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Sample records for brain state classification

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

    Science.gov (United States)

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

    2012-01-01

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

  2. Classification of Medical Brain Images

    Institute of Scientific and Technical Information of China (English)

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

    2003-01-01

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

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

  5. Adaptive multiclass classification for brain computer interfaces.

    Science.gov (United States)

    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

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

  6. Tissue tracking: applications for brain MRI classification

    Science.gov (United States)

    Melonakos, John; Gao, Yi; Tannenbaum, Allen

    2007-03-01

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

  7. Quantum Brain States

    CERN Document Server

    Mould, R A

    2003-01-01

    If conscious observers are to be included in the quantum mechanical universe, we need to find the rules that engage observers with quantum mechanical systems. The author has proposed five rules that are discovered by insisting on empirical completeness; that is, by requiring the rules to draw empirical information from Schrodinger's solutions that is more complete than is currently possible with the (Born) probability interpretation. I discard Born's interpretation, introducing probability solely through probability current. These rules tell us something about brains. They require the existence of observer brain states that are neither conscious nor unconscious. I call them 'ready' brain states because they are on stand-by, ready to become conscious the moment they are stochastically chosen. Two of the rules are selection rules involving ready brain states. The place of these rules in a wider theoretical context is discussed. Key Words: boundary conditions, consciousness, decoherence, macroscopic superpositio...

  8. 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.

  9. 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.

  10. BRAIN TUMOR CLASSIFICATION USING NEURAL NETWORK BASED METHODS

    OpenAIRE

    Kalyani A. Bhawar*, Prof. Nitin K. Bhil

    2016-01-01

    MRI (Magnetic resonance Imaging) brain neoplasm pictures Classification may be a troublesome tasks due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of 3 stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the options connected with tomography pictures victimization d...

  11. Entanglement classification with matrix product states.

    Science.gov (United States)

    Sanz, M; Egusquiza, I L; Di Candia, R; Saberi, H; Lamata, L; Solano, E

    2016-07-26

    We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by .

  12. Classification of CT-brain slices based on local histograms

    Science.gov (United States)

    Avrunin, Oleg G.; Tymkovych, Maksym Y.; Pavlov, Sergii V.; Timchik, Sergii V.; Kisała, Piotr; Orakbaev, Yerbol

    2015-12-01

    Neurosurgical intervention is a very complicated process. Modern operating procedures based on data such as CT, MRI, etc. Automated analysis of these data is an important task for researchers. Some modern methods of brain-slice segmentation use additional data to process these images. Classification can be used to obtain this information. To classify the CT images of the brain, we suggest using local histogram and features extracted from them. The paper shows the process of feature extraction and classification CT-slices of the brain. The process of feature extraction is specialized for axial cross-section of the brain. The work can be applied to medical neurosurgical systems.

  13. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  14. Training brain networks and states.

    Science.gov (United States)

    Tang, Yi-Yuan; Posner, Michael I

    2014-07-01

    Brain training refers to practices that alter the brain in a way that improves cognition, and performance in domains beyond those involved in the training. We argue that brain training includes network training through repetitive practice that exercises specific brain networks and state training, which changes the brain state in a way that influences many networks. This opinion article considers two widely used methods - working memory training (WMT) and meditation training (MT) - to demonstrate the similarities and differences between network and state training. These two forms of training involve different areas of the brain and different forms of generalization. We propose a distinction between network and state training methods to improve understanding of the most effective brain training.

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

    Directory of Open Access Journals (Sweden)

    Norberto Eiji Nawa

    2015-12-01

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

  16. Entanglement classification with matrix product states.

    Science.gov (United States)

    Sanz, M; Egusquiza, I L; Di Candia, R; Saberi, H; Lamata, L; Solano, E

    2016-01-01

    We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by . PMID:27457273

  17. 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.

  18. 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.

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

    Science.gov (United States)

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

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods. PMID:26351901

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

    Science.gov (United States)

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

    2016-08-01

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

  1. Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions.

    Science.gov (United States)

    Zhang, Jinyin; Li, Xin; Foldes, Stephen T; Wang, Wei; Collinger, Jennifer L; Weber, Douglas J; Bagić, Anto

    2016-01-01

    Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace transformation into a unified framework by formulating a constrained optimization problem. Our experimental results based on human subjects demonstrate that RDA can efficiently extract the discriminant pattern from prespecified cortical regions to accurately distinguish different brain states.

  2. A classification scheme for chimera states

    OpenAIRE

    Kemeth, Felix P.; Haugland, Sindre W.; Schmidt, Lennart; Kevrekidis, Ioannis G.; Krischer, Katharina

    2016-01-01

    We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enable a meaningful definition of chimera states as well as their classification into three categories: stationary, turbulent and breathing. In addition, these categories can be further subdivided according to the time-stationarity of these two measures. We demonstrate that this approach both is consistent with previous...

  3. Natural image classification driven by human brain activity

    Science.gov (United States)

    Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

    2016-03-01

    Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

  4. A classification scheme for chimera states

    Science.gov (United States)

    Kemeth, Felix P.; Haugland, Sindre W.; Schmidt, Lennart; Kevrekidis, Ioannis G.; Krischer, Katharina

    2016-09-01

    We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enable a meaningful definition of chimera states as well as their classification into three categories: stationary, turbulent, and breathing. In addition, these categories can be further subdivided according to the time-stationarity of these two measures. We demonstrate that this approach is both consistent with previously recognized chimera states and enables us to classify states as chimeras which have not been categorized as such before. Furthermore, the scheme allows for a qualitative and quantitative comparison of experimental chimeras with chimeras obtained through numerical simulations.

  5. 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.

  6. 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.

  7. Decoding Spontaneous Emotional States in the Human Brain

    Science.gov (United States)

    Kragel, Philip A.; Knodt, Annchen R.; Hariri, Ahmad R.; LaBar, Kevin S.

    2016-01-01

    Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems. PMID:27627738

  8. Decoding Spontaneous Emotional States in the Human Brain.

    Science.gov (United States)

    Kragel, Philip A; Knodt, Annchen R; Hariri, Ahmad R; LaBar, Kevin S

    2016-09-01

    Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems. PMID:27627738

  9. 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.

  10. Improved Classification Methods for Brain Computer Interface System

    Directory of Open Access Journals (Sweden)

    YI Fang

    2012-03-01

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

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

    CERN Document Server

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

    2014-01-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave ...

  12. Optimal Trajectories of Brain State Transitions

    OpenAIRE

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

    2016-01-01

    The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how the organization of white matter architecture constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question from a computational perspective by defining a brain state as a pattern of activity across brain regions. Drawing on recent advances in network control theory, we model the underlying mechanisms of brain state transitions as eli...

  13. 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

  14. 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.

  15. Brain tissue segmentation in 4D CT using voxel classification

    Science.gov (United States)

    van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

    2012-02-01

    A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

  16. Resting state brain activity and functional brain mapping

    Institute of Scientific and Technical Information of China (English)

    Zhao Xiaohu; Wang Peijun; Tang Xiaowei

    2007-01-01

    Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental "rest" conditions. The mental activity that occurs during"rest" might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.

  17. Multiclass imbalance learning:Improving classification of pediatric brain tumors from magnetic resonance spectroscopy

    OpenAIRE

    Zarinabad, Niloufar; Wilson, Martin P; Gill, Simrandip K.; Manias, Karen A; Davies, Nigel P; Peet, Andrew C

    2016-01-01

    PURPOSE: Classification of pediatric brain tumors from (1) H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces difficulties in classifying rare tumor groups. This study assessed different imbalanced multiclass learning techniques and compared the use of complete spectra and quantified metabolite profiles for classification of three main childhood brain tu...

  18. 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.

  19. Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity.

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    Full Text Available BACKGROUND: Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI data, as inputs to efficient classifiers such as support vector machines (SVM and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we introduce a novel framework where in both functional connectivity (FC based on instantaneous temporal correlation and effective connectivity (EC based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects. CONCLUSIONS/SIGNIFICANCE: The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state

  20. Classification of cognitive states using functional MRI data

    Science.gov (United States)

    Yang, Ye; Pal, Ranadip; O'Boyle, Michael

    2010-03-01

    A fundamental goal of the analysis of fMRI data is to locate areas of brain activation that can differentiate various cognitive tasks. Traditionally, researchers have approached fMRI analysis through characterizing the relationship between cognitive variables and individual brain voxels. In recent years, multivariate approaches (analyze more than one voxel at once) to fMRI data analysis have gained importance. But in majority of the multivariate approaches, the voxels used for classification are selected based on prior biological knowledge or discriminating power of individual voxels. We used sequential floating forward search (SFFS) feature selection approach for selecting the voxels and applied it to distinguish the cognitive states of whether a subject is doing a reasoning or a counting task. We obtained superior classifier performance by using the sequential approach as compared to selecting the features with best individual classifier performance. We analyzed the problem of over-fitting in this extremely high dimensional feature space with limited training samples. For estimating the accuracy of the classifier, we employed various estimation methods and discussed their importance in this small sample scenario. Also we modified the feature selection algorithm by adding spatial information to incorporate the biological constraint that spatially nearby voxels tends to represent similar things.

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

    Science.gov (United States)

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

    2008-05-01

    We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor. PMID:18450536

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

    Directory of Open Access Journals (Sweden)

    Ch.Aparna

    2010-12-01

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

  3. 9 CFR 146.44 - Terminology and classification; States.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Terminology and classification; States. 146.44 Section 146.44 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Special Provisions for Meat-Type Turkey Slaughter Plants § 146.44 Terminology and classification;...

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

    NARCIS (Netherlands)

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

    2011-01-01

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

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

    NARCIS (Netherlands)

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

    2011-01-01

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

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

    NARCIS (Netherlands)

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

    2011-01-01

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

  7. 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 ...

  8. COHERENT STATES, FRACTALS AND BRAIN WAVES

    OpenAIRE

    Vitiello, Giuseppe

    2009-01-01

    I show that a functional representation of self-similarity (as the one occurring in fractals) is provided by squeezed coherent states. In this way, the dissipative model of brain is shown to account for the self-similarity in brain background activity suggested by power-law distributions of power spectral densities of electrocorticograms. I also briefly discuss the action-perception cycle in the dissipative model with reference to intentionality in terms of trajectories in the memory state sp...

  9. Brain network adaptability across task states.

    Directory of Open Access Journals (Sweden)

    Elizabeth N Davison

    2015-01-01

    Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

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

    CERN Document Server

    Rajendran, P

    2010-01-01

    An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on prediagnosed database of brain images showed 96 percent and 93 percent sensitivity and accuracy respectively.

  11. 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. PMID:26880873

  12. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    Science.gov (United States)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  13. Brain Content of Branes' States

    CERN Document Server

    Mkrtchyan, R L

    2003-01-01

    The problem of decomposition of unitary irreps of (super) tensorial (i.e. extended with tensorial charges) Poincare algebra w.r.t. its different subgroups is considered. This requires calculation of little groups for different configurations of tensor charges. Particularly, for preon states (i.e. states with maximal supersymmetry) in different dimensions the particle content is calculated, i.e. the spectrum of usual Poincare representations in the preon representation of tensorial Poincare. At d=4 results coincide with (and may provide another point of view on) the Vasiliev's results in field theories in generalized space-time. The translational subgroup of little groups of massless particles and branes is shown to be (and coincide with, at d=4) a subgroup of little groups of "pure branes" algebras, i.e. tensorial Poincare algebras without vector generators. Possible existence of corresponding field theories is discussed. At 11d it is shown that, contrary to lower dimensions, spinors are not homogeneous space...

  14. Psychophysiological classification and staging of mental states during meditative practice.

    Science.gov (United States)

    Hinterberger, Thilo; Kamei, Tsutomu; Walach, Harald

    2011-12-01

    The study of meditation offers a perfect setting for the study of a large variety of states of consciousness. Here, we present a classification paradigm that can be used for staging of individual meditation sessions into a variety of predefined mental states. We have measured 64 channels of the electroencephalogram (EEG) plus peripheral physiological measures in 49 participants with varying experiences in meditation practice. The data recorded in a meditation session of seven meditative tasks were analyzed with respect to EEG power spectral density measures plus peripheral measures. A multiclass linear discriminant analysis classifier was trained for classification of data epochs of the seven standard tasks. The classification results were verified using random partitions of the data. As an overall result, about 83% (±7%) of the epochs could be correctly classified to their originating task. The best classification method was then applied to individual meditation sessions, which allowed for staging of meditation states similarly to the staging possibility of sleep states. This study exemplarily demonstrates the possibility of developing an automatized staging tool that can be used for monitoring changes in the states of consciousness offline or online for training or therapeutic purpose.

  15. Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

    Science.gov (United States)

    Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

    2016-01-01

    We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

  16. Quantum learning: asymptotically optimal classification of qubit states

    International Nuclear Information System (INIS)

    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 (X1, Y1), ... , (Xn, Yn), where Xi represents a feature and Yiin{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.

  17. Brain state-dependent neuronal computation

    Directory of Open Access Journals (Sweden)

    Pascale eQuilichini

    2012-10-01

    Full Text Available Neuronal firing pattern, which includes both the frequency and the timing of action potentials, is a key component of information processing in the brain. Although the relationship between neuronal output (the firing pattern and function (during a task/behavior is not fully understood, there is now considerable evidence that a given neuron can show very different firing patterns according to brain state. Thus, such neurons assembled into neuronal networks generate different rhythms (e.g. theta, gamma, sharp wave ripples, which sign specific brain states (e.g. learning, sleep. This implies that a given neuronal network, defined by its hard-wired physical connectivity, can support different brain state-dependent activities through the modulation of its functional connectivity. Here, we review data demonstrating that not only the firing pattern, but also the functional connections between neurons, can change dynamically. We then explore the possible mechanisms of such versatility, focusing on the intrinsic properties of neurons and the properties of the synapses they establish, and how they can be modified by neuromodulators, i.e. the different ways that neurons can use to switch from one mode of communication to the other.

  18. State-dependencies of learning across brain scales

    OpenAIRE

    Petra eRitter; Jan eBorn; Michael eBrecht; Hubert eDinse; Uwe eHeinemann; Burkhard ePleger; Dietmar eSchmitz; Susanne eSchreiber; Arno eVillringer; Richard eKempter

    2015-01-01

    Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous ‘spontaneous’ shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, parti...

  19. State-dependencies of learning across brain scales

    OpenAIRE

    Ritter, Petra; Born, Jan; Brecht, Michael; Dinse, Hubert R.; Heinemann, Uwe; Pleger, Burkhard; Schmitz, Dietmar; Schreiber, Susanne; Villringer, Arno; Kempter, Richard

    2015-01-01

    Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous "spontaneous" shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, parti...

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

    Directory of Open Access Journals (Sweden)

    V. Anitha

    2014-01-01

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

  1. Identification and classification of hubs in brain networks

    NARCIS (Netherlands)

    Sporns, O.; Honey, C.J.; Kotter, R.

    2007-01-01

    Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identifica

  2. 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 ...

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

    Directory of Open Access Journals (Sweden)

    S. Amoozegar

    2013-06-01

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

  4. Generation and classification of robust remote symmetric Dicke states

    Institute of Scientific and Technical Information of China (English)

    Zhu Yan-Wu; Gao Ke-Lin

    2008-01-01

    In this paper,we present an approach to generating arbitrary symmetric Dicke states with distant trapped ions and linear optics.Distant trapped ions can be prepared in the symmetric Dicke states by using two photon-number-resolving detectors and a polarization beam splitter.The atomic symmetric Dicke states are robust against decoherence,for atoms are in a metastable level.We discuss the experimental feasibility of our scheme with current technology.Finally,we discuss the classification of arbitrary n-qubit symmetric Dicke states under statistical local operation and classical communication and prove the existence of[n/2]inequivalent classes of genuine entanglement of n-qubit symmetric Dicke states.

  5. Neural correlates of establishing, maintaining, and switching brain states.

    Science.gov (United States)

    Tang, Yi-Yuan; Rothbart, Mary K; Posner, Michael I

    2012-06-01

    Although the study of brain states is an old one in neuroscience, there has been growing interest in brain state specification owing to MRI studies tracing brain connectivity at rest. In this review, we summarize recent research on three relatively well-described brain states: the resting, alert, and meditation states. We explore the neural correlates of maintaining a state or switching between states, and argue that the anterior cingulate cortex and striatum play a critical role in state maintenance, whereas the insula has a major role in switching between states. Brain state may serve as a predictor of performance in a variety of perceptual, memory, and problem solving tasks. Thus, understanding brain states is critical for understanding human performance.

  6. WAVELET STATISTICAL TEXTURE FEATURES WITH ORTHOGONAL OPERATORS TUMOUR CLASSIFICATION IN MAGNETIC RESONANCE IMAGING BRAIN

    Directory of Open Access Journals (Sweden)

    R. Meenakshi

    2013-01-01

    Full Text Available Tumors medically also called neoplasms are an abnormal mass of tissue resulting from uncontrolled proliferation or division of cells occurring in the human body. If such growth is located in the brain then it is called as brain tumor. Identification of such tumors is a major challenge in the field of medical science. Early identification of tumors prove to be critical as serious consequences can be averted. Its threat level depends on a combination of various factors like the type of tumor, its location, its size and its developmental stage. Tumor can occur in any part of the body. Magnetic Resonance Imaging (MRI technique is mainly used for analyzing the brain, as the images produced are of high precision and applicability. The main objective of this study is to classify the brain MRI dataset for the existence or non existence of tumors. The proposed method uses Two Dimensional Discrete Wavelet Transform (2D-DWT for pre-processing and further classification with orthogonal operators and SVM. The usage of 2D-DWT for pre-processing improves the classification accuracy by 2% when compared to the existing classification techniques.

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

    Institute of Scientific and Technical Information of China (English)

    Wu Ting; Yan Guozheng; Yang Banghua

    2009-01-01

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

  8. 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

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

    Science.gov (United States)

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

    2015-01-01

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

  10. 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. PMID:23771347

  11. Segmentation and Classification of Brain MRI Images Using Improved Logismos-B Algorithm

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    S. Dilip kumar

    2014-12-01

    Full Text Available Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT and Magnetic resonance (MR imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM, gray matter (GM and cerebrospinal fluid spaces (CSF. These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation

  12. 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.

  13. Quadratic Program Optimization using Support Vector Machine for CT Brain Image Classification

    Directory of Open Access Journals (Sweden)

    J.Umamaheswari

    2012-07-01

    Full Text Available In this paper, an efficient Computer Tomography (CT image classification using Support Vector Machine (SVM with optimized quadratic programming methodology is proposed. Due to manual interpretation of brain images based on visual examination by radiologist/physician that cause incorrect diagnosis, when a large number of CT images are analyzed. To avoid the human error, an automated optimized classification system is proposed for abnormal CT image identification. This is an automated system for content based image retrieval with better classifier accuracy and prediction time. SVM classifier can accurately train up the datas as normal and abnormal brains interpreted manually by the user. The system can retrieve more number of images present in the query data base. The proposed classifier is analyzed with existing Sequential Minimal Optimization (SMO and K Nearest Neighbour classifier KNN. From the experimental analysis, the proposed classifier outperforms all other classifier taken for examination.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Xiaolong Peng

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

  16. 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.

  17. 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.

  18. Changes in cognitive state alter human functional brain networks

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    Malaak Nasser Moussa

    2011-08-01

    Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.

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

    NARCIS (Netherlands)

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

    2006-01-01

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

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

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    Eva Janousova

    2016-08-01

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

  1. Machine learning classification of resting state functional connectivity predicts smoking status

    Directory of Open Access Journals (Sweden)

    Vani ePariyadath

    2014-06-01

    Full Text Available Machine learning-based approaches are now able to examine functional magnetic resonance imaging data in a multivariate manner and extract features predictive of group membership. We applied support vector machine-based classification to resting state functional connectivity data from nicotine-dependent smokers and healthy controls to identify brain-based features predictive of nicotine dependence. By employing a network-centered approach, we observed that within-network functional connectivity measures offered maximal information for predicting smoking status, as opposed to between-network connectivity, or the representativeness of each individual node with respect to its parent network. Further, our analysis suggests that connectivity measures within the executive control and frontoparietal networks are particularly informative in predicting smoking status. Our findings suggest that machine learning-based approaches to classifying resting state functional connectivity data offer a valuable alternative technique to understanding large-scale differences in addiction-related neurobiology.

  2. 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.

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

    Science.gov (United States)

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate 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. PMID:27320688

  4. 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.

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

    Science.gov (United States)

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

    2016-01-15

    Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data. PMID:26481679

  6. 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:…

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

    Directory of Open Access Journals (Sweden)

    C. R. Hema

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    V.P. Gladis Pushpa Rathi

    2015-05-01

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

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

    Science.gov (United States)

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

    2016-08-01

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

  10. 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.

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

    Science.gov (United States)

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

    2016-04-01

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

  12. 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…

  13. 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...

  14. The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

    OpenAIRE

    Sabancı, Kadir; Koklu, Murat

    2015-01-01

    What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for ...

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

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

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

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

    CERN Document Server

    Rusconi, Marco

    2016-01-01

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

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    Liang eZhan

    2015-07-01

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

  19. 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

  20. Antidepressive interventions : On state and vulnerability of the brain

    NARCIS (Netherlands)

    Korf, J

    1996-01-01

    An attempt is made to relate drug and non-drug antidepressive interventions to brain processes. In the present context two concepts are proposed: vulnerability towards depressogenic factors and depression as a state of the brain. Accordingly, it is assumed that the current antidepressants make the b

  1. 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

    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 mor

  2. Addiction Related Alteration in Resting-state Brain Connectivity

    OpenAIRE

    Ma, Ning; Liu, Ying; Li, Nan; Wang, Chang-Xin; Zhang, Hao; Jiang, Xiao-Feng; Xu, Hu-Sheng; Fu, Xian-ming; Hu, Xiaoping; Zhang, Da-Ren

    2009-01-01

    It is widely accepted that addictive drug use is related to abnormal functional organization in the user’s brain. The present study aimed to identify this type of abnormality within the brain networks implicated in addiction by resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI). With fMRI data acquired during resting state from 14 chronic heroin users (12 of whom were being treated with methadone) and 13 non-addicted controls, we investigated the ...

  3. Does State Merit-Based Aid Stem Brain Drain?

    Science.gov (United States)

    Zhang, Liang; Ness, Erik C.

    2010-01-01

    In this study, the authors use college enrollment and migration data to test the brain drain hypothesis. Their results suggest that state merit scholarship programs do indeed stanch the migration of "best and brightest" students to other states. In the aggregate and on average, the implementation of state merit aid programs increases the total…

  4. 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., 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.

  5. Classification of multipartite systems featuring only $|W\\rangle$ and $|GHZ\\rangle$ genuine entangled states

    OpenAIRE

    Holweck, Frédéric; Lévay, Péter

    2015-01-01

    In this paper we present several multipartite quantum systems featuring the same type of genuine (tripartite) entanglement. Based on a geometric interpretation of the so-called $|W\\rangle$ and $|GHZ\\rangle$ states we show that the classification of all multipartite systems featuring those and only those two classes of genuine entanglement can be deduced from earlier work of algebraic geometers. This classification corresponds in fact to classification of fundamental subadjoint varieties and e...

  6. EEG Resting-State Brain Topological Reorganization as a Function of Age

    Directory of Open Access Journals (Sweden)

    Manuela Petti

    2016-01-01

    Full Text Available Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20–63 years. Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults: significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

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

    Directory of Open Access Journals (Sweden)

    Nikolay V. Manyakov

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lawrence A. Farwell

    2014-12-01

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

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

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

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

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

    International Nuclear Information System (INIS)

    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.)

  11. A framework to support automated classification and labeling of brain electromagnetic patterns.

    Science.gov (United States)

    Frishkoff, Gwen A; Frank, Robert M; Rong, Jiawei; Dou, Dejing; Dien, Joseph; Halderman, Laura K

    2007-01-01

    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. PMID:18301711

  12. 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.

  13. The Wechsler Adult Intelligence Scale-III and Malingering in Traumatic Brain Injury: Classification Accuracy in Known Groups

    Science.gov (United States)

    Curtis, Kelly L.; Greve, Kevin W.; Bianchini, Kevin J.

    2009-01-01

    A known-groups design was used to determine the classification accuracy of Wechsler Adult Intelligence Scale-III (WAIS-III) variables in detecting malingered neurocognitive dysfunction (MND) in traumatic brain injury (TBI). TBI patients were classified into the following groups: (a) mild TBI not-MND (n = 26), (b) mild TBI MND (n = 31), and (c)…

  14. 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.

  15. 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.

  16. Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations

    Directory of Open Access Journals (Sweden)

    Adrian Rodriguez Aguinaga

    2015-06-01

    Full Text Available This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes of a 10/20 model to Brodmann regions and reduce computational burden. The classification process were performed by a Support Vector Machines Classification process, achieving a 81.46 percent of classification rate for a multi-class problem and the emotions modeling are based in an adjusted space from the Russell Arousal Valence Space and the Geneva model.

  17. Default network connectivity decodes brain states with simulated microgravity.

    Science.gov (United States)

    Zeng, Ling-Li; Liao, Yang; Zhou, Zongtan; Shen, Hui; Liu, Yadong; Liu, Xufeng; Hu, Dewen

    2016-04-01

    With great progress of space navigation technology, it becomes possible to travel beyond Earth's gravity. So far, it remains unclear whether the human brain can function normally within an environment of microgravity and confinement. Particularly, it is a challenge to figure out some neuroimaging-based markers for rapid screening diagnosis of disrupted brain function in microgravity environment. In this study, a 7-day -6° head down tilt bed rest experiment was used to simulate the microgravity, and twenty healthy male participants underwent resting-state functional magnetic resonance imaging scans at baseline and after the simulated microgravity experiment. We used a multivariate pattern analysis approach to distinguish the brain states with simulated microgravity from normal gravity based on the functional connectivity within the default network, resulting in an accuracy of no less than 85 % via cross-validation. Moreover, most discriminative functional connections were mainly located between the limbic system and cortical areas and were enhanced after simulated microgravity, implying a self-adaption or compensatory enhancement to fulfill the need of complex demand in spatial navigation and motor control functions in microgravity environment. Overall, the findings suggest that the brain states in microgravity are likely different from those in normal gravity and that brain connectome could act as a biomarker to indicate the brain state in microgravity. PMID:27066149

  18. Resting state brain networks and their implications in neurodegenerative disease

    Science.gov (United States)

    Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

    2012-10-01

    Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

  19. Brain imaging of pain: state of the art

    Science.gov (United States)

    Morton, Debbie L; Sandhu, Javin S; Jones, Anthony KP

    2016-01-01

    Pain is a complex sensory and emotional experience that is heavily influenced by prior experience and expectations of pain. Before the development of noninvasive human brain imaging, our grasp of the brain’s role in pain processing was limited to data from postmortem studies, direct recording of brain activity, patient experience and stimulation during neurosurgical procedures, and animal models of pain. Advances made in neuroimaging have bridged the gap between brain activity and the subjective experience of pain and allowed us to better understand the changes in the brain that are associated with both acute and chronic pain. Additionally, cognitive influences on pain such as attention, anticipation, and fear can now be directly observed, allowing for the interpretation of the neural basis of the psychological modulation of pain. The use of functional brain imaging to measure changes in endogenous neurochemistry has increased our understanding of how states of increased resilience and vulnerability to pain are maintained. PMID:27660488

  20. 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. PMID:27255798

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

    Science.gov (United States)

    Vergun, Svyatoslav; Gaggl, Wolfgang; Nair, Veena A.; Suhonen, Joshua I.; Birn, Rasmus M.; Ahmed, Azam S.; Meyerand, M. Elizabeth; Reuss, James; DeYoe, Edgar A.; Prabhakaran, Vivek

    2016-01-01

    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 (RSN) 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 RSNs 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. PMID:27729846

  2. 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%.

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

    International Nuclear Information System (INIS)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-12-15

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

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

    Science.gov (United States)

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

    2011-06-01

    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

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

    International Nuclear Information System (INIS)

    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

  7. Neuromodulation of the conscious state following severe brain injuries.

    Science.gov (United States)

    Fridman, Esteban A; Schiff, Nicholas D

    2014-12-01

    Disorders of consciousness (DOC) following severe structural brain injuries globally affect the conscious state and the expression of goal-directed behaviors. In some subjects, neuromodulation with medications or electrical stimulation can markedly improve the impaired conscious state present in DOC. We briefly review recent studies and provide an organizing framework for considering the apparently widely disparate collection of medications and approaches that may modulate the conscious state in subjects with DOC. We focus on neuromodulation of the anterior forebrain mesocircuit in DOC and briefly compare mechanisms supporting recovery from structural brain injuries to those underlying facilitated emergence from unconsciousness produced by anesthesia. We derive some general principles for approaching the problem of restoration of consciousness after severe structural brain injuries, and suggest directions for future research.

  8. State-Based Models for Light Curve Classification

    Science.gov (United States)

    Becker, A.

    I discuss here the application of continuous time autoregressive models to the characterization of astrophysical variability. These types of models are general enough to represent many classes of variability, and descriptive enough to provide features for lightcurve classification. Importantly, the features of these models may be interpreted in terms of the power spectrum of the lightcurve, enabling constraints on characteristic timescales and periodicity. These models may be extended to include vector-valued inputs, raising the prospect of a fully general modeling and classification environment that uses multi-passband inputs to create a single phenomenological model. These types of spectral-temporal models are an important extension of extant techniques, and necessary in the upcoming eras of Gaia and LSST.

  9. 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 not previo......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...

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

    Energy Technology Data Exchange (ETDEWEB)

    Basso, Vittorio [Istituto Elettrotecnico Nazionale Galileo Ferraris, Strada delle Cacce 91, I-10135 Turin (Italy)]. E-mail: basso@ien.it; Magni, Alessandro [Istituto Elettrotecnico Nazionale Galileo Ferraris, Strada delle Cacce 91, I-10135 Turin (Italy); Bertotti, Giorgio [Istituto Elettrotecnico Nazionale Galileo Ferraris, Strada delle Cacce 91, I-10135 Turin (Italy)

    2006-02-01

    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.

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

    International Nuclear Information System (INIS)

    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

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

    Science.gov (United States)

    Johnson, Sara B; Riis, Jenna L; Noble, Kimberly G

    2016-04-01

    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

  13. Measuring Asymmetric Interactions in Resting State Brain Networks.

    Science.gov (United States)

    Joshi, Anand A; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M

    2015-01-01

    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  14. 9 CFR 146.24 - Terminology and classification; States.

    Science.gov (United States)

    2010-01-01

    ..., DEPARTMENT OF AGRICULTURE LIVESTOCK IMPROVEMENT NATIONAL POULTRY IMPROVEMENT PLAN FOR COMMERCIAL POULTRY... § 146.23(a) of this part; (ii) All egg-type chicken breeding flocks in production within the State are... poultry disease diagnostic services within the State are required to report to the Official State...

  15. Resting State Brain Entropy Alterations in Relapsing Remitting Multiple Sclerosis.

    Science.gov (United States)

    Zhou, Fuqing; Zhuang, Ying; Gong, Honghan; Zhan, Jie; Grossman, Murray; Wang, Ze

    2016-01-01

    Brain entropy (BEN) mapping provides a novel approach to characterize brain temporal dynamics, a key feature of human brain. Using resting state functional magnetic resonance imaging (rsfMRI), reliable and spatially distributed BEN patterns have been identified in normal brain, suggesting a potential use in clinical populations since temporal brain dynamics and entropy may be altered in disease conditions. The purpose of this study was to characterize BEN in multiple sclerosis (MS), a neurodegenerative disease that affects millions of people. Since currently there is no cure for MS, developing treatment or medication that can slow down its progression represents a high research priority, for which validating a brain marker sensitive to disease and the related functional impairments is essential. Because MS can start long time before any measurable symptoms and structural deficits, assessing the dynamic brain activity and correspondingly BEN may provide a critical way to study MS and its progression. Because BEN is new to MS, we aimed to assess BEN alterations in the relapsing-remitting MS (RRMS) patients using a patient versus control design, to examine the correlation of BEN to clinical measurements, and to check the correlation of BEN to structural brain measures which have been more often used in MS studies. As compared to controls, RRMS patients showed increased BEN in motor areas, executive control area, spatial coordinating area, and memory system. Increased BEN was related to greater disease severity as measured by the expanded disability status scale (EDSS) and greater tissue damage as indicated by the mean diffusivity. Patients also showed decreased BEN in other places, which was associated with less disability or fatigue, indicating a disease-related BEN re-distribution. Our results suggest BEN as a novel and useful tool for characterizing RRMS. PMID:26727514

  16. Resting State Brain Entropy Alterations in Relapsing Remitting Multiple Sclerosis.

    Directory of Open Access Journals (Sweden)

    Fuqing Zhou

    Full Text Available Brain entropy (BEN mapping provides a novel approach to characterize brain temporal dynamics, a key feature of human brain. Using resting state functional magnetic resonance imaging (rsfMRI, reliable and spatially distributed BEN patterns have been identified in normal brain, suggesting a potential use in clinical populations since temporal brain dynamics and entropy may be altered in disease conditions. The purpose of this study was to characterize BEN in multiple sclerosis (MS, a neurodegenerative disease that affects millions of people. Since currently there is no cure for MS, developing treatment or medication that can slow down its progression represents a high research priority, for which validating a brain marker sensitive to disease and the related functional impairments is essential. Because MS can start long time before any measurable symptoms and structural deficits, assessing the dynamic brain activity and correspondingly BEN may provide a critical way to study MS and its progression. Because BEN is new to MS, we aimed to assess BEN alterations in the relapsing-remitting MS (RRMS patients using a patient versus control design, to examine the correlation of BEN to clinical measurements, and to check the correlation of BEN to structural brain measures which have been more often used in MS studies. As compared to controls, RRMS patients showed increased BEN in motor areas, executive control area, spatial coordinating area, and memory system. Increased BEN was related to greater disease severity as measured by the expanded disability status scale (EDSS and greater tissue damage as indicated by the mean diffusivity. Patients also showed decreased BEN in other places, which was associated with less disability or fatigue, indicating a disease-related BEN re-distribution. Our results suggest BEN as a novel and useful tool for characterizing RRMS.

  17. LU Invariants and Canonical Forms and SLOCC Classification of Pure 3-Qubit States

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    In this paper the entanglement of pure 3-qubit states is discussed. The local unitary (LU) polynomial invariants that are closely related to the canonical forms are constructed and the relations of the coefficients of the canonical forms are given. Then the stochastic local operations and classical communication (SLOCC) classification of the states are discussed on the basis of the canonical forms, and the symmetric canonical form of the states without 3-tangle is discussed. Finally, we give the relation between the LU polynomial invariants and SLOCC classification.

  18. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Zhao, Qibin; Wang, Xingyu; Cichocki, Andrzej

    2014-02-01

    Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI. PMID:24344691

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

    Science.gov (United States)

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

    2016-01-01

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

  20. Brain drain and productivity growth: are small states different?

    OpenAIRE

    Schiff, Maurice; Wang, Yanling

    2008-01-01

    This paper examines the impact of North-South trade-related technology diffusion on TFP growth in small and large states in the South. The main findings are: i) TFP growth increases with North-South trade-related technology diffusion, with education, and with the interaction between the two, and it decreases with the emigration of skilled labor (brain drain); ii) these effects are substantially (over three times) larger in small states than in large ones. Small states also exhibit a much high...

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Gemma Modinos

    2013-02-01

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

  3. A Plastic Temporal Brain Code for Conscious State Generation

    Directory of Open Access Journals (Sweden)

    Birgitta Dresp-Langley

    2009-01-01

    Full Text Available Consciousness is known to be limited in processing capacity and often described in terms of a unique processing stream across a single dimension: time. In this paper, we discuss a purely temporal pattern code, functionally decoupled from spatial signals, for conscious state generation in the brain. Arguments in favour of such a code include Dehaene et al.'s long-distance reverberation postulate, Ramachandran's remapping hypothesis, evidence for a temporal coherence index and coincidence detectors, and Grossberg's Adaptive Resonance Theory. A time-bin resonance model is developed, where temporal signatures of conscious states are generated on the basis of signal reverberation across large distances in highly plastic neural circuits. The temporal signatures are delivered by neural activity patterns which, beyond a certain statistical threshold, activate, maintain, and terminate a conscious brain state like a bar code would activate, maintain, or inactivate the electronic locks of a safe. Such temporal resonance would reflect a higher level of neural processing, independent from sensorial or perceptual brain mechanisms.

  4. 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.

  5. 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.

  6. 77 FR 16925 - Medical Devices; Neurological Devices; Classification of the Near Infrared Brain Hematoma Detector

    Science.gov (United States)

    2012-03-23

    ... classification will be the initial classification of the device. Within 30 days after the issuance of an order... intend to market this type of device must submit to FDA a premarket notification, prior to marketing the... Orders 12866 and 13563 direct Agencies to assess all costs and benefits of available...

  7. A comparison of classification algorithms in session-to-session generalisation in brain pattern recognition

    OpenAIRE

    Klaesson, Eric; Dahlin, Victor

    2014-01-01

    A brain computer interface (BCI) is a system to interpret a user’s intentionthrough using the users brain activity. The system is todayforemost used for research for disabled people. Electroencephalography(EEG) is the most common method used by BCI to record the brainactivity of the user, where the relevant information is read from thebeta and mu waves in the brain. These brain waves are from the partof the brain that is activated when user are doing problem solving orimagining moving a part ...

  8. Performance of dry electrode with bristle in recording EEG rhythms across brain state changes.

    Science.gov (United States)

    Kitoko, Vangu; Nguyen, Tuan N; Nguyen, Jordan S; Tran, Yvonne; Nguyen, Hung T

    2011-01-01

    In this paper we evaluate the physiological performance of a silver-silver chloride dry electrode with bristle (B-Electrode) in recording EEG data. For this purpose, we compare the performance of the bristle electrode in recording EEG data with the standard wet gold-plated cup electrode (G-Electrode) using two different brain state change tasks including resting condition with eyes-closed and performing mathematical task with eyes-open. Using a 2 channel recording device, eyes-closed command data were collected from each of 6 participants for a period of 20 sec and the same procedure was applied for the mathematical calculation task. These data were used for statistical and classification analyse. Although, B-electrode has shown a slightly higher performance compared with G-electrode in both tasks, but analyse did not reveal any significant differences between both electrodes in all six subjects tested.

  9. 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

  10. Classification of water quality according to Czechoslovak State Standard 83 0602 with respect to radioactivity

    International Nuclear Information System (INIS)

    The example is given of the classification of the quality of surface waters in the catchment of the Berounka river in which the impact was monitored of radioactive raw materials mining. The most sensitive indicator of mining activity was gross alpha activity. Classification of the occurrence of radioactive substances in surface waters was made according to Czechoslovak State Standard 830602 and according to the amended draft standard which allows greater differentiation of quality cateo.ories between areas significantly affected by the source of contamination and areas which are not affected. (M.D.)

  11. 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.

  12. On the classification of flaring states of blazar

    CERN Document Server

    Resconi, E; Gross, A; Costamante, L; Flaccomio, E

    2009-01-01

    The time evolution of the electromagnetic emission from blazars, in particular high frequency peaked sources (HBLs), displays irregular activity not yet understood. In this work we report a methodology capable of characterizing the time behavior of these variable objects. The Maximum Likelihood Blocks (MLBs) is a model-independent estimator which sub-divides the light curve into time blocks, whose length and amplitude are compatible with states of constant emission rate of the observed source. The MLBs yields the statistical significance in the rate variations and strongly suppresses the noise fluctuations in the light curves. We apply 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, which consist of 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 character...

  13. Classification of trivial spin-1 tensor network states on a square lattice

    Science.gov (United States)

    Lee, Hyunyong; Han, Jung Hoon

    2016-09-01

    Classification of possible quantum spin liquid (QSL) states of interacting spin-1/2's in two dimensions has been a fascinating topic of condensed matter for decades, resulting in enormous progress in our understanding of low-dimensional quantum matter. By contrast, relatively little work exists on the identification, let alone classification, of QSL phases for spin-1 systems in dimensions higher than one. Employing the powerful ideas of tensor network theory and its classification, we develop general methods for writing QSL wave functions of spin-1 respecting all the lattice symmetries, spin rotation, and time reversal with trivial gauge structure on the square lattice. We find 25 distinct classes characterized by five binary quantum numbers. Several explicit constructions of such wave functions are given for bond dimensions D ranging from two to four, along with thorough numerical analyses to identify their physical characters. Both gapless and gapped states are found. The topological entanglement entropy of the gapped states is close to zero, indicative of topologically trivial states. In D =4 , several different tensors can be linearly combined to produce a family of states within the same symmetry class. A rich "phase diagram" can be worked out among the phases of these tensors, as well as the phase transitions among them. Among the states we identified in this putative phase diagram is the plaquette-ordered phase, gapped resonating valence bond phase, and a critical phase. A continuous transition separates the plaquette-ordered phase from the resonating valence bond phase.

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

    Science.gov (United States)

    Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

    2016-01-01

    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.

  15. 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.

  16. 4-CLASS MOTOR IMAGERY CLASSIFICATION FOR POST STROKE REHABILITATION USING BRAIN-COMPUTER INTERFACE

    OpenAIRE

    Aarathi Kumar*, Nisha. P. V

    2016-01-01

    Brain-Computer Interface (BCI) is a mechanism that helps in the control/communication of one’s environment through the brain signals obtained directly from the brain via an EEG signal acquisition unit. A BCI incorporating Motor Imagery for post-stroke rehabilitation of upper limbs and knee in fully disabled patients is designed. It helps in restoring some of the activities of the daily living. It aids post-stroke sufferers to carry out functionalities like movement of right an...

  17. 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.

  18. Frequency dependent topological patterns of resting-state brain networks.

    Directory of Open Access Journals (Sweden)

    Long Qian

    Full Text Available The topological organization underlying brain networks has been extensively investigated using resting-state fMRI, focusing on the low frequency band from 0.01 to 0.1 Hz. However, the frequency specificities regarding the corresponding brain networks remain largely unclear. In the current study, a data-driven method named complementary ensemble empirical mode decomposition (CEEMD was introduced to separate the time series of each voxel into several intrinsic oscillation rhythms with distinct frequency bands. Our data indicated that the whole brain BOLD signals could be automatically divided into five specific frequency bands. After applying the CEEMD method, the topological patterns of these five temporally correlated networks were analyzed. The results showed that global topological properties, including the network weighted degree, network efficiency, mean characteristic path length and clustering coefficient, were observed to be most prominent in the ultra-low frequency bands from 0 to 0.015 Hz. Moreover, the saliency of small-world architecture demonstrated frequency-density dependency. Compared to the empirical mode decomposition method (EMD, CEEMD could effectively eliminate the mode-mixing effects. Additionally, the robustness of CEEMD was validated by the similar results derived from a split-half analysis and a conventional frequency division method using the rectangular window band-pass filter. Our findings suggest that CEEMD is a more effective method for extracting the intrinsic oscillation rhythms embedded in the BOLD signals than EMD. The application of CEEMD in fMRI data analysis will provide in-depth insight in investigations of frequency specific topological patterns of the dynamic brain networks.

  19. 78 FR 9929 - Current Traumatic Brain Injury State Implementation Partnership Grantees; Non-Competitive One...

    Science.gov (United States)

    2013-02-12

    ... HUMAN SERVICES Health Resources and Services Administration Current Traumatic Brain Injury State...-Competitive One-Year Extension Funds for Current Traumatic Brain Injury (TBI) State Implementation Partnership... by the Traumatic Brain Injury Act of 1996 (Pub. L. 104-166) and was most recently reauthorized by...

  20. 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 activ

  1. 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

    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 activ

  2. Stability of thalamocortical synaptic transmission across awake brain states.

    Science.gov (United States)

    Stoelzel, Carl R; Bereshpolova, Yulia; Swadlow, Harvey A

    2009-05-27

    Sensory cortical neurons are highly sensitive to brain state, with many neurons showing changes in spatial and/or temporal response properties and some neurons becoming virtually unresponsive when subjects are not alert. Although some of these changes are undoubtedly attributable to state-related filtering at the thalamic level, another likely source of such effects is the thalamocortical (TC) synapse, where activation of nicotinic receptors on TC terminals have been shown to enhance synaptic transmission in vitro. However, monosynaptic TC synaptic transmission has not been directly examined during different states of alertness. Here, in awake rabbits that shifted between alert and non-alert EEG states, we examined the monosynaptic TC responses and short-term synaptic dynamics generated by spontaneous impulses of single visual and somatosensory TC neurons. We did this using spike-triggered current source-density analysis, an approach that enables assessment of monosynaptic extracellular currents generated in different cortical layers by impulses of single TC afferents. Spontaneous firing rates of TC neurons were higher, and burst rates were much lower in the alert state. However, we found no state-related changes in the amplitude of monosynaptic TC responses when TC spikes with similar preceding interspike interval were compared. Moreover, the relationship between the preceding interspike interval of the TC spike and postsynaptic response amplitude was not influenced by state. These data indicate that TC synaptic transmission and dynamics are highly conserved across different states of alertness and that observed state-related changes in receptive field properties that occur at the cortical level result from other mechanisms.

  3. 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.

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

    International Nuclear Information System (INIS)

    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

  5. 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.

  6. Is Brain in a Superfluid State? Physics of Consciousness

    CERN Document Server

    Chakraverty, Benoy

    2010-01-01

    The article "Physics of Consciousness" treats mind as an abstract Hilbert space with a set of orthogonal base vectors to describe information like particles, which are considered to be the elementary excitation of a quantum field. A non-Hermitian operator of Self is introduced to create these information like particles which in turn will constitute a coherent information field. The non - zero average of this self operator is shown to constitute our basic I. Awareness and consciousness is described very simply as a response function of these operators to external world. We show with a very simple neural model how a baby less than two years old develop self-awareness as the neural connectivity achieves a critical value. The all-important I is the basic cognitive order parameter of each human brain and is a result of thermodynamic phase transition from a chaotic disordered state to a symmetry broken coherent ordered state, very akin to physics of superfluidity.

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

    Directory of Open Access Journals (Sweden)

    Mark Plitt

    2015-01-01

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

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

    NARCIS (Netherlands)

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

    1997-01-01

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

  9. Choosing among alternative classification criteria to measure the labour force state

    OpenAIRE

    Battistin, Erich; Rettore, Enrico; Trivellato, Ugo

    2005-01-01

    Current labour force counting relies on general guidelines set by the International Labour Office(ILO) to classify individuals into three labour force states: employment, unemployment and in activity. However, the resulting statistics areknown to be sensitive to slight variations of operational definitions prima facie consistent with the general guidelines. In this paper two alternative classification criteria are considered: a 'strict' criterion followed by Eurostat, which results from a str...

  10. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zimu; DENG Zhidong

    2013-01-01

    A two-stage state recognition method is proposed for asynchronous SSVEP (steady-state visual evoked potential) based brain-computer interface (SBCI) system.The two-stage method is composed of the idle state (IS) detection and control state (CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.

  11. Classification of Sitting States for the Humanoid Robot SJTU-HR1

    Institute of Scientific and Technical Information of China (English)

    Jialun Yang; Feng Gao

    2011-01-01

    The classification of sitting issues is investigated since detailed state classification for humanoid robots plays a key role in the practical application of humanoid robots, particularly for the humanoid robots doing complicated tasks. This paper presents the concept, the characteristics tree, and the prototype of the humanoid robot SJTU-HR1. The basic states for humanoid robots are proposed, including lying, sitting, standing, and handstanding. Moreover, the sitting states are classified into several states from the viewpoint of topology. The GF (generalized function) set theory is applied to achieve the kinematic characteristics of the interested end-effectors of the humanoid robot SJTU-HR1. Finally, the results indicate that a large number of the siring states can be represented by the meaningful notations systematically. Furthermore, the one-to-one correspondence between the state and kinematic characteristics of the interested end-effectors of the SJTU-HR1 leads to deeper insight into the capabilities of the humanoid robot SJTU-HR1.

  12. DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

    Science.gov (United States)

    Yan, Chao-Gan; Wang, Xin-Di; Zuo, Xi-Nian; Zang, Yu-Feng

    2016-07-01

    Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

  13. STATE SPACE POINT DISTRIBUTION PARAMETER FOR SUPPORT VECTOR MACHINE BASED CV UNIT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    N K Narayanan

    2012-01-01

    Full Text Available In this paper we extend Support Vector Machines (SVM for speaker independent Consonant – Vowel (CV unit classification. Here we adopt the technique known as Decision Directed Acyclic Graph (DDAG , which is used to combine many two class classifiers into multiclass classifier. Using Reconstructed State Space (RSS based State Space Point Distribution (SSPD parameters, we obtain an average speaker independent phoneme recognition accuracy of 90% on the Malayalam V/CV speech unit database. The recognition results indicate that this method is efficient and can be adopted for developing a complete speech recognition system for Malayalam language.

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

    OpenAIRE

    Gwen A. Frishkoff; Robert M. Frank; Jiawei Rong; Dejing Dou; Joseph Dien; Laura K. Halderman

    2007-01-01

    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 r...

  15. An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

    Directory of Open Access Journals (Sweden)

    Maria Goreti Rosa-Freitas

    2007-06-01

    Full Text Available Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I, submontane (II, plateau (III, lowland (IV, and alluvial (V. Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima, savannah (VII, cerrado, and wetland (VIII, campinarana. Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.

  16. 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.

  17. 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. PMID:27221544

  18. What should be the roles of conscious states and brain states in theories of mental activity?

    Directory of Open Access Journals (Sweden)

    Donelson E Dulany

    2011-03-01

    Full Text Available 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?

  19. 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...

  20. Outcome Classification of Preschool Children with Autism Spectrum Disorders Using Mri Brain Measures.

    Science.gov (United States)

    Akshoomoff, Natacha; Lord, Catherine; Lincoln, Alan J.; Courchesne, Rachel Y.; Carper, Ruth A.; Townsend, Jeanne; Courchesne, Eric

    2004-01-01

    Objective: To test the hypothesis that a combination of magnetic resonance imaging (MRI) brain measures obtained during early childhood distinguish children with autism spectrum disorders (ASD) from typically developing children and is associated with functional outcome. Method: Quantitative MRI technology was used to measure gray and white matter…

  1. Brain Factor and Its Stating Role in Enterprises’ Competitive Recovery

    OpenAIRE

    Mikhail N. Dudin; Nikolai V. Lyasnikov; Yuri V. Horikov

    2013-01-01

    The article deals with general theses of the brain capital concept as the factor of business competitive recovery, as well as the entrepreneurship aspects in knowledge economy. The article shows the role of brain factor in enterprises’ competitive recovery

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

    Energy Technology Data Exchange (ETDEWEB)

    Magnin, Benoit [UMR-S 678, Inserm, Paris (France)]|[UMR-S 610, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France); Mesrob, Lilia [UMR-S 610, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France); Kinkingnehun, Serge [UMR-S 610, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France)]|[BRAIN, Vitry-sur-Seine (France); Pelegrini-Issac, Melanie [UMR-S 678, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France); Colliot, Olivier [IFR 49, Gif-sur-Yvette (France)]|[UPR 640 LENA, CNRS, Paris (France); Sarazin, Marie; Dubois, Bruno [UMR-S 610, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France)]|[Pitie-Salpetriere Hospital, Department of Neurology, Paris (France); Lehericy, Stephane [UMR-S 610, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France)]|[UMPC Univ. Paris 06, Center for NeuroImaging Research-CENIR, Paris (France)]|[Pitie-Salpetriere Hospital, Department of Neuroradiology, Paris (France); Benali, Habib [UMR-S 678, Inserm, Paris (France)]|[UMPC Univ Paris 06, Faculte de Medecine Pitie-Salpetriere, Paris (France)]|[IFR 49, Gif-sur-Yvette (France)]|[UNF/CRIUGM, Universite de Montreal, Montreal, QC (Canada)

    2009-02-15

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

  3. 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.

  4. 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 stim

  5. Classification of cirrhotic patients with or without minimal hepatic encephalopathy and healthy subjects using resting-state attention-related network analysis.

    Directory of Open Access Journals (Sweden)

    Hua-Jun Chen

    Full Text Available BACKGROUND: Attention deficit is an early and key characteristic of minimal hepatic encephalopathy (MHE and has been used as indicator for MHE detection. The aim of this study is to classify the cirrhotic patients with or without MHE (NMHE and healthy controls (HC using the resting-state attention-related brain network analysis. METHODS AND FINDINGS: Resting-state fMRI was administrated to 20 MHE patients, 21 NMHE patients, and 17 HCs. Three attention-related networks, including dorsal attention network (DAN, ventral attention network (VAN, and default mode network (DMN, were obtained by independent component analysis. One-way analysis of covariance was performed to determine the regions of interest (ROIs showing significant functional connectivity (FC change. With FC strength of ROIs as indicators, Linear Discriminant Analysis (LDA was conducted to differentiate MHE from HC or NMHE. Across three groups, significant FC differences were found within DAN (left superior/inferior parietal lobule and right inferior parietal lobule, VAN (right superior parietal lobule, and DMN (bilateral posterior cingulate gyrus and precuneus, and left inferior parietal lobule. With FC strength of ROIs from three networks as indicators, LDA yielded 94.6% classification accuracy between MHE and HC (100% sensitivity and 88.2% specificity and 85.4% classification accuracy between MHE and NMHE (90.0% sensitivity and 81.0% specificity. CONCLUSIONS: Our results suggest that the resting-state attention-related brain network analysis can be useful in classification of subjects with MHE, NMHE, and HC and may provide a new insight into MHE detection.

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

    International Nuclear Information System (INIS)

    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.)

  7. Computerised cognitive training in acquired brain injury: A systematic review of outcomes using the International Classification of Functioning (ICF).

    Science.gov (United States)

    Sigmundsdottir, Linda; Longley, Wendy A; Tate, Robyn L

    2016-10-01

    Computerised cognitive training (CCT) is an increasingly popular intervention for people experiencing cognitive symptoms. This systematic review evaluated the evidence for CCT in adults with acquired brain injury (ABI), focusing on how outcome measures used reflect efficacy across components of the International Classification of Functioning, Disability and Health. Database searches were conducted of studies investigating CCT to treat cognitive symptoms in adult ABI. Scientific quality was rated using the PEDro-P and RoBiNT Scales. Ninety-six studies met the criteria. Most studies examined outcomes using measures of mental functions (93/96, 97%); fewer studies included measures of activities/participation (41/96, 43%) or body structures (8/96, 8%). Only 14 studies (15%) provided Level 1 evidence (randomised controlled trials with a PEDro-P score ≥ 6/10), with these studies suggesting strong evidence for CCT improving processing speed in multiple sclerosis (MS) and moderate evidence for improving memory in MS and brain tumour populations. There is a large body of research examining the efficacy of CCT, but relatively few Level 1 studies and evidence is largely limited to body function outcomes. The routine use of outcome measures of activities/participation would provide more meaningful evidence for the efficacy of CCT. The use of body structure outcome measures (e.g., neuroimaging) is a newly emerging area, with potential to increase understanding of mechanisms of action for CCT. PMID:26965034

  8. 基于相应簇回声状态网络静态分类方法%Echo State Networks for Static Classification with Corresponding Clusters

    Institute of Scientific and Technical Information of China (English)

    郭嘉; 雷苗; 彭喜元

    2011-01-01

    借鉴模仿哺乳动物大脑皮层分簇结构的复杂网络拓扑结构,提出一种基于相应簇储备池回声状态网络的分类方法.将时间窗函数机制引入到回声状态网络储备池的构建中,利用具体问题中需分类数据的类别数量,生成具有对应分簇数目的储备池,以期提高分类精度.基于标准数据集和模拟电路故障诊断的实验验证结果表明,本文方法与标准回声状态网络等方法相比具有更高的分类精度.%A classification method using echo state networks (ESNs) with corresponding clusters is proposed, which is inspired by complex network topologies imitating cortical networks of the mammalian brain. The time windows functions are adopted to construct multiple-cluster reservoir. The number of clusters corresponds with the number of classes in specific classification problems to improve the classification accuracy. Experimental results based on the standard datasets and analog circuit fault diagnosis show that the proposed method outperforms the original echo state networks.

  9. Plasticity of resting state brain networks in recovery from stress

    Directory of Open Access Journals (Sweden)

    Jose Miguel Soares

    2013-12-01

    Full Text Available Chronic stress has been widely reported to have deleterious impact in multiple biological systems. Specifically, structural and functional remodelling of several brain regions following prolonged stress exposure have been described; importantly, some of these changes are eventually reversible. Recently, we showed the impact of stress on resting state networks (RSNs, but nothing is known about the plasticity of RSNs after recovery from stress. Herein, we examined the plasticity of RSNs, both at functional and structural levels, by comparing the same individuals before and after recovery from the exposure to chronic stress; results were also contrasted with a control group. Here we show that the stressed individuals after recovery displayed a decreased resting functional connectivity in the default mode network (DMN, ventral attention network (VAN and sensorimotor network (SMN when compared to themselves immediately after stress; however, this functional plastic recovery was only partial as when compared with the control group, as there were still areas of increased connectivity in dorsal attention network (DAN, SMN and primary visual network (VN in participants recovered from stress. Data also shows that participants after recovery from stress displayed increased deactivations in DMN, SMN and auditory network (AN, to levels similar to those of controls, showing a normalization of the deactivation pattern in RSNs after recovery from stress. In contrast, structural changes (volumetry of the brain areas involving these networks are absent after the recovery period. These results reveal plastic phenomena in specific RSNs and a functional remodeling of the activation-deactivation pattern following recovery from chronic-stress, which is not accompanied by significant structural plasticity.

  10. Classification of EEG Signals in a Brain-Computer Interface System

    OpenAIRE

    Larsen, Erik Andreas

    2011-01-01

    Electroencephalography (EEG) equipment are becoming more available on thepublic market, which enables more diverse research in a currently narrow field.The Brain-Computer Interface (BCI) community recognize the need for systemsthat makes BCI more user-friendly, real-time, manageable and suited for peoplethat are not forced to use them, like clinical patients, and those who are disabled.Thus, this project is an effort to seek such improvements, having a newly availablemarket product to experim...

  11. 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 ai

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

    Science.gov (United States)

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

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities. PMID:27050535

  13. Brain

    Science.gov (United States)

    ... will return after updating. Resources Archived Modules Updates Brain Cerebrum The cerebrum is the part of the ... the outside of the brain and spinal cord. Brain Stem The brain stem is the part of ...

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

    International Nuclear Information System (INIS)

    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)

  15. TMS-evoked changes in brain-state dynamics quantified by using EEG data.

    Science.gov (United States)

    Mutanen, Tuomas; Nieminen, Jaakko O; Ilmoniemi, Risto J

    2013-01-01

    To improve our understanding of the combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) method in general, it is important to study how the dynamics of the TMS-modulated brain activity differs from the dynamics of spontaneous activity. In this paper, we introduce two quantitative measures based on EEG data, called mean state shift (MSS) and state variance (SV), for evaluating the TMS-evoked changes in the brain-state dynamics. MSS quantifies the immediate TMS-elicited change in the brain state, whereas SV shows whether the rate at which the brain state changes is modulated by TMS. We report a statistically significant increase for a period of 100-200 ms after the TMS pulse in both MSS and SV at the group level. This indicates that the TMS-modulated brain state differs from the spontaneous one. Moreover, the TMS-modulated activity is more vigorous than the natural activity.

  16. Statewide lake classification utilizing LANDSAT imagery for the state of Wisconsin

    International Nuclear Information System (INIS)

    A cooperative program between the Wisconsin Department of Natural Resources and the University of Wisconsin-Madison resulted in the assessment of the trophic condition of approximately 3,000 significant inland lakes in Wisconsin. The feasibility of using both photographic and digital representations of LANDSAT multispectral scanner data for lake classification was investigated. The result was the development of a nearly automated system which, with minimal human interaction, locates and extracts the lake data, then corrects the data for atmospheric effects, and finally classifies all the significant lakes in the state as to trophic condition

  17. Plasticity of brain wave network interactions and evolution across physiologic states

    Directory of Open Access Journals (Sweden)

    Kang K. L. Liu

    2015-10-01

    Full Text Available Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very

  18. Plasticity of brain wave network interactions and evolution across physiologic states.

    Science.gov (United States)

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

    2015-01-01

    Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of

  19. Plasticity of brain wave network interactions and evolution across physiologic states

    Science.gov (United States)

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

    2015-01-01

    Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of

  20. Automatic Brain Lesion Detection and Classification Based on Diffusion-Weighted Imaging using Adaptive Thresholding and a Rule-Based Classifier

    Directory of Open Access Journals (Sweden)

    N.M. Saad

    2014-12-01

    Full Text Available In this paper, a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed. Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke, solid tumor, chronic stroke, and necrosis are analyzed. The analysis is divided into four stages: pre-processing, segmentation, feature extraction, and classification. In the detection and segmentation stage, the image is divided into 8x8 macro-block regions. Adaptive thresholding technique is applied to segment the lesion’s region. Statistical features are measured on the region of interest. A rulebased classifier is used to classify four types of lesions. Jaccard’s similarity index of the segmentation results for acute stroke, solid tumor, chronic stroke, and necrosis are 0.8, 0.55, 0.27, and 0.42, respectively. The classification accuracy is 93% for acute stroke, 73% for solid tumor, 84% for chronic stroke, and 60% for necrosis. Overall, adaptive thresholding provides high segmentation performance for hyper-intensity lesions. The best segmentation and classification performance is achieved for acute stroke. The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions.

  1. Brain Factor and Its Stating Role in Enterprises’ Competitive Recovery

    Directory of Open Access Journals (Sweden)

    Mikhail N. Dudin

    2013-01-01

    Full Text Available The article deals with general theses of the brain capital concept as the factor of business competitive recovery, as well as the entrepreneurship aspects in knowledge economy. The article shows the role of brain factor in enterprises’ competitive recovery

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

    OpenAIRE

    Tijn M Schouten; Marisa Koini; Frank de Vos; Stephan Seiler; Jeroen van der Grond; Anita Lechner; Anne Hafkemeijer; Christiane Möller; Reinhold Schmidt; Mark de Rooij; Rombouts, Serge A.R.B.

    2016-01-01

    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 (...

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

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

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

  4. 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...

  5. The Effect of Aging on Resting-State Brain Function: An fMRI Study

    Directory of Open Access Journals (Sweden)

    A. H. Batouli

    2009-11-01

    Full Text Available Background/Objective: Healthy aging may be accompanied by some types of cognitive impairment; moreover, normal aging may cause natural atrophy in the healthy human brain. The hypothesis of the healthy aging brain is the structural changes together with the functional impairment happening. The brain struggles to over-compensate for those functional age-related impairments to continue as a healthy brain in its functions. Our goal in this study was to evaluate the effects of aging on the resting-state activation network of the brain using the multi-session probabilistic independent component analysis algorithm (PICA. "nPatients and Methods: We compared the resting-state brain activities between two groups of healthy aged and young subjects, so we examined 30 right-handed subjects and finally 12 healthy aging and 11 controls were enrolled in the study. "nResults: Our results showed that during the resting-state, older brains benefit from larger areas of activation, while in young competent brains, higher activation occurs in terms of greater intensity. These results were obtained in prefrontal areas as regions with regard to memory function as well as the posterior cingulate cortex (PCC as parts of the default mode network. Meanwhile, we reached the same results after normalization of activation size with total brain volume. "nConclusion: The difference in activation patterns between the two groups shows the brain's endeavor to compensate the functional impairment.

  6. 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 ai

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

    International Nuclear Information System (INIS)

    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 (18F-FDG PET), proton magnetic resonance spectroscopy (1H 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 18F-FDG PET and 1H 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%. Conclusion: This study suggests

  9. Comparing implementations of magnetic-resonance-guided fluorescence molecular tomography for diagnostic classification of brain tumors

    Science.gov (United States)

    Davis, Scott C.; Samkoe, Kimberley S.; O'Hara, Julia A.; Gibbs-Strauss, Summer L.; Paulsen, Keith D.; Pogue, Brian W.

    2010-09-01

    Fluorescence molecular tomography (FMT) systems coupled to conventional imaging modalities such as magnetic resonance imaging (MRI) and computed tomography provide unique opportunities to combine data sets and improve image quality and content. Yet, the ideal approach to combine these complementary data is still not obvious. This preclinical study compares several methods for incorporating MRI spatial prior information into FMT imaging algorithms in the context of in vivo tissue diagnosis. Populations of mice inoculated with brain tumors that expressed either high or low levels of epidermal growth factor receptor (EGFR) were imaged using an EGF-bound near-infrared dye and a spectrometer-based MRI-FMT scanner. All data were spectrally unmixed to extract the dye fluorescence from the tissue autofluorescence. Methods to combine the two data sets were compared using student's t-tests and receiver operating characteristic analysis. Bulk fluorescence measurements that made up the optical imaging data set were also considered in the comparison. While most techniques were able to distinguish EGFR(+) tumors from EGFR(-) tumors and control animals, with area-under-the-curve values=1, only a handful were able to distinguish EGFR(-) tumors from controls. Bulk fluorescence spectroscopy techniques performed as well as most imaging techniques, suggesting that complex imaging algorithms may be unnecessary to diagnose EGFR status in these tissue volumes.

  10. Automatic Region-Based Brain Classification of MRI-T1 Data.

    Science.gov (United States)

    Yazdani, Sepideh; Yusof, Rubiyah; Karimian, Alireza; Mitsukira, Yasue; Hematian, Amirshahram

    2016-01-01

    Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness. PMID:27096925

  11. Brain Tumors and Brain Tumor Research Progress in Image Classification%脑肿瘤及脑肿瘤图像分类的研究进展

    Institute of Scientific and Technical Information of China (English)

    俞海平; 邬立保

    2011-01-01

    Many methods of brain tumor classification,there is no uniform classification^! A variety of tumors and pathological features of the different tissue, the study of benign and malignant, and things are not the same characteristics. Usually can be classified as histological.-(l) Originated in glial tumors: astrocytoma, less support glial cell tumors, medulloblastoma, etc.(2) Originated in meningeal tumors: meningioma, meningeal sarcoma, arachnoid cyst.(3) Originated in the pituitary tumors: tired color cell adenoma, acidophilic, basophilic cell adenoma.(4) Originated in cranial nerve tumors: acoustic neuroma, trigeminal nerve sheath tumors and other tumors.(S) Originated from residual embryonic tissue: craniopharyngioma, chordoma, dermoid cyst (6) Originated in vascular cells: vascular tumors and vascular reticular cell tumor, etc.(7) Transfer or by other parts of the tumor invasion: a variety of metastatic tumors, and nasopharyn-geal carcinoma, etc.%脑肿瘤分类的方法很多,目前尚无统一的分类方法,并且各种肿瘤的组织发生与病理特征不同,其良性与恶性以及物学特性也不一样.通常按组织学可分类如下:(1)发源于神经胶质的肿瘤:星形细胞瘤、少支胶质细胞瘤、髓母细胞瘤等.(2)发源于脑膜的肿瘤:脑膜瘤、脑膜内瘤、蛛网膜囊肿等.(3)发源于垂体的肿瘤:厌色细胞腺瘤,嗜酸、嗜碱性细胞腺瘤.(4)发源于颅神经的肿瘤:听神经瘤、三叉神经瘤等各种神经鞘瘤.(5)发源于胚胎残余组织:颅咽管瘤、脊索瘤、皮样囊肿等.(6)发源于血管细胞:血管瘤及血管网织细胞瘤等.(7)由其它部位转移或侵入的肿瘤:各种转移瘤及鼻咽癌等.

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

    OpenAIRE

    Mark Plitt; Kelly Anne Barnes; Alex Martin

    2015-01-01

    Objectives: Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis. Methods: Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build ...

  13. Bayesian Models for Life Prediction and Fault-Mode Classification in Solid State Lamps

    Energy Technology Data Exchange (ETDEWEB)

    Lall, Pradeep; Wei, Junchao; Sakalaukus, Peter

    2015-04-19

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classifY failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identifY luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. It is expected that, the new test technique will allow the development of failure distributions without testing till L 70 life for the manifestation of failure.

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

    OpenAIRE

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

    2016-01-01

    Conventional neuroimaging analyses have revealed the computational specificity of localized 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...

  15. Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

    Directory of Open Access Journals (Sweden)

    John R. Pruett, Jr.

    2015-04-01

    Full Text Available Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs classified 6 versus 12 month-old infants (128 datasets above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

  16. 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)

    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.

  17. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    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 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...

  19. Simultaneous imaging of MR angiographic image and brain surface image using steady-state free precession

    Energy Technology Data Exchange (ETDEWEB)

    Takane, Atsushi; Tsuda, Munetaka (Hitachi Ltd., Katsuta, Ibaraki (Japan)); Koizumi, Hideaki; Koyama, Susumu; Yoshida, Takeyuki

    1993-09-01

    Synthesis of a brain surface image and an angiographic image representing brain surface vasculatures can be useful for pre-operational contemplation of brain surgery. Both brain surface images and brain surface vasculature images were successfully acquired simultaneously utilizing both FID signals and time-reversed FID signals created under steady-state free precession (SSFP). This simultaneous imaging method has several advantages. No positional discrepancies between both images and prolongation of scan time are anticipated because of concurrent acquisition of the two kinds of image data. Superimposition and stereo-display of both images enable understanding of their spatial relationship, and therefore afford a useful means for pre-operational simulation of brain surgery. (author).

  20. An abnormal resting-state functional brain network indicates progression towards Alzheimer’s disease*****

    Institute of Scientific and Technical Information of China (English)

    Jie Xiang; Hao Guo; Rui Cao; Hong Liang; Junjie Chen

    2013-01-01

    Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer’s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer’s disease) using the Alzheimer’s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer’s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest-ing-state functional network gradual y increased, while clustering coefficients gradual y decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and Alzhei-mer’s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventual y lead to diffuse brain injury and other cognitive impairments.

  1. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

    Science.gov (United States)

    Griffanti, Ludovica; Zamboni, Giovanna; Khan, Aamira; Li, Linxin; Bonifacio, Guendalina; Sundaresan, Vaanathi; Schulz, Ursula G; Kuker, Wilhelm; Battaglini, Marco; Rothwell, Peter M; Jenkinson, Mark

    2016-11-01

    Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. PMID:27402600

  2. A classification and description of the shrubland vegetation on Platberg, Eastern Free State, South Africa

    Directory of Open Access Journals (Sweden)

    Robert F. Brand

    2009-01-01

    Full Text Available The natural environment is constantly under threat from human-related activities. Platberg, overlooking the town of Harrismith in the Free State, is an inselberg that presents a refuge for indigenous plants and animals. The natural vegetation of the area is threatened by various farming and grazing practices, as well as by commercial development. In order to obtain baseline data and to obtain an improved understanding of the long-term ecological processes, the vegetation of Platberg was investigated to establish Afroalpine floristic links to the Drakensberg, as well as for the management of natural resources. From a Two-Way Indicator-Species Analysis (TWINSPAN classification, refined by Braun–Blanquet methods, four major plant communities were identified, which were subdivided into fynbos, wetland, woody/shrub and grassland. A classification and description of the shrubland is presented in this article. The analysis showed the shrubland divided into 20 different plant communities, which are grouped into eight major communities, 13 sub-communities and eight variants. A total of 450 species was recorded from 109 relevés. A total of 24 endemic, or near-endemic, and Red Data species belonging to the Drakensberg Alpine Centre (DAC was collected, with 22 alien (introduced species also being recorded. Numerous floristic links with the DAC, the Cape Floristic Region and the Grassland Bioregions to the north and west were found.Conservation implications: The floristic composition and community analysis proves Platberg to be an important centre for plant diversity, with high species richness, a variety of habitats, and complex ecosystems. This description of the woodland communities can be used to assist with the setting of criteria for the management and protection of inselbergs in the province.

  3. The 10 Hz Frequency: A Fulcrum For Transitional Brain States

    Science.gov (United States)

    Garcia-Rill, E.; D’Onofrio, S.; Luster, B.; Mahaffey, S.; Urbano, F. J.; Phillips, C.

    2016-01-01

    A 10 Hz rhythm is present in the occipital cortex when the eyes are closed (alpha waves), in the precentral cortex at rest (mu rhythm), in the superior and middle temporal lobe (tau rhythm), in the inferior olive (projection to cerebellar cortex), and in physiological tremor (underlying all voluntary movement). These are all considered resting rhythms in the waking brain which are “replaced” by higher frequency activity with sensorimotor stimulation. That is, the 10 Hz frequency fulcrum is replaced on the one hand by lower frequencies during sleep, or on the other hand by higher frequencies during volition and cognition. The 10 Hz frequency fulcrum is proposed as the natural frequency of the brain during quiet waking, but is replaced by higher frequencies capable of permitting more complex functions, or by lower frequencies during sleep and inactivity. At the center of the transition shifts to and from the resting rhythm is the reticular activating system, a phylogenetically preserved area of the brain essential for preconscious awareness.

  4. Characterization of task-free and task-performance brain states via functional connectome patterns.

    Science.gov (United States)

    Zhang, Xin; Guo, Lei; Li, Xiang; Zhang, Tuo; Zhu, Dajiang; Li, Kaiming; Chen, Hanbo; Lv, Jinglei; Jin, Changfeng; Zhao, Qun; Li, Lingjiang; Liu, Tianming

    2013-12-01

    Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain.

  5. Team Classification in State Correctional Institutions: Its Association with Inmate and Staff Attitudes

    Science.gov (United States)

    Hepburn, John R.; Albonetti, Celesta A.

    1978-01-01

    Team classification seeks to bring together various levels of correctional staff and inmates to discuss and resolve issues pertaining to work and cell assignment, disciplinary action, furlough requests, and merit time considerations. Team classification, when successfully implemented, is positively associated with staff attitudes toward inmates,…

  6. Classification of first-episode schizophrenia patients and healthy subjects by automated MRI measures of regional brain volume and cortical thickness.

    Directory of Open Access Journals (Sweden)

    Yoichiro Takayanagi

    Full Text Available BACKGROUND: Although structural magnetic resonance imaging (MRI studies have repeatedly demonstrated regional brain structural abnormalities in patients with schizophrenia, relatively few MRI-based studies have attempted to distinguish between patients with first-episode schizophrenia and healthy controls. METHOD: Three-dimensional MR images were acquired from 52 (29 males, 23 females first-episode schizophrenia patients and 40 (22 males, 18 females healthy subjects. Multiple brain measures (regional brain volume and cortical thickness were calculated by a fully automated procedure and were used for group comparison and classification by linear discriminant function analysis. RESULTS: Schizophrenia patients showed gray matter volume reductions and cortical thinning in various brain regions predominantly in prefrontal and temporal cortices compared with controls. The classifiers obtained from 66 subjects of the first group successfully assigned 26 subjects of the second group with accuracy above 80%. CONCLUSION: Our results showed that combinations of automated brain measures successfully differentiated first-episode schizophrenia patients from healthy controls. Such neuroimaging approaches may provide objective biological information adjunct to clinical diagnosis of early schizophrenia.

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

    Science.gov (United States)

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

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features—mean, slope, variance, peak, skewness and kurtosis—are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. PMID:27252637

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2016-05-01

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

  10. Towards literature-based feature selection for diagnostic classification: A meta-analysis of resting-state fMRI in depression

    Directory of Open Access Journals (Sweden)

    Benedikt eSundermann

    2014-09-01

    Full Text Available Information derived from functional magnetic resonance imaging (fMRI during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD. Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA. 32 studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic

  11. 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.

  12. 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.

  13. Resting-state fMRI: A window into human brain plasticity

    OpenAIRE

    Guerra-Carrillo, B; Mackey, AP; Bunge, SA

    2014-01-01

    © The Author(s) 2014. Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level-dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-d...

  14. Steady state visually evoked potentials based Brain computer interface test outside the lab

    OpenAIRE

    Eduardo Francisco Caicedo Bravo; Jaiber Evelio Cardona Aristizábal

    2016-01-01

    Context: Steady State Visually Evoked Potentials (SSVEP) are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs) implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where th...

  15. The evolution of brain waves in altered states of consciousness (REM sleep and meditation)

    OpenAIRE

    Irina E. Chiş

    2009-01-01

    Aim: The aim of this study was to investigate the brain activity in REM sleep andmeditation; it was also studied in which way an appropriate musical background would affect theevolution of brain waves in these altered states of consciousness. Material and Method: The recordingswere done with a portable electroencephalograph, on a homogeneous group of human subjects (menaged 30-50 years). The subjects were monitored in their own bed, the length of sleep and how earlythey went to bed was up to ...

  16. Spin-glass model predicts metastable brain states that diminish in anesthesia

    Directory of Open Access Journals (Sweden)

    Anthony G Hudetz

    2014-12-01

    Full Text Available Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions defined on a group-aligned cortical surface map. Each region represented the population activity of a ~20mm2 area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of regions were calculated and averaged across subjects. In the model, each cortical region was allowed to interact with the 16 other regions that had the highest pair-wise correlation values. All regions stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T=0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep and increased activation (hyper-activation moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large

  17. 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...

  18. State of the art of current 3-D scoliosis classifications: a systematic review from a clinical perspective.

    Science.gov (United States)

    Donzelli, Sabrina; Poma, Salvatore; Balzarini, Luca; Borboni, Alberto; Respizzi, Stefano; Villafane, Jorge Hugo; Zaina, Fabio; Negrini, Stefano

    2015-01-01

    Scoliosis is a complex three dimensional (3D) deformity: the current lack of a 3D classification could hide something fundamental for scoliosis prognosis and treatment. A clear picture of the actually existing 3D classifications lacks. The aim of this systematic review was to identify all the 3D classification systems proposed until now in the literature with the aim to identify similarities and differences mainly in a clinical perspective.After a MEDLINE Data Base review, done in November 2013 using the search terms "Scoliosis/classification" [Mesh] and "scoliosis/classification and Imaging, three dimensional" [Mesh], 8 papers were included with a total of 1164 scoliosis patients, 23 hyperkyphosis and 25 controls, aged between 8 and 20 years, with curves from 10° to 81° Cobb, and various curve patterns. Six studies looked at the whole 3D spine and found classificatory parameters according to planes, angles and rotations, including: Plane of Maximal Curvature (PMC), Best Fit Plane, Cobb angles in bodily plane and PMC, Axial rotation of the apical vertebra and of the PMC, and geometric 3D torsion. Two studies used the regional (spinal) Top View of the spine and found classificatory parameters according to its geometrical properties (area, direction and barycenter) including: Ratio of the frontal and the sagittal size, Phase, Directions (total, thoracic and lumbar), and Shift. It was possible to find similarities among 10 out of the 16 the sub-groups identified by different authors with different methods in different populations.In summation, the state of the art of 3D classification systems include 8 studies which showed some comparability, even though of low level. The most useful one in clinical everyday practice, is far from being defined. More than 20 years passed since the definition of the third dimension of the scoliosis deformity, now the time has come for clinicians and bioengineers to start some real clinical application, and develop means to make this

  19. Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.

    Directory of Open Access Journals (Sweden)

    Lingling Yang

    Full Text Available Brain computer interfaces (BCIs offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

  20. A Method for Automated Classification of Parkinson's Disease Diagnosis Using an Ensemble Average Propagator Template Brain Map Estimated from Diffusion MRI.

    Science.gov (United States)

    Banerjee, Monami; Okun, Michael S; Vaillancourt, David E; Vemuri, Baba C

    2016-01-01

    Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder that affects patients in all countries and of all nationalities. Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques utilized for detection of neurologic diseases. Changes in structural biomarkers will likely play an important future role in assessing progression of many neurological diseases inclusive of PD. In this paper, we derived structural biomarkers from diffusion MRI (dMRI), a structural modality that allows for non-invasive inference of neuronal fiber connectivity patterns. The structural biomarker we use is the ensemble average propagator (EAP), a probability density function fully characterizing the diffusion locally at a voxel level. To assess changes with respect to a normal anatomy, we construct an unbiased template brain map from the EAP fields of a control population. Use of an EAP captures both orientation and shape information of the diffusion process at each voxel in the dMRI data, and this feature can be a powerful representation to achieve enhanced PD brain mapping. This template brain map construction method is applicable to small animal models as well as to human brains. The differences between the control template brain map and novel patient data can then be assessed via a nonrigid warping algorithm that transforms the novel data into correspondence with the template brain map, thereby capturing the amount of elastic deformation needed to achieve this correspondence. We present the use of a manifold-valued feature called the Cauchy deformation tensor (CDT), which facilitates morphometric analysis and automated classification of a PD versus a control population. Finally, we present preliminary results of automated discrimination between a group of 22 controls and 46 PD patients using CDT. This method may be possibly applied to larger population sizes and other parkinsonian syndromes in the near future. PMID

  1. A Method for Automated Classification of Parkinson’s Disease Diagnosis Using an Ensemble Average Propagator Template Brain Map Estimated from Diffusion MRI

    Science.gov (United States)

    Banerjee, Monami; Okun, Michael S.; Vaillancourt, David E.; Vemuri, Baba C.

    2016-01-01

    Parkinson’s disease (PD) is a common and debilitating neurodegenerative disorder that affects patients in all countries and of all nationalities. Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques utilized for detection of neurologic diseases. Changes in structural biomarkers will likely play an important future role in assessing progression of many neurological diseases inclusive of PD. In this paper, we derived structural biomarkers from diffusion MRI (dMRI), a structural modality that allows for non-invasive inference of neuronal fiber connectivity patterns. The structural biomarker we use is the ensemble average propagator (EAP), a probability density function fully characterizing the diffusion locally at a voxel level. To assess changes with respect to a normal anatomy, we construct an unbiased template brain map from the EAP fields of a control population. Use of an EAP captures both orientation and shape information of the diffusion process at each voxel in the dMRI data, and this feature can be a powerful representation to achieve enhanced PD brain mapping. This template brain map construction method is applicable to small animal models as well as to human brains. The differences between the control template brain map and novel patient data can then be assessed via a nonrigid warping algorithm that transforms the novel data into correspondence with the template brain map, thereby capturing the amount of elastic deformation needed to achieve this correspondence. We present the use of a manifold-valued feature called the Cauchy deformation tensor (CDT), which facilitates morphometric analysis and automated classification of a PD versus a control population. Finally, we present preliminary results of automated discrimination between a group of 22 controls and 46 PD patients using CDT. This method may be possibly applied to larger population sizes and other parkinsonian syndromes in the near future. PMID

  2. A Method for Automated Classification of Parkinson's Disease Diagnosis Using an Ensemble Average Propagator Template Brain Map Estimated from Diffusion MRI.

    Science.gov (United States)

    Banerjee, Monami; Okun, Michael S; Vaillancourt, David E; Vemuri, Baba C

    2016-01-01

    Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder that affects patients in all countries and of all nationalities. Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques utilized for detection of neurologic diseases. Changes in structural biomarkers will likely play an important future role in assessing progression of many neurological diseases inclusive of PD. In this paper, we derived structural biomarkers from diffusion MRI (dMRI), a structural modality that allows for non-invasive inference of neuronal fiber connectivity patterns. The structural biomarker we use is the ensemble average propagator (EAP), a probability density function fully characterizing the diffusion locally at a voxel level. To assess changes with respect to a normal anatomy, we construct an unbiased template brain map from the EAP fields of a control population. Use of an EAP captures both orientation and shape information of the diffusion process at each voxel in the dMRI data, and this feature can be a powerful representation to achieve enhanced PD brain mapping. This template brain map construction method is applicable to small animal models as well as to human brains. The differences between the control template brain map and novel patient data can then be assessed via a nonrigid warping algorithm that transforms the novel data into correspondence with the template brain map, thereby capturing the amount of elastic deformation needed to achieve this correspondence. We present the use of a manifold-valued feature called the Cauchy deformation tensor (CDT), which facilitates morphometric analysis and automated classification of a PD versus a control population. Finally, we present preliminary results of automated discrimination between a group of 22 controls and 46 PD patients using CDT. This method may be possibly applied to larger population sizes and other parkinsonian syndromes in the near future.

  3. 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

  4. Meal Replacement: Calming the Hot-State Brain Network of Appetite

    OpenAIRE

    Brielle ePaolini; Laurienti, Paul J.; James eNorris; W. Jack eRejeski

    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 re...

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

    OpenAIRE

    Dan Wu; Chaoyi Li; Yu Yin; Changzheng Zhou; Dezhong Yao

    2010-01-01

    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...

  6. Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.

    Science.gov (United States)

    Frank, Lawrence R; Galinsky, Vitaly L

    2016-09-01

    The ability of functional magnetic resonance imaging (FMRI) to noninvasively measure fluctuations in brain activity in the absence of an applied stimulus offers the possibility of discerning functional networks in the resting state of the brain. However, the reconstruction of brain networks from these signal fluctuations poses a significant challenge because they are generally nonlinear and nongaussian and can overlap in both their spatial and temporal extent. Moreover, because there is no explicit input stimulus, there is no signal model with which to compare the brain responses. A variety of techniques have been devised to address this problem, but the predominant approaches are based on the presupposition of statistical properties of complex brain signal parameters, which are unprovable but facilitate the analysis. In this article, we address this problem with a new method, entropy field decomposition, for estimating structure within spatiotemporal data. This method is based on a general information field-theoretic formulation of Bayesian probability theory incorporating prior coupling information that allows the enumeration of the most probable parameter configurations without the need for unjustified statistical assumptions. This approach facilitates the construction of brain activation modes directly from the spatial-temporal correlation structure of the data. These modes and their associated spatial-temporal correlation structure can then be used to generate space-time activity probability trajectories, called functional connectivity pathways, which provide a characterization of functional brain networks. PMID:27391678

  7. Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia.

    Science.gov (United States)

    Zhu, Jiajia; Zhuo, Chuanjun; Liu, Feng; Qin, Wen; Xu, Lixue; Yu, Chunshui

    2016-01-01

    Clinical and brain structural differences have been reported between patients with familial and sporadic schizophrenia; however, little is known about the brain functional differences between the two subtypes of schizophrenia. Twenty-six patients with familial schizophrenia (PFS), 26 patients with sporadic schizophrenia (PSS) and 26 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging. The whole-brain functional network was constructed and analyzed using graph theoretical approaches. Topological properties (including global, nodal and edge measures) were compared among the three groups. We found that PFS, PSS and HC exhibited common small-world architecture of the functional brain networks. However, at a global level, only PFS showed significantly lower normalized clustering coefficient, small-worldness, and local efficiency, indicating a randomization shift of their brain networks. At a regional level, PFS and PSS disrupted different neural circuits, consisting of abnormal nodes (increased or decreased nodal centrality) and edges (decreased functional connectivity strength), which were widely distributed throughout the entire brain. Furthermore, some of these altered network measures were significantly correlated with severity of psychotic symptoms. These results suggest that familial and sporadic schizophrenia had segregated disruptions in the topological organization of the intrinsic functional brain network, which may be due to different etiological contributions. PMID:27032817

  8. Neuronal networks and mediators of cortical neurovascular coupling responses in normal and altered brain states.

    Science.gov (United States)

    Lecrux, C; Hamel, E

    2016-10-01

    Brain imaging techniques that use vascular signals to map changes in neuronal activity, such as blood oxygenation level-dependent functional magnetic resonance imaging, rely on the spatial and temporal coupling between changes in neurophysiology and haemodynamics, known as 'neurovascular coupling (NVC)'. Accordingly, NVC responses, mapped by changes in brain haemodynamics, have been validated for different stimuli under physiological conditions. In the cerebral cortex, the networks of excitatory pyramidal cells and inhibitory interneurons generating the changes in neural activity and the key mediators that signal to the vascular unit have been identified for some incoming afferent pathways. The neural circuits recruited by whisker glutamatergic-, basal forebrain cholinergic- or locus coeruleus noradrenergic pathway stimulation were found to be highly specific and discriminative, particularly when comparing the two modulatory systems to the sensory response. However, it is largely unknown whether or not NVC is still reliable when brain states are altered or in disease conditions. This lack of knowledge is surprising since brain imaging is broadly used in humans and, ultimately, in conditions that deviate from baseline brain function. Using the whisker-to-barrel pathway as a model of NVC, we can interrogate the reliability of NVC under enhanced cholinergic or noradrenergic modulation of cortical circuits that alters brain states.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

  9. Classification of carbon materials for developing structure-properties relationships based on the aggregate state of the precursors

    Institute of Scientific and Technical Information of China (English)

    Oleksiy V. Khavryuchenko; Volodymyr D.Khavryuchenko

    2014-01-01

    Modern carbon science lacks an efficient structure-related classi-fication of materials. We present an approach based on dividing carbon materials by the aggregate state of the precursor. The common features in the structure of carbon particles that allow putting them into a group are discussed, with particular attention to the potential energy stored in the carbon structure from differ-ent rates of relaxation during the synthesis and prearrangement of structural motifs due to the effect of the precursor structure.

  10. Application of the Köppen classification for climatic zoning in the state of Minas Gerais, Brazil

    Science.gov (United States)

    de Sá Júnior, Arionaldo; de Carvalho, Luiz Gonsaga; da Silva, Fábio Fernandes; de Carvalho Alves, Marcelo

    2012-04-01

    The knowledge of the climatic conditions of a region is crucial for its agricultural development. It is also extremely important for understanding the fact that certain cultures have to develop under prevailing temperature and humidity conditions and assist in the adoption of a suitable irrigation technique, as well as its management and operationalization. The Köppen system of climate classification is widely used for the identification of homogeneous climate zones as it considers only rainfall and temperature as the meteorological elements for classification. For this study, we used climatic databases of rainfall and temperature in a raster format, with a spatial resolution of 30″ of arc (an approximate area of 0.86 km2 pixel-1), from 1961 to 1990. Through geoprocessing techniques, we obtained a map of climatic classification for the state of Minas Gerais. We found that the state has the following three major climatic groups: A, B and C, which correspond to tropical rainy, dry and warm temperate climates, respectively. The climate classes obtained were Aw, Am, BSh, Cwa and Cwb, with Aw, Cwa and Cwb classes occupying 99.89% of the territorial area of the state. The validation of the results showed a satisfactory agreement, with 93.75% reliability.

  11. Geomagnetic Storms and their Influence on the Human Brain Functional State

    Directory of Open Access Journals (Sweden)

    Elchin S. Babayev

    2005-01-01

    Full Text Available An investigation of the influence of geomagnetic storms of various intensities on healthy adults' human brain activity and its functional state was conducted. Results of electroencephalogram (EEG investigations were used as the most objective method reflecting functional state of the human brain. Studies on the influence of geomagnetic storms on the human brain functional state of healthy adult women patients (permanent group in states of relaxation, photo-stimulation and hyper-ventilation have revealed a negative influence of severe geomagnetic storms on functional state of the human brain. As a rule, during periods of strong geomagnetic disturbances, indisposition, weakness and presence of indistinct localized headaches were recorded for majority of patients. Complex of nonspecific shifts on EEG reflects disorganization of functional activity of cortex of large hemispheres of the human brain at geomagnetically disturbed days, which is likely connected with dysfunction of integrative subcortical systems, with disbalance of its ascending synchronizing and desynchronizing influences. Imbalance of activating and deactivating mechanisms including dysfunctions of ergo- and tropho-tropic over-segmentary centers was registered. Strengthening cortical connections in the right cortical hemisphere and their short circuit on temporal sections during geomagnetically disturbed days were observed, while, in geomagnetically quiet days, a profile of correlation interrelations reflected weak internal- and inter-hemispheric connections. The threshold of convulsive (spasmodic readiness of the human brain is reduced, which is especially dangerous for risk group persons. It is established that, in general, weak and moderate geomagnetic storms exert stimulating influence while strong disturbances of geomagnetic conditions activate braking (inhibiting processes.

  12. 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.

  13. 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.

  14. A state of the art ash classification system - China Light and Power Company Limited, Hong Kong

    Energy Technology Data Exchange (ETDEWEB)

    Meijers, S.J.; Brunskill, J. [Bateman Material Handling Ltd., Wilbart (South Africa)

    1994-12-31

    The pressures from environmentalists are ever increasing in todays highly polluted world. One of the hardest hit are the power stations generating gaseous liquid and solid pollutants. In recent times, the waste fly ash has proven itself to be an important commodity in the cementitious industry if used in the correct form. In order to meet world standards for the particle size criteria, sophisticated equipment needs to be used in a fly ash classification system. This paper highlights the major operating areas in the worlds latest fly ash classification system recently commissioned in Hong Kong. 3 refs., 5 figs.

  15. Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial.

    Science.gov (United States)

    Anderson, Ariana; Cohen, Mark S

    2013-01-01

    Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

  16. 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

  17. 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....

  18. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

    OpenAIRE

    Yijun Wang; Dan Zhang; Xiaorong Gao; Bo Hong; Shangkai Gao

    2007-01-01

    For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this ...

  19. Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms.

    Science.gov (United States)

    Petzold, Anne; Valencia, Miguel; Pál, Balázs; Mena-Segovia, Juan

    2015-01-01

    Cholinergic neurons of the pedunculopontine nucleus (PPN) are most active during the waking state. Their activation is deemed to cause a switch in the global brain activity from sleep to wakefulness, while their sustained discharge may contribute to upholding the waking state and enhancing arousal. Similarly, non-cholinergic PPN neurons are responsive to brain state transitions and their activation may influence some of the same targets of cholinergic neurons, suggesting that they operate in coordination. Yet, it is not clear how the discharge of distinct classes of PPN neurons organize during brain states. Here, we monitored the in vivo network activity of PPN neurons in the anesthetized rat across two distinct levels of cortical dynamics and their transitions. We identified a highly structured configuration in PPN network activity during slow-wave activity that was replaced by decorrelated activity during the activated state (AS). During the transition, neurons were predominantly excited (phasically or tonically), but some were inhibited. Identified cholinergic neurons displayed phasic and short latency responses to sensory stimulation, whereas the majority of non-cholinergic showed tonic responses and remained at high discharge rates beyond the state transition. In vitro recordings demonstrate that cholinergic neurons exhibit fast adaptation that prevents them from discharging at high rates over prolonged time periods. Our data shows that PPN neurons have distinct but complementary roles during brain state transitions, where cholinergic neurons provide a fast and transient response to sensory events that drive state transitions, whereas non-cholinergic neurons maintain an elevated firing rate during global activation.

  20. Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms

    Directory of Open Access Journals (Sweden)

    Anne ePetzold

    2015-10-01

    Full Text Available Cholinergic neurons of the pedunculopontine nucleus (PPN are most active during the waking state. Their activation is deemed to cause a switch in the global brain activity from sleep to wakefulness, while their sustained discharge may contribute to upholding the waking state and enhancing arousal. Similarly, non-cholinergic PPN neurons are responsive to brain state transitions and their activation may influence some of the same targets of cholinergic neurons, suggesting that they operate in coordination. Yet, it is not clear how the discharge of distinct classes of PPN neurons organize during brain states. Here we monitored the in vivo network activity of PPN neurons in the anesthetized rat across two distinct levels of cortical dynamics and their transitions. We identified a highly structured configuration in PPN network activity during slow-wave activity that was replaced by decorrelated activity during the activated state. During the transition, neurons were predominantly excited (phasically or tonically, but some were inhibited. Identified cholinergic neurons displayed phasic and short latency responses to sensory stimulation, whereas the majority of non-cholinergic showed tonic responses and remained at high discharge rates beyond the state transition. In vitro recordings demonstrate that cholinergic neurons exhibit fast adaptation that prevents them from discharging at high rates over prolonged time periods. Our data shows that PPN neurons have distinct but complementary roles during brain state transitions, where cholinergic neurons provide a fast and transient response to sensory events that drive state transitions, whereas non-cholinergic neurons maintain an elevated firing rate during global activation.

  1. Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms

    Science.gov (United States)

    Petzold, Anne; Valencia, Miguel; Pál, Balázs; Mena-Segovia, Juan

    2015-01-01

    Cholinergic neurons of the pedunculopontine nucleus (PPN) are most active during the waking state. Their activation is deemed to cause a switch in the global brain activity from sleep to wakefulness, while their sustained discharge may contribute to upholding the waking state and enhancing arousal. Similarly, non-cholinergic PPN neurons are responsive to brain state transitions and their activation may influence some of the same targets of cholinergic neurons, suggesting that they operate in coordination. Yet, it is not clear how the discharge of distinct classes of PPN neurons organize during brain states. Here, we monitored the in vivo network activity of PPN neurons in the anesthetized rat across two distinct levels of cortical dynamics and their transitions. We identified a highly structured configuration in PPN network activity during slow-wave activity that was replaced by decorrelated activity during the activated state (AS). During the transition, neurons were predominantly excited (phasically or tonically), but some were inhibited. Identified cholinergic neurons displayed phasic and short latency responses to sensory stimulation, whereas the majority of non-cholinergic showed tonic responses and remained at high discharge rates beyond the state transition. In vitro recordings demonstrate that cholinergic neurons exhibit fast adaptation that prevents them from discharging at high rates over prolonged time periods. Our data shows that PPN neurons have distinct but complementary roles during brain state transitions, where cholinergic neurons provide a fast and transient response to sensory events that drive state transitions, whereas non-cholinergic neurons maintain an elevated firing rate during global activation. PMID:26582977

  2. Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

    OpenAIRE

    Poldrack, Russell A.; Halchenko, Yaroslav ,; Hanson, Stephen José

    2009-01-01

    Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes ar...

  3. 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.

  4. Analysis of Brain Cognitive State for Arithmetic Task and Motor Task Using Electroencephalography Signal

    Directory of Open Access Journals (Sweden)

    R Kalpana

    2013-08-01

    Full Text Available To localize the brain dynamics for cognitive processes from EEG signature has been a challenging taskfrom last two decades. In this paper we explore the spatial-temporal correlations of brain electricalneuronal activity for cognitive task such as Arithmetic and Motor Task using 3D cortical distributionmethod. Ten healthy right handed volunteers participated in the experiment. EEG signal was acquiredduring resting state with eyes open and eyes closed; performing motor task and arithmetic calculations.The signal was then computed for three dimensional cortical distributions on realistic head model withMNI152 template using standardized low resolution brain electromagnetic tomography (sLORETA. Thiswas followed by an appropriate standardization of the current density, producing images of electricneuronal activity without localization bias. Neuronal generators responsible for cognitive state such asArithmetic Task and Motor Task were localized. The result was correlated with the previous neuroimaging(fMRI study investigation. Hence our result directed that the neuronal activity from EEG signal can bedemonstrated in cortical level with good spatial resolution. 3D cortical distribution method, thus, may beused to obtain both spatial and temporal information from EEG signal and may prove to be a significanttechnique to investigate the cognitive functions in mental health and brain dysfunctions. Also, it may behelpful for brain/human computer interfacing.

  5. Classification of 4-qubit Entangled Graph States According to Bipartite Entanglement, Multipartite Entanglement and Non-local Properties

    Science.gov (United States)

    Assadi, Leila; Jafarpour, Mojtaba

    2016-07-01

    We use concurrence to study bipartite entanglement, Meyer-Wallach measure and its generalizations to study multi-partite entanglement and MABK and SASA inequalities to study the non-local properties of the 4-qubit entangled graph states, quantitatively. Then, we present 3 classifications, each one in accordance with one of the aforementioned properties. We also observe that the classification according to multipartite entanglement does exactly coincide with that according to nonlocal properties, but does not match with that according to bipartite entanglement. This observation signifies the fact that non-locality and multipartite entanglement enjoy the same basic underlying principles, while bipartite entanglement may not reveal the non-locality issue in its entirety.

  6. 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

    2016-01-01

    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. PMID:27488504

  7. 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.

  8. Resting State Brain Connectivity After Surgical and Behavioral Weight Loss

    Science.gov (United States)

    Lepping, Rebecca J.; Bruce, Amanda S.; Francisco, Alex; Yeh, Hung-Wen; Martin, Laura E.; Powell, Joshua N.; Hancock, Laura; Patrician, Trisha M.; Breslin, Florence J.; Selim, Niazy; Donnelly, Joseph E.; Brooks, William M.; Savage, Cary R.; Simmons, W. Kyle; Bruce, Jared M.

    2015-01-01

    OBJECTIVE We previously reported changes in food-cue neural reactivity associated with behavioral and surgical weight loss interventions. Resting functional connectivity represents tonic neural activity that may contribute to weight loss success. Here we explore whether intervention type is associated with differences in functional connectivity after weight loss. METHODS Fifteen obese participants were recruited prior to adjustable gastric banding surgery. Thirteen demographically matched obese participants were selected from a separate behavioral diet intervention. Resting state fMRI was collected three months after surgery/behavioral intervention. ANOVA was used to examine post-weight loss differences between the two groups in connectivity to seed regions previously identified as showing differential cue-reactivity after weight loss. RESULTS Following weight loss, behavioral dieters exhibited increased connectivity between left precuneus/superior parietal lobule (SPL) and bilateral insula pre- to post-meal and bariatric patients exhibited decreased connectivity between these regions pre- to post-meal (pcorrected<.05). CONCLUSIONS Behavioral dieters showed increased connectivity pre- to post-meal between a region associated with processing of self-referent information (precuneus/SPL) and a region associated with interoception (insula) whereas bariatric patients showed decreased connectivity between these regions. This may reflect increased attention to hunger signals following surgical procedures, and increased attention to satiety signals following behavioral diet interventions. PMID:26053145

  9. Brain activation and inhibition after acupuncture at Taichong and Taixi: resting-state functional magnetic resonance imaging

    OpenAIRE

    Shao-qun Zhang; Yan-jie Wang; Ji-ping Zhang; Jun-qi Chen; Chun-xiao Wu; Zhi-peng Li; Jia-rong Chen; Huai-liang Ouyang; Yong Huang; Chun-zhi Tang

    2015-01-01

    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 acupunctur...

  10. Meal replacement: calming the hot-state brain network of appetite

    OpenAIRE

    Brielle M Paolini; 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 ...

  11. Steady-state brain glucose transport kinetics re-evaluated with a four-state conformational model

    Directory of Open Access Journals (Sweden)

    João M N Duarte

    2009-10-01

    Full Text Available Glucose supply from blood to brain occurs through facilitative transporter proteins. A near linear relation between brain and plasma glucose has been experimentally determined and described by a reversible model of enzyme kinetics. A conformational four-state exchange model accounting for trans-acceleration and asymmetry of the carrier was included in a recently developed multi-compartmental model of glucose transport. Based on this model, we demonstrate that brain glucose (Gbrain as function of plasma glucose (Gplasma can be described by a single analytical equation namely comprising three kinetic compartments: blood, endothelial cells and brain. Transport was described by four parameters: apparent half saturation constant Kt, apparent maximum rate constant Tmax, glucose consumption rate CMRglc, and the iso-inhibition constant Kii that suggests Gbrain as inhibitor of the isomerisation of the unloaded carrier. Previous published data, where Gbrain was quantified as a function of plasma glucose by either biochemical methods or NMR spectroscopy, were used to determine the aforementioned kinetic parameters. Glucose transport was characterized by Kt ranging from 1.5 to 3.5 mM, Tmax/CMRglc from 4.6 to 5.6, and Kii from 51 to 149 mM. It was noteworthy that Kt was on the order of a few mM, as previously determined from the reversible model. The conformational four-state exchange model of glucose transport into the brain includes both efflux and transport inhibition by Gbrain, predicting that Gbrain eventually approaches a maximum concentration. However, since Kii largely exceeds Gplasma, iso-inhibition is unlikely to be of substantial importance for plasma glucose below 25 mM. As a consequence, the reversible model can account for most experimental observations under euglycaemia and moderate cases of hypo- and hyperglycaemia.

  12. Probing Intrinsic Resting-State Networks in the Infant Rat Brain

    Science.gov (United States)

    Bajic, Dusica; Craig, Michael M.; Borsook, David; Becerra, Lino

    2016-01-01

    Resting-state functional magnetic resonance imaging (rs-fMRI) measures spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signal in the absence of external stimuli. It has become a powerful tool for mapping large-scale brain networks in humans and animal models. Several rs-fMRI studies have been conducted in anesthetized and awake adult rats, reporting consistent patterns of brain activity at the systems level. However, the evolution to adult patterns of resting-state activity has not yet been evaluated and quantified in the developing rat brain. In this study, we hypothesized that large-scale intrinsic networks would be easily detectable but not fully established as specific patterns of activity in lightly anesthetized 2-week-old rats (N = 11). Independent component analysis (ICA) identified 8 networks in 2-week-old-rats. These included Default mode, Sensory (Exteroceptive), Salience (Interoceptive), Basal Ganglia-Thalamic-Hippocampal, Basal Ganglia, Autonomic, Cerebellar, as well as Thalamic-Brainstem networks. Many of these networks consisted of more than one component, possibly indicative of immature, underdeveloped networks at this early time point. Except for the Autonomic network, infant rat networks showed reduced connectivity with subcortical structures in comparison to previously published adult networks. Reported slow fluctuations in the BOLD signal that correspond to functionally relevant resting-state networks in 2-week-old rats can serve as an important tool for future studies of brain development in the settings of different pharmacological applications or disease. PMID:27803653

  13. Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review.

    Science.gov (United States)

    Andellini, Martina; Cannatà, Vittorio; Gazzellini, Simone; Bernardi, Bruno; Napolitano, Antonio

    2015-09-30

    The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements. PMID:26072249

  14. Soil mapping and classification in the Alps: Current state and future challenges

    Science.gov (United States)

    Baruck, Jasmin; Gruber, Fabian; Geitner, Clemens

    2014-05-01

    Soil is an essential, non-renewable resource, which fundamentally needs sustainable management. Soils in mountain regions like the Alps have a diverse small-scale distribution and they are characterized by a slow soil development and multilayer profiles. This is mainly caused by high process dynamics and harsh climate conditions. Therefore, soils are particularly vulnerable and require a sustainable management approach. Furthermore, the global change, especially the climate and land use change, leads to new demands on the soil. Thus, high-resolution spatial informations on soil properties are required to protect this resource and to consider its properties in spatial planning and decision making. In the Alpine region soil maps are mostly confined to small (mostly agriculture) areas. Especially, in higher altitudes of the Alps pedologic research and data collection are lacking. However, nowadays and in the future systematic soil mapping works are and will be no longer applied. Another methodical problem arises because each Alpine country has its own national soil mapping and classification system which are not adapted to Alpine areas. Therefore, appropriate methods of working practices for the Alpine region are mostly missing. The central aim of the research project "ReBo - Terrain Classification based on airborne laser scanning data to support soil mapping in the Alps", founded by the Autonomous Province of Bolzano - South Tyrol, is to develop and verify a concept, which allows the collection of soil data through an optimized interaction of soil mapping and geomorphometric analysis. The test sites are located in South Tyrol (Italy). The workflow shall minimise the required pedologic field work and shall provide a reliable strategy for transferring punctual soil informations into spatial soil maps. However, for a detailed analysis a systematic pedologic field work is still indispensable. As in the Alps reliable soil mapping and classification standards are lacking

  15. 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.

  16. [Functional connectivity analysis of the brain network using resting-state FMRI].

    Science.gov (United States)

    Hayashi, Toshihiro

    2011-12-01

    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. PMID:22147450

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

    International Nuclear Information System (INIS)

    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)

  18. 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.

  19. Classification of Manifolds by Single-Layer Neural Networks

    OpenAIRE

    Chung, SueYeon; Lee, Daniel D.; Sompolinsky, Haim

    2015-01-01

    The neuronal representation of objects exhibit enormous variability due to changes in the object's physical features such as location, size, orientation, and intensity. How the brain copes with the variability across these manifolds of neuronal states and generates invariant perception of objects remains poorly understood. Here we present a theory of neuronal classification of manifolds, extending Gardner's replica theory of classification of isolated points by a single layer perceptron. We e...

  20. State-dependent and environmental modulation of brain hyperthermic effects of psychoactive drugs of abuse

    Science.gov (United States)

    Kiyatkin, Eugene A.

    2014-01-01

    Hyperthermia is a known effect induced by psychomotor stimulants and pathological hyperthermia is a prominent symptom of acute intoxication with these drugs in humans. In this manuscript, I will review our recent work concerning the brain hyperthermic effects of several known and recently appeared psychostimulant drugs of abuse (cocaine, methamphetamine, MDMA, methylone, and MDPV). Specifically, I will consider the role of activity state and environmental conditions in modulating the brain temperature effects of these drugs and their acute toxicity. Although some of these drugs are structurally similar and interact with the same brain substrates, there are important differences in their temperature effects in quiet resting conditions and the type of modulation of these temperature effects under conditions that mimic basic aspects of human drug use (social interaction, moderately warm environments). These data could be important for understanding the potential dangers of each drug and ultimately preventing adverse health complications associated with acute drug-induced intoxication.

  1. 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.

  2. An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification

    Science.gov (United States)

    Fasel, Timothy R.; Todd, Michael D.

    2010-07-01

    The combination of chaotically amplitude-modulated ultrasonic waves and time series prediction algorithms has shown the ability to locate and classify various bond state damage conditions of a composite bonded joint. This study examines the ability of a new two-part supervised learning classification scheme not only to classify disbond size but also to classify whether a bond for which there is no baseline data is undamaged or has some form of disbond. This classification is performed using data from a similarly configured composite bond for which baseline data are available. The test structures are analogous to a wing skin-to-spar bonded joint. An active excitation signal is imparted to the structure through a macro fiber composite (MFC) patch on one side of the bonded joint and sensed using an equivalent MFC patch on the opposite side of the joint. There is an MFC actuator/sensor pair for each bond condition to be identified. The classification approach compares features derived from an autoregressive (AR) model coefficient vector cross-assurance criterion.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Distinct resting-state brain activity in patients with functional constipation.

    Science.gov (United States)

    Zhu, Qiang; Cai, Weiwei; Zheng, Jianyong; Li, Guanya; Meng, Qianqian; Liu, Qiaoyun; Zhao, Jizheng; von Deneen, Karen M; Wang, Yuanyuan; Cui, Guangbin; Duan, Shijun; Han, Yu; Wang, Huaning; Tian, Jie; Zhang, Yi; Nie, Yongzhan

    2016-10-01

    Functional constipation (FC) is a common functional gastrointestinal disorder (FGID) with a higher prevalence in clinical practice. The primary brain regions involved in emotional arousal regulation, somatic, sensory and motor control processing have been identified with neuroimaging in FGID. It remains unclear how these factors interact to influence the baseline brain activity of patients with FC. In the current study, we combined resting-state fMRI (RS-fMRI) with Granger causality analysis (GCA) to investigate the causal interactions of the brain areas in 14 patients with FC and in 26 healthy controls (HC). Our data showed significant differences in baseline brain activities in a number of major brain regions implicated in emotional process modulation (i.e. dorsal anterior cingulate cortex-dACC, anterior insula-aINS, orbitofrontal cortex-OFC, hippocampus-HIPP), somatic and sensory processing, and motor control (i.e., supplementary motor area-SMA, precentral gyrus-PreCen) (Ppropel limbic regions at the aINS and HIPP to induce abnormal emotional processing regulating visceral responses; and weaker effective connectivity from the SMA and PreCen, which are regions involved with somatic, sensory and motor control, propel the aINS and HIPP, suggesting abnormalities of sensory and behavioral responses. Such information of basal level functional abnormalities expands our current understanding of neural mechanisms underlying functional constipation.

  6. 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.

  7. The McKern Taxonomic System and Archaeological Culture Classification in the Midwestern United States: A History and Evaluation

    Directory of Open Access Journals (Sweden)

    B. K. Swartz

    1996-05-01

    Full Text Available In the first half of the 20th century three major archaeological culture unit classifications were formulated in the United States. The most curious one was the Midwestern Taxonomic System, a scheme that ignored time and space. Alton K. Fisher suggested to W. C. McKern in the late 1920's that the Linnean model of morphological classifi­cation, which was employed in biology at a time of pre-evolutionary thinking, might be adapted to archaeologi­cal culture classification (Fisher 1986. On the basis of this idea McKern conceived the Midwestern Taxonomic System and planned to present his concept in a paper at the Central Section of the American Anthropological Association at Ann Arbor, Michigan, in April, 1932. Illness prevented him from making the presentation. The first public statement was before a small group of archaeologists at the time of an archaeological symposium, Illinois Academy of Science, May 1932 (Griffin 1943:327. After input from various archaeologists a formal account was prepared as a manuscript entitled "Culture Type Classification for Midwestern North American Archaeology" at the Chicago Conference, December 10, 1932. Other participants at this conference were Samuel A. Barrett, Fay­ Cooper Cole, Thorne Deuel, Carl E. Guthe, A. R. Kelly (Cole and Deuel 1937a:34 and James B. Griffin (as a graduate student, personal communication, 1986. This classification method was more fully and formally presented three years later, in December 1935, at the original Indianapolis Archaeological Conference (Guthe 1937. A more detailed history of the origins of the McKern system is provided by Griffin (1943.

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

    Science.gov (United States)

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

    2015-01-01

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

  9. Brain regions involved in dispositional mindfulness during resting state and their relation with well-being.

    Science.gov (United States)

    Kong, Feng; Wang, Xu; Song, Yiying; Liu, Jia

    2016-08-01

    Mindfulness can be viewed as an important dispositional characteristic that reflects the tendency to be mindful in daily life, which is beneficial for improving individuals' both hedonic and eudaimonic well-being. However, no study to date has examined the brain regions involved in individual differences in dispositional mindfulness during the resting state and its relation with hedonic and eudaimonic well-being. To investigate this issue, the present study employed resting-state functional magnetic resonance imaging (rs-fMRI) to evaluate the regional homogeneity (ReHo) that measures the local synchronization of spontaneous brain activity in a large sample. We found that dispositional mindfulness was positively associated with the ReHo in the left orbitofrontal cortex (OFC), left parahippocampal gyrus (PHG), and right insula implicated in emotion processing, body awareness, and self-referential processing, and negatively associated with the ReHo in right inferior frontal gyrus (IFG) implicated in response inhibition and attentional control. Furthermore, we found different neural associations with hedonic (i.e., positive and negative affect) and eudaimonic well-being (i.e., the meaningful and purposeful life). Specifically, the ReHo in the IFG predicted eudaimonic well-being whereas the OFC predicted positive affect, both of which were mediated by dispositional mindfulness. Taken together, our study provides the first evidence for linking individual differences in dispositional mindfulness to spontaneous brain activity and demonstrates that dispositional mindfulness engages multiple brain mechanisms that differentially influence hedonic and eudaimonic well-being. PMID:26360907

  10. 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

  11. Resting-State and Task-Based Functional Brain Connectivity in Developmental Dyslexia.

    Science.gov (United States)

    Schurz, Matthias; Wimmer, Heinz; Richlan, Fabio; Ludersdorfer, Philipp; Klackl, Johannes; Kronbichler, Martin

    2015-10-01

    Reading requires the interaction between multiple cognitive processes situated in distant brain areas. This makes the study of functional brain connectivity highly relevant for understanding developmental dyslexia. We used seed-voxel correlation mapping to analyse connectivity in a left-hemispheric network for task-based and resting-state fMRI data. Our main finding was reduced connectivity in dyslexic readers between left posterior temporal areas (fusiform, inferior temporal, middle temporal, superior temporal) and the left inferior frontal gyrus. Reduced connectivity in these networks was consistently present for 2 reading-related tasks and for the resting state, showing a permanent disruption which is also present in the absence of explicit task demands and potential group differences in performance. Furthermore, we found that connectivity between multiple reading-related areas and areas of the default mode network, in particular the precuneus, was stronger in dyslexic compared with nonimpaired readers.

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Chanan S. Syan

    2010-01-01

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

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

    OpenAIRE

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

    2010-01-01

    We studied visual perception using an annular random-dot motion stimulus called the racetrack. We recorded neural activity using magnetoencephalography while subjects viewed variants of this stimulus that contained no inherent motion or various degrees of embedded motion. Subjects reported seeing rotary motion during viewing of all stimuli. We found that, in the absence of any motion signals, patterns of brain activity differed between states of motion perception and nonperception. Furthermor...

  15. Information-geometric measures estimate neural interactions during oscillatory brain states

    OpenAIRE

    Jean-Marc Fellous; Masami Tatsuno

    2014-01-01

    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...

  16. Mapping Thalamocortical Networks in Rat Brain using Resting-State Functional Connectivity

    OpenAIRE

    Liang, Zhifeng; Li, Tao; King, Jean; Zhang, Nanyin

    2013-01-01

    Thalamocortical connectivity plays a vital role in brain function. The anatomy and function of thalamocortical networks have been extensively studied in animals by numerous invasive techniques. Non-invasively mapping thalamocortical networks in humans has also been demonstrated by utilizing resting-state functional magnetic resonance imaging (rsfMRI). However, success in simultaneously imaging multiple thalamocortical networks in animals is rather limited. This is largely due to the profound ...

  17. 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

    2016-01-01

    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. PMID:27488504

  18. 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. PMID:24110165

  19. 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.

  20. 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.

  1. A Novel Approach for Pass Word Authentication using Brain -State -In -A Box (BSB) Model

    CERN Document Server

    Chakravarthy, A S N; Avadhani, P S

    2011-01-01

    Authentication is the act of confirming the truth of an attribute of a datum or entity. This might involve confirming the identity of a person, tracing the origins of an artefact, ensuring that a product is what it's packaging and labelling claims to be, or assuring that a computer program is a trusted one. The authentication of information can pose special problems (especially man-in-the-middle attacks), and is often wrapped up with authenticating identity. Password authentication using Brain-State -In-A Box is presented in this paper. Here in this paper we discuss Brain-State -In-A Box Scheme for Textual and graphical passwords which will be converted in to probabilistic values Password. We observe how to get password authentication Probabilistic values for Text and Graphical image. This study proposes the use of a Brain-State -In-A Box technique for password authentication. In comparison to existing layered neural network techniques, the proposed method provides better accuracy and quicker response time to...

  2. The evolution of brain waves in altered states of consciousness (REM sleep and meditation

    Directory of Open Access Journals (Sweden)

    Irina E. Chiş

    2009-12-01

    Full Text Available Aim: The aim of this study was to investigate the brain activity in REM sleep andmeditation; it was also studied in which way an appropriate musical background would affect theevolution of brain waves in these altered states of consciousness. Material and Method: The recordingswere done with a portable electroencephalograph, on a homogeneous group of human subjects (menaged 30-50 years. The subjects were monitored in their own bed, the length of sleep and how earlythey went to bed was up to them. This was made to avoid errors that could compromise the wholestudy. Results: It was shown that an appropriate musical background has a positive effect on brainactivity and especially on alpha waves. There were no significant results regarding REM sleep, althougha slight increase in the frequency by which the periods of REM sleep occurred was noticed. On theother hand, in meditation, the appropriate musical background had a major influence on the period inwhich the subjects entered the alpha state. This period was considerably reduced. Conclusion: Anadequate type of music can help our brain entering in, and maintaining the alpha state.

  3. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

    Science.gov (United States)

    Park, Jong-Uk; Lee, Hyo-Ki; Lee, Junghun; Urtnasan, Erdenebayar; Kim, Hojoong; Lee, Kyoung-Joung

    2015-09-01

    This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter. PMID:26261097

  4. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

    Science.gov (United States)

    Park, Jong-Uk; Lee, Hyo-Ki; Lee, Junghun; Urtnasan, Erdenebayar; Kim, Hojoong; Lee, Kyoung-Joung

    2015-09-01

    This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.

  5. Robust brain parcellation using sparse representation on resting-state fMRI.

    Science.gov (United States)

    Zhang, Yu; Caspers, Svenja; Fan, Lingzhong; Fan, Yong; Song, Ming; Liu, Cirong; Mo, Yin; Roski, Christian; Eickhoff, Simon; Amunts, Katrin; Jiang, Tianzi

    2015-11-01

    Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.

  6. 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. PMID:27480732

  7. Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self.

    Science.gov (United States)

    Lehmann, D; Faber, P L; Achermann, P; Jeanmonod, D; Gianotti, L R; Pizzagalli, D

    2001-11-30

    Multichannel EEG of an advanced meditator was recorded during four different, repeated meditations. Locations of intracerebral source gravity centers as well as Low Resolution Electromagnetic Tomography (LORETA) functional images of the EEG 'gamma' (35-44 Hz) frequency band activity differed significantly between meditations. Thus, during volitionally self-initiated, altered states of consciousness that were associated with different subjective meditation states, different brain neuronal populations were active. The brain areas predominantly involved during the self-induced meditation states aiming at visualization (right posterior) and verbalization (left central) agreed with known brain functional neuroanatomy. The brain areas involved in the self-induced, meditational dissolution and reconstitution of the experience of the self (right fronto-temporal) are discussed in the context of neural substrates implicated in normal self-representation and reality testing, as well as in depersonalization disorders and detachment from self after brain lesions. PMID:11738545

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

    OpenAIRE

    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 use...

  9. MTR variations in normal adult brain structures using balanced steady-state free precession

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, Meritxell; Wetzel, Stephan G.; Radue, Ernst-Wilhelm [University of Basel Hospital, Department of Neuroradiology, Institute of Radiology, Basel (Switzerland); Gloor, Monika; Bieri, Oliver; Scheffler, Klaus [University of Basel Hospital, Division of Radiological Physics, Institute of Radiology, Basel (Switzerland)

    2011-03-15

    Magnetization transfer (MT) is sensitive to the macromolecular environment of water protons and thereby provides information not obtainable from conventional magnetic resonance imaging (MRI). Compared to standard methods, MT-sensitized balanced steady-state free precession (bSSFP) offers high-resolution images with significantly reduced acquisition times. In this study, high-resolution magnetization transfer ratio (MTR) images from normal appearing brain structures were acquired with bSSFP. Twelve subjects were studied on a 1.5 T scanner. MTR values were calculated from MT images acquired in 3D with 1.3 mm isotropic resolution. The complete MT data set was acquired within less than 3.5 min. Forty-one brain structures of the white matter (WM) and gray matter (GM) were identified for each subject. MTR values were higher for WM than GM. In general, MTR values of the WM and GM structures were in good accordance with the literature. However, MTR values showed more homogenous values within WM and GM structures than previous studies. MT-sensitized bSSFP provides isotropic high-resolution MTR images and hereby allows assessment of reliable MTR data in also very small brain structures in clinically feasible acquisition times and is thus a promising sequence for being widely used in the clinical routine. The present normative data can serve as a reference for the future characterization of brain pathologies. (orig.)

  10. Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Maharatna, Koushik; Pan, Indranil; Kuyucu, Doga

    2015-09-01

    Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3-5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration

  11. 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.

  12. Classification of EGC output and Mental State Transition Networkusing Self Organizing Map

    OpenAIRE

    Mera, Kazuya; Ichimura, Takumi

    2011-01-01

    Mental State Transition Network which consists of mental states connected one another is a basic concept of approximating to human psychological and mental responses. It can represent transition from an emotional state to other one with stimulus by calculating Emotion Generating Calculations method. However, this method ignores most of emotions except for an emotion which has the strongest effect although EGC can calculate the degree of 20 emotions in parallel. In this pa...

  13. Resting-State Brain Functional Connectivity Is Altered in Type 2 Diabetes

    OpenAIRE

    Musen, Gail; Jacobson, Alan M.; Bolo, Nicolas R.; Simonson, Donald C.; Martha E. Shenton; McCartney, Richard L.; Flores, Veronica L.; Hoogenboom, Wouter S.

    2012-01-01

    Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer disease (AD). Populations at risk for AD show altered brain activity in the default mode network (DMN) before cognitive dysfunction. We evaluated this brain pattern in T2DM patients. We compared T2DM patients (n = 10, age = 56 ± 2.2 years, fasting plasma glucose [FPG] = 8.4 ± 1.3 mmol/L, HbA1c = 7.5 ± 0.54%) with nondiabetic age-matched control subjects (n = 11, age = 54 ± 1.8 years, FPG = 4.8 ± 0.2 mmol/L) using resting-state fun...

  14. 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

  15. Testing a dual-systems model of adolescent brain development using resting-state connectivity analyses.

    Science.gov (United States)

    van Duijvenvoorde, A C K; Achterberg, M; Braams, B R; Peters, S; Crone, E A

    2016-01-01

    The current study aimed to test a dual-systems model of adolescent brain development by studying changes in intrinsic functional connectivity within and across networks typically associated with cognitive-control and affective-motivational processes. To this end, resting-state and task-related fMRI data were collected of 269 participants (ages 8-25). Resting-state analyses focused on seeds derived from task-related neural activation in the same participants: the dorsal lateral prefrontal cortex (dlPFC) from a cognitive rule-learning paradigm and the nucleus accumbens (NAcc) from a reward-paradigm. Whole-brain seed-based resting-state analyses showed an age-related increase in dlPFC connectivity with the caudate and thalamus, and an age-related decrease in connectivity with the (pre)motor cortex. nAcc connectivity showed a strengthening of connectivity with the dorsal anterior cingulate cortex (ACC) and subcortical structures such as the hippocampus, and a specific age-related decrease in connectivity with the ventral medial PFC (vmPFC). Behavioral measures from both functional paradigms correlated with resting-state connectivity strength with their respective seed. That is, age-related change in learning performance was mediated by connectivity between the dlPFC and thalamus, and age-related change in winning pleasure was mediated by connectivity between the nAcc and vmPFC. These patterns indicate (i) strengthening of connectivity between regions that support control and learning, (ii) more independent functioning of regions that support motor and control networks, and (iii) more independent functioning of regions that support motivation and valuation networks with age. These results are interpreted vis-à-vis a dual-systems model of adolescent brain development. PMID:25969399

  16. Testing a dual-systems model of adolescent brain development using resting-state connectivity analyses.

    Science.gov (United States)

    van Duijvenvoorde, A C K; Achterberg, M; Braams, B R; Peters, S; Crone, E A

    2016-01-01

    The current study aimed to test a dual-systems model of adolescent brain development by studying changes in intrinsic functional connectivity within and across networks typically associated with cognitive-control and affective-motivational processes. To this end, resting-state and task-related fMRI data were collected of 269 participants (ages 8-25). Resting-state analyses focused on seeds derived from task-related neural activation in the same participants: the dorsal lateral prefrontal cortex (dlPFC) from a cognitive rule-learning paradigm and the nucleus accumbens (NAcc) from a reward-paradigm. Whole-brain seed-based resting-state analyses showed an age-related increase in dlPFC connectivity with the caudate and thalamus, and an age-related decrease in connectivity with the (pre)motor cortex. nAcc connectivity showed a strengthening of connectivity with the dorsal anterior cingulate cortex (ACC) and subcortical structures such as the hippocampus, and a specific age-related decrease in connectivity with the ventral medial PFC (vmPFC). Behavioral measures from both functional paradigms correlated with resting-state connectivity strength with their respective seed. That is, age-related change in learning performance was mediated by connectivity between the dlPFC and thalamus, and age-related change in winning pleasure was mediated by connectivity between the nAcc and vmPFC. These patterns indicate (i) strengthening of connectivity between regions that support control and learning, (ii) more independent functioning of regions that support motor and control networks, and (iii) more independent functioning of regions that support motivation and valuation networks with age. These results are interpreted vis-à-vis a dual-systems model of adolescent brain development.

  17. Brain state-dependent closed-loop modulation of paired associative stimulation controlled by sensorimotor desynchronization

    Directory of Open Access Journals (Sweden)

    Vladislav eRoyter

    2016-05-01

    Full Text Available Background: Pairing peripheral electrical stimulation (ES and transcranial magnetic stimulation (TMS increases corticospinal excitability when applied with a specific temporal pattern. When the two stimulation techniques are applied separately, motor imagery (MI-related oscillatory modulation amplifies both ES-related cortical effects -sensorimotor event-related desynchronization (ERD - and TMS-induced peripheral responses - motor-evoked potentials (MEP. However, the influence of brain self-regulation on the associative pairing of these stimulation techniques is still unclear.Objective: The aim of this pilot study was to investigate the effects of MI-related ERD during associative ES and TMS on subsequent corticospinal excitability. Method: The paired application of functional electrical stimulation (FES of the extensor digitorum communis (EDC muscle and subsequent single-pulse TMS (110% resting motor threshold of the contralateral primary motor cortex was controlled by beta-band (16-22Hz ERD during motor-imagery of finger extension and applied within a brain-machine interface environment in six healthy subjects. Neural correlates were probed by acquiring the stimulus-response curve (SRC of both MEP peak-to-peak amplitude and area under the curve (AUC before and after the intervention. Result: The application of approximately 150 pairs of associative FES and TMS resulted in a significant increase of MEP amplitudes and AUC, indicating that the induced increase of corticospinal excitability was mediated by the recruitment of additional neuronal pools. MEP increases were brain-state dependent and correlated with beta-band ERD, but not with the background EDC muscle activity; this finding was independent of the FES intensity applied.Conclusion: These results could be relevant for developing closed-loop therapeutic approaches such as the application of brain state-dependent, paired associative stimulation in the context of neurorehabilitation.

  18. Brain metabolism in patients with vegetative state after post-resuscitated hypoxic-ischemic brain injury: statistical parametric mapping analysis of F-18 fluorodeoxyglucose positron emission tomography

    Institute of Scientific and Technical Information of China (English)

    Yong Wook Kim; Hyoung Seop Kim; Young-Sil An

    2013-01-01

    Background Hypoxic-ischemic brain injury (HIBI) after cardiopulmonary resuscitation is one of the most devastating neurological conditions that causing the impaired consciousness.However,there were few studies investigated the changes of brain metabolism in patients with vegetative state (VS) after post-resuscitated HIBI.This study aimed to analyze the change of overall brain metabolism and elucidated the brain area correlated with the level of consciousness (LOC) in patients with VS after post-resuscitated HIBI.Methods We consecutively enrolled 17 patients with VS after HIBI,who experienced cardiopulmonary resuscitation.Overall brain metabolism was measured by F-18 fluorodeoxyglucose positron emission tomography (F-18 FDG PET) and we compared regional brain metabolic patterns from t7 patients with those from 15 normal controls using voxel-by-voxel based statistical parametric mapping analysis.Additionally,we correlated the LOC measured by the JFK-coma recovery scale-revised of each patient with brain metabolism by covariance analysis.Results Compared with normal controls,the patients with VS after post-resuscitated HIBI revealed significantly decreased brain metabolism in bilateral precuneus,bilateral posterior cingulate gyrus,bilateral middle frontal gyri,bilateral superior parietal gyri,bilateral middle occipital gyri,bilateral precentral gyri (PFEw correctecd <0.0001),and increased brain metabolism in bilateral insula,bilateral cerebella,and the brainstem (PFEw correctecd <0.0001).In covariance analysis,the LOC was significantly correlated with brain metabolism in bilateral fusiform and superior temporal gyri (P uncorrected <0.005).Conclusions Our study demonstrated that the precuneus,the posterior cingulate area and the frontoparietal cortex,which is a component of neural correlate for consciousness,may be relevant structure for impaired consciousness in patient with VS after post-resuscitated HIBI.In post-resuscitated HIBI,measurement of brain

  19. 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

  20. fNIRS-based brain-computer interfaces: a review

    OpenAIRE

    Noman eNaseer; Keum-Shik eHong

    2015-01-01

    A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The mos...

  1. Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data

    Directory of Open Access Journals (Sweden)

    Willem J. D. van Leeuwen

    2010-02-01

    Full Text Available This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS Normalized Difference Vegetation Index (NDVI time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR-MODIS NDVI translation algorithm and for various biogeographic studies.

  2. Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

    OpenAIRE

    Selvaraj, Jerritta; Murugappan, Murugappan; Wan, Khairunizam; Yaacob, Sazali

    2013-01-01

    Background Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional patter...

  3. 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...

  4. [Music-Acoustic Signals Controlled by Subject's Brain Potentials in the Correction of Unfavorable Functional States].

    Science.gov (United States)

    Fedotchev, A I; Bondar, A T; Bakhchina, A V; Parin, S B; Polevaya, S A; Radchenko, G S

    2016-01-01

    Literature review and the results of own studies on the development and experimental testing of musical EEG neurofeedback technology are presented. The technology is based on exposure of subjects to music or music-like signals that are organized in strict accordance with the current values of brain potentials of the patient. The main attention is paid to the analysis of the effectiveness of several versions of the technology, using specific and meaningful for the individual narrow-frequency EEG oscillators during the correction of unfavorable changes of the functional state. PMID:27149824

  5. A Bayesian Framework for the Classification of Microbial Gene Activity States

    Science.gov (United States)

    Disselkoen, Craig; Greco, Brian; Cook, Kaitlyn; Koch, Kristin; Lerebours, Reginald; Viss, Chase; Cape, Joshua; Held, Elizabeth; Ashenafi, Yonatan; Fischer, Karen; Acosta, Allyson; Cunningham, Mark; Best, Aaron A.; DeJongh, Matthew; Tintle, Nathan

    2016-01-01

    Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics. PMID:27555837

  6. Classification of lying states for the humanoid robot SJTU-HR1

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The humanoid robot SJTU-HR1’s concept is introduced and its characteristics tree is given.The basic states for SJTU-HR1 are proposed,including lying,sitting,standing and handstanding,abstracted from the daily exercises of human beings.The GF(generalized function) set theory is exploited to achieve the kinematic characteristics of the interested EEs(end-effectors) of SJTU-HR1 for the lying states.Finally,the results show that the large amounts of states can be described using the abbreviations in a systematic manner.Although we have focused on the application of the GF set theory to humanoid robots,particularly the SJTU-HR1,this methodology can also be applied to quadruped robots and hexapedal robots,especially when the desired tasks are complex.

  7. Classification of lying states for the humanoid robot SJTU-HR1

    Institute of Scientific and Technical Information of China (English)

    YANG JiaLun; GAO Feng; JIN ZhenLin; SHI LiFeng

    2009-01-01

    The humanoid robot SJTU-HRI's concept is introduced and its characteristics tree is given. The basic states for SJTU-HR1 are proposed, including lying, sitting, standing and handstsnding, abstracted from the daily exercises of human beings. The GF (generalized function) set theory is exploited to achieve the kinematic characteristics of the interested EEs (end-effectors) of SJTU-HR1 for the lying states.Finally, the results show that the large amounts of states can be described using the abbreviations in a systematic manner. Although we have focused on the application of the GF set theory to humanoid robots, particularly the SJTU-HR1, this methodology can also be applied to quadruped robots and hexapedal robots, especially when the desired tasks are complex.

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

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2006-10-01

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

  9. Plasticity of brain wave network interactions and evolution across physiologic states

    OpenAIRE

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

    2015-01-01

    Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other a...

  10. 22 CFR 9.6 - Derivative classification.

    Science.gov (United States)

    2010-04-01

    ... CFR 2001.22. (c) Department of State Classification Guide. The Department of State Classification... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Derivative classification. 9.6 Section 9.6... classification. (a) Definition. Derivative classification is the incorporating, paraphrasing, restating...

  11. Altered baseline brain activity in children with bipolar disorder during mania state: a resting-state study

    Directory of Open Access Journals (Sweden)

    Lu D

    2014-02-01

    Full Text Available Dali Lu,1 Qing Jiao,2 Yuan Zhong,3,4 Weijia Gao,1 Qian Xiao,1 Xiaoqun Liu,1 Xiaoling Lin,5 Wentao Cheng,6 Lanzhu Luo,6 Chuanjian Xu,3 Guangming Lu,2 Linyan Su1 1Mental Health Institute of the Second Xiangya Hospital, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, People's Republic of China; 2Department of Radiology, Taishan Medical University, Taian, People's Republic of China; 3Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, People's Republic of China; 4School of Psychology, Nanjing Normal University, Nanjing, People's Republic of China; 5School of Nursing of Central South University, Changsha, People's Republic of China; 6Department of Pediatric and Geriatric Psychiatry, Fuzhou Neuropsychiatric Hospital, Fuzhou, People's Republic of China Background: Previous functional magnetic resonance imaging (fMRI studies have shown abnormal functional connectivity in regions involved in emotion processing and regulation in pediatric bipolar disorder (PBD. Recent studies indicate, however, that task-dependent neural changes only represent a small fraction of the brain's total activity. How the brain allocates the majority of its resources at resting state is still unknown. We used the amplitude of low-frequency fluctuation (ALFF method of fMRI to explore the spontaneous neuronal activity in resting state in PBD patients. Methods: Eighteen PBD patients during the mania phase and 18 sex-, age- and education-matched healthy subjects were enrolled in this study and all patients underwent fMRI scanning. The ALFF method was used to compare the resting-state spontaneous neuronal activity between groups. Correlation analysis was performed between the ALFF values and Young Mania Rating Scale scores. Results: Compared with healthy controls, PBD patients presented increased ALFF in bilateral caudate and left pallidum as well as decreased ALFF in left precuneus

  12. Classification methods of sonar signal. Part One: State of the art

    International Nuclear Information System (INIS)

    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

  13. Induced arousal following zolpidem treatment in a vegetative state after brain injury in 7 cases Analysis using visual single photon emission computerized tomography and digitized cerebral state monitor

    Institute of Scientific and Technical Information of China (English)

    Bo Du; Aijun Shan; Di Yang; Wei Xiang

    2008-01-01

    BACKGROUND: Several studies have reported the use of zolpidem for induced arousal after permanent vegetative states. However, changes in brain function and EMG after zolpidem treatment requires further investigation. OBJECTIVE: To investigate the effect of zolpidem, an unconventional drug, on inducing arousal in patients in a permanent vegetative state after brain injury using visual single photon emission computerized tomography and digitized cerebral state monitor. DESIGN: A self-controlled observation. SETTING: Shenzhen People's Hospital.PARTICIPANTS: Seven patients in a permanent vegetative state were selected from the Department of Neurosurgery, Shenzhen People's Hospital from March 2005 to May 2007. The group included 5 males and 2 females, 24–55 years of age, with a mean age of 38.5 years. All seven patients had been in a permanent vegetative statement for at least six months. The patient group included three comatose patients, who had sustained injuries to the cerebral cortex, basal ganglia, or thalamus in motor vehicle accidents, and four patients, who had suffered primary/secondary brain stem injury. Informed consents were obtained from the patients’ relatives. METHODS: The patients brains were imaged by 99Tcm ECD single photon emission computerized tomography prior to treatment with zolpidem [Sanofi Winthrop Industrie, France, code number approved by the State Food & Drug Administration (SFDA) J20040033, specification 10 mg per tablet. At 8:00 p.m., 10 mg zolpidem was dissolved with distilled water and administered through a nasogastric tube at 1 hour before and after treatment and 1 week following treatment, respectively. Visual analysis of cerebral perfusion changes in the injured brain regions before and after treatment was performed. Simultaneously, three monitoring parameters were obtained though a cerebral state monitor, which included cerebral state index, electromyographic index, and burst suppression index. MAIN OUTCOME MEASURES: Comparison

  14. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

    Directory of Open Access Journals (Sweden)

    José del R. Millán

    2010-09-01

    Full Text Available In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT. In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication & Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI to improve BCI usability, and the development of novel BCI technology including better EEG devices.

  15. Brain-computer interfaces in the completely locked-in state and chronic stroke.

    Science.gov (United States)

    Chaudhary, U; Birbaumer, N; Ramos-Murguialday, A

    2016-01-01

    Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery. PMID:27590968

  16. 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.

  17. Hierarchical Finite-State Modeling for Texture Segmentation with Application to Forest Classification

    OpenAIRE

    Scarpa, Giuseppe; Haindl, Michal; Zerubia, Josiane

    2006-01-01

    The authors would like to thank the “French Forest Inventory” In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- ...

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

    OpenAIRE

    Oliveira Lindomário Barros de; Ferreira Maria da Graça de Vasconcelos Xavier; Marques Flávio Adriano

    2004-01-01

    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...

  19. 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

  20. Inhibition in multiclass classification

    OpenAIRE

    Huerta, Ramón; Vembu, Shankar; Amigó, José M.; Nowotny, Thomas; Elkan, Charles

    2012-01-01

    The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and ...

  1. Supermultiplet classification of higher intrashell doubly excited states of H- and He

    International Nuclear Information System (INIS)

    We compute new estimates of energy levels of doubly excited states with two electrons in the same shell, for all principal quantum numbers N- and He. We investigate the structure of these intrashell spectra using a recent ''supermultiplet'' approach which originates in the O(4) shell structure of one-electron atoms. We provide new interpretations of the electron correlation underlying the O(4) states and supermultiplets of two-electron atoms; this accounts qualitatively for approximate separability of rotationlike and vibrationlike progressions of levels found in the computed spectra. Certain diamond-shaped patterns of supermultiplet energies are found to contain near degeneracies of levels with ΔL= +- 2; this accounts in part for level clustering we find in the unresolved spectra. Our investigation of scaling of the apparent rotational and vibrational parts of the energy with higher N shows consistency with a simple model of the atom which has electrons on the surface of a spherical shell, with radius Rapprox. =N2. We investigate the model group theoretically, and find two different O(4) groups which describe angular electron correlation in limiting cases of large or small shell radius for each principal quantum number N. A third O(4) group is related to intrashell radiative transitions

  2. Territorial classification of indigenous Amazon: Contributions from the diversity to The Colombian state-nation

    International Nuclear Information System (INIS)

    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

  3. 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.

  4. Life Prediction and Classification of Failure Modes in Solid State Luminaires Using Bayesian Probabilistic Models

    Energy Technology Data Exchange (ETDEWEB)

    Lall, Pradeep; Wei, Junchao; Sakalaukus, Peter

    2014-05-27

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. It is expected that, the new test technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.

  5. Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Lall, Pradeep [Auburn Univ., Auburn, AL (United States); Wei, Junchao [Auburn Univ., Auburn, AL (United States); Sakalaukus, Peter [Auburn Univ., Auburn, AL (United States)

    2014-06-22

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The α-λ plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.

  6. 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.

  7. 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.

  8. 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.

  9. Acute effects of modafinil on brain resting state networks in young healthy subjects.

    Directory of Open Access Journals (Sweden)

    Roberto Esposito

    Full Text Available BACKGROUND: There is growing debate on the use of drugs that promote cognitive enhancement. Amphetamine-like drugs have been employed as cognitive enhancers, but they show important side effects and induce addiction. In this study, we investigated the use of modafinil which appears to have less side effects compared to other amphetamine-like drugs. We analyzed effects on cognitive performances and brain resting state network activity of 26 healthy young subjects. METHODOLOGY: A single dose (100 mg of modafinil was administered in a double-blind and placebo-controlled study. Both groups were tested for neuropsychological performances with the Raven's Advanced Progressive Matrices II set (APM before and three hours after administration of drug or placebo. Resting state functional magnetic resonance (rs-FMRI was also used, before and after three hours, to investigate changes in the activity of resting state brain networks. Diffusion Tensor Imaging (DTI was employed to evaluate differences in structural connectivity between the two groups. Protocol ID: Modrest_2011; NCT01684306; http://clinicaltrials.gov/ct2/show/NCT01684306. PRINCIPAL FINDINGS: Results indicate that a single dose of modafinil improves cognitive performance as assessed by APM. Rs-fMRI showed that the drug produces a statistically significant increased activation of Frontal Parietal Control (FPC; p<0.04 and Dorsal Attention (DAN; p<0.04 networks. No modifications in structural connectivity were observed. CONCLUSIONS AND SIGNIFICANCE: Overall, our findings support the notion that modafinil has cognitive enhancing properties and provide functional connectivity data to support these effects. TRIAL REGISTRATION: ClinicalTrials.gov NCT01684306 http://clinicaltrials.gov/ct2/show/NCT01684306.

  10. Brain Network Reconfiguration and Perceptual Decoupling During an Absorptive State of Consciousness.

    Science.gov (United States)

    Hove, Michael J; Stelzer, Johannes; Nierhaus, Till; Thiel, Sabrina D; Gundlach, Christopher; Margulies, Daniel S; Van Dijk, Koene R A; Turner, Robert; Keller, Peter E; Merker, Björn

    2016-07-01

    Trance is an absorptive state of consciousness characterized by narrowed awareness of external surroundings and has long been used-for example, by shamans-to gain insight. Shamans across cultures often induce trance by listening to rhythmic drumming. Using functional magnetic resonance imaging (fMRI), we examined the brain-network configuration associated with trance. Experienced shamanic practitioners (n = 15) listened to rhythmic drumming, and either entered a trance state or remained in a nontrance state during 8-min scans. We analyzed changes in network connectivity. Trance was associated with higher eigenvector centrality (i.e., stronger hubs) in 3 regions: posterior cingulate cortex (PCC), dorsal anterior cingulate cortex (dACC), and left insula/operculum. Seed-based analysis revealed increased coactivation of the PCC (a default network hub involved in internally oriented cognitive states) with the dACC and insula (control-network regions involved in maintaining relevant neural streams). This coactivation suggests that an internally oriented neural stream was amplified by the modulatory control network. Additionally, during trance, seeds within the auditory pathway were less connected, possibly indicating perceptual decoupling and suppression of the repetitive auditory stimuli. In sum, trance involved coactive default and control networks, and decoupled sensory processing. This network reconfiguration may promote an extended internal train of thought wherein integration and insight can occur. PMID:26108612

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

    Institute of Scientific and Technical Information of China (English)

    郭正红; 赵丙辰

    2013-01-01

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

  12. Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state

    Science.gov (United States)

    Power, Sarah D.; Kushki, Azadeh; Chau, Tom

    2011-10-01

    Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to

  13. THE STATE OF THE WATER IN BRAIN TISSUE IN PRESENCE OF TS-100 NANOPARTICLES

    Directory of Open Access Journals (Sweden)

    T. V.

    2015-12-01

    Full Text Available By the method of low-temperature 1Н NMR spectroscopy the structure of the hydrate layers of water associated with brain cells, the changes of these parameters during necrotic lesions (stroke and in the presence of trifluoroacetic acid, which allows differentiating intracellular water clusters according to their ability to dissolve the acid, were studied. Also the impact of silica TS-100 nanoparticles on the state of water in brain tissue, namely on the water binding parameters in the air and in the presence of a weakly polar solvent was considered. The distributions by the radii and change of Gibbs free energy for clusters of strongly bound interfacial water were obtained. It was shown that the hydration properties of the native brain tissue differ from the hydration properties of necrotic damaged tissue by the structure of weakly bound water clusters. In intact tissue all the water is associated and is a part of clusters and domains, most of which have a radii R = 2 and 20 nm. The media with chloroform stabilizes water polyassociates with the radius up to R = 100 nm and trifluoroacetic acid stabilizes water polyassociates with radii R = 7–20 nm. It was found that the partial dehydration of the investigated tissue samples is accompanied by decreasing of weakly bound water amount and some increasing of strongly bound water that indicates a change of molecular interactions between the components of cells-nanoparticles composite system. The ischemic necrosis area presence leads to a decrease of water binding due to the average size water polyassociates increasing. This effect is observed both in air and in a weakly polar organic solvent medium (deuterochloroform.

  14. 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.

  15. Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients.

    Science.gov (United States)

    Chen, Guangyu; Zhang, Hong-Ying; Xie, Chunming; Chen, Gang; Zhang, Zhi-Jun; Teng, Gao-Jun; Li, Shi-Jiang

    2013-01-01

    Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

  16. Nominal classification

    OpenAIRE

    Senft, G.

    2007-01-01

    This handbook chapter summarizes some of the problems of nominal classification in language, presents and illustrates the various systems or techniques of nominal classification, and points out why nominal classification is one of the most interesting topics in Cognitive Linguistics.

  17. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect

    OpenAIRE

    Chuan-Chih Yang; Alfonso Barrós-Loscertales; Daniel Pinazo; Noelia Ventura-Campos; Viola Borchardt; Juan-Carlos Bustamante; Aina Rodríguez-Pujadas; Paola Fuentes-Claramonte; Raúl Balaguer; César Ávila; Martin Walter

    2016-01-01

    The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common res...

  18. 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.

  19. 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. PMID

  20. 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...... 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...... 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...

  1. Activated and deactivated functional brain areas in the Deqi state A functional MRI study

    Institute of Scientific and Technical Information of China (English)

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

    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,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.

  2. 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

  3. Resting-state, functional MRI on regional homogeneity changes of brain in the heavy smokers

    International Nuclear Information System (INIS)

    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)

  4. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

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    Yijun Wang

    2007-07-01

    Full Text Available For a robust brain-computer interface (BCI system based on motor imagery (MI, it should be able to tell when the subject is not concentrating on MI tasks (the “idle state” so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD was used to extract the features of event-related desynchronization (ERD in two motor imagery tasks. Then Fisher discriminant analysis (FDA was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task.

  5. Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

    Science.gov (United States)

    Abu-Alqumsan, Mohammad; Peer, Angelika

    2016-06-01

    Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

  6. Histamine from Brain Resident MAST Cells Promotes Wakefulness and Modulates Behavioral States

    OpenAIRE

    Sachiko Chikahisa; Tohru Kodama; Atsushi Soya; Yohei Sagawa; Yuji Ishimaru; Hiroyoshi Séi; Seiji Nishino

    2013-01-01

    Mast cell activation and degranulation can result in the release of various chemical mediators, such as histamine and cytokines, which significantly affect sleep. Mast cells also exist in the central nervous system (CNS). Since up to 50% of histamine contents in the brain are from brain mast cells, mediators from brain mast cells may significantly influence sleep and other behaviors. In this study, we examined potential involvement of brain mast cells in sleep/wake regulations, focusing espec...

  7. Brain activation and inhibition after acupuncture at Taichong and Taixi: resting-state functional magnetic resonance imaging

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    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.

  8. 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. PMID:25883630

  9. Brain activation and inhibition after acupuncture at Taichong andTaixi:resting-state functional magnetic resonance imaging

    Institute of Scientific and Technical Information of China (English)

    Shao-qun Zhang; Chun-zhi Tang; Yan-jie Wang; Ji-ping Zhang; Jun-qi Chen; Chun-xiao Wu; Zhi-peng Li; Jia-rong Chen; Huai-liang Ouyang; Yong Huang

    2015-01-01

    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 atTaichong (LR3) andTaixi (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 atTaichong andTaixi 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 res-onance imaging, which revealed that the amplitude of low-frequency lfuctuation, 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), inferi-or frontal gyrus (Brodmann area 44) and the center of the posterior lobe of the cerebellum. The present ifndings indicate that acupuncture atTaichong andTaixi speciifcally promote blood lfow 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.

  10. Voxel-based statistical analysis of cerebral glucose metabolism in patients with permanent vegetative state after acquired brain injury

    Institute of Scientific and Technical Information of China (English)

    Yong Wook Kim; Hyoung Seop Kim; Young-Sil An; Sang Hee Im

    2010-01-01

    Background Permanent vegetative state is defined as the impaired level of consciousness longer than 12 months after traumatic causes and 3 months after non-traumatic causes of brain injury. Although many studies assessed the cerebral metabolism in patients with acute and persistent vegetative state after brain injury, few studies investigated the cerebral metabolism in patients with permanent vegetative state. In this study, we performed the voxel-based analysis of cerebral glucose metabolism and investigated the relationship between regional cerebral glucose metabolism and the severity of impaired consciousness in patients with permanent vegetative state after acquired brain injury.Methods We compared the regional cerebral glucose metabolism as demonstrated by F-18 fluorodeoxyglucose positron emission tomography from 12 patients with permanent vegetative state after acquired brain injury with those from 12 control subjects. Additionally, covariance analysis was performed to identify regions where decreased changes in regional cerebral glucose metabolism significantly correlated with a decrease of level of consciousness measured by JFK-coma recovery scare. Statistical analysis was performed using statistical parametric mapping.Results Compared with controls, patients with permanent vegetative state demonstrated decreased cerebral glucose metabolism in the left precuneus, both posterior cingulate cortices, the left superior parietal lobule (Pcorrected <0.001), and increased cerebral glucose metabolism in the both cerebellum and the right supramarginal cortices (Pcorrected <0.001). In the covariance analysis, a decrease in the level of consciousness was significantly correlated with decreased cerebral glucose metabolism in the both posterior cingulate cortices (Puncorrected <0.005).Conclusion Our findings suggest that the posteromedial parietal cortex, which are part of neural network for consciousness, may be relevant structure for pathophysiological mechanism

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

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    Ilya M. Veer

    2010-09-01

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

  12. Reduced brain resting-state network specificity in infants compared with adults

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    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

  13. The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism.

    Science.gov (United States)

    Thompson, Garth J; Riedl, Valentin; Grimmer, Timo; Drzezga, Alexander; Herman, Peter; Hyder, Fahmeed

    2016-07-01

    The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states. PMID:27029438

  14. Evaluation of brain functional states based on projections of electroencephalographic spectral parameters on 2-dimensional canonical space.

    Science.gov (United States)

    Won, Seung-Hee; Jang, Hwan-Soo; Lee, Ho-Won; Jang, Il-Sung; Lee, Maan-Gee

    2012-10-15

    Electroencephalographic (EEG) activities reflect the functional state of the brain, but it is difficult to objectively describe functional brain states. Here, we describe two statistical divergence measures, Mahalanobis distance and Hellinger distance of projections to the reference spaces, to evaluate their state-discriminating ability. Last, divergence measures of 30-min segments after caffeine treatment were compared to evaluate the dose- and time-dependent arousal effects of caffeine to the best reference space. EEG was recorded from Sprague-Dawley rats during pre- and post-administration of caffeine. Several two-dimensional reference spaces were constructed from subsets of the normalized 7 relative band powers pooled from the pre-drug period of all recordings for each cortex: two reference spaces from data sets of the frontal and parietal cortex, and four reference spaces from data sets of active wake, slow-wave sleep, paradoxical sleep state, and all states. Sleep-wake states used as test states were plotted onto the reference spaces, and then, two divergence measures were derived to measure state-discriminating ability of each reference space. First, the reference space of the same cortex as test data was better for discriminating test states than another cortical reference space. Second, the one reference space constructed from data of all states was better for discriminating test states than the other reference spaces. Third, divergence measures were well correlated with sleep-wake durations after caffeine administration and showed the temporal trends of caffeine-induced arousal effect. These results suggest that two statistical measures can objectively describe brain functional states and drug-induced states.

  15. SECONDARY BRAIN INJURY

    OpenAIRE

    Ida Ayu Basmatika

    2013-01-01

    Secondary brain injury is a condision that occurs at some times after the primary impact and can be largely prevented and treated. Most brain injury ends with deadly consequences which is caused by secondary damage to the brain. Traumatic brain injured still represents the leading cause of morbidity and mortality in individuals under the age of 45 years in the world. The classification of secondary brain injured is divided into extracranial and intracranial causes. The cause of extracranial s...

  16. 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.

  17. Steady state visually evoked potentials based Brain computer interface test outside the lab

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    Eduardo Francisco Caicedo Bravo

    2016-06-01

    Full Text Available Context: Steady State Visually Evoked Potentials (SSVEP are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where the user could move an avatar looking for a target that is represented by a house.  In order to move the avatar, the volunteer must stare at one of the four visual stimuli; the four visual stimuli represent the four directions: right, up, left, and down. The system is proven without any calibration procedure.Results: 32 volunteers utilized the system and 20 achieved the target with an accuracy above 60%, including 9 with an accuracy of 100%, 7 achieved the target with an accuracy below 60% and 5 left without achieving the goal. For the volunteers who reached accuracy above 60%, the results of the performance achieved an average of 6,4s for command detections, precision of 79% and information transfer rate (ITR of 8,78 bits/s.Conclusions: We showed a SSVEP based BCI system with low cost, it was proved in a public event, it did not have calibration procedures, it was easy to install, and it was used for people in a wide age range. The results show that it is possible to bring this kind of systems to environments outside the laboratory.

  18. Using auditory steady state responses to outline the functional connectivity in the tinnitus brain.

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    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.

  19. Frequency specificity of regional homogeneity in the resting-state human brain.

    Directory of Open Access Journals (Sweden)

    Xiaopeng Song

    Full Text Available Resting state-fMRI studies have found that the inter-areal correlations in cortical networks concentrate within ultra-low frequencies (0.01-0.04 Hz while long-distance connections within subcortical networks distribute over a wider frequency range (0.01-0.14 Hz. However, the frequency characteristics of regional homogeneity (ReHo in different areas are still unclear. To examine the ReHo properties in different frequency bands, a data-driven method, Empirical Mode Decomposition (EMD, was adopted to decompose the time series of each voxel into several components with distinct frequency bands. ReHo values in each of the components were then calculated. Our results showed that ReHo in cortical areas were higher and more frequency-dependent than those in the subcortical regions. BOLD oscillations of 0.02-0.04 Hz mainly contributed to the cortical ReHo, whereas the ReHo in limbic areas involved a wider frequency range and were dominated by higher-frequency BOLD oscillations (>0.08 Hz. The frequency characteristics of ReHo are distinct between different parts of the striatum, with the frequency band of 0.04-0.1 Hz contributing the most to ReHo in caudate nucleus, and oscillations lower than 0.02 Hz contributing more to ReHo in putamen. The distinct frequency-specific ReHo properties of different brain areas may arise from the assorted cytoarchitecture or synaptic types in these areas. Our work may advance the understanding of the neural-physiological basis of local BOLD activities and the functional specificity of different brain regions.

  20. Whole-brain perfusion imaging with balanced steady-state free precession arterial spin labeling.

    Science.gov (United States)

    Han, Paul Kyu; Ye, Jong Chul; Kim, Eung Yeop; Choi, Seung Hong; Park, Sung-Hong

    2016-03-01

    Recently, balanced steady-state free precession (bSSFP) readout has been proposed for arterial spin labeling (ASL) perfusion imaging to reduce susceptibility artifacts at a relatively high spatial resolution and signal-to-noise ratio (SNR). However, the main limitation of bSSFP-ASL is the low spatial coverage. In this work, methods to increase the spatial coverage of bSSFP-ASL are proposed for distortion-free, high-resolution, whole-brain perfusion imaging. Three strategies of (i) segmentation, (ii) compressed sensing (CS) and (iii) a hybrid approach combining the two methods were tested to increase the spatial coverage of pseudo-continuous ASL (pCASL) with three-dimensional bSSFP readout. The spatial coverage was increased by factors of two, four and six using each of the three approaches, whilst maintaining the same total scan time (5.3 min). The number of segments and/or CS acceleration rate (R) correspondingly increased to maintain the same bSSFP readout time (1.2 s). The segmentation approach allowed whole-brain perfusion imaging for pCASL-bSSFP with no penalty in SNR and/or total scan time. The CS approach increased the spatial coverage of pCASL-bSSFP whilst maintaining the temporal resolution, with minimal impact on the image quality. The hybrid approach provided compromised effects between the two methods. Balanced SSFP-based ASL allows the acquisition of perfusion images with wide spatial coverage, high spatial resolution and SNR, and reduced susceptibility artifacts, and thus may become a good choice for clinical and neurological studies. Copyright © 2015 John Wiley & Sons, Ltd.

  1. Abnormal baseline brain activity in patients with neuromyelitis optica: A resting-state fMRI study

    International Nuclear Information System (INIS)

    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.

  2. 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.

  3. 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 (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.

  4. 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.

  5. Voxel Scale Complex Networks of Functional Connectivity in the Rat Brain: Neurochemical State Dependence of Global and Local Topological Properties

    Directory of Open Access Journals (Sweden)

    Adam J. Schwarz

    2012-01-01

    Full Text Available Network analysis of functional imaging data reveals emergent features of the brain as a function of its topological properties. However, the brain is not a homogeneous network, and the dependence of functional connectivity parameters on neuroanatomical substrate and parcellation scale is a key issue. Moreover, the extent to which these topological properties depend on underlying neurochemical changes remains unclear. In the present study, we investigated both global statistical properties and the local, voxel-scale distribution of connectivity parameters of the rat brain. Different neurotransmitter systems were stimulated by pharmacological challenge (d-amphetamine, fluoxetine, and nicotine to discriminate between stimulus-specific functional connectivity and more general features of the rat brain architecture. Although global connectivity parameters were similar, mapping of local connectivity parameters at high spatial resolution revealed strong neuroanatomical dependence of functional connectivity in the rat brain, with clear differentiation between the neocortex and older brain regions. Localized foci of high functional connectivity independent of drug challenge were found in the sensorimotor cortices, consistent with the high neuronal connectivity in these regions. Conversely, the topological properties and node roles in subcortical regions varied with neurochemical state and were dependent on the specific dynamics of the different functional processes elicited.

  6. Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self

    OpenAIRE

    D. Lehmann(Darmstadt, GSI); Faber, P L; Achermann, P.; Jeanmonod, D; Gianotti, L. R.; Pizzagalli, D.

    2001-01-01

    Multichannel EEG of an advanced meditator was recorded during four different, repeated meditations. Locations of intracerebral source gravity centers as well as Low Resolution Electromagnetic Tomography (LORETA) functional images of the EEG 'gamma' (35-44 Hz) frequency band activity differed significantly between meditations. Thus, during volitionally self-initiated, altered states of consciousness that were associated with different subjective meditation states, different brain neuronal popu...

  7. North-South Trade-related Technology Diffusion, Brain Drain and Productivity Growth: Are Small States Different?

    OpenAIRE

    Schiff, Maurice; Wang, Yanling

    2009-01-01

    The economies of small developing states tend to be more fragile than those of large ones. This paper examines this issue in a dynamic context by focusing on the impact of the brain drain on North-South trade-related technology diffusion and total factor productivity growth in small and large states in the South. There are three main findings. First, productivity growth increases with Nort...

  8. Whole-brain functional connectivity during emotional word classification in medication-free Major Depressive Disorder: Abnormal salience circuitry and relations to positive emotionality☆

    Science.gov (United States)

    van Tol, Marie-José; Veer, Ilya M.; van der Wee, Nic J.A.; Aleman, André; van Buchem, Mark A.; Rombouts, Serge A.R.B.; Zitman, Frans G.; Veltman, Dick J.; Johnstone, Tom

    2013-01-01

    Major Depressive Disorder (MDD) has been associated with biased processing and abnormal regulation of negative and positive information, which may result from compromised coordinated activity of prefrontal and subcortical brain regions involved in evaluating emotional information. We tested whether patients with MDD show distributed changes in functional connectivity with a set of independently derived brain networks that have shown high correspondence with different task demands, including stimulus salience and emotional processing. We further explored if connectivity during emotional word processing related to the tendency to engage in positive or negative emotional states. In this study, 25 medication-free MDD patients without current or past comorbidity and matched controls (n = 25) performed an emotional word-evaluation task during functional MRI. Using a dual regression approach, individual spatial connectivity maps representing each subject's connectivity with each standard network were used to evaluate between-group differences and effects of positive and negative emotionality (extraversion and neuroticism, respectively, as measured with the NEO-FFI). Results showed decreased functional connectivity of the medial prefrontal cortex, ventrolateral prefrontal cortex, and ventral striatum with the fronto-opercular salience network in MDD patients compared to controls. In patients, abnormal connectivity was related to extraversion, but not neuroticism. These results confirm the hypothesis of a relative (para)limbic–cortical decoupling that may explain dysregulated affect in MDD. As connectivity of these regions with the salience network was related to extraversion, but not to general depression severity or negative emotionality, dysfunction of this network may be responsible for the failure to sustain engagement in rewarding behavior. PMID:24179829

  9. Probing of brain states in real-time: Introducing the ConSole environment

    Directory of Open Access Journals (Sweden)

    Thomas eHartmann

    2011-03-01

    Full Text Available Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e. treating it as a source of "noise". Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of "brain states" that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a posthoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces ConSole (CONstance System for OnLine Eeg, a software package capable of analyzing ongoing EEG / MEG in real-time and presenting auditory and visual stimuli via internal routines. Stimulation via external devices (e.g. TMS or third-party software (e.g. Psyscope X is possible by sending TTL-triggers. With ConSole it is thus possible to target the stimulation at specific brain-states. In contrast to many available applications, ConSole is open-source. Its modular design enhances the power of the software as it can be easily adapted to new challenges and writing new experiments is an easy task. ConSole is already pre-equipped with modules performing standard signal processing steps. The software is also independent from the EEG / MEG system, as long as a driver can be written (currently 2 EEG systems are supported. Besides a general introduction, we present benchmark data regarding performance and validity of the calculations used, as well as three example applications of ConSole in different settings. ConSole can be downloaded at: http://console-kn.sf.net.

  10. Individual human brain areas can be identified from their characteristic spectral activation fingerprints

    OpenAIRE

    Keitel, Anne; Gross, Joachim

    2016-01-01

    The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified f...

  11. Classification of hydrogeologic areas and hydrogeologic flow systems in the basin and range physiographic province, southwestern United States

    Science.gov (United States)

    Anning, David W.; Konieczki, Alice D.

    2005-01-01

    The hydrogeology of the Basin and Range Physiographic Province in parts of Arizona, California, New Mexico, Utah, and most of Nevada was classified at basin and larger scales to facilitate information transfer and to provide a synthesis of results from many previous hydrologic investigations. A conceptual model for the spatial hierarchy of the hydrogeology was developed for the Basin and Range Physiographic Province and consists, in order of increasing spatial scale, of hydrogeologic components, hydrogeologic areas, hydrogeologic flow systems, and hydrogeologic regions. This hierarchy formed a framework for hydrogeologic classification. Hydrogeologic areas consist of coincident ground-water and surface-water basins and were delineated on the basis of existing sets of basin boundaries that were used in past investigations by State and Federal government agencies. Within the study area, 344 hydrogeologic areas were identified and delineated. This set of basins not only provides a framework for the classification developed in this report, but also has value for regional and subregional purposes of inventory, study, analysis, and planning throughout the Basin and Range Physiographic Province. The fact that nearly all of the province is delineated by the hydrogeologic areas makes this set well suited to support regional-scale investigations. Hydrogeologic areas are conceptualized as a control volume consisting of three hydrogeologic components: the soils and streams, basin fill, and consolidated rocks. The soils and streams hydrogeologic component consists of all surface-water bodies and soils extending to the bottom of the plant root zone. The basin-fill hydrogeologic component consists of unconsolidated and semiconsolidated sediment deposited in the structural basin. The consolidated-rocks hydrogeologic component consists of the crystalline and sedimentary rocks that form the mountain blocks and basement rock of the structural basin. Hydrogeologic areas were

  12. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately. PMID:27147955

  13. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  14. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  15. Advancing brain-machine interfaces: Moving beyond linear state space models

    Directory of Open Access Journals (Sweden)

    Adam G Rouse

    2015-07-01

    Full Text Available Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs. Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider i the dynamic range and precision of natural movements, ii differences between cortical activity and actual body movement, iii kinematic and muscular synergies, and iv the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.

  16. Social-sparsity brain decoders: faster spatial sparsity

    OpenAIRE

    Varoquaux, Gaël; Kowalski, Matthieu; Thirion, Bertrand

    2016-01-01

    Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as ...

  17. Altered baseline brain activity with 72 h of simulated microgravity--initial evidence from resting-state fMRI.

    Science.gov (United States)

    Liao, Yang; Zhang, Jinsong; Huang, Zhiping; Xi, Yibin; Zhang, Qianru; Zhu, Tianli; Liu, Xufeng

    2012-01-01

    To provide the basis and reference to further insights into the neural activity of the human brain in a microgravity environment, we discuss the amplitude changes of low-frequency brain activity fluctuations using a simulated microgravity model. Twelve male participants between 24 and 31 years old received resting-state fMRI scans in both a normal condition and after 72 hours in a -6° head down tilt (HDT). A paired sample t-test was used to test the amplitude differences of low-frequency brain activity fluctuations between these two conditions. With 72 hours in a -6° HDT, the participants showed a decreased amplitude of low-frequency fluctuations in the left thalamus compared with the normal condition (a combined threshold of Pmicrogravity environment. PMID:23285086

  18. Adult sports-related traumatic brain injury in United States trauma centers.

    Science.gov (United States)

    Winkler, Ethan A; Yue, John K; Burke, John F; Chan, Andrew K; Dhall, Sanjay S; Berger, Mitchel S; Manley, Geoffrey T; Tarapore, Phiroz E

    2016-04-01

    OBJECTIVE Sports-related traumatic brain injury (TBI) is an important public health concern estimated to affect 300,000 to 3.8 million people annually in the United States. Although injuries to professional athletes dominate the media, this group represents only a small proportion of the overall population. Here, the authors characterize the demographics of sports-related TBI in adults from a community-based trauma population and identify predictors of prolonged hospitalization and increased morbidity and mortality rates. METHODS Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from adults (age ≥ 18 years) across 5 sporting categories-fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged hospital length of stay (LOS), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α sports-related TBIs were documented in the NTDB, which represented 18,310 incidents nationally. Equestrian sports were the greatest contributors to sports-related TBI (45.2%). Mild TBI represented nearly 86% of injuries overall. Mean (± SEM) LOSs in the hospital or intensive care unit (ICU) were 4.25 ± 0.09 days and 1.60 ± 0.06 days, respectively. The mortality rate was 3.0% across all patients, but was statistically higher in TBI from roller sports (4.1%) and aquatic sports (7.7%). Age, hypotension on admission to the emergency department (ED), and the severity of head and extracranial injuries were statistically significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Traumatic brain injury during aquatic sports was similarly associated with prolonged ICU and hospital LOSs, medical complications, and failure to be discharged to

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

    Institute of Scientific and Technical Information of China (English)

    李定健

    2016-01-01

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

  20. A subject-independent pattern-based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Andreas Markus Ray

    2015-10-01

    Full Text Available While earlier Brain-Computer Interface (BCI studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e. happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to match their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.

  1. A subject-independent pattern-based Brain-Computer Interface

    Science.gov (United States)

    Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

    2015-01-01

    While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to “match” their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders. PMID:26539089

  2. Evaluation of the WHO classification of dengue disease severity during an epidemic in 2011 in the state of Ceara, Brazil

    Directory of Open Access Journals (Sweden)

    Luciano Pamplona de Goes Cavalcanti

    2014-02-01

    Full Text Available In 2009, the World Health Organization (WHO issued a new guideline that stratifies dengue-affected patients into severe (SD and non-severe dengue (NSD (with or without warning signs. To evaluate the new recommendations, we completed a retrospective cross-sectional study of the dengue haemorrhagic fever (DHF cases reported during an outbreak in 2011 in northeastern Brazil. We investigated 84 suspected DHF patients, including 45 (53.6% males and 39 (46.4% females. The ages of the patients ranged from five-83 years and the median age was 29. According to the DHF/dengue shock syndrome classification, 53 (63.1% patients were classified as having dengue fever and 31 (36.9% as having DHF. According to the 2009 WHO classification, 32 (38.1% patients were grouped as having NSD [4 (4.8% without warning signs and 28 (33.3% with warning signs] and 52 (61.9% as having SD. A better performance of the revised classification in the detection of severe clinical manifestations allows for an improved detection of patients with SD and may reduce deaths. The revised classification will not only facilitate effective screening and patient management, but will also enable the collection of standardised surveillance data for future epidemiological and clinical studies.

  3. Thirty minute transcutaneous electric acupoint stimulation modulates resting state brain activities: a perfusion and BOLD fMRI study.

    Science.gov (United States)

    Jiang, Yin; Hao, Ying; Zhang, Yue; Liu, Jing; Wang, Xiaoying; Han, Jisheng; Fang, Jing; Zhang, Jue; Cui, Cailian

    2012-05-31

    Increasing neuroimaging studies have focused on the sustained after effects of acupuncture, especially for the changes of brain activities in rest. However, short-period stimuli have mostly been chosen in these works. The present study aimed to investigate how the resting state brain activities in healthy subjects were modulated by relatively long-period (30 min) acupuncture, a widely used modality in clinical practice. Transcutaneous electric acupoint stimulation (TEAS) or intermittent minimal TEAS (MTEAS) were given for 30 min to 40 subjects. Functional MRI (fMRI) data were collected including the pre-stimulation resting state and the post-stimulation resting state, using dual-echo arterial spin labeling (ASL) techniques, representing both cerebral blood flow (CBF) signals and blood oxygen-dependent level (BOLD) signals simultaneously. Following 30 min TEAS, but not MTEAS, the mean global CBF decreased, and a significant decrease of regional CBF was observed in SI, insula, STG, MOG and IFG. Functional connectivity analysis showed more secure and spatially extended connectivity of both the DMN and SMN after 30 min TEAS. Our results implied that modulation of the regional brain activities and network connectivity induced by thirty minute TEAS may associate with the acupuncture-related therapeutic effects. Furthermore, the resting state regional CBF quantified by ASL perfusion fMRI may serve as a potential biomarker in future acupuncture studies. PMID:22541167

  4. Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Robert eBauer

    2015-02-01

    Full Text Available Neurofeedback training with brain-computer interfaces is currently studied in a variety of neurological and neuropsychiatric conditions to reduce disorder-specific symptoms. For this purpose, a variety of classification algorithms have been explored to distinguish different brain states. These neural states, e.g. self-regulated brain activity versus rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy have been introduced to evaluate the performance of these algorithms. Interestingly, the very same measures are often used to estimate the subject’s ability to perform brain self-regulation. This is surprising, as the goal of improving the tool that differentiates brain states is different from the aim of optimizing neurofeedback for the subject who performs brain self-regulation. For the latter, knowledge about mental resources and work load is essential to adapt the difficulty of the intervention.In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development as a measure of a subject’s cognitive resources and the instructional efficacy of neurofeedback. This approach is based on a reconsideration of item-response theory and cognitive load theory for instructional design, and combines them with the classification accuracy curve as a measure of BCI performance.

  5. Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study.

    Directory of Open Access Journals (Sweden)

    Qingbao Yu

    Full Text Available Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs by group independent component analysis (ICA. FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS. These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

  6. Effects of methylphenidate on resting-state brain activity in normal adults: an fMRI study

    Institute of Scientific and Technical Information of China (English)

    Yihong Zhu; Bin Gao; Jianming Hua; Weibo Liu; Yichao Deng; Lijie Zhang; Biao Jiang

    2013-01-01

    Methylphenidate (MPH) is one of the most commonly used stimulants for the treatment of attention deficit hyperactivity disorder (ADHD).Although several studies have evaluated the effects of MPH on human brain activation during specific cognitive tasks using functional magnetic resonance imaging (fMRI),few studies have focused on spontaneous brain activity.In the current study,we investigated the effect of MPH on the intra-regional synchronization of spontaneous brain activity during the resting state in 18normal adult males.A handedness questionnaire and the Wechsler Adult Intelligence Scale were applied before medication,and a resting-state fMRI scan was obtained 1 h after medication (20 mg MPH or placebo,order counterbalanced between participants).We demonstrated that:(1) there were no significant differences in the performance of behavioral tasks between the MPH and placebo groups; (2) the left middle and superior temporal gyri had stronger MPH-related regional homogeneity (ReHo); and (3) the left lingual gyrus had weaker MPH-related ReHo.Our findings showed that the ReHo in some brain areas changes with MPH compared to placebo in normal adults,even though there are no behavioral differences.This method can be applied to patients with mental illness who may be treated with MPH,and be used to compare the difference between patients taking MPH and normal participants,to help reveal the mechanism of how MPH works.

  7. Spike avalanches in vivo suggest a driven, slightly subcritical brain state

    Directory of Open Access Journals (Sweden)

    Viola ePriesemann

    2014-06-01

    Full Text Available In self-organized critical (SOC systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore we analyzed highly parallel spike recordings from awake rats and monkeys, anaesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1 subsampling, (2 increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition, and (3 making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes, and for the dependence of all these measures on the temporal bin size.Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.

  8. Life Expectancy after Inpatient Rehabilitation for Traumatic Brain Injury in the United States.

    Science.gov (United States)

    Harrison-Felix, Cynthia; Pretz, Christopher; Hammond, Flora M; Cuthbert, Jeffrey P; Bell, Jeneita; Corrigan, John; Miller, A Cate; Haarbauer-Krupa, Juliet

    2015-12-01

    This study characterized life expectancy after traumatic brain injury (TBI). The TBI Model Systems (TBIMS) National Database (NDB) was weighted to represent those ≥16 years of age completing inpatient rehabilitation for TBI in the United States (US) between 2001 and 2010. Analyses included Standardized Mortality Ratios (SMRs), Cox regression, and life expectancy. The US mortality rates by age, sex, race, and cause of death for 2005 and 2010 were used for comparison purposes. Results indicated that a total of 1325 deaths occurred in the weighted cohort of 6913 individuals. Individuals with TBI were 2.23 times more likely to die than individuals of comparable age, sex, and race in the general population, with a reduced average life expectancy of 9 years. Independent risk factors for death were: older age, male gender, less-than-high school education, previously married at injury, not employed at injury, more recent year of injury, fall-related TBI, not discharged home after rehabilitation, less functional independence, and greater disability. Individuals with TBI were at greatest risk of death from seizures; accidental poisonings; sepsis; aspiration pneumonia; respiratory, mental/behavioral, or nervous system conditions; and other external causes of injury and poisoning, compared with individuals in the general population of similar age, gender, and race. This study confirms prior life expectancy study findings, and provides evidence that the TBIMS NDB is representative of the larger population of adults receiving inpatient rehabilitation for TBI in the US. There is an increased risk of death for individuals with TBI requiring inpatient rehabilitation.

  9. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect

    Directory of Open Access Journals (Sweden)

    Chuan-Chih Yang

    2016-01-01

    Full Text Available The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation and between time points (before versus after training were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression.

  10. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect

    Science.gov (United States)

    Yang, Chuan-Chih; Barrós-Loscertales, Alfonso; Pinazo, Daniel; Ventura-Campos, Noelia; Borchardt, Viola; Bustamante, Juan-Carlos; Rodríguez-Pujadas, Aina; Fuentes-Claramonte, Paola; Balaguer, Raúl; Ávila, César; Walter, Martin

    2016-01-01

    The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation) and between time points (before versus after training) were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression. PMID:26998365

  11. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect.

    Science.gov (United States)

    Yang, Chuan-Chih; Barrós-Loscertales, Alfonso; Pinazo, Daniel; Ventura-Campos, Noelia; Borchardt, Viola; Bustamante, Juan-Carlos; Rodríguez-Pujadas, Aina; Fuentes-Claramonte, Paola; Balaguer, Raúl; Ávila, César; Walter, Martin

    2016-01-01

    The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation) and between time points (before versus after training) were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression. PMID:26998365

  12. [Music therapy and "brain music": state of the art, problems and perspectives].

    Science.gov (United States)

    2013-01-01

    Recent literature on the problem of interaction between music and the brain is reviewed and summarized. Mechanisms and effects of two most popular music therapy applications are picked out, including music listening and music making. Special attention is paid to relatively new line of investigations that is called "music of the brain" and deals with transformation of bioelectric processes of human organism into music. Unresolved questions of music therapy are identified and some promising lines of future investigations are delineated. PMID:25508092

  13. Ovarian steroids in rat and human brain : effects of different endocrine states

    OpenAIRE

    Bixo, Marie

    1987-01-01

    Ovarian steroid hormones are known to produce several different effects in the brain. In addition to their role in gonadotropin release, ovulation and sexual behaviour they also seem to affect mood and emotions, as shown in women with the premenstrual tension syndrome. Some steroids have the ability to affect brain excitability. Estradiol decreases the electroshock threshold while progesterone acts as an anti-convulsant and anaesthetic in both animals and humans. Several earlier studies have ...

  14. MRI Study on the Functional and Spatial Consistency of Resting State-Related Independent Components of the Brain Network

    Energy Technology Data Exchange (ETDEWEB)

    Jeong, Bum Seok [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Choi, Jee Wook [Daejeon St. Mary' s Hospital, The Catholic University of Korea College of Medicine, Daejeon (Korea, Republic of); Kim, Ji Woong [College of Medical Science, Konyang University, Daejeon(Korea, Republic of)

    2012-06-15

    Resting-state networks (RSNs), including the default mode network (DMN), have been considered as markers of brain status such as consciousness, developmental change, and treatment effects. The consistency of functional connectivity among RSNs has not been fully explored, especially among resting-state-related independent components (RSICs). This resting-state fMRI study addressed the consistency of functional connectivity among RSICs as well as their spatial consistency between 'at day 1' and 'after 4 weeks' in 13 healthy volunteers. We found that most RSICs, especially the DMN, are reproducible across time, whereas some RSICs were variable in either their spatial characteristics or their functional connectivity. Relatively low spatial consistency was found in the basal ganglia, a parietal region of left frontoparietal network, and the supplementary motor area. The functional connectivity between two independent components, the bilateral angular/supramarginal gyri/intraparietal lobule and bilateral middle temporal/occipital gyri, was decreased across time regardless of the correlation analysis method employed, (Pearson's or partial correlation). RSICs showing variable consistency are different between spatial characteristics and functional connectivity. To understand the brain as a dynamic network, we recommend further investigation of both changes in the activation of specific regions and the modulation of functional connectivity in the brain network.

  15. Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study.

    Directory of Open Access Journals (Sweden)

    Xiaohu Zhao

    Full Text Available The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD. However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI of carefully selected moderate AD patients and normal controls (NCs. Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.

  16. Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

    Directory of Open Access Journals (Sweden)

    Delong eZhang

    2015-02-01

    Full Text Available The present study examined directional connections in the brain among resting-state networks (RSNs when the participant had their eyes open (EO or had their eyes closed (EC. The resting-state fMRI data were collected from 20 healthy participants (11 males, 20.17 ± 2.74 years under the EO and EC states. Independent component analysis (ICA was applied to identify the separated RSNs (i.e., the primary/high-level visual, primary sensory-motor, ventral motor, salience/dorsal attention, and anterior/posterior default-mode networks, and the Gaussian Bayesian network (BN learning approach was then used to explore the conditional dependencies among these RSNs. The network-to-network directional connections related to EO and EC were depicted, and a support vector machine (SVM was further employed to identify the directional connection patterns that could effectively discriminate between the two states. The results indicated that the connections among RSNs are directionally connected within a BN during the EO and EC states. The directional connections from the salient attention network to the anterior/posterior default-mode networks and the high-level to primary-level visual network were the obvious characteristics of both the EO and EC resting-state BNs. Of the directional connections in BN, the attention (salient and dorsal-related directional connections were observed to be discriminative between the EO and EC states. In particular, we noted that the properties of the salient and dorsal attention networks were in opposite directions. Overall, the present study described the directional connections of RSNs using a BN learning approach during the EO and EC states, and the results suggested that the attention system (the salient and the dorsal attention network might have important roles in resting-state brain networks and the neural substrate underpinning of switching between the EO and EC states.

  17. Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine

    Science.gov (United States)

    Jansen, Patrick; Vollnhals, Michael; Renner, Daniel; Vergossen, David; John, Werner; Götze, Jürgen

    2016-09-01

    Further improvements on the novel method for state of charge (SOC) determination of lithium iron phosphate (LFP) cells based on the impedance spectra classification are presented. A Support Vector Machine (SVM) is applied to impedance spectra of a LFP cell, with each impedance spectrum representing a distinct SOC for a predefined temperature. As a SVM is a binary classifier, only the distinction between two SOC can be computed in one iteration of the algorithm. Therefore a search pattern is necessary. A balanced tree search was implemented with good results. In order to further improvements of the SVM method, this paper discusses two new search pattern, namely a linear search and an imbalanced tree search, the later one based on an initial educated guess. All three search pattern were compared under various aspects like accuracy, efficiency, tolerance of disturbances and temperature dependancy. The imbalanced search tree shows to be the most efficient search pattern if the initial guess is within less than ±5 % SOC of the original SOC in both directions and exhibits the best tolerance for high disturbances. Linear search improves the rate of exact classifications for almost every temperature. It also improves the robustness against high disturbances and can even detect a certain number of false classifications which makes this search pattern unique. The downside is a much lower efficiency as all impedance spectra have to be evaluated while the tree search pattern only evaluate those on the tree path.

  18. Financial Accounting: Classifications and Standard Terminology for Local and State School Systems. State Educational Records and Reports Series: Handbook II, Revised.

    Science.gov (United States)

    Roberts, Charles T., Comp.; Lichtenberger, Allan R., Comp.

    This handbook has been prepared as a vehicle or mechanism for program cost accounting and as a guide to standard school accounting terminology for use in all types of local and intermediate education agencies. In addition to classification descriptions, program accounting definitions, and proration of cost procedures, some units of measure and…

  19. Multisite functional connectivity MRI classification of autism: ABIDE results

    Directory of Open Access Journals (Sweden)

    Jared A Nielsen

    2013-09-01

    Full Text Available Background: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange including resting state fMRI data obtained from 964 subjects and 16 separate international sites.Methods: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million "connections" after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier.Results: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampal and fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times.Conclusions: Multisite functional connectivity classification of autism outperformed chance using a simple leave

  20. 22 CFR 42.11 - Classification symbols.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification symbols. 42.11 Section 42.11... NATIONALITY ACT, AS AMENDED Classification and Foreign State Chargeability § 42.11 Classification symbols. A... visa symbol to show the classification of the alien. Immigrants Symbol Class Section of law...

  1. 22 CFR 9.8 - Classification challenges.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification challenges. 9.8 Section 9.8 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.8 Classification... classification status is improper are expected and encouraged to challenge the classification status of...

  2. 7 CFR 51.1860 - Color classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Color classification. 51.1860 Section 51.1860... STANDARDS) United States Standards for Fresh Tomatoes 1 Color Classification § 51.1860 Color classification... illustrating the color classification requirements, as set forth in this section. This visual aid may...

  3. Oxidative state and oxidative metabolism in the brain of rats with adjuvant-induced arthritis.

    Science.gov (United States)

    Wendt, Mariana Marques Nogueira; de Sá-Nakanishi, Anacharis Babeto; de Castro Ghizoni, Cristiane Vizioli; Bersani Amado, Ciomar Aparecida; Peralta, Rosane Marina; Bracht, Adelar; Comar, Jurandir Fernando

    2015-06-01

    The purpose of the present study was to evaluate the oxidative status of the brain of arthritic rats, based mainly on the observation that arthritis induces a pronounced oxidative stress in the liver of arthritis rats and that morphological alterations have been reported to occur in patients with rheumatoid arthritis. Rats with adjuvant-induced arthritis were used. These animals presented higher levels of reactive oxygen species (ROS) in the total brain homogenate (25% higher) and in the mitochondria (+55%) when compared to healthy rats. The nitrite plus nitrate contents, nitric oxide (NO) markers, were also increased in both mitochondria (+27%) and cytosol (+14%). Arthritic rats also presented higher levels of protein carbonyl groups in the total homogenate (+43%), mitochondria (+69%) and cytosol (+145%). Arthritis caused a diminution of oxygen consumption in isolated brain mitochondria only when ascorbate was the electron donor. The disease diminished the mitochondrial cytochrome c oxidase activity by 55%, but increased the transmembrane potential by 16%. The pro-oxidant enzyme xanthine oxidase was 150%, 110% and 283% higher, respectively, in the brain homogenate, mitochondria and cytosol of arthritic animals. The same occurred with the calcium-independent NO-synthase activity that was higher in the brain homogenate (90%) and cytosol (122%) of arthritic rats. The catalase activity, on the other hand, was diminished by arthritis in all cellular fractions (between 30 and 40%). It is apparent that the brain of rats with adjuvant-induced arthritis presents a pronounced oxidative stress and a significant injury to lipids and proteins, a situation that possibly contributes to the brain symptoms of the arthritis disease.

  4. 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

    OpenAIRE

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

    2016-01-01

    Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both 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,...

  5. Classification of First-Episode Schizophrenia Patients and Healthy Subjects by Automated MRI Measures of Regional Brain Volume and Cortical Thickness

    OpenAIRE

    Yoichiro Takayanagi; Tsutomu Takahashi; Lina Orikabe; Yuriko Mozue; Yasuhiro Kawasaki; Kazue Nakamura; Yoko Sato; Masanari Itokawa; Hidenori Yamasue; Kiyoto Kasai; Masayoshi Kurachi; Yuji Okazaki; Michio Suzuki

    2011-01-01

    BACKGROUND: Although structural magnetic resonance imaging (MRI) studies have repeatedly demonstrated regional brain structural abnormalities in patients with schizophrenia, relatively few MRI-based studies have attempted to distinguish between patients with first-episode schizophrenia and healthy controls. METHOD: Three-dimensional MR images were acquired from 52 (29 males, 23 females) first-episode schizophrenia patients and 40 (22 males, 18 females) healthy subjects. Multiple brain measure...

  6. Brain Computer Interfaces, a Review

    Science.gov (United States)

    Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

    2012-01-01

    A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices. PMID:22438708

  7. Brain computer interfaces, a review.

    Science.gov (United States)

    Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

    2012-01-01

    A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

  8. Brain Computer Interfaces, a Review

    Directory of Open Access Journals (Sweden)

    Luis Fernando Nicolas-Alonso

    2012-01-01

    Full Text Available A brain-computer interface (BCI is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

  9. Pediatric sports-related traumatic brain injury in United States trauma centers.

    Science.gov (United States)

    Yue, John K; Winkler, Ethan A; Burke, John F; Chan, Andrew K; Dhall, Sanjay S; Berger, Mitchel S; Manley, Geoffrey T; Tarapore, Phiroz E

    2016-04-01

    OBJECTIVE Traumatic brain injury (TBI) in children is a significant public health concern estimated to result in over 500,000 emergency department (ED) visits and more than 60,000 hospitalizations in the United States annually. Sports activities are one important mechanism leading to pediatric TBI. In this study, the authors characterize the demographics of sports-related TBI in the pediatric population and identify predictors of prolonged hospitalization and of increased morbidity and mortality rates. METHODS Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from children (age 0-17 years) across 5 sports categories: fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged length of stay (LOS) in the hospital or intensive care unit (ICU), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α pediatric sports-related TBIs were recorded in the NTDB, and these injuries represented 11,614 incidents nationally after sample weighting. Fall or interpersonal contact events were the greatest contributors to sports-related TBI (47.4%). Mild TBI represented 87.1% of the injuries overall. Mean (± SEM) LOSs in the hospital and ICU were 2.68 ± 0.07 days and 2.73 ± 0.12 days, respectively. The overall mortality rate was 0.8%, and the prevalence of medical complications was 2.1% across all patients. Severities of head and extracranial injuries were significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Hypotension on admission to the ED was a significant predictor of failure to discharge to home (OR 0.05, 95% CI 0.03-0.07, p pediatric sports-related TBI, the severities of head and extracranial traumas are important

  10. Pediatric sports-related traumatic brain injury in United States trauma centers.

    Science.gov (United States)

    Yue, John K; Winkler, Ethan A; Burke, John F; Chan, Andrew K; Dhall, Sanjay S; Berger, Mitchel S; Manley, Geoffrey T; Tarapore, Phiroz E

    2016-04-01

    OBJECTIVE Traumatic brain injury (TBI) in children is a significant public health concern estimated to result in over 500,000 emergency department (ED) visits and more than 60,000 hospitalizations in the United States annually. Sports activities are one important mechanism leading to pediatric TBI. In this study, the authors characterize the demographics of sports-related TBI in the pediatric population and identify predictors of prolonged hospitalization and of increased morbidity and mortality rates. METHODS Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from children (age 0-17 years) across 5 sports categories: fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged length of stay (LOS) in the hospital or intensive care unit (ICU), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α sports-related TBIs were recorded in the NTDB, and these injuries represented 11,614 incidents nationally after sample weighting. Fall or interpersonal contact events were the greatest contributors to sports-related TBI (47.4%). Mild TBI represented 87.1% of the injuries overall. Mean (± SEM) LOSs in the hospital and ICU were 2.68 ± 0.07 days and 2.73 ± 0.12 days, respectively. The overall mortality rate was 0.8%, and the prevalence of medical complications was 2.1% across all patients. Severities of head and extracranial injuries were significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Hypotension on admission to the ED was a significant predictor of failure to discharge to home (OR 0.05, 95% CI 0.03-0.07, p injury incurred during roller sports was independently associated with prolonged hospital LOS compared

  11. Classification of EEG for Affect Recognition: An Adaptive Approach

    Science.gov (United States)

    Alzoubi, Omar; Calvo, Rafael A.; Stevens, Ronald H.

    Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.

  12. Towards ultrahigh resting-state functional connectivity in the mouse brain using photoacoustic microscopy

    Science.gov (United States)

    Hariri, Ali; Bely, Nicholas; Chen, Chen; Nasiriavanaki, Mohammadreza

    2016-03-01

    The increasing use of mouse models for human brain disease studies, coupled with the fact that existing high-resolution functional imaging modalities cannot be easily applied to mice, presents an emerging need for a new functional imaging modality. Utilizing both mechanical and optical scanning in the photoacoustic microscopy, we can image spontaneous cerebral hemodynamic fluctuations and their associated functional connections in the mouse brain. The images is going to be acquired noninvasively with a fast frame rate, a large field of view, and a high spatial resolution. We developed an optical resolution photoacoustic microscopy (OR-PAM) with diode laser. Laser light was raster scanned due to XY-stage movement. Images from ultra-high OR-PAM can then be used to study brain disorders such as stroke, Alzheimer's, schizophrenia, multiple sclerosis, autism, and epilepsy.

  13. A supervised clustering approach for fMRI-based inference of brain states

    CERN Document Server

    Michel, Vincent; Varoquaux, Gaël; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand; 10.1016/j.patcog.2011.04.006

    2011-01-01

    We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus ac...

  14. Brain emotional learning based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Abdolreza Asadi Ghanbari

    2012-09-01

    Full Text Available A brain computer interface (BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain-computer interfaces (BCIs bring forward some challenging problems to the machine learning community, of which classification of time-varying electrophysiological signals is a crucial one. Constructing adaptive classifiers is a promising approach to deal with this problem. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC, which is based on emotional learning process. The main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius, as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. The developed method was compared with other methods used for EEG signals classification (support vector machine (SVM and two different neural network types (MLP, PNN. The result analysis demonstrated an efficiency of the proposed approach.

  15. Altered Functional Connectivity within and between Brain Modules in Absence Epilepsy: A Resting-State Functional Magnetic Resonance Imaging Study

    Directory of Open Access Journals (Sweden)

    Cui-Ping Xu

    2013-01-01

    Full Text Available Functional connectivity has been correlated with a patient’s level of consciousness and has been found to be altered in several neuropsychiatric disorders. Absence epilepsy patients, who experience a loss of consciousness, are assumed to suffer from alterations in thalamocortical networks; however, previous studies have not explored the changes at a functional module level. We used resting-state functional magnetic resonance imaging to examine the alteration in functional connectivity that occurs in absence epilepsy patients. By parcellating the brain into 90 brain regions/nodes, we uncovered an altered functional connectivity within and between functional modules. Some brain regions had a greater number of altered connections and therefore behaved as key nodes in the changed network pattern; these regions included the superior frontal gyrus, the amygdala, and the putamen. In particular, the superior frontal gyrus demonstrated both an increased value of connections with other nodes of the frontal default mode network and a decreased value of connections with the limbic system. This divergence is positively correlated with epilepsy duration. These findings provide a new perspective and shed light on how functional connectivity and the balance of within/between module connections may contribute to both the state of consciousness and the development of absence epilepsy.

  16. Decreased Regional Homogeneity in Patients With Acute Mild Traumatic Brain Injury: A Resting-State fMRI Study.

    Science.gov (United States)

    Zhan, Jie; Gao, Lei; Zhou, Fuqing; Kuang, Hongmei; Zhao, Jing; Wang, Siyong; He, Laichang; Zeng, Xianjun; Gong, Honghan

    2015-10-01

    Mild traumatic brain injury (mTBI) is characterized by structural disconnection and large-scale neural network dysfunction in the resting state. However, little is known concerning the intrinsic changes in local spontaneous brain activity in patients with mTBI. The aim of the current study was to assess regional synchronization in acute mTBI patients. Fifteen acute mTBI patients and 15 sex-, age-, and education-matched healthy controls (HCs) were studied. We used the regional homogeneity (ReHo) method to map local connectivity across the whole brain and performed a two-sample t-test between the two groups. Compared with HCs, patients with acute mTBI showed significantly decreased ReHo in the left insula, left precentral/postcentral gyrus, and left supramarginal gyrus (p Mental State Examination (MMSE) scores across all acute mTBI patients (p < 0.05, uncorrected). The ReHo method may provide an objective biomarker for evaluating the functional abnormity of mTBI in the acute setting. PMID:26348589

  17. Altered baseline brain activity with 72 h of simulated microgravity--initial evidence from resting-state fMRI.

    Directory of Open Access Journals (Sweden)

    Yang Liao

    Full Text Available To provide the basis and reference to further insights into the neural activity of the human brain in a microgravity environment, we discuss the amplitude changes of low-frequency brain activity fluctuations using a simulated microgravity model. Twelve male participants between 24 and 31 years old received resting-state fMRI scans in both a normal condition and after 72 hours in a -6° head down tilt (HDT. A paired sample t-test was used to test the amplitude differences of low-frequency brain activity fluctuations between these two conditions. With 72 hours in a -6° HDT, the participants showed a decreased amplitude of low-frequency fluctuations in the left thalamus compared with the normal condition (a combined threshold of P<0.005 and a minimum cluster size of 351 mm(3 (13 voxels, which corresponded with the corrected threshold of P<0.05 determined by AlphaSim. Our findings indicate that a gravity change-induced redistribution of body fluid may disrupt the function of the left thalamus in the resting state, which may contribute to reduced motor control abilities and multiple executive functions in astronauts in a microgravity environment.

  18. Histamine from brain resident MAST cells promotes wakefulness and modulates behavioral states.

    Directory of Open Access Journals (Sweden)

    Sachiko Chikahisa

    Full Text Available Mast cell activation and degranulation can result in the release of various chemical mediators, such as histamine and cytokines, which significantly affect sleep. Mast cells also exist in the central nervous system (CNS. Since up to 50% of histamine contents in the brain are from brain mast cells, mediators from brain mast cells may significantly influence sleep and other behaviors. In this study, we examined potential involvement of brain mast cells in sleep/wake regulations, focusing especially on the histaminergic system, using mast cell deficient (W/W(v mice. No significant difference was found in the basal amount of sleep/wake between W/W(v mice and their wild-type littermates (WT, although W/W(v mice showed increased EEG delta power and attenuated rebound response after sleep deprivation. Intracerebroventricular injection of compound 48/80, a histamine releaser from mast cells, significantly increased histamine levels in the ventricular region and enhanced wakefulness in WT mice, while it had no effect in W/W(v mice. Injection of H1 antagonists (triprolidine and mepyramine significantly increased the amounts of slow-wave sleep in WT mice, but not in W/W(v mice. Most strikingly, the food-seeking behavior observed in WT mice during food deprivation was completely abolished in W/W(v mice. W/W(v mice also exhibited higher anxiety and depression levels compared to WT mice. Our findings suggest that histamine released from brain mast cells is wake-promoting, and emphasizes the physiological and pharmacological importance of brain mast cells in the regulation of sleep and fundamental neurobehavior.

  19. Histamine from brain resident MAST cells promotes wakefulness and modulates behavioral states.

    Science.gov (United States)

    Chikahisa, Sachiko; Kodama, Tohru; Soya, Atsushi; Sagawa, Yohei; Ishimaru, Yuji; Séi, Hiroyoshi; Nishino, Seiji

    2013-01-01

    Mast cell activation and degranulation can result in the release of various chemical mediators, such as histamine and cytokines, which significantly affect sleep. Mast cells also exist in the central nervous system (CNS). Since up to 50% of histamine contents in the brain are from brain mast cells, mediators from brain mast cells may significantly influence sleep and other behaviors. In this study, we examined potential involvement of brain mast cells in sleep/wake regulations, focusing especially on the histaminergic system, using mast cell deficient (W/W(v)) mice. No significant difference was found in the basal amount of sleep/wake between W/W(v) mice and their wild-type littermates (WT), although W/W(v) mice showed increased EEG delta power and attenuated rebound response after sleep deprivation. Intracerebroventricular injection of compound 48/80, a histamine releaser from mast cells, significantly increased histamine levels in the ventricular region and enhanced wakefulness in WT mice, while it had no effect in W/W(v) mice. Injection of H1 antagonists (triprolidine and mepyramine) significantly increased the amounts of slow-wave sleep in WT mice, but not in W/W(v) mice. Most strikingly, the food-seeking behavior observed in WT mice during food deprivation was completely abolished in W/W(v) mice. W/W(v) mice also exhibited higher anxiety and depression levels compared to WT mice. Our findings suggest that histamine released from brain mast cells is wake-promoting, and emphasizes the physiological and pharmacological importance of brain mast cells in the regulation of sleep and fundamental neurobehavior.

  20. Neocortical-hippocampal dynamics of working memory in healthy and diseased brain states based on functional connectivity

    Directory of Open Access Journals (Sweden)

    Pablo eCampo

    2012-03-01

    Full Text Available Working memory is the ability to transiently maintain and manipulate internal representations beyond its external availability to the senses. This process is thought to support high level cognitive abilities and been shown to be strongly predictive of individual intelligence and reasoning abilities. While early models of working memory have relied on a modular perspective of brain functioning, more recent evidence suggests that cognitive functions emerge from the interactions of multiple brain regions to generate large-scale networks. Here we will review the current research on functional connectivity of working memory processes to highlight the critical role played by neural interactions in healthy and pathological brain states. Recent findings demonstrate that working memory abilities are not determined solely by local brain activity, but also rely on the functional coupling of neocortical-hippocampal regions to support working memory processes. Although the hippocampus has long been held to be important for long-term declarative memory, recent evidence suggests that the hippocampus may also be necessary to coordinate disparate cortical regions supporting the periodic reactivation of internal representations in working memory. Furthermore, recent brain imaging studies using connectivity measures, have shown that changes in cortico-limbic interactions can be useful to characterize working memory impairments observed in different neuropathological conditions. Recent advances in electrophysiological and neuroimaging techniques to model network activity has led to important insights into how neocortical and hippocampal regions support working memory processes and how disruptions along this network can lead to the memory impairments commonly reported in many neuropathological populations.

  1. Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study.

    Science.gov (United States)

    Suo, Xueling; Lei, Du; Li, Kaiming; Chen, Fuqin; Li, Fei; Li, Lei; Huang, Xiaoqi; Lui, Su; Li, Lingjiang; Kemp, Graham J; Gong, Qiyong

    2015-09-01

    Children exposed to natural disasters are vulnerable to the development of posttraumatic stress disorder (PTSD). Recent studies of other neuropsychiatric disorders have used graph-based theoretical analysis to investigate the topological properties of the functional brain connectome. However, little is known about this connectome in pediatric PTSD. Twenty-eight pediatric PTSD patients and 26 trauma-exposed non-PTSD patients were recruited from 4,200 screened subjects after the 2008 Sichuan earthquake to undergo a resting-state functional magnetic resonance imaging scan. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was then used to examine the group-specific topological properties of the two functional connectomes. Both the PTSD and non-PTSD control groups exhibited "small-world" brain network topology. However, the functional connectome of the PTSD group showed a significant increase in the clustering coefficient and a normalized characteristic path length and local efficiency, suggesting a shift toward regular networks. Furthermore, the PTSD connectomes showed both enhanced nodal centralities, mainly in the default mode- and salience-related regions, and reduced nodal centralities, mainly in the central-executive network regions. The clustering coefficient and nodal efficiency of the left superior frontal gyrus were positively correlated with the Clinician-Administered PTSD Scale. These disrupted topological properties of the functional connectome help to clarify the pathogenesis of pediatric PTSD and could be potential biomarkers of brain abnormalities. PMID:26096541

  2. Spatiotemporal Psychopathology II: How does a psychopathology of the brain's resting state look like? Spatiotemporal approach and the history of psychopathology.

    Science.gov (United States)

    Northoff, Georg

    2016-01-15

    Psychopathology as the investigation and classification of experience, behavior and symptoms in psychiatric patients is an old discipline that ranges back to the end of the 19th century. Since then different approaches to psychopathology have been suggested. Recent investigations showing abnormalities in the brain on different levels raise the question how the gap between brain and psyche, between neural abnormalities and alteration in experience and behavior can be bridged. Historical approaches like descriptive (Jaspers) and structural (Minkoswki) psychopathology as well as the more current phenomenological psychopathology (Paarnas, Fuchs, Sass, Stanghellini) remain on the side of the psyche giving detailed description of the phenomenal level of experience while leaving open the link to the brain. In contrast, the recently introduced Research Domain Classification (RDoC) aims at explicitly linking brain and psyche by starting from so-called 'neuro-behavioral constructs'. How does Spatiotemporal Psychopathology, as demonstrated in the first paper on depression, stand in relation to these approaches? In a nutshell, Spatiotemporal Psychopathology aims to bridge the gap between brain and psyche. Specifically, as demonstrated in depression in the first paper, the focus is on the spatiotemporal features of the brain's intrinsic activity and how they are transformed into corresponding spatiotemporal features in experience on the phenomenal level and behavioral changes, which can well account for the symptoms in these patients. This second paper focuses on some of the theoretical background assumptions in Spatiotemporal Psychopathology by directly comparing it to descriptive, structural, and phenomenological psychopathology as well as to RDoC. PMID:26071797

  3. Does any aspect of mind survive brain damage that typically leads to a persistent vegetative state? Ethical considerations

    Directory of Open Access Journals (Sweden)

    Fuchs Thomas

    2007-12-01

    Full Text Available Abstract Recent neuroscientific evidence brings into question the conclusion that all aspects of consciousness are gone in patients who have descended into a persistent vegetative state (PVS. Here we summarize the evidence from human brain imaging as well as neurological damage in animals and humans suggesting that some form of consciousness can survive brain damage that commonly causes PVS. We also raise the issue that neuroscientific evidence indicates that raw emotional feelings (primary-process affects can exist without any cognitive awareness of those feelings. Likewise, the basic brain mechanisms for thirst and hunger exist in brain regions typically not damaged by PVS. If affective feelings can exist without cognitive awareness of those feelings, then it is possible that the instinctual emotional actions and pain "reflexes" often exhibited by PVS patients may indicate some level of mentality remaining in PVS patients. Indeed, it is possible such raw affective feelings are intensified when PVS patients are removed from life-supports. They may still experience a variety of primary-process affective states that could constitute forms of suffering. If so, withdrawal of life-support may violate the principle of nonmaleficence and be tantamount to inflicting inadvertent "cruel and unusual punishment" on patients whose potential distress, during the process of dying, needs to be considered in ethical decision-making about how such individuals should be treated, especially when their lives are ended by termination of life-supports. Medical wisdom may dictate the use of more rapid pharmacological forms of euthanasia that minimize distress than the de facto euthanasia of life-support termination that may lead to excruciating feelings of pure thirst and other negative affective feelings in the absence of any reflective awareness.

  4. Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks

    Directory of Open Access Journals (Sweden)

    Mathias eVukelić

    2015-07-01

    Full Text Available The mechanisms of learning involved in brain self-regulation have still to be unveiled to exploit the full potential of this methodology for therapeutic interventions. This skill of volitionally changing brain activity presumably resembles motor skill learning which in turn is accompanied by plastic changes modulating resting state networks. Along these lines, we hypothesized that brain regulation and neurofeedback would similarly modify intrinsic networks at rest while presenting a distinct spatio-temporal pattern. High-resolution EEG preceded and followed a single neurofeedback training intervention of modulating circumscribed sensorimotor low β -activity by motor imagery in eleven healthy participants. They were kept in the deliberative phase of skill acquisition with high demands for learning self-regulation through stepwise increases of task difficulty. By applying the corrected imaginary part of the coherency function, we observed increased functional connectivity of both the primary motor and the primary somatosensory cortex with their respective contralateral homologous cortices in the low β-frequency band which was self-regulated during feedback. At the same time, the primary motor cortex - but none of the surrounding cortical areas - showed connectivity to contralateral supplementary motor and dorsal premotor areas in the high β-band. Simultaneously, the neurofeedback target displayed a specific increase of functional connectivity with an ipsilateral fronto-parietal network in the α-band while presenting a de-coupling with contralateral primary and secondary sensorimotor areas in the very same frequency band.Brain self-regulating modifies resting state connections spatially selective to the neurofeedback target of the dominant hemisphere. These are anatomically distinct with regard to the cortico-cortical connectivity pattern and are functionally specific with regard to the time domain of coherent activity consistent with a Hebbian

  5. Intraaxial brain tumors

    International Nuclear Information System (INIS)

    The incidence of primary intracranial tumors in the United States is approximately 15,0000 new cases per year. It has been estimated that 80--85% of all intracranial tumors occur in adults; the majority are situated in the supratentorial compartment. In the pediatric population, intracranial tumors are extraordinarily common---the CNS is the second most common site of pediatric neoplasia. Excluding the first year of life and adolescence, the location of intracranial tumors in the pediatric age group is infratentorial in 60--70% of cases, of which 75% involve the cerebellum and 25% reside in the brainstem. The limitations of neuroimaging are often revealed by understanding the microscopic pathology of these lesions, just as the neuropathologist would find if he or she relied solely on gross pathology. The general correlation between pathology and imaging will be stressed in this paper. Innumerable schemes for tumor classification have been devised; unfortunately, no classification is perfect. For the purposes of this discussion, the author has modified the proposed classifications of tumors in an attempt to combine typical neuroanatomic sites with the complex divisions traditionally formed on the basis of histopathology, since it is well recognized that the clinical behavior of brain tumors can depend largely on their sites of origin

  6. Hubble Classification

    Science.gov (United States)

    Murdin, P.

    2000-11-01

    A classification scheme for galaxies, devised in its original form in 1925 by Edwin P Hubble (1889-1953), and still widely used today. The Hubble classification recognizes four principal types of galaxy—elliptical, spiral, barred spiral and irregular—and arranges these in a sequence that is called the tuning-fork diagram....

  7. Is Traumatic Brain Injury Associated with Reduced Inter-Hemispheric Functional Connectivity? A Study of Large-Scale Resting State Networks following Traumatic Brain Injury.

    Science.gov (United States)

    Rigon, Arianna; Duff, Melissa C; McAuley, Edward; Kramer, Arthur F; Voss, Michelle W

    2016-06-01

    Traumatic brain injury (TBI) often has long-term debilitating sequelae in cognitive and behavioral domains. Understanding how TBI impacts functional integrity of brain networks that underlie these domains is key to guiding future approaches to TBI rehabilitation. In the current study, we investigated the differences in inter-hemispheric functional connectivity (FC) of resting state networks (RSNs) between chronic mild-to-severe TBI patients and normal comparisons (NC), focusing on two externally oriented networks (i.e., the fronto-parietal network [FPN] and the executive control network [ECN]), one internally oriented network (i.e., the default mode network [DMN]), and one somato-motor network (SMN). Seed voxel correlation analysis revealed that TBI patients displayed significantly less FC between lateralized seeds and both homologous and non-homologous regions in the opposite hemisphere for externally oriented networks but not for DMN or SMN; conversely, TBI patients showed increased FC within regions of the DMN, especially precuneus and parahippocampal gyrus. Region of interest correlation analyses confirmed the presence of significantly higher inter-hemispheric FC in NC for the FPN (p  0.05) or SMN (p > 0.05). Further analysis revealed that performance on a neuropsychological test measuring organizational skills and visuo-spatial abilities administered to the TBI group, the Rey-Osterrieth Complex Figure Test, positively correlated with FC between the right FPN and homologous regions. Our findings suggest that distinct RSNs display specific patterns of aberrant FC following TBI; this represents a step forward in the search for biomarkers useful for early diagnosis and treatment of TBI-related cognitive impairment. PMID:25719433

  8. Baseline brain activity changes in patients with clinically isolated syndrome revealed by resting-state functional MRI

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou; Duan, Yunyun; Liang, Peipeng; Jia, Xiuqin; Yu, Chunshui [Dept. of Radiology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Ye, Jing [Dept. of Neurology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Butzkueven, Helmut [Dept. of Medicine, Univ. of Melbourne, Melbourne (Australia); Dong, Huiqing [Dept. of Neurology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Li, Kuncheng [Dept. of Radiology, Xuanwu Hospital, Capital Medical Univ., Beijing (China); Beijing Key Laboratory of MRI and Brain Informatics, Beijing (China)], E-mail: likuncheng1955@yahoo.com.cn

    2012-11-15

    Background A clinically isolated syndrome (CIS) is the first manifestation of multiple sclerosis (MS). Previous task-related functional MRI studies demonstrate functional reorganization in patients with CIS. Purpose To assess baseline brain activity changes in patients with CIS by using the technique of regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI. Material and Methods Resting-state fMRIs data acquired from 37 patients with CIS and 37 age- and sex-matched normal controls were compared to investigate ALFF differences. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration, and T2 lesion volume (T2LV) were further explored. Results Patients with CIS had significantly decreased ALFF in the right anterior cingulate cortex, right caudate, right lingual gyrus, and right cuneus (P < 0.05 corrected for multiple comparisons using Monte Carlo simulation) compared to normal controls, while no significantly increased ALFF were observed in CIS. No significant correlation was found between the EDSS, disease duration, T2LV, and ALFF in regions with significant group differences. Conclusion In patients with CIS, resting-state fMRI demonstrates decreased activity in several brain regions. These results are in contrast to patients with established MS, in whom ALFF demonstrates several regions of increased activity. It is possible that this shift from decreased activity in CIS to increased activity in MS could reflect the dynamics of cortical reorganization.

  9. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

    Science.gov (United States)

    2016-01-01

    An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

  10. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

    Science.gov (United States)

    Li, Guangye; Zhang, Dingguo

    2016-01-01

    An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

  11. Is lactate a Volume Transmitter of Metabolic States of the Brain?

    Directory of Open Access Journals (Sweden)

    Linda H. Bergersen

    2012-03-01

    Full Text Available We present the perspective that lactate is a volume transmitter of cellular signals in brain that acutely and chronically regulate the energy metabolism of large neuronal ensembles. From this perspective, we interpret recent evidence to mean that lactate transmission serves the maintenance of network metabolism by two different mechanisms, one by regulating the formation of cAMP via the lactate receptor GPR81, the other by adjusting the NADH/NAD+ redox ratios, both linked to the maintenance of brain energy turnover and possibly cerebral blood flow. The roles of lactate as mediator of metabolic information rather than metabolic substrate answer a number of questions raised by the controversial oxidativeness of astrocytic metabolism and its contribution to neuronal function.

  12. Novel Polyomavirus associated with Brain Tumors in Free-Ranging Raccoons, Western United States

    Science.gov (United States)

    Dela Cruz, Florante N.; Giannitti, Federico; Li, Linlin; Woods, Leslie W.; Del Valle, Luis; Delwart, Eric

    2013-01-01

    Tumors of any type are exceedingly rare in raccoons. High-grade brain tumors, consistently located in the frontal lobes and olfactory tracts, were detected in 10 raccoons during March 2010–May 2012 in California and Oregon, suggesting an emerging, infectious origin. We have identified a candidate etiologic agent, dubbed raccoon polyomavirus, that was present in the tumor tissue of all affected animals but not in tissues from 20 unaffected animals. Southern blot hybridization and rolling circle amplification showed the episomal viral genome in the tumors. The multifunctional nuclear protein large T-antigen was detectable by immunohistochemical analyses in a subset of neoplastic cells. Raccoon polyomavirus may contribute to the development of malignant brain tumors of raccoons. PMID:23260029

  13. [Effects of low doses of essential oil on the antioxidant state of the erythrocytes, liver, and the brains of mice].

    Science.gov (United States)

    Misharina, T A; Fatkullina, L D; Alinkina, E S; Kozachenko, A I; Nagler, L G; Medvedeva, I B; Goloshchapov, A N; Burlakova, E B

    2014-01-01

    We studied the effects of essential oil from oregano and clove and a mixture of lemon essential oil and a ginger extract on the antioxidant state of organs in intact and three experimental groups of Bulb mice. We found that the essential oil was an efficient in vivo bioantioxidant when mice were treated with it for 6 months even at very low doses, such as 300 ng/day. All essential oil studied inhibited lipid peroxidation (LPO) in the membranes of erythrocytes that resulted in increased membrane resistance to spontaneous hemolysis, decreased membrane microviscosity, maintenance of their structural integrity, and functional activity. The essential oil substantially decreased the LPO intensity in the liver and the brains of mice and increased the resistance of liver and brain lipids to oxidation and the activity of antioxidant enzymes in the liver. The most expressed bioantioxidant effect on erythrocytes was observed after clove oil treatment, whereas on the liver and brain, after treatment with a mixture of lemon essential oil and a ginger extract. PMID:25272759

  14. Transitions between dynamical states of differing stability in the human brain

    OpenAIRE

    Meyer-Lindenberg, Andreas; Ziemann, Ulf; Hajak, Göran; Cohen, Leonardo; Berman, Karen Faith

    2002-01-01

    What mechanisms underlie the flexible formation, adaptation, synchronization, and dissolution of large-scale neural assemblies from the 1010 densely interconnected, continuously active neurons of the human brain? Nonlinear dynamics provides a unifying perspective on self-organization. It shows that the emergence of patterns in open, nonequilibrium systems is governed by their stability in response to small disturbances and predicts macroscopic transitions between patterns of differing stabili...

  15. The Acute Inflammatory Response in Trauma / Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects

    OpenAIRE

    R, Namas; A, Ghuma; L, Hermus; R, Zamora; DO Okonkwo; TR, Billiar; Y, Vodovotz

    2009-01-01

    Traumatic injury/hemorrhagic shock (T/HS) elicits an acute inflammatory response that may result in death. Inflammation describes a coordinated series of molecular, cellular, tissue, organ, and systemic responses that drive the pathology of various diseases including T/HS and traumatic brain injury (TBI). Inflammation is a finely tuned, dynamic, highly-regulated process that is not inherently detrimental, but rather required for immune surveillance, optimal post-injury tissue repair, and rege...

  16. Effects of age and underlying brain dysfunction on the postictal state

    OpenAIRE

    Theodore, William H

    2010-01-01

    There is relatively little information on the underlying parameters that affect clinical features of the postictal period. Age-related physiological changes, including alterations in cerebral blood flow and metabolism, neurotransmitter function, and responses of the brain to seizure activity may affect postictal clinical phenomena. Some conclusions can be drawn. Elderly adults and children, particularly in the presence of diffuse cerebral dysfunction, may have more prolonged postictal confusi...

  17. Affect and the brain's functional organization: a resting-state connectivity approach.

    Directory of Open Access Journals (Sweden)

    Christiane S Rohr

    Full Text Available The question of how affective processing is organized in the brain is still a matter of controversial discussions. Based on previous initial evidence, several suggestions have been put forward regarding the involved brain areas: (a right-lateralized dominance in emotional processing, (b hemispheric dominance according to positive or negative valence, (c one network for all emotional processing and (d region-specific discrete emotion matching. We examined these hypotheses by investigating intrinsic functional connectivity patterns that covary with results of the Positive and Negative Affective Schedule (PANAS from 65 participants. This approach has the advantage of being able to test connectivity rather than activation, and not requiring a potentially confounding task. Voxelwise functional connectivity from 200 regions-of-interest covering the whole brain was assessed. Positive and negative affect covaried with functional connectivity involving a shared set of regions, including the medial prefrontal cortex, the anterior cingulate, the visual cortex and the cerebellum. In addition, each affective domain had unique connectivity patterns, and the lateralization index showed a right hemispheric dominance for negative affect. Therefore, our results suggest a predominantly right-hemispheric network with affect-specific elements as the underlying organization of emotional processes.

  18. Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States

    Science.gov (United States)

    Shibata, Kazuhisa; Watanabe, Takeo; Kawato, Mitsuo; Sasaki, Yuka

    2016-01-01

    In human studies, how averaged activation in a brain region relates to human behavior has been extensively investigated. This approach has led to the finding that positive and negative facial preferences are represented by different brain regions. However, using a functional magnetic resonance imaging (fMRI) decoded neurofeedback (DecNef) method, we found that different patterns of neural activations within the cingulate cortex (CC) play roles in representing opposite directions of facial preference. In the present study, while neutrally preferred faces were presented, multi-voxel activation patterns in the CC that corresponded to higher (or lower) preference were repeatedly induced by fMRI DecNef. As a result, previously neutrally preferred faces became more (or less) preferred. We conclude that a different activation pattern in the CC, rather than averaged activation in a different area, represents and suffices to determine positive or negative facial preference. This new approach may reveal the importance of an activation pattern within a brain region in many cognitive functions. PMID:27608359

  19. Use of Stereotactic Radiosurgery for Brain Metastases From Non-Small Cell Lung Cancer in the United States

    Energy Technology Data Exchange (ETDEWEB)

    Halasz, Lia M., E-mail: lhalasz@uw.edu [Department of Radiation Oncology, University of Washington, Seattle, Washington (United States); Harvard Radiation Oncology Program, Harvard Medical School, Boston, Massachusetts (United States); Weeks, Jane C.; Neville, Bridget A.; Taback, Nathan [Division of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts (United States); Punglia, Rinaa S. [Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts (United States)

    2013-02-01

    Purpose: The indications for treatment of brain metastases from non-small cell lung cancer (NSCLC) with stereotactic radiosurgery (SRS) remain controversial. We studied patterns, predictors, and cost of SRS use in elderly patients with NSCLC. Methods and Materials: Using the Surveillance, Epidemiology, and End Results-Medicare (SEER-Medicare) database, we identified patients with NSCLC who were diagnosed with brain metastases between 2000 and 2007. Our cohort included patients treated with radiation therapy and not surgical resection as initial treatment for brain metastases. Results: We identified 7684 patients treated with radiation therapy within 2 months after brain metastases diagnosis, of whom 469 (6.1%) cases had billing codes for SRS. Annual SRS use increased from 3.0% in 2000 to 8.2% in 2005 and varied from 3.4% to 12.5% by specific SEER registry site. After controlling for clinical and sociodemographic characteristics, we found SRS use was significantly associated with increasing year of diagnosis, specific SEER registry, higher socioeconomic status, admission to a teaching hospital, no history of participation in low-income state buy-in programs (a proxy for Medicaid eligibility), no extracranial metastases, and longer intervals from NSCLC diagnosis. The average cost per patient associated with radiation therapy was 2.19 times greater for those who received SRS than for those who did not. Conclusions: The use of SRS in patients with metastatic NSCLC increased almost 3-fold from 2000 to 2005. In addition, we found significant variations in SRS use across SEER registries and socioeconomic quartiles. National practice patterns in this study suggested both a lack of consensus and an overall limited use of the approach among elderly patients before 2008.

  20. Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks.

    Science.gov (United States)

    Karahanoğlu, Fikret Işik; Van De Ville, Dimitri

    2015-07-16

    Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art signal processing techniques, we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial; typically, three to four iCAPs occur simultaneously in combinations that are consistent with their behaviour profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, we instead find evidence for two DMN-related iCAPs consisting the posterior cingulate cortex that differentially interact with the attention network. These findings demonstrate how the fMRI resting state can be functionally decomposed into spatially and temporally overlapping building blocks using iCAPs.

  1. ADHD classification using bag of words approach on network features

    Science.gov (United States)

    Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak

    2012-02-01

    Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.

  2. 78 FR 39765 - Notice of Proposed Classification of Public Lands/Minerals for State Indemnity Selection, Colorado

    Science.gov (United States)

    2013-07-02

    ..., Routt and San Miguel counties, Colorado, and are described as follows: New Mexico Principal Meridian...\\ (geothermal steam); Sec. 18, N\\1/2\\SE\\1/4\\ and SW\\1/4\\SE\\1/4\\ (geothermal steam). T. 4 S., R. 83 W., Sec. 17... to the State or may be reserved by the United States. Oil and gas, geothermal, or other leases...

  3. Brain-computer interface research a state-of-the-art summary 3

    CERN Document Server

    Guger, Christoph; Allison, Brendan

    2014-01-01

    This book provides a cutting-edge overview of the latest developments in Brain-Computer-Interfaces (BCIs), reported by leading research groups. As the reader will discover, BCI research is moving ahead rapidly, with many new ideas, research initiatives, and improved technologies, e.g. BCIs that enable people to communicate just by thinking - without any movement at all. Several different groups are helping severely disabled users communicate using BCIs, and BCI technology is also being extended to facilitate recovery from stroke, epilepsy, and other conditions. Each year, hundreds of the top

  4. Brain-computer interface research a state-of-the-art summary

    CERN Document Server

    Allison, Brendan; Edlinger, Günter; Leuthardt, E C

    Brain-computer interfaces (BCIs) are rapidly developing into a mainstream, worldwide research endeavor. With so many new groups and projects, it can be difficult to identify the best ones. This book summarizes ten leading projects from around the world. About 60 submissions were received in 2011 for the highly competitive BCI Research Award, and an international jury selected the top ten. This Brief gives a concise but carefully illustrated and fully up-to-date description of each of these projects, together with an introduction and concluding chapter by the editors.

  5. Cognitive Rehabilitation in Patients with Gliomas and Other Brain Tumors: State of the Art

    Directory of Open Access Journals (Sweden)

    E. Bergo

    2016-01-01

    Full Text Available Disease prognosis is very poor in patients with brain tumors. Cognitive deficits due to disease or due to its treatment have an important weight on the quality of life of patients and caregivers. Studies often take into account quality of life as a fundamental element in the management of disease and interventions have been developed for cognitive rehabilitation of neuropsychological deficits with the aim of improving the quality of life and daily-life autonomy of patients. In this literature review, we will consider the published studies of cognitive rehabilitation over the past 20 years.

  6. Multiple steady-states in the terrestrial atmosphere-biosphere system: a result of a discrete vegetation classification?

    Directory of Open Access Journals (Sweden)

    A. Kleidon

    2007-08-01

    Full Text Available Multiple steady states in the atmosphere-biosphere system can arise as a consequence of interactions and positive feedbacks. While atmospheric conditions affect vegetation productivity in terms of available light, water, and heat, different levels of vegetation productivity can result in differing energy- and water partitioning at the land surface, thereby leading to different atmospheric conditions. Here we investigate the emergence of multiple steady states in the terrestrial atmosphere-biosphere system and focus on the role of how vegetation is represented in the model: (i in terms of a few, discrete vegetation classes, or (ii a continuous representation. We then conduct sensitivity simulations with respect to initial conditions and to the number of discrete vegetation classes in order to investigate the emergence of multiple steady states. We find that multiple steady states occur in our model only if vegetation is represented by a few vegetation classes. With an increased number of classes, the difference between the number of multiple steady states diminishes, and disappears completely in our model when vegetation is represented by 8 classes or more. Despite the convergence of the multiple steady states into a single one, the resulting climate-vegetation state is nevertheless less productive when compared to the emerging state associated with the continuous vegetation parameterization. We conclude from these results that the representation of vegetation in terms of a few, discrete vegetation classes can result (a in an artificial emergence of multiple steady states and (b in a underestimation of vegetation productivity. Both of these aspects are important limitations to be considered when global vegetation-atmosphere models are to be applied to topics of global change.

  7. Multiple steady-states in the terrestrial atmosphere-biosphere system: a result of a discrete vegetation classification?

    Directory of Open Access Journals (Sweden)

    A. Kleidon

    2007-02-01

    Full Text Available Multiple steady states in the atmosphere-biosphere system can arise as a consequence of interactions and positive feedbacks. While atmospheric conditions affect vegetation productivity in terms of available light, water, and heat, different levels of vegetation productivity can result in differing energy- and water partitioning at the land surface, thereby leading to different atmospheric conditions. Here we investigate the emergence of multiple steady states in the terrestrial atmosphere-biosphere system and focus on the role of how vegetation is represented in the model: (i in terms of a few, discrete vegetation classes, or (ii a continuous representation. We then conduct sensitivity simulations with respect to initial conditions and to the number of discrete vegetation classes in order to investigate the emergence of multiple steady states. We find that multiple steady states occur in our model only if vegetation is represented by a few vegetation classes. With an increased number of classes, the difference between the number of multiple steady states diminishes, and disappears completely in our model when vegetation is represented by 8 classes or more. Despite the convergence of the multiple steady states into a single one, the resulting climate-vegetation state is nevertheless less productive when compared to the emerging state associated with the continuous vegetation parameterization. We conclude from these results that the representation of vegetation in terms of a few, discrete vegetation classes can result (a in an artificial emergence of multiple steady states and (b in a underestimation of vegetation productivity. Both of these aspects are important limitations to be considered when global vegetation-atmosphere models are to be applied to topics of global change.

  8. Negative mood state enhances the susceptibility to unpleasant events: neural correlates from a music-primed emotion classification task.

    Directory of Open Access Journals (Sweden)

    Jiajin Yuan

    Full Text Available BACKGROUND: Various affective disorders are linked with enhanced processing of unpleasant stimuli. However, this link is likely a result of the dominant negative mood derived from the disorder, rather than a result of the disorder itself. Additionally, little is currently known about the influence of mood on the susceptibility to emotional events in healthy populations. METHOD: Event-Related Potentials (ERP were recorded for pleasant, neutral and unpleasant pictures while subjects performed an emotional/neutral picture classification task during positive, neutral, or negative mood induced by instrumental Chinese music. RESULTS: Late Positive Potential (LPP amplitudes were positively related to the affective arousal of pictures. The emotional responding to unpleasant pictures, indicated by the unpleasant-neutral differences in LPPs, was enhanced during negative compared to neutral and positive moods in the entire LPP time window (600-1000 ms. The magnitude of this enhancement was larger with increasing self-reported negative mood. In contrast, this responding was reduced during positive compared to neutral mood in the 800-1000 ms interval. Additionally, LPP reactions to pleasant stimuli were similar across positive, neutral and negative moods except those in the 800-900 ms interval. IMPLICATIONS: Negative mood intensifies the humans' susceptibility to unpleasant events in healthy individuals. In contrast, music-induced happy mood is effective in reducing the susceptibility to these events. Practical implications of these findings were discussed.

  9. Sensitivity of International Classification of Diseases codes for hyponatremia among commercially insured outpatients in the United States

    Directory of Open Access Journals (Sweden)

    Curtis Lesley H

    2008-06-01

    Full Text Available Abstract Background Administrative claims are a rich source of information for epidemiological and health services research; however, the ability to accurately capture specific diseases or complications using claims data has been debated. In this study, the authors examined the validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM diagnosis codes for the identification of hyponatremia in an outpatient managed care population. Methods We analyzed outpatient laboratory and professional claims for patients aged 18 years and older in the National Managed Care Benchmark Database from Integrated Healthcare Information Services. We obtained all claims for outpatient serum sodium laboratory tests performed in 2004 and 2005, and all outpatient professional claims with a primary or secondary ICD-9-CM diagnosis code of hyponatremia (276.1. Results A total of 40,668 outpatient serum sodium laboratory results were identified as hyponatremic (serum sodium 99% for all cutoff points. Conclusion ICD-9-CM codes in administrative data are insufficient to identify hyponatremia in an outpatient population.

  10. Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics

    Directory of Open Access Journals (Sweden)

    Martin eDinov

    2016-05-01

    Full Text Available Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches and the sum squared error (SSE from a multilayer perceptron (MLP prediction of the EEG timeseries, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach.

  11. Novel Modeling of Task vs. Rest Brain State Predictability Using a Dynamic Time Warping Spectrum: Comparisons and Contrasts with Other Standard Measures of Brain Dynamics

    Science.gov (United States)

    Dinov, Martin; Lorenz, Romy; Scott, Gregory; Sharp, David J.; Fagerholm, Erik D.; Leech, Robert

    2016-01-01

    Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach. PMID:27242502

  12. Rabi-coupled two-component Bose-Einstein condensates: Classification of the ground states, defects, and energy estimates

    Science.gov (United States)

    Aftalion, Amandine; Mason, Peter

    2016-08-01

    We classify the ground states and topological defects of two-component Bose-Einstein condensates under the effect of internal coherent Rabi coupling. We present numerical phase diagrams which show the boundaries between symmetry-breaking components and various vortex patterns (triangular, square, bound state between vortices). We estimate the Rabi energy in the Thomas-Fermi limit which allows us to have an analytical description of the point energy leading to the formation of the various vortex patterns.

  13. Correlation between the Effects of Acupuncture at Taichong (LR3) and Functional Brain Areas: A Resting-State Functional Magnetic Resonance Imaging Study Using True versus Sham Acupuncture

    OpenAIRE

    Chunxiao Wu; Shanshan Qu; Jiping Zhang; Junqi Chen; Shaoqun Zhang; Zhipeng Li; Jiarong Chen; Huailiang Ouyang; Yong Huang; Chunzhi Tang

    2014-01-01

    Functional magnetic resonance imaging (fMRI) has been shown to detect the specificity of acupuncture points, as proved by numerous studies. In this study, resting-state fMRI was used to observe brain areas activated by acupuncture at the Taichong (LR3) acupoint. A total of 15 healthy subjects received brain resting-state fMRI before acupuncture and after sham and true acupuncture, respectively, at LR3. Image data processing was performed using Data Processing Assistant for Resting-State fMRI ...

  14. Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.

    Science.gov (United States)

    Robinson, Neethu; Zaidi, Ali Danish; Rana, Mohit; Prasad, Vinod A; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

    2016-01-01

    Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

  15. A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

    NARCIS (Netherlands)

    Mühl, Christian; Allison, Brandan; Nijholt, Anton; Chanel, Guillaume

    2014-01-01

    Affective states, moods and emotions, are an integral part of the human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial ex

  16. Multimodal MRI classification in vascular mild cognitive impairment.

    Science.gov (United States)

    Diciotti, Stefano; Ciulli, Stefano; Ginestroni, Andrea; Salvadori, Emilia; Poggesi, Anna; Pantoni, Leonardo; Inzitari, Domenico; Mascalchi, Mario; Toschi, Nicola

    2015-08-01

    Vascular mild cognitive impairment (VMCI) is a disorder in which multimodal MRI can add significant value by combining diffusion tensor imaging (DTI) with brain morphometry. In this study we implemented and compared machine learning techniques for multimodal classification between 58 VMCI patients and 29 healthy subjects as well as for discrimination (within the VMCI group) between patients with different cognitive performances. For each subject, a cortical feature vector was constructed based on cortical parcellation and cortical and subcortical volumetric segmentation and a DTI feature vector was formed by combining descriptive statistical metrics related to the distribution of DTI invariants within white matter. We employed both a sequential minimal optimization and a functional tree classifier, using feature selection and 10-fold cross-validation, and compared their performances in monomodal and multimodal classification for both classification problems (healthy subjects vs VMCI and prediction of cognitive performance). While monomodal classification resulted in satisfactory performance in most cases, turning from monomodal to multimodal classification resulted in an improvement of the performance in the discrimination between VMCI patients with low cognitive performance and healthy subjects by up to 10% in sensitivity (leaving specificity unchanged). We therefore are able to confirm the usefulness of machine learning techniques in discriminating diseased states based on neuroimaging data. PMID:26737240

  17. Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

    Science.gov (United States)

    2016-01-01

    Abstract When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation. PMID:27752540

  18. Detection of Rabies Antigen in the Saliva and Brains of Apparently Healthy Dogs Slaughtered for Human Consumption and Its Public Health Implications in Abia State, Nigeria

    OpenAIRE

    Mshelbwala, P. P.; Ogunkoya, A. B.; B. V. Maikai

    2013-01-01

    The study was carried out in eight dogs slaughtering outlets within four Local Government Areas of the State for the determination of rabies antigen in the saliva and brain of apparently healthy dogs slaughtered for human consumption. A total of one hundred (100) samples each of saliva and brain were collected before and after slaughter, respectively, between April to June, 2013, in the selected areas. The saliva was subjected to rapid immune-chromatographic test (RICT) while direct fluoresce...

  19. Altered baseline brain activities before food intake in obese men: a resting state fMRI study.

    Science.gov (United States)

    Zhang, Bin; Tian, Derun; Yu, Chunshui; Zhang, Jing; Tian, Xiao; von Deneen, Karen M; Zang, Yufeng; Walter, Martin; Liu, Yijun

    2015-01-01

    Obesity as a chronic disease has become a global epidemic. However, why obese individuals eat more still remains unclear. Recent functional neuroimaging studies have found abnormal brain activations in obese people. In the present study, we used resting state functional MRI to observe spontaneous blood-oxygen-level dependent (BOLD) signal fluctuations during both hunger and satiety states in 20 lean and 20 obese men. Using a regional homogeneity (ReHo) analysis method, we measured temporal homogeneity of the regional BOLD signals. We found that, before food intake, obese men had significantly increased synchronicity of activity in the left putamen relative to lean men. Decreased synchronicity of activity was found in the orbitofrontal cortex (OFC) and medial prefrontal cortex(MPFC) in the obese subjects. And, the ratings of hunger of the obese subjects were higher than those of the lean subjects before food intake. After food intake, we did not find the significant differences between the obese men and the lean men. In all participations, synchronicity of activity increased from the fasted to the satiated state in the OFC. The results indicated that OFC plays an important role in feeding behavior, and OFC signaling may be disordered in obesity. Obese men show less inhibitory control during fasting state. This study has provided strong evidence supporting the hypothesis that there is a hypo-functioning reward circuitry in obese individuals, in which the frontal cortex may fail to inhibit the striatum, and consequently lead to overeating and obesity. PMID:25459293

  20. [Definition and classification of epilepsy].

    Science.gov (United States)

    Jibiki, Itsuki

    2014-05-01

    The concept or definition of epilepsy was mentioned as a chronic disease of the brain consisting of repetitions of EEG paroxysm and clinical seizures caused by excessive discharges of the cerebral neurons, in reference with Gastaut's opinion and the other statements. Further, we referred to diseases to be excluded from epilepsy such as isolated, occasional and subclinical seizures and so on. Next, new classifications of seizures and epilepsies were explained on the basis of revised terminology and concepts for organization of seizures and epilepsies in Report of the ILAE Communication in Classification and Terminology, 2005-09, in comparison with the Classification of Epileptic Seizures in 1981 and the Classification of Epilepsies and Epileptic Syndromes in 1989.

  1. Assessment of brain activities during an emotional stress state using fMRI

    International Nuclear Information System (INIS)

    We investigated cerebrum activation using functional magnetic resonance imaging during a mental stress state. Thirty-four healthy adults participated. Before the experiment, we assessed their stress states using the Stress Self-rating Scale and divided the participants into Stress and Non-stress groups. The experiment consisted of 6 trials. Each trial consisted of a 20-s block of emotional audio-visual stimuli (4-s stimulation x 5 slides) and a fixation point. These processes were performed 3 times continuously (Relaxed, Pleasant, Unpleasant stimuli) in a random order. These results showed that the Non-stress group indicated activation of the amygdala and hippocampus in the Pleasant and Unpleasant stimuli while the Stress group indicated activation of the hippocampus in Pleasant stimuli, and the amygdala and hippocampus in Unpleasant stimuli. These findings suggested that the mental stress state engages the reduction of emotional processing. Also, the responsiveness of the memory system remained during and after the emotional stress state. (author)

  2. Modular Reorganization of Brain Resting State Networks and Its Independent Validation in Alzheimer’s Disease Patients

    Directory of Open Access Journals (Sweden)

    Guangyu eChen

    2013-08-01

    Full Text Available Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer’s Disease (AD. However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN group. Our main finding is that the largest homotopic module (defined as the insula module in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups. Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

  3. Autonomic nervous system dynamics for mood and emotional-state recognition significant advances in data acquisition, signal processing and classification

    CERN Document Server

    Valenza, Gaetano

    2014-01-01

    This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and ...

  4. Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.

    Science.gov (United States)

    Wang, Ching-Cheng; Lai, Wen-Chung; Chuang, Woei-Jer

    2016-09-01

    A tool for predicting the redox state and secondary structure of cysteine residues using multi-dimensional analyses of different combinations of nuclear magnetic resonance (NMR) chemical shifts has been developed. A data set of cysteine [Formula: see text], (13)C(α), (13)C(β), (1)H(α), (1)H(N), and (15)N(H) chemical shifts was created, classified according to redox state and secondary structure, using a library of 540 re-referenced BioMagResBank (BMRB) entries. Multi-dimensional analyses of three, four, five, and six chemical shifts were used to derive rules for predicting the structural states of cysteine residues. The results from 60 BMRB entries containing 122 cysteines showed that four-dimensional analysis of the C(α), C(β), H(α), and N(H) chemical shifts had the highest prediction accuracy of 100 and 95.9 % for the redox state and secondary structure, respectively. The prediction of secondary structure using 3D, 5D, and 6D analyses had the accuracy of ~90 %, suggesting that H(N) and [Formula: see text] chemical shifts may be noisy and made the discrimination worse. A web server (6DCSi) was established to enable users to submit NMR chemical shifts, either in BMRB or key-in formats, for prediction. 6DCSi displays predictions using sets of 3, 4, 5, and 6 chemical shifts, which shows their consistency and allows users to draw their own conclusions. This web-based tool can be used to rapidly obtain structural information regarding cysteine residues directly from experimental NMR data.

  5. The brain on silent: mind wandering, mindful awareness, and states of mental tranquility.

    Science.gov (United States)

    Vago, David R; Zeidan, Fadel

    2016-06-01

    Mind wandering and mindfulness are often described as divergent mental states with opposing effects on cognitive performance and mental health. Spontaneous mind wandering is typically associated with self-reflective states that contribute to negative processing of the past, worrying/fantasizing about the future, and disruption of primary task performance. On the other hand, mindful awareness is frequently described as a focus on present sensory input without cognitive elaboration or emotional reactivity, and is associated with improved task performance and decreased stress-related symptomology. Unfortunately, such distinctions fail to acknowledge similarities and interactions between the two states. Instead of an inverse relationship between mindfulness and mind wandering, a more nuanced characterization of mindfulness may involve skillful toggling back and forth between conceptual and nonconceptual processes and networks supporting each state, to meet the contextually specified demands of the situation. In this article, we present a theoretical analysis and plausible neurocognitive framework of the restful mind, in which we attempt to clarify potentially adaptive contributions of both mind wandering and mindful awareness through the lens of the extant neurocognitive literature on intrinsic network activity, meditation, and emerging descriptions of stillness and nonduality. A neurophenomenological approach to probing modality-specific forms of concentration and nonconceptual awareness is presented that may improve our understanding of the resting state. Implications for future research are discussed.

  6. The brain on silent: mind wandering, mindful awareness, and states of mental tranquility.

    Science.gov (United States)

    Vago, David R; Zeidan, Fadel

    2016-06-01

    Mind wandering and mindfulness are often described as divergent mental states with opposing effects on cognitive performance and mental health. Spontaneous mind wandering is typically associated with self-reflective states that contribute to negative processing of the past, worrying/fantasizing about the future, and disruption of primary task performance. On the other hand, mindful awareness is frequently described as a focus on present sensory input without cognitive elaboration or emotional reactivity, and is associated with improved task performance and decreased stress-related symptomology. Unfortunately, such distinctions fail to acknowledge similarities and interactions between the two states. Instead of an inverse relationship between mindfulness and mind wandering, a more nuanced characterization of mindfulness may involve skillful toggling back and forth between conceptual and nonconceptual processes and networks supporting each state, to meet the contextually specified demands of the situation. In this article, we present a theoretical analysis and plausible neurocognitive framework of the restful mind, in which we attempt to clarify potentially adaptive contributions of both mind wandering and mindful awareness through the lens of the extant neurocognitive literature on intrinsic network activity, meditation, and emerging descriptions of stillness and nonduality. A neurophenomenological approach to probing modality-specific forms of concentration and nonconceptual awareness is presented that may improve our understanding of the resting state. Implications for future research are discussed. PMID:27398642

  7. Using brain-computer interfaces to overcome the extinction of goal-directed thinking in minimally conscious state patients.

    Science.gov (United States)

    Liberati, Giulia; Birbaumer, Niels

    2012-08-01

    Minimally conscious state (MCS) is a condition of severely altered consciousness, in which patients appear to be wakeful and exhibit fluctuating but reproducible signs of awareness. MCS patients do not respond and are therefore dependent on others. In agreement with the embodied cognition assumption that motor actions influence our cognition, the absence of movement and the decrease in consequences for any type of covert or overt response may cause an extinction of goal-directed thinking. Brain-computer interfaces, which allow a direct output without muscular involvement, may be used to promote goal-directed thinking by allowing the performance of spatial and motor imagery tasks and could facilitate the interaction of MCS patients with their environment, possibly regaining some degree of communication and autonomy.

  8. 7 CFR 51.1904 - Maturity classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Maturity classification. 51.1904 Section 51.1904... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1904 Maturity classification. Tomatoes which are characteristically red when ripe, but are not overripe or...

  9. 7 CFR 51.1903 - Size classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1903 Section 51.1903... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1903 Size classification. The following terms may be used for describing the size of the tomatoes in any...

  10. 7 CFR 51.1402 - Size classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1402 Section 51.1402... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Size Classification § 51.1402 Size classification. Size of pecans may be specified in connection with the grade in accordance with one of...

  11. 12 CFR 403.4 - Derivative classification.

    Science.gov (United States)

    2010-01-01

    ... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Derivative classification. 403.4 Section 403.4 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION, AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION § 403.4 Derivative classification. (a) Use of...

  12. A state of nuclear power in Japan and world and bring up best brains

    International Nuclear Information System (INIS)

    History of nuclear power and the related policy in Japan is explained. Safety of nuclear power has not been cleared to people. The specialists have to show people information how to decide nuclear power policy. There are two lines such as 1) uranium and plutonium line and 2) thorium cycle line. With developing an accelerator driven system of plutonium, we should develop thorium cycle in order to be desirable for non-proliferation and residual activity. Today, the budget for energy development and nuclear power in Japan has to be reconsidered. The budget for development of natural energy in this country is the largest in the world, but the fruits are very small. Many universities stopped nuclear reactor for research and only five reactors for research are operating in Japan. We must find new way of a corporation combined JAERI (Japan Atomic Energy Research Institute) with JNC (Japan Nuclear Cycle Development Institute) and reconstruction to share the use of facilities. The progressive researches bring up best brains using small reactor. Other countries use many kinds of energies and begin to study new researches. (S.Y.)

  13. The Acute Inflammatory Response in Trauma / Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects

    Directory of Open Access Journals (Sweden)

    Y Vodovotz

    2009-01-01

    Full Text Available Traumatic injury/hemorrhagic shock (T/HS elicits an acute inflammatory response that may result in death. Inflammation describes a coordinated series of molecular, cellular, tissue, organ, and systemic responses that drive the pathology of various diseases including T/HS and traumatic brain injury (TBI. Inflammation is a finely tuned, dynamic, highly-regulated process that is not inherentlydetrimental, but rather required for immune surveillance, optimal post-injury tissue repair, and regeneration. The inflammatory response is driven by cytokines and chemokines and is partiallypropagated by damaged tissue-derived products (Damage-associated Molecular Patterns; DAMP’s.DAMPs perpetuate inflammation through the release of pro-inflammatory cytokines, but may also inhibit anti-inflammatory cytokines. Various animal models of T/HS in mice, rats, pigs, dogs, and nonhumanprimates have been utilized in an attempt to move from bench to bedside. Novel approaches, including those from the field of systems biology, may yield therapeutic breakthroughs in T/HS andTBI in the near future.

  14. [Animal Models of Depression: Behavior as the Basis for Methodology, Assessment Criteria and Classifications].

    Science.gov (United States)

    Grigoryan, G A; Gulyaeva, N V

    2015-01-01

    Analysis of the current state modeling of depression in animals is presented. Criteria and classification systems of the existing models are considered as well as approaches to the assessment of model validity. Though numerous approaches to modeling of depressive states based on disturbances of both motivational and emotional brain mechanisms have been elaborated, no satisfactory model of stable depression state has been developed yet. However, the diversity of existing models is quite positive since it allows performing targeted studies of selected neurobiological mechanisms and laws of depressive state development, as well as to investigate mechanisms of action and predict pharmacological profiles of potential antidepressants. PMID:26841653

  15. Global Integration of the Hot-State Brain Network of Appetite Predicts Short Term Weight Loss in Older Adult

    Directory of Open Access Journals (Sweden)

    Brielle M Paolini

    2015-05-01

    Full Text Available Obesity is a public health crisis in North America. While lifestyle interventions for weight loss (WL remain popular, the rate of success is highly variable. Clearly, self-regulation of eating behavior is a challenge and patterns of activity across the brain may be an important determinant of success. The current study prospectively examined whether integration across the Hot-State Brain Network of Appetite (HBN-A predicts WL after 6-months of treatment in older adults. Our metric for network integration was global efficiency (GE. The present work is a sub-study (n = 56 of an ongoing randomized clinical trial involving WL. Imaging involved a baseline food-cue visualization functional MRI (fMRI scan following an overnight fast. Using graph theory to build functional brain networks, we demonstrated that regions of the HBN-A (insula, anterior cingulate cortex (ACC, superior temporal pole, amygdala and the parahippocampal gyrus were highly integrated as evidenced by the results of a principal component analysis. After accounting for known correlates of WL (baseline weight, age, sex, and self-regulatory efficacy and treatment condition, which together contributed 36.9% of the variance in WL, greater GE in the HBN-A was associated with an additional 19% of the variance. The ACC of the HBN-A was the primary driver of this effect, accounting for 14.5% of the variance in WL when entered in a stepwise regression following the covariates, p = 0.0001. The HBN-A is comprised of limbic regions important in the processing of emotions and visceral sensations and the ACC is key for translating such processing into behavioral consequences. The improved integration of these regions may enhance awareness of body and emotional states leading to more successful self-regulation and to greater WL. This is the first study among older adults to prospectively demonstrate that, following an overnight fast, GE of the HBN-A during a food visualization task is predictive of

  16. Global integration of the hot-state brain network of appetite predicts short term weight loss in older adult.

    Science.gov (United States)

    Paolini, Brielle M; Laurienti, Paul J; Simpson, Sean L; Burdette, Jonathan H; Lyday, Robert G; Rejeski, W Jack

    2015-01-01

    Obesity is a public health crisis in North America. While lifestyle interventions for weight loss (WL) remain popular, the rate of success is highly variable. Clearly, self-regulation of eating behavior is a challenge and patterns of activity across the brain may be an important determinant of success. The current study prospectively examined whether integration across the Hot-State Brain Network of Appetite (HBN-A) predicts WL after 6-months of treatment in older adults. Our metric for network integration was global efficiency (GE). The present work is a sub-study (n = 56) of an ongoing randomized clinical trial involving WL. Imaging involved a baseline food-cue visualization functional MRI (fMRI) scan following an overnight fast. Using graph theory to build functional brain networks, we demonstrated that regions of the HBN-A (insula, anterior cingulate cortex (ACC), superior temporal pole (STP), amygdala and the parahippocampal gyrus) were highly integrated as evidenced by the results of a principal component analysis (PCA). After accounting for known correlates of WL (baseline weight, age, sex, and self-regulatory efficacy) and treatment condition, which together contributed 36.9% of the variance in WL, greater GE in the HBN-A was associated with an additional 19% of the variance. The ACC of the HBN-A was the primary driver of this effect, accounting for 14.5% of the variance in WL when entered in a stepwise regression following the covariates, p = 0.0001. The HBN-A is comprised of limbic regions important in the processing of emotions and visceral sensations and the ACC is key for translating such processing into behavioral consequences. The improved integration of these regions may enhance awareness of body and emotional states leading to more successful self-regulation and to greater WL. This is the first study among older adults to prospectively demonstrate that, following an overnight fast, GE of the HBN-A during a food visualization task is predictive of

  17. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  18. Low-level and high-level modulations of fixational saccades and high frequency oscillatory brain activity in a visual object classification task

    Directory of Open Access Journals (Sweden)

    Maciej eKosilo

    2013-12-01

    Full Text Available Until recently induced gamma-band activity (GBA was considered a neural marker of cortical object representation. However induced GBA in the electroencephalogram (EEG is susceptible to artifacts caused by miniature fixational saccades. Recent studies have demonstrated that fixational saccades also reflect high-level representational processes. Do high-level as opposed to low-level factors influence fixational saccades? What is the effect of these factors on artifact-free GBA? To investigate this, we conducted separate eye tracking and EEG experiments using identical designs. Participants classified line drawings as objects or non-objects. To introduce low-level differences, contours were defined along different directions in cardinal colour space: S-cone-isolating, intermediate isoluminant, or a full-colour stimulus, the latter containing an additional achromatic component. Prior to the classification task, object discrimination thresholds were measured and stimuli were scaled to matching suprathreshold levels for each participant. In both experiments, behavioural performance was best for full-colour stimuli and worst for S-cone isolating stimuli. Saccade rates 200-700 ms after stimulus onset were modulated independently by low and high-level factors, being higher for full-colour stimuli than for S-cone isolating stimuli and higher for objects. Low-amplitude evoked GBA and total GBA were observed in very few conditions, showing that paradigms with isoluminant stimuli may not be ideal for eliciting such responses. We conclude that cortical loops involved in the processing of objects are preferentially excited by stimuli that contain achromatic information. Their activation can lead to relatively early exploratory eye movements even for foveally-presented stimuli.

  19. CLASSIFICATION OF EEG SIGNAL DATA USING HYBRID OF NEURO-FUZZY

    OpenAIRE

    Pratibha Rana*, Ms. Jyotsna Singh

    2016-01-01

    Brain Computing interface technology represents a very highly growing field now-a-days for the research because of its unique applications system. In this paper we investigate classification methods of mental commands based on EEG data for BCI. The aim of this study is to present the work of training an artificial neural network (ANN) and Neuro-Fuzzy system provided with the data of a few healthy people who are in different mental states thinking about different activities like eating, walkin...

  20. Using concurrent EEG and fMRI to probe the state of the brain in schizophrenia.

    Science.gov (United States)

    Ford, Judith M; Roach, Brian J; Palzes, Vanessa A; Mathalon, Daniel H

    2016-01-01

    Perceptional abnormalities in schizophrenia are associated with hallucinations and delusions, but also with negative symptoms and poor functional outcome. Perception can be studied using EEG-derived event related potentials (ERPs). Because of their excellent temporal resolution, ERPs have been used to ask when perception is affected by schizophrenia. Because of its excellent spatial resolution, functional magnetic resonance imaging (fMRI) has been used to ask where in the brain these effects are seen. We acquired EEG and fMRI data simultaneously to explore when and where auditory perception is affected by schizophrenia. Thirty schizophrenia (SZ) patients and 23 healthy comparison subjects (HC) listened to 1000 Hz tones occurring about every second. We used joint independent components analysis (jICA) to combine EEG-based event-related potential (ERP) and fMRI responses to tones. Five ERP-fMRI joint independent components (JIC) were extracted. The "N100" JIC had temporal weights during N100 (peaking at 100 ms post-tone onset) and fMRI spatial weights in superior and middle temporal gyri (STG/MTG); however, it did not differ between groups. The "P200" JIC had temporal weights during P200 and positive fMRI spatial weights in STG/MTG and frontal areas, and negative spatial weights in the nodes of the default mode network (DMN) and visual cortex. Groups differed on the "P200" JIC: SZ had smaller "P200" JIC, especially those with more severe avolition/apathy. This is consistent with negative symptoms being related to perceptual deficits, and suggests patients with avolition/apathy may allocate too few resources to processing external auditory events and too many to processing internal events. PMID:27622140