentiates directly from the neuroectoderm of the brain midline. Lazarillo expression appears at the onset of axogenesis as the lateral cell projects an axon laterally toward the next Lazarillo-expressing cell cluster. The cells of this target cluster direct axons into separate brain regions, thereby establishing an orthogonally organized scaffold that the lateral cell axon follows as it navigates away from the brain midline. The primary axon scaffold of the brain results from a stepwise interlinking of discrete brain regions, as exemplified by axons from neighboring Lazarillo-expressing cell clusters.

  12. 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…

  13. 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…

  14. Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues.

    Science.gov (United States)

    Bozek, Katarzyna; Wei, Yuning; Yan, Zheng; Liu, Xiling; Xiong, Jieyi; Sugimoto, Masahiro; Tomita, Masaru; Pääbo, Svante; Sherwood, Chet C; Hof, Patrick R; Ely, John J; Li, Yan; Steinhauser, Dirk; Willmitzer, Lothar; Giavalisco, Patrick; Khaitovich, Philipp

    2015-02-18

    Lipids are prominent components of the nervous system. Here we performed a large-scale mass spectrometry-based analysis of the lipid composition of three brain regions as well as kidney and skeletal muscle of humans, chimpanzees, rhesus macaques, and mice. The human brain shows the most distinct lipid composition: 76% of 5,713 lipid compounds examined in our study are either enriched or depleted in the human brain. Concentration levels of lipids enriched in the brain evolve approximately four times faster among primates compared with lipids characteristic of non-neural tissues and show further acceleration of change in human neocortical regions but not in the cerebellum. Human-specific concentration changes are supported by human-specific expression changes for corresponding enzymes. These results provide the first insights into the role of lipids in human brain evolution. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy

    Science.gov (United States)

    Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to "yes" or "no" intentions (e.g., mental arithmetic calculation for "yes"). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient's internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an "fNIRS-based direct intention decoding" paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing "yes" or "no" intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ±1.39 and 74.08% ±2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for pcommunication systems for patients with motor disabilities.

  16. 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

  17. RNA Sequencing Analysis Reveals Interactions between Breast Cancer or Melanoma Cells and the Tissue Microenvironment during Brain Metastasis

    Directory of Open Access Journals (Sweden)

    Ryo Sato

    2017-01-01

    Full Text Available Metastasis is the main cause of treatment failure and death in cancer patients. Metastasis of tumor cells to the brain occurs frequently in individuals with breast cancer, non–small cell lung cancer, or melanoma. Despite recent advances in our understanding of the causes and in the treatment of primary tumors, the biological and molecular mechanisms underlying the metastasis of cancer cells to the brain have remained unclear. Metastasizing cancer cells interact with their microenvironment in the brain to establish metastases. We have now developed mouse models of brain metastasis based on intracardiac injection of human breast cancer or melanoma cell lines, and we have performed RNA sequencing analysis to identify genes in mouse brain tissue and the human cancer cells whose expression is associated specifically with metastasis. We found that the expressions of the mouse genes Tph2, Sspo, Ptprq, and Pole as well as those of the human genes CXCR4, PLLP, TNFSF4, VCAM1, SLC8A2, and SLC7A11 were upregulated in brain tissue harboring metastases. Further characterization of such genes that contribute to the establishment of brain metastases may provide a basis for the development of new therapeutic strategies and consequent improvement in the prognosis of cancer patients.

  18. Real-time in vivo imaging reveals the ability of neutrophils to remove Cryptococcus neoformans directly from the brain vasculature.

    Science.gov (United States)

    Zhang, Mingshun; Sun, Donglei; Liu, Gongguan; Wu, Hui; Zhou, Hong; Shi, Meiqing

    2016-03-01

    Although neutrophils are typically the first immune cells attracted to an infection site, little is known about how neutrophils dynamically interact with invading pathogens in vivo. Here, with the use of intravital microscopy, we demonstrate that neutrophils migrate to the arrested Cryptococcus neoformans, a leading agent to cause meningoencephalitis, in the brain microvasculature. Following interactions with C. neoformans, neutrophils were seen to internalize the organism and then circulate back into the bloodstream, resulting in a direct removal of the organism from the endothelial surface before its transmigration into the brain parenchyma. C. neoformans infection led to enhanced expression of adhesion molecules macrophage 1 antigen on neutrophils and ICAM-1 on brain endothelial cells. Depletion of neutrophils enhanced the brain fungal burden. Complement C3 was critically involved in the recognition of C. neoformans by neutrophils and subsequent clearance of the organism from the brain. Together, our finding of the direct removal of C. neoformans by neutrophils from its arrested site may represent a novel mechanism of host defense in the brain, in addition to the known, direct killing of microorganisms at the infection sites. These data are the first to characterize directly the dynamic interactions of leukocytes with a microbe in the brain of a living animal.

  19. 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.

  20. Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification.

    Science.gov (United States)

    Min, Rui; Cheng, Jian; Price, True; Wu, Guorong; Shen, Dinggang

    2014-01-01

    In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.

  1. 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

  2. Natural Minor Scale is More Natural to the Brain than Harmonic Minor Scale as Revealed by Magnetoencephalography

    Science.gov (United States)

    Ando, Hiromitsu; Nemoto, Iku; Oda, Shoichiro

    Minor mode is known to elicit stronger emotional responses than major mode in the brain. The present work focused on the minor scales and natural and harmonic minor scales were compared in automatic brain responses in an oddball paradigm. The standard stimulus was either the natural or harmonic minor scale, and a deviant stimulus lacked one scale tone of the corresponding complete minor scale. The brain responded to omission of every tone but omission of the tone B flat in the natural minor experiment elicited larger response than that of the other tones. In particular, the response was significantly larger than that to omission of B in the harmonic minor experiment. This result suggested that the brain felt the natural minor scale to be actually more natural than the harmonic minor scale.

  3. Neuroarchitecture of the arcuate body in the brain of the spider Cupiennius salei (Araneae, Chelicerata) revealed by allatostatin-, proctolin-, and CCAP-immunocytochemistry and its evolutionary implications.

    Science.gov (United States)

    Loesel, Rudi; Seyfarth, Ernst-August; Bräunig, Peter; Agricola, Hans-Jürgen

    2011-05-01

    Here we describe the neuronal organization of the arcuate body in the brain of the wandering spider Cupiennius salei. The internal anatomy of this major brain center is analyzed in detail based on allatostatin-, proctolin-, and crustacean cardioactive peptide (CCAP)-immunohistochemistry. Prominent neuronal features are demonstrated in graphic reconstructions. The stainings revealed that the neuroarchitecture of the arcuate body is characterized by several distinct layers some of which comprise nerve terminals that are organized in columnar, palisade-like arrays. The anatomy of the spider's arcuate body exhibits similarities as well as differences when compared to the central complex in the protocerebrum of the Tetraconata. Arguments for and against a possible homology of the arcuate body of the Chelicerata and the central complex of the Tetraconata and their consequences for the understanding of arthropod brain evolution are discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. A Comparative Antibody Analysis of Pannexin1 Expression in Four Rat Brain Regions Reveals Varying Subcellular Localizations

    OpenAIRE

    Cone, Angela C.; Ambrosi, Cinzia; Scemes, Eliana; Maryann E. Martone; Sosinsky, Gina E.

    2013-01-01

    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 p...

  5. Comparison of locus-specific databases for BRCA1 and BRCA2 variants reveals disparity in variant classification within and among databases.

    Science.gov (United States)

    Vail, Paris J; Morris, Brian; van Kan, Aric; Burdett, Brianna C; Moyes, Kelsey; Theisen, Aaron; Kerr, Iain D; Wenstrup, Richard J; Eggington, Julie M

    2015-10-01

    Genetic variants of uncertain clinical significance (VUSs) are a common outcome of clinical genetic testing. Locus-specific variant databases (LSDBs) have been established for numerous disease-associated genes as a research tool for the interpretation of genetic sequence variants to facilitate variant interpretation via aggregated data. If LSDBs are to be used for clinical practice, consistent and transparent criteria regarding the deposition and interpretation of variants are vital, as variant classifications are often used to make important and irreversible clinical decisions. In this study, we performed a retrospective analysis of 2017 consecutive BRCA1 and BRCA2 genetic variants identified from 24,650 consecutive patient samples referred to our laboratory to establish an unbiased dataset representative of the types of variants seen in the US patient population, submitted by clinicians and researchers for BRCA1 and BRCA2 testing. We compared the clinical classifications of these variants among five publicly accessible BRCA1 and BRCA2 variant databases: BIC, ClinVar, HGMD (paid version), LOVD, and the UMD databases. Our results show substantial disparity of variant classifications among publicly accessible databases. Furthermore, it appears that discrepant classifications are not the result of a single outlier but widespread disagreement among databases. This study also shows that databases sometimes favor a clinical classification when current best practice guidelines (ACMG/AMP/CAP) would suggest an uncertain classification. Although LSDBs have been well established for research applications, our results suggest several challenges preclude their wider use in clinical practice.

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

    Science.gov (United States)

    Cuaya, Laura V; Hernández-Pérez, Raúl; Concha, Luis

    2016-01-01

    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.

  7. Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2010-01-01

    Full Text Available Abstract Background We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits. We performed a whole-genome gene expression experiment and applied a systems biology approach using weighted gene co-expression network analysis. Results We were able to identify modules of co-expressed genes that distinguish a strain or brain region. Analysis of the networks that are most informative for hippocampus and amygdala revealed enrichment in neurologically, genetically and psychologically related pathways. Close examination of the strain-specific gene expression profiles, however, revealed no functional relevance but a significant enrichment of single nucleotide polymorphisms in the probe sequences used for array hybridization. This artifact was not observed for the modules of co-expressed genes that distinguish amygdala and hippocampus. Conclusions The brain-region specific modules were found to be independent of genetic background and are therefore likely to represent biologically relevant molecular networks that can be studied to complement our knowledge about pathways in neuropsychiatric disease.

  8. Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions.

    Science.gov (United States)

    Pannek, Kerstin; Boyd, Roslyn N; Fiori, Simona; Guzzetta, Andrea; Rose, Stephen E

    2014-01-01

    Cerebral palsy (CP) is a term to describe the spectrum of disorders of impaired motor and sensory function caused by a brain lesion occurring early during development. Diffusion MRI and tractography have been shown to be useful in the study of white matter (WM) microstructure in tracts likely to be impacted by the static brain lesion. The purpose of this study was to identify WM pathways with altered connectivity in children with unilateral CP caused by periventricular white matter lesions using a whole-brain connectivity approach. Data of 50 children with unilateral CP caused by periventricular white matter lesions (5-17 years; manual ability classification system [MACS] I = 25/II = 25) and 17 children with typical development (CTD; 7-16 years) were analysed. Structural and High Angular Resolution Diffusion weighted Images (HARDI; 64 directions, b = 3000 s/mm(2)) were acquired at 3 T. Connectomes were calculated using whole-brain probabilistic tractography in combination with structural parcellation of the cortex and subcortical structures. Connections with altered fractional anisotropy (FA) in children with unilateral CP compared to CTD were identified using network-based statistics (NBS). The relationship between FA and performance of the impaired hand in bimanual tasks (Assisting Hand Assessment-AHA) was assessed in connections that showed significant differences in FA compared to CTD. FA was reduced in children with unilateral CP compared to CTD. Seven pathways, including the corticospinal, thalamocortical, and fronto-parietal association pathways were identified simultaneously in children with left and right unilateral CP. There was a positive relationship between performance of the impaired hand in bimanual tasks and FA within the cortico-spinal and thalamo-cortical pathways (r(2) = 0.16-0.44; p treatment may elucidate the neurological correlates of improved functioning due to intervention.

  9. Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis

    Science.gov (United States)

    Gonzalez-Castillo, Javier; Saad, Ziad S.; Handwerker, Daniel A.; Inati, Souheil J.; Brenowitz, Noah; Bandettini, Peter A.

    2012-01-01

    The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models. PMID:22431587

  10. EEG-Based Classification of New Imagery Tasks Using Three-Layer Feedforward Neural Network Classifier for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2006-10-01

    In this paper proposes the classification method of new imagery tasks for simple binary commands approach to a brain-computer interface (BCI). An analysis of imaginary tasks as “yes/no” have been proposed. Since BCI is very helpful technology for the patients who are suffering from severe motor disabilities. The BCI applications can be realized by using an electroencephalogram (EEG) signals recording at the scalp surface through the electrodes. Six healthy subjects (three males and three females), aged 23-30 years, were volunteered to participate in the experiment. During the experiment, 10-questions were used to be stimuli. The feature extraction of the event-related synchronization and event-related desynchronization (ERD/ERS) responses can be determined by the slope coefficient and Euclidian distance (SCED) method. The method uses the three-layer feedforward neural network based on a simple backpropagation algorithm to classify the two feature vectors. The experimental results of the proposed method show the average accuracy rates of 81.5 and 78.8% when the subjects imagine to “yes” and “no”, respectively.

  11. What can volumes reveal about human brain evolution? A framework for bridging behavioral, histometric and volumetric perspectives

    Directory of Open Access Journals (Sweden)

    Alexandra A de Sousa

    2014-06-01

    Full Text Available An overall relationship between brain size and cognitive ability exists across primates. Can more specific information about neural function be gleaned from cortical area volumes? Numerous studies have found significant relationships between brain structures and behaviors. However, few studies have speculated about brain structure-function relationships from the microanatomical to the macroanatomical level. Here we address this problem in comparative neuroanatomy, where the functional relevance of overall brain size and the sizes of cortical regions have been poorly understood, by considering comparative psychology, with measures of visual acuity and the perception of visual illusions. We outline a model where the macroscopic size (volume or surface area of a cortical region (such as the primary visual cortex, V1 is related to the microstructure of discrete brain regions. The hypothesis developed here is that a larger absolute V1 can process more information with greater fidelity due to having more neurons to represent a field of space. This is the first time that the necessary comparative neuroanatomical research at the microstructural level has been brought to bear on the issue. The evidence suggests that as the size of V1 increases: the number of neurons increases, the neuron density decreases, and the density of neuronal connections increases. Thus, we describe how information about gross neuromorphology, using V1 as a model for the study of other cortical areas, may permit interpretations of cortical function.

  12. What can volumes reveal about human brain evolution? A framework for bridging behavioral, histometric, and volumetric perspectives.

    Science.gov (United States)

    de Sousa, Alexandra A; Proulx, Michael J

    2014-01-01

    An overall relationship between brain size and cognitive ability exists across primates. Can more specific information about neural function be gleaned from cortical area volumes? Numerous studies have found significant relationships between brain structures and behaviors. However, few studies have speculated about brain structure-function relationships from the microanatomical to the macroanatomical level. Here we address this problem in comparative neuroanatomy, where the functional relevance of overall brain size and the sizes of cortical regions have been poorly understood, by considering comparative psychology, with measures of visual acuity and the perception of visual illusions. We outline a model where the macroscopic size (volume or surface area) of a cortical region (such as the primary visual cortex, V1) is related to the microstructure of discrete brain regions. The hypothesis developed here is that an absolutely larger V1 can process more information with greater fidelity due to having more neurons to represent a field of space. This is the first time that the necessary comparative neuroanatomical research at the microstructural level has been brought to bear on the issue. The evidence suggests that as the size of V1 increases: the number of neurons increases, the neuron density decreases, and the density of neuronal connections increases. Thus, we describe how information about gross neuromorphology, using V1 as a model for the study of other cortical areas, may permit interpretations of cortical function.

  13. Small-World Brain Functional Networks in Children With Attention-Deficit/Hyperactivity Disorder Revealed by EEG Synchrony.

    Science.gov (United States)

    Liu, Tian; Chen, Yanni; Lin, Pan; Wang, Jue

    2015-07-01

    We investigated the topologic properties of human brain attention-related functional networks associated with Multi-Source Interference Task (MSIT) performance using electroencephalography (EEG). Data were obtained from 13 children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and 13 normal control children. Functional connectivity between all pairwise combinations of EEG channels was established by calculating synchronization likelihood (SL). The cluster coefficients and path lengths were computed as a function of degree K. The results showed that brain attention functional networks of normal control subjects had efficient small-world topologic properties, whereas these topologic properties were altered in ADHD. In particular, increased local characteristics combined with decreased global characteristics in ADHD led to a disorder-related shift of the network topologic structure toward ordered networks. These findings are consistent with a hypothesis of dysfunctional segregation and integration of the brain in ADHD, and enhance our understanding of the underlying pathophysiologic mechanism of this illness.

  14. White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities.

    Science.gov (United States)

    Sullivan, Sarah; Eucker, Stephanie A; Gabrieli, David; Bradfield, Connor; Coats, Brittany; Maltese, Matthew R; Lee, Jongho; Smith, Colin; Margulies, Susan S

    2015-08-01

    A systematic correlation between finite element models (FEMs) and histopathology is needed to define deformation thresholds associated with traumatic brain injury (TBI). In this study, a FEM of a transected piglet brain was used to reverse engineer the range of optimal shear moduli for infant (5 days old, 553-658 Pa) and 4-week-old toddler piglet brain (692-811 Pa) from comparisons with measured in situ tissue strains. The more mature brain modulus was found to have significant strain and strain rate dependencies not observed with the infant brain. Age-appropriate FEMs were then used to simulate experimental TBI in infant (n=36) and preadolescent (n=17) piglets undergoing a range of rotational head loads. The experimental animals were evaluated for the presence of clinically significant traumatic axonal injury (TAI), which was then correlated with FEM-calculated measures of overall and white matter tract-oriented tissue deformations, and used to identify the metric with the highest sensitivity and specificity for detecting TAI. The best predictors of TAI were the tract-oriented strain (6-7%), strain rate (38-40 s(-1), and strain times strain rate (1.3-1.8 s(-1) values exceeded by 90% of the brain. These tract-oriented strain and strain rate thresholds for TAI were comparable to those found in isolated axonal stretch studies. Furthermore, we proposed that the higher degree of agreement between tissue distortion aligned with white matter tracts and TAI may be the underlying mechanism responsible for more severe TAI after horizontal and sagittal head rotations in our porcine model of nonimpact TAI than coronal plane rotations.

  15. White matter tract oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities

    Science.gov (United States)

    Sullivan, Sarah; Eucker, Stephanie A.; Gabrieli, David; Bradfield, Connor; Coats, Brittany; Maltese, Matthew R.; Lee, Jongho; Smith, Colin; Margulies, Susan S.

    2015-01-01

    A systematic correlation between finite element models (FEMs) and histopathology is needed to define deformation thresholds associated with traumatic brain injury (TBI). In this study, a FEM of a transected piglet brain was used to reverse engineer the range of optimal shear moduli for infant (5-day-old, 553-658 Pa) and 4-week old toddler piglet brain, (692-811 Pa) from comparisons with measured in situ tissue strains. The more mature brain modulus was found to have significant strain and strain rate dependencies not observed with the infant brain. Age-appropriate FEMs were then used to simulate experimental TBI in infant (n=36) and pre-adolescent (n=17) piglets undergoing a range of rotational head loads. The experimental animals were evaluated for the presence of clinically significant traumatic axonal injury (TAI), which was then correlated with FEM-calculated measures of overall and white matter tract-oriented tissue deformations, and used to identify the metric with the highest sensitivity and specificity for detecting TAI. The best predictors of TAI were the tract-oriented strain (6–7%), strain rate (38–40 s−1), and strain times strain rate (1.3–1.8 s−1) values exceeded by 90% of the brain. These tract-oriented strain and strain rate thresholds for TAI were comparable to those found in isolated axonal stretch studies. Furthermore, we proposed that the higher degree of agreement between tissue distortion aligned with white matter tracts and TAI may be the underlying mechanism responsible for more severe TAI after horizontal and sagittal head rotations in our porcine model of nonimpact TAI than coronal plane rotations. PMID:25547650

  16. Mitochondrial proteome analysis reveals depression of the Ndufs3 subunit and activity of complex I in diabetic rat brain.

    Science.gov (United States)

    Taurino, Federica; Stanca, Eleonora; Siculella, Luisa; Trentadue, Raffaella; Papa, Sergio; Zanotti, Franco; Gnoni, Antonio

    2012-04-18

    Type-1 diabetes resulting from defective insulin secretion and consequent hyperglycemia, is associated with "diabetic encephalopathy." This is characterized by brain neurophysiological and structural changes resulting in impairment of cognitive function. The present proteomic analysis of brain mitochondrial proteins from streptozotocin-induced type-1 diabetic rats, shows a large decrement of the Ndufs3 protein subunit of complex I, decreased level of the mRNA and impaired catalytic activity of the complex in the diabetic rats as compared to controls. The severe depression of the expression and enzymatic activity of complex I can represent a critical contributing factor to the onset of the diabetic encephalopathy in type-1 diabetes.

  17. Relationship between brain network pattern and cognitive performance of children revealed by MEG signals during free viewing of video.

    Science.gov (United States)

    Duan, Fang; Watanabe, Katsumi; Yoshimura, Yuko; Kikuchi, Mitsuru; Minabe, Yoshio; Aihara, Kazuyuki

    2014-04-01

    Application of graph theory to analysis of functional networks in the brain is an important research trend. Extensive research on the resting state has shown a "small-world" organization of the brain network as a whole. However, the small-worldness of children's brain networks in a working state has not yet been well characterized. In this paper, we used a custom-made, child-sized magnetoencephalography (MEG) device to collect data from children while they were watching cartoon videos. Network structures were analyzed and compared with scores on the Kaufman Assessment Battery for Children (K-ABC). The results of network analysis showed that (1) the small-world scalar showed a negative correlation with the simultaneous processing raw score, a measure of visual processing (Gv) ability, and (2) the children with higher simultaneous processing raw scores possessed network structures that can be more efficient for local information processing than children with lower scores. These results were compatible with previous studies on the adult working state. Additional results obtained from further analysis of the frontal and occipital lobes indicated that high cognitive performance could represent better local efficiency in task-related sub-networks. Under free viewing of cartoon videos, brain networks were no longer confined to their strongest small-world states; connections became clustered in local areas such as the frontal and occipital lobes, which might be a more useful configuration for handling visual processing tasks.

  18. 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...

  19. RNA Sequence Analysis of Human Huntington Disease Brain Reveals an Extensive Increase in Inflammatory and Developmental Gene Expression.

    Directory of Open Access Journals (Sweden)

    Adam Labadorf

    Full Text Available Huntington's Disease (HD is a devastating neurodegenerative disorder that is caused by an expanded CAG trinucleotide repeat in the Huntingtin (HTT gene. Transcriptional dysregulation in the human HD brain has been documented but is incompletely understood. Here we present a genome-wide analysis of mRNA expression in human prefrontal cortex from 20 HD and 49 neuropathologically normal controls using next generation high-throughput sequencing. Surprisingly, 19% (5,480 of the 28,087 confidently detected genes are differentially expressed (FDR<0.05 and are predominantly up-regulated. A novel hypothesis-free geneset enrichment method that dissects large gene lists into functionally and transcriptionally related groups discovers that the differentially expressed genes are enriched for immune response, neuroinflammation, and developmental genes. Markers for all major brain cell types are observed, suggesting that HD invokes a systemic response in the brain area studied. Unexpectedly, the most strongly differentially expressed genes are a homeotic gene set (represented by Hox and other homeobox genes, that are almost exclusively expressed in HD, a profile not widely implicated in HD pathogenesis. The significance of transcriptional changes of developmental processes in the HD brain is poorly understood and warrants further investigation. The role of inflammation and the significance of non-neuronal involvement in HD pathogenesis suggest anti-inflammatory therapeutics may offer important opportunities in treating HD.

  20. 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.

  1. Functional magnetic resonance imaging reveals abnormal brain connectivity in EGR3 gene transfected rat model of schizophrenia.

    Science.gov (United States)

    Song, Tianbin; Nie, Binbin; Ma, Ensen; Che, Jing; Sun, Shilong; Wang, Yuli; Shan, Baoci; Liu, Yawu; Luo, Senlin; Ma, Guolin; Li, Kefeng

    2015-05-01

    Schizophrenia is characterized by the disorder of "social brain". However, the alternation of connectivity density in brain areas of schizophrenia patients remains largely unknown. In this study, we successfully created a rat model of schizophrenia by the transfection of EGR3 gene into rat brain. We then investigated the connectivity density of schizophrenia susceptible regions in rat brain using functional magnetic resonance imaging (fMRI) in combination with multivariate Granger causality (GC) model. We found that the average signal strength in prefrontal lobe and hippocampus of schizophrenia model group was significantly higher than the control group. Bidirectional Granger causality connection was observed between hippocampus and thalamic in schizophrenia model group. Both connectivity density and Granger causality connection were changed in prefrontal lobe, hippocampus and thalamus after risperidone treatment. Our results indicated that fMRI in combination with GC connection analysis may be used as an important method in diagnosis of schizophrenia and evaluation the effect of antipsychotic treatment. These findings support the connectivity disorder hypothesis of schizophrenia and increase our understanding of the neural mechanisms of schizophrenia.

  2. 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.

  3. 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.

  4. 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.

  5. Dynamic Analyses of PrP and PrPsc in Brain Tissues of Golden Hamsters Infected With Scraple Strain 263K Revealed Various PrP Forms

    Institute of Scientific and Technical Information of China (English)

    JIAN-MEI GAO; AND XIAO-PING DONG; CHEN GAO; JUN HAN; XIAO-BO ZHOU; XIN-LI XIAO; JIN ZHANG; LAN CHEN; BAO-YUN ZHANG; TAO HONG

    2004-01-01

    Objective To expatiate dynamic changes in hamsters infected with scrapie strain 263K, to observe the presence and aggravation of various forms of PrP and PrPSc during incubation period, and to probe primarily the relationship between the onset of clinic manifestations and the presence of different PrPSc forms. Methods Hamster-adapted scrapie strain 263K was intracerebrally inoculated into hamsters. Different forms of PrP and PrPSc were monitored dynamically by Western blot and immuno-histochemical assays. The presence of scrapie-associated fibril (SAF) was assayedby electron microscopy analysis (EM) and immuno-golden EM. Results PrPSc was initiallydetected in the brain tissues of the animals in 20 days post-inoculation by immunohistochemistry and 40 days with Western blot. Quantitative evaluations revealed that the amounts of PrP and PrPSc inbrain tissues increased along with the incubation. Several high and low molecular masses of PrP wereseen in the brains of the long-life span infected animals. Deglycosylation assays identified that the truncated PrP in the infected brains showed similar glycosylation patterns as the full-length PrP. The presence of short fragments was seemed to relate with the onset of clinical conditions. Conclusion These results indicate that infectious agents exist and accumulate in central nerve system prior to the onset of the illness. Various molecular patterns of PrPSc may indwell in brain tissues during the infection.

  6. Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation.

    Science.gov (United States)

    Plassard, Andrew J; Kelly, Patrick D; Asman, Andrew J; Kang, Hakmook; Patel, Mayur B; Landman, Bennett A

    2015-03-20

    Medical imaging plays a key role in guiding treatment of traumatic brain injury (TBI) and for diagnosing intracranial hemorrhage; most commonly rapid computed tomography (CT) imaging is performed. Outcomes for patients with TBI are variable and difficult to predict upon hospital admission. Quantitative outcome scales (e.g., the Marshall classification) have been proposed to grade TBI severity on CT, but such measures have had relatively low value in staging patients by prognosis. Herein, we examine a cohort of 1,003 subjects admitted for TBI and imaged clinically to identify potential prognostic metrics using a "big data" paradigm. For all patients, a brain scan was segmented with multi-atlas labeling, and intensity/volume/texture features were computed in a localized manner. In a 10-fold cross-validation approach, the explanatory value of the image-derived features is assessed for length of hospital stay (days), discharge disposition (five point scale from death to return home), and the Rancho Los Amigos functional outcome score (Rancho Score). Image-derived features increased the predictive R(2) to 0.38 (from 0.18) for length of stay, to 0.51 (from 0.4) for discharge disposition, and to 0.31 (from 0.16) for Rancho Score (over models consisting only of non-imaging admission metrics, but including positive/negative radiological CT findings). This study demonstrates that high volume retrospective analysis of clinical imaging data can reveal imaging signatures with prognostic value. These targets are suited for follow-up validation and represent targets for future feature selection efforts. Moreover, the increase in prognostic value would improve staging for intervention assessment and provide more reliable guidance for patients.

  7. Brain CT image classification based on least squares support vector machine opti-mized by improved harmony search algorithm%改进和声搜索算法优化LSSVM的脑CT图像分类

    Institute of Scientific and Technical Information of China (English)

    郭正红; 赵丙辰

    2013-01-01

    In order to improve the brain CT image classification accuracy, this paper proposes brain CT mage classification mod-el(IHS-LSSVM)based on the least squares support vector machine and harmony search algorithm. Firstly, the LSSVM parame-ters are taken as different musical tone combination, and then the harmony search algorithm is used to find the optimal parame-ters, and the optimal position adjustment strategy is introduced to enhance the ability of jumping out of local minima, the brain CT image classification model is established according to the optimal parameters, and the performance of the model is tested. The simulation results show that, compared with the other models, IHS-LSSVM not only improves the image classification accu-racy, but also accelerates the classification speed, so it is an effective brain CT image classification model.%为了提高脑CT图像的分类正确率,针对分类器中的最小二乘支持向量机(LSSVM)参数优化问题,提出一种改进和声搜索算法优化LSSVM的脑CT图像分类模型(IHS-LSSVM)。将LSSVM参数看作不同乐器的声调组合,通过和声搜索算法的“调音”找到最优参数,并在寻优过程中引入粒子群算法的最优位置更新策略,增强了算法跳出局部极小值的能力,根据最优参数建立脑CT图像分类模型,并对模型的性能进行仿真测试。仿真结果表明,相对于对比模型,IHS-LSSVM不仅提高了脑CT图像分类正确率,而且加快分类速度,是一种有效的脑CT图像分类模型。

  8. A human blood-brain barrier transcytosis assay reveals antibody transcytosis influenced by pH-dependent receptor binding.

    Directory of Open Access Journals (Sweden)

    Hadassah Sade

    Full Text Available We have adapted an in vitro model of the human blood-brain barrier, the immortalized human cerebral microvascular endothelial cells (hCMEC/D3, to quantitatively measure protein transcytosis. After validating the receptor-mediated transport using transferrin, the system was used to measure transcytosis rates of antibodies directed against potential brain shuttle receptors. While an antibody to the insulin-like growth factor 1 receptor (IGF1R was exclusively recycled to the apical compartment, the fate of antibodies to the transferrin receptor (TfR was determined by their relative affinities at extracellular and endosomal pH. An antibody with reduced affinity at pH5.5 showed significant transcytosis, while pH-independent antibodies of comparable affinities at pH 7.4 remained associated with intracellular vesicular compartments and were finally targeted for degradation.

  9. A Voxel-Based Morphometry Study Reveals Local Brain Structural Alterations associated With Ambient Fine Particles in older women

    Directory of Open Access Journals (Sweden)

    Ramon Casanova

    2016-10-01

    Full Text Available Objective: Exposure to ambient fine particulate matter (PM2.5: PM with aerodynamic diameters <2.5µm has been linked with cognitive deficits in older adults. Using fine-grained voxel-wise analyses, we examined whether PM2.5 exposure also affects brain structure.Methods: Brain MRI data were obtained from 1,365 women (aged 71-89 in the Women’s Health Initiative Memory Study and local brain volumes were estimated using RAVENS (regional analysis of volumes in normalized space. Based on geocoded residential locations and air monitoring data from the U.S. Environmental Protection Agency, we employed a spatiotemporal model to estimate long-term (3-year average exposure to ambient PM2.5 preceding MRI scans. Voxel-wise linear regression models were fit separately to gray matter (GM and white matter (WM maps to analyze associations between brain structure and PM2.5 exposure, with adjustment for potential confounders. Results: Increased PM2.5 exposure was associated with smaller volumes in both cortical GM and subcortical WM areas. For GM, associations were clustered in the bilateral superior, middle, and medial frontal gyri. For WM, the largest clusters were in the frontal lobe, with smaller clusters in the temporal, parietal, and occipital lobes. No statistically significant associations were observed between PM2.5 exposure and hippocampal volumes. Conclusions: Long-term PM2.5 exposures may accelerate loss of both GM and WM in older women. While our previous work linked WM decreased volumes to PM2.5 air pollution, this is the first neuroimaging study reporting associations between air pollution exposure and smaller volumes of cortical GM. Our data support the hypothesized synaptic neurotoxicity of airborne particles.

  10. 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.

  11. A Voxel-Based Morphometry Study Reveals Local Brain Structural Alterations Associated with Ambient Fine Particles in Older Women

    Science.gov (United States)

    Casanova, Ramon; Wang, Xinhui; Reyes, Jeanette; Akita, Yasuyuki; Serre, Marc L.; Vizuete, William; Chui, Helena C.; Driscoll, Ira; Resnick, Susan M.; Espeland, Mark A.; Chen, Jiu-Chiuan; Wassertheil-Smoller, Sylvia; Goodwin, Mimi; DeNise, Richard; Lipton, Michael; Hannigan, James; Carpini, Anthony; Noble, David; Guzman, Wilton; Kotchen, Jane Morley; Goveas, Joseph; Kerwin, Diana; Ulmer, John; Censky, Steve; Flinton, Troy; Matusewic, Tracy; Prost, Robert; Stefanick, Marcia L.; Swope, Sue; Sawyer-Glover, Anne Marie; Hartley, Susan; Jackson, Rebecca; Hallarn, Rose; Kennedy, Bonnie; Bolognone, Jill; Casimir, Lindsay; Kochis, Amanda; Robbins, John; Zaragoza, Sophia; Carter, Cameron; Ryan, John; Macias, Denise; Sonico, Jerry; Nathan, Lauren; Voigt, Barbara; Villablanca, Pablo; Nyborg, Glen; Godinez, Sergio; Perrymann, Adele; Limacher, Marian; Anderson, Sheila; Toombs, Mary Ellen; Bennett, Jeffrey; Jones, Kevin; Brum, Sandy; Chatfield, Shane; Vantrees, Kevin; Robinson, Jennifer; Wilson, Candy; Koch, Kevin; Hart, Suzette; Carroll, Jennifer; Cherrico, Mary; Ockene, Judith; Churchill, Linda; Fellows, Douglas; Serio, Anthony; Jackson, Sharon; Spavich, Deidre; Margolis, Karen; Bjerk, Cindy; Truwitt, Chip; Peitso, Margaret; Camcrena, Alexa; Grim, Richard; Levin, Julie; Perron, Mary; Brunner, Robert; Golding, Ross; Pansky, Leslie; Arguello, Sandie; Hammons, Jane; Peterson, Nikki; Murphy, Carol; Morgan, Maggie; Castillo, Mauricio; Beckman, Thomas; Huang, Benjamin; Kuller, Lewis; McHugh, Pat; Meltzer, Carolyn; Davis, Denise; Davis, Joyce; Kost, Piera; Lucas, Kim; Potter, Tom; Tarr, Lee; Shumaker, Sally; Espeland, Mark; Coker, Laura; Williamson, Jeff; Felton, Debbie; Gleiser, LeeAnn; Rapp, Steve; Legault, Claudine; Dailey, Maggie; Casanova, Ramon; Robertson, Julia; Hogan, Patricia; Gaussoin, Sarah; Nance, Pam; Summerville, Cheryl; Peral, Ricardo; Tan, Josh; Bryan, Nick; Davatzikos, Christos; Desiderio, Lisa; Buckholtz, Neil; Molchan, Susan; Resnick, Susan; Rossouw, Jacques; Pottern, Linda

    2016-01-01

    Objective: Exposure to ambient fine particulate matter (PM2.5: PM with aerodynamic diameters voxel-wise analyses, we examined whether PM2.5 exposure also affects brain structure. Methods: Brain MRI data were obtained from 1365 women (aged 71–89) in the Women's Health Initiative Memory Study and local brain volumes were estimated using RAVENS (regional analysis of volumes in normalized space). Based on geocoded residential locations and air monitoring data from the U.S. Environmental Protection Agency, we employed a spatiotemporal model to estimate long-term (3-year average) exposure to ambient PM2.5 preceding MRI scans. Voxel-wise linear regression models were fit separately to gray matter (GM) and white matter (WM) maps to analyze associations between brain structure and PM2.5 exposure, with adjustment for potential confounders. Results: Increased PM2.5 exposure was associated with smaller volumes in both cortical GM and subcortical WM areas. For GM, associations were clustered in the bilateral superior, middle, and medial frontal gyri. For WM, the largest clusters were in the frontal lobe, with smaller clusters in the temporal, parietal, and occipital lobes. No statistically significant associations were observed between PM2.5 exposure and hippocampal volumes. Conclusions: Long-term PM2.5 exposures may accelerate loss of both GM and WM in older women. While our previous work linked smaller WM volumes to PM2.5, this is the first neuroimaging study reporting associations between air pollution exposure and smaller volumes of cortical GM. Our data support the hypothesized synaptic neurotoxicity of airborne particles.

  12. Novel biochemical manipulation of brain serotonin reveals a role of serotonin in the circadian rhythm of sleep-wake cycles.

    Science.gov (United States)

    Nakamaru-Ogiso, Eiko; Miyamoto, Hiroyuki; Hamada, Kozo; Tsukada, Koji; Takai, Katsuji

    2012-06-01

    Serotonin (5-HT) neurons have been implicated in the modulation of many physiological functions, including mood regulation, feeding, and sleep. Impaired or altered 5-HT neurotransmission appears to be involved in depression and anxiety symptoms, as well as in sleep disorders. To investigate brain 5-HT functions in sleep, we induced 5-HT deficiency through acute tryptophan depletion in rats by intraperitoneally injecting a tryptophan-degrading enzyme called tryptophan side chain oxidase I (TSOI). After the administration of TSOI (20 units), plasma tryptophan levels selectively decreased to 1-2% of those of controls within 2 h, remained under 1% for 12-24 h, and then recovered between 72 and 96 h. Following plasma tryptophan levels, brain 5-HT levels decreased to ∼30% of the control level after 6 h, remained at this low level for 20-30 h, and returned to normal after 72 h. In contrast, brain norepinephreine and dopamine levels remained unchanged. After TSOI injection, the circadian rhythms of the sleep-wake cycle and locomotive activity were lost and broken into minute(s) ultradian alternations. The hourly slow-wave sleep (SWS) time significantly increased at night, but decreased during the day, whereas rapid eye movement sleep was significantly reduced during the day. However, daily total (cumulative) SWS time was retained at the normal level. As brain 5-HT levels gradually recovered 48 h after TSOI injection, the circadian rhythms of sleep-wake cycles and locomotive activity returned to normal. Our results suggest that 5-HT with a rapid turnover rate plays an important role in the circadian rhythm of sleep-wake cycles.

  13. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

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    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.)

  14. 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

  15. Evans Blue Staining Reveals Vascular Leakage Associated with Focal Areas of Host-Parasite Interaction in Brains of Pigs Infected with Taenia solium

    Science.gov (United States)

    Paredes, Adriana; Cangalaya, Carla; Rivera, Andrea; Gonzalez, Armando E.; Mahanty, Siddhartha; Garcia, Hector H.; Nash, Theodore E.

    2014-01-01

    Cysticidal drug treatment of viable Taenia solium brain parenchymal cysts leads to an acute pericystic host inflammatory response and blood brain barrier breakdown (BBB), commonly resulting in seizures. Naturally infected pigs, untreated or treated one time with praziquantel were sacrificed at 48 hr and 120 hr following the injection of Evans blue (EB) to assess the effect of treatment on larval parasites and surrounding tissue. Examination of harvested non encapsulated muscle cysts unexpectedly revealed one or more small, focal round region(s) of Evans blue dye infiltration (REBI) on the surface of otherwise non dye-stained muscle cysts. Histopathological analysis of REBI revealed focal areas of eosinophil-rich inflammatory infiltrates that migrated from the capsule into the tegument and internal structures of the parasite. In addition some encapsulated brain cysts, in which the presence of REBI could not be directly assessed, showed histopathology identical to that of the REBI. Muscle cysts with REBI were more frequent in pigs that had received praziquantel (6.6% of 3736 cysts; n = 6 pigs) than in those that were untreated (0.2% of 3172 cysts; n = 2 pigs). Similar results were found in the brain, where 20.7% of 29 cysts showed histopathology identical to muscle REBI cysts in praziquantel-treated pigs compared to the 4.3% of 47 cysts in untreated pigs. Closer examination of REBI infiltrates showed that EB was taken up only by eosinophils, a major component of the cellular infiltrates, which likely explains persistence of EB in the REBI. REBI likely represent early damaging host responses to T. solium cysts and highlight the focal nature of this initial host response and the importance of eosinophils at sites of host-parasite interaction. These findings suggest new avenues for immunomodulation to reduce inflammatory side effects of anthelmintic therapy. PMID:24915533

  16. Evans blue staining reveals vascular leakage associated with focal areas of host-parasite interaction in brains of pigs infected with Taenia solium.

    Science.gov (United States)

    Marzal, Miguel; Guerra-Giraldez, Cristina; Paredes, Adriana; Cangalaya, Carla; Rivera, Andrea; Gonzalez, Armando E; Mahanty, Siddhartha; Garcia, Hector H; Nash, Theodore E

    2014-01-01

    Cysticidal drug treatment of viable Taenia solium brain parenchymal cysts leads to an acute pericystic host inflammatory response and blood brain barrier breakdown (BBB), commonly resulting in seizures. Naturally infected pigs, untreated or treated one time with praziquantel were sacrificed at 48 hr and 120 hr following the injection of Evans blue (EB) to assess the effect of treatment on larval parasites and surrounding tissue. Examination of harvested non encapsulated muscle cysts unexpectedly revealed one or more small, focal round region(s) of Evans blue dye infiltration (REBI) on the surface of otherwise non dye-stained muscle cysts. Histopathological analysis of REBI revealed focal areas of eosinophil-rich inflammatory infiltrates that migrated from the capsule into the tegument and internal structures of the parasite. In addition some encapsulated brain cysts, in which the presence of REBI could not be directly assessed, showed histopathology identical to that of the REBI. Muscle cysts with REBI were more frequent in pigs that had received praziquantel (6.6% of 3736 cysts; n = 6 pigs) than in those that were untreated (0.2% of 3172 cysts; n = 2 pigs). Similar results were found in the brain, where 20.7% of 29 cysts showed histopathology identical to muscle REBI cysts in praziquantel-treated pigs compared to the 4.3% of 47 cysts in untreated pigs. Closer examination of REBI infiltrates showed that EB was taken up only by eosinophils, a major component of the cellular infiltrates, which likely explains persistence of EB in the REBI. REBI likely represent early damaging host responses to T. solium cysts and highlight the focal nature of this initial host response and the importance of eosinophils at sites of host-parasite interaction. These findings suggest new avenues for immunomodulation to reduce inflammatory side effects of anthelmintic therapy.

  17. Mouse embryonic stem cell-derived cells reveal niches that support neuronal differentiation in the adult rat brain.

    Science.gov (United States)

    Maya-Espinosa, Guadalupe; Collazo-Navarrete, Omar; Millán-Aldaco, Diana; Palomero-Rivero, Marcela; Guerrero-Flores, Gilda; Drucker-Colín, René; Covarrubias, Luis; Guerra-Crespo, Magdalena

    2015-02-01

    A neurogenic niche can be identified by the proliferation and differentiation of its naturally residing neural stem cells. However, it remains unclear whether "silent" neurogenic niches or regions suitable for neural differentiation, other than the areas of active neurogenesis, exist in the adult brain. Embryoid body (EB) cells derived from embryonic stem cells (ESCs) are endowed with a high potential to respond to specification and neuralization signals of the embryo. Hence, to identify microenvironments in the postnatal and adult rat brain with the capacity to support neuronal differentiation, we transplanted dissociated EB cells to conventional neurogenic and non-neurogenic regions. Our results show a neuronal differentiation pattern of EB cells that was dependent on the host region. Efficient neuronal differentiation of EB cells occurred within an adjacent region to the rostral migratory stream. EB cell differentiation was initially patchy and progressed toward an even distribution along the graft by 15-21 days post-transplantation, giving rise mostly to GABAergic neurons. EB cells in the striatum displayed a lower level of neuronal differentiation and derived into a significant number of astrocytes. Remarkably, when EB cells were transplanted to the striatum of adult rats after a local ischemic stroke, increased number of neuroblasts and neurons were observed. Unexpectedly, we determined that the adult substantia nigra pars compacta, considered a non-neurogenic area, harbors a robust neurogenic environment. Therefore, neurally uncommitted cells derived from ESCs can detect regions that support neuronal differentiation within the adult brain, a fundamental step for the development of stem cell-based replacement therapies. © 2014 AlphaMed Press.

  18. 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

  19. A Comparative Antibody Analysis of Pannexin1 Expression in Four Rat Brain Regions Reveals Varying Subcellular Localizations

    Science.gov (United States)

    Cone, Angela C.; Ambrosi, Cinzia; Scemes, Eliana; Martone, Maryann E.; Sosinsky, Gina E.

    2012-01-01

    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-line viewing, and

  20. 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 combi

  1. Next-Generation Sequencing Techniques Reveal that Genomic Imprinting Is Absent in Day-Old Gallus gallus domesticus Brains.

    Science.gov (United States)

    Wang, Qiong; Li, Kaiyang; Zhang, Daixi; Li, Junying; Xu, Guiyun; Zheng, Jiangxia; Yang, Ning; Qu, Lujiang

    2015-01-01

    Genomic imprinting is a phenomenon characterized by parent-of-origin-specific gene expression. While widely documented in viviparous mammals and plants, imprinting in oviparous birds remains controversial. Because genomic imprinting is temporal- and tissue-specific, we investigated this phenomenon only in the brain tissues of 1-day-old chickens (Gallus gallus). We used next-generation sequencing technology to compare four transcriptomes pooled from 11 chickens, generated from reciprocally crossed families, to the DNA sequences of their parents. Candidate imprinted genes were then selected from these sequence alignments and subjected to verification experiments that excluded all but one SNP. Subsequent experiments performed with two new sets of reciprocally crossed families resulted in the exclusion of that candidate SNP as well. Attempts to find evidence of genomic imprinting from long non-coding RNAs yielded negative results. We therefore conclude that genomic imprinting is absent in the brains of 1-day-old chickens. However, due to the temporal and tissue specificity of imprinting, our results cannot be extended to all growth stages and tissue types.

  2. 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.

  3. Live imaging reveals a new role for the sigma-1 (σ1) receptor in allowing microglia to leave brain injuries.

    Science.gov (United States)

    Moritz, Christian; Berardi, Francesco; Abate, Carmen; Peri, Francesca

    2015-03-30

    Microglial cells are responsible for clearing and maintaining the central nervous system (CNS) microenvironment. Upon brain damage, they move toward injuries to clear the area by engulfing dying neurons. However, in the context of many neurological disorders chronic microglial responses are responsible for neurodegeneration. Therefore, it is important to understand how these cells can be "switched-off" and regain their ramified state. Current research suggests that microglial inflammatory responses can be inhibited by sigma (σ) receptor activation. Here, we take advantage of the optical transparency of the zebrafish embryo to study the role of σ1 receptor in microglia in an intact living brain. By combining chemical approaches with real time imaging we found that treatment with PB190, a σ1 agonist, blocks microglial migration toward injuries leaving cellular baseline motility and the engulfment of apoptotic neurons unaffected. Most importantly, by taking a reverse genetic approach, we discovered that the role of σ1in vivo is to "switch-off" microglia after they responded to an injury allowing for these cells to leave the site of damage. This indicates that pharmacological manipulation of σ1 receptor modulates microglial responses providing new approaches to reduce the devastating impact that microglia have in neurodegenerative diseases.

  4. 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.

  5. 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.

  6. Classification and regression tree (CART analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

    Directory of Open Access Journals (Sweden)

    Betsey Dexter Dyer

    2008-01-01

    Full Text Available 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.

  7. Extensive Direct Subcortical Cerebellum-Basal Ganglia Connections in Human Brain as Revealed by Constrained Spherical Deconvolution Tractography

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    Demetrio eMilardi

    2016-03-01

    Full Text Available The connections between the cerebellum and basal ganglia were assumed to occur at the level of neocortex. However evidences from animal data have challenged this old perspective showing extensive subcortical pathways linking the cerebellum with the basal ganglia. Here we tested the hypothesis if these connections also exist between the cerebellum and basal ganglia in the human brain by using diffusion magnetic resonance imaging and tractography. Fifteen healthy subjects were analyzed by using constrained spherical deconvolution technique obtained with a 3T magnetic resonance imaging scanner. We found extensive connections running between the subthalamic nucleus and cerebellar cortex and, as novel result, we demonstrated a direct route linking the dentate nucleus to the internal globus pallidus as well as to the substantia nigra. These findings may open a new scenario on the interpretation of basal ganglia disorders.

  8. Brain-decoding fMRI reveals how wholes relate to the sum of parts.

    Science.gov (United States)

    Kubilius, Jonas; Baeck, Annelies; Wagemans, Johan; Op de Beeck, Hans P

    2015-11-01

    The human brain performs many nonlinear operations in order to extract relevant information from local inputs. How can we observe and quantify these effects within and across large patches of cortex? In this paper, we discuss the application of multi-voxel pattern analysis (MVPA) in functional magnetic resonance imaging (fMRI) to address this issue. Specifically, we show how MVPA (i) allows to compare various possibilities of part combinations into wholes, such as taking the mean, weighted mean, or the maximum of responses to the parts; (ii) can be used to quantify the parameters of these combinations; and (iii) can be applied in various experimental paradigms. Through these procedures, fMRI helps to obtain a computational understanding of how local information is integrated into larger wholes in various cortical regions.

  9. Naturalistic fMRI Mapping Reveals Superior Temporal Sulcus as the Hub for the Distributed Brain Network for Social Perception

    Science.gov (United States)

    Lahnakoski, Juha M.; Glerean, Enrico; Salmi, Juha; Jääskeläinen, Iiro P.; Sams, Mikko; Hari, Riitta; Nummenmaa, Lauri

    2012-01-01

    Despite the abundant data on brain networks processing static social signals, such as pictures of faces, the neural systems supporting social perception in naturalistic conditions are still poorly understood. Here we delineated brain networks subserving social perception under naturalistic conditions in 19 healthy humans who watched, during 3-T functional magnetic resonance imaging (fMRI), a set of 137 short (approximately 16 s each, total 27 min) audiovisual movie clips depicting pre-selected social signals. Two independent raters estimated how well each clip represented eight social features (faces, human bodies, biological motion, goal-oriented actions, emotion, social interaction, pain, and speech) and six filler features (places, objects, rigid motion, people not in social interaction, non-goal-oriented action, and non-human sounds) lacking social content. These ratings were used as predictors in the fMRI analysis. The posterior superior temporal sulcus (STS) responded to all social features but not to any non-social features, and the anterior STS responded to all social features except bodies and biological motion. We also found four partially segregated, extended networks for processing of specific social signals: (1) a fronto-temporal network responding to multiple social categories, (2) a fronto-parietal network preferentially activated to bodies, motion, and pain, (3) a temporo-amygdalar network responding to faces, social interaction, and speech, and (4) a fronto-insular network responding to pain, emotions, social interactions, and speech. Our results highlight the role of the pSTS in processing multiple aspects of social information, as well as the feasibility and efficiency of fMRI mapping under conditions that resemble the complexity of real life. PMID:22905026

  10. Naturalistic FMRI mapping reveals superior temporal sulcus as the hub for the distributed brain network for social perception.

    Science.gov (United States)

    Lahnakoski, Juha M; Glerean, Enrico; Salmi, Juha; Jääskeläinen, Iiro P; Sams, Mikko; Hari, Riitta; Nummenmaa, Lauri

    2012-01-01

    Despite the abundant data on brain networks processing static social signals, such as pictures of faces, the neural systems supporting social perception in naturalistic conditions are still poorly understood. Here we delineated brain networks subserving social perception under naturalistic conditions in 19 healthy humans who watched, during 3-T functional magnetic resonance imaging (fMRI), a set of 137 short (approximately 16 s each, total 27 min) audiovisual movie clips depicting pre-selected social signals. Two independent raters estimated how well each clip represented eight social features (faces, human bodies, biological motion, goal-oriented actions, emotion, social interaction, pain, and speech) and six filler features (places, objects, rigid motion, people not in social interaction, non-goal-oriented action, and non-human sounds) lacking social content. These ratings were used as predictors in the fMRI analysis. The posterior superior temporal sulcus (STS) responded to all social features but not to any non-social features, and the anterior STS responded to all social features except bodies and biological motion. We also found four partially segregated, extended networks for processing of specific social signals: (1) a fronto-temporal network responding to multiple social categories, (2) a fronto-parietal network preferentially activated to bodies, motion, and pain, (3) a temporo-amygdalar network responding to faces, social interaction, and speech, and (4) a fronto-insular network responding to pain, emotions, social interactions, and speech. Our results highlight the role of the pSTS in processing multiple aspects of social information, as well as the feasibility and efficiency of fMRI mapping under conditions that resemble the complexity of real life.

  11. Investigations of transcript expression in fathead minnow (Pimephales promelas) brain tissue reveal toxicological impacts of RDX exposure.

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    Gust, Kurt A; Wilbanks, Mitchell S; Guan, Xin; Pirooznia, Mehdi; Habib, Tanwir; Yoo, Leslie; Wintz, Henri; Vulpe, Chris D; Perkins, Edward J

    2011-01-17

    Production, usage and disposal of the munitions constituent (MC) cyclotrimethylenetrinitramine (RDX) has led to environmental releases on military facilities. The chemical attributes of RDX are conducive for leaching to surface water which may put aquatic organisms at risk of exposure. Because RDX has been observed to cause aberrant neuromuscular effects across a wide range of animal phyla, we assessed the effects of RDX on central nervous system (CNS) functions in the representative aquatic ecotoxicological model species, fathead minnow (Pimephales promelas). We developed a fathead minnow brain-tissue cDNA library enriched for transcripts differentially expressed in response to RDX and trinitrotoluene (TNT) exposure. All 4,128 cDNAs were sequenced, quality filtered and assembled yielding 2230 unique sequences and 945 significant blastx matches (E ≤10(-5)). The cDNA library was leveraged to create custom-spotted microarrays for use in transcript expression assays. The impact of RDX on transcript expression in brain tissue was examined in fathead minnows exposed to RDX at 0.625, 2.5, 5, 10mg/L or an acetone-spike control for 10 days. Overt toxicity of RDX in fathead minnow occurred only at the highest exposure concentration resulting in 50% mortality and weight loss. Conversely, Bayesian analysis of microarray data indicated significant changes in transcript expression at concentrations as low as 0.625 mg/L. In total, 154 cDNAs representing 44 unique transcripts were differentially expressed in RDX exposures, the majority of which were validated by reverse transcriptase-quantitative PCR (RT-qPCR). Investigation of molecular pathways, gene ontology (GO) and individual gene functions affected by RDX exposures indicated changes in metabolic processes involved in: oxygen transport, neurological function, calcium binding/signaling, energy metabolism, cell growth/division, oxidative stress and ubiquitination. In total, our study indicated that RDX exposure affected

  12. Naturalistic fMRI mapping reveals superior temporal sulcus as the hub for the distributed brain network for social perception

    Directory of Open Access Journals (Sweden)

    Juha Marko Lahnakoski

    2012-08-01

    Full Text Available Despite the abundant data on brain networks processing static social signals, such as pictures of faces, the neural systems supporting social perception in naturalistic conditions are still poorly understood. Here we delineated brain networks subserving social perception under naturalistic conditions in 19 healthy humans who watched, during 3-tesla functional magnetic imaging (fMRI, a set of 137 short (~16 s each, total 27 min audiovisual movie clips depicting pre-selected social signals. Two independent raters estimated how well each clip represented eight social features (faces, human bodies, biological motion, goal-oriented actions, emotion, social interaction, pain, and speech and six filler features (places, objects, rigid motion, people not in social interaction, non-goal-oriented action and non-human sounds lacking social content. These ratings were used as predictors in the fMRI analysis. The posterior superior temporal sulcus (STS responded to all social features but not to any non-social features, and the anterior STS responded to all social features except bodies and biological motion. We also found four partially segregated, extended networks for processing of specific social signals: 1 a fronto-temporal network responding to multiple social categories, 2 a fronto-parietal network preferentially activated to bodies, motion and pain, 3 a temporo-amygdalar network responding to faces, social interaction and speech, and 4 a fronto-insular network responding to pain, emotions, social interactions, and speech. Our results highlight the role of the posterior STS in processing multiple aspects of social information, as well as the feasibility and efficiency of fMRI mapping under conditions that resemble the complexity of real life.

  13. Functional interactions within the parahippocampal region revealed by voltage-sensitive dye imaging in the isolated guinea pig brain.

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    Biella, Gerardo; Spaiardi, Paolo; Toselli, Mauro; de Curtis, Marco; Gnatkovsky, Vadym

    2010-02-01

    The massive transfer of information from the neocortex to the entorhinal cortex (and vice versa) is hindered by a powerful inhibitory control generated in the perirhinal cortex. In vivo and in vitro experiments performed in rodents and cats support this conclusion, further extended in the present study to the analysis of the interaction between the entorhinal cortex and other parahippocampal areas, such as the postrhinal and the retrosplenial cortices. The experiments were performed in the in vitro isolated guinea pig brain by a combined approach based on electrophysiological recordings and fast imaging of optical signals generated by voltage-sensitive dyes applied to the entire brain by arterial perfusion. Local stimuli delivered in different portions of the perirhinal, postrhinal, and retrosplenial cortex evoked local responses that did not propagate to the entorhinal cortex. Neither high- and low-frequency-patterned stimulation nor paired associative stimuli facilitated the propagation of activity to the entorhinal region. Similar stimulations performed during cholinergic neuromodulation with carbachol were also ineffective in overcoming the inhibitory network that controls propagation to the entorhinal cortex. The pharmacological inactivation of GABAergic transmission by local application of bicuculline (1 mM) in area 36 of the perirhinal cortex facilitated the longitudinal (rostrocaudal) propagation of activity into the perirhinal/postrhinal cortices but did not cause propagation into the entorhinal cortex. Bicuculline injection in both area 35 and medial entorhinal cortex released the inhibitory control and allowed the propagation of the neural activity to the entorhinal cortex. These results demonstrate that, as for the perirhinal-entorhinal reciprocal interactions, also the connections between the postrhinal/retrosplenial cortices and the entorhinal region are subject to a powerful inhibitory control.

  14. Is the shock index based classification of hypovolemic shock applicable in multiple injured patients with severe traumatic brain injury?-an analysis of the TraumaRegister DGU(®).

    Science.gov (United States)

    Fröhlich, Matthias; Driessen, Arne; Böhmer, Andreas; Nienaber, Ulrike; Igressa, Alhadi; Probst, Christian; Bouillon, Bertil; Maegele, Marc; Mutschler, Manuel

    2016-12-12

    A new classification of hypovolemic shock based on the shock index (SI) was proposed in 2013. This classification contains four classes of shock and shows good correlation with acidosis, blood product need and mortality. Since their applicability was questioned, the aim of this study was to verify the validity of the new classification in multiple injured patients with traumatic brain injury. Between 2002 and 2013, data from 40 888 patients from the TraumaRegister DGU(®) were analysed. Patients were classified according to their initial SI at hospital admission (Class I: SI shock based on universally available parameters. Although the pathophysiology in TBI and Non TBI patients and early treatment methods such as the use of vasopressors differ, both groups showed an identical probability of recieving blood products within the respective SI class. Regardless of the presence of TBI, the classification of hypovolemic shock based on the SI enables a fast and reliable assessment of hypovolemic shock in the emergency department. Therefore, the presented study supports the SI as a feasible tool to assess patients at risk for blood product transfusions, even in the presence of severe TBI.

  15. Task-Related Edge Density (TED-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

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    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.

  16. 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.

  17. Gene expression profiling in brain of mice exposed to the marine neurotoxin ciguatoxin reveals an acute anti-inflammatory, neuroprotective response

    Directory of Open Access Journals (Sweden)

    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

  18. 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

  19. 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.

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    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

  20. Suppression subtraction hybridization (SSH) and macroarray techniques reveal differential gene expression profiles in brain of sea bream infected with nodavirus.

    Science.gov (United States)

    Dios, S; Poisa-Beiro, L; Figueras, A; Novoa, B

    2007-03-01

    Despite of the impact that viruses have on aquatic organisms, relatively little is known on how fish fight against these infections. In this work, the brain gene expression pattern of sea bream (Sparus aurata) in response to nodavirus infection was investigated. We used the suppression subtractive hybridization (SSH) method to generate a subtracted cDNA library enriched with gene transcripts differentially expressed after 1 day post-infection. Some of the ESTs from the infected tissues fell in gene categories related to stress and immune responses. For the reverse library (ESTs expressed in controls compared with infected tissues) the most abundant transcripts were of ribosomal and mitochondrial nature. Several ESTs potentially induced by virus exposure were selected for in vivo expression studies. We observed a clear difference in expression between infected and control samples for two candidate genes, ubiquitin conjugating enzyme 7 interacting protein, which seems to play an important role in apoptosis and the interferon induced protein with helicase C domain 1 (mda-5) that contributes to apoptosis and regulates the type I IFN production, a key molecule of the antiviral innate response in most organisms.

  1. Event-related brain potentials reveal the time-course of language change detection in early bilinguals.

    Science.gov (United States)

    Kuipers, Jan-Rouke; Thierry, Guillaume

    2010-05-01

    Using event-related brain potentials, we investigated the temporal course of language change detection in proficient bilinguals as compared to matched controls. Welsh-English bilingual participants and English controls were presented with a variant of the oddball paradigm involving picture-word pairs. The language of the spoken word was manipulated such that English was the frequent stimulus (75%) and Welsh the infrequent stimulus (25%). We also manipulated semantic relatedness between pictures and words, such that only half of the pictures were followed by a word that corresponded with the identity of the picture. The P2 wave was significantly modulated by language in the bilingual group only, suggesting that this group detected a language change as early as 200 ms after word onset. Monolinguals also reliably detected the language change, but at a later stage of semantic integration (N400 range), since Welsh words were perceived as meaningless. The early detection of a language change in bilinguals triggered stimulus re-evaluation mechanisms reflected by a significant P600 modulation by Welsh words. Furthermore, compared to English unrelated words, English words matching the picture identity elicited significantly greater P2 amplitudes in the bilingual group only, suggesting that proficient bilinguals validate an incoming word against their expectation based on the context. Overall, highly proficient bilinguals appear to detect language changes very early on during speech perception and to consciously monitor language changes when they occur.

  2. Functionally Brain Network Connected to the Retrosplenial Cortex of Rats Revealed by 7T fMRI.

    Science.gov (United States)

    Wang, Jingjuan; Nie, Binbin; Duan, Shaofeng; Zhu, Haitao; Liu, Hua; Shan, Baoci

    2016-01-01

    Functional networks are regarded as important mechanisms for increasing our understanding of brain function in healthy and diseased states, and increased interest has been focused on extending the study of functional networks to animal models because such models provide a functional understanding of disease progression, therapy and repair. In rodents, the retrosplenial cortex (RSC) is an important cortical region because it has a large size and presents transitional patterns of lamination between the neocortex and archicortex. In addition, a number of invasive studies have highlighted the importance of the RSC for many functions. However, the network based on the RSC in rodents remains unclear. Based on the critical importance of the RSC, we defined the bilateral RSCs as two regions of interest and estimated the network based on the RSC. The results showed that the related regions include the parietal association cortex, hippocampus, thalamus nucleus, midbrain structures, and hypothalamic mammillary bodies. Our findings indicate two possible major networks: a sensory-cognitive network that has a hub in the RSCs and processes sensory information, spatial learning, and episodic memory; and a second network that is involved in the regulation of visceral functions and arousal. In addition, functional asymmetry between the bilateral RSCs was observed.

  3. Memory traces for spoken words in the brain as revealed by the hemodynamic correlate of the mismatch negativity.

    Science.gov (United States)

    Shtyrov, Yury; Osswald, Katja; Pulvermüller, Friedemann

    2008-01-01

    The mismatch negativity response, considered a brain correlate of automatic preattentive auditory processing, is enhanced for word stimuli as compared with acoustically matched pseudowords. This lexical enhancement, taken as a signature of activation of language-specific long-term memory traces, was investigated here using functional magnetic resonance imaging to complement the previous electrophysiological studies. In passive oddball paradigm, word stimuli were randomly presented as rare deviants among frequent pseudowords; the reverse conditions employed infrequent pseudowords among word stimuli. Random-effect analysis indicated clearly distinct patterns for the different lexical types. Whereas the hemodynamic mismatch response was significant for the word deviants, it did not reach significance for the pseudoword conditions. This difference, more pronounced in the left than right hemisphere, was also assessed by analyzing average parameter estimates in regions of interests within both temporal lobes. A significant hemisphere-by-lexicality interaction confirmed stronger blood oxygenation level-dependent mismatch responses to words than pseudowords in the left but not in the right superior temporal cortex. The increased left superior temporal activation and the laterality of cortical sources elicited by spoken words compared with pseudowords may indicate the activation of cortical circuits for lexical material even in passive oddball conditions and suggest involvement of the left superior temporal areas in housing such word-processing neuronal circuits.

  4. NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication

    Directory of Open Access Journals (Sweden)

    Ning eLiu

    2016-03-01

    Full Text Available 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 non-cooperative 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, during cooperative and obstructive interaction. This synchrony was not observed during the parallel game play condition and the dialogue 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 region (dmPFC, in particular Brodmann 9, during cooperative interaction only. These additional findings suggest that BA9 may be particularly engaged when theory-of-mind is required for cooperative social interaction. The new methods described here have the potential to significantly extend fNIRS applications to social cognitive research.

  5. 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.

  6. Brain processing of biologically relevant odors in the awake rat, as revealed by manganese-enhanced MRI.

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    Benoist Lehallier

    Full Text Available BACKGROUND: So far, an overall view of olfactory structures activated by natural biologically relevant odors in the awake rat is not available. Manganese-enhanced MRI (MEMRI is appropriate for this purpose. While MEMRI has been used for anatomical labeling of olfactory pathways, functional imaging analyses have not yet been performed beyond the olfactory bulb. Here, we have used MEMRI for functional imaging of rat central olfactory structures and for comparing activation maps obtained with odors conveying different biological messages. METHODOLOGY/PRINCIPAL FINDINGS: Odors of male fox feces and of chocolate flavored cereals were used to stimulate conscious rats previously treated by intranasal instillation of manganese (Mn. MEMRI activation maps showed Mn enhancement all along the primary olfactory cortex. Mn enhancement elicited by male fox feces odor and to a lesser extent that elicited by chocolate odor, differed from that elicited by deodorized air. This result was partly confirmed by c-Fos immunohistochemistry in the piriform cortex. CONCLUSION/SIGNIFICANCE: By providing an overall image of brain structures activated in awake rats by odorous stimulation, and by showing that Mn enhancement is differently sensitive to different stimulating odors, the present results demonstrate the interest of MEMRI for functional studies of olfaction in the primary olfactory cortex of laboratory small animals, under conditions close to natural perception. Finally, the factors that may cause the variability of the MEMRI signal in response to different odor are discussed.

  7. Relational and procedural memory systems in the goldfish brain revealed by trace and delay eyeblink-like conditioning.

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    Gómez, A; Rodríguez-Expósito, B; Durán, E; Martín-Monzón, I; Broglio, C; Salas, C; Rodríguez, F

    2016-12-01

    The presence of multiple memory systems supported by different neural substrata has been demonstrated in animal and human studies. In mammals, two variants of eyeblink classical conditioning, differing only in the temporal relationships between the conditioned stimulus (CS) and the unconditioned stimulus (US), have been widely used to study the neural substrata of these different memory systems. Delay conditioning, in which both stimuli coincide in time, depends on a non-relational memory system supported by the cerebellum and associated brainstem circuits. In contrast, trace conditioning, in which a stimulus-free time gap separates the CS and the US, requires a declarative or relational memory system, thus depending on forebrain structures in addition to the cerebellum. The distinction between the explicit or relational and the implicit or procedural memory systems that support trace and delay classical conditioning has been extensively studied in mammals, but studies in other vertebrate groups are relatively scarce. In the present experiment we analyzed the differential involvement of the cerebellum and the telencephalon in delay and trace eyeblink-like classical conditioning in goldfish. The results show that whereas the cerebellum lesion prevented the eyeblink-like conditioning in both procedures, the telencephalon ablation impaired exclusively the acquisition of the trace conditioning. These data showing that comparable neural systems support delay and trace eyeblink conditioning in teleost fish and mammals suggest that these separate memory systems and their neural bases could be a shared ancestral brain feature of the vertebrate lineage. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. The Novel Graph Kernel for Brain Networks with Application to MCI Classification%面向脑网络的新型图核及其在 MCI 分类上的应用

    Institute of Scientific and Technical Information of China (English)

    接标; 张道强

    2016-01-01

    Graph kernel,as a similarity measure of graphs,has been proposed for computing the similarity of a pair of brain networks and applied for classification of brain diseases,such as Alzheimer’s disease (AD)as well as its early stage,i.e.,mild cognitive impairment (MCI). However,existing graph kernels are mainly constructed on general graphs and thus ignore the intrinsic property of brain networks,such as the uniqueness of each node,i.e.,each node corresponds to a unique brain regions,which may affect the performance of brain network analysis (classification).To address this problem,in this paper,a novel graph kernel is proposed for measuring the similarity of brain networks.Specifically,a group of sub-networks are first constructed on each node to reflect the local and multi-level topological properties of brain network.Then, according the uniqueness of each node,a function is defined to measure the similarity of a pair of sub-network groups across different subjects.Finally,the graph kernel on brain network can be defined through computing the similarity of all pairs of sub-network groups.Different from existing graph kernels,our proposed graph kernel not only considers the specific property of brain networks,but also preserves the local connectivity properties of brain networks.The experimental results on both real MCI datasets show that our proposed graph kernel can significantly improve the classification performance in comparison with state-of-the-art graph kernels.%作为一种图的相似性度量,图核已经被提出用于计算脑网络的相似性,并用于分类一些脑疾病,如阿尔茨海默病(Alzheimer’s Disease,AD)以及它的早期阶段,即轻度认知功能障碍(Mild Cognitive Impairment,MCI)。然而,已有图核主要面向一般图而构建,从而忽略了脑网络自身特有的特性,如节点的唯一性(即每个节点对应着唯一的脑区),这可能影响到脑网络分析

  9. Longitudinal metabolic changes in the hippocampus and thalamus of the maternal brain revealed by proton magnetic resonance spectroscopy.

    Science.gov (United States)

    Zhou, Iris Y; Chan, Russell W; Ho, Leon C; Wu, Ed X

    2013-10-11

    Pregnancy is accompanied by dramatic hormonal changes, which are essential for the display of maternal behaviors. Reproductive hormones have been shown to remodel the neuronal structure and function of the female brain. However, most previous studies have examined the structural and functional changes elicited by transient fluctuations in reproductive hormones. The impact of naturally elevated and more sustained hormonal alterations during pregnancy and lactation are not fully understood. Further alterations in neurochemistry, which may result in substantial changes in the structure and function of neurons that are associated with behavioral modifications in the maternal female, are difficult to capture in a longitudinal and non-invasive manner. In this study, neurobiological alterations during pregnancy and motherhood were investigated longitudinally using non-invasive proton magnetic resonance spectroscopy ((1)H MRS) at 7T in regions related to learning and memory, such as the hippocampus, and in structures involved in alertness and attention, such as the thalamus. Pregnant primiparous rats (N=15) were studied at three days before mating, gestational day 17, lactation day 7 and post-weaning day 7. Age-matched nulliparous female rats (N=9) served as non-pregnant controls. Significantly higher N-acetylaspartate (NAA) levels were observed in the hippocampus and thalamus of rats at gestational day 17. These increases may be associated with increased dendritic sprouting, synaptogenesis or neurogenesis, thereby facilitating supporting behaviors that involve spatial learning and memory and alleviating fear and stress. The (1)H MRS detection of ongoing neurochemical changes induced by pregnancy, especially in the hippocampus, can shed light on the neurochemical underpinnings of behavioral modifications, including the improvement in spatial learning and memory, during pregnancy.

  10. Asynchronous presentation of global and local information reveals effects of attention on brain electrical activity specific to each level.

    Science.gov (United States)

    Iglesias-Fuster, Jorge; Santos-Rodríguez, Yusniel; Trujillo-Barreto, Nelson; Valdés-Sosa, Mitchell J

    2014-01-01

    The neural basis of selective attention within hierarchically organized Navon figures has been extensively studied with event related potentials (ERPs), by contrasting responses obtained when attending the global and the local echelons. The findings are inherently ambiguous because both levels are always presented together. Thus, only a mixture of the brain responses to two levels can be observed. Here, we use a method that allows unveiling of global and local letters at distinct times, enabling estimation of separate ERPs related to each level. Two interspersed oddball streams were presented, each using letters from one level and comprised of frequent distracters and rare targets. Previous work and our Experiment 1 show that it is difficult to divide attention between two such streams of stimuli. ERP recording in Experiment 2 evinced an early selection negativity (SN, with latencies to the 50% area of about 266 ms for global distracters and 276 ms for local distracters) that was larger for attended relative to unattended distracters. The SN was larger over right posterior occipito-temporal derivations for global stimuli and over left posterior occipito-temporal derivations for local stimuli (although the latter was less strongly lateralized). A discrimination negativity (DN, accompanied by a P3b) was larger for attended targets relative to attended distracters, with latencies to the 50% area of about 316 ms for global stimuli and 301 ms for local stimuli, which presented a similar distribution for both levels over left temporo-parietal electrodes. The two negativities apparently index successive stages in the processing of a selected level within a compound figure. By resolving the ambiguity of traditional designs, our method allowed us to observe the effects of attention for each hierarchical level on its own.

  11. Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum.

    Science.gov (United States)

    Roalf, D R; Nanga, R P R; Rupert, P E; Hariharan, H; Quarmley, M; Calkins, M E; Dress, E; Prabhakaran, K; Elliott, M A; Moberg, P J; Gur, R C; Gur, R E; Reddy, R; Turetsky, B I

    2017-01-24

    Psychosis commonly develops in adolescence or early adulthood. Youths at clinical high risk (CHR) for psychosis exhibit similar, subtle symptoms to those with schizophrenia (SZ). Malfunctioning neurotransmitter systems, such as glutamate, are implicated in the disease progression of psychosis. Yet, in vivo imaging techniques for measuring glutamate across the cortex are limited. Here, we use a novel 7 Tesla MRI glutamate imaging technique (GluCEST) to estimate changes in glutamate levels across cortical and subcortical regions in young healthy individuals and ones on the psychosis spectrum. Individuals on the psychosis spectrum (PS; n=19) and healthy young individuals (HC; n=17) underwent MRI imaging at 3 and 7 T. At 7 T, a single slice GluCEST technique was used to estimate in vivo glutamate. GluCEST contrast was compared within and across the subcortex, frontal, parietal and occipital lobes. Subcortical (χ(2) (1)=4.65, P=0.031) and lobular (χ(2) (1)=5.17, P=0.023) GluCEST contrast levels were lower in PS compared with HC. Abnormal GluCEST contrast levels were evident in both CHR (n=14) and SZ (n=5) subjects, and correlated differentially, across regions, with clinical symptoms. Our findings describe a pattern of abnormal brain neurochemistry early in the course of psychosis. Specifically, CHR and young SZ exhibit diffuse abnormalities in GluCEST contrast attributable to a major contribution from glutamate. We suggest that neurochemical profiles of GluCEST contrast across cortex and subcortex may be considered markers of early psychosis. GluCEST methodology thus shows promise to further elucidate the progression of the psychosis disease state.Molecular Psychiatry advance online publication, 24 January 2017; doi:10.1038/mp.2016.258.

  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. 阿片受体类型和功能及其在猪脑中的个体发育特点%Classification and Functions of Opioid Receptors and Their Ontogenic Characterization in Pig Brain

    Institute of Scientific and Technical Information of China (English)

    李定健

    2016-01-01

    The research progress on classification of opioid receptors and introduced functions of four main types of receptors were summarized. In animal brains, changes in the opioid receptor number and their afifnity to opioid ligands can affect opioidergic control of brain functions or central nervous control of endocrine system. In order to provide the references for changes of opioid receptors in pig brain, the ontogenic characterization of opioid receptors in pig brains was elaborated.%总结阿片受体类型的研究进展,介绍4种主要类型受体的功能。动物脑中阿片受体的数量以及它们与阿片配体亲和力的改变能够对脑功能的阿片控制或内分泌系统的中枢神经控制产生影响。为明确猪脑中阿片受体的变化,阐述了阿片受体在猪脑中的个体发育特点。

  14. Multivariate imaging-genetics study of MRI gray matter volume and SNPs reveals biological pathways correlated with brain structural differences in Attention Deficit Hyperactivity Disorder

    Directory of Open Access Journals (Sweden)

    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

  15. 'Hit & Run' model of closed-skull traumatic brain injury (TBI) reveals complex patterns of post-traumatic AQP4 dysregulation.

    Science.gov (United States)

    Ren, Zeguang; Iliff, Jeffrey J; Yang, Lijun; Yang, Jiankai; Chen, Xiaolin; Chen, Michael J; Giese, Rebecca N; Wang, Baozhi; Shi, Xuefang; Nedergaard, Maiken

    2013-06-01

    Cerebral edema is a major contributor to morbidity associated with traumatic brain injury (TBI). The methods involved in most rodent models of TBI, including head fixation, opening of the skull, and prolonged anesthesia, likely alter TBI development and reduce secondary injury. We report the development of a closed-skull model of murine TBI, which minimizes time of anesthesia, allows the monitoring of intracranial pressure (ICP), and can be modulated to produce mild and moderate grade TBI. In this model, we characterized changes in aquaporin-4 (AQP4) expression and localization after mild and moderate TBI. We found that global AQP4 expression after TBI was generally increased; however, analysis of AQP4 localization revealed that the most prominent effect of TBI on AQP4 was the loss of polarized localization at endfoot processes of reactive astrocytes. This AQP4 dysregulation peaked at 7 days after injury and was largely indistinguishable between mild and moderate grade TBI for the first 2 weeks after injury. Within the same model, blood-brain barrieranalysis of variance permeability, cerebral edema, and ICP largely normalized within 7 days after moderate TBI. These findings suggest that changes in AQP4 expression and localization may not contribute to cerebral edema formation, but rather may represent a compensatory mechanism to facilitate its resolution.

  16. Recombinant adeno-associated virus-mediated microRNA delivery into the postnatal mouse brain reveals a role for miR-134 in dendritogenesis in vivo

    Directory of Open Access Journals (Sweden)

    Mette Christensen

    2010-01-01

    Full Text Available Recent studies using primary neuronal cultures have revealed important roles of the microRNA pathway in the regulation of neuronal development and morphology. For example, miR-134 is involved in dendritogenesis and spine development in hippocampal neurons by regulating local mRNA translation in dendrites. The in vivo roles of microRNAs in these processes are still uninvestigated, partly due to the lack of tools enabling stable in vivo delivery of microRNAs or microRNA inhibitors into neurons of the mammalian brain. Here we describe the construction and validation of a vector-based tool for stable 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 previous findings obtained with cultured neurons. Our system provides researchers with a unique tool to study the role of any candidate microRNA in vivo and can easily be adapted to microRNA loss-of-function studies. This platform should therefore greatly facilitate investigations on the role of microRNAs in synapse development, plasticity and behavior in vivo.

  17. Comparative transcriptome analysis in induced neural stem cells reveals defined neural cell identities in vitro and after transplantation into the adult rodent brain.

    Science.gov (United States)

    Hallmann, Anna-Lena; Araúzo-Bravo, Marcos J; Zerfass, Christina; Senner, Volker; Ehrlich, Marc; Psathaki, Olympia E; Han, Dong Wook; Tapia, Natalia; Zaehres, Holm; Schöler, Hans R; Kuhlmann, Tanja; Hargus, Gunnar

    2016-05-01

    Reprogramming technology enables the production of neural progenitor cells (NPCs) from somatic cells by direct transdifferentiation. However, little is known on how neural programs in these induced neural stem cells (iNSCs) differ from those of alternative stem cell populations in vitro and in vivo. Here, we performed transcriptome analyses on murine iNSCs in comparison to brain-derived neural stem cells (NSCs) and pluripotent stem cell-derived NPCs, which revealed distinct global, neural, metabolic and cell cycle-associated marks in these populations. iNSCs carried a hindbrain/posterior cell identity, which could be shifted towards caudal, partially to rostral but not towards ventral fates in vitro. iNSCs survived after transplantation into the rodent brain and exhibited in vivo-characteristics, neural and metabolic programs similar to transplanted NSCs. However, iNSCs vastly retained caudal identities demonstrating cell-autonomy of regional programs in vivo. These data could have significant implications for a variety of in vitro- and in vivo-applications using iNSCs.

  18. Network-Based Logistic Classification with an Enhanced L1/2 Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer

    Directory of Open Access Journals (Sweden)

    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. A Classification Scheme for Young Stellar Objects Using the WIDE-FIELD INFRARED SURVEY EXPLORER ALLWISE Catalog: Revealing Low-Density Star Formation in the Outer Galaxy

    Science.gov (United States)

    Koening, X. P.; Leisawitz, D. T.

    2014-01-01

    We present an assessment of the performance of WISE and the AllWISE data release in a section of the Galactic Plane. We lay out an approach to increasing the reliability of point source photometry extracted from the AllWISE catalog in Galactic Plane regions using parameters provided in the catalog. We use the resulting catalog to construct a new, revised young star detection and classification scheme combining WISE and 2MASS near and mid-infrared colors and magnitudes and test it in a section of the Outer Milky Way. The clustering properties of the candidate Class I and II stars using a nearest neighbor density calculation and the two-point correlation function suggest that the majority of stars do form in massive star forming regions, and any isolated mode of star formation is at most a small fraction of the total star forming output of the Galaxy. We also show that the isolated component may be very small and could represent the tail end of a single mechanism of star formation in line with models of molecular cloud collapse with supersonic turbulence and not a separate mode all to itself.

  20. 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.

  1. 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.

  2. Tracking the Emergence of Host-Specific Simian Immunodeficiency Virus env and nef Populations Reveals nef Early Adaptation and Convergent Evolution in Brain of Naturally Progressing Rhesus Macaques.

    Science.gov (United States)

    Lamers, Susanna L; Nolan, David J; Rife, Brittany D; Fogel, Gary B; McGrath, Michael S; Burdo, Tricia H; Autissier, Patrick; Williams, Kenneth C; Goodenow, Maureen M; Salemi, Marco

    2015-08-01

    evolutionary patterns in the gp120 and nef genes leading to the emergence of host-specific viral populations and potentially linked to disease progression. Although each macaque exhibited unique immune profiles, macaque-specific nef sequences evolving under selection were consistently detected in plasma samples at 3 months postinfection, significantly earlier than in gp120 macaque-specific sequences. On the other hand, nef sequences in brain tissues, collected at necropsy of two animals with detectable infection in the central nervous system (CNS), revealed convergent evolution. The results not only indicate that early adaptation of nef in the new host may be essential for successful infection, but also suggest that specific nef variants may be required for SIV to efficiently invade CNS macrophages and/or enhance macrophage migration, resulting in HIV neuropathology. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  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; Woldorff, Marty G

    2016-01-20

    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. Analysis of Post-Traumatic Brain Injury Gene Expression Signature Reveals Tubulins, Nfe2l2, Nfkb, Cd44, and S100a4 as Treatment Targets.

    Science.gov (United States)

    Lipponen, Anssi; Paananen, Jussi; Puhakka, Noora; Pitkänen, Asla

    2016-08-17

    We aimed to define the chronically altered gene expression signature of traumatic brain injury (TBI-sig) to discover novel treatments to reverse pathologic gene expression or reinforce the expression of recovery-related genes. Genome-wide RNA-sequencing was performed at 3 months post-TBI induced by lateral fluid-percussion injury in rats. We found 4964 regulated genes in the perilesional cortex and 1966 in the thalamus (FDR < 0.05). TBI-sig was used for a LINCS analysis which identified 11 compounds that showed a strong connectivity with the TBI-sig in neuronal cell lines. Of these, celecoxib and sirolimus were recently reported to have a disease-modifying effect in in vivo animal models of epilepsy. Other compounds revealed by the analysis were BRD-K91844626, BRD-A11009626, NO-ASA, BRD-K55260239, SDZ-NKT-343, STK-661558, BRD-K75971499, ionomycin, and desmethylclomipramine. Network analysis of overlapping genes revealed the effects on tubulins (Tubb2a, Tubb3, Tubb4b), Nfe2l2, S100a4, Cd44, and Nfkb2, all of which are linked to TBI-relevant outcomes, including epileptogenesis and tissue repair. Desmethylclomipramine modulated most of the gene targets considered favorable for TBI outcome. Our data demonstrate long-lasting transcriptomics changes after TBI. LINCS analysis predicted that these changes could be modulated by various compounds, some of which are already in clinical use but never tested in TBI.

  5. 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

  6. Complex interplay between brain function and structure during cerebral amyloidosis in APP transgenic mouse strains revealed by multi-parametric MRI comparison.

    Science.gov (United States)

    Grandjean, Joanes; Derungs, Rebecca; Kulic, Luka; Welt, Tobias; Henkelman, Mark; Nitsch, Roger M; Rudin, Markus

    2016-07-01

    Alzheimer's disease is a fatal neurodegenerative disorder affecting the aging population. Neuroimaging methods, in particular magnetic resonance imaging (MRI), have helped reveal alterations in the brain structure, metabolism, and function of patients and in groups at risk of developing AD, yet the nature of these alterations is poorly understood. Neuroimaging in mice is attractive for investigating mechanisms underlying functional and structural changes associated with AD pathology. Several preclinical murine models of AD have been generated based on transgenic insertion of human mutated APP genes. Depending on the specific mutations, mouse strains express different aspects of amyloid pathology, e.g. intracellular amyloid-β (Aβ) aggregates, parenchymal plaques, or cerebral amyloid angiopathy. We have applied multi-parametric MRI in three transgenic mouse lines to compare changes in brain function with resting-state fMRI and structure with diffusion tensor imaging and high resolution anatomical imaging. E22ΔAβ developing intracellular Aβ aggregates did not present functional or structural alterations compared to their wild-type littermates. PSAPP mice displaying parenchymal amyloid plaques displayed mild functional changes within the supplementary and barrel field cortices, and increased isocortical volume relative to controls. Extensive reduction in functional connectivity in the sensory-motor cortices and within the default mode network, as well as local volume increase in the midbrain relative to wild-type have been observed in ArcAβ mice bearing intracellular Aβ aggregates as well as parenchymal and vascular amyloid deposits. Patterns of functional and structural changes appear to be strain-specific and not directly related to amyloid deposition.

  7. A Translational Murine Model of Sub-Lethal Intoxication with Shiga Toxin 2 Reveals Novel Ultrastructural Findings in the Brain Striatum

    Science.gov (United States)

    Tironi-Farinati, Carla; Geoghegan, Patricia A.; Cangelosi, Adriana; Pinto, Alipio; Loidl, C. Fabian; Goldstein, Jorge

    2013-01-01

    Infection by Shiga toxin-producing Escherichia coli causes hemorrhagic colitis, hemolytic uremic syndrome (HUS), acute renal failure, and also central nervous system complications in around 30% of the children affected. Besides, neurological deficits are one of the most unrepairable and untreatable outcomes of HUS. Study of the striatum is relevant because basal ganglia are one of the brain areas most commonly affected in patients that have suffered from HUS and since the deleterious effects of a sub-lethal dose of Shiga toxin have never been studied in the striatum, the purpose of this study was to attempt to simulate an infection by Shiga toxin-producing E. coli in a murine model. To this end, intravenous administration of a sub-lethal dose of Shiga toxin 2 (0.5 ηg per mouse) was used and the correlation between neurological manifestations and ultrastructural changes in striatal brain cells was studied in detail. Neurological manifestations included significant motor behavior abnormalities in spontaneous motor activity, gait, pelvic elevation and hind limb activity eight days after administration of the toxin. Transmission electron microscopy revealed that the toxin caused early perivascular edema two days after administration, as well as significant damage in astrocytes four days after administration and significant damage in neurons and oligodendrocytes eight days after administration. Interrupted synapses and mast cell extravasation were also found eight days after administration of the toxin. We thus conclude that the chronological order of events observed in the striatum could explain the neurological disorders found eight days after administration of the toxin. PMID:23383285

  8. A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.

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    Blair Kaneshiro

    Full Text Available The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG study, we used single-trial classification to perform a Representational Similarity Analysis (RSA of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single

  9. Microbial and metabolic profiling reveal strong influence of water table and land-use patterns on classification of degraded tropical peatlands

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    Mishra, S.; Lee, W. A.; Hooijer, A.; Reuben, S.; Sudiana, I. M.; Idris, A.; Swarup, S.

    2014-04-01

    Tropical peatlands from southeast Asia are undergoing extensive drainage, deforestation and degradation for agriculture and human settlement purposes. This is resulting in biomass loss and subsidence of peat from its oxidation. Molecular profiling approaches were used to understand the relative influences of different land-use patterns, hydrological and physicochemical parameters on the state of degraded tropical peatlands. As microbial communities play a critical role in biogeochemical cascades in the functioning of peatlands, we used microbial and metabolic profiles as surrogates of community structure and functions, respectively. Profiles were generated from 230 bacterial 16 S rDNA fragments and 145 metabolic markers of 46 samples from 10 sites, including those from above and below water table in a contiguous area of 48 km2 covering five land-use types. These were degraded forest, degraded land, oil palm plantation, mixed crop plantation and settlements. Bacterial profiles were most influenced by variations in water table and land-use patterns, followed by age of drainage and peat thickness in that order. Bacterial profiling revealed differences in sites, based on the duration and frequency of water table fluctuations and on oxygen availability. Mixed crop plantations had the most diverse bacterial and metabolic profiles. Metabolic profiling, being closely associated with biogeochemical functions, could distinguish communities not only based on land-use types but also their geographic locations, thus providing a finer resolution than bacterial profiles. Agricultural inputs, such as nitrates, were highly associated with bacterial community structure of oil palm plantations, whereas phosphates and dissolved organic carbon influenced those from mixed crop plantations and settlements. Our results provide a basis for adopting molecular marker-based approaches to classify peatlands and determine relative importance of factors that influence peat functioning. Our

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