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Sample records for brain state classification

  1. 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.

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

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

    2007-10-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

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    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

  5. Classification of Cortical Brain Malformations

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2008-03-01

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

  6. Unsupervised classification of operator workload from brain signals

    Science.gov (United States)

    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.

  7. Local Kernel for Brains Classification in Schizophrenia

    Science.gov (United States)

    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.

  8. 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

  9. Werner State Structure and Entanglement Classification

    Directory of Open Access Journals (Sweden)

    David W. Lyons

    2012-01-01

    Full Text Available We present applications of the representation theory of Lie groups to the analysis of structure and local unitary classification of Werner states, sometimes called the decoherence-free states, which are states of n quantum bits left unchanged by local transformations that are the same on each particle. We introduce a multiqubit generalization of the singlet state and a construction that assembles these qubits into Werner states.

  10. Simple Fully Automated Group Classification on Brain fMRI

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  11. 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.

  12. [Mixed states: evolution of classifications].

    Science.gov (United States)

    Pringuey, D; Cherikh, F; Giordana, B; Fakra, E; Dassa, D; Cermolacce, M; Belzeaux, R; Maurel, M; Azorin, J-M

    2013-12-01

    The nosological position of mixed states has followed the course of classifying methods in psychiatry, the steps of the invention of the clinic, progress in the organization of care, including the discoveries of psychopharmacology. The clinical observation of a mixture of symptoms emerging from usually opposite clinical conditions is classical. In the 70s, a syndromic specification fixed the main symptom combinations but that incongruous assortment failed to stabilize the nosological concept. Then stricter criteriology was proposed. To be too restrictive, a consensus operates a dimensional opening that attempts to meet the pragmatic requirements of nosology validating the usefulness of the class system. This alternation between rigor of categorization and return to a more flexible criteriological option reflects the search for the right balance between nosology and diagnosis. The definition of mixed states is best determined by their clinical and prognostic severity, related to the risk of suicide, their lower therapeutic response, the importance of their psychiatric comorbidities, anxiety, emotional lability, alcohol abuse. Trying to compensate for the lack of categorical definitions and better reflecting the clinical field problems, new definitions complement criteriology with dimensional aspects, particularly taking into account temperaments. Copyright © 2013 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

  13. Brain tumor classification using Probabilistic Neural Network

    African Journals Online (AJOL)

    pc

    2018-03-05

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

  14. A proposed United States resource classification system

    International Nuclear Information System (INIS)

    Masters, C.D.

    1980-01-01

    Energy is a world-wide problem calling for world-wide communication to resolve the many supply and distribution problems. Essential to a communication problem are a definition and comparability of elements being communicated. The US Geological Survey, with the co-operation of the US Bureau of Mines and the US Department of Energy, has devised a classification system for all mineral resources, the principles of which, it is felt, offer the possibility of world communication. At present several other systems, extant or under development (Potential Gas Committee of the USA, United Nations Resource Committee, and the American Society of Testing and Materials) are internally consistent and provide easy communication linkage. The system in use by the uranium community in the United States of America, however, ties resource quantities to forward-cost dollar values rendering them inconsistent with other classifications and therefore not comparable. This paper develops the rationale for the new USGS resource classification and notes its benefits relative to a forward-cost classification and its relationship specifically to other current classifications. (author)

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

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    Ferreira da Silva, Adelino R

    2007-04-01

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

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

    Czech Academy of Sciences Publication Activity Database

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

    2011-01-01

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

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

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

    2017-01-01

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

  18. Building the United States National Vegetation Classification

    Science.gov (United States)

    Franklin, S.B.; Faber-Langendoen, D.; Jennings, M.; Keeler-Wolf, T.; Loucks, O.; Peet, R.; Roberts, D.; McKerrow, A.

    2012-01-01

    The Federal Geographic Data Committee (FGDC) Vegetation Subcommittee, the Ecological Society of America Panel on Vegetation Classification, and NatureServe have worked together to develop the United States National Vegetation Classification (USNVC). The current standard was accepted in 2008 and fosters consistency across Federal agencies and non-federal partners for the description of each vegetation concept and its hierarchical classification. The USNVC is structured as a dynamic standard, where changes to types at any level may be proposed at any time as new information comes in. But, because much information already exists from previous work, the NVC partners first established methods for screening existing types to determine their acceptability with respect to the 2008 standard. Current efforts include a screening process to assign confidence to Association and Group level descriptions, and a review of the upper three levels of the classification. For the upper levels especially, the expectation is that the review process includes international scientists. Immediate future efforts include the review of remaining levels and the development of a proposal review process.

  19. The brain MRI classification problem from wavelets perspective

    Science.gov (United States)

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

    2015-02-01

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

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

    Science.gov (United States)

    Tayebi Meybodi, Ali; Lawton, Michael T

    2018-05-04

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

  1. 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.

  2. Application of machine learning on brain cancer multiclass classification

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

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

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

    2012-01-01

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

  7. Brain communication in the locked-in state.

    Science.gov (United States)

    De Massari, Daniele; Ruf, Carolin A; Furdea, Adrian; Matuz, Tamara; van der Heiden, Linda; Halder, Sebastian; Silvoni, Stefano; Birbaumer, Niels

    2013-06-01

    Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional

  8. State of the Art in the Cramer Classification Scheme and ...

    Science.gov (United States)

    Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD. Slide presentation at the SOT FDA Colloquium on State of the Art in the Cramer Classification Scheme and Threshold of Toxicological Concern in College Park, MD.

  9. 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.

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

    Science.gov (United States)

    Dai, Mengxi; Liu, Shucong; Zhang, Pengju

    2018-01-01

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

  11. Solid state conformational classification of eight-membered rings

    DEFF Research Database (Denmark)

    Pérez, J.; García, L.; Kessler, M.

    2005-01-01

    A statistical classification of the solid state conformation in the title complexes using data retrieved from the Cambridge Structural Database (CSD) has been made. Phosphate and phosphinate complexes show a chair conformation preferably. In phosphonate complexes, the most frequent conformations...

  12. Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

    Science.gov (United States)

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

    2017-08-01

    Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

  13. Optimal trajectories of brain state transitions.

    Science.gov (United States)

    Gu, Shi; Betzel, Richard F; Mattar, Marcelo G; Cieslak, Matthew; Delio, Philip R; Grafton, Scott T; Pasqualetti, Fabio; Bassett, Danielle S

    2017-03-01

    The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Possession States: Approaches to Clinical Evaluation and Classification

    Directory of Open Access Journals (Sweden)

    S. McCormick

    1992-01-01

    Full Text Available The fields of anthropology and sociology have produced a large quantity of literature on possession states, physicians however rarely report on such phenomena. As a result clinical description of possession states has suffered, even though these states may be more common and less deviant than supposed. Both ICD-10 and DSM-IV may include specific criteria for possession disorders. The authors briefly review Western notions about possession and kindred states and present guidelines for evaluation and classification.

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

    Science.gov (United States)

    Wegmayr, Viktor; Aitharaju, Sai; Buhmann, Joachim

    2018-02-01

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

  16. Failure diagnosis using deep belief learning based health state classification

    International Nuclear Information System (INIS)

    Tamilselvan, Prasanna; Wang, Pingfeng

    2013-01-01

    Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  20. Assessing the Approaches to Classification of the State Financial Control

    OpenAIRE

    Baraniuk Yurii R.

    2017-01-01

    The article is aimed at assessing the approaches to classification of the State financial control, as well as disclosing the relationship and differences between its forms, types and methods. The results of comparative analysis of existing classifications of the State financial control have been covered. The substantiation of its identification by forms, types and methods of control was explored. Clarification of the interpretation of the concepts of «form of control», «type of control», «sub...

  1. The classification of flaring states of blazars

    Science.gov (United States)

    Resconi, E.; Franco, D.; Gross, A.; Costamante, L.; Flaccomio, E.

    2009-08-01

    Aims: The time evolution of the electromagnetic emission from blazars, in particular high-frequency peaked sources (HBLs), displays irregular activity that has not yet been understood. In this work we report a methodology capable of characterizing the time behavior of these variable objects. Methods: The maximum likelihood blocks (MLBs) is a model-independent estimator that subdivides the light curve into time blocks, whose length and amplitude are compatible with states of constant emission rate of the observed source. The MLBs yield the statistical significance in the rate variations and strongly suppresses the noise fluctuations in the light curves. We applied the MLBs for the first time on the long term X-ray light curves (RXTE/ASM) of Mkn 421, Mkn 501, 1ES 1959+650, and 1ES 2155-304, more than 10 years of observational data (1996-2007). Using the MLBs interpretation of RXTE/ASM data, the integrated time flux distribution is determined for each single source considered. We identify in these distributions the characteristic level, as well as the flaring states of the blazars. Results: All the distributions show a significant component at negative flux values, most probably caused by an uncertainty in the background subtraction and by intrinsic fluctuations of RXTE/ASM. This effect concerns in particular short time observations. To quantify the probability that the intrinsic fluctuations give rise to a false identification of a flare, we study a population of very faint sources and their integrated time-flux distribution. We determine duty cycle or fraction of time a source spent in the flaring state of the source Mkn 421, Mkn 501, 1ES 1959+650 and 1ES 2155-304. Moreover, we study the random coincidences between flares and generic sporadic events such as high-energy neutrinos or flares in other wavelengths.

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

    Science.gov (United States)

    Banan, Rouzbeh; Hartmann, Christian

    2017-03-01

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

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

    Science.gov (United States)

    Hong, Keum-Shik; Khan, Muhammad Jawad

    2017-01-01

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

  4. Assessing the Approaches to Classification of the State Financial Control

    Directory of Open Access Journals (Sweden)

    Baraniuk Yurii R.

    2017-11-01

    Full Text Available The article is aimed at assessing the approaches to classification of the State financial control, as well as disclosing the relationship and differences between its forms, types and methods. The results of comparative analysis of existing classifications of the State financial control have been covered. The substantiation of its identification by forms, types and methods of control was explored. Clarification of the interpretation of the concepts of «form of control», «type of control», «subtype of control», «method of control», «methodical reception of control» has been provided. It has been determined that the form of the State financial control is a manifestation of the internal organization of control and the methods of its carrying out; a model of classification of the State financial control has been substantiated; attributes of the first and second order have been allocated; substantiation of methods and techniques has been improved; their composition and structure have been identified. This approach allows to divide general questions of the State financial control into theoretical and practical and, taking into consideration the expansion of the list of objects of the State financial control, will help to improve its methodology.

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

    Science.gov (United States)

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

    2017-07-01

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

  6. State-of-the-Art Methods for Brain Tissue Segmentation: A Review.

    Science.gov (United States)

    Dora, Lingraj; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

    2017-01-01

    Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.

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

    Science.gov (United States)

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

    2017-12-01

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

  8. Quantum learning: asymptotically optimal classification of qubit states

    International Nuclear Information System (INIS)

    Guta, Madalin; Kotlowski, Wojciech

    2010-01-01

    Pattern recognition is a central topic in learning theory, with numerous applications such as voice and text recognition, image analysis and computer diagnosis. The statistical setup in classification is the following: we are given an i.i.d. training set (X 1 , Y 1 ), ... , (X n , Y n ), where X i represents a feature and Y i in{0, 1} is a label attached to that feature. The underlying joint distribution of (X, Y) is unknown, but we can learn about it from the training set, and we aim at devising low error classifiers f: X→Y used to predict the label of new incoming features. In this paper, we solve a quantum analogue of this problem, namely the classification of two arbitrary unknown mixed qubit states. Given a number of 'training' copies from each of the states, we would like to 'learn' about them by performing a measurement on the training set. The outcome is then used to design measurements for the classification of future systems with unknown labels. We found the asymptotically optimal classification strategy and show that typically it performs strictly better than a plug-in strategy, which consists of estimating the states separately and then discriminating between them using the Helstrom measurement. The figure of merit is given by the excess risk equal to the difference between the probability of error and the probability of error of the optimal measurement for known states. We show that the excess risk scales as n -1 and compute the exact constant of the rate.

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  10. Entanglement Classification of extended Greenberger-Horne-Zeilinger-Symmetric States

    OpenAIRE

    Jung, Eylee; Park, DaeKil

    2013-01-01

    In this paper we analyze entanglement classification of extended Greenberger-Horne-Zeilinger-symmetric states $\\rho^{ES}$, which is parametrized by four real parameters $x$, $y_1$, $y_2$ and $y_3$. The condition for separable states of $\\rho^{ES}$ is analytically derived. The higher classes such as bi-separable, W, and Greenberger-Horne-Zeilinger classes are roughly classified by making use of the class-specific optimal witnesses or map from the extended Greenberger-Horne-Zeilinger symmetry t...

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

    Science.gov (United States)

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

    2015-08-01

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

  12. Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

    Science.gov (United States)

    Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

    2014-01-01

    Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

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

    Science.gov (United States)

    Grossman, Rachel; Ram, Zvi

    2014-12-01

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

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

    Science.gov (United States)

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

    2015-09-01

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

  15. 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

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

    Science.gov (United States)

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

    2018-06-01

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

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

    Science.gov (United States)

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

    2015-11-01

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

  18. 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.

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

    Science.gov (United States)

    Damayanti, A.; Werdiningsih, I.

    2018-03-01

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

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

    International Nuclear Information System (INIS)

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

    1995-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-10-01

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

  2. Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM

    Science.gov (United States)

    Mokhtari, Fatemeh; Bakhtiari, Shahab K.; Hossein-Zadeh, Gholam Ali; Soltanian-Zadeh, Hamid

    2012-02-01

    Decoding techniques have opened new windows to explore the brain function and information encoding in brain activity. In the current study, we design a recursive support vector machine which is enriched by a subtree graph kernel. We apply the classifier to discriminate between attentional cueing task and resting state from a block design fMRI dataset. The classifier is trained using weighted fMRI graphs constructed from activated regions during the two mentioned states. The proposed method leads to classification accuracy of 1. It is also able to elicit discriminative regions and connectivities between the two states using a backward edge elimination algorithm. This algorithm shows the importance of regions including cerebellum, insula, left middle superior frontal gyrus, post cingulate cortex, and connectivities between them to enhance the correct classification rate.

  3. Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Tijn M. Schouten

    2016-01-01

    Full Text Available Magnetic resonance imaging (MRI is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD, and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N=77 from the prospective registry on dementia study and controls (N=173 from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC of 0.760 (full correlations between functional networks to 0.909 (grey matter density. When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

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

    Science.gov (United States)

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

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Ying Mei

    2017-06-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  7. Classification of wetlands and deepwater habitats of the United States

    Science.gov (United States)

    Cowardin, L.M.; Carter, V.; Golet, F.C.; LaRoe, E.T.

    1985-01-01

    This classification, to be used in a new inventory of wetlands and deepwater habitats of the United States, is intended to describe ecological taxa, arrange them in a system useful to resource managers, furnish units for mapping, and provide uniformity of concepts and terms. Wetlands are defined by plants (hydrophytes), soils (hydric soils), and frequency of flooding. Ecologically related areas of deep water, traditionally not considered wetlands, are included in the classification as deepwater habitats.Systems form the highest level of the classification hierarchy; five are defined-Marine, Estuarine, Riverine, Lacustrine, and Palustrine. Marine and Estuarine Systems each have two Subsystems, Subtidal and Intertidal; the Riverine System has four Subsystems, Tidal, Lower Perennial, Upper Perennial, and Intermittent; the Lacustrine has two, Littoral and Limnetic; and the Palustrine has no Subsystems.Within the Subsystems, Classes are based on substrate material and flooding regime, or on vegetative life form. The same Classes may appear under one or more of the Systems or Subsystems. Six Classes are based on substrate and flooding regime: (1) Rock Bottom with a substrate of bedrock, boulders, or stones; (2) Unconsolidated Bottom with a substrate of cobbles, gravel, sand, mud, or organic material; (3) Rocky Shore with the same substrates as Rock Bottom; (4) Unconsolidated Shore with the same substrates as Unconsolidated Bottom; (5) Streambed with any of the substrates; and (6) Reef with a substrate composed of the living and dead remains of invertebrates (corals, mollusks, or worms). The bottom Classes, (1) and (2) above, are flooded all or most of the time and the shore Classes, (3) and (4), are exposed most of the time. The Class Streambed is restricted to channels of intermittent streams and tidal channels that are dewatered at low tide. The life form of the dominant vegetation defines the five Classes based on vegetative form: (1) Aquatic Bed, dominated by plants

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

    NARCIS (Netherlands)

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

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

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

    NARCIS (Netherlands)

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

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

  10. 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.

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

    Science.gov (United States)

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

    2015-09-01

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

  12. Thyroid hormones states and brain development interactions.

    Science.gov (United States)

    Ahmed, Osama M; El-Gareib, A W; El-Bakry, A M; Abd El-Tawab, S M; Ahmed, R G

    2008-04-01

    The action of thyroid hormones (THs) in the brain is strictly regulated, since these hormones play a crucial role in the development and physiological functioning of the central nervous system (CNS). Disorders of the thyroid gland are among the most common endocrine maladies. Therefore, the objective of this study was to identify in broad terms the interactions between thyroid hormone states or actions and brain development. THs regulate the neuronal cytoarchitecture, neuronal growth and synaptogenesis, and their receptors are widely distributed in the CNS. Any deficiency or increase of them (hypo- or hyperthyroidism) during these periods may result in an irreversible impairment, morphological and cytoarchitecture abnormalities, disorganization, maldevelopment and physical retardation. This includes abnormal neuronal proliferation, migration, decreased dendritic densities and dendritic arborizations. This drastic effect may be responsible for the loss of neurons vital functions and may lead, in turn, to the biochemical dysfunctions. This could explain the physiological and behavioral changes observed in the animals or human during thyroid dysfunction. It can be hypothesized that the sensitive to the thyroid hormones is not only remarked in the neonatal period but also prior to birth, and THs change during the development may lead to the brain damage if not corrected shortly after the birth. Thus, the hypothesis that neurodevelopmental abnormalities might be related to the thyroid hormones is plausible. Taken together, the alterations of neurotransmitters and disturbance in the GABA, adenosine and pro/antioxidant systems in CNS due to the thyroid dysfunction may retard the neurogenesis and CNS growth and the reverse is true. In general, THs disorder during early life may lead to distortions rather than synchronized shifts in the relative development of several central transmitter systems that leads to a multitude of irreversible morphological and biochemical

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

    Science.gov (United States)

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

    2014-01-01

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

  14. Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity.

    Directory of Open Access Journals (Sweden)

    Vasily A Vakorin

    2016-12-01

    Full Text Available Accurate means to detect mild traumatic brain injury (mTBI using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz, together with increased connectivity in the slower alpha band (8-12 Hz. A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  17. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  18. 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…

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

    Science.gov (United States)

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

    2012-03-28

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

  20. 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.

  1. 9 CFR 77.3 - Tuberculosis classifications of States and zones.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Tuberculosis classifications of States... TUBERCULOSIS General Provisions § 77.3 Tuberculosis classifications of States and zones. The Administrator shall classify each State for tuberculosis in accordance with this part. A zone comprising less than an...

  2. 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...

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

    Science.gov (United States)

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

    2004-09-01

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

  4. Quality of Life Following Brain Injury: Perspectives from Brain Injury Association of America State Affiliates

    Science.gov (United States)

    Degeneffe, Charles Edmund; Tucker, Mark

    2012-01-01

    Objective: to examine the perspectives of brain injury professionals concerning family members' feelings about the quality of life experienced by individuals with brain injuries. Participants: participating in the study were 28 individuals in leadership positions with the state affiliates of the Brain Injury Association of America (BIAA). Methods:…

  5. 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.

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

    Science.gov (United States)

    Tayebi Meybodi, Ali; Lawton, Michael T

    2018-02-23

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

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

    Directory of Open Access Journals (Sweden)

    Toni Maree Dwan

    2015-03-01

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

  8. Secondary structure classification of amino-acid sequences using state-space modeling

    OpenAIRE

    Brunnert, Marcus; Krahnke, Tillmann; Urfer, Wolfgang

    2001-01-01

    The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial...

  9. [CLASSIFICATION OF ACUTE PANCREATITIS: CURRENT STATE OF THE ISSUE].

    Science.gov (United States)

    Bagnenko, S F; Gol'tsov, V P; Savello, V E; Vashetko, R V

    2015-01-01

    The article analyzed disadvantages of "Atlanta-92" classification of acute pancreatitis and its two modifications: APCWG-2012 and IAP-2011. The school of Saint-Petersburg pancreatologists suggested the classification AP of Russian Surgical Society (2014), which represented the concept of disease staging.

  10. 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.

  11. The State of the NIH BRAIN Initiative.

    Science.gov (United States)

    Koroshetz, Walter; Gordon, Joshua; Adams, Amy; Beckel-Mitchener, Andrea; Churchill, James; Farber, Gregory; Freund, Michelle; Gnadt, Jim; Hsu, Nina; Langhals, Nicholas; Lisanby, Sarah; Liu, Guoying; Peng, Grace; Ramos, Khara; Steinmetz, Michael; Talley, Edmund; White, Samantha

    2018-06-19

    The BRAIN Initiative® arose from a grand challenge to "accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought." The BRAIN Initiative is a public-private effort focused on the development and use of powerful tools for acquiring fundamental insights about how information processing occurs in the central nervous system. As the Initiative enters its fifth year, NIH has supported over 500 principal investigators, who have answered the Initiative's challenge via hundreds of publications describing novel tools, methods, and discoveries that address the Initiative's seven scientific priorities. We describe scientific advances produced by individual labs, multi-investigator teams, and entire consortia that, over the coming decades, will produce more comprehensive and dynamic maps of the brain, deepen our understanding of how circuit activity can produce a rich tapestry of behaviors, and lay the foundation for understanding how its circuitry is disrupted in brain disorders. Much more work remains to bring this vision to fruition, and NIH continues to look to the diverse scientific community, from mathematics, to physics, chemistry, engineering, neuroethics, and neuroscience, to ensure that the greatest scientific benefit arises from this unique research Initiative. Copyright © 2018 the authors.

  12. 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.

  13. 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

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

    Directory of Open Access Journals (Sweden)

    Jenessa Lancaster

    2018-02-01

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

  15. Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data

    Directory of Open Access Journals (Sweden)

    Svyatoslav Vergun

    2016-09-01

    Full Text Available Functional magnetic resonance imaging studies have significantly expanded the field’s understanding of functional brain activity of healthy and patient populations. Resting state (rs- fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RNS labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor and executive control resting state networks from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects. ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron. The method’s utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians’ expert interpretation.

  16. Hierarchical Functional Modularity in the Resting-State Human Brain

    NARCIS (Netherlands)

    Ferrarini, Luca; Veer, Ilya M.; Baerends, Evelinda; van Tol, Marie-Jose; Renken, Remco J.; van der Wee, Nic J. A.; Veltman, Dirk. J.; Aleman, Andre; Zitman, Frans G.; Penninx, Brenda W. J. H.; van Buchem, Mark A.; Reiber, Johan H. C.; Rombouts, Serge A. R. B.; Milles, Julien

    Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a

  17. Hierarchical Functional Modularity in the Resting-State Human Brain

    NARCIS (Netherlands)

    Ferrarini, L.; Veer, I.M.; Baerends, E.; van Tol, M.J.; Renken, R.J.; van der Wee, N.J.A.; Veltman, D.J.; Aleman, A.; Zitman, F.G.; Penninx, B.W.J.H.; van Buchem, M.A.; Reiber, J.H.C.; Rombouts, S.A.R.B.; Milles, J.

    2009-01-01

    Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a

  18. EEG Oscillatory States: Universality, Uniqueness and Specificity across Healthy-Normal, Altered and Pathological Brain Conditions

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

    For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations’ functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal. PMID:24505292

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

    NARCIS (Netherlands)

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

    2017-01-01

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

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

    NARCIS (Netherlands)

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

    2018-01-01

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

  1. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

    Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

  2. Baseline brain energy supports the state of consciousness.

    Science.gov (United States)

    Shulman, Robert G; Hyder, Fahmeed; Rothman, Douglas L

    2009-07-07

    An individual, human or animal, is defined to be in a conscious state empirically by the behavioral ability to respond meaningfully to stimuli, whereas the loss of consciousness is defined by unresponsiveness. PET measurements of glucose or oxygen consumption show a widespread approximately 45% reduction in cerebral energy consumption with anesthesia-induced loss of consciousness. Because baseline brain energy consumption has been shown by (13)C magnetic resonance spectroscopy to be almost exclusively dedicated to neuronal signaling, we propose that the high level of brain energy is a necessary property of the conscious state. Two additional neuronal properties of the conscious state change with anesthesia. The delocalized fMRI activity patterns in rat brain during sensory stimulation at a higher energy state (close to the awake) collapse to a contralateral somatosensory response at lower energy state (deep anesthesia). Firing rates of an ensemble of neurons in the rat somatosensory cortex shift from the gamma-band range (20-40 Hz) at higher energy state to energy state. With the conscious state defined by the individual's behavior and maintained by high cerebral energy, measurable properties of that state are the widespread fMRI patterns and high frequency neuronal activity, both of which support the extensive interregional communication characteristic of consciousness. This usage of high brain energies when the person is in the "state" of consciousness differs from most studies, which attend the smaller energy increments observed during the stimulations that form the "contents" of that state.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  4. 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.

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

    Science.gov (United States)

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

    2012-03-01

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

  6. Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving.

    Science.gov (United States)

    Garcia, Javier O; Brooks, Justin; Kerick, Scott; Johnson, Tony; Mullen, Tim R; Vettel, Jean M

    2017-04-15

    Conventional neuroimaging analyses have ascribed function to particular brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity as measured with EEG and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands (delta: 2-3Hz; theta: 4-7Hz; alpha: 8-12Hz; beta: 13-25Hz) in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction using a variant of Granger causality. We applied this approach to a simulated driving task and examined directed relationships between brain activity and continuous driving performance (steering behavior or vehicle heading error). Our results indicated that two neuro-behavioral states may be explored with this methodology: a Proactive brain state that actively plans the response to the sensory information and is characterized by delta-beta activity, and a Reactive brain state that processes incoming information and reacts to environmental statistics primarily within the alpha band. Published by Elsevier Inc.

  7. 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

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

    NARCIS (Netherlands)

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

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2014-04-01

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

  11. Emotional State Classification in Virtual Reality Using Wearable Electroencephalography

    Science.gov (United States)

    Suhaimi, N. S.; Teo, J.; Mountstephens, J.

    2018-03-01

    This paper presents the classification of emotions on EEG signals. One of the key issues in this research is the lack of mental classification using VR as the medium to stimulate emotion. The approach towards this research is by using K-nearest neighbor (KNN) and Support Vector Machine (SVM). Firstly, each of the participant will be required to wear the EEG headset and recording their brainwaves when they are immersed inside the VR. The data points are then marked if they showed any physical signs of emotion or by observing the brainwave pattern. Secondly, the data will then be tested and trained with KNN and SVM algorithms. The accuracy achieved from both methods were approximately 82% throughout the brainwave spectrum (α, β, γ, δ, θ). These methods showed promising results and will be further enhanced using other machine learning approaches in VR stimulus.

  12. Classification of single normal and Alzheimer’s disease individuals from cortical sources of resting state EEG rhythms

    Directory of Open Access Journals (Sweden)

    Claudio eBabiloni

    2016-02-01

    Full Text Available Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG rhythms in groups of Alzheimer’s disease (AD compared to healthy elderly (Nold subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz, theta (4-8 Hz, alpha 1 (8-10.5 Hz, alpha 2 (10.5-13 Hz, beta 1 (13-20 Hz, beta 2 (20-30 Hz, and gamma (30-40 Hz were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC higher than 0.7 as a threshold for a moderate classification rate (i.e. 70%. Results showed that the following EEG markers overcame this threshold: (i central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii frontal theta/alpha 1 current density; (iv occipital delta/alpha 1 inter-hemispherical connectivity; (v occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%. These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

  13. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    Directory of Open Access Journals (Sweden)

    Ole eJensen

    2011-05-01

    Full Text Available Large efforts are currently being made to develop and improve online analysis of brain activity which can be used e.g. for brain-computer interfacing (BCI. A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from EEG/MEG studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2016-09-01

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

  16. First steps towards a state classification in the random-field Ising model

    International Nuclear Information System (INIS)

    Basso, Vittorio; Magni, Alessandro; Bertotti, Giorgio

    2006-01-01

    The properties of locally stable states of the random-field Ising model are studied. A map is defined for the dynamics driven by the field starting from a locally stable state. The fixed points of the map are connected with the limit hysteresis loops that appear in the classification of the states

  17. Methodical approaches to development of classification state methods of regulation business activity in fishery

    OpenAIRE

    She Son Gun

    2014-01-01

    Approaches to development of classification of the state methods of regulation of economy are considered. On the basis of the provided review the complex method of state regulation of business activity is reasonable. The offered principles allow improving public administration and can be used in industry concepts and state programs on support of small business in fishery.

  18. Hierarchical functional modularity in the resting-state human brain.

    Science.gov (United States)

    Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien

    2009-07-01

    Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc

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

    Science.gov (United States)

    Sheejakumari, V.; Sankara Gomathi, B.

    2015-01-01

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

  20. Investigating the Intersession Reliability of Dynamic Brain-State Properties.

    Science.gov (United States)

    Smith, Derek M; Zhao, Yrian; Keilholz, Shella D; Schumacher, Eric H

    2018-06-01

    Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Rosaleena Mohanty

    2018-05-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

  5. Effect of higher frequency on the classification of steady-state visual evoked potentials

    Science.gov (United States)

    Won, Dong-Ok; Hwang, Han-Jeong; Dähne, Sven; Müller, Klaus-Robert; Lee, Seong-Whan

    2016-02-01

    Objective. Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. Approach. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. Main results. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. Significance. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

  6. [The physiological classification of human thermal states under high environmental temperatures].

    Science.gov (United States)

    Bobrov, A F; Kuznets, E I

    1995-01-01

    The paper deals with the physiological classification of human thermal states in a hot environment. A review of the basic systems of classifications of thermal states is given, their main drawbacks are discussed. On the basis of human functional state research in a broad range of environmental temperatures the system of evaluation and classification of human thermal states is proposed. New integral one-dimensional multi-parametric criteria for evaluation are used. For the development of these criteria methods of factor, cluster and canonical correlation analyses are applied. Stochastic nomograms capable of identification of human thermal state for different intensity of influence are given. In this case evaluation of intensity is estimated according to one-dimensional criteria taking into account environmental temperature, physical load and time of man's staying in overheating conditions.

  7. 9 CFR 145.34 - Terminology and classification; States.

    Science.gov (United States)

    2010-01-01

    ... Special Provisions for Multiplier Meat-Type Chicken Breeding Flocks and Products § 145.34 Terminology and... found within the preceding 24 months in waterfowl, exhibition poultry, and game bird breeding flocks... State, Meat-Type Chickens. (1) A State will be declared a U.S. M. Gallisepticum Clean State, Meat-Type...

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

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  9. Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

    Science.gov (United States)

    Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.

    2018-05-01

    This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.

  10. Characterizing Resting-State Brain Function Using Arterial Spin Labeling

    Science.gov (United States)

    Jann, Kay; Wang, Danny J.J.

    2015-01-01

    Abstract Arterial spin labeling (ASL) is an increasingly established magnetic resonance imaging (MRI) technique that is finding broader applications in studying the healthy and diseased brain. This review addresses the use of ASL to assess brain function in the resting state. Following a brief technical description, we discuss the use of ASL in the following main categories: (1) resting-state functional connectivity (FC) measurement: the use of ASL-based cerebral blood flow (CBF) measurements as an alternative to the blood oxygen level-dependent (BOLD) technique to assess resting-state FC; (2) the link between network CBF and FC measurements: the use of network CBF as a surrogate of the metabolic activity within corresponding networks; and (3) the study of resting-state dynamic CBF-BOLD coupling and cerebral metabolism: the use of dynamic CBF information obtained using ASL to assess dynamic CBF-BOLD coupling and oxidative metabolism in the resting state. In addition, we summarize some future challenges and interesting research directions for ASL, including slice-accelerated (multiband) imaging as well as the effects of motion and other physiological confounds on perfusion-based FC measurement. In summary, this work reviews the state-of-the-art of ASL and establishes it as an increasingly viable MRI technique with high translational value in studying resting-state brain function. PMID:26106930

  11. 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.

  12. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...

  13. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

    Science.gov (United States)

    Sørensen, Lauge; Nielsen, Mads

    2018-05-15

    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. State of the Art Review: Poverty and the Developing Brain

    Science.gov (United States)

    Riis, Jenna L.; Noble, Kimberly G.

    2016-01-01

    In the United States, >40% of children are either poor or near-poor. As a group, children in poverty are more likely to experience worse health and more developmental delay, lower achievement, and more behavioral and emotional problems than their more advantaged peers; however, there is broad variability in outcomes among children exposed to similar conditions. Building on a robust literature from animal models showing that environmental deprivation or enrichment shapes the brain, there has been increasing interest in understanding how the experience of poverty may shape the brain in humans. In this review, we summarize research on the relationship between socioeconomic status and brain development, focusing on studies published in the last 5 years. Drawing on a conceptual framework informed by animal models, we highlight neural plasticity, epigenetics, material deprivation (eg, cognitive stimulation, nutrient deficiencies), stress (eg, negative parenting behaviors), and environmental toxins as factors that may shape the developing brain. We then summarize the existing evidence for the relationship between child poverty and brain structure and function, focusing on brain areas that support memory, emotion regulation, and higher-order cognitive functioning (ie, hippocampus, amygdala, prefrontal cortex) and regions that support language and literacy (ie, cortical areas of the left hemisphere). We then consider some limitations of the current literature and discuss the implications of neuroscience concepts and methods for interventions in the pediatric medical home. PMID:26952506

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  16. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  17. Method and System for Controlling a Dexterous Robot Execution Sequence Using State Classification

    Science.gov (United States)

    Sanders, Adam M. (Inventor); Platt, Robert J., Jr. (Inventor); Quillin, Nathaniel (Inventor); Permenter, Frank Noble (Inventor); Pfeiffer, Joseph (Inventor)

    2014-01-01

    A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. 78 FR 39765 - Notice of Proposed Classification of Public Lands/Minerals for State Indemnity Selection, Colorado

    Science.gov (United States)

    2013-07-02

    ... Proposed Classification of Public Lands/Minerals for State Indemnity Selection, Colorado AGENCY: Bureau of Land Management, Interior. ACTION: Notice of Proposed Classification. SUMMARY: The Colorado State Board... public lands and mineral estate in lieu of lands to which the State was entitled but did not receive...

  20. Operational classification and quantification of multipartite entangled states

    International Nuclear Information System (INIS)

    Rigolin, Gustavo; Oliveira, Thiago R. de; Oliveira, Marcos C. de

    2006-01-01

    We formalize and extend an operational multipartite entanglement measure introduced by T. R. Oliveira, G. Rigolin, and M. C. de Oliveira, Phys. Rev. A 73, 010305(R) (2006), through the generalization of global entanglement (GE) [D. A. Meyer and N. R. Wallach, J. Math. Phys. 43, 4273 (2002)]. Contrarily to GE the main feature of this measure lies in the fact that we study the mean linear entropy of all possible partitions of a multipartite system. This allows the construction of an operational multipartite entanglement measure which is able to distinguish among different multipartite entangled states that GE failed to discriminate. Furthermore, it is also maximum at the critical point of the Ising chain in a transverse magnetic field, being thus able to detect a quantum phase transition

  1. Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

    Directory of Open Access Journals (Sweden)

    Joseph St. Peter

    2018-03-01

    Full Text Available Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.

  2. Magnetic translation group and classification of states of an itinerant electron

    International Nuclear Information System (INIS)

    Wal, Andrzej

    2006-01-01

    We consider an itinerant electron on two-dimensional finite square lattice in a magneticfield. A magnetic translation group (MTG) for this system with the periodic Born- Karman conditions has been introduced. The irreducible representation of MTG is used for classification of energy levels of electron states for this model

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

    Directory of Open Access Journals (Sweden)

    Gemma Modinos

    2013-02-01

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

  4. 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.

  5. Correspondence between EQ-5D health state classifications and EQ VAS scores

    Directory of Open Access Journals (Sweden)

    Whynes David K

    2008-11-01

    Full Text Available Abstract Background The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS. The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified. Methods A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress. Results In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress. Conclusion In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although

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

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Bo Peng

    2017-05-01

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

  8. Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

    Science.gov (United States)

    Hearn, Tristan A.

    2015-01-01

    This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

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

    Science.gov (United States)

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

    2015-01-01

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

  10. Energy landscapes of resting-state brain networks

    Directory of Open Access Journals (Sweden)

    Takamitsu eWatanabe

    2014-02-01

    Full Text Available During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs including the default-mode network (DMN and frontoparietal network (FPN. Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics, the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant energy were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.

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

    Directory of Open Access Journals (Sweden)

    C. Arunkumar

    2014-05-01

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

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

    Science.gov (United States)

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

    2010-10-01

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

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

    Science.gov (United States)

    Chan, Vincy; Thurairajah, Pravheen; Colantonio, Angela

    2015-02-04

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

  14. Resting state brain networks in the prairie vole.

    Science.gov (United States)

    Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

    2018-01-19

    Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

  15. Resting-state brain activity in adult males who stutter.

    Directory of Open Access Journals (Sweden)

    Yun Xuan

    Full Text Available Although developmental stuttering has been extensively studied with structural and task-based functional magnetic resonance imaging (fMRI, few studies have focused on resting-state brain activity in this disorder. We investigated resting-state brain activity of stuttering subjects by analyzing the amplitude of low-frequency fluctuation (ALFF, region of interest (ROI-based functional connectivity (FC and independent component analysis (ICA-based FC. Forty-four adult males with developmental stuttering and 46 age-matched fluent male controls were scanned using resting-state fMRI. ALFF, ROI-based FCs and ICA-based FCs were compared between male stuttering subjects and fluent controls in a voxel-wise manner. Compared with fluent controls, stuttering subjects showed increased ALFF in left brain areas related to speech motor and auditory functions and bilateral prefrontal cortices related to cognitive control. However, stuttering subjects showed decreased ALFF in the left posterior language reception area and bilateral non-speech motor areas. ROI-based FC analysis revealed decreased FC between the posterior language area involved in the perception and decoding of sensory information and anterior brain area involved in the initiation of speech motor function, as well as increased FC within anterior or posterior speech- and language-associated areas and between the prefrontal areas and default-mode network (DMN in stuttering subjects. ICA showed that stuttering subjects had decreased FC in the DMN and increased FC in the sensorimotor network. Our findings support the concept that stuttering subjects have deficits in multiple functional systems (motor, language, auditory and DMN and in the connections between them.

  16. Resting-State Brain Activity in Adult Males Who Stutter

    Science.gov (United States)

    Zhu, Chaozhe; Wang, Liang; Yan, Qian; Lin, Chunlan; Yu, Chunshui

    2012-01-01

    Although developmental stuttering has been extensively studied with structural and task-based functional magnetic resonance imaging (fMRI), few studies have focused on resting-state brain activity in this disorder. We investigated resting-state brain activity of stuttering subjects by analyzing the amplitude of low-frequency fluctuation (ALFF), region of interest (ROI)-based functional connectivity (FC) and independent component analysis (ICA)-based FC. Forty-four adult males with developmental stuttering and 46 age-matched fluent male controls were scanned using resting-state fMRI. ALFF, ROI-based FCs and ICA-based FCs were compared between male stuttering subjects and fluent controls in a voxel-wise manner. Compared with fluent controls, stuttering subjects showed increased ALFF in left brain areas related to speech motor and auditory functions and bilateral prefrontal cortices related to cognitive control. However, stuttering subjects showed decreased ALFF in the left posterior language reception area and bilateral non-speech motor areas. ROI-based FC analysis revealed decreased FC between the posterior language area involved in the perception and decoding of sensory information and anterior brain area involved in the initiation of speech motor function, as well as increased FC within anterior or posterior speech- and language-associated areas and between the prefrontal areas and default-mode network (DMN) in stuttering subjects. ICA showed that stuttering subjects had decreased FC in the DMN and increased FC in the sensorimotor network. Our findings support the concept that stuttering subjects have deficits in multiple functional systems (motor, language, auditory and DMN) and in the connections between them. PMID:22276215

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

    Directory of Open Access Journals (Sweden)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Fundamental Frequency Extraction Method using Central Clipping and its Importance for the Classification of Emotional State

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2012-01-01

    Full Text Available The paper deals with a classification of emotional state. We implemented a method for extracting the fundamental speech signal frequency by means of a central clipping and examined a correlation between emotional state and fundamental speech frequency. For this purpose, we applied an approach of exploratory data analysis. The ANOVA (Analysis of variance test confirmed that a modification in the speaker's emotional state changes the fundamental frequency of human vocal tract. The main contribution of the paper lies in investigation, of central clipping method by the ANOVA.

  20. Three particle Poincare states and SU(6) x SU(3) as a classification group for baryons

    International Nuclear Information System (INIS)

    Buccella, F.; Sciarrino, A.; Sorba, P.

    1975-05-01

    A complete set of democratic quantum numbers is introduced to classify the states of an irreducible unitary representation (IUR) of the Poincare group obtained from the decomposition of the direct products of three I.U.R. Such states are identified with the baryon states constituted of three free relativistic quarks. The transformation from current to constituent quarks is then easily reobtained. Moreover, the group SU(6) x SU(3) appears naturally as a collinear classification group for baryons. Results similar to those of the symmetric harmonic oscillator quark model are obtained [fr

  1. Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

    Science.gov (United States)

    Harrivel, Angela R.; Liles, Charles; Stephens, Chad L.; Ellis, Kyle K.; Prinzel, Lawrence J.; Pope, Alan T.

    2016-01-01

    Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

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

    Science.gov (United States)

    Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

    2010-06-01

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

  3. 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.

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

    NARCIS (Netherlands)

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

    2017-01-01

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

  5. Activated and deactivated functional brain areas in the Deqi state

    OpenAIRE

    Huang, Yong; Zeng, Tongjun; Zhang, Guifeng; Li, Ganlong; Lu, Na; Lai, Xinsheng; Lu, Yangjia; Chen, Jiarong

    2012-01-01

    We compared the activities of functional regions of the brain in the Deqi versus non-Deqi state, as reported by physicians and subjects during acupuncture. Twelve healthy volunteers received sham and true needling at the Waiguan (TE5) acupoint. Real-time cerebral functional MRI showed that compared with non-sensation after sham needling, true needling activated Brodmann areas 3, 6, 8, 9, 10, 11, 13, 20, 21, 37, 39, 40, 43, and 47, the head of the caudate nucleus, the parahippocampal gyrus, th...

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

    Directory of Open Access Journals (Sweden)

    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

  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. Copyright © 2016 John Wiley & Sons, Ltd.

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

    Directory of Open Access Journals (Sweden)

    Jianing Zhang

    2018-04-01

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

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

    Science.gov (United States)

    Kim, Yong-Ku; Na, Kyoung-Sae

    2018-01-03

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

  10. Training echo state networks for rotation-invariant bone marrow cell classification.

    Science.gov (United States)

    Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

    2017-01-01

    The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

  11. The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study

    NARCIS (Netherlands)

    Bosma, I.; Stam, C.; Douw, L.; Bartolomei, F.; Heimans, J.; Dijk, van B.; Postma, T.; Klein, M.; Reijneveld, J.

    2008-01-01

    Purpose: In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain

  12. The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study

    NARCIS (Netherlands)

    Bosma, I.; Stam, C. J.; Douw, L.; Bartolomei, F.; Heimans, J. J.; van Dijk, B. W.; Postma, T. J.; Klein, M.; Reijneveld, J. C.

    2008-01-01

    In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain activity

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

    Science.gov (United States)

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

    2007-11-05

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

  14. 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

  15. Classification of schizophrenia patients based on resting-state functional network connectivity

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Arbabshirani

    2013-07-01

    Full Text Available There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity features to classify schizophrenia.

  16. Axonal Conduction Delays, Brain State, and Corticogeniculate Communication.

    Science.gov (United States)

    Stoelzel, Carl R; Bereshpolova, Yulia; Alonso, Jose-Manuel; Swadlow, Harvey A

    2017-06-28

    Thalamocortical conduction times are short, but layer 6 corticothalamic axons display an enormous range of conduction times, some exceeding 40-50 ms. Here, we investigate (1) how axonal conduction times of corticogeniculate (CG) neurons are related to the visual information conveyed to the thalamus, and (2) how alert versus nonalert awake brain states affect visual processing across the spectrum of CG conduction times. In awake female Dutch-Belted rabbits, we found 58% of CG neurons to be visually responsive, and 42% to be unresponsive. All responsive CG neurons had simple, orientation-selective receptive fields, and generated sustained responses to stationary stimuli. CG axonal conduction times were strongly related to modulated firing rates (F1 values) generated by drifting grating stimuli, and their associated interspike interval distributions, suggesting a continuum of visual responsiveness spanning the spectrum of axonal conduction times. CG conduction times were also significantly related to visual response latency, contrast sensitivity (C-50 values), directional selectivity, and optimal stimulus velocity. Increasing alertness did not cause visually unresponsive CG neurons to become responsive and did not change the response linearity (F1/F0 ratios) of visually responsive CG neurons. However, for visually responsive CG neurons, increased alertness nearly doubled the modulated response amplitude to optimal visual stimulation (F1 values), significantly shortened response latency, and dramatically increased response reliability. These effects of alertness were uniform across the broad spectrum of CG axonal conduction times. SIGNIFICANCE STATEMENT Corticothalamic neurons of layer 6 send a dense feedback projection to thalamic nuclei that provide input to sensory neocortex. While sensory information reaches the cortex after brief thalamocortical axonal delays, corticothalamic axons can exhibit conduction delays of <2 ms to 40-50 ms. Here, in the corticogeniculate

  17. 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...

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

    Science.gov (United States)

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

    2017-06-01

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

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

    Science.gov (United States)

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

    2005-01-01

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

  20. 77 FR 16925 - Medical Devices; Neurological Devices; Classification of the Near Infrared Brain Hematoma Detector

    Science.gov (United States)

    2012-03-23

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

  1. Brain electrical activity and subjective experience during altered states of consciousness: ganzfeld and hypnagogic states.

    Science.gov (United States)

    Wackermann, Jiri; Pütz, Peter; Büchi, Simone; Strauch, Inge; Lehmann, Dietrich

    2002-11-01

    Manifestations of experimentally induced altered states of consciousness in the brain's electrical activity as well as in subjective experience were explored via the hypnagogic state at sleep onset, and the state induced by exposure to an unstructured perceptual field (ganzfeld). Twelve female paid volunteers participated in sessions involving sleep onset, ganzfeld, and eyes-closed relaxed waking, and were repeatedly prompted for recall of their momentary mentation, according to a predefined schedule. Nineteen channel EEG, two channels EOG and EMG were recorded simultaneously. The mentation reports were followed by the subjects' ratings of their experience on a number of ordinal scales. Two-hundred and forty-one mentation reports were collected. EEG epochs immediately preceding the mentation reports were FFT-analysed and the spectra compared between states. The ganzfeld EEG spectrum, showing no signs of decreased vigilance, was very similar to the EEG spectrum of waking states, even showed a minor acceleration of alpha activity. The subjective experience data were reduced to four principal components: Factor I represented the subjective vigilance dimension, as confirmed by correlations with EEG spectral indices. Only Factor IV, the 'absorption' dimension, differentiated between the ganzfeld state (more absorption) and other states. In waking states and in ganzfeld, the subjects estimated elapsed time periods significantly shorter than in states at sleep onset. The results did not support the assumption of a hypnagogic nature of the ganzfeld imagery. Dream-like imagery can occur in various global functional states of the brain; hypnagogic and ganzfeld-induced states should be conceived as special cases of a broader class of 'hypnagoid' phenomena.

  2. State-related functional integration and functional segregation brain networks in schizophrenia.

    Science.gov (United States)

    Yu, Qingbao; Sui, Jing; Kiehl, Kent A; Pearlson, Godfrey; Calhoun, Vince D

    2013-11-01

    Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder. © 2013.

  3. 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).

  4. The development of an automatic classification system of nuclear power plant states

    International Nuclear Information System (INIS)

    Mitomo, Nobuo; Matsuoka, Takeshi

    2000-01-01

    For the future autonomous plant, automatic control and diagnostics are being incorporated and operators are mainly engaged in the high levels of diagnosis and decision-making in emergencies. Therefore these matters will be performed through the Man-Machine Interface(MMI). Ship Research Institute has been carrying out the research on the MMI system for autonomous power plants. The automatic classification system of plant states is one of the functions of this MMI and the system utilizes COBWEB, which is known as a way of clustering data to acquire concepts. In this paper, many plant states produced by a plant simulator we examined in order to confirm the effectiveness of this system. The system has well classified plant states produced by a plant simulator. (author)

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

    Directory of Open Access Journals (Sweden)

    Justin Ronald Estepp

    2015-03-01

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

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

    Science.gov (United States)

    Estepp, Justin R; Christensen, James C

    2015-01-01

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

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

    International Nuclear Information System (INIS)

    Peitgen, N.; Papanagiotou, P.

    2017-01-01

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

  8. Non-invasive brain stimulation of the aging brain: State of the art and future perspectives.

    Science.gov (United States)

    Tatti, Elisa; Rossi, Simone; Innocenti, Iglis; Rossi, Alessandro; Santarnecchi, Emiliano

    2016-08-01

    Favored by increased life expectancy and reduced birth rate, worldwide demography is rapidly shifting to older ages. The golden age of aging is not only an achievement but also a big challenge because of the load of the elderly on social and medical health care systems. Moreover, the impact of age-related decline of attention, memory, reasoning and executive functions on self-sufficiency emphasizes the need of interventions to maintain cognitive abilities at a useful degree in old age. Recently, neuroscientific research explored the chance to apply Non-Invasive Brain Stimulation (NiBS) techniques (as transcranial electrical and magnetic stimulation) to healthy aging population to preserve or enhance physiologically-declining cognitive functions. The present review will update and address the current state of the art on NiBS in healthy aging. Feasibility of NiBS techniques will be discussed in light of recent neuroimaging (either structural or functional) and neurophysiological models proposed to explain neural substrates of the physiologically aging brain. Further, the chance to design multidisciplinary interventions to maximize the efficacy of NiBS techniques will be introduced as a necessary future direction. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

    Science.gov (United States)

    Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

    2013-01-01

    Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

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

    OpenAIRE

    Bach Cuadra, Meritxell; Thiran, Jean-Philippe

    2005-01-01

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

  11. Does Global Astrocytic Calcium Signaling Participate in Awake Brain State Transitions and Neuronal Circuit Function?

    DEFF Research Database (Denmark)

    Kjaerby, Celia; Rasmussen, Rune; Andersen, Mie

    2017-01-01

    of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca(2+) signaling in response to activation of the noradrenergic system, but whether...... astrocytic Ca(2+) signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca(2+) signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore......We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release...

  12. Touch-based Brain Computer Interfaces: State of the art

    NARCIS (Netherlands)

    Erp, J.B.F. van; Brouwer, A.M.

    2014-01-01

    Brain Computer Interfaces (BCIs) rely on the user's brain activity to control equipment or computer devices. Many BCIs are based on imagined movement (called active BCIs) or the fact that brain patterns differ in reaction to relevant or attended stimuli in comparison to irrelevant or unattended

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1990-11-01

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

  14. MRI patterns in prolonged low response states following traumatic brain injury in children and adolescents.

    Science.gov (United States)

    Patrick, Peter D; Mabry, Jennifer L; Gurka, Matthew J; Buck, Marcia L; Boatwright, Evelyn; Blackman, James A

    2007-01-01

    To explore the relationship between location and pattern of brain injury identified on MRI and prolonged low response state in children post-traumatic brain injury (TBI). This observational study compared 15 children who spontaneously recovered within 30 days post-TBI to 17 who remained in a prolonged low response state. 92.9% of children with brain stem injury were in the low response group. The predicted probability was 0.81 for brain stem injury alone, increasing to 0.95 with a regional pattern of injury to the brain stem, basal ganglia, and thalamus. Low response state in children post-TBI is strongly correlated with two distinctive regions of injury: the brain stem alone, and an injury pattern to the brain stem, basal ganglia, and thalamus. This study demonstrates the need for large-scale clinical studies using MRI as a tool for outcome assessment in children and adolescents following severe TBI.

  15. Steady-state cerebral glucose concentrations and transport in the human brain

    OpenAIRE

    Gruetter, R.; Ugurbil, K.; Seaquist, E. R.

    1998-01-01

    Understanding the mechanism of brain glucose transport across the blood- brain barrier is of importance to understanding brain energy metabolism. The specific kinetics of glucose transport nave been generally described using standard Michaelis-Menten kinetics. These models predict that the steady- state glucose concentration approaches an upper limit in the human brain when the plasma glucose level is well above the Michaelis-Menten constant for half-maximal transport, K(t). In experiments wh...

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  18. Classification of matrix-product ground states corresponding to one-dimensional chains of two-state sites of nearest neighbor interactions

    International Nuclear Information System (INIS)

    Fatollahi, Amir H.; Khorrami, Mohammad; Shariati, Ahmad; Aghamohammadi, Amir

    2011-01-01

    A complete classification is given for one-dimensional chains with nearest-neighbor interactions having two states in each site, for which a matrix product ground state exists. The Hamiltonians and their corresponding matrix product ground states are explicitly obtained.

  19. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    NARCIS (Netherlands)

    Jensen, O.; Bahramisharif, A.; Oostenveld, R.; Klanke, S.; Hadjipapas, A.; Okazaki, Y.O.; Gerven, M.A.J. van

    2011-01-01

    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for

  20. Progress in clinical research and application of resting state functional brain imaging

    International Nuclear Information System (INIS)

    Long Miaomiao; Ni Hongyan

    2013-01-01

    Resting state functional brain imaging experimental design is free of stimulus task and offers various parametric maps through different data-driven post processing methods with endogenous BOLD signal changes as the source of imaging. Mechanism of resting state brain activities could be extensively studied with improved patient compliance and clinical application compared with task related functional brain imaging. Also resting state functional brain imaging can be used as a method of data acquisition, with implicit neuronal activity as a kind of experimental design, to reveal characteristic brain activities of epileptic patient. Even resting state functional brain imaging data processing method can be used to analyze task related functional MRI data, opening new horizons of task related functional MRI study. (authors)

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

    Science.gov (United States)

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

    2018-01-01

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

  2. Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

    Science.gov (United States)

    Combaz, Adrien; Van Hulle, Marc M

    2015-01-01

    We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

  3. State-Dependent Changes of Connectivity Patterns and Functional Brain Network Topology in Autism Spectrum Disorder

    Science.gov (United States)

    Barttfeld, Pablo; Wicker, Bruno; Cukier, Sebastian; Navarta, Silvana; Lew, Sergio; Leiguarda, Ramon; Sigman, Mariano

    2012-01-01

    Anatomical and functional brain studies have converged to the hypothesis that autism spectrum disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that…

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

    Science.gov (United States)

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

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

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

    Directory of Open Access Journals (Sweden)

    Thanh G. Phan

    2018-03-01

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

  6. Is the Internet gaming-addicted brain close to be in a pathological state?

    Science.gov (United States)

    Park, Chang-Hyun; Chun, Ji-Won; Cho, Huyn; Jung, Young-Chul; Choi, Jihye; Kim, Dai Jin

    2017-01-01

    Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks. © 2015 Society for the Study of Addiction.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-02-15

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

  8. Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface

    Science.gov (United States)

    Ng, Kian B.; Bradley, Andrew P.; Cunnington, Ross

    2012-06-01

    The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

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

    Science.gov (United States)

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

    2017-11-01

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

  10. 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).

  11. 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.

  12. Land classification of the standing stone state forest and state park on the eastern highland rim in Tennessee: the interaction of geology, topography, and soils

    Science.gov (United States)

    Glendon W. Smalley; Carlie McCowan; S. David Todd; Phillip M. Morrissey; J. Andrew McBride

    2013-01-01

    This paper summarizes the application of a land classification system developed by the senior author to the Standing Stone State Forest and State Park (SSSF&SP) on the Eastern Highland Rim. Landtypes are the most detailed level in the hierarchical system and represent distinct units of the landscape (mapped at a scale of 1:24,000) as defined by climate, geology,...

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

    Science.gov (United States)

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

    2016-01-01

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

  14. Deep brain stimulation and spinal cord stimulation for vegetative state and minimally conscious state.

    Science.gov (United States)

    Yamamoto, Takamitsu; Katayama, Yoichi; Obuchi, Toshiki; Kobayashi, Kazutaka; Oshima, Hideki; Fukaya, Chikashi

    2013-01-01

    On the basis of the findings of the electrophysiological evaluation of vegetative state (VS) and minimally conscious state (MCS), the effect of deep brain stimulation (DBS) was examined according to long-term follow-up results. The results of spinal cord stimulation (SCS) on MCS was also examined and compared with that of DBS. One hundred seven patients in VS and 21 patients in MCS were evaluated neurologically and electrophysiologically over 3 months after the onset of brain injury. Among the 107 VS patients, 21 were treated by DBS. Among the 21 MCS patients, 5 were treated by DBS and 10 by SCS. Eight of the 21 patients recovered from VS and were able to follow verbal instructions. These eight patients showed desynchronization on continuous electroencephalographic frequency analysis. The Vth wave of the auditory brainstem response and N20 of somatosensory evoked potential were recorded even with a prolonged latency, and pain-related P250 was recorded with an amplitude of more than 7 μV. In addition, DBS and SCS induced a marked functional recovery in MCS patients who satisfied the electrophysiological inclusion criteria. DBS for VS and MCS patients and SCS for MCS patients may be useful, when the candidates are selected on the basis of the electrophysiological inclusion criteria. Only 16 (14.9%) of the 107 VS patients and 15 (71.4%) of the 21 MCS patients satisfied the electrophysiological inclusion criteria. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. What Should Be the Roles of Conscious States and Brain States in Theories of Mental Activity?**

    Science.gov (United States)

    Dulany, Donelson E.

    2011-01-01

    Answers to the title’s question have been influenced by a history in which an early science of consciousness was rejected by behaviourists on the argument that this entails commitment to ontological dualism and “free will” in the sense of indeterminism. This is, however, a confusion of theoretical assertions with metaphysical assertions. Nevertheless, a legacy within computational and information-processing views of mind rejects or de-emphasises a role for consciousness. This paper sketches a mentalistic metatheory in which conscious states are the sole carriers of symbolic representations, and thus have a central role in the explanation of mental activity and action-while specifying determinism and materialism as useful working assumptions. A mentalistic theory of causal learning, experimentally examined with phenomenal reports, is followed by examination of these questions: Are there common roles for phenomenal reports and brain imaging? Is there defensible evidence for unconscious brain states carrying symbolic representations? Are there interesting dissociations within consciousness? PMID:21694964

  16. Classification of the European Union member states according to the relative level of sustainable development.

    Science.gov (United States)

    Anna, Bluszcz

    Nowadays methods of measurement and assessment of the level of sustained development at the international, national and regional level are a current research problem, which requires multi-dimensional analysis. The relative assessment of the sustainability level of the European Union member states and the comparative analysis of the position of Poland relative to other countries was the aim of the conducted studies in the article. EU member states were treated as objects in the multi-dimensional space. Dimensions of space were specified by ten diagnostic variables describing the sustainability level of UE countries in three dimensions, i.e., social, economic and environmental. Because the compiled statistical data were expressed in different units of measure, taxonomic methods were used for building an aggregated measure to assess the level of sustainable development of EU member states, which through normalisation of variables enabled the comparative analysis between countries. Methodology of studies consisted of eight stages, which included, among others: defining data matrices, calculating the variability coefficient for all variables, which variability coefficient was under 10 %, division of variables into stimulants and destimulants, selection of the method of variable normalisation, developing matrices of normalised data, selection of the formula and calculating the aggregated indicator of the relative level of sustainable development of the EU countries, calculating partial development indicators for three studies dimensions: social, economic and environmental and the classification of the EU countries according to the relative level of sustainable development. Statistical date were collected based on the Polish Central Statistical Office publication.

  17. Twisted injectivity in projected entangled pair states and the classification of quantum phases

    Energy Technology Data Exchange (ETDEWEB)

    Buerschaper, Oliver, E-mail: obuerschaper@perimeterinstitute.ca

    2014-12-15

    We introduce a class of projected entangled pair states (PEPS) which is based on a group symmetry twisted by a 3-cocycle of the group. This twisted symmetry is expressed as a matrix product operator (MPO) with bond dimension greater than 1 and acts on the virtual boundary of a PEPS tensor. We show that it gives rise to a new standard form for PEPS from which we construct a family of local Hamiltonians which are gapped, frustration-free and include fixed points of the renormalization group flow. Based on this insight, we advance the classification of 2D gapped quantum spin systems by showing how this new standard form for PEPS determines the emergent topological order of these local Hamiltonians. Specifically, we identify their universality class as DIJKGRAAF–WITTEN topological quantum field theory (TQFT). - Highlights: • We introduce a new standard form for projected entangled pair states via a twisted group symmetry which is given by nontrivial matrix product operators. • We construct a large family of gapped, frustration-free Hamiltonians in two dimensions from this new standard form. • We rigorously show how this new standard form for low energy states determines the emergent topological order.

  18. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    NARCIS (Netherlands)

    Jensen, O.; Bahramisharif, A.; Oostenveld, R.; Klanke, S.; Hadjipapas, A.; Okazaki, Y.O.; Gerven, M.A.J. van

    2011-01-01

    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for

  19. An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States

    Science.gov (United States)

    2015-12-22

    AFRL-AFOSR-VA-TR-2016-0037 An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States Adrian Lee UNIVERSITY OF WASHINGTON...to 14-09-2015 4. TITLE AND SUBTITLE An Integrated Neuroscience and Engineering Approach to Classifying Human Brain- States 5a.  CONTRACT NUMBER 5b...specific cognitive states remains elusive, owing perhaps to limited crosstalk between the fields of neuroscience and engineering. Here, we report a

  20. Resting-state oscillatory brain dynamics in Alzheimer disease

    NARCIS (Netherlands)

    de Haan, W.; Stam, C.J.; Jones, B.F.; Zuiderwijk, I.M.; van Dijk, B.W.; Scheltens, P.

    2008-01-01

    Altered oscillatory brain activity in Alzheimer disease (AD) may reflect underlying neuropathological changes, and its characterization might lead to new diagnostic possibilities. The present study using quantitative magnetoencephalography was set up to examine power spectrum changes in AD patients,

  1. Brain radioligands. State of the art and new trends

    International Nuclear Information System (INIS)

    Halldin, C.; Gulyas, B.; Langer, O.; Farde, L.

    2001-01-01

    Non-invasive radioligand imaging methods for brain receptor studies use either short-lived positron-emitting radionuclides such as 11 C and 18 F for positron emission tomography (PET) or single photon-emitting radionuclides such as 123 I for single photon emission computed tomography (SPECT). PET and SPECT use radioligands which are injected intravenously into experimental animals, human volunteers or patients. The main applications of radioligands in brain research concern human neuro psychopharmacology and the discovery and development of novel drugs to be used in the therapy of neurological and psychiatric disorders. A basic problem in PET and SPECT brain receptor studies is the lack of useful radioligands with appropriate binding characteristics. Prerequisite criteria need to be satisfied for a radioligand to reveal target binding sites in vivo. This section will discuss these important criteria and also review recent examples in neuro receptor radioligand development such as selective radioligands for brain monoamine transporters

  2. Brain entropy and human intelligence: A resting-state fMRI study.

    Science.gov (United States)

    Saxe, Glenn N; Calderone, Daniel; Morales, Leah J

    2018-01-01

    Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

  3. Brain entropy and human intelligence: A resting-state fMRI study

    Science.gov (United States)

    Calderone, Daniel; Morales, Leah J.

    2018-01-01

    Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. PMID:29432427

  4. Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

    Science.gov (United States)

    Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

    2018-04-01

    In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

  5. Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Schmah, Tanya; Madsen, Kristoffer Hougaard

    2012-01-01

    Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting...... the underlying neural encoding of an experiment defining multiple brain states. In this relation there is a great desire for the researcher to generate brain maps, that highlight brain locations of importance to the classifiers decisions. Based on sensitivity analysis, we develop further procedures for model...... direction the individual locations influence the classification. We illustrate the visualization procedure on a real data from a simple functional magnetic resonance imaging experiment....

  6. Classification of the NPP core and fuel assembly states by the pattern recoguition method

    International Nuclear Information System (INIS)

    Egorov, Yu.A.; Ivanov, E.A.; Kazakov, S.V.; Tolstykh, V.D.

    1981-01-01

    The patern recognition methods used for solving the problems of analysis of radiohazard states of fuel assemblies (FA) and uranium-graphite reactor core as a whole are considered. The problem under consideration is formulated as the problem of studying the deformation of signal space for the system of fuel can tightness control on the background of fuel assembly character space as characteristics, reflecting the FA living conditions in a core power, coolant flow rate, coolant steam content and pipeline length up to the detector of the system of fuel can tightness control are chosen. The analysis of deformation of the fuel can tightness control system signal space is completed by its division into two spaces: the background signal space and the valid signal space. For solving the problem the method of basic components and variational approach have been used. The conclusion is drawn that as the extent of FA failure and valid signals of by-channel system of fuel can tightness control are in one-to-one correspondence it is advantageous to solve the problem of FA state classification in the space of valid signals [ru

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  8. Brain State-dependent Functional Hemispheric Specialization in Men but not in Women

    OpenAIRE

    Mohr, Christine; Michel, Christoph M.; Lantz, Goran; Ortigue, Stephanie; Viaud-Delmon, Isabelle; Landis, Theodor

    2017-01-01

    Hemispheric specialization is reliably demonstrated in patients with unilateral lesions or disconnected hemispheres, but is inconsistent in healthy populations. The reason for this paradox is unclear. We propose that functional hemispheric specialization in healthy participants depends upon functional brain states at stimulus arrival (FBS). Brain activity was recorded from 123 surface electrodes while 22 participants (11 women) performed lateralized lexical decisions (left hemisphere processi...

  9. An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging.

    Directory of Open Access Journals (Sweden)

    Jared A Nielsen

    Full Text Available Lateralized brain regions subserve functions such as language and visuospatial processing. It has been conjectured that individuals may be left-brain dominant or right-brain dominant based on personality and cognitive style, but neuroimaging data has not provided clear evidence whether such phenotypic differences in the strength of left-dominant or right-dominant networks exist. We evaluated whether strongly lateralized connections covaried within the same individuals. Data were analyzed from publicly available resting state scans for 1011 individuals between the ages of 7 and 29. For each subject, functional lateralization was measured for each pair of 7266 regions covering the gray matter at 5-mm resolution as a difference in correlation before and after inverting images across the midsagittal plane. The difference in gray matter density between homotopic coordinates was used as a regressor to reduce the effect of structural asymmetries on functional lateralization. Nine left- and 11 right-lateralized hubs were identified as peaks in the degree map from the graph of significantly lateralized connections. The left-lateralized hubs included regions from the default mode network (medial prefrontal cortex, posterior cingulate cortex, and temporoparietal junction and language regions (e.g., Broca Area and Wernicke Area, whereas the right-lateralized hubs included regions from the attention control network (e.g., lateral intraparietal sulcus, anterior insula, area MT, and frontal eye fields. Left- and right-lateralized hubs formed two separable networks of mutually lateralized regions. Connections involving only left- or only right-lateralized hubs showed positive correlation across subjects, but only for connections sharing a node. Lateralization of brain connections appears to be a local rather than global property of brain networks, and our data are not consistent with a whole-brain phenotype of greater "left-brained" or greater "right-brained

  10. An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging.

    Science.gov (United States)

    Nielsen, Jared A; Zielinski, Brandon A; Ferguson, Michael A; Lainhart, Janet E; Anderson, Jeffrey S

    2013-01-01

    Lateralized brain regions subserve functions such as language and visuospatial processing. It has been conjectured that individuals may be left-brain dominant or right-brain dominant based on personality and cognitive style, but neuroimaging data has not provided clear evidence whether such phenotypic differences in the strength of left-dominant or right-dominant networks exist. We evaluated whether strongly lateralized connections covaried within the same individuals. Data were analyzed from publicly available resting state scans for 1011 individuals between the ages of 7 and 29. For each subject, functional lateralization was measured for each pair of 7266 regions covering the gray matter at 5-mm resolution as a difference in correlation before and after inverting images across the midsagittal plane. The difference in gray matter density between homotopic coordinates was used as a regressor to reduce the effect of structural asymmetries on functional lateralization. Nine left- and 11 right-lateralized hubs were identified as peaks in the degree map from the graph of significantly lateralized connections. The left-lateralized hubs included regions from the default mode network (medial prefrontal cortex, posterior cingulate cortex, and temporoparietal junction) and language regions (e.g., Broca Area and Wernicke Area), whereas the right-lateralized hubs included regions from the attention control network (e.g., lateral intraparietal sulcus, anterior insula, area MT, and frontal eye fields). Left- and right-lateralized hubs formed two separable networks of mutually lateralized regions. Connections involving only left- or only right-lateralized hubs showed positive correlation across subjects, but only for connections sharing a node. Lateralization of brain connections appears to be a local rather than global property of brain networks, and our data are not consistent with a whole-brain phenotype of greater "left-brained" or greater "right-brained" network strength

  11. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression.

    Directory of Open Access Journals (Sweden)

    Kosuke Yoshida

    Full Text Available In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS regression to resting-state functional magnetic resonance imaging (rs-fMRI data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area.

  12. Classification of fMRI resting-state maps using machine learning techniques: A comparative study

    Science.gov (United States)

    Gallos, Ioannis; Siettos, Constantinos

    2017-11-01

    We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

  13. [The current state of the brain-computer interface problem].

    Science.gov (United States)

    Shurkhay, V A; Aleksandrova, E V; Potapov, A A; Goryainov, S A

    2015-01-01

    It was only 40 years ago that the first PC appeared. Over this period, rather short in historical terms, we have witnessed the revolutionary changes in lives of individuals and the entire society. Computer technologies are tightly connected with any field, either directly or indirectly. We can currently claim that computers are manifold superior to a human mind in terms of a number of parameters; however, machines lack the key feature: they are incapable of independent thinking (like a human). However, the key to successful development of humankind is collaboration between the brain and the computer rather than competition. Such collaboration when a computer broadens, supplements, or replaces some brain functions is known as the brain-computer interface. Our review focuses on real-life implementation of this collaboration.

  14. Altered resting state brain networks in Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Martin Göttlich

    Full Text Available Parkinson's disease (PD is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37 compared to healthy controls (n = 20. Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine, but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence

  15. Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

    Directory of Open Access Journals (Sweden)

    Burke R

    2005-01-01

    Full Text Available This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

  16. Modeling epileptic brain states using EEG spectral analysis and topographic mapping.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César; Ribeiro, Bernardete; Castelo-Branco, Miguel; Sales, Francisco; Dourado, António

    2012-09-30

    Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated from EEG. Using normalized-cuts segmentation algorithm, points of interest are identified in the topographic mappings and their trajectories over time are used for finding out relations with epileptogenic propagations in the brain. These trajectories are used to train a Hidden Markov Model (HMM), which models the different epileptic brain states and the transition among them. Applied to 10 patients suffering from focal seizures, with a total of 30 seizures over 497.3h of data, the methodology shows good results (an average point-by-point accuracy of 89.31%) for the identification of the four brain states--interictal, preictal, ictal and postictal. The results suggest that the spatio-temporal dynamics captured by the proposed methodology are related to the epileptic brain states and transitions involved in focal seizures. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach.

    Science.gov (United States)

    Subbaraju, Vigneshwaran; Suresh, Mahanand Belathur; Sundaram, Suresh; Narasimhan, Sundararajan

    2017-01-01

    This paper presents a new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI. Further the method also extracts discriminative features for an accurate diagnosis of ASD. The proposed approach determines a spatial filter that projects the covariance matrices of the Blood Oxygen Level Dependent (BOLD) time-series signals from both the ASD patients and neurotypical subjects in orthogonal directions such that they are highly separable. The inverse of this filter also provides a spatial pattern map within the brain that highlights those regions responsible for the distinguishable activities between the ASD patients and neurotypical subjects. For a better classification, highly discriminative log-variance features providing the maximum separation between the two classes are extracted from the projected BOLD time-series data. A detailed study has been carried out using the publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) consortium for the different gender and age-groups. The study results indicate that for all the above categories, the regional differences in resting state activities are more commonly found in the right hemisphere compared to the left hemisphere of the brain. Among males, a clear shift in activities to the prefrontal cortex is observed for ASD patients while other parts of the brain show diminished activities compared to neurotypical subjects. Among females, such a clear shift is not evident; however, several regions, especially in the posterior and medial portions of the brain show diminished activities due to ASD. Finally, the classification performance obtained using the log-variance features is found to be better when compared to earlier studies in the literature. Copyright © 2016 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2018-02-01

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

  19. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    Science.gov (United States)

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  20. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation

    Directory of Open Access Journals (Sweden)

    Alireza eGharabaghi

    2014-03-01

    Full Text Available Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.

  1. Qualitative and quantitative estimations of the effect of geomagnetic field variations on human brain functional state

    International Nuclear Information System (INIS)

    Belisheva, N.K.; Popov, A.N.; Petukhova, N.V.; Pavlova, L.P.; Osipov, K.S.; Tkachenko, S.Eh.; Baranova, T.I.

    1995-01-01

    The comparison of functional dynamics of human brain with reference to qualitative and quantitative characteristics of local geomagnetic field (GMF) variations was conducted. Steady and unsteady states of human brain can be determined: by geomagnetic disturbances before the observation period; by structure and doses of GMF variations; by different combinations of qualitative and quantitative characteristics of GMF variations. Decrease of optimal GMF activity level and the appearance of aperiodic disturbances of GMF can be a reason of unsteady brain's state. 18 refs.; 3 figs

  2. Characterization and classification of two soils derived from basic rocks in Pernambuco State Coast, Northeast Brazil

    Directory of Open Access Journals (Sweden)

    Oliveira Lindomário Barros de

    2004-01-01

    Full Text Available Geomorphic surfaces that present soils derived from basic rocks under warm and humid climate are unique scenarios for studying tropical soils. This paper aimed to characterize and classify two pedons derived from basalt at the Atlantic Forest Zone, Pernambuco State, Northeastern coast of Brazil. Two representative pedons (P1 and P2 were selected on a hillslope at the Cabo de Santo Agostinho municipality. Field macromorphological descriptions were carried out and soil horizon were sampled for physical, chemical, mineralogical and micromorphological characterization. The soils were classified, according to the Brazilian System of Soil Classification (and US Soil Taxonomy as: "Latossolo Vermelho-Amarelo distroférrico argissólico" (Typic Hapludox (P1 and "Nitossolo Vermelho distroférrico típico" (Rhodic Paleudult (P2. Pedon 1 differs from Pedon 2 in some aspects. For instance, P1 presents more yellowish colors, absence of clay illuviation, more friable consistence and the prismatic structure undergoes transformation to angular and subangular blocks. Pedon 2 presents ferri-argilans and leptocutans which indicate that vertical and lateral illuviation of clay is an active process in their formation. These chemically poor and mineralogically uniform soils are a result of the high temperature and rainfall of the studied area.

  3. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Science.gov (United States)

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  4. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  5. Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

    Science.gov (United States)

    Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

    2017-08-01

    Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

  6. Handedness- and Brain Size-Related Efficiency Differences in Small-World Brain Networks: A Resting-State Functional Magnetic Resonance Imaging Study

    OpenAIRE

    Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

    2015-01-01

    The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical...

  7. Immanuel Kant's mind and the brain's resting state.

    Science.gov (United States)

    Northoff, Georg

    2012-07-01

    The early philosopher Immanuel Kant suggested that the mind’s intrinsic features are intimately linked to the extrinsic stimuli of the environment it processes. Currently, the field faces an analogous problem with regard to the brain. Kant’s ideas may provide novel insights into how the brain’s intrinsic features must be so that they can be linked to the neural processing of extrinsic stimuli to enable the latter’s association with consciousness and self.

  8. Classification of binary intentions for individuals with impaired oculomotor function: ‘eyes-closed’ SSVEP-based brain-computer interface (BCI)

    Science.gov (United States)

    Lim, Jeong-Hwan; Hwang, Han-Jeong; Han, Chang-Hee; Jung, Ki-Young; Im, Chang-Hwan

    2013-04-01

    Objective. Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. Approach. The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). Main results. Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min-1. A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. Significance. The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our ‘eyes-closed’ SSVEP-based BCI system can be potentially used for communication of

  9. An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.

    Science.gov (United States)

    Li, Rui; Zhang, Xiaodong; Li, Hanzhe; Zhang, Liming; Lu, Zhufeng; Chen, Jiangcheng

    2018-08-01

    Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Complex network analysis of resting-state fMRI of the brain.

    Science.gov (United States)

    Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

    2016-08-01

    Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

  11. Glymphatic clearance controls state-dependent changes in brain lactate concentration

    DEFF Research Database (Denmark)

    Lundgaard, Iben; Lu, Minh Lon; Yang, Ezra

    2017-01-01

    Brain lactate concentration is higher during wakefulness than in sleep. However, it is unknown why arousal is linked to an increase in brain lactate and why lactate declines within minutes of sleep. Here, we show that the glymphatic system is responsible for state-dependent changes in brain lacta......-lymphatic clearance. This analysis provides fundamental new insight into brain energy metabolism by demonstrating that glucose that is not fully oxidized can be exported as lactate via glymphatic-lymphatic fluid transport.......Brain lactate concentration is higher during wakefulness than in sleep. However, it is unknown why arousal is linked to an increase in brain lactate and why lactate declines within minutes of sleep. Here, we show that the glymphatic system is responsible for state-dependent changes in brain lactate...... concentration. Suppression of glymphatic function via acetazolamide treatment, cisterna magna puncture, aquaporin 4 deletion, or changes in body position reduced the decline in brain lactate normally observed when awake mice transition into sleep or anesthesia. Concurrently, the same manipulations diminished...

  12. Accuracy of automated classification of major depressive disorder as a function of symptom severity

    Directory of Open Access Journals (Sweden)

    Rajamannar Ramasubbu, MD, FRCPC, MSc

    2016-01-01

    Conclusions: Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

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

    Science.gov (United States)

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

    2017-04-15

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

  14. New York State Thruway Authority automatic vehicle classification (AVC) : research report.

    Science.gov (United States)

    2008-03-31

    In December 2007, the N.Y.S. Thruway Authority (Thruway) concluded a Federal : funded research effort to study technology and develop a design for retrofitting : devices required in implementing a fully automated vehicle classification system i...

  15. An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to...

  16. Decreased integration and information capacity in stroke measured by whole brain models of resting state activity.

    Science.gov (United States)

    Adhikari, Mohit H; Hacker, Carl D; Siegel, Josh S; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo; Corbetta, Maurizio

    2017-04-01

    While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to

  17. Resting state cerebral blood flow with arterial spin labeling MRI in developing human brains.

    Science.gov (United States)

    Liu, Feng; Duan, Yunsuo; Peterson, Bradley S; Asllani, Iris; Zelaya, Fernando; Lythgoe, David; Kangarlu, Alayar

    2018-07-01

    The development of brain circuits is coupled with changes in neurovascular coupling, which refers to the close relationship between neural activity and cerebral blood flow (CBF). Studying the characteristics of CBF during resting state in developing brain can be a complementary way to understand the functional connectivity of the developing brain. Arterial spin labeling (ASL), as a noninvasive MR technique, is particularly attractive for studying cerebral perfusion in children and even newborns. We have collected pulsed ASL data in resting state for 47 healthy subjects from young children to adolescence (aged from 6 to 20 years old). In addition to studying the developmental change of static CBF maps during resting state, we also analyzed the CBF time series to reveal the dynamic characteristics of CBF in differing age groups. We used the seed-based correlation analysis to examine the temporal relationship of CBF time series between the selected ROIs and other brain regions. We have shown the developmental patterns in both static CBF maps and dynamic characteristics of CBF. While higher CBF of default mode network (DMN) in all age groups supports that DMN is the prominent active network during the resting state, the CBF connectivity patterns of some typical resting state networks show distinct patterns of metabolic activity during the resting state in the developing brains. Copyright © 2018 European Paediatric Neurology Society. All rights reserved.

  18. Radioactive-waste classification in the United States: history and current predicaments

    International Nuclear Information System (INIS)

    Lowenthal, M. D.

    1997-01-01

    repository thus far. It is not yet clear whether this is a simple or a difficult problem to resolve. We have now in the United States a unique opportunity to restructure the waste-classification system. The above discussion indicates that there is a strong linkage between actual disposal facilities and officially established waste classes. Only three commercial low-level waste disposal facilities are currently operating and the nation has not yet opened for operations disposal facilities for any other kind of waste (tailings and byproduct wastes excepted). Thus we have an opportunity to redefine waste classes, to base them more on the factors that are important for waste management, before directions are set and inertia is established. But this opportunity is a window that will not stay open for long. Several more commercial LLW disposal facilities are hoped to begin operations in the next decade. WIPP is scheduled to begin accepting waste in the year 1998. Plans for cleanup of the DOE complex have been put onto a fast track, a ten-year plan announced in 1996 by DOE's head of Environmental Management. Any new system of classification needs to include considerations that drive both the top-down systems and the bottom-up systems: they exist for good reason. But our current system does not do a good job of matching the waste to its destination. When the disposal is not properly suited to the waste or the waste is not suited for the disposal, it is possible to spend more for less protection. Thus, the cost of keeping our current disorderly classes and not integrating the interests of generators and disposers into a systematic scheme may be higher risks, higher expenditures, or both

  19. Experience-based utility and own health state valuation for a health state classification system: why and how to do it.

    Science.gov (United States)

    Brazier, John; Rowen, Donna; Karimi, Milad; Peasgood, Tessa; Tsuchiya, Aki; Ratcliffe, Julie

    2017-10-11

    In the estimation of population value sets for health state classification systems such as the EuroQOL five dimensions questionnaire (EQ-5D), there is increasing interest in asking respondents to value their own health state, sometimes referred to as "experience-based utility values" or, more correctly, own rather than hypothetical health states. Own health state values differ to hypothetical health state values, and this may be attributable to many reasons. This paper critically examines whose values matter; why there is a difference between own and hypothetical values; how to measure own health state values; and why to use own health state values. Finally, the paper examines other ways that own health state values can be taken into account, such as including the use of informed general population preferences that may better take into account experience-based values.

  20. Music and natural sounds in an auditory steady-state response based brain-computer interface to increase user acceptance.

    Science.gov (United States)

    Heo, Jeong; Baek, Hyun Jae; Hong, Seunghyeok; Chang, Min Hye; Lee, Jeong Su; Park, Kwang Suk

    2017-05-01

    Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain-computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. [Between psyche and brain : State of the art in psychiatry].

    Science.gov (United States)

    Fuchs, T

    2017-05-01

    Since its development around 1800 psychiatry has been oscillating between the poles of the sciences and the humanities, being directed towards subjective experience on the one hand and towards the neural substrate on the other hand. Today, this dualism seems to have been overcome by a naturalism, which identifies subjective experience with neural processes, according to Griesinger's frequently quoted statement "mental diseases are brain diseases". The progress achieved by the neurobiological paradigm on the level of a fundamental science is in contrast to the tendency to isolate mental illnesses from the patients' social relationships and to neglect subjectivity and intersubjectivity in their explanation. What should be searched for is therefore an overarching paradigm that is able to establish psychiatry as a relational medicine in an encompassing sense: as a science and practice of biological, psychological and social relationships and their disorders. Within such a paradigm, the brain may be understood and investigated as the central "relational organ" without reductionist constrictions.

  2. Reverse brain drain in South Korea: state-led model.

    Science.gov (United States)

    Yoon, B L

    1992-01-01

    Korea's reverse brain drain (RBD) has been an organized government effort, rather than a spontaneous social phenomenon, in that various policies and the political support of President Park, Chung-Hee were instrumental in laying the groundwork for its success. Particular features of Korea's RBD policies are the creation of a conducive domestic environment (i.e., government-sponsored strategic R & D institution-building, legal, and administrative reforms), and importantly, the empowerment of returnees (via, i.e., exceptionally good maternal benefits, guarantees of research autonomy). President Park played the cardinal role in empowering repatriates at the expense of his own civil bureaucracy, and his capacity for such patronage derived from Korea's bureaucratic-authoritarian political system. Returning scientists and engineers directly benefitted from this political system as well as Park's personal guardianship. For Park, empowerment of returning "brains" was necessary to accomplish his national industrialization plan, thereby enhancing his political legitimacy in domestic politics. An alliance with the R & D cadre was functionally necessary to successfully consolidate strong presidential power, and politically nonthreatening due to the particular form of "pact of domination" in Korea's power structure. RBD in Korea will continue in the near future given Korea's drive for high technology, and the remarkable expansion of local industrial and educational sectors. Korea's future RBD, however, needs to pay closer attention to the following 4 problems: research autonomy; equality issues; skill-based repatriation of technicians and engineers rather than Ph.Ds; and subsidies to small and medium industry for RBD.

  3. [To what extent is Mexican emigration to the United States a brain drain?].

    Science.gov (United States)

    Osborn, T N; Newton, J R

    1979-01-01

    The authors examine the problem of legal Mexican emigration to the United States, analyzing its effects on both countries. Characteristics of the Mexican emigrants are examined with respect to level of employment, salary, and employment opportunities. The total number of emigrants in professional and technical capacities is investigated in order to determine the likelihood of a brain drain from Mexico to the United States

  4. Stability of whole brain and regional network topology within and between resting and cognitive states.

    Science.gov (United States)

    Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

    2013-01-01

    Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

  5. Stability of whole brain and regional network topology within and between resting and cognitive states.

    Directory of Open Access Journals (Sweden)

    Justyna K Rzucidlo

    Full Text Available BACKGROUND: Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. METHODOLOGY/PRINCIPAL FINDINGS: fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. CONCLUSIONS/SIGNIFICANCE: These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

  6. Role of mitochondrial calcium uptake homeostasis in resting state fMRI brain networks.

    Science.gov (United States)

    Kannurpatti, Sridhar S; Sanganahalli, Basavaraju G; Herman, Peter; Hyder, Fahmeed

    2015-11-01

    Mitochondrial Ca(2+) uptake influences both brain energy metabolism and neural signaling. Given that brain mitochondrial organelles are distributed in relation to vascular density, which varies considerably across brain regions, we hypothesized different physiological impacts of mitochondrial Ca(2+) uptake across brain regions. We tested the hypothesis by monitoring brain "intrinsic activity" derived from the resting state functional MRI (fMRI) blood oxygen level dependent (BOLD) fluctuations in different functional networks spanning the somatosensory cortex, caudate putamen, hippocampus and thalamus, in normal and perturbed mitochondrial Ca(2+) uptake states. In anesthetized rats at 11.7 T, mitochondrial Ca(2+) uptake was inhibited or enhanced respectively by treatments with Ru360 or kaempferol. Surprisingly, mitochondrial Ca(2+) uptake inhibition by Ru360 and enhancement by kaempferol led to similar dose-dependent decreases in brain-wide intrinsic activities in both the frequency domain (spectral amplitude) and temporal domain (resting state functional connectivity; RSFC). The fact that there were similar dose-dependent decreases in the frequency and temporal domains of the resting state fMRI-BOLD fluctuations during mitochondrial Ca(2+) uptake inhibition or enhancement indicated that mitochondrial Ca(2+) uptake and its homeostasis may strongly influence the brain's functional organization at rest. Interestingly, the resting state fMRI-derived intrinsic activities in the caudate putamen and thalamic regions saturated much faster with increasing dosage of either drug treatment than the drug-induced trends observed in cortical and hippocampal regions. Regional differences in how the spectral amplitude and RSFC changed with treatment indicate distinct mitochondrion-mediated spontaneous neuronal activity coupling within the various RSFC networks determined by resting state fMRI. Copyright © 2015 John Wiley & Sons, Ltd.

  7. High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain

    OpenAIRE

    Nasiriavanaki, Mohammadreza; Xia, Jun; Wan, Hanlin; Bauer, Adam Quentin; Culver, Joseph P.; Wang, Lihong V.

    2013-01-01

    The increasing use of mouse models for human brain disease studies presents an emerging need for a new functional imaging modality. Using optical excitation and acoustic detection, we developed a functional connectivity photoacoustic tomography system, which allows noninvasive imaging of resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight functional regions, including the olfactory bu...

  8. Multiscale energy reallocation during low-frequency steady-state brain response.

    Science.gov (United States)

    Wang, Yifeng; Chen, Wang; Ye, Liangkai; Biswal, Bharat B; Yang, Xuezhi; Zou, Qijun; Yang, Pu; Yang, Qi; Wang, Xinqi; Cui, Qian; Duan, Xujun; Liao, Wei; Chen, Huafu

    2018-05-01

    Traditional task-evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low-frequency steady-state brain response (lfSSBR) reflects frequency-tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra-slow frequency band ( .955) of their spatial distribution and brain-behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency-specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks. © 2018 Wiley Periodicals, Inc.

  9. State of the Art: Novel Applications for Deep Brain Stimulation.

    Science.gov (United States)

    Roy, Holly A; Green, Alexander L; Aziz, Tipu Z

    2018-02-01

    Deep brain stimulation (DBS) is a rapidly developing field of neurosurgery with potential therapeutic applications that are relevant to conditions traditionally viewed as beyond the limits of neurosurgery. Our objective, in this review, is to highlight some of the emerging applications of DBS within three distinct but overlapping spheres, namely trauma, neuropsychiatry, and autonomic physiology. An extensive literature review was carried out in MEDLINE, to identify relevant studies and review articles describing applications of DBS in the areas of trauma, neuropsychiatry and autonomic neuroscience. A wide range of applications of DBS in these spheres was identified, some having only been tested in one or two cases, others much better studied. We have identified various avenues for DBS to be applied for patient benefit in cases relevant to trauma, neuropsychiatry and autonomic neuroscience. Further developments in DBS technology and clinical trial design will enable these novel applications to be effectively and rigorously assessed and utilized most effectively. © 2017 International Neuromodulation Society.

  10. Interaction Effects of BDNF and COMT Genes on Resting-State Brain Activity and Working Memory

    Science.gov (United States)

    Chen, Wen; Chen, Chunhui; Xia, Mingrui; Wu, Karen; Chen, Chuansheng; He, Qinghua; Xue, Gui; Wang, Wenjing; He, Yong; Dong, Qi

    2016-01-01

    Catechol-O-methyltransferase (COMT) and brain-derived neurotrophic factor (BDNF) genes have been found to interactively influence working memory (WM) as well as brain activation during WM tasks. However, whether the two genes have interactive effects on resting-state activities of the brain and whether these spontaneous activations correlate with WM are still unknown. This study included behavioral data from WM tasks and genetic data (COMT rs4680 and BDNF Val66Met) from 417 healthy Chinese adults and resting-state fMRI data from 298 of them. Significant interactive effects of BDNF and COMT were found for WM performance as well as for resting-state regional homogeneity (ReHo) in WM-related brain areas, including the left medial frontal gyrus (lMeFG), left superior frontal gyrus (lSFG), right superior and medial frontal gyrus (rSMFG), right medial orbitofrontal gyrus (rMOFG), right middle frontal gyrus (rMFG), precuneus, bilateral superior temporal gyrus, left superior occipital gyrus, right middle occipital gyrus, and right inferior parietal lobule. Simple effects analyses showed that compared to other genotypes, subjects with COMT-VV/BDNF-VV had higher WM and lower ReHo in all five frontal brain areas. The results supported the hypothesis that COMT and BDNF polymorphisms influence WM performance and spontaneous brain activity (i.e., ReHo). PMID:27853425

  11. Incidence of Brain Atrophy and Decline in Mini-Mental State Examination Score After Whole-Brain Radiotherapy in Patients With Brain Metastases: A Prospective Study

    International Nuclear Information System (INIS)

    Shibamoto, Yuta; Baba, Fumiya; Oda, Kyota; Hayashi, Shinya; Kokubo, Masaki; Ishihara, Shun-Ichi; Itoh, Yoshiyuki; Ogino, Hiroyuki; Koizumi, Masahiko

    2008-01-01

    Purpose: To determine the incidence of brain atrophy and dementia after whole-brain radiotherapy (WBRT) in patients with brain metastases not undergoing surgery. Methods and Materials: Eligible patients underwent WBRT to 40 Gy in 20 fractions with or without a 10-Gy boost. Brain magnetic resonance imaging or computed tomography and Mini-Mental State Examination (MMSE) were performed before and soon after radiotherapy, every 3 months for 18 months, and every 6 months thereafter. Brain atrophy was evaluated by change in cerebrospinal fluid-cranial ratio (CCR), and the atrophy index was defined as postradiation CCR divided by preradiation CCR. Results: Of 101 patients (median age, 62 years) entering the study, 92 completed WBRT, and 45, 25, and 10 patients were assessable at 6, 12, and 18 months, respectively. Mean atrophy index was 1.24 ± 0.39 (SD) at 6 months and 1.32 ± 0.40 at 12 months, and 18% and 28% of the patients had an increase in the atrophy index by 30% or greater, respectively. No apparent decrease in mean MMSE score was observed after WBRT. Individually, MMSE scores decreased by four or more points in 11% at 6 months, 12% at 12 months, and 0% at 18 months. However, about half the decrease in MMSE scores was associated with a decrease in performance status caused by systemic disease progression. Conclusions: Brain atrophy developed in up to 30% of patients, but it was not necessarily accompanied by MMSE score decrease. Dementia after WBRT unaccompanied by tumor recurrence was infrequent

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

    Directory of Open Access Journals (Sweden)

    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.

  13. Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

    Science.gov (United States)

    Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

    2016-08-01

    Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

  14. Objective classification of latent behavioral states in bio-logging data using multivariate-normal hidden Markov models.

    Science.gov (United States)

    Phillips, Joe Scutt; Patterson, Toby A; Leroy, Bruno; Pilling, Graham M; Nicol, Simon J

    2015-07-01

    Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions. Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching. Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna. HMMs provide easily interpretable models for the objective classification of

  15. Modulation classification for MIMO systems: State of the art and research directions

    International Nuclear Information System (INIS)

    Bahloul, Mohammad Rida; Yusoff, Mohd Zuki; Abdel-Aty, Abdel-Haleem; Saad, M. Naufal M.; Al-Jemeli, Marwan

    2016-01-01

    Blind techniques and algorithms for Multiple-Input Multiple-Output (MIMO) signals interception have recently attracted a great deal of research efforts. This is due to their important applications in the military and civil telecommunications domains. One essential step in the signal interception process is to blindly recognize the modulation scheme of the MIMO signals. This process is formally called Modulation Classification (MC). This paper discusses the modulation classification for MIMO systems and presents a comprehensive and critical literature review of the existing MC algorithms for MIMO systems; where possible, gaps in the knowledge base are identified and future directions for the research work are suggested.

  16. Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

    Science.gov (United States)

    Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

    2016-06-01

    Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  17. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    Science.gov (United States)

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  18. The neural sociometer: brain mechanisms underlying state self-esteem.

    Science.gov (United States)

    Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

    2011-11-01

    On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

  19. CLASSIFICATION AND COMPLEX STATE VALUE OF SHOPPING CENTERS: PROJECT-ORIENTED APPROACH

    Directory of Open Access Journals (Sweden)

    Юрій Павлович РАК

    2016-02-01

    Full Text Available Was done the analysis of projects objects of trade and entertainment centers from the perspective of improving the life safety and is proposed the definition of "Trade and entertainment center", "Trade and entertainment center" and "Complex value of trade and entertainment center." A classification of shopping centers on the classification criteria and the criteria are characterized by increased security status and attractiveness of their operation. The classification of trade and entertainment centers on the criteria of classification features were made. It characterizes the security situation and will increase the attractiveness of their operation. In the nearest future the most secure and modern TEC will be those buildings who will have unique qualities such as safety systems, excellent customer service, and thus by a high level of trust (the client to the mall. The important role will play those TEC, who have clearly formed value oriented project management, including communication values using innovative methods and models. Trade and entertainment centers as an organization are included in the complex process of interaction management. They being both as an enterprise that serves the public and satisfying a great range of his interests and architectural site, which is leased and increases the business attractiveness of the district of TEC location. This duality of the essence of TEC center makes difficult to assess the effectiveness of its security.

  20. FMCW radar system for detection and classification of small vessels in high sea state conditions

    NARCIS (Netherlands)

    Wasselin, J.-P.; Mazuel, S.; Itcia, E.; Huizing, A.G.; Theil, A.

    2012-01-01

    The ROCKWELL COLLINS France radar department is currently developing a FMCW radar system for the detection and the classification of small maritime targets in the frame of the SISMARIS, SARGOS & I2C projects. Several test campaigns have been conducted since 2009 to develop a sensor as well as an

  1. The regulation of brain states by neuroactive substances distributed via the cerebrospinal fluid; a review

    Directory of Open Access Journals (Sweden)

    Veening Jan G

    2010-01-01

    Full Text Available Abstract The cerebrospinal fluid (CSF system provides nutrients to and removes waste products from the brain. Recent findings suggest, however, that in addition, the CSF contains message molecules in the form of actively released neuroactive substances. The concentrations of these vary between locations, suggesting they are important for the changes in brain activity that underlie different brain states, and induce different sensory input and behavioral output relationships. The cranial CSF displays a rapid caudally-directed ventricular flow followed by a slower rostrally-directed subarachnoid flow (mainly towards the cribriform plate and from there into the nasal lymphatics. Thus, many brain areas are exposed to and can be influenced by substances contained in the CSF. In this review we discuss the production and flow of the CSF, including the mechanisms involved in the regulation of its composition. In addition, the available evidence for the release of neuropeptides and other neuroactive substances into the CSF is reviewed, with particular attention to the selective effects of these on distant downstream receptive brain areas. As a conclusion we suggest that (1 the flowing CSF is involved in more than just nutrient and waste control, but is also used as a broadcasting system consisting of coordinated messages to a variety of nearby and distant brain areas; (2 this special form of volume transmission underlies changes in behavioral states.

  2. Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction.

    Science.gov (United States)

    Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard

    2016-04-01

    The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Glymphatic clearance controls state-dependent changes in brain lactate concentration.

    Science.gov (United States)

    Lundgaard, Iben; Lu, Minh Lon; Yang, Ezra; Peng, Weiguo; Mestre, Humberto; Hitomi, Emi; Deane, Rashid; Nedergaard, Maiken

    2017-06-01

    Brain lactate concentration is higher during wakefulness than in sleep. However, it is unknown why arousal is linked to an increase in brain lactate and why lactate declines within minutes of sleep. Here, we show that the glymphatic system is responsible for state-dependent changes in brain lactate concentration. Suppression of glymphatic function via acetazolamide treatment, cisterna magna puncture, aquaporin 4 deletion, or changes in body position reduced the decline in brain lactate normally observed when awake mice transition into sleep or anesthesia. Concurrently, the same manipulations diminished accumulation of lactate in cervical, but not in inguinal lymph nodes when mice were anesthetized. Thus, our study suggests that brain lactate is an excellent biomarker of the sleep-wake cycle and increases further during sleep deprivation, because brain lactate is inversely correlated with glymphatic-lymphatic clearance. This analysis provides fundamental new insight into brain energy metabolism by demonstrating that glucose that is not fully oxidized can be exported as lactate via glymphatic-lymphatic fluid transport.

  4. Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state.

    Science.gov (United States)

    Dørum, Erlend S; Kaufmann, Tobias; Alnæs, Dag; Andreassen, Ole A; Richard, Geneviève; Kolskår, Knut K; Nordvik, Jan Egil; Westlye, Lars T

    2017-03-01

    Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task

  5. High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain

    Science.gov (United States)

    Nasiriavanaki, Mohammadreza; Xia, Jun; Wan, Hanlin; Bauer, Adam Quentin; Culver, Joseph P.; Wang, Lihong V.

    2014-01-01

    The increasing use of mouse models for human brain disease studies presents an emerging need for a new functional imaging modality. Using optical excitation and acoustic detection, we developed a functional connectivity photoacoustic tomography system, which allows noninvasive imaging of resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight functional regions, including the olfactory bulb, limbic, parietal, somatosensory, retrosplenial, visual, motor, and temporal regions, as well as in several subregions. The borders and locations of these regions agreed well with the Paxinos mouse brain atlas. By subjecting the mouse to alternating hyperoxic and hypoxic conditions, strong and weak functional connectivities were observed, respectively. In addition to connectivity images, vascular images were simultaneously acquired. These studies show that functional connectivity photoacoustic tomography is a promising, noninvasive technique for functional imaging of the mouse brain. PMID:24367107

  6. Use of Ancillary Tests When Determining Brain Death in Pediatric Patients in the United States.

    Science.gov (United States)

    Lewis, Ariane; Adams, Nellie; Chopra, Arun; Kirschen, Matthew P

    2017-10-01

    Although pediatric brain death guidelines stipulate when ancillary testing should be used during brain death determination, little is known about the way these recommendations are implemented in clinical practice. We conducted a survey of pediatric intensivists and neurologists in the United States on the use of ancillary testing. Although most respondents noted they only performed an ancillary test if the clinical examination and apnea test could not be completed, 20% of 195 respondents performed an ancillary test for other reasons, including (1) to convince a family that objected to the brain death determination that a patient is truly dead (n = 21), (2) personal preference (n = 14), and (3) institutional requirement (n = 5). Our findings suggest that pediatricians use ancillary tests for a variety of reasons during brain death determination. Medical societies and governmental regulatory bodies must reinforce the need for homogeneity in practice.

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

    Science.gov (United States)

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

    2010-08-11

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

  8. 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.

  9. Increased power spectral density in resting-state pain-related brain networks in fibromyalgia.

    Science.gov (United States)

    Kim, Ji-Young; Kim, Seong-Ho; Seo, Jeehye; Kim, Sang-Hyon; Han, Seung Woo; Nam, Eon Jeong; Kim, Seong-Kyu; Lee, Hui Joong; Lee, Seung-Jae; Kim, Yang-Tae; Chang, Yongmin

    2013-09-01

    Fibromyalgia (FM), characterized by chronic widespread pain, is known to be associated with heightened responses to painful stimuli and atypical resting-state functional connectivity among pain-related regions of the brain. Previous studies of FM using resting-state functional magnetic resonance imaging (rs-fMRI) have focused on intrinsic functional connectivity, which maps the spatial distribution of temporal correlations among spontaneous low-frequency fluctuation in functional MRI (fMRI) resting-state data. In the current study, using rs-fMRI data in the frequency domain, we investigated the possible alteration of power spectral density (PSD) of low-frequency fluctuation in brain regions associated with central pain processing in patients with FM. rsfMRI data were obtained from 19 patients with FM and 20 age-matched healthy female control subjects. For each subject, the PSDs for each brain region identified from functional connectivity maps were computed for the frequency band of 0.01 to 0.25 Hz. For each group, the average PSD was determined for each brain region and a 2-sample t test was performed to determine the difference in power between the 2 groups. According to the results, patients with FM exhibited significantly increased frequency power in the primary somatosensory cortex (S1), supplementary motor area (SMA), dorsolateral prefrontal cortex, and amygdala. In patients with FM, the increase in PSD did not show an association with depression or anxiety. Therefore, our findings of atypical increased frequency power during the resting state in pain-related brain regions may implicate the enhanced resting-state baseline neural activity in several brain regions associated with pain processing in FM. Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

  10. Relation of visual creative imagery manipulation to resting-state brain oscillations.

    Science.gov (United States)

    Cai, Yuxuan; Zhang, Delong; Liang, Bishan; Wang, Zengjian; Li, Junchao; Gao, Zhenni; Gao, Mengxia; Chang, Song; Jiao, Bingqing; Huang, Ruiwang; Liu, Ming

    2018-02-01

    Visual creative imagery (VCI) manipulation is the key component of visual creativity; however, it remains largely unclear how it occurs in the brain. The present study investigated the brain neural response to VCI manipulation and its relation to intrinsic brain activity. We collected functional magnetic resonance imaging (fMRI) datasets related to a VCI task and a control task as well as pre- and post-task resting states in sequential sessions. A general linear model (GLM) was subsequently used to assess the specific activation of the VCI task compared with the control task. The changes in brain oscillation amplitudes across the pre-, on-, and post-task states were measured to investigate the modulation of the VCI task. Furthermore, we applied a Granger causal analysis (GCA) to demonstrate the dynamic neural interactions that underlie the modulation effect. We determined that the VCI task specifically activated the left inferior frontal gyrus pars triangularis (IFGtriang) and the right superior frontal gyrus (SFG), as well as the temporoparietal areas, including the left inferior temporal gyrus, right precuneus, and bilateral superior parietal gyrus. Furthermore, the VCI task modulated the intrinsic brain activity of the right IFGtriang (0.01-0.08 Hz) and the left caudate nucleus (0.2-0.25 Hz). Importantly, an inhibitory effect (negative) may exist from the left SFG to the right IFGtriang in the on-VCI task state, in the frequency of 0.01-0.08 Hz, whereas this effect shifted to an excitatory effect (positive) in the subsequent post-task resting state. Taken together, the present findings provide experimental evidence for the existence of a common mechanism that governs the brain activity of many regions at resting state and whose neural activity may engage during the VCI manipulation task, which may facilitate an understanding of the neural substrate of visual creativity.

  11. Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

    Science.gov (United States)

    Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

    2017-08-30

    Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that

  12. Structurally-constrained relationships between cognitive states in the human brain.

    Directory of Open Access Journals (Sweden)

    Ann M Hermundstad

    2014-05-01

    Full Text Available The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

  13. Progesterone mediates brain functional connectivity changes during the menstrual cycle - A pilot resting state MRI study

    Directory of Open Access Journals (Sweden)

    Katrin eArelin

    2015-02-01

    Full Text Available The growing interest in intrinsic brain organization has sparked various innovative approaches to generating comprehensive connectivity-based maps of the human brain. Prior reports point to a sexual dimorphism of the structural and functional human connectome. However, it is uncertain whether subtle changes in sex hormones, as occur during the monthly menstrual cycle, substantially impact the functional architecture of the female brain. Here, we performed eigenvector centrality (EC mapping in 32 longitudinal resting state fMRI scans of a single healthy subject without oral contraceptive use, across four menstrual cycles, and assessed estrogen and progesterone levels. To investigate associations between cycle-dependent hormones and brain connectivity, we performed correlation analyses between the EC maps and the respective hormone levels. On the whole brain level, we found a significant positive correlation between progesterone and EC in the bilateral DLPFC and bilateral sensorimotor cortex. In a secondary region-of-interest analysis, we detected a progesterone-modulated increase in functional connectivity of both bilateral DLPFC and bilateral sensorimotor cortex with the hippocampus. Our results suggest that the menstrual cycle substantially impacts intrinsic functional connectivity, particularly in brain areas associated with contextual memory-regulation, such as the hippocampus. These findings are the first to link the subtle hormonal fluctuations that occur during the menstrual cycle, to significant changes in regional functional connectivity in the hippocampus in a longitudinal design, given the limitation of data acquisition in a single subject. Our study demonstrates the feasibility of such a longitudinal rs-fMRI design and illustrates a means of creating a personalized map of the human brain by integrating potential mediators of brain states, such as menstrual cycle phase.

  14. Resting state functional MRI in Parkinson's disease: the impact of deep brain stimulation on 'effective' connectivity.

    Science.gov (United States)

    Kahan, Joshua; Urner, Maren; Moran, Rosalyn; Flandin, Guillaume; Marreiros, Andre; Mancini, Laura; White, Mark; Thornton, John; Yousry, Tarek; Zrinzo, Ludvic; Hariz, Marwan; Limousin, Patricia; Friston, Karl; Foltynie, Tom

    2014-04-01

    Depleted of dopamine, the dynamics of the parkinsonian brain impact on both 'action' and 'resting' motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the 'effective' connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network-disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.

  15. Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

    Science.gov (United States)

    Cheng, Lin; Zhu, Yang; Sun, Junfeng; Deng, Lifu; He, Naying; Yang, Yang; Ling, Huawei; Ayaz, Hasan; Fu, Yi; Tong, Shanbao

    2018-01-25

    Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique

  16. Quantitative Rates of Brain Glucose Metabolism Distinguish Minimally Conscious from Vegetative State Patients

    DEFF Research Database (Denmark)

    Stender, Johan; Kupers, Ron; Rodell, Anders

    2015-01-01

    of these patients. However, no quantitative comparisons of cerebral glucose metabolism in VS/UWS and MCS have yet been reported. We calculated the regional and whole-brain CMRglc of 41 patients in the states of VS/UWS (n=14), MCS (n=21) or emergence from MCS (EMCS, n=6), and healthy volunteers (n=29). Global......The differentiation of the vegetative or unresponsive wakefulness syndrome (VS/UWS) from the minimally conscious state (MCS) is an important clinical issue. The cerebral metabolic rate of glucose (CMRglc) declines when consciousness is lost, and may reveal the residual cognitive function...... these results reveal a significant correlation between whole-brain energy metabolism and level of consciousness, suggesting that quantitative values of CMRglc reveal consciousness in severely brain-injured patients.Journal of Cerebral Blood Flow & Metabolism advance online publication, 8 October 2014; doi:10...

  17. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  18. Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

    Science.gov (United States)

    Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

    2013-12-01

    Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

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

    Science.gov (United States)

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

    2016-10-15

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

  20. Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

    Science.gov (United States)

    Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

    2018-01-01

    In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

  1. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

    Science.gov (United States)

    Carhart-Harris, Robin L.; Leech, Robert; Hellyer, Peter J.; Shanahan, Murray; Feilding, Amanda; Tagliazucchi, Enzo; Chialvo, Dante R.; Nutt, David

    2014-01-01

    Entropy is a dimensionless quantity that is used for measuring uncertainty about the state of a system but it can also imply physical qualities, where high entropy is synonymous with high disorder. Entropy is applied here in the context of states of consciousness and their associated neurodynamics, with a particular focus on the psychedelic state. The psychedelic state is considered an exemplar of a primitive or primary state of consciousness that preceded the development of modern, adult, human, normal waking consciousness. Based on neuroimaging data with psilocybin, a classic psychedelic drug, it is argued that the defining feature of “primary states” is elevated entropy in certain aspects of brain function, such as the repertoire of functional connectivity motifs that form and fragment across time. Indeed, since there is a greater repertoire of connectivity motifs in the psychedelic state than in normal waking consciousness, this implies that primary states may exhibit “criticality,” i.e., the property of being poised at a “critical” point in a transition zone between order and disorder where certain phenomena such as power-law scaling appear. Moreover, if primary states are critical, then this suggests that entropy is suppressed in normal waking consciousness, meaning that the brain operates just below criticality. It is argued that this entropy suppression furnishes normal waking consciousness with a constrained quality and associated metacognitive functions, including reality-testing and self-awareness. It is also proposed that entry into primary states depends on a collapse of the normally highly organized activity within the default-mode network (DMN) and a decoupling between the DMN and the medial temporal lobes (which are normally significantly coupled). These hypotheses can be tested by examining brain activity and associated cognition in other candidate primary states such as rapid eye movement (REM) sleep and early psychosis and comparing

  2. Brain state-dependence of electrically evoked potentials monitored with head-mounted electronics.

    Science.gov (United States)

    Richardson, Andrew G; Fetz, Eberhard E

    2012-11-01

    Inferring changes in brain connectivity is critical to studies of learning-related plasticity and stimulus-induced conditioning of neural circuits. In addition, monitoring spontaneous fluctuations in connectivity can provide insight into information processing during different brain states. Here, we quantified state-dependent connectivity changes throughout the 24-h sleep-wake cycle in freely behaving monkeys. A novel, head-mounted electronic device was used to electrically stimulate at one site and record evoked potentials at other sites. Electrically evoked potentials (EEPs) revealed the connectivity pattern between several cortical sites and the basal forebrain. We quantified state-dependent changes in the EEPs. Cortico-cortical EEP amplitude increased during slow-wave sleep, compared to wakefulness, while basal-cortical EEP amplitude decreased. The results demonstrate the utility of using portable electronics to document state-dependent connectivity changes in freely behaving primates.

  3. Mixed-phenotype acute leukemia: state-of-the-art of the diagnosis, classification and treatment.

    Science.gov (United States)

    Cernan, Martin; Szotkowski, Tomas; Pikalova, Zuzana

    2017-09-01

    Mixed-phenotype acute leukemia (MPAL) is a heterogeneous group of hematopoietic malignancies in which blasts show markers of multiple developmental lineages and cannot be clearly classified as acute myeloid or lymphoblastic leukemias. Historically, various names and classifications were used for this rare entity accounting for 2-5% of all acute leukemias depending on the diagnostic criterias used. The currently valid classification of myeloid neoplasms and acute leukemia published by the World Health Organization (WHO) in 2016 refers to this group of diseases as MPAL. Because adverse cytogenetic abnormalities are frequently present, MPAL is generally considered a disease with a poor prognosis. Knowledge of its treatment is limited to retrospective analyses of small patient cohorts. So far, no treatment recommendations verified by prospective studies have been published. The reported data suggest that induction therapy for acute lymphoblastic leukemia followed by allogeneic hematopoietic cell transplantation is more effective than induction therapy for acute myeloid leukemia or consolidation chemotherapy. The establishment of cooperative groups and international registries based on the recent WHO criterias are required to ensure further progress in understanding and treatment of MPAL. This review summarizes current knowledge on the diagnosis, classification, prognosis and treatment of MPAL patients.

  4. Linking state regulation, brain laterality, and self-reported ADHD symptoms in adults

    NARCIS (Netherlands)

    Mohamed, Saleh

    2016-01-01

    Aim: Difficulties in regulating the motor activation state and atypical brain laterality have been suggested to be key factors in Attention-Deficit/Hyperactivity Disorder (ADHD). So far, the link between the two factors has not been directly tested, which is the aim of the present study. Method:

  5. Postoperative mortality after surgery for brain tumors by patient insurance status in the United States

    NARCIS (Netherlands)

    Momin, E.N.; Adams, H.; Shinohara, R.T.; Frangakis, C.; Brem, H.; Quinones-Hinojosa, A.

    2012-01-01

    OBJECTIVE To examine whether being uninsured is associated with higher in-hospital postoperative mortality when undergoing surgery in the United States for a brain tumor. DESIGN Retrospective cohort study using the Nationwide Inpatient Sample, January 1, 1999, through December 31, 2008. SETTING The

  6. Decreased levels of brain-derived neurotrophic factor in the remitted state of unipolar depressive disorder

    DEFF Research Database (Denmark)

    Hasselbalch, Jacob; Knorr, U; Bennike, B

    2012-01-01

    Decreased levels of peripheral brain-derived neurotrophic factor (BDNF) have been associated with depression. It is uncertain whether abnormally low levels of BDNF in blood are present beyond the depressive state and whether levels of BDNF are associated with the course of clinical illness....

  7. Short-lived brain state after cued motor imagery in naive subjects

    NARCIS (Netherlands)

    Pfurtscheller, G.; Scherer, R.; Müller-Putz, G.R.; Lopes da Silva, F.H.

    2008-01-01

    Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects)

  8. Dissociative states in dreams and brain chaos: Implications for creative awareness

    Directory of Open Access Journals (Sweden)

    Petr eBob

    2015-09-01

    Full Text Available This article reviews recent findings indicating some common brain processes during dissociative states and dreaming with the aim to outline a perspective that neural chaotic states during dreaming can be closely related to dissociative states that may manifest in dreams scenery. These data are in agreement with various clinical findings that dissociated states can be projected into the dream scenery in REM sleep periods and dreams may represent their specific interactions that may uncover unusual psychological potential of creativity in psychotherapy, art and scientific discoveries.

  9. Developing a dementia-specific health state classification system for a new preference-based instrument AD-5D.

    Science.gov (United States)

    Nguyen, Kim-Huong; Mulhern, Brendan; Kularatna, Sanjeewa; Byrnes, Joshua; Moyle, Wendy; Comans, Tracy

    2017-01-25

    With an ageing population, the number of people with dementia is rising. The economic impact on the health care system is considerable and new treatment methods and approaches to dementia care must be cost effective. Economic evaluation requires valid patient reported outcome measures, and this study aims to develop a dementia-specific health state classification system based on the Quality of Life for Alzheimer's disease (QOL-AD) instrument (nursing home version). This classification system will subsequently be valued to generate a preference-based measure for use in the economic evaluation of interventions for people with dementia. We assessed the dimensionality of the QOL-AD to develop a new classification system. This was done using exploratory and confirmatory factor analysis and further assessment of the structure of the measure to ensure coverage of the key areas of quality of life. Secondly, we used Rasch analysis to test the psychometric performance of the items, and select item(s) to describe each dimension. This was done on 13 items of the QOL-AD (excluding two general health items) using a sample of 284 residents living in long-term care facilities in Australia who had a diagnosis of dementia. A five dimension classification system is proposed resulting from the three factor structure (defined as 'interpersonal environment', 'physical health' and 'self-functioning') derived from the factor analysis and two factors ('memory' and 'mood') from the accompanying review. For the first three dimensions, Rasch analysis selected three questions of the QOL-AD ('living situation', 'physical health', and 'do fun things') with memory and mood questions representing their own dimensions. The resulting classification system (AD-5D) includes many of the health-related quality of life dimensions considered important to people with dementia, including mood, global function and skill in daily living. The development of the AD-5D classification system is an important step

  10. Love-related changes in the brain: A resting-state functional magnetic resonance imaging study

    OpenAIRE

    Hongwen eSong; Zhiling eZou; Juan eKou; Yang eLiu; LiZhuang eYang; Anna ezilverstand; Federicod’Oleire eUquillas; Xiaochu eZhang; Xiaochu eZhang; Xiaochu eZhang

    2015-01-01

    Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Studies with functional magnetic resonance imaging (fMRI) have found activation increases in brain regions involved in processing of reward, emotion, motivation when romantic lovers view photographs of their partners. However, not much is known on whether romantic love affects the brain’s functional architecture during rest. In the present study, resting state...

  11. Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players.

    Science.gov (United States)

    Muraskin, Jordan; Dodhia, Sonam; Lieberman, Gregory; Garcia, Javier O; Verstynen, Timothy; Vettel, Jean M; Sherwin, Jason; Sajda, Paul

    2016-12-01

    Post-task resting state dynamics can be viewed as a task-driven state where behavioral performance is improved through endogenous, non-explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post-task resting state dynamics that promote learning. We measured simultaneous fMRI/EEG and DTI in Division-1 collegiate baseball players and compared to a group of controls, examining differences in both functional and structural connectivity. Participants performed a surrogate baseball pitch Go/No-Go task before a resting state scan, and we compared post-task resting state connectivity using a seed-based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Although both groups were equally trained on the task, the experts showed differential activity in their post-task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA-L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD-alpha oscillation correlations between groups suggests variability in modulatory attention in the post-task state, and (3) group differences between BOLD-beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find that brain dynamics in the post-task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity. Hum Brain Mapp 37:4454-4471, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals

  12. Insulin-Resistant Brain State: the culprit in sporadic Alzheimer’s Disease?

    Science.gov (United States)

    Correia, Sónia C.; Santos, Renato X.; Perry, George; Zhu, Xiongwei; Moreira, Paula I.; Smith, Mark A.

    2011-01-01

    Severe abnormalities in brain glucose/energy metabolism and insulin signaling have been documented to take a pivotal role in early sporadic Alzheimer’s disease (sAD) pathology. Indeed, the “insulin-resistant brain state” has been hypothesized to form the core of the neurodegenerative events that occur in sAD. In this vein, intracerebroventricular administration of subdiabetogenic doses of streptozotocin (STZ) in rats can induce an insulin-resistant brain state, which is proposed as a suitable experimental model of sAD. This review highlights the involvement of disturbed brain insulin metabolism in sAD etiopathogenesis. Furthermore, current knowledge demonstrates that central STZ administration produces brain pathology and behavioral changes that resemble changes found in sAD patients. The STZ-intracerebroventricularly treated rat represents a promising experimental tool in this field by providing new insights concerning early brain alterations in sAD, which can be translated in novel etiopathogenic and therapeutic approaches in this disease. PMID:21262392

  13. At risk of being risky: The relationship between "brain age" under emotional states and risk preference.

    Science.gov (United States)

    Rudolph, Marc D; Miranda-Domínguez, Oscar; Cohen, Alexandra O; Breiner, Kaitlyn; Steinberg, Laurence; Bonnie, Richard J; Scott, Elizabeth S; Taylor-Thompson, Kim; Chein, Jason; Fettich, Karla C; Richeson, Jennifer A; Dellarco, Danielle V; Galván, Adriana; Casey, B J; Fair, Damien A

    2017-04-01

    Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky." Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention

    Science.gov (United States)

    Ewell, Laura A.; Liang, Liang; Armstrong, Caren; Soltész, Ivan; Leutgeb, Stefan

    2015-01-01

    Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention. SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single

  15. Resting state brain dynamics and its transients: a combined TMS-EEG study.

    Science.gov (United States)

    Bonnard, Mireille; Chen, Sophie; Gaychet, Jérôme; Carrere, Marcel; Woodman, Marmaduke; Giusiano, Bernard; Jirsa, Viktor

    2016-08-04

    The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain's relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL, and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

  16. A computational study of whole-brain connectivity in resting state and task fMRI

    Science.gov (United States)

    Goparaju, Balaji; Rana, Kunjan D.; Calabro, Finnegan J.; Vaina, Lucia Maria

    2014-01-01

    Background We compared the functional brain connectivity produced during resting-state in which subjects were not actively engaged in a task with that produced while they actively performed a visual motion task (task-state). Material/Methods In this paper we employed graph-theoretical measures and network statistics in novel ways to compare, in the same group of human subjects, functional brain connectivity during resting-state fMRI with brain connectivity during performance of a high level visual task. We performed a whole-brain connectivity analysis to compare network statistics in resting and task states among anatomically defined Brodmann areas to investigate how brain networks spanning the cortex changed when subjects were engaged in task performance. Results In the resting state, we found strong connectivity among the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and hippocampal formation, consistent with previous reports of the default mode network (DMN). The connections among these areas were strengthened while subjects actively performed an event-related visual motion task, indicating a continued and strong engagement of the DMN during task processing. Regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), showed that task performance induces stronger inter-regional connections, leading to a denser processing network, but that this does not imply a more efficient system as shown by the integration measures such as path length and global efficiency, and from global measures such as small-worldness. Conclusions In spite of the maintenance of connectivity and the “hub-like” behavior of areas, our results suggest that the network paths may be rerouted when performing the task condition. PMID:24947491

  17. Classification of Four-Qubit States by Means of a Stochastic Local Operation and the Classical Communication Invariant

    International Nuclear Information System (INIS)

    Zha Xin-Wei; Ma Gang-Long

    2011-01-01

    It is a recent observation that entanglement classification for qubits is closely related to stochastic local operations and classical communication (SLOCC) invariants. Verstraete et al.[Phys. Rev. A 65 (2002) 052112] showed that for pure states of four qubits there are nine different degenerate SLOCC entanglement classes. Li et al.[Phys. Rev. A 76 (2007) 052311] showed that there are at feast 28 distinct true SLOCC entanglement classes for four qubits by means of the SLOCC invariant and semi-invariant. We give 16 different entanglement classes for four qubits by means of basic SLOCC invariants. (general)

  18. Ketamine changes the local resting-state functional properties of anesthetized-monkey brain.

    Science.gov (United States)

    Rao, Jia-Sheng; Liu, Zuxiang; Zhao, Can; Wei, Rui-Han; Zhao, Wen; Tian, Peng-Yu; Zhou, Xia; Yang, Zhao-Yang; Li, Xiao-Guang

    2017-11-01

    Ketamine is a well-known anesthetic. 'Recreational' use of ketamine common induces psychosis-like symptoms and cognitive impairments. The acute and chronic effects of ketamine on relevant brain circuits have been studied, but the effects of single-dose ketamine administration on the local resting-state functional properties of the brain remain unknown. In this study, we aimed to assess the effects of single-dose ketamine administration on the brain local intrinsic properties. We used resting-state functional magnetic resonance imaging (rs-fMRI) to explore the ketamine-induced alterations of brain intrinsic properties. Seven adult rhesus monkeys were imaged with rs-fMRI to examine the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) in the brain before and after ketamine injection. Paired comparisons were used to detect the significantly altered regions. Results showed that the fALFF of the prefrontal cortex (p=0.046), caudate nucleus (left side, p=0.018; right side, p=0.025), and putamen (p=0.020) in post-injection stage significantly increased compared with those in pre-injection period. The ReHo of nucleus accumbens (p=0.049), caudate nucleus (p=0.037), and hippocampus (p=0.025) increased after ketamine injection, but that of prefrontal cortex decreased (pketamine administration can change the regional intensity and synchronism of brain activity, thereby providing evidence of ketamine-induced abnormal resting-state functional properties in primates. This evidence may help further elucidate the effects of ketamine on the cerebral resting status. Copyright © 2017. Published by Elsevier Inc.

  19. Directed connectivity of brain default networks in resting state using GCA and motif.

    Science.gov (United States)

    Jiao, Zhuqing; Wang, Huan; Ma, Kai; Zou, Ling; Xiang, Jianbo

    2017-06-01

    Nowadays, there is a lot of interest in assessing functional interactions between key brain regions. In this paper, Granger causality analysis (GCA) and motif structure are adopted to study directed connectivity of brain default mode networks (DMNs) in resting state. Firstly, the time series of functional magnetic resonance imaging (fMRI) data in resting state were extracted, and the causal relationship values of the nodes representing related brain regions are analyzed in time domain to construct a default network. Then, the network structures were searched from the default networks of controls and patients to determine the fixed connection mode in the networks. The important degree of motif structures in directed connectivity of default networks was judged according to p-value and Z-score. Both node degree and average distance were used to analyze the effect degree an information transfer rate of brain regions in motifs and default networks, and efficiency of the network. Finally, activity and functional connectivity strength of the default brain regions are researched according to the change of energy distributions between the normals and the patients' brain regions. Experimental results demonstrate that, both normal subjects and stroke patients have some corresponding fixed connection mode of three nodes, and the efficiency and power spectrum of the patient's default network is somewhat lower than that of the normal person. In particular, the Right Posterior Cingulate Gyrus (PCG.R) has a larger change in functional connectivity and its activity. The research results verify the feasibility of the application of GCA and motif structure to study the functional connectivity of default networks in resting state.

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

    Science.gov (United States)

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

    2015-07-30

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

  1. Fractals as macroscopic manifestation of squeezed coherent states and brain dynamics

    International Nuclear Information System (INIS)

    Vitiello, Giuseppe

    2012-01-01

    Recent results on the relation between self-similarity and squeezed coherent states are presented. I consider fractals which are generated iteratively according to a prescribed recipe, the so-called deterministic fractals. Fractal properties are incorporated in the framework of the theory of the entire analytical functions and deformed coherent states. Conversely, fractal properties of squeezed coherent states are recognized. This sheds some light on the understanding of the dynamical origin of fractals and their global nature emerging from local deformation processes. The self-similarity in brain background activity suggested by laboratory observations of power-law distributions of power spectral densities of electrocorticograms is also discussed and accounted in the frame of the dissipative many-body model of brain.

  2. Music Composition from the Brain Signal: Representing the Mental State by Music

    Directory of Open Access Journals (Sweden)

    Dan Wu

    2010-01-01

    Full Text Available This paper proposes a method to translate human EEG into music, so as to represent mental state by music. The arousal levels of the brain mental state and music emotion are implicitly used as the bridge between the mind world and the music. The arousal level of the brain is based on the EEG features extracted mainly by wavelet analysis, and the music arousal level is related to the musical parameters such as pitch, tempo, rhythm, and tonality. While composing, some music principles (harmonics and structure were taken into consideration. With EEGs during various sleep stages as an example, the music generated from them had different patterns of pitch, rhythm, and tonality. 35 volunteers listened to the music pieces, and significant difference in music arousal levels was found. It implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, and so forth.

  3. Spontaneous sleep-like brain state alternations and breathing characteristics in urethane anesthetized mice.

    Directory of Open Access Journals (Sweden)

    Silvia Pagliardini

    Full Text Available Brain state alternations resembling those of sleep spontaneously occur in rats under urethane anesthesia and they are closely linked with sleep-like respiratory changes. Although rats are a common model for both sleep and respiratory physiology, we sought to determine if similar brain state and respiratory changes occur in mice under urethane. We made local field potential recordings from the hippocampus and measured respiratory activity by means of EMG recordings in intercostal, genioglossus, and abdominal muscles. Similar to results in adult rats, urethane anesthetized mice displayed quasi-periodic spontaneous forebrain state alternations between deactivated patterns resembling slow wave sleep (SWS and activated patterns resembling rapid eye movement (REM sleep. These alternations were associated with an increase in breathing rate, respiratory variability, a depression of inspiratory related activity in genioglossus muscle and an increase in expiratory-related abdominal muscle activity when comparing deactivated (SWS-like to activated (REM-like states. These results demonstrate that urethane anesthesia consistently induces sleep-like brain state alternations and correlated changes in respiratory activity across different rodent species. They open up the powerful possibility of utilizing transgenic mouse technology for the advancement and translation of knowledge regarding sleep cycle alternations and their impact on respiration.

  4. Functional connectivity analysis of the brain network using resting-state fMRI

    International Nuclear Information System (INIS)

    Hayashi, Toshihiro

    2011-01-01

    Spatial patterns of spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signals reflect the underlying neural architecture. The study of the brain network based on these self-organized patterns is termed resting-state functional MRI (fMRI). This review article aims at briefly reviewing a basic concept of this technology and discussing its implications for neuropsychological studies. First, the technical aspects of resting-state fMRI, including signal sources, physiological artifacts, image acquisition, and analytical methods such as seed-based correlation analysis and independent component analysis, are explained, followed by a discussion on the major resting-state networks, including the default mode network. In addition, the structure-function correlation studied using diffuse tensor imaging and resting-state fMRI is briefly discussed. Second, I have discussed the reservations and potential pitfalls of 2 major imaging methods: voxel-based lesion-symptom mapping and task fMRI. Problems encountered with voxel-based lesion-symptom mapping can be overcome by using resting-state fMRI and evaluating undamaged brain networks in patients. Regarding task fMRI in patients, I have also emphasized the importance of evaluating the baseline brain activity because the amplitude of activation in BOLD fMRI is hard to interpret as the same baseline cannot be assumed for both patient and normal groups. (author)

  5. Abnormal resting-state brain activities in patients with first-episode obsessive-compulsive disorder.

    Science.gov (United States)

    Niu, Qihui; Yang, Lei; Song, Xueqin; Chu, Congying; Liu, Hao; Zhang, Lifang; Li, Yan; Zhang, Xiang; Cheng, Jingliang; Li, Youhui

    2017-01-01

    This paper attempts to explore the brain activity of patients with obsessive-compulsive disorder (OCD) and its correlation with the disease at resting duration in patients with first-episode OCD, providing a forceful imaging basis for clinic diagnosis and pathogenesis of OCD. Twenty-six patients with first-episode OCD and 25 healthy controls (HC group; matched for age, sex, and education level) underwent functional magnetic resonance imaging (fMRI) scanning at resting state. Statistical parametric mapping 8, data processing assistant for resting-state fMRI analysis toolkit, and resting state fMRI data analysis toolkit packages were used to process the fMRI data on Matlab 2012a platform, and the difference of regional homogeneity (ReHo) values between the OCD group and HC group was detected with independent two-sample t -test. With age as a concomitant variable, the Pearson correlation analysis was adopted to study the correlation between the disease duration and ReHo value of whole brain. Compared with HC group, the ReHo values in OCD group were decreased in brain regions, including left thalamus, right thalamus, right paracentral lobule, right postcentral gyrus, and the ReHo value was increased in the left angular gyrus region. There was a negative correlation between disease duration and ReHo value in the bilateral orbitofrontal cortex (OFC). OCD is a multifactorial disease generally caused by abnormal activities of many brain regions at resting state. Worse brain activity of the OFC is related to the OCD duration, which provides a new insight to the pathogenesis of OCD.

  6. Urodynamic function during sleep-like brain states in urethane anesthetized rats.

    Science.gov (United States)

    Crook, J; Lovick, T

    2016-01-28

    The aim was to investigate urodynamic parameters and functional excitability of the periaqueductal gray matter (PAG) during changes in sleep-like brain states in urethane anesthetized rats. Simultaneous recordings of detrusor pressure, external urethral sphincter (EUS) electromyogram (EMG), cortical electroencephalogram (EEG), and single-unit activity in the PAG were made during repeated voiding induced by continuous infusion of saline into the bladder. The EEG cycled between synchronized, high-amplitude slow wave activity (SWA) and desynchronized low-amplitude fast activity similar to slow wave and 'activated' sleep-like brain states. During (SWA, 0.5-1.5 Hz synchronized oscillation of the EEG waveform) voiding became more irregular than in the 'activated' brain state (2-5 Hz low-amplitude desynchronized EEG waveform) and detrusor void pressure threshold, void volume threshold and the duration of bursting activity in the external urethral sphincter EMG were raised. The spontaneous firing rate of 23/52 neurons recorded within the caudal PAG and adjacent tegmentum was linked to the EEG state, with the majority of responsive cells (92%) firing more slowly during SWA. Almost a quarter of the cells recorded (12/52) showed phasic changes in firing rate that were linked to the occurrence of voids. Inhibition (n=6), excitation (n=4) or excitation/inhibition (n=2) was seen. The spontaneous firing rate of 83% of the micturition-responsive cells was sensitive to changes in EEG state. In nine of the 12 responsive cells (75%) the responses were reduced during SWA. We propose that during different sleep-like brain states changes in urodynamic properties occur which may be linked to changing excitability of the micturition circuitry in the periaqueductal gray. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. The entropic brain:A theory of conscious states informed by neuroimaging research with psychedelic drugs

    Directory of Open Access Journals (Sweden)

    Robin Lester Carhart-Harris

    2014-02-01

    Full Text Available Entropy is a dimensionless quantity that is used for measuring uncertainty about the state of a system but it can also imply physical qualities, where high entropy is synonymous with high disorder. Entropy is applied here in the context of states of consciousness and their associated neural dynamics, with a particular focus on the psychedelic state. The psychedelic state is considered an exemplar of a primitive or primary state of consciousness that preceded the development of modern, adult, human, normal waking consciousness. Based on neuroimaging data with psilocybin, a classic psychedelic drug, it is argued that the defining feature of ‘primary states’ is elevated entropy in certain aspects of brain function, such as the repertoire of functional connectivity motifs that form and fragment across time. It is noted that elevated entropy in this sense, is a characteristic of systems exhibiting ‘self-organised criticality’, i.e., a property of systems that gravitate towards a ‘critical’ point in a transition zone between order and disorder in which certain phenomena such as power-law scaling appear. This implies that entropy is suppressed in normal waking consciousness, meaning that the brain operates just below criticality. It is argued that this entropy suppression furnishes consciousness with a constrained quality and associated metacognitive functions, including reality-testing and self-awareness. It is also proposed that entry into primary states depends on a collapse of the normally highly organised activity within the default-mode network (DMN and a decoupling between the DMN and the medial temporal lobes (which are normally significantly coupled. These hypotheses can be tested by examining brain activity and associated cognition in other candidate primary states such as REM sleep and early psychosis and comparing these with non-primary states such as normal waking consciousness and the anaesthetised state.

  8. An accelerated framework for the classification of biological targets from solid-state micropore data.

    Science.gov (United States)

    Hanif, Madiha; Hafeez, Abdul; Suleman, Yusuf; Mustafa Rafique, M; Butt, Ali R; Iqbal, Samir M

    2016-10-01

    Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. An up-to-date comparison of state-of-the-art classification algorithms

    KAUST Repository

    Zhang, Chongsheng

    2017-04-05

    Current benchmark reports of classification algorithms generally concern common classifiers and their variants but do not include many algorithms that have been introduced in recent years. Moreover, important properties such as the dependency on number of classes and features and CPU running time are typically not examined. In this paper, we carry out a comparative empirical study on both established classifiers and more recently proposed ones on 71 data sets originating from different domains, publicly available at UCI and KEEL repositories. The list of 11 algorithms studied includes Extreme Learning Machine (ELM), Sparse Representation based Classification (SRC), and Deep Learning (DL), which have not been thoroughly investigated in existing comparative studies. It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines (SVM) and Random Forests (RF), while being the fastest algorithm in terms of prediction efficiency. ELM also yields good accuracy results, ranking in the top-5, alongside GBDT, RF, SVM, and C4.5 but this performance varies widely across all data sets. Unsurprisingly, top accuracy performers have average or slow training time efficiency. DL is the worst performer in terms of accuracy but second fastest in prediction efficiency. SRC shows good accuracy performance but it is the slowest classifier in both training and testing.

  10. An up-to-date comparison of state-of-the-art classification algorithms

    KAUST Repository

    Zhang, Chongsheng; Liu, Changchang; Zhang, Xiangliang; Almpanidis, George

    2017-01-01

    Current benchmark reports of classification algorithms generally concern common classifiers and their variants but do not include many algorithms that have been introduced in recent years. Moreover, important properties such as the dependency on number of classes and features and CPU running time are typically not examined. In this paper, we carry out a comparative empirical study on both established classifiers and more recently proposed ones on 71 data sets originating from different domains, publicly available at UCI and KEEL repositories. The list of 11 algorithms studied includes Extreme Learning Machine (ELM), Sparse Representation based Classification (SRC), and Deep Learning (DL), which have not been thoroughly investigated in existing comparative studies. It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines (SVM) and Random Forests (RF), while being the fastest algorithm in terms of prediction efficiency. ELM also yields good accuracy results, ranking in the top-5, alongside GBDT, RF, SVM, and C4.5 but this performance varies widely across all data sets. Unsurprisingly, top accuracy performers have average or slow training time efficiency. DL is the worst performer in terms of accuracy but second fastest in prediction efficiency. SRC shows good accuracy performance but it is the slowest classifier in both training and testing.

  11. Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks.

    Science.gov (United States)

    Carbonell, Felix; Nagano-Saito, Atsuko; Leyton, Marco; Cisek, Paul; Benkelfat, Chawki; He, Yong; Dagher, Alain

    2014-09-01

    Spatial patterns of functional connectivity derived from resting brain activity may be used to elucidate the topological properties of brain networks. Such networks are amenable to study using graph theory, which shows that they possess small world properties and can be used to differentiate healthy subjects and patient populations. Of particular interest is the possibility that some of these differences are related to alterations in the dopamine system. To investigate the role of dopamine in the topological organization of brain networks at rest, we tested the effects of reducing dopamine synthesis in 13 healthy subjects undergoing functional magnetic resonance imaging. All subjects were scanned twice, in a resting state, following ingestion of one of two amino acid drinks in a randomized, double-blind manner. One drink was a nutritionally balanced amino acid mixture, and the other was tyrosine and phenylalanine deficient. Functional connectivity between 90 cortical and subcortical regions was estimated for each individual subject under each dopaminergic condition. The lowered dopamine state caused the following network changes: reduced global and local efficiency of the whole brain network, reduced regional efficiency in limbic areas, reduced modularity of brain networks, and greater connection between the normally anti-correlated task-positive and default-mode networks. We conclude that dopamine plays a role in maintaining the efficient small-world properties and high modularity of functional brain networks, and in segregating the task-positive and default-mode networks. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2012-01-01

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

  13. Deep brain stimulation modulates synchrony within spatially and spectrally distinct resting state networks in Parkinson's disease.

    Science.gov (United States)

    Oswal, Ashwini; Beudel, Martijn; Zrinzo, Ludvic; Limousin, Patricia; Hariz, Marwan; Foltynie, Tom; Litvak, Vladimir; Brown, Peter

    2016-05-01

    Chronic dopamine depletion in Parkinson's disease leads to progressive motor and cognitive impairment, which is associated with the emergence of characteristic patterns of synchronous oscillatory activity within cortico-basal-ganglia circuits. Deep brain stimulation of the subthalamic nucleus is an effective treatment for Parkinson's disease, but its influence on synchronous activity in cortico-basal-ganglia loops remains to be fully characterized. Here, we demonstrate that deep brain stimulation selectively suppresses certain spatially and spectrally segregated resting state subthalamic nucleus-cortical networks. To this end we used a validated and novel approach for performing simultaneous recordings of the subthalamic nucleus and cortex using magnetoencephalography (during concurrent subthalamic nucleus deep brain stimulation). Our results highlight that clinically effective subthalamic nucleus deep brain stimulation suppresses synchrony locally within the subthalamic nucleus in the low beta oscillatory range and furthermore that the degree of this suppression correlates with clinical motor improvement. Moreover, deep brain stimulation relatively selectively suppressed synchronization of activity between the subthalamic nucleus and mesial premotor regions, including the supplementary motor areas. These mesial premotor regions were predominantly coupled to the subthalamic nucleus in the high beta frequency range, but the degree of deep brain stimulation-associated suppression in their coupling to the subthalamic nucleus was not found to correlate with motor improvement. Beta band coupling between the subthalamic nucleus and lateral motor areas was not influenced by deep brain stimulation. Motor cortical coupling with subthalamic nucleus predominantly involved driving of the subthalamic nucleus, with those drives in the higher beta frequency band having much shorter net delays to subthalamic nucleus than those in the lower beta band. These observations raise the

  14. Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

    Science.gov (United States)

    Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

    2015-05-01

    The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

  15. Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.

    Science.gov (United States)

    Cao, Lei; Ju, Zhengyu; Li, Jie; Jian, Rongjun; Jiang, Changjun

    2015-09-30

    Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum. In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition. The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants. These conclusions demonstrated that our proposed method was promising for a high-speed online BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Resting state fMRI entropy probes complexity of brain activity in adults with ADHD.

    Science.gov (United States)

    Sokunbi, Moses O; Fung, Wilson; Sawlani, Vijay; Choppin, Sabine; Linden, David E J; Thome, Johannes

    2013-12-30

    In patients with attention deficit hyperactivity disorder (ADHD), quantitative neuroimaging techniques have revealed abnormalities in various brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Nonlinear signal processing techniques such as sample entropy have been used to probe the regularity of brain magnetoencephalography signals in patients with ADHD. In the present study, we extend this technique to analyse the complex output patterns of the 4 dimensional resting state functional magnetic resonance imaging signals in adult patients with ADHD. After adjusting for the effect of age, we found whole brain entropy differences (P=0.002) between groups and negative correlation (r=-0.45) between symptom scores and mean whole brain entropy values, indicating lower complexity in patients. In the regional analysis, patients showed reduced entropy in frontal and occipital regions bilaterally and a significant negative correlation between the symptom scores and the entropy maps at a family-wise error corrected cluster level of Pentropy is a useful tool in revealing abnormalities in the brain dynamics of patients with psychiatric disorders. © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Alterations in regional homogeneity of resting-state brain activity in internet gaming addicts

    Directory of Open Access Journals (Sweden)

    Dong Guangheng

    2012-08-01

    Full Text Available Abstract Backgrounds Internet gaming addiction (IGA, as a subtype of internet addiction disorder, is rapidly becoming a prevalent mental health concern around the world. The neurobiological underpinnings of IGA should be studied to unravel the potential heterogeneity of IGA. This study investigated the brain functions in IGA patients with resting-state fMRI. Methods Fifteen IGA subjects and fourteen healthy controls participated in this study. Regional homogeneity (ReHo measures were used to detect the abnormal functional integrations. Results Comparing to the healthy controls, IGA subjects show enhanced ReHo in brainstem, inferior parietal lobule, left posterior cerebellum, and left middle frontal gyrus. All of these regions are thought related with sensory-motor coordination. In addition, IGA subjects show decreased ReHo in temporal, occipital and parietal brain regions. These regions are thought responsible for visual and auditory functions. Conclusions Our results suggest that long-time online game playing enhanced the brain synchronization in sensory-motor coordination related brain regions and decreased the excitability in visual and auditory related brain regions.

  18. Conscious access is linked to ongoing brain state: electrophysiological evidence from the attentional blink.

    Science.gov (United States)

    Pincham, Hannah L; Szucs, Dénes

    2012-10-01

    Neuroscience explanations of conscious access focus on neural events elicited by stimuli. In contrast, here, we used the attentional blink paradigm in combination with event-related brain potentials to examine whether the ongoing state of the brain before a stimulus can determine both conscious access and the poststimulus neural events associated with consciousness. Participants were required to detect 2 target letters from digit distractors while their brain activity was being recorded. Trials were classified based on whether the secondcritical target (T2) was detected. We found that T2-detection was predetermined by brain activity prior to the onset of the stimulation stream. Specifically, T2-detected trials were predicated by a frontocentral positive going deflection that started more than 200 ms before the stream began. Accurate T2 detection was also accompanied by enhanced poststimulus neural activity, as reflected by a larger P3b component. Furthermore, prestimulus and poststimulus markers of T2-detection were highly correlated with one another. We therefore argue that conscious experiences are shaped by potentially random fluctuations in neural activity. Overall, the results reveal that conscious access is underpinned by an important relationship involving predictive prestimulus neural activity and responsive poststimulus brain activity.

  19. Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment.

    Science.gov (United States)

    Yi, Li-Ye; Liang, Xia; Liu, Da-Ming; Sun, Bo; Ying, Sun; Yang, Dong-Bo; Li, Qing-Bin; Jiang, Chuan-Lu; Han, Ying

    2015-10-01

    Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (svMCI). However, whether and how these changes alter functional brain network organization remains largely unknown. We recruited 21 patients with svMCI and 26 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks. Compared with the HC individuals, the patients with svMCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the svMCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The svMCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the svMCI patients. Together, our results indicate that svMCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of svMCI. © 2015 John Wiley & Sons Ltd.

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

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2013-10-11

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

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

    Science.gov (United States)

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

    2018-03-21

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

  2. Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

    Science.gov (United States)

    Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

    2018-04-01

    Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

  3. Spinal Cord Injury Disrupts Resting-State Networks in the Human Brain.

    Science.gov (United States)

    Hawasli, Ammar H; Rutlin, Jerrel; Roland, Jarod L; Murphy, Rory K J; Song, Sheng-Kwei; Leuthardt, Eric C; Shimony, Joshua S; Ray, Wilson Z

    2018-03-15

    Despite 253,000 spinal cord injury (SCI) patients in the United States, little is known about how SCI affects brain networks. Spinal MRI provides only structural information with no insight into functional connectivity. Resting-state functional MRI (RS-fMRI) quantifies network connectivity through the identification of resting-state networks (RSNs) and allows detection of functionally relevant changes during disease. Given the robust network of spinal cord afferents to the brain, we hypothesized that SCI produces meaningful changes in brain RSNs. RS-fMRIs and functional assessments were performed on 10 SCI subjects. Blood oxygen-dependent RS-fMRI sequences were acquired. Seed-based correlation mapping was performed using five RSNs: default-mode (DMN), dorsal-attention (DAN), salience (SAL), control (CON), and somatomotor (SMN). RSNs were compared with normal control subjects using false-discovery rate-corrected two way t tests. SCI reduced brain network connectivity within the SAL, SMN, and DMN and disrupted anti-correlated connectivity between CON and SMN. When divided into separate cohorts, complete but not incomplete SCI disrupted connectivity within SAL, DAN, SMN and DMN and between CON and SMN. Finally, connectivity changed over time after SCI: the primary motor cortex decreased connectivity with the primary somatosensory cortex, the visual cortex decreased connectivity with the primary motor cortex, and the visual cortex decreased connectivity with the sensory parietal cortex. These unique findings demonstrate the functional network plasticity that occurs in the brain as a result of injury to the spinal cord. Connectivity changes after SCI may serve as biomarkers to predict functional recovery following an SCI and guide future therapy.

  4. Altered spontaneous brain activity in patients with hemifacial spasm: a resting-state functional MRI study.

    Directory of Open Access Journals (Sweden)

    Ye Tu

    Full Text Available Resting-state functional magnetic resonance imaging (fMRI has been used to detect the alterations of spontaneous neuronal activity in various neurological and neuropsychiatric diseases, but rarely in hemifacial spasm (HFS, a nervous system disorder. We used resting-state fMRI with regional homogeneity (ReHo analysis to investigate changes in spontaneous brain activity of patients with HFS and to determine the relationship of these functional changes with clinical features. Thirty patients with HFS and 33 age-, sex-, and education-matched healthy controls were included in this study. Compared with controls, HFS patients had significantly decreased ReHo values in left middle frontal gyrus (MFG, left medial cingulate cortex (MCC, left lingual gyrus, right superior temporal gyrus (STG and right precuneus; and increased ReHo values in left precentral gyrus, anterior cingulate cortex (ACC, right brainstem, and right cerebellum. Furthermore, the mean ReHo value in brainstem showed a positive correlation with the spasm severity (r = 0.404, p = 0.027, and the mean ReHo value in MFG was inversely related with spasm severity in HFS group (r = -0.398, p = 0.028. This study reveals that HFS is associated with abnormal spontaneous brain activity in brain regions most involved in motor control and blinking movement. The disturbances of spontaneous brain activity reflected by ReHo measurements may provide insights into the neurological pathophysiology of HFS.

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Science.gov (United States)

    Amanpour, Behzad; Erfanian, Abbas

    2013-01-01

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

  8. Chimera states in brain networks: Empirical neural vs. modular fractal connectivity

    Science.gov (United States)

    Chouzouris, Teresa; Omelchenko, Iryna; Zakharova, Anna; Hlinka, Jaroslav; Jiruska, Premysl; Schöll, Eckehard

    2018-04-01

    Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

  9. Towards Development of a 3-State Self-Paced Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Ali Bashashati

    2007-01-01

    the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement. It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1% in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1% in the context of a 2-state self-paced BCI.

  10. Brain cancer incidence trends in relation to cellular telephone use in the United States

    OpenAIRE

    Inskip, Peter D.; Hoover, Robert N.; Devesa, Susan S.

    2010-01-01

    The use of cellular telephones has grown explosively during the past two decades, and there are now more than 279 million wireless subscribers in the United States. If cellular phone use causes brain cancer, as some suggest, the potential public health implications could be considerable. One might expect the effects of such a prevalent exposure to be reflected in general population incidence rates, unless the induction period is very long or confined to very long-term users. To address this i...

  11. Love-related changes in the brain: a resting-state functional magnetic resonance imaging study

    OpenAIRE

    Song, Hongwen; Zou, Zhiling; Kou, Juan; Liu, Yang; Yang, Lizhuang; Zilverstand, Anna; d’Oleire Uquillas, Federico; Zhang, Xiaochu

    2015-01-01

    Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Functional magnetic resonance imaging (fMRI) studies have found activation increases in brain regions involved in the processing of reward, motivation and emotion regulation, when romantic lovers view photographs of their partners. However, not much is known about whether romantic love affects the brain’s functional architecture during rest. In the present stu...

  12. Resting-state brain activity in the motor cortex reflects task-induced activity: A multi-voxel pattern analysis.

    Science.gov (United States)

    Kusano, Toshiki; Kurashige, Hiroki; Nambu, Isao; Moriguchi, Yoshiya; Hanakawa, Takashi; Wada, Yasuhiro; Osu, Rieko

    2015-08-01

    It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

  13. Classification of economic and legal remedies for the realization of the state regional policy

    Directory of Open Access Journals (Sweden)

    Лілія Сергіївна Тертишна

    2017-09-01

    Full Text Available The article is devoted to the study of the economic and legal mechanism for ensuring the functioning of the state regional policy in Ukraine. The existing legal and regulatory framework on the availability in it necessary and sufficient conceptual apparatus, a list of economic and legal items or the effective implementation of the state regional policy is analyzed. The main defects of the legislation regulating the state regional policy are defined. State regional policy is a fragment of social and economic policies in the scale of its specific administrative units – regions, and in content – by the means of a synthesis of economic and legal methods aimed at solving problems of state investment, innovation, foreign trade, sectoral policies, where the economic and legal means are regarding the economic part of this policy. It was substantiated that the efficiency of national regional policy depends on how successfully the state can combine and integrate all the directions and types of policies and especially their economic and legal means. It is established, that the general means of state regulation are defined in the Commercial Code of Ukraine as a key. However, there is a need to identify and consolidate the entire system of economic and legal means of state regulation that can be applied in the implementation of the state regional policy and to determine the specific implementation mechanisms. It was found that for all state regional policy objects the universal are such economic law enforcement actions as a public-private partnership and special modes of management.

  14. Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

    Directory of Open Access Journals (Sweden)

    Yan-li Yang

    2015-01-01

    Full Text Available It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

  15. Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

    Science.gov (United States)

    Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

    2017-01-01

    Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

  16. Extraversion and neuroticism relate to topological properties of resting-state brain networks.

    Science.gov (United States)

    Gao, Qing; Xu, Qiang; Duan, Xujun; Liao, Wei; Ding, Jurong; Zhang, Zhiqiang; Li, Yuan; Lu, Guangming; Chen, Huafu

    2013-01-01

    With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here, we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI) data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e., betweenness centrality (BC), was positively associated with neuroticism scores in the right precentral gyrus (PreCG), right caudate nucleus, right olfactory cortex, and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus (MTG), indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

  17. Extraversion and Neuroticism relate to topological properties of resting-state brain networks

    Directory of Open Access Journals (Sweden)

    Qing eGao

    2013-06-01

    Full Text Available With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e. betweenness centrality (BC, was positively associated with neuroticism scores in the right precentral gyrus, right caudate nucleus, right olfactory cortex and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus, indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

  18. At risk of being risky: The relationship between “brain age” under emotional states and risk preference

    OpenAIRE

    Marc D. Rudolph; Oscar Miranda-Domínguez; Alexandra O. Cohen; Kaitlyn Breiner; Laurence Steinberg; Richard J. Bonnie; Elizabeth S. Scott; Kim Taylor-Thompson; Jason Chein; Karla C. Fettich; Jennifer A. Richeson; Danielle V. Dellarco; Adriana Galván; B.J. Casey; Damien A. Fair

    2017-01-01

    Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N = 212; 10–25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the “brain age” of a ...

  19. Assessment and classification of hydromorphological state of the Breń River

    Directory of Open Access Journals (Sweden)

    Borek Łukasz

    2016-09-01

    Full Text Available The paper presents the classification of the hydromorphological condition of the Breń River according to the River Habitat Survey (RHS. The research of the hydromorphological assessment of the Breń River, which is a right-bank tributary of the Vistula River and almost entirely flows through the area of the Dąbrowa Tarnowska district was conducted in June 2015. The research sites were situated on the border of the Tarnów Plateau and the Vistula Lowland. The Breń River in these sections flows through rural areas used for agricultural purposes with low-density housing. The analysis of qualitative parameters describing the morphological characteristics were based on two synthetic indices of stream quality: Habitat Quality Assesment (HQA and Habitat Modification Score (HMS. The calculated numerical values of the two indices proved that the sections of the Breń River correspond with the third and fifth class, which means a moderate (III and very bad (V hydromorphological condition.

  20. Classification of cyclic initial states and geometric phase for the spin-j system

    Energy Technology Data Exchange (ETDEWEB)

    Skrynnikov, N.R.; Zhou, J.; Sanctuary, B.C. [Dept. of Chem., McGill Univ., Montreal, PQ (Canada)

    1994-09-21

    Quantum states which evolve cyclically in their projective Hilbert space give rise to a geometric (or Aharonov-Anandan) phase. An aspect of primary interest is stable cyclic behaviour as realized under a periodic Hamiltonian. The problem has been handled by use of time-dependent transformations treated along the lines of Floquet's theory as well as in terms of exponential operators with a goal to examine the variety of initial states exhibiting cyclic behaviour. A particular case of special cyclic initial states is described which is shown to be important for nuclear magnetic resonance experiments aimed at the study of the effects of the geometric phase. An example of arbitrary spin j in a precessing magnetic field and spin j=1 subject to both axially symmetric quadrupolar interaction and a precessing magnetic field are presented. The invariant (Kobe's) geometric phase is calculated for special cyclic states. (author)

  1. Regional homogeneity of resting-state brain abnormalities in bipolar and unipolar depression.

    Science.gov (United States)

    Liu, Chun-Hong; Ma, Xin; Wu, Xia; Zhang, Yu; Zhou, Fu-Chun; Li, Feng; Tie, Chang-Le; Dong, Jie; Wang, Yong-Jun; Yang, Zhi; Wang, Chuan-Yue

    2013-03-05

    Bipolar disorder patients experiencing a depressive episode (BD-dep) without an observed history of mania are often misdiagnosed and are consequently treated as having unipolar depression (UD), leading to inadequate treatment and poor outcomes. An essential solution to this problem is to identify objective biological markers that distinguish BD-dep and UD patients at an early stage. However, studies directly comparing the brain dysfunctions associated with BD-dep and UD are rare. More importantly, the specificity of the differences in brain activity between these mental disorders has not been examined. With whole-brain regional homogeneity analysis and region-of-interest (ROI) based receiver operating characteristic (ROC) analysis, we aimed to compare the resting-state brain activity of BD-dep and UD patients. Furthermore, we examined the specific differences and whether these differences were attributed to the brain abnormality caused by BD-dep, UD, or both. Twenty-one bipolar and 21 unipolar depressed patients, as well as 26 healthy subjects matched for gender, age, and educational levels, participated in the study. We compared the differences in the regional homogeneity (ReHo) of the BD-dep and UD groups and further identified their pathophysiological abnormality. In the brain regions showing a difference between the BD-dep and UD groups, we further conducted receptive operation characteristic (ROC) analyses to confirm the effectiveness of the identified difference in classifying the patients. We observed ReHo differences between the BD-dep and UD groups in the right ventrolateral middle frontal gyrus, right dorsal anterior insular, right ventral anterior insular, right cerebellum posterior gyrus, right posterior cingulate cortex, right parahippocampal gyrus, and left cerebellum anterior gyrus. Further ROI comparisons and ROC analysis on these ROIs showed that the right parahippocampal gyrus reflected abnormality specific to the BD-dep group, while the right

  2. Cortical neurons and networks are dormant but fully responsive during isoelectric brain state.

    Science.gov (United States)

    Altwegg-Boussac, Tristan; Schramm, Adrien E; Ballestero, Jimena; Grosselin, Fanny; Chavez, Mario; Lecas, Sarah; Baulac, Michel; Naccache, Lionel; Demeret, Sophie; Navarro, Vincent; Mahon, Séverine; Charpier, Stéphane

    2017-09-01

    A continuous isoelectric electroencephalogram reflects an interruption of endogenously-generated activity in cortical networks and systematically results in a complete dissolution of conscious processes. This electro-cerebral inactivity occurs during various brain disorders, including hypothermia, drug intoxication, long-lasting anoxia and brain trauma. It can also be induced in a therapeutic context, following the administration of high doses of barbiturate-derived compounds, to interrupt a hyper-refractory status epilepticus. Although altered sensory responses can be occasionally observed on an isoelectric electroencephalogram, the electrical membrane properties and synaptic responses of individual neurons during this cerebral state remain largely unknown. The aim of the present study was to characterize the intracellular correlates of a barbiturate-induced isoelectric electroencephalogram and to analyse the sensory-evoked synaptic responses that can emerge from a brain deprived of spontaneous electrical activity. We first examined the sensory responsiveness from patients suffering from intractable status epilepticus and treated by administration of thiopental. Multimodal sensory responses could be evoked on the flat electroencephalogram, including visually-evoked potentials that were significantly amplified and delayed, with a high trial-to-trial reproducibility compared to awake healthy subjects. Using an analogous pharmacological procedure to induce prolonged electro-cerebral inactivity in the rat, we could describe its cortical and subcortical intracellular counterparts. Neocortical, hippocampal and thalamo-cortical neurons were all silent during the isoelectric state and displayed a flat membrane potential significantly hyperpolarized compared with spontaneously active control states. Nonetheless, all recorded neurons could fire action potentials in response to intracellularly injected depolarizing current pulses and their specific intrinsic

  3. The Impact of Age and Cognitive Reserve on Resting-State Brain Connectivity

    Directory of Open Access Journals (Sweden)

    Jessica I. Fleck

    2017-12-01

    Full Text Available Cognitive reserve (CR is a protective mechanism that supports sustained cognitive function following damage to the physical brain associated with age, injury, or disease. The goal of the research was to identify relationships between age, CR, and brain connectivity. A sample of 90 cognitively normal adults, ages 45–64 years, had their resting-state brain activity recorded with electroencephalography (EEG and completed a series of memory and executive function assessments. CR was estimated using years of education and verbal IQ scores. Participants were divided into younger and older age groups and low- and high-CR groups. We observed greater left- than right-hemisphere coherence in younger participants, and greater right- than left-hemisphere coherence in older participants. In addition, greater coherence was observed under eyes-closed than eyes-open recording conditions for both low-CR and high-CR participants, with a more substantial difference between recording conditions in individuals high in CR regardless of age. Finally, younger participants low in CR exhibited greater mean coherence than younger participants high in CR, whereas the opposite pattern was observed in older participants, with greater coherence in older participants high in CR. Together, these findings suggest the possibility of a shift in the relationship between CR and brain connectivity during aging.

  4. Regional homogeneity of the resting-state brain activity correlates with individual intelligence.

    Science.gov (United States)

    Wang, Leiqiong; Song, Ming; Jiang, Tianzi; Zhang, Yunting; Yu, Chunshui

    2011-01-25

    Resting-state functional magnetic resonance imaging has confirmed that the strengths of the long distance functional connectivity between different brain areas are correlated with individual differences in intelligence. However, the association between the local connectivity within a specific brain region and intelligence during rest remains largely unknown. The aim of this study is to investigate the relationship between local connectivity and intelligence. Fifty-nine right-handed healthy adults participated in the study. The regional homogeneity (ReHo) was used to assess the strength of local connectivity. The associations between ReHo and full-scale intelligence quotient (FSIQ) scores were studied in a voxel-wise manner using partial correlation analysis controlling for age and sex. We found that the FSIQ scores were positively correlated with the ReHo values of the bilateral inferior parietal lobules, middle frontal, parahippocampal and inferior temporal gyri, the right thalamus, superior frontal and fusiform gyri, and the left superior parietal lobule. The main findings are consistent with the parieto-frontal integration theory (P-FIT) of intelligence, supporting the view that general intelligence involves multiple brain regions throughout the brain. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  5. Brain physiological state evaluated by real-time multiparametric tissue spectroscopy in vivo

    Science.gov (United States)

    Mayevsky, Avraham; Barbiro-Michaely, Efrat; Kutai-Asis, Hofit; Deutsch, Assaf; Jaronkin, Alex

    2004-07-01

    The significance of normal mitochondrial function in cellular energy homeostasis as well as its involvement in acute and chronic neurodegenerative disease was reviewed recently (Nicholls & Budd. Physiol Rev. 80: 315-360, 2000). Nevertheless, monitoring of mitochondrial function in vivo and real time mode was not used by many investigators and is very rare in clinical practice. The main principle tool available for the evaluation of mitochondrial function is the monitoring of NADH fluorescence. In order to interpret correctly the changes in NADH redox state in vivo, it is necessary to correlate this signal to other parameters, reflecting O2 supply to the brain. Therefore, we have developed and applied a multiparametric optical monitoring system, by which microcirculatory blood flow and hemoglobin oxygenation is measured, together with mitochondrial NADH fluorescence. Since the calibration of these signals is not in absolute units, the simultaneous monitoring provide a practical tool for the interpretation of brain functional state under various pathophysiological conditions. The monitoring system combines a time-sharing fluorometer-reflectometer for the measurement of NADH fluorescence and hemoglobin oxygenation as well as a laser Doppler flowmeter for the recording of microcirculatory blood flow. A combined fiber optic probe was located on the surface of the brain using a skull cemented cannula. Rats and gerbils were exposed to anoxia, ischemia and spreading depression and the functional state of the brain was evaluated. The results showed a clear correlation between O2 supply/demand as well as, energy balance under the various pathophysiological conditions. This monitoring approach could be adapted to clinical monitoring of tissue vitality.

  6. Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

    Directory of Open Access Journals (Sweden)

    Chandler R. L. Mongerson

    2017-08-01

    Full Text Available Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1 present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA, such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2 review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3 discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

  7. Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality.

    Science.gov (United States)

    Mongerson, Chandler R L; Jennings, Russell W; Borsook, David; Becerra, Lino; Bajic, Dusica

    2017-01-01

    Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

  8. Disrupted resting-state functional architecture of the brain after 45-day simulated microgravity

    Science.gov (United States)

    Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Liang, Zhu-Yuan; Chen, Xiao-Ping; Zheng, Dang; Tan, Cheng; Tian, Zhi-Qiang; Wang, Chun-Hui; Bai, Yan-Qiang; Chen, Shan-Guang; Li, Shu

    2014-01-01

    Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI) to study whether the functional architecture of the brain is altered after 45 days of −6° head-down tilt (HDT) bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of −6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC), to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC) analysis. We found decreased DC in two regions, the left anterior insula (aINS) and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies), in the male volunteers after 45 days of −6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS–MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function, and central neural activity. PMID:24926242

  9. Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity

    Directory of Open Access Journals (Sweden)

    Yuan eZhou

    2014-06-01

    Full Text Available Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI to study whether the functional architecture of the brain is altered after 45 days of -6° head-down tilt (HDT bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of -6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC, to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC analysis. We found decreased DC in two regions, the left anterior insula (aINS and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies, in the male volunteers after 45 days of -6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS–MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function and central neural activity.

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

    Science.gov (United States)

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

    2016-01-01

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

  11. Formulations development for improving the classification of ceramic tile manufactured in the Sergipe state - part one: mineralogical characterization

    International Nuclear Information System (INIS)

    Goes, J.R.; Azevedo, T.F.; Barreto, L.S.

    2011-01-01

    The ceramic tiles manufactured in Sergipe State are classified in Absorption 'BIIb' Group. Studies have been developed to obtain the classification 'BIIa' Group. This first part is about the mineralogical characterization of raw materials used for ceramics tiles, collected for three different fields. The mineralogical characterization was made with: X-ray Diffraction, Infrared, Thermogravimetric and Differential Thermal Analysis, and was also obtained clays plasticity indices. The samples were heated up to 500 deg C, 900° C and 1100° C. Clays were classified as highly plastics and moderately plastics with a large number of grain with size order smaller than 0,074 mm. The main minerals identified were: kaolinite, illite, montmorillonite, quartz, feldspar and calcite. Two of the three studied fields had high calcite content. The Calcite retards the sintering process causing higher porosity to the ceramic tiles. (author)

  12. Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

  13. Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

  14. Quantitative rates of brain glucose metabolism distinguish minimally conscious from vegetative state patients.

    Science.gov (United States)

    Stender, Johan; Kupers, Ron; Rodell, Anders; Thibaut, Aurore; Chatelle, Camille; Bruno, Marie-Aurélie; Gejl, Michael; Bernard, Claire; Hustinx, Roland; Laureys, Steven; Gjedde, Albert

    2015-01-01

    The differentiation of the vegetative or unresponsive wakefulness syndrome (VS/UWS) from the minimally conscious state (MCS) is an important clinical issue. The cerebral metabolic rate of glucose (CMRglc) declines when consciousness is lost, and may reveal the residual cognitive function of these patients. However, no quantitative comparisons of cerebral glucose metabolism in VS/UWS and MCS have yet been reported. We calculated the regional and whole-brain CMRglc of 41 patients in the states of VS/UWS (n=14), MCS (n=21) or emergence from MCS (EMCS, n=6), and healthy volunteers (n=29). Global cortical CMRglc in VS/UWS and MCS averaged 42% and 55% of normal, respectively. Differences between VS/UWS and MCS were most pronounced in the frontoparietal cortex, at 42% and 60% of normal. In brainstem and thalamus, metabolism declined equally in the two conditions. In EMCS, metabolic rates were indistinguishable from those of MCS. Ordinal logistic regression predicted that patients are likely to emerge into MCS at CMRglc above 45% of normal. Receiver-operating characteristics showed that patients in MCS and VS/UWS can be differentiated with 82% accuracy, based on cortical metabolism. Together these results reveal a significant correlation between whole-brain energy metabolism and level of consciousness, suggesting that quantitative values of CMRglc reveal consciousness in severely brain-injured patients.

  15. Central thalamic deep brain stimulation for support of forebrain arousal regulation in the minimally conscious state.

    Science.gov (United States)

    Schiff, Nicholas D

    2013-01-01

    This chapter considers the use of central thalamic deep brain stimulation (CT/DBS) to support arousal regulation mechanisms in the minimally conscious state (MCS). CT/DBS for selected patients in a MCS is first placed in the historical context of prior efforts to use thalamic electrical brain stimulation to treat the unconscious clinical conditions of coma and vegetative state. These previous studies and a proof of concept result from a single-subject study of a patient in a MCS are reviewed against the background of new population data providing benchmarks of the natural history of vegetative and MCSs. The conceptual foundations for CT/DBS in selected patients in a MCS are then presented with consideration of both circuit and cellular mechanisms underlying recovery of consciousness identified from empirical studies. Directions for developing future generalizable criteria for CT/DBS that focus on the integrity of necessary brain systems and behavioral profiles in patients in a MCS that may optimally response to support of arousal regulation mechanisms are proposed. © 2013 Elsevier B.V. All rights reserved.

  16. [Brain activitivation of euthymic patients with Type I bipolar disorder in resting state Default Mode Network].

    Science.gov (United States)

    Vargas, Cristian; Pineda, Julián; Calvo, Víctor; López-Jaramillo, Carlos

    2014-01-01

    As there are still doubts about brain connectivity in type I bipolar disorder (BID), resting-state functional magnetic resonance imaging (RS-fMRI) studies are necessary during euthymia for a better control of confounding factors. To evaluate the differences in brain activation between euthymic BID patients and control subjects using resting state- functional-magnetic resonance imaging (RS-fMRI), and to identify the lithium effect in these activations. A cross-sectional study was conducted on 21 BID patients (10 receiving lithium only, and 11 non-medicated) and 12 healthy control subjects, using RS fMRI and independent component analysis (ICA). Increased activation was found in the right hippocampus (P=.049) and posterior cingulate (P=.040) within the Default Mode Network (DMN) when BID and control group were compared. No statistically significant differences were identified between BID on lithium only therapy and non-medicated BID patients. The results suggest that there are changes in brain activation and connectivity in BID even during euthymic phase and mainly within the DMN network, which could be relevant in affect regulation. Copyright © 2013 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

  17. An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging

    OpenAIRE

    Nielsen, Jared A.; Zielinski, Brandon A.; Ferguson, Michael A.; Lainhart, Janet E.; Anderson, Jeffrey S.

    2013-01-01

    Lateralized brain regions subserve functions such as language and visuospatial processing. It has been conjectured that individuals may be left-brain dominant or right-brain dominant based on personality and cognitive style, but neuroimaging data has not provided clear evidence whether such phenotypic differences in the strength of left-dominant or right-dominant networks exist. We evaluated whether strongly lateralized connections covaried within the same individuals. Data were analyzed from...

  18. 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

  19. Changes in Brain Resting-state Functional Connectivity Associated with Peripheral Nerve Block: A Pilot Study.

    Science.gov (United States)

    Melton, M Stephen; Browndyke, Jeffrey N; Harshbarger, Todd B; Madden, David J; Nielsen, Karen C; Klein, Stephen M

    2016-08-01

    Limited information exists on the effects of temporary functional deafferentation (TFD) on brain activity after peripheral nerve block (PNB) in healthy humans. Increasingly, resting-state functional connectivity (RSFC) is being used to study brain activity and organization. The purpose of this study was to test the hypothesis that TFD through PNB will influence changes in RSFC plasticity in central sensorimotor functional brain networks in healthy human participants. The authors achieved TFD using a supraclavicular PNB model with 10 healthy human participants undergoing functional connectivity magnetic resonance imaging before PNB, during active PNB, and during PNB recovery. RSFC differences among study conditions were determined by multiple-comparison-corrected (false discovery rate-corrected P value less than 0.05) random-effects, between-condition, and seed-to-voxel analyses using the left and right manual motor regions. The results of this pilot study demonstrated disruption of interhemispheric left-to-right manual motor region RSFC (e.g., mean Fisher-transformed z [effect size] at pre-PNB 1.05 vs. 0.55 during PNB) but preservation of intrahemispheric RSFC of these regions during PNB. Additionally, there was increased RSFC between the left motor region of interest (PNB-affected area) and bilateral higher order visual cortex regions after clinical PNB resolution (e.g., Fisher z between left motor region of interest and right and left lingual gyrus regions during PNB, -0.1 and -0.6 vs. 0.22 and 0.18 after PNB resolution, respectively). This pilot study provides evidence that PNB has features consistent with other models of deafferentation, making it a potentially useful approach to investigate brain plasticity. The findings provide insight into RSFC of sensorimotor functional brain networks during PNB and PNB recovery and support modulation of the sensory-motor integration feedback loop as a mechanism for explaining the behavioral correlates of peripherally

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

    Science.gov (United States)

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

    2017-10-01

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

  1. A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data

    KAUST Repository

    Kang, Hakmook; Ombao, Hernando; Fonnesbeck, Christopher; Ding, Zhaohua; Morgan, Victoria L.

    2017-01-01

    DTI that could potentially enhance estimation of resting-state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatiotemporal model that incorporates structural connectivity (SC

  2. Classification methods of sonar signal. Part One: State of the art

    International Nuclear Information System (INIS)

    Colao, F.; Dell'Erba, R.

    2009-01-01

    STSS500 camera is an innovative instrument dedicated to submarine robot planned in order to increase human operator perception; the objectives include the possibility to operate in hostile atmospheres, the overcoming of the visible and/or infrared vision limits in turbid waters. STSS500 operates lighting by sound the scene with an array of acoustic rays with known electric characteristics. Later it listens to the reception antenna in order to record the reflected and/or diffuse acoustic echoes from the present obstacles in the scene. The camera is stereoscopic. An opportune analysis of signals receipted from the reception antenna renders the construction of synthetic data; the data fill in voxel a portion of the space place in front of the STS500. With simplifications of the physical phenomena the analysis of geometry of the lighted portion and the acoustic echoes is possible the localizations in the space and the physical dimensions of the voxel, and therefore to obtain the trend in the time of the reflected and/or diffused acoustic intensity from every given volume element. The main problem, that up to the present it is little studied, is dealing with the elaboration of the return echoes turns with the aim to extract those features related to the physical characteristics of the target, and whose visualization can help the process of understanding of the scene resumption from STS500. This the aspect is here inquired in detail, by means of the deepening and the study of the technical notes in literature that regard the identification and automatic classification and, the possible application to the case of acoustic data with typical characteristics dates from the camera that is to our disposition. Topic in this job is the use of well known techniques used in optical elaboration of satellite imaging, (methods PCA, SAM, SCM, etc). In this case we assuming that the specificities of the means and of the interaction has formally similar scale laws as in the event

  3. Cognitive and default-mode resting state networks: do male and female brains "rest" differently?

    Science.gov (United States)

    Weissman-Fogel, Irit; Moayedi, Massieh; Taylor, Keri S; Pope, Geoff; Davis, Karen D

    2010-11-01

    Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally-correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task-free state. Both task-related and task-free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task-free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty-nine healthy subjects (26 females, 23 males) underwent a 5-min task-free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best-fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self-organizing group-level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. © 2010 Wiley-Liss, Inc.

  4. Brain cancer incidence trends in relation to cellular telephone use in the United States.

    Science.gov (United States)

    Inskip, Peter D; Hoover, Robert N; Devesa, Susan S

    2010-11-01

    The use of cellular telephones has grown explosively during the past two decades, and there are now more than 279 million wireless subscribers in the United States. If cellular phone use causes brain cancer, as some suggest, the potential public health implications could be considerable. One might expect the effects of such a prevalent exposure to be reflected in general population incidence rates, unless the induction period is very long or confined to very long-term users. To address this issue, we examined temporal trends in brain cancer incidence rates in the United States, using data collected by the Surveillance, Epidemiology, and End Results (SEER) Program. Log-linear models were used to estimate the annual percent change in rates among whites. With the exception of the 20-29-year age group, the trends for 1992-2006 were downward or flat. Among those aged 20-29 years, there was a statistically significant increasing trend between 1992 and 2006 among females but not among males. The recent trend in 20-29-year-old women was driven by a rising incidence of frontal lobe cancers. No increases were apparent for temporal or parietal lobe cancers, or cancers of the cerebellum, which involve the parts of the brain that would be more highly exposed to radiofrequency radiation from cellular phones. Frontal lobe cancer rates also rose among 20-29-year-old males, but the increase began earlier than among females and before cell phone use was highly prevalent. Overall, these incidence data do not provide support to the view that cellular phone use causes brain cancer.

  5. Children and young adults in a vegetative or minimally conscious state after brain injury. Diagnosis, rehabilitation and outcome.

    NARCIS (Netherlands)

    Eilander, H.J.

    2008-01-01

    Severe brain injury can result in long lasting loss of consciousness. After recovering from a comatose state, some patients move over into a vegetative state that remains for weeks, months or even years. The presence of patients in a prolonged unconscious state is demanding for families, as well as

  6. Selection and Classification of United States Military Officers: A fifty-Year Bibliography (1937-1986)

    Science.gov (United States)

    1989-03-01

    orientation in Naval aviation cadets. Journal of Educational Psychology , 45, 91-109. 908 Creelman , J.A. (1954). An analysis of the physical fitness index...International Index to Periodicals. (e) Military Testing Association Proceedings, and (f) Psychological Abstracts. The research organizations of the armed...Texas, can trace its beginning to a number of Psychological Research Units of the Medical Division of the United States Army Air Corps. Some of the

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

    Science.gov (United States)

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

    2017-05-01

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

  8. An insect-inspired bionic sensor for tactile localisation and material classification with state-dependent modulation

    Directory of Open Access Journals (Sweden)

    Luca ePatanè

    2012-08-01

    Full Text Available Insects carry a pair of antennae on their head: multimodal sensory organs that serve a wide range of sensory-guided behaviours. During locomotion, antennae are involved in near-range orientation, for example in detecting, localising, probing and negotiating obstacles.Here we present a bionic, active tactile sensing system inspired by insect antennae. It comprises an actuated elastic rod equipped with a terminal acceleration sensor. The measurement principle is based on the analysis of damped harmonic oscillations registered upon contact with an object. The dominant frequency of the oscillation is extracted to determine the distance of the contact point along the probe, and basal angular encoders allow tactile localisation in a polar coordinate system. Finally, the damping behaviour of the registered signal is exploited to determine the most likely material.The tactile sensor is tested in four approaches with increasing neural plausibility: First, we show that peak extraction from the Fourier spectrum is sufficient for tactile localisation with position errors below 1%. Also, the damping property of the extracted frequency is used for material classification. Second, we show that the Fourier spectrum can be analysed by an Artificial Neural Network which can be trained to decode contact distance and to classify contact materials. Thirdly, we show how efficiency can be improved by band-pass filtering the Fourier spectrum by application of non-negative matrix factorisation. This reduces the input dimension by 95% while reducing classification performance by 8% only. Finally, we replace the FFT by an array of spiking neurons with gradually differing resonance properties, such that their spike rate is a function of the input frequency. We show that this network can be applied to detect tactile contact events of a wheeled robot, and how detrimental effects of robot velocity on antennal dynamics can be suppressed by state-dependent modulation of the

  9. Differential structural and resting state connectivity between insular subdivisions and other pain-related brain regions.

    Science.gov (United States)

    Wiech, K; Jbabdi, S; Lin, C S; Andersson, J; Tracey, I

    2014-10-01

    Functional neuroimaging studies suggest that the anterior, mid, and posterior division of the insula subserve different functions in the perception of pain. The anterior insula (AI) has predominantly been associated with cognitive-affective aspects of pain, while the mid and posterior divisions have been implicated in sensory-discriminative processing. We examined whether this functional segregation is paralleled by differences in (1) structural and (2) resting state connectivity and (3) in correlations with pain-relevant psychological traits. Analyses were restricted to the 3 insular subdivisions and other pain-related brain regions. Both type of analyses revealed largely overlapping results. The AI division was predominantly connected to the ventrolateral prefrontal cortex (structural and resting state connectivity) and orbitofrontal cortex (structural connectivity). In contrast, the posterior insula showed strong connections to the primary somatosensory cortex (SI; structural connectivity) and secondary somatosensory cortex (SII; structural and resting state connectivity). The mid insula displayed a hybrid connectivity pattern with strong connections with the ventrolateral prefrontal cortex, SII (structural and resting state connectivity) and SI (structural connectivity). Moreover, resting state connectivity revealed strong connectivity of all 3 subdivisions with the thalamus. On the behavioural level, AI structural connectivity was related to the individual degree of pain vigilance and awareness that showed a positive correlation with AI-amygdala connectivity and a negative correlation with AI-rostral anterior cingulate cortex connectivity. In sum, our findings show a differential structural and resting state connectivity for the anterior, mid, and posterior insula with other pain-relevant brain regions, which might at least partly explain their different functional profiles in pain processing. Copyright © 2014 The Authors. Published by Elsevier B.V. All

  10. Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez Aviles, Camila A. [ORNL; Rao, Nageswara S. [ORNL

    2018-01-01

    We consider the problem of inferring the operational state of a reactor facility by using measurements from a radiation sensor network, which is deployed around the facility’s ventilation stack. The radiation emissions from the stack decay with distance, and the corresponding measurements are inherently random with parameters determined by radiation intensity levels at the sensor locations. We fuse measurements from network sensors to estimate the intensity at the stack, and use this estimate in a one-sided Sequential Probability Ratio Test (SPRT) to infer the on/off state of the reactor facility. We demonstrate the superior performance of this method over conventional majority vote fusers and individual sensors using (i) test measurements from a network of NaI sensors, and (ii) emulated measurements using radioactive effluents collected at a reactor facility stack. We analytically quantify the performance improvements of individual sensors and their networks with adaptive thresholds over those with fixed ones, by using the packing number of the radiation intensity space.

  11. State of charge classification for lithium-ion batteries using impedance based features

    Directory of Open Access Journals (Sweden)

    M. P. Felder

    2017-09-01

    Full Text Available Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV to state of charge (SoC characteristic of lithium ion batteries, methods employed in applications with other cell chemistries cannot be adapted. Exploiting the higher significance of the impedance for state estimation for that chemistry, new impedance based features are proposed by this work. To evaluate the suitability of these features, simulations have been conducted using a simplified on-board power supply net as excitation source. The simulation outcome has been investigated regarding the cross correlation factor rxy and in a polynomial regression scenario. The results of the simulations show a best case error below 1 % SoC, which is 3 percentage points lower than using terminal voltage and impedance. When increasing the measurement uncertainty, the difference remains around 2 percent points.

  12. Territorial classification of indigenous Amazon: Contributions from the diversity to The Colombian state-nation

    International Nuclear Information System (INIS)

    Franky Calvo, Carlos Eduardo

    2001-01-01

    The author mention that In the last years the globalization, the internationalization of the economy, the post-modernity and their relationships to each other and with the local thing they have monopolized good part of the attention of the academic discussions, being the topic of the identity one of the privileged fields of analysis. In these discussions it has been emphasized in the crisis of the pattern, intellectual, cultural, political and economic, that consolidated the state-nation. A lot it has also been written on how the historical development of the capitalism needed this consolidation and that of some national markets, of some centers of power and of accumulation of the capital and of some dependent peripheries to expand. In this consolidation process, the state-nation compared The Nation with a dominant ethnic group or with the integration of a block of dominant classes of diverse ethnic groups that they concentrated its efforts on to erase or to tinge the ethnic differences of its respective country, in homogenizing its socio-cultural manifestations and in transforming some symbols common to the whole population

  13. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures

    Science.gov (United States)

    Robinson, P. A.; Rennie, C. J.; Rowe, D. L.

    2002-04-01

    Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

  14. Activated and deactivated functional brain areas in the Deqi state: A functional MRI study.

    Science.gov (United States)

    Huang, Yong; Zeng, Tongjun; Zhang, Guifeng; Li, Ganlong; Lu, Na; Lai, Xinsheng; Lu, Yangjia; Chen, Jiarong

    2012-10-25

    We compared the activities of functional regions of the brain in the Deqi versus non-Deqi state, as reported by physicians and subjects during acupuncture. Twelve healthy volunteers received sham and true needling at the Waiguan (TE5) acupoint. Real-time cerebral functional MRI showed that compared with non-sensation after sham needling, true needling activated Brodmann areas 3, 6, 8, 9, 10, 11, 13, 20, 21, 37, 39, 40, 43, and 47, the head of the caudate nucleus, the parahippocampal gyrus, thalamus and red nucleus. True needling also deactivated Brodmann areas 1, 2, 3, 4, 5, 6, 7, 9, 10, 18, 24, 31, 40 and 46.

  15. Examination of corticothalamic fiber projections in United States service members with mild traumatic brain injury

    Science.gov (United States)

    Rashid, Faisal M.; Dennis, Emily L.; Villalon-Reina, Julio E.; Jin, Yan; Lewis, Jeffrey D.; York, Gerald E.; Thompson, Paul M.; Tate, David F.

    2017-11-01

    Mild traumatic brain injury (mTBI) is characterized clinically by a closed head injury involving differential or rotational movement of the brain inside the skull. Over 3 million mTBIs occur annually in the United States alone. Many of the individuals who sustain an mTBI go on to recover fully, but around 20% experience persistent symptoms. These symptoms often last for many weeks to several months. The thalamus, a structure known to serve as a global networking or relay system for the rest of the brain, may play a critical role in neurorehabiliation and its integrity and connectivity after injury may also affect cognitive outcomes. To examine the thalamus, conventional tractography methods to map corticothalamic pathways with diffusion-weighted MRI (DWI) lead to sparse reconstructions that may contain false positive fibers that are anatomically inaccurate. Using a specialized method to zero in on corticothalamic pathways with greater robustness, we noninvasively examined corticothalamic fiber projections using DWI, in 68 service members. We found significantly lower fractional anisotropy (FA), a measure of white matter microstructural integrity, in pathways projecting to the left pre- and postcentral gyri - consistent with sensorimotor deficits often found post-mTBI. Mapping of neural circuitry in mTBI may help to further our understanding of mechanisms underlying recovery post-TBI.

  16. Resting State Brain Network Disturbances Related to Hypomania and Depression in Medication-Free Bipolar Disorder.

    Science.gov (United States)

    Spielberg, Jeffrey M; Beall, Erik B; Hulvershorn, Leslie A; Altinay, Murat; Karne, Harish; Anand, Amit

    2016-12-01

    Research on resting functional brain networks in bipolar disorder (BP) has been unable to differentiate between disturbances related to mania or depression, which is necessary to understand the mechanisms leading to each state. Past research has also been unable to elucidate the impact of BP-related network disturbances on the organizational properties of the brain (eg, communication efficiency). Thus, the present work sought to isolate network disturbances related to BP, fractionate these into components associated with manic and depressive symptoms, and characterize the impact of disturbances on network function. Graph theory was used to analyze resting functional magnetic resonance imaging data from 60 medication-free patients meeting the criteria for BP and either a current hypomanic (n=30) or depressed (n=30) episode and 30 closely age/sex-matched healthy controls. Correction for multiple comparisons was carried out. Compared with controls, BP patients evidenced hyperconnectivity in a network involving right amygdala. Fractionation revealed that (hypo)manic symptoms were associated with hyperconnectivity in an overlapping network and disruptions in the brain's 'small-world' network organization. Depressive symptoms predicted hyperconnectivity in a network involving orbitofrontal cortex along with a less resilient global network organization. Findings provide deeper insight into the differential pathophysiological processes associated with hypomania and depression, along with the particular impact these differential processes have on network function.

  17. Association between number of cell phone contracts and brain tumor incidence in nineteen U.S. States.

    Science.gov (United States)

    Lehrer, Steven; Green, Sheryl; Stock, Richard G

    2011-02-01

    Some concern has arisen about adverse health effects of cell phones, especially the possibility that the low power microwave-frequency signal transmitted by the antennas on handsets might cause brain tumors or accelerate the growth of subclinical tumors. We analyzed data from the Statistical Report: Primary Brain Tumors in the United States, 2000-2004 and 2007 cell phone subscription data from the Governing State and Local Sourcebook. There was a significant correlation between number of cell phone subscriptions and brain tumors in nineteen US states (r = 0.950, P cell phone subscriptions and brain tumors could be due solely to the fact that some states, such as New York, have much larger populations than other states, such as North Dakota, multiple linear regression was performed with number of brain tumors as the dependent variable, cell phone subscriptions, population, mean family income and mean age as independent variables. The effect of cell phone subscriptions was significant (P = 0.017), and independent of the effect of mean family income (P = 0.894), population (P = 0.003) and age (0.499). The very linear relationship between cell phone usage and brain tumor incidence is disturbing and certainly needs further epidemiological evaluation. In the meantime, it would be prudent to limit exposure to all sources of electro-magnetic radiation.

  18. Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

    Directory of Open Access Journals (Sweden)

    Bin Wang

    2017-11-01

    Full Text Available Alzheimer's disease (AD is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs, 33 early MCI (EMCI, 32 late MCI (LMCI, and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ scores and global Clinical Dementia Rating (CDR scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE

  19. Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

  20. Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

  1. Alcohol affects the brain's resting-state network in social drinkers.

    Directory of Open Access Journals (Sweden)

    Chrysa Lithari

    Full Text Available Acute alcohol intake is known to enhance inhibition through facilitation of GABA(A receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN. To test our hypothesis, electroencephalographic (EEG measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC on standardized Low Resolution Electromagnetic Tomography (sLORETA solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected in alpha, beta (eyes-open and theta bands (eyes-closed following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05. Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo. Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as

  2. Sex differences in associations of arginine vasopressin and oxytocin with resting-state functional brain connectivity.

    Science.gov (United States)

    Rubin, Leah H; Yao, Li; Keedy, Sarah K; Reilly, James L; Bishop, Jeffrey R; Carter, C Sue; Pournajafi-Nazarloo, Hossein; Drogos, Lauren L; Tamminga, Carol A; Pearlson, Godfrey D; Keshavan, Matcheri S; Clementz, Brett A; Hill, Scot K; Liao, Wei; Ji, Gong-Jun; Lui, Su; Sweeney, John A

    2017-01-02

    Oxytocin (OT) and arginine vasopressin (AVP) exert robust and sexually dimorphic influences on cognition and emotion. How these hormones regulate relevant functional brain systems is not well understood. OT and AVP serum concentrations were assayed in 60 healthy individuals (36 women). Brain functional networks assessed with resting-state functional magnetic resonance imaging (rs-fMRI) were constructed with graph theory-based approaches that characterize brain networks as connected nodes. Sex differences were demonstrated in rs-fMRI. Men showed higher nodal degree (connectedness) and efficiency (information propagation capacity) in left inferior frontal gyrus (IFG) and bilateral superior temporal gyrus (STG) and higher nodal degree in left rolandic operculum. Women showed higher nodal betweenness (being part of paths between nodes) in right putamen and left inferior parietal gyrus (IPG). Higher hormone levels were associated with less intrinsic connectivity. In men, higher AVP was associated with lower nodal degree and efficiency in left IFG (pars orbitalis) and left STG and less efficiency in left IFG (pars triangularis). In women, higher AVP was associated with lower betweenness in left IPG, and higher OT was associated with lower nodal degree in left IFG (pars orbitalis). Hormones differentially correlate with brain networks that are important for emotion processing and cognition in men and women. AVP in men and OT in women may regulate orbital frontal cortex connectivity, which is important in emotion processing. Hormone associations with STG and pars triangularis in men and parietal cortex in women may account for well-established sex differences in verbal and visuospatial abilities, respectively. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  3. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

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

  4. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Directory of Open Access Journals (Sweden)

    Cai Wingfield

    2017-09-01

    Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  5. Cesarean Delivery in the United States 2005 - 2014: A Population-Based Analysis Using the Robson Ten Group Classification System.

    Science.gov (United States)

    Hehir, Mark P; Ananth, Cande V; Siddiq, Zainab; Flood, Karen; Friedman, Alexander M; D'Alton, Mary E

    2018-04-12

    Cesarean delivery has increased steadily in the United States over recent decades with significant downstream health consequences. The World Health Organization has endorsed the Robson Ten Group Classification System (TGCS) as a global standard to facilitate analysis and comparison of cesarean delivery rates. Our objective was to apply the TGCS to a nationwide cohort in the United States over a 10-year period. This population-based analysis applied the TGCS to all births in the United States from 2005-2014, recorded in the 2003-revised birth certificate format. Over the study 10-year period 27,044,217 deliveries met inclusion criteria. Five parameters (parity including previous cesarean, gestational age, labor onset, fetal presentation and plurality), identifiable on presentation for delivery, were used to classify all women included into one of ten groups. The overall cesarean rate was 31.6%. Group 3 births (singleton, term, cephalic multiparas in spontaneous labor) were most common, while Group 5 births (those with a previous cesarean) accounted for the most cesarean deliveries increasing from 27% of all cesareans in 2005-06 to over 34% in 2013-14. Breech pregnancies (Groups 6 and 7) had cesarean rates above 90%. Primiparous and multiparous women who had a prelabor cesarean [Groups 2(b) and 4(b)] accounted for over one quarter of all cesarean deliveries. Women with a previous cesarean delivery represent an increasing proportion of cesarean deliveries. Use of the Robson criteria allows standardised comparisons of data and identifies clinical scenarios driving changes in cesarean rates. Hospitals and health organisations can use the TGCS to evaluate quality and processes associated with cesarean delivery. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Information-geometric measures estimate neural interactions during oscillatory brain states

    Directory of Open Access Journals (Sweden)

    Yimin eNie

    2014-02-01

    Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

  7. Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity.

    Science.gov (United States)

    Li, Zhengjun; Zang, Yu-Feng; Ding, Jianping; Wang, Ze

    2017-04-01

    The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

  8. Genetic Bio-Ancestry and Social Construction of Racial Classification in Social Surveys in the Contemporary United States

    Science.gov (United States)

    Guo, Guang; Fu, Yilan; Lee, Hedwig; Cai, Tianji; Harris, Kathleen Mullan; Li, Yi

    2013-01-01

    Self-reported race is generally considered the basis for racial classification in social surveys, including the U.S. census. Drawing on recent advances in human molecular genetics and social science perspectives of socially constructed race, our study takes into account both genetic bio-ancestry and social context in understanding racial classification. This article accomplishes two objectives. First, our research establishes geographic genetic bio-ancestry as a component of racial classification. Second, it shows how social forces trump biology in racial classification and/or how social context interacts with bio-ancestry in shaping racial classification. The findings were replicated in two racially and ethnically diverse data sets: the College Roommate Study (N = 2,065) and the National Longitudinal Study of Adolescent Health (N = 2,281). PMID:24019100

  9. Phylogeny and Classification of Yersinia pestis Through the Lens of Strains From the Plague Foci of Commonwealth of Independent States

    Directory of Open Access Journals (Sweden)

    Vladimir V. Kutyrev

    2018-05-01

    Full Text Available The established phylogeny of the etiological agent of plague, Yersinia pestis, is not perfect, as it does not take into account the strains from numerous natural foci of Commonwealth of Independent States (CIS. We have carried out PCR and SNP typing of 359 strains and whole genome sequencing of 51 strains from these plague foci and determined the phylogenetic diversity of the strains circulating here. They belong to 0.ANT3, 0.ANT5, 2.ANT3, 4.ANT branches of antique biovar, 2.MED0, 2.MED1 branches of medieval biovar and to 0.PE2, 0.PE4a. 0.PE4h, 0.PE4t branches. Based on the studies of 178 strains from 23 plague foci of CIS countries, it was determined that the population structure of 2.MED strains is subdivided into Caucasian–Caspian and Central Asian–Chinese branches. In Central-Caucasian high-mountain plague foci in the Russian Federation (RF the most deeply diverged branch of medieval biovar, 2.MED0, has been found. With the data obtained, the current population structure of Y. pestis species has been refined. New subspecies classification is developed, comprising seven subspecies: pestis, caucasica (0.PE2, angolica (0.PE3, central asiatica (0.PE4, tibetica (0.PE7, ulegeica (0.PE5, and qinghaica (0.PE10.

  10. Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

    NARCIS (Netherlands)

    Zhang, Delong; Liang, Bishan; Wu, Xia; Wang, Zengjian; Xu, Pengfei; Chang, Song; Liu, Bo; Liu, Ming; Huang, Ruiwang

    2015-01-01

    The present study examined directional connections in the brain among resting-state networks (RSNs) when the participant had their eyes open (E0) or had their eyes closed (EC). The resting state fMRI data were collected from 20 healthy participants (9 males, 20.17 +/- 2.74 years) under the EO and EC

  11. Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces—an Offline Study

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    Yawei Zhao

    2018-02-01

    Full Text Available Brain-computer interfaces (BCIs, independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximum r = −0.48, p < 0.001 between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.

  12. Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

    Science.gov (United States)

    Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

    2017-08-01

    Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

  13. Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

    OpenAIRE

    Tingting Xu; Kathryn R. Cullen; Bryon Mueller; Mindy W. Schreiner; Kelvin O. Lim; S. Charles Schulz; Keshab K. Parhi

    2016-01-01

    Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and construc...

  14. Classification of Flotation Frothers

    Directory of Open Access Journals (Sweden)

    Jan Drzymala

    2018-02-01

    Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

  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

    2015-01-01

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

  16. Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization.

    Directory of Open Access Journals (Sweden)

    Melle J W van der Molen

    Full Text Available Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI, a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior and short-range (frontal-frontal and posterior-posterior clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

  17. Love-related changes in the brain: A resting-state functional magnetic resonance imaging study

    Directory of Open Access Journals (Sweden)

    Hongwen eSong

    2015-02-01

    Full Text Available Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Studies with functional magnetic resonance imaging (fMRI have found activation increases in brain regions involved in processing of reward, emotion, motivation when romantic lovers view photographs of their partners. However, not much is known on whether romantic love affects the brain’s functional architecture during rest. In the present study, resting state functional magnetic resonance imaging (rsfMRI data was collected to compare the regional homogeneity (ReHo and functional connectivity (FC across a lover group (LG, N=34, currently intensely in love, ended-love group (ELG, N=34, romantic relationship ended recently, and single group (SG, N=32, never fallen in love.The results showed that:1 ReHo of the left dorsal anterior cingulate cortex (dACC was significantly increased in the LG (in comparison to the ELG and the SG; 2 ReHo of the left dACC was positively correlated with length of time in love in the LG, and negatively correlated with the lovelorn duration since breakup in the ELG; 3 functional connectivity (FC within the reward, motivation, and emotion network (dACC, insula, caudate, amygdala and nucleus accumbens and the social cognition network (temporo-parietal junction (TPJ, posterior cingulate cortex (PCC, medial prefrontal cortex (MPFC, inferior parietal, precuneus and temporal lobe was significantly increased in the LG (in comparison to the ELG and SG; 4 in most regions within both networks FC was positively correlated with the love duration in the LG but negatively correlated with the lovelorn duration in the ELG. This study provides first empirical evidence of love-related alterations of brain functional architecture. The results shed light on the underlying neural mechanisms of romantic love, and demonstrate the possibility of applying a resting state approach for investigating romantic love.

  18. Meal Replacement: Calming the Hot-State Brain Network of Appetite

    Directory of Open Access Journals (Sweden)

    Brielle ePaolini

    2014-03-01

    Full Text Available There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to hot states which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A that exists after a brief period of food restraint followed either by the consumption of a meal replacement (MR or water. Fourteen overweight/obese adults came to our laboratory on two different occasions. Both times they consumed a controlled breakfast meal and then were restricted from eating for 2.5 hours prior to an MRI scan. On one visit, they consumed a meal replacement (MR liquid meal after this period of food restriction; on the other visit they consumed an equal amount of water. After these manipulations, the participants underwent a resting fMRI scan. Our first study aim employed an exploratory, data-driven approach to identify hubs relevant to the HBN-A. Using data from the water condition, five regions were found to be the hubs or nodes of the HBN-A: insula, anterior cingulated cortex, the superior temporal pole, the amygdala, and the hippocampus. We then demonstrated that the consumption of a liquid MR dampened interconnectivity between the nodes of the HBN-A as compared to water. Importantly and consistent with these network data, the consumption of a MR beverage also lowered state cravings and hunger.

  19. Meal replacement: calming the hot-state brain network of appetite.

    Science.gov (United States)

    Paolini, Brielle M; Laurienti, Paul J; Norris, James; Rejeski, W Jack

    2014-01-01

    There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to "hot states" which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A) that exists after a brief period of food restraint followed either by the consumption of a meal replacement (MR) or water. Fourteen overweight/obese adults came to our laboratory on two different occasions. Both times they consumed a controlled breakfast meal and then were restricted from eating for 2.5 h prior to an MRI scan. On one visit, they consumed a meal replacement (MR) liquid meal after this period of food restriction; on the other visit they consumed an equal amount of water. After these manipulations, the participants underwent a resting fMRI scan. Our first study aim employed an exploratory, data-driven approach to identify hubs relevant to the HBN-A. Using data from the water condition, five regions were found to be the hubs or nodes of the HBN-A: insula, anterior cingulated cortex, the superior temporal pole, the amygdala, and the hippocampus. We then demonstrated that the consumption of a liquid MR dampened interconnectivity between the nodes of the HBN-A as compared to water. Importantly and consistent with these network data, the consumption of a MR beverage also lowered state cravings and hunger.

  20. Altered causal connectivity of resting state brain networks in amnesic MCI.

    Directory of Open Access Journals (Sweden)

    Peipeng Liang

    Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

  1. Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

    Science.gov (United States)

    Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

    2017-10-01

    Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Love-related changes in the brain: a resting-state functional magnetic resonance imaging study.

    Science.gov (United States)

    Song, Hongwen; Zou, Zhiling; Kou, Juan; Liu, Yang; Yang, Lizhuang; Zilverstand, Anna; d'Oleire Uquillas, Federico; Zhang, Xiaochu

    2015-01-01

    Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Functional magnetic resonance imaging (fMRI) studies have found activation increases in brain regions involved in the processing of reward, motivation and emotion regulation, when romantic lovers view photographs of their partners. However, not much is known about whether romantic love affects the brain's functional architecture during rest. In the present study, resting state functional magnetic resonance imaging (rsfMRI) data was collected to compare the regional homogeneity (ReHo) and functional connectivity (FC) across an "in-love" group (LG, N = 34, currently intensely in love), an "ended-love" group (ELG, N = 34, ended romantic relationship recently), and a "single" group (SG, N = 32, never fallen in love). Results show that: (1) ReHo of the left dorsal anterior cingulate cortex (dACC) was significantly increased in the LG (in comparison to the ELG and the SG); (2) ReHo of the left dACC was positively correlated with length of time in love in the LG, and negatively correlated with the lovelorn duration since breakup in the ELG; (3) FC within the reward, motivation, and emotion regulation network (dACC, insula, caudate, amygdala, and nucleus accumbens) as well as FC in the social cognition network [temporo-parietal junction (TPJ), posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal, precuneus, and temporal lobe] was significantly increased in the LG (in comparison to the ELG and SG); (4) in most regions within both networks FC was positively correlated with the duration of love in the LG but negatively correlated with the lovelorn duration of time since breakup in the ELG. This study provides first empirical evidence of love-related alterations in brain functional architecture. Furthermore, the results shed light on the underlying neural mechanisms of romantic love, and demonstrate the

  3. Moral competence and brain connectivity: a resting-state fMRI study

    Science.gov (United States)

    Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo; Korczykowski, Marc; Kable, Joseph W.; Rao, Hengyi; Robertson, Diana C.

    2016-01-01

    Moral competence (MC) refers to the ability to apply certain moral orientations in a consistent and differentiated manner when judging moral issues. People greatly differ in terms of MC, however, little is known about how these differences are implemented in the brain. To investigate this question, we used functional magnetic resonance imaging and examined resting-state functional connectivity (RSFC) in n=31 individuals with MC scores in the highest 15% of the population and n=33 individuals with MC scores in the lowest 15%, selected from a large sample of 730 Master of Business Administration (MBA) students. Compared to individuals with lower MC, individuals with higher MC showed greater amygdala-ventromedial prefrontal connectivity, which may reflect better ability to cope with emotional conflicts elicited by moral dilemmas. Moreover, individuals with higher MC showed less inter-network connectivity between the amygdalar and fronto-parietal networks, suggesting a more independent operation of these networks. Our findings provide novel insights into how individual differences in moral judgment are associated with RSFC in brain circuits related to emotion processing and cognitive control. PMID:27456537

  4. Moral competence and brain connectivity: A resting-state fMRI study.

    Science.gov (United States)

    Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo; Korczykowski, Marc; Kable, Joseph W; Rao, Hengyi; Robertson, Diana C

    2016-11-01

    Moral competence (MC) refers to the ability to apply certain moral orientations in a consistent and differentiated manner when judging moral issues. People greatly differ in terms of MC, however, little is known about how these differences are implemented in the brain. To investigate this question, we used functional magnetic resonance imaging and examined resting-state functional connectivity (RSFC) in n=31 individuals with MC scores in the highest 15% of the population and n=33 individuals with MC scores in the lowest 15%, selected from a large sample of 730 Master of Business Administration (MBA) students. Compared to individuals with lower MC, individuals with higher MC showed greater amygdala-ventromedial prefrontal connectivity, which may reflect better ability to cope with emotional conflicts elicited by moral dilemmas. Moreover, individuals with higher MC showed less inter-network connectivity between the amygdalar and fronto-parietal networks, suggesting a more independent operation of these networks. Our findings provide novel insights into how individual differences in moral judgment are associated with RSFC in brain circuits related to emotion processing and cognitive control. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Deep brain stimulation for the early treatment of the minimally conscious state and vegetative state: experience in 14 patients.

    Science.gov (United States)

    Chudy, Darko; Deletis, Vedran; Almahariq, Fadi; Marčinković, Petar; Škrlin, Jasenka; Paradžik, Veronika

    2018-04-01

    OBJECTIVE An effective treatment of patients in a minimally conscious state (MCS) or vegetative state (VS) caused by hypoxic encephalopathy or traumatic brain injury (TBI) is not yet available. Deep brain stimulation (DBS) of the thalamic reticular nuclei has been attempted as a therapeutic procedure mainly in patients with TBI. The purpose of this study was to investigate the therapeutic use of DBS for patients in VS or MCS. METHODS Fourteen of 49 patients in VS or MCS qualified for inclusion in this study and underwent DBS. Of these 14 patients, 4 were in MCS and 10 were in VS. The etiology of VS or MCS was TBI in 4 cases and hypoxic encephalopathy due to cardiac arrest in 10. The selection criteria for DBS, evaluating the status of the cerebral cortex and thalamocortical reticular formation, included: neurological evaluation, electrophysiological evaluation, and the results of positron emission tomography (PET) and MRI examinations. The target for DBS was the centromedian-parafascicular (CM-pf) complex. The duration of follow-up ranged from 38 to 60 months. RESULTS Two MCS patients regained consciousness and regained their ability to walk, speak fluently, and live independently. One MCS patient reached the level of consciousness, but was still in a wheelchair at the time the article was written. One VS patient (who had suffered a cerebral ischemic lesion) improved to the level of consciousness and currently responds to simple commands. Three VS patients died of respiratory infection, sepsis, or cerebrovascular insult (1 of each). The other 7 patients remained without substantial improvement of consciousness. CONCLUSIONS Spontaneous recovery from MCS/VS to the level of consciousness with no or minimal need for assistance in everyday life is very rare. Therefore, if a patient in VS or MCS fulfills the selection criteria (presence of somatosensory evoked potentials from upper extremities, motor and brainstem auditory evoked potentials, with cerebral glucose

  6. The spectral diversity of resting-state fluctuations in the human brain.

    Directory of Open Access Journals (Sweden)

    Klaudius Kalcher

    Full Text Available In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms and compared to 20 resting-state datasets from standard, high-TR (1800 ms EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1-0.25 Hz; 0.25-0.75 Hz; 0.75-1.4 Hz was computed for both the low-TR and (for the two lower-frequency ranges the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low

  7. High spatial resolution brain functional MRI using submillimeter balanced steady-state free precession acquisition

    International Nuclear Information System (INIS)

    Wu, Pei-Hsin; Chung, Hsiao-Wen; Tsai, Ping-Huei; Wu, Ming-Long; Chuang, Tzu-Chao; Shih, Yi-Yu; Huang, Teng-Yi

    2013-01-01

    Purpose: One of the technical advantages of functional magnetic resonance imaging (fMRI) is its precise localization of changes from neuronal activities. While current practice of fMRI acquisition at voxel size around 3 × 3 × 3 mm 3 achieves satisfactory results in studies of basic brain functions, higher spatial resolution is required in order to resolve finer cortical structures. This study investigated spatial resolution effects on brain fMRI experiments using balanced steady-state free precession (bSSFP) imaging with 0.37 mm 3 voxel volume at 3.0 T. Methods: In fMRI experiments, full and unilateral visual field 5 Hz flashing checkerboard stimulations were given to healthy subjects. The bSSFP imaging experiments were performed at three different frequency offsets to widen the coverage, with functional activations in the primary visual cortex analyzed using the general linear model. Variations of the spatial resolution were achieved by removing outerk-space data components. Results: Results show that a reduction in voxel volume from 3.44 × 3.44 × 2 mm 3 to 0.43 × 0.43 × 2 mm 3 has resulted in an increase of the functional activation signals from (7.7 ± 1.7)% to (20.9 ± 2.0)% at 3.0 T, despite of the threefold SNR decreases in the original images, leading to nearly invariant functional contrast-to-noise ratios (fCNR) even at high spatial resolution. Activation signals aligning nicely with gray matter sulci at high spatial resolution would, on the other hand, have possibly been mistaken as noise at low spatial resolution. Conclusions: It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain

  8. High spatial resolution brain functional MRI using submillimeter balanced steady-state free precession acquisition

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Pei-Hsin; Chung, Hsiao-Wen [Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan (China); Tsai, Ping-Huei [Imaging Research Center, Taipei Medical University, Taipei 11031, Taiwan and Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei 11031, Taiwan (China); Wu, Ming-Long, E-mail: minglong.wu@csie.ncku.edu.tw [Institute of Medical Informatics, National Cheng-Kung University, Tainan 70101, Taiwan and Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China); Chuang, Tzu-Chao [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China); Shih, Yi-Yu [Siemens Limited Healthcare Sector, Taipei 11503, Taiwan (China); Huang, Teng-Yi [Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan (China)

    2013-12-15

    Purpose: One of the technical advantages of functional magnetic resonance imaging (fMRI) is its precise localization of changes from neuronal activities. While current practice of fMRI acquisition at voxel size around 3 × 3 × 3 mm{sup 3} achieves satisfactory results in studies of basic brain functions, higher spatial resolution is required in order to resolve finer cortical structures. This study investigated spatial resolution effects on brain fMRI experiments using balanced steady-state free precession (bSSFP) imaging with 0.37 mm{sup 3} voxel volume at 3.0 T. Methods: In fMRI experiments, full and unilateral visual field 5 Hz flashing checkerboard stimulations were given to healthy subjects. The bSSFP imaging experiments were performed at three different frequency offsets to widen the coverage, with functional activations in the primary visual cortex analyzed using the general linear model. Variations of the spatial resolution were achieved by removing outerk-space data components. Results: Results show that a reduction in voxel volume from 3.44 × 3.44 × 2 mm{sup 3} to 0.43 × 0.43 × 2 mm{sup 3} has resulted in an increase of the functional activation signals from (7.7 ± 1.7)% to (20.9 ± 2.0)% at 3.0 T, despite of the threefold SNR decreases in the original images, leading to nearly invariant functional contrast-to-noise ratios (fCNR) even at high spatial resolution. Activation signals aligning nicely with gray matter sulci at high spatial resolution would, on the other hand, have possibly been mistaken as noise at low spatial resolution. Conclusions: It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain.

  9. An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.

    Science.gov (United States)

    Zhang, Dan; Huang, Bisheng; Wu, Wei; Li, Siliang

    2015-11-01

    Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.

  10. P3-24: Pre-Existing Brain States Predict Aesthetic Judgments

    Directory of Open Access Journals (Sweden)

    Po-Jang Hsieh

    2012-10-01

    Full Text Available Intuition and an assumption of basic rationality would suggest that people evaluate a stimulus on the basis of its properties and their underlying utility. However, various findings suggest that evaluations often depend not only on the thing evaluated, but also on a variety of contextual factors. Here we demonstrate a further departure from normative decision making: Aesthetic evaluations of abstract fractal art by human subjects were predicted with up to 75% accuracy by their brain states before the stimuli were presented. These predictions were based on cross-validation tests of pre-stimulus patterns of BOLD fMRI signals across a distributed network of regions in the frontal lobes. This predictive power did not simply reflect motor biases in favor of pressing a particular button. Our findings suggest that endogenous neural signals that exist before trial onset can bias people's decisions when evaluating visual stimuli.

  11. Steady State Visual Evoked Potential Based Brain-Computer Interface for Cognitive Assessment

    DEFF Research Database (Denmark)

    Westergren, Nicolai; Bendtsen, Rasmus L.; Kjær, Troels W.

    2016-01-01

    decline is important. Cognitive decline may be detected using fullyautomated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer...... interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% ± 0%. The test...... subjects achieved an information transfer rate (ITR) of 14:64 bits/min ± 7:63 bits=min and a subject test performance of 47:22% ± 34:10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system...

  12. Reduced brain resting-state network specificity in infants compared with adults.

    Science.gov (United States)

    Wylie, Korey P; Rojas, Donald C; Ross, Randal G; Hunter, Sharon K; Maharajh, Keeran; Cornier, Marc-Andre; Tregellas, Jason R

    2014-01-01

    Infant resting-state networks do not exhibit the same connectivity patterns as those of young children and adults. Current theories of brain development emphasize developmental progression in regional and network specialization. We compared infant and adult functional connectivity, predicting that infants would exhibit less regional specificity and greater internetwork communication compared with adults. Functional magnetic resonance imaging at rest was acquired in 12 healthy, term infants and 17 adults. Resting-state networks were extracted, using independent components analysis, and the resulting components were then compared between the adult and infant groups. Adults exhibited stronger connectivity in the posterior cingulate cortex node of the default mode network, but infants had higher connectivity in medial prefrontal cortex/anterior cingulate cortex than adults. Adult connectivity was typically higher than infant connectivity within structures previously associated with the various networks, whereas infant connectivity was frequently higher outside of these structures. Internetwork communication was significantly higher in infants than in adults. We interpret these findings as consistent with evidence suggesting that resting-state network development is associated with increasing spatial specificity, possibly reflecting the corresponding functional specialization of regions and their interconnections through experience.

  13. Resting-state, functional MRI on regional homogeneity changes of brain in the heavy smokers

    International Nuclear Information System (INIS)

    Yang Shiqi; Wu Guangyao; Lin Fuchun; Kong Xiangquan; Zhou Guofeng; Pang Haopeng; Zhu Ling; Liu Guobing; Lei Hao

    2012-01-01

    Objective: To explore the mechanism of self-awareness in the heavy smokers (HS) by using regional homogeneity (ReHo) combined with resting-state functional MRI (fMRI). Methods: Thirty HS and 31 healthy non-smokers (NS) matched for age and sex underwent a 3.0 T resting-state fMRI. The data were post-processed by SPM 5 and then the ReHo values were calculated by REST software. The ReHo values between the two groups were compared by two-sample t-test. The brain map with significant difference of ReHo value was obtained. Results: Compared with that in NS group, the regions with decreased ReHo value included the bilateral precuneus, superior frontal gyrus,medial prefrontal cortex, right angular gyrus, inferior frontal gyrus, inferior occipital gyrus, cerebellum, and left middle frontal gyrus in HS group. The regions of increased ReHo value included the bilateral insula, parahippocampal gyrus, white matter of parietal lobe, pons, left inferior parietal lobule, lingual gyrus, thalamus, inferior orbital gyrus, white matter of temporal-frontal lobe, and cerebellum. The difference was more obvious in the left hemisphere. Conclusions: In HS, abnormal ReHo on a resting state which reflects network of smoking addiction. This method may be helpful in understanding the mechanism of self-awareness in HS. (authors)

  14. Extraversion modulates functional connectivity hubs of resting-state brain networks.

    Science.gov (United States)

    Pang, Yajing; Cui, Qian; Duan, Xujun; Chen, Heng; Zeng, Ling; Zhang, Zhiqiang; Lu, Guangming; Chen, Huafu

    2017-09-01

    Personality dimension extraversion describes individual differences in social behaviour and socio-emotional functioning. The intrinsic functional connectivity patterns of the brain are reportedly associated with extraversion. However, whether or not extraversion is associated with functional hubs warrants clarification. Functional hubs are involved in the rapid integration of neural processing, and their dysfunction contributes to the development of neuropsychiatric disorders. In this study, we employed the functional connectivity density (FCD) method for the first time to distinguish the energy-efficient hubs associated with extraversion. The resting-state functional magnetic resonance imaging data of 71 healthy subjects were used in the analysis. Short-range FCD was positively correlated with extraversion in the left cuneus, revealing a link between the local functional activity of this region and extraversion in risk-taking. Long-range FCD was negatively correlated with extraversion in the right superior frontal gyrus and the inferior frontal gyrus. Seed-based resting-state functional connectivity (RSFC) analyses revealed that a decreased long-range FCD in individuals with high extraversion scores showed a low long-range functional connectivity pattern between the medial and dorsolateral prefrontal cortex, middle temporal gyrus, and anterior cingulate cortex. This result suggests that decreased RSFC patterns are responsible for self-esteem, self-evaluation, and inhibitory behaviour system that account for the modulation and shaping of extraversion. Overall, our results emphasize specific brain hubs, and reveal long-range functional connections in relation to extraversion, thereby providing a neurobiological basis of extraversion. © 2015 The British Psychological Society.

  15. Comparison of tactile, auditory and visual modality for brain-computer interface use: A case study with a patient in the locked-in state

    Directory of Open Access Journals (Sweden)

    Tobias eKaufmann

    2013-07-01

    Full Text Available This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials elicited in different modalities for use in brain-computer interface systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5 in the visual, auditory and tactile modality were conducted (2 runs per modality. Classifiers were built on one run and tested offline on another run (and vice versa. In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g. partner scanning. Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the

  16. Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state.

    Science.gov (United States)

    Kaufmann, Tobias; Holz, Elisa M; Kübler, Andrea

    2013-01-01

    This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials (ERP) elicited in different modalities for use in brain-computer interface (BCI) systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5) in the visual, auditory and tactile modality were conducted (2 runs per modality). Classifiers were built on one run and tested offline on another run (and vice versa). In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g., partner scanning). Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the

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

    Science.gov (United States)

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

    2015-01-01

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

  18. Brain activation and inhibition after acupuncture at Taichong and Taixi: resting-state functional magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Shao-qun Zhang

    2015-01-01

    Full Text Available Acupuncture can induce changes in the brain. However, the majority of studies to date have focused on a single acupoint at a time. In the present study, we observed activity changes in the brains of healthy volunteers before and after acupuncture at Taichong (LR3 and Taixi (KI3 using resting-state functional magnetic resonance imaging. Fifteen healthy volunteers underwent resting-state functional magnetic resonance imaging of the brain 15 minutes before acupuncture, then received acupuncture at Taichong and Taixi using the nail-pressing needle insertion method, after which the needle was retained in place for 30 minutes. Fifteen minutes after withdrawal of the needle, the volunteers underwent a further session of resting-state functional magnetic resonance imaging, which revealed that the amplitude of low-frequency fluctuation, a measure of spontaneous neuronal activity, increased mainly in the cerebral occipital lobe and middle occipital gyrus (Brodmann area 18/19, inferior occipital gyrus (Brodmann area 18 and cuneus (Brodmann area 18, but decreased mainly in the gyrus rectus of the frontal lobe (Brodmann area 11, inferior frontal gyrus (Brodmann area 44 and the center of the posterior lobe of the cerebellum. The present findings indicate that acupuncture at Taichong and Taixi specifically promote blood flow and activation in the brain areas related to vision, emotion and cognition, and inhibit brain areas related to emotion, attention, phonological and semantic processing, and memory.

  19. Brain activation and inhibition after acupuncture at Taichong and Taixi: resting-state functional magnetic resonance imaging.

    Science.gov (United States)

    Zhang, Shao-Qun; Wang, Yan-Jie; Zhang, Ji-Ping; Chen, Jun-Qi; Wu, Chun-Xiao; Li, Zhi-Peng; Chen, Jia-Rong; Ouyang, Huai-Liang; Huang, Yong; Tang, Chun-Zhi

    2015-02-01

    Acupuncture can induce changes in the brain. However, the majority of studies to date have focused on a single acupoint at a time. In the present study, we observed activity changes in the brains of healthy volunteers before and after acupuncture at Taichong (LR3) and Taixi (KI3) using resting-state functional magnetic resonance imaging. Fifteen healthy volunteers underwent resting-state functional magnetic resonance imaging of the brain 15 minutes before acupuncture, then received acupuncture at Taichong and Taixi using the nail-pressing needle insertion method, after which the needle was retained in place for 30 minutes. Fifteen minutes after withdrawal of the needle, the volunteers underwent a further session of resting-state functional magnetic resonance imaging, which revealed that the amplitude of low-frequency fluctuation, a measure of spontaneous neuronal activity, increased mainly in the cerebral occipital lobe and middle occipital gyrus (Brodmann area 18/19), inferior occipital gyrus (Brodmann area 18) and cuneus (Brodmann area 18), but decreased mainly in the gyrus rectus of the frontal lobe (Brodmann area 11), inferior frontal gyrus (Brodmann area 44) and the center of the posterior lobe of the cerebellum. The present findings indicate that acupuncture at Taichong and Taixi specifically promote blood flow and activation in the brain areas related to vision, emotion and cognition, and inhibit brain areas related to emotion, attention, phonological and semantic processing, and memory.

  20. Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation

    NARCIS (Netherlands)

    Boersma, M.; Smit, D.J.A.; de Bie, H.M.A.; van Baal, G.C.M.; Boomsma, D.I.; de Geus, E.J.C.; Delemarre-van de Waal, H.A.; Stam, C.J.

    2011-01-01

    During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths.

  1. 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.

  2. Reduced brain resting-state network specificity in infants compared with adults

    Directory of Open Access Journals (Sweden)

    Wylie KP

    2014-07-01

    Full Text Available Korey P Wylie,1,* Donald C Rojas,1,* Randal G Ross,1 Sharon K Hunter,1 Keeran Maharajh,1 Marc-Andre Cornier,2 Jason R Tregellas1,3 1Department of Psychiatry, 2Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 3Denver Veterans Affairs Medical Center, Denver, CO, USA *These authors contributed equally to this work Purpose: Infant resting-state networks do not exhibit the same connectivity patterns as those of young children and adults. Current theories of brain development emphasize developmental progression in regional and network specialization. We compared infant and adult functional connectivity, predicting that infants would exhibit less regional specificity and greater internetwork communication compared with adults.Patients and methods: Functional magnetic resonance imaging at rest was acquired in 12 healthy, term infants and 17 adults. Resting-state networks were extracted, using independent components analysis, and the resulting components were then compared between the adult and infant groups.Results: Adults exhibited stronger connectivity in the posterior cingulate cortex node of the default mode network, but infants had higher connectivity in medial prefrontal cortex/anterior cingulate cortex than adults. Adult connectivity was typically higher than infant connectivity within structures previously associated with the various networks, whereas infant connectivity was frequently higher outside of these structures. Internetwork communication was significantly higher in infants than in adults.Conclusion: We interpret these findings as consistent with evidence suggesting that resting-state network development is associated with increasing spatial specificity, possibly reflecting the corresponding functional specialization of regions and their interconnections through experience. Keywords: functional connectivity magnetic resonance imaging

  3. State of catecxolaminergine systems of the brain in forming of sydnocarb psychosis

    Directory of Open Access Journals (Sweden)

    Al Nasir Eiad

    2014-03-01

    Full Text Available Violations of mnestic reactions are one of substantial signs of disorders of nervous activity. On the basis of it, as a criterion of forming of experimental psychosis, in our supervisions, the state of processes of conditionally-reflex memory was studied in rats. To cover up mechanisms of derangements of conditionally reflex activity in the process of forming of psychotic symptomatic complex, maintenance of adrenalin, noradrenalinum and neurospecific albumen S - 100 in the brain structures, that take a direct part in the processes of memory was studied. Derangements of cognitive function, that are the result of neurotoxic action of sydnocarb, are related to reduction of maintenance of noradrenalinum in the frontal cortex, as well as adrenalin in the pons varolii. That is, sydnocarb psychosis is accompanied by reduction of activating role of the cortex and trunk structures, negatively affecting the state of mnestic reactions. In the hippocampus and striate body excitation causes violation of memory processes and on the contrary, concentration of noradrenalinum rose. Thus, the presented model of experimental psychosis, created by subacute introduction of sydnocarb, is an adequate and alternative methodology of psychotic disorders forming in animals resulted from direct participation of the catecholaminergetic systems of CNS.

  4. Patients in a vegetative state following traumatic brain injury display a reduced intracortical modulation.

    Science.gov (United States)

    Bagnato, Sergio; Boccagni, Cristina; Sant'Angelo, Antonino; Prestandrea, Caterina; Rizzo, Silvia; Galardi, Giuseppe

    2012-10-01

    Patients in coma who fail to wake develop a condition known as a vegetative state (VS). While we know that some cortical activities exist in patients in VS, it remains unclear whether interneuronal modulation can be abnormal in the cerebral cortex of these patients. The aim of the study was to evaluate the inhibitory and excitatory interneuronal circuits in patients in VS following a traumatic brain injury. Cortical excitability was studied in 5 VS patients and in 10 healthy subjects using paired pulses transcranial magnetic stimulation (TMS). Resting motor threshold and intracortical inhibition and facilitation at short intervals (2 and 10 ms, respectively) were evaluated. Two patients were studied again after their level of consciousness transitioned into a minimally conscious state (MCS). Both intracortical inhibition and facilitation were significantly reduced in patients compared to healthy subjects (p<0.05). In addition, these results did not significantly change in the 2 patients who evolved into a MCS. This is the first report showing an abnormal cortical excitability in patients in VS. Our findings suggest a pathophysiological base for future work aiming to restore the lack of interneuronal transmission in patients in VS. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  5. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS)

    OpenAIRE

    Fox, Michael D.; Halko, Mark A.; Eldaief, Mark C.; Pascual-Leone, Alvaro

    2012-01-01

    Both resting state functional magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) are increasingly popular techniques that can be used to non-invasively measure brain connectivity in human subjects. TMS shows additional promise as a method to manipulate brain connectivity. In this review we discuss how these two complimentary tools can be combined to optimally study brain connectivity and manipulate distributed brain networks. Important clinical applications include...

  6. Resting-state brain networks in patients with Parkinson's disease and impulse control disorders.

    Science.gov (United States)

    Tessitore, Alessandro; Santangelo, Gabriella; De Micco, Rosa; Giordano, Alfonso; Raimo, Simona; Amboni, Marianna; Esposito, Fabrizio; Barone, Paolo; Tedeschi, Gioacchino; Vitale, Carmine

    2017-09-01

    To investigate intrinsic neural networks connectivity changes in Parkinson's disease (PD) patients with and without impulse control disorders (ICD). Fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched healthy controls (HC) were enrolled in the study. To identify patients with and without ICD and/or punding, we used the Minnesota Impulsive Disorders Interview (MIDI) and a clinical interview based on diagnostic criteria for each symptom. All patients underwent a detailed neuropsychological evaluation. Whole brain structural and functional imaging was performed on a 3T GE MR scanner. Statistical analysis of functional data was completed using BrainVoyager QX software. Voxel-based morphometry (VBM) was used to test whether between-group differences in resting-state connectivity were related to structural abnormalities. The presence of ICD symptoms was associated with an increased connectivity within the salience and default-mode networks, as well as with a decreased connectivity within the central executive network (p < .05 corrected). ICD severity was correlated with both salience and default mode networks connectivity changes only in the ICD+ group. VBM analysis did not reveal any statistically significant differences in local grey matter volume between ICD+ and ICD- patients and between all patients and HC (p < .05. FWE). The presence of a disrupted connectivity within the three core neurocognitive networks may be considered as a potential neural correlate of ICD presence in patients with PD. Our findings provide additional insights into the mechanisms underlying ICD in PD, confirming the crucial role of an abnormal prefrontal-limbic-striatal homeostasis in their development. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Using auditory steady state responses to outline the functional connectivity in the tinnitus brain.

    Directory of Open Access Journals (Sweden)

    Winfried Schlee

    Full Text Available BACKGROUND: Tinnitus is an auditory phantom perception that is most likely generated in the central nervous system. Most of the tinnitus research has concentrated on the auditory system. However, it was suggested recently that also non-auditory structures are involved in a global network that encodes subjective tinnitus. We tested this assumption using auditory steady state responses to entrain the tinnitus network and investigated long-range functional connectivity across various non-auditory brain regions. METHODS AND FINDINGS: Using whole-head magnetoencephalography we investigated cortical connectivity by means of phase synchronization in tinnitus subjects and healthy controls. We found evidence for a deviating pattern of long-range functional connectivity in tinnitus that was strongly correlated with individual ratings of the tinnitus percept. Phase couplings between the anterior cingulum and the right frontal lobe and phase couplings between the anterior cingulum and the right parietal lobe showed significant condition x group interactions and were correlated with the individual tinnitus distress ratings only in the tinnitus condition and not in the control conditions. CONCLUSIONS: To the best of our knowledge this is the first study that demonstrates existence of a global tinnitus network of long-range cortical connections outside the central auditory system. This result extends the current knowledge of how tinnitus is generated in the brain. We propose that this global extend of the tinnitus network is crucial for the continuos perception of the tinnitus tone and a therapeutical intervention that is able to change this network should result in relief of tinnitus.

  8. Neural markers of loss aversion in resting-state brain activity.

    Science.gov (United States)

    Canessa, Nicola; Crespi, Chiara; Baud-Bovy, Gabriel; Dodich, Alessandra; Falini, Andrea; Antonellis, Giulia; Cappa, Stefano F

    2017-02-01

    Neural responses in striatal, limbic and somatosensory brain regions track individual differences in loss aversion, i.e. the higher sensitivity to potential losses compared with equivalent gains in decision-making under risk. The engagement of structures involved in the processing of aversive stimuli and experiences raises a further question, i.e. whether the tendency to avoid losses rather than acquire gains represents a transient fearful overreaction elicited by choice-related information, or rather a stable component of one's own preference function, reflecting a specific pattern of neural activity. We tested the latter hypothesis by assessing in 57 healthy human subjects whether the relationship between behavioral and neural loss aversion holds at rest, i.e. when the BOLD signal is collected during 5minutes of cross-fixation in the absence of an explicit task. Within the resting-state networks highlighted by a spatial group Independent Component Analysis (gICA), we found a significant correlation between strength of activity and behavioral loss aversion in the left ventral striatum and right posterior insula/supramarginal gyrus, i.e. the very same regions displaying a pattern of neural loss aversion during explicit choices. Cross-study analyses confirmed that this correlation holds when voxels identified by gICA are used as regions of interest in task-related activity and vice versa. These results suggest that the individual degree of (neural) loss aversion represents a stable dimension of decision-making, which reflects in specific metrics of intrinsic brain activity at rest possibly modulating cortical excitability at choice. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms.

    Science.gov (United States)

    Northoff, Georg

    2016-01-15

    Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Abnormal baseline brain activity in patients with neuromyelitis optica: A resting-state fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Liu Yaou [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Liang Peipeng [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); International WIC institute, Beijing University of Technology, Beijing 100024 (China); Duan Yunyun; Jia Xiuqin; Wang Fei; Yu Chunshui; Qin Wen [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Dong Huiqing; Ye Jing [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Li Kuncheng, E-mail: likuncheng1955@yahoo.com.cn [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China)

    2011-11-15

    Purpose: Recent immunopathologic and MRI findings suggest that tissue damage in neuromyelitis optica (NMO) is not limited to spinal cord and optic nerve, but also in brain. Baseline brain activity can reveal the brain functional changes to the tissue damages and give clues to the pathophysiology of NMO, however, it has never been explored by resting-state functional MRI (fMRI). We used regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI to investigate how baseline brain activity changes in patients with NMO. Methods: Resting-state fMRIs collected from seventeen NMO patients and seventeen age- and sex-matched normal controls were compared to investigate the ALFF difference between the two groups. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration were further explored. Results: Our results showed that NMO patients had significantly decreased ALFF in precuneus, posterior cingulate cortex (PCC) and lingual gyrus; and increased ALFF in middle frontal gyrus, caudate nucleus and thalamus, compared to normal controls. Moderate negative correlations were found between the EDSS and ALFF in the left middle frontal gyrus (r = -0.436, p = 0.040) and the left caudate (r = -0.542, p = 0.012). Conclusion: The abnormal baseline brain activity shown by resting-state fMRI in NMO is relevant to cognition, visual and motor systems. It implicates a complex baseline brain status of both functional impairments and adaptations caused by tissue damages in these systems, which gives clues to the pathophysiology of NMO.

  11. Abnormal baseline brain activity in patients with neuromyelitis optica: A resting-state fMRI study

    International Nuclear Information System (INIS)

    Liu Yaou; Liang Peipeng; Duan Yunyun; Jia Xiuqin; Wang Fei; Yu Chunshui; Qin Wen; Dong Huiqing; Ye Jing; Li Kuncheng

    2011-01-01

    Purpose: Recent immunopathologic and MRI findings suggest that tissue damage in neuromyelitis optica (NMO) is not limited to spinal cord and optic nerve, but also in brain. Baseline brain activity can reveal the brain functional changes to the tissue damages and give clues to the pathophysiology of NMO, however, it has never been explored by resting-state functional MRI (fMRI). We used regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI to investigate how baseline brain activity changes in patients with NMO. Methods: Resting-state fMRIs collected from seventeen NMO patients and seventeen age- and sex-matched normal controls were compared to investigate the ALFF difference between the two groups. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration were further explored. Results: Our results showed that NMO patients had significantly decreased ALFF in precuneus, posterior cingulate cortex (PCC) and lingual gyrus; and increased ALFF in middle frontal gyrus, caudate nucleus and thalamus, compared to normal controls. Moderate negative correlations were found between the EDSS and ALFF in the left middle frontal gyrus (r = -0.436, p = 0.040) and the left caudate (r = -0.542, p = 0.012). Conclusion: The abnormal baseline brain activity shown by resting-state fMRI in NMO is relevant to cognition, visual and motor systems. It implicates a complex baseline brain status of both functional impairments and adaptations caused by tissue damages in these systems, which gives clues to the pathophysiology of NMO.

  12. Frequency-dependent brain regional homogeneity alterations in patients with mild cognitive impairment during working memory state relative to resting state

    Directory of Open Access Journals (Sweden)

    Pengyun eWang

    2016-03-01

    Full Text Available Several studies have reported working memory deficits in patients with mild cognitive impairment (MCI. However, previous studies investigating the neural mechanisms of MCI have primarily focused on brain activity alterations during working memory tasks. No study to date has compared brain network alterations in the working memory state between MCI patients and normal control subjects. Therefore, using the index of regional homogeneity (ReHo, we explored brain network impairments in MCI patients during a working memory task relative to the resting state, and identified frequency-dependent effects in separate frequency bands.Our results indicate that, in MCI patients, ReHo is altered in the posterior cingulate cortex in the slow-3 band (0.073–0.198 Hz, and in the bottom of the right occipital lobe and part of the right cerebellum, the right thalamus, a diffusing region in the bilateral prefrontal cortex, the left and right parietal-occipital regions, and the right angular gyrus in the slow-5 band (0.01–0.027 Hz. Furthermore, in normal controls, the value of ReHo in clusters belonging to the default mode network decreased, while the value of ReHo in clusters belonging to the attentional network increased during the task state. However, this pattern was reversed in MCI patients, and was associated with decreased working memory performance. In addition, we identified altered functional connectivity of the abovementioned regions with other parts of the brain in MCI patients.This is the first study to compare frequency-dependent alterations of ReHo in MCI patients between resting and working memory states. The results provide a new perspective regarding the neural mechanisms of working memory deficits in MCI patients, and extend our knowledge of altered brain patterns in resting and task-evoked states.

  13. Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

    Science.gov (United States)

    Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

    2016-01-01

    Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

  14. Whole brain, high resolution spin-echo resting state fMRI using PINS multiplexing at 7 T

    NARCIS (Netherlands)

    Koopmans, P.J.; Boyacioglu, R.; Barth, M.; Norris, David Gordon

    2012-01-01

    This article demonstrates the application of spin-echo EPI for resting state fMRI at 7 T. A short repetition time of 1860 ms was made possible by the use of slice multiplexing which permitted whole brain coverage at high spatial resolution (84 slices of 1.6 mm thickness). Radiofrequency power

  15. At risk of being risky: The relationship between “brain age” under emotional states and risk preference

    Directory of Open Access Journals (Sweden)

    Marc D. Rudolph

    2017-04-01

    Full Text Available Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N = 212; 10–25y examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the “brain age” of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that “brain age” across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts relates to a group mean difference in risk perception − a pattern exemplified greatest in young-adults (ages 18–21. The results are suggestive of a specified functional brain phenotype that relates to being at “risk to be risky.”

  16. At risk of being risky: the relationship between “brain age” under emotional states and risk preference

    Science.gov (United States)

    Rudolph, Marc D.; Miranda-Dominguez, Oscar; Cohen, Alexandra O.; Breiner, Kaitlyn; Steinberg, Laurence; Bonnie, Richard J.; Scott, Elizabeth S.; Taylor-Thompson, Kim A.; Chein, Jason; Fettich, Karla C.; Richeson, Jennifer A.; Dellarco, Danielle V.; Galván, Adriana; Casey, BJ; Fair, Damien A.

    2017-01-01

    Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10–25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the “brain age” of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that “brain age” across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception – a pattern exemplified greatest in young-adults (ages 18–21). The results are suggestive of a specified functional brain phenotype that relates to being at “risk to be risky.” PMID:28279917

  17. Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

    Science.gov (United States)

    Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

    2011-01-01

    We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world