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Sample records for brain state classification

  1. Behavioral state classification in epileptic brain using intracranial electrophysiology

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

    2017-04-01

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

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

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    Mohit eRana

    2013-10-01

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

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

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

    2012-01-01

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

  4. 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.

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

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    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.

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

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

    2016-02-01

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

  7. Adaptive multiclass classification for brain computer interfaces.

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    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

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

  8. GLCM textural features for Brain Tumor Classification

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    N S Zulpe

    2012-05-01

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

  9. Tissue tracking: applications for brain MRI classification

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    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.

  10. Unsupervised classification of operator workload from brain signals

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

    2016-06-01

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

  11. Local Kernel for Brains Classification in Schizophrenia

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

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

  12. Entanglement classification with matrix product states.

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    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 .

  13. Training brain networks and states.

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    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.

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

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

    2016-01-01

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

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

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

    2011-01-01

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

  16. Werner State Structure and Entanglement Classification

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    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.

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

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    Norberto Eiji Nawa

    2015-12-01

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

  18. Simple Fully Automated Group Classification on Brain fMRI

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

    2010-04-14

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

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

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

    2016-12-01

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

  20. [Mixed states: evolution of classifications].

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

    2013-12-01

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

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

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    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

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    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.

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

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    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.

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

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

    2017-01-01

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

  5. Building the United States National Vegetation Classification

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    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.

  6. Classification of Brain Tumor Using Support Vector Machine Classfiers

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    Dr.D. J. Pete

    2014-03-01

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

  7. Leveraging Human Brain Activity to Improve Object Classification

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    Fong, Ruth Catherine

    2015-01-01

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

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

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    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.

  9. 9 CFR 145.54 - 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. 145.54 Section 145.54 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Terminology and classification; States. (a) U.S. Pullorum-Typhoid Clean State. (1) A State will be declared...

  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. Brain communication in the locked-in state.

    Science.gov (United States)

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

    2013-06-01

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

  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.

    Science.gov (United States)

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

    2012-01-01

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

  15. 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.

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

    Science.gov (United States)

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

  18. 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.

  19. 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.

  20. 9 CFR 146.24 - 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. 146.24 Section 146.24 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Special Provisions for Commercial Table-Egg Layer Flocks § 146.24 Terminology and classification;...

  1. 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;...

  2. 9 CFR 145.44 - 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. 145.44 Section 145.44 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Special Provisions for Turkey Breeding Flocks and Products § 145.44 Terminology and classification;...

  3. Traumatic brain injury among Indiana state prisoners.

    Science.gov (United States)

    Ray, Bradley; Sapp, Dona; Kincaid, Ashley

    2014-09-01

    Research on traumatic brain injury among inmates has focused on comparing the rate of traumatic brain injury among offenders to the general population, but also how best to screen for traumatic brain injury among this population. This study administered the short version of the Ohio State University Traumatic Brain Injury Identification Method to all male inmates admitted into Indiana state prisons were screened for a month (N = 831). Results indicate that 35.7% of the inmates reported experiencing a traumatic brain injury during their lifetime and that these inmates were more likely to have a psychiatric disorder and a prior period of incarceration than those without. Logistic regression analysis finds that a traumatic brain injury predicts the likelihood of prior incarceration net of age, race, education, and psychiatric disorder. This study suggests that brief instruments can be successfully implemented into prison screenings to help divert inmates into needed treatment.

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

    Science.gov (United States)

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

    2015-07-27

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

  5. 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.

  6. 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.

  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. 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.

  9. 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

  10. 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

  11. 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

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

    OpenAIRE

    Rajendran, P.; M.Madheswaran

    2010-01-01

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

  13. Classification of Entanglement in Symmetric States

    CERN Document Server

    Aulbach, Martin

    2011-01-01

    Quantum states that are symmetric with respect to permutations of their subsystems appear in a wide range of physical settings, and they have a variety of promising applications in quantum information science. In this thesis the entanglement of symmetric multipartite states is categorised, with a particular focus on the pure multi-qubit case and the geometric measure of entanglement. An essential tool for this analysis is the Majorana representation, a generalisation of the single-qubit Bloch sphere representation, which allows for a unique representation of symmetric n qubit states by n points on the surface of a sphere. Here this representation is employed to search for the maximally entangled symmetric states of up to 12 qubits in terms of the geometric measure, and an intuitive visual understanding of the upper bound on the maximal symmetric entanglement is given. Furthermore, it will be seen that the Majorana representation facilitates the characterisation of entanglement equivalence classes such as Stoc...

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

    Science.gov (United States)

    Rajendran, P; Madheswaran, M

    2012-04-01

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

  15. Data analytics for drilling operational states classifications

    OpenAIRE

    Veres, Galina; Sabeur, Zoheir

    2015-01-01

    This paper provides benchmarks for the identification of best performance classifiers for the detection of operational states in industrial drilling operations. Multiple scenarios for the detection of the operational states are tested on a rig with various drilling wells. Drilling data are extremely challenging due to their non-linear and stochastic natures, notwithstanding the embedded noise in them and unbalancing. Nevertheless, there is a possibility to deploy robust classifiers to overcom...

  16. 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...

  17. 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.

  18. 9 CFR 78.41 - State/area classification.

    Science.gov (United States)

    2010-01-01

    ... AGRICULTURE INTERSTATE TRANSPORTATION OF ANIMALS (INCLUDING POULTRY) AND ANIMAL PRODUCTS BRUCELLOSIS Designation of Brucellosis Areas § 78.41 State/area classification. (a) Class Free. Alabama, Alaska, Arizona..., Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina,...

  19. 9 CFR 145.24 - 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. 145.24 Section 145.24 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Special Provisions for Multiplier Egg-Type Chicken Breeding Flocks and Products § 145.24 Terminology...

  20. 9 CFR 145.34 - 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. 145.34 Section 145.34 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE... Special Provisions for Multiplier Meat-Type Chicken Breeding Flocks and Products § 145.34 Terminology...

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Directory of Open Access Journals (Sweden)

    P. Rajendran

    2009-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Finitha Joseph

    2015-11-01

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

  4. Classification of Implantable Rotary Blood Pump States With Class Noise.

    Science.gov (United States)

    Ooi, Hui-Lee; Seera, Manjeevan; Ng, Siew-Cheok; Lim, Chee Peng; Loo, Chu Kiong; Lovell, Nigel H; Redmond, Stephen J; Lim, Einly

    2016-05-01

    A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise.

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

    Directory of Open Access Journals (Sweden)

    P.Muthu Krishnammal

    2015-06-01

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

  6. 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.

  7. Neural mass model-based tracking of anesthetic brain states

    NARCIS (Netherlands)

    Kuhlmann, Levin; Freestone, Dean R.; Manton, Jonathan H.; Heyse, Bjorn; Vereecke, Hugo E. M.; Lipping, Tarmo; Struys, Michel M. R. F.; Liley, David T. J.

    2016-01-01

    Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain state

  8. 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.

  9. 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.

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

    Science.gov (United States)

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

    2014-10-01

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

  11. 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.

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

    Science.gov (United States)

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

    2012-02-01

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

  13. 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.

  14. 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

  15. 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

  16. The Soil Series in Soil Classifications of the United States

    Science.gov (United States)

    Indorante, Samuel; Beaudette, Dylan; Brevik, Eric C.

    2014-05-01

    Organized national soil survey began in the United States in 1899, with soil types as the units being mapped. The soil series concept was introduced into the U.S. soil survey in 1903 as a way to relate soils being mapped in one area to the soils of other areas. The original concept of a soil series was all soil types formed in the same parent materials that were of the same geologic age. However, within about 15 years soil series became the primary units being mapped in U.S. soil survey. Soil types became subdivisions of soil series, with the subdivisions based on changes in texture. As the soil series became the primary mapping unit the concept of what a soil series was also changed. Instead of being based on parent materials and geologic age, the soil series of the 1920s was based on the morphology and composition of the soil profile. Another major change in the concept of soil series occurred when U.S. Soil Taxonomy was released in 1975. Under Soil Taxonomy, the soil series subdivisions were based on the uses the soils might be put to, particularly their agricultural uses (Simonson, 1997). While the concept of the soil series has changed over the years, the term soil series has been the longest-lived term in U.S. soil classification. It has appeared in every official classification system used by the U.S. soil survey (Brevik and Hartemink, 2013). The first classification system was put together by Milton Whitney in 1909 and had soil series at its second lowest level, with soil type at the lowest level. The second classification system used by the U.S. soil survey was developed by C.F. Marbut, H.H. Bennett, J.E. Lapham, and M.H. Lapham in 1913. It had soil series at the second highest level, with soil classes and soil types at more detailed levels. This was followed by another system in 1938 developed by M. Baldwin, C.E. Kellogg, and J. Thorp. In this system soil series were again at the second lowest level with soil types at the lowest level. The soil type

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

    Science.gov (United States)

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

    2008-01-01

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

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

    Science.gov (United States)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

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

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

    Science.gov (United States)

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

  20. 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.

  1. Changes in cognitive state alter human functional brain networks

    Directory of Open Access Journals (Sweden)

    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.

  2. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jun Cheng

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

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

    Science.gov (United States)

    Dhas, DAS; Madheswaran, M

    2015-06-09

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

  6. 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.

  7. 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.

  8. 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.

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

    Science.gov (United States)

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

    2014-01-01

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

  10. 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

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

    Directory of Open Access Journals (Sweden)

    Eva Janousova

    2016-08-01

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

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

    Science.gov (United States)

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

    2011-11-01

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

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

    Science.gov (United States)

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

    2013-11-01

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

  14. 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:…

  15. 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...

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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…

  1. 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.

  2. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

  4. 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...

  5. 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.

  6. 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

  7. 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

  8. 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.

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

    Science.gov (United States)

    Li, Tao; Li, Wu; Yang, Yehui

    2017-01-01

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

  10. 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.

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

    Science.gov (United States)

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

    2016-10-01

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

  12. 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…

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

    Science.gov (United States)

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

    2013-04-01

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

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

    Science.gov (United States)

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

    2016-01-01

    Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles. Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles. PMID:27826224

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

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

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

  16. 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.

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

    Science.gov (United States)

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

    2014-06-01

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

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

    Science.gov (United States)

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

    2017-02-22

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

  19. 9 CFR 145.10 - Terminology and classification; flocks, products, and States.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Terminology and classification; flocks, products, and States. 145.10 Section 145.10 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION... POULTRY General Provisions § 145.10 Terminology and classification; flocks, products, and...

  20. 9 CFR 146.9 - Terminology and classification; flocks, products, and States.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Terminology and classification; flocks, products, and States. 146.9 Section 146.9 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION... POULTRY General Provisions § 146.9 Terminology and classification; flocks, products, and...

  1. 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.

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

    Science.gov (United States)

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

    2016-03-01

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

  3. 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.

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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.

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

    NARCIS (Netherlands)

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

    1993-01-01

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

  10. 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%.

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

    Directory of Open Access Journals (Sweden)

    de Certaines Jacques D

    2008-12-01

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

  12. 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.

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

    Science.gov (United States)

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

    2012-12-01

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

  14. 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.

  15. 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...... not previously seen by the system on the basis of 1 sec long EEGs....

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

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

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

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-06-07

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

  19. 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.

  20. Operator functional state classification using least-square support vector machine based recursive feature elimination technique.

    Science.gov (United States)

    Yin, Zhong; Zhang, Jianhua

    2014-01-01

    This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

  1. Brain imaging of pain: state of the art

    Directory of Open Access Journals (Sweden)

    Morton DL

    2016-09-01

    Full Text Available Debbie L Morton, Javin S Sandhu, Anthony KP Jones Human Pain Research Group, Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester, UK Abstract: 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. Keywords: fMRI, PET, EEG, arthritis, fibromyalgia

  2. 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.

  3. 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.

  4. Information content in cortical spike trains during brain state transitions.

    Science.gov (United States)

    Arnold, Maria M; Szczepanski, Janusz; Montejo, Noelia; Amigó, José M; Wajnryb, Eligiusz; Sanchez-Vives, Maria V

    2013-02-01

    Even in the absence of external stimuli there is ongoing activity in the cerebral cortex as a result of recurrent connectivity. This paper attempts to characterize one aspect of this ongoing activity by examining how the information content carried by specific neurons varies as a function of brain state. We recorded from rats chronically implanted with tetrodes in the primary visual cortex during awake and sleep periods. Electro-encephalogram and spike trains were recorded during 30-min periods, and 2-4 neuronal spikes were isolated per tetrode off-line. All the activity included in the analysis was spontaneous, being recorded from the visual cortex in the absence of visual stimuli. The brain state was determined through a combination of behavior evaluation, electroencephalogram and electromyogram analysis. Information in the spike trains was determined by using Lempel-Ziv Complexity. Complexity was used to estimate the entropy of neural discharges and thus the information content (Amigóet al. Neural Comput., 2004, 16: 717-736). The information content in spike trains (range 4-70 bits s(-1) ) was evaluated during different brain states and particularly during the transition periods. Transitions toward states of deeper sleep coincided with a decrease of information, while transitions to the awake state resulted in an increase in information. Changes in both directions were of the same magnitude, about 30%. Information in spike trains showed a high temporal correlation between neurons, reinforcing the idea of the impact of the brain state in the information content of spike trains.

  5. A multi-signature brain-computer interface: use of transient and steady-state responses

    Science.gov (United States)

    Severens, Marianne; Farquhar, Jason; Duysens, Jacques; Desain, Peter

    2013-04-01

    Objective. The aim of this paper was to increase the information transfer in brain-computer interfaces (BCI). Therefore, a multi-signature BCI was developed and investigated. Stimuli were designed to simultaneously evoke transient somatosensory event-related potentials (ERPs) and steady-state somatosensory potentials (SSSEPs) and the ERPs and SSSEPs in isolation. Approach. Twelve subjects participated in two sessions. In the first session, the single and combined stimulation conditions were compared on these somatosensory responses and on the classification performance. In the second session the on-line performance with the combined stimulation was evaluated while subjects received feedback. Furthermore, in both sessions, the performance based on ERP and SSSEP features was compared. Main results. No difference was found in the ERPs and SSSEPs between stimulation conditions. The combination of ERP and SSSEP features did not perform better than with ERP features only. In both sessions, the classification performances based on ERP and combined features were higher than the classification based on SSSEP features. Significance. Although the multi-signature BCI did not increase performance, it also did not negatively impact it. Therefore, such stimuli could be used and the best performing feature set could then be chosen individually.

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

    Science.gov (United States)

    Farquhar, J; Hill, N J

    2013-04-01

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

  7. 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.

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

    Science.gov (United States)

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

    2005-12-01

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

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

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    Noman Naseer

    2016-01-01

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

  10. 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.

  11. EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

    Science.gov (United States)

    Acqualagna, Laura; Bosse, Sebastian; Porbadnigk, Anne K.; Curio, Gabriel; Müller, Klaus-Robert; Wiegand, Thomas; Blankertz, Benjamin

    2015-04-01

    Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.

  12. Intrinsic brain activity in altered states of consciousness: how conscious is the default mode of brain function?

    Science.gov (United States)

    Boly, M; Phillips, C; Tshibanda, L; Vanhaudenhuyse, A; Schabus, M; Dang-Vu, T T; Moonen, G; Hustinx, R; Maquet, P; Laureys, S

    2008-01-01

    Spontaneous brain activity has recently received increasing interest in the neuroimaging community. However, the value of resting-state studies to a better understanding of brain-behavior relationships has been challenged. That altered states of consciousness are a privileged way to study the relationships between spontaneous brain activity and behavior is proposed, and common resting-state brain activity features observed in various states of altered consciousness are reviewed. Early positron emission tomography studies showed that states of extremely low or high brain activity are often associated with unconsciousness. However, this relationship is not absolute, and the precise link between global brain metabolism and awareness remains yet difficult to assert. In contrast, voxel-based analyses identified a systematic impairment of associative frontoparieto-cingulate areas in altered states of consciousness, such as sleep, anesthesia, coma, vegetative state, epileptic loss of consciousness, and somnambulism. In parallel, recent functional magnetic resonance imaging studies have identified structured patterns of slow neuronal oscillations in the resting human brain. Similar coherent blood oxygen level-dependent (BOLD) systemwide patterns can also be found, in particular in the default-mode network, in several states of unconsciousness, such as coma, anesthesia, and slow-wave sleep. The latter results suggest that slow coherent spontaneous BOLD fluctuations cannot be exclusively a reflection of conscious mental activity, but may reflect default brain connectivity shaping brain areas of most likely interactions in a way that transcends levels of consciousness, and whose functional significance remains largely in the dark.

  13. 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.

  14. Source Localization of Brain States Associated with Canonical Neuroimaging Postures.

    Science.gov (United States)

    Lifshitz, Michael; Thibault, Robert T; Roth, Raquel R; Raz, Amir

    2017-02-14

    Cognitive neuroscientists rarely consider the influence that body position exerts on brain activity; yet, postural variation holds important implications for the acquisition and interpretation of neuroimaging data. Whereas participants in most behavioral and EEG experiments sit upright, many prominent brain imaging techniques (e.g., fMRI) require participants to lie supine. Here we demonstrate that physical comportment profoundly alters baseline brain activity as measured by magnetoencephalography (MEG)-an imaging modality that permits multipostural acquisition. We collected resting-state MEG data from 12 healthy participants in three postures (lying supine, reclining at 45°, and sitting upright). Source-modeling analysis revealed a broadly distributed influence of posture on resting brain function. Sitting upright versus lying supine was associated with greater high-frequency (i.e., beta and gamma) activity in widespread parieto-occipital cortex. Moreover, sitting upright and reclined postures correlated with dampened activity in prefrontal regions across a range of bandwidths (i.e., from alpha to low gamma). The observed effects were large, with a mean Cohen's d of 0.95 (SD = 0.23). In addition to neural activity, physiological parameters such as muscle tension and eye blinks may have contributed to these posture-dependent changes in brain signal. Regardless of the underlying mechanisms, however, the present results have important implications for the acquisition and interpretation of multimodal imaging data (e.g., studies combining fMRI or PET with EEG or MEG). More broadly, our findings indicate that generalizing results-from supine neuroimaging measurements to erect positions typical of ecological human behavior-would call for considering the influence that posture wields on brain dynamics.

  15. Energy landscapes of resting-state brain networks

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    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.

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

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    Yun Xuan

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

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

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    Whynes David K

    2008-11-01

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

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

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    Gemma Modinos

    2013-02-01

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

  19. 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.

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

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    Ling Zhu

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

  1. 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.

  2. 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...

  3. 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.

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

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    D. Mudali

    2015-01-01

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

  5. Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes.

    Science.gov (United States)

    Boschmann, Alexander; Nofen, Barbara; Platzner, Marco

    2013-01-01

    Pattern recognition of myoelectric signals in upper-limb prosthesis control has been subject to intense research for several years. However, few systems have yet been successfully clinically implemented. One possible explanation for this discrepancy is that published reports mostly focus on classification accuracy of myoelectric signals recorded under laboratory conditions as the metric for the system's performance. These data are usually acquired only during the static state of the contraction in a fixed seated position. This supports the test subject in performing repeatable contractions throughout the experiment and generally results in an unrealistically high classification accuracy. In clinical testing however, subjects have to perform various activities of daily living, causing the limb to move in different positions. These variations in limb positions can significantly decrease robustness and usability of myoelectric control systems. Recent reports have shown that the so-called limb position effect can be resolved for the static state of the signal by adding accelerometer data to the feature vector. Including data from the transient state of the signals for classifier training generally significantly increases the classification error so it is mostly not considered in published reports. In this paper, we investigate the classification accuracy of transient EMG data, taking into account the limb position effect. We demonstrate that a classifier trained with features from EMG, accelerometer and gyroscope outperforms classifiers using only EMG or EMG and accelerometer data when classifying transient EMG data.

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

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    B. Balakumar

    2013-09-01

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

  7. 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

  8. 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

  9. 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.

  10. Deep brain stimulation for the treatment of vegetative state.

    Science.gov (United States)

    Yamamoto, Takamitsu; Katayama, Yoichi; Kobayashi, Kazutaka; Oshima, Hideki; Fukaya, Chikashi; Tsubokawa, Takashi

    2010-10-01

    One hundred and seven patients in vegetative state (VS) were evaluated neurologically and electrophysiologically over 3 months (90 days) after the onset of brain injury. Among these patients, 21 were treated with deep brain stimulation (DBS). The stimulation sites were the mesencephalic reticular formation (two patients) and centromedian-parafascicularis nucleus complex (19 cases). Eight of the patients recovered from VS and were able to obey verbal commands at 13 and 10 months in the case of head trauma and at 19, 14, 13, 12, 12 and 8 months in the case of vascular disease after comatose brain injury, and no patients without DBS recovered from VS spontaneously within 24 months after brain injury. The eight patients who recovered from VS showed desynchronization on continuous EEG frequency analysis. The Vth wave of the auditory brainstem response and N20 of the somatosensory evoked potential could be recorded, although with a prolonged latency, and the pain-related P250 was recorded with an amplitude of > 7 μV. Sixteen (14.9%) of the 107 VS patients satisfied these criteria in our electrophysiological evaluation, 10 of whom were treated with DBS and six of whom were not treated with DBS. In these 16 patients, the recovery rate from VS was different between the DBS therapy group and the no DBS therapy group (P < 0.01, Fisher's exact probability test) These findings indicate that DBS may be useful for the recovery of patients from VS if the candidates are selected on the basis of electrophysiological criteria.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

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

    Science.gov (United States)

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

    2007-04-15

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

  16. 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.

  17. 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.

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    Demonet Jean

    2009-06-01

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

  20. 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.

  1. Metabolic resting-state brain networks in health and disease.

    Science.gov (United States)

    Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C; Nazem, Amir; Sako, Wataru; Peng, Shichun; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

    2015-02-24

    The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

  2. 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.

  3. Brain mechanisms of altered conscious states during epileptic seizures.

    Science.gov (United States)

    Cavanna, Andrea Eugenio; Monaco, Francesco

    2009-05-01

    Impaired consciousness has long been considered the hallmark of epileptic seizures. Both generalized seizures and complex partial seizures are characterized by a multifaceted spectrum of altered conscious states, in terms of the general level of awareness and the subjective contents of consciousness. Complete loss of consciousness occurs when epileptic activity involves both cortical and subcortical structures, as in tonic-clonic seizures and absence seizures. Medial temporal lobe discharges can selectively impair experience in complex partial seizures (with affected responsiveness) and certain simple partial seizures (with unaffected responsiveness). Electrical stimulation of temporal lobe structures has been shown to evoke similar subjective experiences. Findings from neurophysiological and brain-imaging studies in epilepsy have now demonstrated that involvement of the bilateral thalamus and upper brainstem leads to selective impairment of frontoparietal association cortices and midline 'default mode' networks, which results in ictal loss of consciousness. The spread of epileptic discharges from the medial temporal lobe to the same subcortical structures can ultimately cause impairment in the level of consciousness in the late ictal and immediate postictal phase of complex partial seizures. This paper reviews novel insights into the brain mechanisms that underlie alterations of consciousness during epileptic seizures and the implications for clinical practice in terms of diagnosis and management.

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

    Science.gov (United States)

    Tohka, Jussi

    2014-11-28

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

  5. 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.

  6. 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

  7. 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.

  8. Resting-state functional brain networks in Parkinson's disease.

    Science.gov (United States)

    Baggio, Hugo C; Segura, Bàrbara; Junque, Carme

    2015-10-01

    The network approach is increasingly being applied to the investigation of normal brain function and its impairment. In the present review, we introduce the main methodological approaches employed for the analysis of resting-state neuroimaging data in Parkinson's disease studies. We then summarize the results of recent studies that used a functional network perspective to evaluate the changes underlying different manifestations of Parkinson's disease, with an emphasis on its cognitive symptoms. Despite the variability reported by many studies, these methods show promise as tools for shedding light on the pathophysiological substrates of different aspects of Parkinson's disease, as well as for differential diagnosis, treatment monitoring and establishment of imaging biomarkers for more severe clinical outcomes.

  9. 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?

  10. Identification and classification of physiologically significant pumping states in an implantable rotary blood pump.

    Science.gov (United States)

    Karantonis, Dean M; Lovell, Nigel H; Ayre, Peter J; Mason, David G; Cloherty, Shaun L

    2006-09-01

    In a clinical setting it is necessary to control the speed of rotary blood pumps used as left ventricular assist devices to prevent possible severe complications associated with over- or underpumping. The hypothesis is that by using only the noninvasive measure of instantaneous pump impeller speed to assess flow dynamics, it is possible to detect physiologically significant pumping states (without the need for additional implantable sensors). By varying pump speed in an animal model, five such states were identified: regurgitant pump flow, ventricular ejection (VE), nonopening of the aortic valve over the cardiac cycle (ANO), and partial collapse (intermittent and continuous) of the ventricle wall (PVC-I and PVC-C). These states are described in detail and a strategy for their noninvasive detection has been developed and validated using (n = 6) ex vivo porcine experiments. Employing a classification and regression tree, the strategy was able to detect pumping states with a high degree of sensitivity and specificity: state VE-99.2/100.0% (sensitivity/specificity); state ANO-100.0/100.0%; state PVC-I- 95.7/91.2%; state PVC-C-69.7/98.7%. With a simplified binary scheme differentiating suction (PVC-I, PVC-C) and nonsuction (VE, ANO) states, both such states were detected with 100% sensitivity.

  11. 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.

  12. 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 lacta...

  13. 78 FR 9929 - Current Traumatic Brain Injury State Implementation Partnership Grantees; Non-Competitive One...

    Science.gov (United States)

    2013-02-12

    ...-Competitive One-Year Extension Funds for Current Traumatic Brain Injury (TBI) State Implementation Partnership... Traumatic Brain Injury Act of 2008 (Pub. L. 110- 206). Under this authority, the HRSA TBI Program is charged... HUMAN SERVICES Health Resources and Services Administration Current Traumatic Brain Injury......

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

    DEFF Research Database (Denmark)

    Kjaerby, Celia; Rasmussen, Rune; Andersen, Mie

    2017-01-01

    We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release...... of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca2+ signaling in response to activation of the noradrenergic system, but whether...... astrocytic Ca2+ signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca2+ signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore, we...

  15. 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

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

    Science.gov (United States)

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

    2012-11-01

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

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

    OpenAIRE

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

    2015-01-01

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

  18. 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.

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

    Science.gov (United States)

    2007-10-01

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

  1. Rotational Rydberg states of polar molecules: Hund's classification and Zeeman effect

    Science.gov (United States)

    Danilyan, A. V.; Chernov, V. E.

    2008-01-01

    The rotational Rydberg states of polar molecules, which arise as a result of the interaction of a Rydberg electron with core rotations, are considered. A large number of angular momenta in the core-electron system lead to a considerably greater number of possible coupling schemes of these momenta compared to the number of schemes determined by the classical five Hund's cases for lower excited electron states of molecules. As a result of such detailed Hund's classification, more than 30 different coupling schemes (Hund's subcases) are constructed for rotational Rydberg states of molecules. The conditions of their realization are indicated in terms of the relative quantities of intramolecular interactions, for which analytical estimates are presented. For a large number of subcases, analytical expressions for the molecular matrix elements are found. These expressions can be useful in classification of the experimental spectra of highly excited molecules. As an application, for each of the subcases considered, analytical expressions are given, which describe the linear Zeeman effect and the Paschen-Back effect.

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

    Science.gov (United States)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

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

  3. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

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

    OpenAIRE

    Corso, Jason J.; Eitan Sharon; Alan Yuille

    2006-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Marlene Huml

    2013-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Charmi Sunil Mehta

    2014-04-01

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

  9. Contact-state classification in human-demonstrated robot compliant motion tasks using the boosting algorithm.

    Science.gov (United States)

    Cabras, Stefano; Castellanos, María Eugenia; Staffetti, Ernesto

    2010-10-01

    Robot programming by demonstration is a robot programming paradigm in which a human operator directly demonstrates the task to be performed. In this paper, we focus on programming by demonstration of compliant motion tasks, which are tasks that involve contacts between an object manipulated by the robot and the environment in which it operates. Critical issues in this paradigm are to distinguish essential actions from those that are not relevant for the correct execution of the task and to transform this information into a robot-independent representation. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states that occur during a demonstration, called contact classification or contact segmentation. We propose a contact classification algorithm based on a supervised learning algorithm, in particular on a stochastic gradient boosting algorithm. The approach described in this paper is accurate and does not depend on the geometric model of the objects involved in the demonstration. It neither relies on the kinestatic model of the contact interactions nor on the contact state graph, whose computation is usually of prohibitive complexity even for very simple geometric object models.

  10. Number of superclasses of four-qubit entangled states under the inductive entanglement classification

    Science.gov (United States)

    Backens, Miriam

    2017-02-01

    L. Lamata et al. use an inductive approach to classify the entangled pure states of four qubits under stochastic local operations and classical communication (SLOCC) [Phys. Rev. A 75, 022318 (2007), 10.1103/PhysRevA.75.022318]. The inductive method yields a priori ten entanglement superclasses, of which they discard three as empty. One of the remaining superclasses is split into two, resulting in eight superclasses of genuine four-qubit entanglement. Here, we show that two of the three discarded superclasses are in fact nonempty and should have been retained. We give explicit expressions for the canonical states of those superclasses, up to SLOCC and qubit permutations. Furthermore, we confirm that the third discarded superclass is indeed empty, yielding a total of ten superclasses of genuine four-qubit entanglement under the inductive classification scheme.

  11. [Carbohydrate metabolism in the brain in comatose states].

    Science.gov (United States)

    Khapiĭ, Kh Kh; Gruzman, A B

    1990-01-01

    The article confirms an earlier discovered phenomenon that during comas and in post-coma periods the brain releases glucose and consumes lactate. It is suggested that the phenomenon is based on glucogenesis taking place in the brain from non-carbohydrate glucose precursors, which is phylogenetically predetermined and biologically expedient.

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

    Science.gov (United States)

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

    2016-05-01

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

  13. 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.

  14. Classification of physiologically significant pumping states in an implantable rotary blood pump: patient trial results.

    Science.gov (United States)

    Karantonis, Dean M; Mason, David G; Salamonsen, Robert F; Ayre, Peter J; Cloherty, Shaun L; Lovell, Nigel H

    2007-01-01

    An integral component in the development of a control strategy for implantable rotary blood pumps is the task of reliably detecting the occurrence of left ventricular collapse due to overpumping of the native heart. Using the noninvasive pump feedback signal of impeller speed, an approach to distinguish between overpumping (or ventricular collapse) and the normal pumping state has been developed. Noninvasive pump signals from 10 human pump recipients were collected, and the pumping state was categorized as either normal or suction, based on expert opinion aided by transesophageal echocardiographic images. A number of indices derived from the pump speed waveform were incorporated into a classification and regression tree model, which acted as the pumping state classifier. When validating the model on 12,990 segments of unseen data, this methodology yielded a peak sensitivity/specificity for detecting suction of 99.11%/98.76%. After performing a 10-fold cross-validation on all of the available data, a minimum estimated error of 0.53% was achieved. The results presented suggest that techniques for pumping state detection, previously investigated in preliminary in vivo studies, are applicable and sufficient for use in the clinical environment.

  15. State of the art survey on MRI brain tumor segmentation.

    Science.gov (United States)

    Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

    2013-10-01

    Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.

  16. 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.

  17. 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

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

    Science.gov (United States)

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

    2016-02-01

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

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

    Science.gov (United States)

    Anna, Bluszcz

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Hui Wang

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

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

    Science.gov (United States)

    Wang, Hui; Chen, Chen; Fushing, Hsieh

    2012-01-01

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

  2. 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.

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

    Science.gov (United States)

    Lalor, E. C.; Kelly, S. P.; Finucane, C.; Burke, R.; Smith, R.; Reilly, R. B.; McDarby, G.

    2005-12-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Martin Göttlich

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2010-12-28

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

  7. 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.

  8. 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).

  9. 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.

  10. Current state of our knowledge on brain tumor epidemiology.

    Science.gov (United States)

    Ostrom, Quinn T; Barnholtz-Sloan, Jill S

    2011-06-01

    The overall incidence of brain tumors for benign and malignant tumors combined is 18.71 per 100,000 person-years; 11.52 per 100,000 person-years for benign tumors and 7.19 per 100,000 person-years for malignant tumors. Incidence, response to treatment, and survival after diagnosis vary greatly by age at diagnosis, histologic type of tumor, and degree of neurologic compromise. The only established environmental risk factor for brain tumors is ionizing radiation exposure. Exposure to radiofrequency electromagnetic fields via cell phone use has gained a lot of attention as a potential risk factor for brain tumor development. However, studies have been inconsistent and inconclusive due to systematic differences in study designs and difficulty of accurately measuring cell phone use. Recently studies of genetic risk factors for brain tumors have expanded to genome-wide association studies. In addition, genome-wide studies of somatic genetic changes in tumors show correlation with clinical outcomes.

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

    Directory of Open Access Journals (Sweden)

    Alireza eGharabaghi

    2014-03-01

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

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

    OpenAIRE

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

    2016-01-01

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

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

    Science.gov (United States)

    Kjaerby, Celia; Rasmussen, Rune; Andersen, Mie; Nedergaard, Maiken

    2017-02-16

    We continuously need to adapt to changing conditions within our surrounding environment, and our brain needs to quickly shift between resting and working activity states in order to allow appropriate behaviors. These global state shifts are intimately linked to the brain-wide release of the neuromodulators, noradrenaline and acetylcholine. Astrocytes have emerged as a new player participating in the regulation of brain activity, and have recently been implicated in brain state shifts. Astrocytes display global Ca(2+) signaling in response to activation of the noradrenergic system, but whether astrocytic Ca(2+) signaling is causative or correlative for shifts in brain state and neural activity patterns is not known. Here we review the current available literature on astrocytic Ca(2+) signaling in awake animals in order to explore the role of astrocytic signaling in brain state shifts. Furthermore, we look at the development and availability of innovative new methodological tools that are opening up for new ways of visualizing and perturbing astrocyte activity in awake behaving animals. With these new tools at hand, the field of astrocyte research will likely be able to elucidate the causal and mechanistic roles of astrocytes in complex behaviors within a very near future.

  14. 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.

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

    Institute of Scientific and Technical Information of China (English)

    接标; 张道强

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Rajamannar Ramasubbu, MD, FRCPC, MSc

    2016-01-01

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

  17. 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.

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

    Science.gov (United States)

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

    2017-03-18

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

  19. 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

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

    Science.gov (United States)

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

    2013-04-01

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

  1. Brain state and changes of mind: Probing the neural bases of multi-stable perceptual dynamics

    NARCIS (Netherlands)

    Kloosterman, N.A.

    2015-01-01

    The internal state of our brain changes constantly, affecting the way in which the cerebral cortex processes information. Changes of cortical state have traditionally been associated with slow and largely automatic fluctuations of wakefulness and arousal, but they can also occur on a rapid (sub-seco

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

    Science.gov (United States)

    Hudetz, Anthony G; Humphries, Colin J; Binder, Jeffrey R

    2014-01-01

    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 (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm(2) area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites 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-scale brain states

  3. A pairwise maximum entropy model accurately describes resting-state human brain networks.

    Science.gov (United States)

    Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

    2013-01-01

    The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks.

  4. 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.

  5. 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.

  6. 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...

  7. 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.

  8. 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

    2015-10-16

    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

  9. Mental states as macrostates emerging from brain electrical dynamics

    Science.gov (United States)

    Allefeld, Carsten; Atmanspacher, Harald; Wackermann, Jiří

    2009-03-01

    Psychophysiological correlations form the basis for different medical and scientific disciplines, but the nature of this relation has not yet been fully understood. One conceptual option is to understand the mental as "emerging" from neural processes in the specific sense that psychology and physiology provide two different descriptions of the same system. Stating these descriptions in terms of coarser- and finer-grained system states (macro- and microstates), the two descriptions may be equally adequate if the coarse-graining preserves the possibility to obtain a dynamical rule for the system. To test the empirical viability of our approach, we describe an algorithm to obtain a specific form of such a coarse-graining from data, and illustrate its operation using a simulated dynamical system. We then apply the method to an electroencephalographic recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject.

  10. 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.

  11. 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...

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

    Science.gov (United States)

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

    2011-11-01

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

  13. 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.

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

    Science.gov (United States)

    Lecrux, C; Hamel, E

    2016-10-05

    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'.

  15. 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.

  16. Can hyper-synchrony in meditation lead to seizures? Similarities in meditative and epileptic brain states.

    Science.gov (United States)

    Lindsay, Shane

    2014-10-01

    Meditation is used worldwide by millions of people for relaxation and stress relief. Given sufficient practice, meditators may also experience a variety of altered states of consciousness. These states can lead to a variety of unusual experiences, including physical, emotional and psychic disturbances. This paper highlights the correspondences between brain states associated with these experiences and the symptoms and neurophysiology of epileptic simple partial seizures. Seizures, like meditation practice, can result in both positive and negative experiences. The neurophysiology and chemistry underlying simple partial seizures are characterised by a high degree of excitability and high levels of neuronal synchrony in gamma-band brain activity. Following a survey of the literature that shows that meditation practice is also linked to high power gamma activity, an account of how meditation could cause such activity is provided. This paper discusses the diagnostic challenges for the claim that meditation practices lead to brain states similar to those found in epileptic seizures, and seeks to develop our understanding of the range of pathological and non-pathological states that result from a hyper-excited and hyper-synchronous brain.

  17. Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

    Directory of Open Access Journals (Sweden)

    Lubin Wang

    Full Text Available The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI. In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

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

    Science.gov (United States)

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

    2014-04-01

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

  19. 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.

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

    NARCIS (Netherlands)

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

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's dise

  1. Auditory Hallucinations and the Brain's Resting-State Networks : Findings and Methodological Observations

    NARCIS (Netherlands)

    Alderson-Day, Ben; Diederen, Kelly; Fernyhough, Charles; Ford, Judith M; Horga, Guillermo; Margulies, Daniel S; McCarthy-Jones, Simon; Northoff, Georg; Shine, James M; Turner, Jessica; van de Ven, Vincent; van Lutterveld, Remko; Waters, Flavie; Jardri, Renaud

    2016-01-01

    In recent years, there has been increasing interest in the potential for alterations to the brain's resting-state networks (RSNs) to explain various kinds of psychopathology. RSNs provide an intriguing new explanatory framework for hallucinations, which can occur in different modalities and populati

  2. Hypoxic-state estimation of brain cells by using wireless near-infrared spectroscopy.

    Science.gov (United States)

    Kuo, Jinn-Rung; Lin, Bor-Shyh; Cheng, Chih-Lun; Chio, Chung-Ching

    2014-01-01

    Near-infrared spectroscopy (NIRS) is a modern measuring technology in neuroscience. It can be used to noninvasively measure the relative concentrations of oxyhemoglobin (OxyHb) and deoxyhemoglobin (DeoHb), which can reflect information related to cerebral blood volume and cerebral oxygen saturation. Therefore, it has the potential for noninvasive monitoring of cerebral ischemia. However, there is still a lack of reliable physiological information on the relationship between the concentrations of OxyHb and DeoHb in cerebral blood and the exact hypoxic state of brain cells under cerebral ischemia. In this study, we describe a wireless multichannel NIRS system, which we designed to noninvasively monitor the relative concentrations of OxyHb and DeoHb in bilateral cerebral blood before, during, and after middle cerebral artery occlusion. By comparing the results with the lactate/pyruvate ratio measured by microdialysis, we investigated the correlation between the relative concentrations of OxyHb and DeoHb in cerebral blood and the hypoxic state of brain cells. The results showed that the relationship between the concentration changes of DeoHb in cerebral blood and the hypoxic state of brain cells was significant. Therefore, by monitoring the changes in concentrations of DeoHb, the wireless NIRS can be used to estimate the hypoxic state of brain cells indirectly.

  3. 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....

  4. 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.

  5. 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

  6. 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.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

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

  9. The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

    Science.gov (United States)

    2013-03-01

    Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients. Journal of Neurophysiology, 104(5), 2900...and clinical Neurophysiology, 86, 210-223. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer . Cauwenberghs, G

  10. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury.

    Science.gov (United States)

    Stevens, Michael C; Lovejoy, David; Kim, Jinsuh; Oakes, Howard; Kureshi, Inam; Witt, Suzanne T

    2012-06-01

    Several reports show that traumatic brain injury (TBI) results in abnormalities in the coordinated activation among brain regions. Because most previous studies examined moderate/severe TBI, the extensiveness of functional connectivity abnormalities and their relationship to postconcussive complaints or white matter microstructural damage are unclear in mild TBI. This study characterized widespread injury effects on multiple integrated neural networks typically observed during a task-unconstrained "resting state" in mild TBI patients. Whole brain functional connectivity for twelve separate networks was identified using independent component analysis (ICA) of fMRI data collected from thirty mild TBI patients mostly free of macroscopic intracerebral injury and thirty demographically-matched healthy control participants. Voxelwise group comparisons found abnormal mild TBI functional connectivity in every brain network identified by ICA, including visual processing, motor, limbic, and numerous circuits believed to underlie executive cognition. Abnormalities not only included functional connectivity deficits, but also enhancements possibly reflecting compensatory neural processes. Postconcussive symptom severity was linked to abnormal regional connectivity within nearly every brain network identified, particularly anterior cingulate. A recently developed multivariate technique that identifies links between whole brain profiles of functional and anatomical connectivity identified several novel mild TBI abnormalities, and represents a potentially important new tool in the study of the complex neurobiological sequelae of TBI.

  11. 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.

  12. Resting-state functional connectivity imaging of the mouse brain using photoacoustic tomography

    Science.gov (United States)

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

    2014-03-01

    Resting-state functional connectivity (RSFC) imaging is an emerging neuroimaging approach that aims to identify spontaneous cerebral hemodynamic fluctuations and their associated functional connections. Clinical studies have demonstrated that RSFC is altered in brain disorders such as stroke, Alzheimer's, autism, and epilepsy. However, conventional neuroimaging modalities cannot easily be applied to mice, the most widely used model species for human brain disease studies. For instance, functional magnetic resonance imaging (fMRI) of mice requires a very high magnetic field to obtain a sufficient signal-to-noise ratio and spatial resolution. Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) is an alternative method. Due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments have been performed using an exposed skull preparation. In this study, we show for the first time, the use of photoacoustic computed tomography (PACT) to noninvasively image resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight regions, as well as several subregions. These findings agreed well with the Paxinos mouse brain atlas. This study showed that PACT is a promising, non-invasive modality for small-animal functional brain imaging.

  13. 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.

  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. A novel EEG for alpha brain state training, neurobiofeedback and behavior change.

    Science.gov (United States)

    Stinson, Bruce; Arthur, David

    2013-08-01

    Mindfulness meditation, with the resulting alpha brain state, is gaining a strong following as an adjunct to health, so too is applying self-affirmation to stimulate behavior change through subconscious re-programming. Until recently the EEG technology needed to demonstrate this has been cumbersome and required specialist training. This paper reports a pilot study using a remote EEG headband, which through a sophisticated algorithm, provides a real-time EEG readout unencumbered by conventional artifacts. In a convenience sample of 13, the difference in brain waves was examined while the subjects were occupied in an 'attention' and an 'alpha mind state' exercise. There was a significant difference in the mean scores for theta, delta, beta and gamma brain waves. Alpha brain waves remained static suggesting an ability of the headset to discriminate a mindful state and to provide real-time, easy to interpret feedback for the facilitator and subject. The findings provide encouragement for research applications in health care activities providing neurobiofeedback to subjects involved in mindfulness behavior change activities.

  16. 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

  17. Neuroethics and Disorders of Consciousness: Discerning Brain States in Clinical Practice and Research.

    Science.gov (United States)

    Fins, Joseph J

    2016-12-01

    Decisions about end-of-life care and participation in clinical research for patients with disorders of consciousness begin with diagnostic discernment. Accurately distinguishing between brain states clarifies clinicians' ethical obligations and responsibilities. Central to this effort is the obligation to provide neuropalliative care for patients in the minimally conscious state who can perceive pain and to restore functional communication through neuroprosthetics, drugs, and rehabilitation to patients with intact but underactivated neural networks. Efforts to bring scientific advances to patients with disorders of consciousness are reviewed, including the investigational use of deep brain stimulation in patients in the minimally conscious state. These efforts help to affirm the civil rights of a population long on the margins.

  18. 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.

  19. Music composition from the brain signal: representing the mental state by music.

    Science.gov (United States)

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

    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 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.

  20. 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.

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

    Science.gov (United States)

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

    2017-01-01

    Objective This paper attempts to explore the brain activity of patients with obsessive-compulsive disorder (OCD) and its correlation with the disease at resting duration in patients with first-episode OCD, providing a forceful imaging basis for clinic diagnosis and pathogenesis of OCD. Methods Twenty-six patients with first-episode OCD and 25 healthy controls (HC group; matched for age, sex, and education level) underwent functional magnetic resonance imaging (fMRI) scanning at resting state. Statistical parametric mapping 8, data processing assistant for resting-state fMRI analysis toolkit, and resting state fMRI data analysis toolkit packages were used to process the fMRI data on Matlab 2012a platform, and the difference of regional homogeneity (ReHo) values between the OCD group and HC group was detected with independent two-sample t-test. With age as a concomitant variable, the Pearson correlation analysis was adopted to study the correlation between the disease duration and ReHo value of whole brain. Results Compared with HC group, the ReHo values in OCD group were decreased in brain regions, including left thalamus, right thalamus, right paracentral lobule, right postcentral gyrus, and the ReHo value was increased in the left angular gyrus region. There was a negative correlation between disease duration and ReHo value in the bilateral orbitofrontal cortex (OFC). Conclusion OCD is a multifactorial disease generally caused by abnormal activities of many brain regions at resting state. Worse brain activity of the OFC is related to the OCD duration, which provides a new insight to the pathogenesis of OCD. PMID:28243104

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

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    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.

  3. Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset.

    Science.gov (United States)

    Leutheuser, Heike; Schuldhaus, Dominik; Eskofier, Bjoern M

    2013-01-01

    Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.

  4. 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.

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

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    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.

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

    Science.gov (United States)

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

    2015-05-01

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

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

    Science.gov (United States)

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

    2013-12-30

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

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

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    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.

  9. 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.

  10. Abnormal functional MRI BOLD contrast in the vegetative state after severe traumatic brain injury.

    Science.gov (United States)

    Heelmann, Volker; Lippert-Grüner, Marcela; Rommel, Thomas; Wedekind, Christoph

    2010-06-01

    For the rehabilitation process, the treatment of patients surviving brain injury in a vegetative state is still a serious challenge. The aim of this study was to investigate patients exhibiting severely disturbed consciousness using functional magnetic resonance imaging. Five cases of posttraumatic vegetative state and one with minimal consciousness close to the vegetative state were studied clinically, electrophysiologically, and by means of functional magnetic resonance imaging. Visual, sensory, and acoustic paradigms were used for stimulation. In three patients examined less than 2 months after trauma, a consistent decrease in blood oxygen level dependent (BOLD) signal ('negative activation') was observed for visual stimulation; one case even showed a decrease in BOLD activation for all three activation paradigms. In the remaining three cases examined more than 6 months after trauma, visual stimulation yielded positive BOLD contrast or no activation. In all cases, sensory stimulation was followed by a decrease in BOLD signal or no activation, whereas auditory stimulation failed to elicit any activation with the exception of one case. Functional magnetic resonance imaging in the vegetative state indicates retained yet abnormal brain function; this abnormality can be attributed to the impairment of cerebral vascular autoregulation or an increase in the energy consumption of activated neocortex in severe traumatic brain injury.

  11. Classification of physiologically significant pumping states in an implantable rotary blood pump: effects of cardiac rhythm disturbances.

    Science.gov (United States)

    Karantonis, Dean M; Lovell, Nigel H; Ayre, Peter J; Mason, David G; Cloherty, Shaun L

    2007-06-01

    Methods of speed control for implantable rotary blood pumps (iRBPs) are vital for providing implant recipients with sufficient blood flow to cater for their physiological requirements. The detection of pumping states that reflect the physiological state of the native heart forms a major component of such a control method. Employing data from a number of acute animal experiments, five such pumping states have been previously identified: regurgitant pump flow, ventricular ejection (VE), nonopening of the aortic valve (ANO), and partial collapse (intermittent [PVC-I] and continuous [PVC-C]) of the ventricle wall. An automated approach that noninvasively detects such pumping states, employing a classification and regression tree (CART), has also been developed. An extension to this technique, involving an investigation into the effects of cardiac rhythm disturbances on the state detection process, is discussed. When incorporating animal data containing arrhythmic events into the CART model, the strategy showed a marked improvement in detecting pumping states as compared to the model devoid of arrhythmic data: state VE--57.4/91.7% (sensitivity/specificity) improved to 97.1/100.0%; state PVC-I--66.7/83.1% improved to 100.0/88.3%, and state PVC-C--11.1/66.2% changed to 0.0/100%. With a simplified binary scheme differentiating suction (PVC-I, PVC-C) and nonsuction (VE, ANO) states, suction was initially detected with 100/98.5% sensitivity/specificity, whereas with the subsequent improved model, both these states were detected with 100% sensitivity. The accuracy achieved demonstrates the robustness of the technique presented, and substantiates its inclusion into any iRBP control methodology.

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

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    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.

  13. 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.

  14. Crosstalk of Signaling and Metabolism Mediated by the NAD(+)/NADH Redox State in Brain Cells.

    Science.gov (United States)

    Winkler, Ulrike; Hirrlinger, Johannes

    2015-12-01

    The energy metabolism of the brain has to be precisely adjusted to activity to cope with the organ's energy demand, implying that signaling regulates metabolism and metabolic states feedback to signaling. The NAD(+)/NADH redox state constitutes a metabolic node well suited for integration of metabolic and signaling events. It is affected by flux through metabolic pathways within a cell, but also by the metabolic state of neighboring cells, for example by lactate transferred between cells. Furthermore, signaling events both in neurons and astrocytes have been reported to change the NAD(+)/NADH redox state. Vice versa, a number of signaling events like astroglial Ca(2+) signals, neuronal NMDA-receptors as well as the activity of transcription factors are modulated by the NAD(+)/NADH redox state. In this short review, this bidirectional interdependence of signaling and metabolism involving the NAD(+)/NADH redox state as well as its potential relevance for the physiology of the brain and the whole organism in respect to blood glucose regulation and body weight control are discussed.

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

    Science.gov (United States)

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

    2016-01-01

    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

  16. 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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  19. 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.

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

    Institute of Scientific and Technical Information of China (English)

    ZHA Xin-Wei; MA Gang-Long

    2011-01-01

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

  1. A default mode of brain function in altered states of consciousness.

    Science.gov (United States)

    Guldenmund, P; Vanhaudenhuyse, A; Boly, M; Laureys, S; Soddu, A

    2012-01-01

    Using modern brain imaging techniques, new discoveries are being made concerning the spontaneous activity of the brain when it is devoid of attention-demanding tasks. Spatially separated patches of neuronal assemblies have been found to show synchronized oscillatory activity behavior and are said to be functionally connected. One of the most robust of these is the default mode network, which is associated with intrinsic processes like mind wandering and self-projection. Furthermore, activity in this network is anticorrelated with activity in a network that is linked to attention to external stimuli. The integrity of both networks is disturbed in altered states of consciousness, like sleep, general anesthesia and hypnosis. In coma and related disorders of consciousness, encompassing the vegetative state (unresponsive wakefulness syndrome) and minimally conscious state, default mode network integrity correlates with the level of remaining consciousness, offering the possibility of using this information for diagnostic and prognostic purposes. Functional brain imaging is currently being validated as a valuable addition to the standardized behavioral assessments that are already in use.

  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. 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.

  4. Brain functional network connectivity based on a visual task:visual information processing-related brain regions are signiifcantly activated in the task state

    Institute of Scientific and Technical Information of China (English)

    Yan-li Yang; Hong-xia Deng; Gui-yang Xing; Xiao-luan Xia; Hai-fang Li

    2015-01-01

    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 inves-tigated 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, conifrming 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 ifndings 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.

  5. 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.

  6. 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.

  7. Representation, Classification and Information Fusion for Robust and Efficient Multimodal Human States Recognition

    Science.gov (United States)

    Li, Ming

    2013-01-01

    The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered as a joint term for identifying/verifing various kinds of human related states, such as biometric identity, language spoken, age, gender, emotion, intoxication level, physical activity, vocal…

  8. 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.

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

    Science.gov (United States)

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

    2011-06-01

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

  10. Synthesis of Brain-State-in-a-Box (BSB) based associative memories.

    Science.gov (United States)

    Lillo, W E; Miller, D C; Hui, S; Zak, S H

    1994-01-01

    Presents a novel synthesis procedure to realize an associative memory using the Generalized-Brain-State-in-a-Box (GBSB) neural model. The implementation yields an interconnection structure that guarantees that the desired memory patterns are stored as asymptotically stable equilibrium points and that possesses very few spurious states. Furthermore, the interconnection structure is in general non-symmetric. Simulation examples are given to illustrate the effectiveness of the proposed synthesis method. The results obtained for the GBSB model are successfully applied to other neural network models.

  11. 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

  12. 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.

  13. The impact of normalization and segmentation on resting-state brain networks.

    Science.gov (United States)

    Magalhães, Ricardo; Marques, Paulo; Soares, José; Alves, Victor; Sousa, Nuno

    2015-04-01

    Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.

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

    Directory of Open Access Journals (Sweden)

    Ting Xue

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

  15. 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.)

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

    Institute of Scientific and Technical Information of China (English)

    朱学才; 李梅; 邹思轩

    2011-01-01

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

  17. 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

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

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

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

  19. Central thalamic deep brain stimulation for support of forebrain arousal regulation in the minimally conscious state.

    Science.gov (United States)

    Schiff, Nicholas D

    2013-01-01

    This chapter considers the use of central thalamic deep brain stimulation (CT/DBS) to support arousal regulation mechanisms in the minimally conscious state (MCS). CT/DBS for selected patients in a MCS is first placed in the historical context of prior efforts to use thalamic electrical brain stimulation to treat the unconscious clinical conditions of coma and vegetative state. These previous studies and a proof of concept result from a single-subject study of a patient in a MCS are reviewed against the background of new population data providing benchmarks of the natural history of vegetative and MCSs. The conceptual foundations for CT/DBS in selected patients in a MCS are then presented with consideration of both circuit and cellular mechanisms underlying recovery of consciousness identified from empirical studies. Directions for developing future generalizable criteria for CT/DBS that focus on the integrity of necessary brain systems and behavioral profiles in patients in a MCS that may optimally response to support of arousal regulation mechanisms are proposed.

  20. Changes in the regional homogeneity of resting-state brain activity in minimal hepatic encephalopathy.

    Science.gov (United States)

    Chen, Hua-Jun; Zhu, Xi-Qi; Yang, Ming; Liu, Bin; Zhang, Yi; Wang, Yu; Teng, Gao-Jun

    2012-01-17

    Resting-state functional magnetic resonance imaging (fMRI) has facilitated the study of spontaneous brain activity by measuring low-frequency oscillations in blood-oxygen-level-dependent signals. Analyses of regional homogeneity (ReHo), which reflects the local synchrony of neural activity, have been used to reveal the mechanisms underlying the brain dysfunction in various neuropsychiatric diseases. However, it is not known whether the ReHo is altered in cirrhotic patients with minimal hepatic encephalopathy (MHE). We recruited 18 healthy controls and 18 patients with MHE. The ReHo was calculated to assess the strength of the local signal synchrony. Compared with the healthy controls, the patients with MHE had significantly decreased ReHo in the cuneus and adjacent precuneus, and left inferior parietal lobe, whereas the regions showing increased ReHo in patients with MHE included the left parahippocampal gyrus, right cerebellar vermis, and bilateral anterior cerebellar lobes. We found a positive correlation between the mean ReHo in the cuneus and adjacent precuneus and the score on the digit-symbol test in the patient group. In conclusion, the analysis of the regional homogeneity of resting-state brain activity may provide additional information with respect to a clinical definition of MHE.

  1. 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.

  2. Brain regions responsible for tinnitus distress and loudness: a resting-state FMRI study.

    Directory of Open Access Journals (Sweden)

    Takashi Ueyama

    Full Text Available Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC strength is affected by the regional autocorrelation coefficient (AC. In this study, we used resting-state functional MRI (fMRI and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC and with taking into account the effect of AC (rGCa to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.

  3. Brain regions responsible for tinnitus distress and loudness: a resting-state FMRI study.

    Science.gov (United States)

    Ueyama, Takashi; Donishi, Tomohiro; Ukai, Satoshi; Ikeda, Yorihiko; Hotomi, Muneki; Yamanaka, Noboru; Shinosaki, Kazuhiro; Terada, Masaki; Kaneoke, Yoshiki

    2013-01-01

    Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC) strength is affected by the regional autocorrelation coefficient (AC). In this study, we used resting-state functional MRI (fMRI) and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC) and with taking into account the effect of AC (rGCa) to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.

  4. 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

  5. Exploring altered consciousness states by magnetic resonance imaging in brain injury.

    Science.gov (United States)

    Lescot, Thomas; Galanaud, Damien; Puybasset, Louis

    2009-03-01

    Traumatic brain injury (TBI) occurs abruptly, involves multiple specialized teams, calls on the health-care system in its emergency dimension, and engages the well-being of the patient and his relatives for a lifetime period. Clinicians in charge of these patients are faced with issues of uppermost importance: medical issues such as predicting the long-term neurological outcome of the comatose patient; ethical issues because of the influence of intensive care on the long-term survival of patients in a vegetative and minimally conscious state; legal issues because of the law that has set the concept of proportionality of care as the legal rule; and social issues as the result of the very high cost of these pathologies. Today's larger availability of magnetic resonance imaging (MRI) in ventilated patients and the recent improvements in hardware and in imaging techniques that have made the last-developed imaging techniques such as diffusion tensor imaging and magnetic resonance spectroscopy available in brain-trauma patients, are changing the paradigm in neurointensive care regarding outcome prediction. The old paradigm that no individual prognosis could be made at the subacute phase in TBI patients does not hold true anymore. This major change opens new challenging ethical questions. This review focuses on the brain explorations that are required, such as MRI, magnetic resonance spectroscopy, and diffusion tensor imaging, to provide the clinician with a multimodal assessment of the brain state to predict outcome of coma. Such an assessment will become mandatory in the near future to answer the crucial question of proportionality of care in these patients.

  6. Assessment and classification of hydromorphological state of the Breń River

    Directory of Open Access Journals (Sweden)

    Borek Łukasz

    2016-09-01

    Full Text Available The paper presents the classification of the hydromorphological condition of the Breń River according to the River Habitat Survey (RHS. The research of the hydromorphological assessment of the Breń River, which is a right-bank tributary of the Vistula River and almost entirely flows through the area of the Dąbrowa Tarnowska district was conducted in June 2015. The research sites were situated on the border of the Tarnów Plateau and the Vistula Lowland. The Breń River in these sections flows through rural areas used for agricultural purposes with low-density housing. The analysis of qualitative parameters describing the morphological characteristics were based on two synthetic indices of stream quality: Habitat Quality Assesment (HQA and Habitat Modification Score (HMS. The calculated numerical values of the two indices proved that the sections of the Breń River correspond with the third and fifth class, which means a moderate (III and very bad (V hydromorphological condition.

  7. Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization.

    Science.gov (United States)

    Depeursinge, Adrien; Iavindrasana, Jimison; Hidki, Asmâa; Cohen, Gilles; Geissbuhler, Antoine; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

    2010-02-01

    In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar's statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROIs.

  8. 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.

  9. Urban empty spaces and derelict infrastructures. An opportunity for the classification of state assets on the territory

    Directory of Open Access Journals (Sweden)

    Piero Pedrocco

    2013-07-01

    Full Text Available Marked by brownfield areas, the contemporary city looks like an untidy scenery, without boundaries, signed by buildings often no more useful for the original functional needs. Urban empty spaces penetrate into the neighbourhoods, without any formal logic, in a crescendo sometimes difficult to deal with. The process of starting from the classification of derelict  State assets, in the perspective of their valorisation, turns out as a description at the basis of the project. This issue opens a wide reflection, under both descriptive and planning points of view, though it is now in a dynamic evolution and therefore uncertain. Our methodology, based on the evaluation of specific characteristics and indicators, addresses us to possibilities of effective regeneration interventions, but that is not enough; subsequently multi-objectives analyses should be used in order to develop wider decision-making dialectics about regeneration, avoiding choices made case by case.

  10. The Brain Functional State of Music Creation: an fMRI Study of Composers.

    Science.gov (United States)

    Lu, Jing; Yang, Hua; Zhang, Xingxing; He, Hui; Luo, Cheng; Yao, Dezhong

    2015-07-23

    In this study, we used functional magnetic resonance imaging (fMRI) to explore the functional networks in professional composers during the creation of music. We compared the composing state and resting state imagery of 17 composers and found that the functional connectivity of primary networks in the bilateral occipital lobe and bilateral postcentral cortex decreased during the composing period. However, significantly stronger functional connectivity appeared between the anterior cingulate cortex (ACC), the right angular gyrus and the bilateral superior frontal gyrus during composition. These findings indicate that a specific brain state of musical creation is formed when professional composers are composing, in which the integration of the primary visual and motor areas is not necessary. Instead, the neurons of these areas are recruited to enhance the functional connectivity between the ACC and the default mode network (DMN) to plan the integration of musical notes with emotion.

  11. 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

  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 pinternal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

  13. A Bayesian Double Fusion Model for Resting State Brain Connectivity Using Joint Functional and Structural Data.

    Science.gov (United States)

    Kang, Hakmook; Ombao, Hernando; Fonnesbeck, Christopher; Ding, Zhaohua; Morgan, Victoria L

    2017-03-19

    Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is that they do not take advantage of the information from DTI that could potentially enhance estimation of resting state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatio-temporal model that incorporates structural connectivity into estimating FC. In our proposed approach, structural connectivity (SC) based on DTI data is used to construct an informative prior for functional connectivity based on resting state fMRI data via the Cholesky decomposition. Simulation studies showed that incorporating the two data produced significantly reduced mean squared errors compared to the standard approach of separately analyzing the two data from different modalities. We applied our model to analyze the resting state DTI and fMRI data collected to estimate FC between the brain regions that were hypothetically important in the origination and spread of temporal lobe epilepsy seizures. Our analysis concludes that the proposed model achieves smaller false positive rates and is much robust to data decimation compared to the conventional approach.

  14. 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.

  15. 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.

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

    Science.gov (United States)

    Vergallo, P; Lay-Ekuakille, A

    2013-08-01

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

  17. 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

    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....... The model was optimized using 50 subjects and validated on 76 subjects. Results: The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 +/- 7.44 (% mu +/-sigma...

  18. 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...

  19. [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.

  20. Critical issues in state-of-the-art brain-computer interface signal processing.

    Science.gov (United States)

    Krusienski, Dean J; Grosse-Wentrup, Moritz; Galán, Ferran; Coyle, Damien; Miller, Kai J; Forney, Elliott; Anderson, Charles W

    2011-04-01

    This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.

  1. Brain activation, affect, and aerobic exercise: an examination of both state-independent and state-dependent relationships.

    Science.gov (United States)

    Petruzzello, S J; Tate, A K

    1997-09-01

    Resting electroencephalograph (EEG) asymmetry is a biological marker of the propensity to respond affectively to, and a measure of change in affect associated with, acute aerobic exercise. This study examined the EEG-affect-exercise relationship. Twenty participants performed each of three randomly assigned 30-min conditions: (a) a nonexercise control, (b) a cycling exercise at 55% VO2max, and (c) a cycling exercise at 70% VO2max. EEG and affect were assessed pre- and 0, 5, 10, 20, and 30 min postcondition. No significant results were seen in the control or 55% conditions. In the 70% exercise condition, greater relative left frontal activation preexercise predicted increased positive affect and reduced state anxiety postexercise. Participants (n = 7) with extreme relative left frontal activation postexercise reported concomitant decreases in anxiety, whereas participants (n = 7) with extreme relative right frontal activation postexercise reported increases in anxiety. These findings (a) replicate prior work, (b) suggest a dose-response intensity effect, and (c) support the idea that exercise is an emotion-eliciting event. Affective responses seem to be mediated in part by differential resting levels of activation in the anterior brain regions. Ongoing anterior brain activation reflected concurrent postexercise affect.

  2. High transition frequencies of dynamic functional connectivity states in the creative brain

    Science.gov (United States)

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

    2017-01-01

    Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity. PMID:28383052

  3. High transition frequencies of dynamic functional connectivity states in the creative brain.

    Science.gov (United States)

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

    2017-04-06

    Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity.

  4. Spontaneous conscious covert cognition states and brain electric spectral states in canonical correlations.

    Science.gov (United States)

    Lehmann, D; Grass, P; Meier, B

    1995-02-01

    Correlations between subjective, conscious, spontaneous cognitions and EEG power spectral profiles were investigated in 20 normal volunteers (2 sessions each) during relaxation-drowsiness-sleep onset. Four-channel EEG (temporal-parietal and parietal-central, left and right) was continuously recorded. The subjects were prompted 15 times per session to give brief reports of their ongoing thoughts. The reports were rated on 23 scales, and the 16 seconds of EEG recording preceding the prompts were spectral analyzed. Canonical correlation analysis was applied to the data (23 cognition ratings and 124 EEG spectral values for each of the 538 prompts). Four of the 23 pairs of canonical EEG variables and cognition variables were significant (p covert, cognitive-emotional states in a no-input, no-task, no-response paradigm.

  5. Sex differences in associations of arginine vasopressin and oxytocin with resting-state functional brain connectivity.

    Science.gov (United States)

    Rubin, Leah H; Yao, Li; Keedy, Sarah K; Reilly, James L; Bishop, Jeffrey R; Carter, C Sue; Pournajafi-Nazarloo, Hossein; Drogos, Lauren L; Tamminga, Carol A; Pearlson, Godfrey D; Keshavan, Matcheri S; Clementz, Brett A; Hill, Scot K; Liao, Wei; Ji, Gong-Jun; Lui, Su; Sweeney, John A

    2017-01-02

    Oxytocin (OT) and arginine vasopressin (AVP) exert robust and sexually dimorphic influences on cognition and emotion. How these hormones regulate relevant functional brain systems is not well understood. OT and AVP serum concentrations were assayed in 60 healthy individuals (36 women). Brain functional networks assessed with resting-state functional magnetic resonance imaging (rs-fMRI) were constructed with graph theory-based approaches that characterize brain networks as connected nodes. Sex differences were demonstrated in rs-fMRI. Men showed higher nodal degree (connectedness) and efficiency (information propagation capacity) in left inferior frontal gyrus (IFG) and bilateral superior temporal gyrus (STG) and higher nodal degree in left rolandic operculum. Women showed higher nodal betweenness (being part of paths between nodes) in right putamen and left inferior parietal gyrus (IPG). Higher hormone levels were associated with less intrinsic connectivity. In men, higher AVP was associated with lower nodal degree and efficiency in left IFG (pars orbitalis) and left STG and less efficiency in left IFG (pars triangularis). In women, higher AVP was associated with lower betweenness in left IPG, and higher OT was associated with lower nodal degree in left IFG (pars orbitalis). Hormones differentially correlate with brain networks that are important for emotion processing and cognition in men and women. AVP in men and OT in women may regulate orbital frontal cortex connectivity, which is important in emotion processing. Hormone associations with STG and pars triangularis in men and parietal cortex in women may account for well-established sex differences in verbal and visuospatial abilities, respectively. © 2016 Wiley Periodicals, Inc.

  6. 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

  7. Prediction and classification of the degradation state of plastic materials used in modern and contemporary art

    Science.gov (United States)

    Manfredi, M.; Barberis, E.; Marengo, E.

    2017-01-01

    Today, artworks partially or completely made of plastic materials can be found in almost all international museums and collections. The deterioration of these objects is now becoming evident mainly because these synthetic materials are not designed for a long life and the characterization of their state of conservation can help curators and conservators. In this research we investigated the applicability of a portable attenuated total reflection (ATR) infrared spectrometer for the non-invasive characterization and for monitoring the degradation of plastics used in modern and contemporary art. Several polypropylene and polycarbonate samples were artificially aged in solar box, simulating about 200 years of museum light exposure, and they were monitored with the portable ATR, creating an infrared library of the conservation state of plastics. Through the use of chemometric techniques like principal component analysis-linear discriminant analysis and partial least square—discriminant analysis, we built a robust degradation model of each material that can be used to predict and classify the degradation state of artworks and to identify the priority of intervention in the museum collections. Portable ATR coupled to multivariate statistics can be employed for taking care of plastic artworks as it is non-invasive, the analysis is very fast and it can be performed directly in situ.

  8. An insect-inspired bionic sensor for tactile localisation and material classification with state-dependent modulation

    Directory of Open Access Journals (Sweden)

    Luca ePatanè

    2012-08-01

    Full Text Available Insects carry a pair of antennae on their head: multimodal sensory organs that serve a wide range of sensory-guided behaviours. During locomotion, antennae are involved in near-range orientation, for example in detecting, localising, probing and negotiating obstacles.Here we present a bionic, active tactile sensing system inspired by insect antennae. It comprises an actuated elastic rod equipped with a terminal acceleration sensor. The measurement principle is based on the analysis of damped harmonic oscillations registered upon contact with an object. The dominant frequency of the oscillation is extracted to determine the distance of the contact point along the probe, and basal angular encoders allow tactile localisation in a polar coordinate system. Finally, the damping behaviour of the registered signal is exploited to determine the most likely material.The tactile sensor is tested in four approaches with increasing neural plausibility: First, we show that peak extraction from the Fourier spectrum is sufficient for tactile localisation with position errors below 1%. Also, the damping property of the extracted frequency is used for material classification. Second, we show that the Fourier spectrum can be analysed by an Artificial Neural Network which can be trained to decode contact distance and to classify contact materials. Thirdly, we show how efficiency can be improved by band-pass filtering the Fourier spectrum by application of non-negative matrix factorisation. This reduces the input dimension by 95% while reducing classification performance by 8% only. Finally, we replace the FFT by an array of spiking neurons with gradually differing resonance properties, such that their spike rate is a function of the input frequency. We show that this network can be applied to detect tactile contact events of a wheeled robot, and how detrimental effects of robot velocity on antennal dynamics can be suppressed by state-dependent modulation of the

  9. 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...

  10. 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.

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

    Science.gov (United States)

    Millán, J D R; Rupp, R; Müller-Putz, G R; Murray-Smith, R; Giugliemma, C; Tangermann, M; Vidaurre, C; Cincotti, F; Kübler, A; Leeb, R; Neuper, C; Müller, K-R; Mattia, D

    2010-01-01

    In recent years, new research has brought the field of electroencephalogram (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 and 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.

  12. 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.

  13. A MATLAB toolbox for classification and visualization of heterogenous multi-scale human data using the Disease State Fingerprint method.

    Science.gov (United States)

    Cluitmans, Luc; Mattila, Jussi; Runtti, Hilkka; van Gils, Mark; Lötjönen, Jyrki

    2013-01-01

    As the amount of data acquired from humans is constantly increasing, efficient tools are needed for extracting relevant information from this data. This paper presents a Matlab implementation of a method to classify and visually explore (highly) multi-variate patient data. The method uses the so-called Disease State Index (DSI) which measures the fit of a test subject's data to two classes present in the data (e.g. 'controls' and 'positives'). DSI values of the different variables measured from a patient can be combined and visualized in a tree-like form using the Disease State Fingerprint (DSF) method. This allows a researcher to explore and understand the relevance of the different variables in classification problems. Moreover, the method is robust with respect to missing data. After giving an introduction to the DSF and DSI methods, the paper describes the steps required to use the methods and presents a MATLAB toolbox to perform these steps. To demonstrate the methods' versatility, the paper illustrates the usage of the toolbox in a few different contexts in which personal health data is to be classified. With this implementation, a powerful and flexible tool is made available to the biomedical research community.

  14. A Comparison of Brain Death Criteria between China and the United States

    Directory of Open Access Journals (Sweden)

    Ze-Yu Ding

    2015-01-01

    Full Text Available Background: Criteria for determining brain death (BD vary between China and the United States. We reported the results of an investigation designed to compare procedures to determine BD in two countries. Methods: The latest criteria in the United states were published in 2010. The latest criteria in China were published in 2009. We used these two types of BD criteria to evaluate patients who were considered to be BD. The time, cost, and accuracy of the diagnosis were compared. Results: From January 1, 2012 to October 8, 2013, there were 37 patients which were applied for BD evaluation in the Neurological Intensive Care Unit of Beijing Tiantan Hospital. The cause of coma were known as subarachnoid hemorrhage (18 patients, 48.6%, intracerebral hemorrhage (8 patients, 21.6%, cerebral ischemia (9 patients, 24.3%, brain stem tumor (1 patient, 2.7%, and intracranial infection (1 patient, 2.7%. The clinical examinations were done for all of the patients except 1 patient who had low blood pressure. Three patients had brainstem reflexes that were excluded from BD. Twenty-five patients had apnea tests, and 20 tests were completed that were all positive. Confirmatory tests were completed differently: Transcranial Doppler (30 patients, positive rate 86.7%, electroencephalogram (25 patients, positive rate 100%, and somatosensory evoked potential (16 patients, positive rate 100%. Thirty-three patients were diagnosed BD by criteria of the United States. Only 9 patients were diagnosed BD by Chinese criteria. The use of time and money in the USA criteria was obviously fewer than those in Chinese criteria (P = 0.000. Conclusion: Compared with BD criteria of the United States, Chinese criteria were stricter, lower positive rate, more cost in money and time, and more reliable by families and doctors.

  15. 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.

  16. 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.

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

    Science.gov (United States)

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

    2015-03-03

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

  18. 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.

  19. 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.

  20. Altered causal connectivity of resting state brain networks in amnesic MCI.

    Directory of Open Access Journals (Sweden)

    Peipeng Liang

    Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

  1. 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

  2. Neural correlates of envy: Regional homogeneity of resting-state brain activity predicts dispositional envy.

    Science.gov (United States)

    Xiang, Yanhui; Kong, Feng; Wen, Xue; Wu, Qihan; Mo, Lei

    2016-11-15

    Envy differs from common negative emotions across cultures. Although previous studies have explored the neural basis of episodic envy via functional magnetic resonance imaging (fMRI), little is known about the neural processes associated with dispositional envy. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in dispositional envy, as measured by the Dispositional Envy Scale (DES). Results showed that ReHo in the inferior/middle frontal gyrus (IFG/MFG) and dorsomedial prefrontal cortex (DMPFC) positively predicted dispositional envy. Moreover, of all the personality traits measured by the Revised NEO Personality Inventory (NEO-PI-R), only neuroticism was significantly associated with dispositional envy. Furthermore, neuroticism mediated the underlying association between the ReHo of the IFG/MFG and dispositional envy. Hence, to the best of our knowledge, this study provides the first evidence that spontaneous brain activity in multiple regions related to self-evaluation, social perception, and social emotion contributes to dispositional envy. In addition, our findings reveal that neuroticism may play an important role in the cognitive processing of dispositional envy.

  3. Moral competence and brain connectivity: A resting-state fMRI study.

    Science.gov (United States)

    Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo; Korczykowski, Marc; Kable, Joseph W; Rao, Hengyi; Robertson, Diana C

    2016-11-01

    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.

  4. Impact of classification of hilar cholangiocarcinomas (Klatskin tumors) on the incidence of intra- and extrahepatic cholangiocarcinoma in the United States.

    Science.gov (United States)

    Welzel, Tania M; McGlynn, Katherine A; Hsing, Ann W; O'Brien, Thomas R; Pfeiffer, Ruth M

    2006-06-21

    Cholangiocarcinomas are topographically categorized as intrahepatic or extrahepatic by the International Classification of Diseases for Oncology (ICD-O). Although hilar cholangiocarcinomas (Klatskin tumors) are extrahepatic cholangiocarcinomas, the second edition of the ICD-O (ICD-O-2) assigned them a histology code 8162/3, Klatskin, which was cross-referenced to intrahepatic cholangiocarcinoma. Recent studies in the United States that included this code (8162/3, Klatskin) with intrahepatic cholangiocarcinoma reported an increasing incidence of intrahepatic cholangiocarcinoma and a decreasing incidence of extrahepatic cholangiocarcinoma. To investigate the impact of this misclassification on site-specific cholangiocarcinoma incidence rates, we calculated annual percent changes (APCs) with data from the Surveillance, Epidemiology, and End Results (SEER) program using a Poisson regression model that was age-adjusted to the year 2000 U.S. population. All statistical tests were two-sided. During 1992-2000, when SEER used ICD-O-2, 1710 intrahepatic cholangiocarcinomas, 1371 extrahepatic cholangiocarcinomas, and 269 hilar cholangiocarcinomas identified by code 8162/3, Klatskin were diagnosed. Ninety-one percent (246 of 269) of the hilar cholangiocarcinomas were incorrectly coded as intrahepatic cholangiocarcinomas, resulting in an overestimation of intrahepatic cholangiocarcinoma incidence by 13% and underestimation of extrahepatic cholangiocarcinomas incidence by 15%. However, even after the exclusion of tumors that were coded to the histology code 8162/3, Klatskin, age-adjusted annual intrahepatic cholangiocarcinoma incidence increased during this period (APC = 4%, 95% confidence interval = 2% to 6%, P<.001).

  5. 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.

  6. 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...

  7. 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.

  8. Optimal and robust design of brain-state-in-a-box neural associative memories.

    Science.gov (United States)

    Park, Yonmook

    2010-03-01

    This paper presents a new optimization approach to the design of associative memories via the brain-state-in-a-box (BSB) neural network. The optimization approach proposed in this paper provides the large and uniform domains of attraction of the prototype patterns, the large robustness margin for the weight matrix of the perturbed BSB neural network, the asymptotic stability of the prototype patterns, and the global stability of the BSB neural network. Based on some known qualitative properties of the BSB neural network and theoretical results presented in this paper, a synthesis method of the BSB-based associative memory is established. The synthesis method presented in this paper is given in the form of a linear matrix inequality-based optimization problem, which can be efficiently solved by a readily available software. Design examples are given to demonstrate the applicability of the proposed method and to compare with the existing synthesis methods.

  9. 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

  10. 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.

  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. A Comparison of Brain Death Criteria between China and the United States

    Institute of Scientific and Technical Information of China (English)

    Ze-Yu Ding; Qian Zhang; Jian-Wei Wu; Zhong-Hua Yang; Xing-Quan Zhao

    2015-01-01

    Background:Criteria for determining brain death (BD) vary between China and the United States.We reported the results of an investigation designed to compare procedures to determine BD in two countries.Methods:The latest criteria in the United states were published in 2010.The latest criteria in China were published in 2009.We used these two types of BD criteria to evaluate patients who were considered to be BD.The time,cost,and accuracy of the diagnosis were compared.Results:From January 1,2012 to October 8,2013,there were 37 patients which were applied for BD evaluation in the Neurological Intensive Care Unit of Beijing Tiantan Hospital.The cause of coma were known as subarachnoid hemorrhage (18 patients,48.6%),intracerebral hemorrhage (8 patients,21.6%),cerebral ischemia (9 patients,24.3%),brain stem tumor (1 patient,2.7%),and intracranial infection (1 patient,2.7%).The clinical examinations were done for all of the patients except 1 patient who had low blood pressure.Three patients had brainstem reflexes that were excluded from BD.Twenty-five patients had apnea tests,and 20 tests were completed that were all positive.Confirmatory tests were completed differently:Transcranial Doppler (30 patients,positive rate 86.7%),electroencephalogram (25 patients,positive rate 100%),and somatosensory evoked potential (16 patients,positive rate 100%).Thirty-three patients were diagnosed BD by criteria of the United States.Only 9 patients were diagnosed BD by Chinese criteria.The use of time and money in the USA criteria was obviously fewer than those in Chinese criteria (P =0.000).Conclusion:Compared with BD criteria of the United States,Chinese criteria were stricter,lower positive rate,more cost in money and time,and more reliable by families and doctors.

  13. 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

  14. 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.

  15. 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.

  16. 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...

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

    Directory of Open Access Journals (Sweden)

    Ilya M. Veer

    2010-09-01

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

  18. Intrinsic brain connectivity in chronic pain: A resting-state fMRI study in patients with rheumatoid arthritis.

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    Pär eFlodin

    2016-03-01

    Full Text Available AbstractBackground. Rheumatoid arthritis (RA is commonly accompanied by pain that is discordant with the degree of peripheral pathology. Very little is known about the cerebral processes involved in pain processing in RA. Here we investigated resting-state brain connectivity associated with prolonged pain in RA. Methods. 24 RA subjects and 19 matched controls were compared with regard to both behavioral measures of pain perception and resting-resting state fMRI data acquired subsequently to fMRI sessions involving pain stimuli. The resting-state fMRI brain connectivity was investigated using 159 seed regions located in cardinal pain processing brain regions. Additional principal component based multivariate pattern analysis of the whole brain connectivity pattern was carried out in a data driven analysis to localize group differences in functional connectivity. Results. When RA patients were compared to controls, we observed significantly lower pain resilience for pressure on the affected finger joints (i.e. P50-joint and an overall heightened level of perceived global pain in RA patients. Relative to controls, RA patients displayed increased brain connectivity predominately for the supplementary motor areas, mid-cingulate cortex and the primary sensorimotor cortex. Additionally, we observed an increase in brain connectivity between the insula and prefrontal cortex as well as between anterior cingulate cortex and occipital areas for RA patients. None of the group differences in brain connectivity were significantly correlated with behavioral parameters.Conclusion. Our study provides experimental evidence of increased connectivity between frontal midline regions that are implicated in affective pain processing and bilateral sensorimotor regions in RA patients.

  19. State of catecxolaminergine systems of the brain in forming of sydnocarb psychosis

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    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.

  20. Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT).

    Science.gov (United States)

    Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao

    2013-07-01

    [Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness - 50.28%; happiness - 79.03%; fear - 77.78%; disgust - 88.69%; and neutral - 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.

  1. 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.

  2. The effect of lithium on resting-state brain networks in patients with bipolar depression

    Institute of Scientific and Technical Information of China (English)

    Chunhong Liu; Xin Ma; Yuan Zhen; Yu Zhang; Lirong Tang; Feng Li; Changle Tie; Chuanyue Wang

    2016-01-01

    Objective: Although lithium has been a commonly prescribed neurotrophic/neuroprotective mood-stabilizing agents, its effect on spontaneous brain activity in patients with bipolar depression remains un-clear. The aim of this study is to reveal the basic mech-anism underlying the pathological influences of lithium on resting-state brain function of bipolar depression pa-tients. Methods:97 subjects including 9 bipolar depres-sion patients with lithium treatment, 19 bipolar depres-sion patients without lithium treatment and 69 healthy controls, were recruited to participate in this study. Amplitude of low-frequency fluctuation ( ALFF ) and fractional amplitude of low-frequency fluctuation ( fALFF) were used to capture the changes of spontane-ous brain activity among different groups. In addition, further analysis in terms of Hamilton Depression Rating Scale, the number of depressive episodes, and illness duration in pooled bipolar depression patients were con-ducted, which combined FLEF and fALEF to identify the basic neural features of bipolar depression patients. Results: It was observed from the imaging results that both the bipolar depression patients receiving lithium treatment and healthy control subjects showed signifi-cantly decreased ALFF/fALFF values in the right anteri-or cingulate cortex and right middle frontal gyrus com-pared to that from the bipolar depression patients with-out lithium treatmetn. The ALFF values of the right middle temporal gyrus was also found to be negative re-lated to the number of depressive episode and the total episodes. Conclusions:Our findings suggested that the bipolar depression subjects were identified to have ab-normal ALFF/ fALFF in the cortico-limbic systems, in-cluding regions like right anterior cingulate cortex, bi-lateral middle frontal gyrus, right orbital frontal gyrus, and right middle temporal gyrus. In addition, it was al-so revealed that the decreased ALFF/fALFF in the right anterior cingulate cortex and right

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Altered Resting-State Brain Activity and Connectivity in Depressed Parkinson's Disease.

    Directory of Open Access Journals (Sweden)

    Xiao Hu

    Full Text Available Depressive symptoms are common in Parkinson's disease (PD, but the neurophysiological mechanisms of depression in PD are poorly understood. The current study attempted to examine disrupted spontaneous local brain activities and functional connectivities that underlie the depression in PD. We recruited a total of 20 depressed PD patients (DPD, 40 non-depressed PD patients (NDPD and 43 matched healthy controls (HC. All the subjects underwent neuropsychological tests and resting-state fMRI scanning. The between-group differences in the amplitude of low frequency fluctuations (ALFF of BOLD signals were examined using post-hoc tests after the analysis of covariance. Compared with the NDPD and HC, the DPD group showed significantly increased ALFF in the left median cingulated cortex (MCC. The functional connectivity (FC between left MCC and all the other voxels in the brain were then calculated. Compared with the HC and NDPD group, the DPD patients showed stronger FC between the left MCC and some of the major nodes of the default mode network (DMN, including the post cingulated cortex/precuneus, medial prefrontal cortex, inferior frontal gyrus, and cerebellum. Correlation analysis revealed that both the ALFF values in the left MCC and the FC between the left MCC and the nodes of DMN were significantly correlated with the Hamilton Depression Rating Scale score. Moreover, higher local activities in the left MCC were associated with increased functional connections between the MCC and the nodes of DMN in PD. These abnormal activities and connectivities of the limbic-cortical circuit may indicate impaired high-order cortical control or uncontrol of negative mood in DPD, which suggested a possible neural mechanism of the depression in PD.

  8. 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.

  9. 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.

  10. A study of the brain's resting state based on alpha band power, heart rate and fMRI

    NARCIS (Netherlands)

    de Munck, J.C.; Goncalves, S.I.; Faes, T.J.C.; Kuijer, J.P.A.; Pouwels, P.J.W.; Heethaar, R.M.; Lopes da Silva, F.H.

    2008-01-01

    Considering that there are several theoretical reasons why fMRI data is correlated to variations in heart rate, these correlations are explored using experimental resting state data. In particular, the possibility is discussed that the "default network", being a brain area that deactivates during no

  11. Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach.

    Science.gov (United States)

    Liu, Feng; Xie, Bing; Wang, Yifeng; Guo, Wenbin; Fouche, Jean-Paul; Long, Zhiliang; Wang, Wenqin; Chen, Heng; Li, Meiling; Duan, Xujun; Zhang, Jiang; Qiu, Mingguo; Chen, Huafu

    2015-03-01

    Functional neuroimaging studies have found intra-regional activity and inter-regional connectivity alterations in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are based on group-level statistics and therefore it is unclear whether PTSD can be discriminated at single-subject level, for instance using the machine learning approach. Here, we proposed a novel framework to identify PTSD using multi-level measures derived from resting-state functional MRI (fMRI). Specifically, three levels of measures were extracted as classification features: (1) regional amplitude of low-frequency fluctuations (univariate feature), which represents local spontaneous synchronous neural activity; (2) temporal functional connectivity (bivariate feature), which represents the extent of similarity of local activity between two regions, and (3) spatial functional connectivity (multivariate feature), which represents the extent of similarity of temporal correlation maps between two regions. Our method was evaluated on 20 PTSD patients and 20 demographically matched healthy controls. The experimental results showed that the features of each level could successfully discriminate PTSD patients from healthy controls. Furthermore, the combination of multi-level features using multi-kernel learning can further improve the classification performance. Specifically, the classification accuracy obtained by the proposed framework was 92.5 %, which was an increase of at least 5 and 17.5 % from the two-level and single-level feature based methods, respectively. Particularly, the limbic structure and prefrontal cortex provided the most discriminant features for classification, consistent with results reported in previous studies. Together, this study demonstrated for the first time that patients with PTSD can be identified at the individual level using resting-state fMRI data. The promising classification results indicated that this method may provide a

  12. 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.

  13. Altered brain functional networks in people with Internet gaming disorder: Evidence from resting-state fMRI.

    Science.gov (United States)

    Wang, Lingxiao; Wu, Lingdan; Lin, Xiao; Zhang, Yifen; Zhou, Hongli; Du, Xiaoxia; Dong, Guangheng

    2016-08-30

    Although numerous neuroimaging studies have detected structural and functional abnormality in specific brain regions and connections in subjects with Internet gaming disorder (IGD), the topological organization of the whole-brain network in IGD remain unclear. In this study, we applied graph theoretical analysis to explore the intrinsic topological properties of brain networks in Internet gaming disorder (IGD). 37 IGD subjects and 35 matched healthy control (HC) subjects underwent a resting-state functional magnetic resonance imaging scan. The functional networks were constructed by thresholding partial correlation matrices of 90 brain regions. Then we applied graph-based approaches to analysis their topological attributes, including small-worldness, nodal metrics, and efficiency. Both IGD and HC subjects show efficient and economic brain network, and small-world topology. Although there was no significant group difference in global topology metrics, the IGD subjects showed reduced regional centralities in the prefrontal cortex, left posterior cingulate cortex, right amygdala, and bilateral lingual gyrus, and increased functional connectivity in sensory-motor-related brain networks compared to the HC subjects. These results imply that people with IGD may be associated with functional network dysfunction, including impaired executive control and emotional management, but enhanced coordination among visual, sensorimotor, auditory and visuospatial systems.

  14. Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

    Science.gov (United States)

    Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

    2011-01-01

    We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition.

  15. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1989-09-01

    Certain governmental information must be classified for national security reasons. However, the national security benefits from classifying information are usually accompanied by significant costs -- those due to a citizenry not fully informed on governmental activities, the extra costs of operating classified programs and procuring classified materials (e.g., weapons), the losses to our nation when advances made in classified programs cannot be utilized in unclassified programs. The goal of a classification system should be to clearly identify that information which must be protected for national security reasons and to ensure that information not needing such protection is not classified. This document was prepared to help attain that goal. This document is the first of a planned four-volume work that comprehensively discusses the security classification of information. Volume 1 broadly describes the need for classification, the basis for classification, and the history of classification in the United States from colonial times until World War 2. Classification of information since World War 2, under Executive Orders and the Atomic Energy Acts of 1946 and 1954, is discussed in more detail, with particular emphasis on the classification of atomic energy information. Adverse impacts of classification are also described. Subsequent volumes will discuss classification principles, classification management, and the control of certain unclassified scientific and technical information. 340 refs., 6 tabs.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. Detection of electroporation-induced membrane permeabilization states in the brain using diffusion-weighted MRI

    DEFF Research Database (Denmark)

    Mahmood, Faisal; Hansen, Rasmus H; Agerholm-Larsen, Birgit

    2015-01-01

    (DW-MRI) as a quantitative method for detecting EP-induced membrane permeabilization of brain tissue using a rat brain model. MATERIAL AND METHODS: Fifty-four anesthetized Sprague-Dawley male rats were electroporated in the right hemisphere, using different voltage levels to induce no permeabilization......-induced permeabilization of brain tissue and to some extent of differentiating NP, TMP and PMP using appropriate scan timing....

  1. Deep brain stimulation for psychiatric disorders--state of the art.

    Science.gov (United States)

    Schläpfer, T E; Bewernick, B H

    2009-01-01

    A substantial number of patients suffering from severe neuropsychiatric disorders do not respond to conventional therapeutic approaches. Results from functional neuroimaging research and the development of neuromodulatory treatments lead to novel putative strategies. Recently, one of those methods, deep brain stimulation (DBS) has been applied in selected patient with major depression and obsessive-compulsive disorder (OCD) and major depression. We summarize in this review, the state of art of knowledge about the neurobiology of depression and OCD and historical treatment methods. Principles of DBS and reasons for the use of DBS in neuropsychiatry are discussed. Different targets have been chosen in a hypothesis-guided way and first results have demonstrated that DBS might be able to modulate dysfunctional neural networks in both major depression and OCD. Although DBS is a unique and promising method for otherwise treatment resistant psychiatric patients, mandatory treatment standards have to be applied for patient and target selection. Therefore, a distinct focus of this review lies on ethical aspects for DBS in neuropsychiatric disorders.

  2. Approaching dysphoric mood: state-effects of mindfulness meditation on frontal brain asymmetry.

    Science.gov (United States)

    Keune, Philipp M; Bostanov, Vladimir; Hautzinger, Martin; Kotchoubey, Boris

    2013-04-01

    Meditation-based interventions reduce the relapse risk in recurrently depressed patients. Randomized trials utilizing neurophysiologic outcome measures, however, have yielded inconsistent results with regard to a prophylactic effect. Although frontal brain asymmetry, assessed through electroencephalographic (EEG) alpha activity (8-13 Hz), is indicative of approach vs. withdrawal-related response dispositions and represents a vulnerability marker of depression, clinical trials have provided mixed results as to whether meditation has beneficial effects on alpha asymmetry. Inconsistencies might have arisen since such trials relied on resting-state recordings, instead of active paradigms under challenge, as suggested by contemporary notions of alpha asymmetry. We examined two groups of remitted, recurrently depressed females. In a "mindfulness support group", EEG was recorded during neutral rest, and rest following a negative mood induction. Subsequently, participants received initial meditation instructions. EEG was then obtained during an active period of guided mindfulness meditation and rest following the active period. In a "rumination challenge group", EEG was obtained during the same resting conditions, whereas in the active period, initial meditation instructions were followed by a rumination challenge. A significant shift in mid-frontal asymmetry, yielding a pattern indicative of approach motivation, was observed in the mindfulness support group, specifically during the meditation period. This indicates that mindfulness meditation may have a transient beneficial effect, which enables patients to take an approach-related motivational stance, particularly under circumstances of risk.

  3. Learning and Forgetting in Generalized Brain-state-in-a-box (BSB) Neural Associative Memories.

    Science.gov (United States)

    Hui, Stefen; Lillo, Walter E.; Zak, Stanislaw H.

    1996-07-01

    We propose learning and forgetting techniques for the generalized brain-state-in-a-box (BSB) based associative memories. A generalization of the BSB model allows each neuron to have its own bias and the synaptic weight matrix does not have to be symmetric. A pattern is learned by a memory if its noisy or an incomplete version presented to the memory is mapped back to this pattern. A pattern, previously stored, is forgotten or deleted from the memory if a stimulus that is a perturbed version of the pattern, when presented to the memory, is not mapped back to this pattern. In this paper we propose "on-line" memory storage and deletion methods using an iterative method of computing the pseudo-inverse of a given matrix. The proposed methods allow one to "add" or "delete" a memory pattern by updating, rather than recomputing from scratch, the current synaptic weight matrix in a single step. We first analyze the desired characteristics of neural network associative memories. After that, we review the existing methods for design of neural associative memories. Then we discuss the generalized BSB neural model and its possible function as an associative memory and proffer arguments in support of using such models for neural associative memories. In particular, the generalized BSB type models are easier to analyze, synthesize, and implement than other neural networks. The results obtained are illustrated by numerical examples. Copyright 1996 Elsevier Science Ltd

  4. 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

  5. Brain state-dependent abnormal LFP activity in the auditory cortex of a schizophrenia mouse model.

    Science.gov (United States)

    Nakao, Kazuhito; Nakazawa, Kazu

    2014-01-01

    In schizophrenia, evoked 40-Hz auditory steady-state responses (ASSRs) are impaired, which reflects the sensory deficits in this disorder, and baseline spontaneous oscillatory activity also appears to be abnormal. It has been debated whether the evoked ASSR impairments are due to the possible increase in baseline power. GABAergic interneuron-specific NMDA receptor (NMDAR) hypofunction mutant mice mimic some behavioral and pathophysiological aspects of schizophrenia. To determine the presence and extent of sensory deficits in these mutant mice, we recorded spontaneous local field potential (LFP) activity and its click-train evoked ASSRs from primary auditory cortex of awake, head-restrained mice. Baseline spontaneous LFP power in the pre-stimulus period before application of the first click trains was augmented at a wide range of frequencies. However, when repetitive ASSR stimuli were presented every 20 s, averaged spontaneous LFP power amplitudes during the inter-ASSR stimulus intervals in the mutant mice became indistinguishable from the levels of control mice. Nonetheless, the evoked 40-Hz ASSR power and their phase locking to click trains were robustly impaired in the mutants, although the evoked 20-Hz ASSRs were also somewhat diminished. These results suggested that NMDAR hypofunction in cortical GABAergic neurons confers two brain state-dependent LFP abnormalities in the auditory cortex; (1) a broadband increase in spontaneous LFP power in the absence of external inputs, and (2) a robust deficit in the evoked ASSR power and its phase-locking despite of normal baseline LFP power magnitude during the repetitive auditory stimuli. The "paradoxically" high spontaneous LFP activity of the primary auditory cortex in the absence of external stimuli may possibly contribute to the emergence of schizophrenia-related aberrant auditory perception.

  6. Brain state-dependent abnormal LFP activity in the auditory cortex of a schizophrenia mouse model

    Directory of Open Access Journals (Sweden)

    Kazuhito eNakao

    2014-07-01

    Full Text Available In schizophrenia, evoked 40-Hz auditory steady-state responses (ASSRs are impaired, which reflects the sensory deficits in this disorder, and baseline spontaneous oscillatory activity also appears to be abnormal. It has been debated whether the evoked ASSR impairments are due to the possible increase in baseline power. GABAergic interneuron-specific NMDA receptor (NMDAR hypofunction mutant mice mimic some behavioral and pathophysiological aspects of schizophrenia. To determine the presence and extent of sensory deficits in these mutant mice, we recorded spontaneous local field potential (LFP activity and its click-train evoked ASSRs from primary auditory cortex of awake, head-restrained mice. Baseline spontaneous LFP power in the pre-stimulus period before application of the first click trains was augmented at a wide range of frequencies. However, when repetitive ASSR stimuli were presented every 20 sec, averaged spontaneous LFP power amplitudes during the inter-ASSR stimulus intervals in the mutant mice became indistinguishable from the levels of control mice. Nonetheless, the evoked 40-Hz ASSR power and their phase locking to click trains were robustly impaired in the mutants, although the evoked 20-Hz ASSRs were also somewhat diminished. These results suggested that NMDAR hypofunction in cortical GABAergic neurons confers two brain state-dependent LFP abnormalities in the auditory cortex; (1 a broadband increase in spontaneous LFP power in the absence of external inputs, and (2 a robust deficit in the evoked ASSR power and its phase-locking despite of normal baseline LFP power magnitude during the repetitive auditory stimuli. The paradoxically high spontaneous LFP activity of the primary auditory cortex in the absence of external stimuli may possibly contribute to the emergence of schizophrenia-related aberrant auditory perception.

  7. An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters

    Directory of Open Access Journals (Sweden)

    Rempe MJ

    2015-09-01

    Full Text Available Michael J Rempe,1,2 William C Clegern,2 Jonathan P Wisor2 1Mathematics and Computer Science, Whitworth University, Spokane, WA, USA; 2College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USAIntroduction: Rodent sleep research uses electroencephalography (EEG and electromyography (EMG to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2–10 seconds, and each epoch is scored as wake, slow-wave sleep (SWS, or rapid-eye-movement sleep (REMS, on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs to 28% (2-second epochs were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring

  8. 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

  9. Neural Mechanisms Linking Mild Traumatic Brain Injury and Anxiety States in an Animal Model

    Science.gov (United States)

    2012-03-01

    region cortices) as defined by Paxinos and Watson (1998). The volume of each brain region of interest was measured from cresyl violet stained...Kurume Med. J. 56, 49-59. Paxinos , C., Watson, C., 1997. The Rat Brain in Stereotaxic Coordinates, 3rd ed. Academic Press, New York. 30 Ptito

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

    Directory of Open Access Journals (Sweden)

    Yu-Chen Chen

    2014-01-01

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

  11. 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.

  12. Cerebral microdialysis in traumatic brain injury and subarachnoid hemorrhage: state of the art.

    Science.gov (United States)

    de Lima Oliveira, Marcelo; Kairalla, Ana Carolina; Fonoff, Erich Talamoni; Martinez, Raquel Chacon Ruiz; Teixeira, Manoel Jacobsen; Bor-Seng-Shu, Edson

    2014-08-01

    Cerebral microdialysis (CMD) is a laboratory tool that provides on-line analysis of brain biochemistry via a thin, fenestrated, double-lumen dialysis catheter that is inserted into the interstitium of the brain. A solute is slowly infused into the catheter at a constant velocity. The fenestrated membranes at the tip of the catheter permit free diffusion of molecules between the brain interstitium and the perfusate, which is subsequently collected for laboratory analysis. The major molecules studied using this method are glucose, lactate, pyruvate, glutamate, and glycerol. The collected substances provide insight into the neurochemical features of secondary injury following traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH) and valuable information about changes in brain metabolism within a short time frame. In this review, the authors detail the CMD technique and its associated markers and then describe pertinent findings from the literature about the clinical application of CMD in TBI and SAH.

  13. Adolescent brain development and underage drinking in the United States: identifying risks of alcohol use in college populations.

    Science.gov (United States)

    Silveri, Marisa M

    2012-01-01

    Alcohol use typically is initiated during adolescence, a period that coincides with critical structural and functional maturation of the brain. Brain maturation and associated improvements in decision making continue into the third decade of life, reaching a plateau within the period referred to as emerging adulthood (18-24 years). This particular period covers that of traditionally aged college students, and includes the age (21 years) when alcohol consumption becomes legal in the United States. This review highlights neurobiological evidence indicating the vulnerabilities of the emerging-adult brain to the effects of alcohol. Factors increasing the risks associated with underage alcohol use include the age group's reduced sensitivity to alcohol sedation and increased sensitivity to alcohol-related disruptions in memory. On the individual level, factors increasing those risks are a positive family history of alcoholism, which has a demonstrated effect on brain structure and function, and emerging comorbid psychiatric conditions. These vulnerabilities-of the age group, in general, as well as of particular individuals-likely contribute to excessive and unsupervised drinking in college students. Discouraging alcohol consumption until neurobiological adulthood is reached is important for minimizing alcohol-related disruptions in brain development and decision-making capacity, and for reducing the negative behavioral consequences associated with underage alcohol use.

  14. Structural and Functional Brain Remodeling during Pregnancy with Diffusion Tensor MRI and Resting-State Functional MRI.

    Directory of Open Access Journals (Sweden)

    Russell W Chan

    Full Text Available Although pregnancy-induced hormonal changes have been shown to alter the brain at the neuronal level, the exact effects of pregnancy on brain at the tissue level remain unclear. In this study, diffusion tensor imaging (DTI and resting-state functional MRI (rsfMRI were employed to investigate and document the effects of pregnancy on the structure and function of the brain tissues. Fifteen Sprague-Dawley female rats were longitudinally studied at three days before mating (baseline and seventeen days after mating (G17. G17 is equivalent to the early stage of the third trimester in humans. Seven age-matched nulliparous female rats served as non-pregnant controls and were scanned at the same time-points. For DTI, diffusivity was found to generally increase in the whole brain during pregnancy, indicating structural changes at microscopic levels that facilitated water molecular movement. Regionally, mean diffusivity increased more pronouncedly in the dorsal hippocampus while fractional anisotropy in the dorsal dentate gyrus increased significantly during pregnancy. For rsfMRI, bilateral functional connectivity in the hippocampus increased significantly during pregnancy. Moreover, fractional anisotropy increase in the dentate gyrus appeared to correlate with the bilateral functional connectivity increase in the hippocampus. These findings revealed tissue structural modifications in the whole brain during pregnancy, and that the hippocampus was structurally and functionally remodeled in a more marked manner.

  15. Structural and Functional Brain Remodeling during Pregnancy with Diffusion Tensor MRI and Resting-State Functional MRI.

    Science.gov (United States)

    Chan, Russell W; Ho, Leon C; Zhou, Iris Y; Gao, Patrick P; Chan, Kevin C; Wu, Ed X

    2015-01-01

    Although pregnancy-induced hormonal changes have been shown to alter the brain at the neuronal level, the exact effects of pregnancy on brain at the tissue level remain unclear. In this study, diffusion tensor imaging (DTI) and resting-state functional MRI (rsfMRI) were employed to investigate and document the effects of pregnancy on the structure and function of the brain tissues. Fifteen Sprague-Dawley female rats were longitudinally studied at three days before mating (baseline) and seventeen days after mating (G17). G17 is equivalent to the early stage of the third trimester in humans. Seven age-matched nulliparous female rats served as non-pregnant controls and were scanned at the same time-points. For DTI, diffusivity was found to generally increase in the whole brain during pregnancy, indicating structural changes at microscopic levels that facilitated water molecular movement. Regionally, mean diffusivity increased more pronouncedly in the dorsal hippocampus while fractional anisotropy in the dorsal dentate gyrus increased significantly during pregnancy. For rsfMRI, bilateral functional connectivity in the hippocampus increased significantly during pregnancy. Moreover, fractional anisotropy increase in the dentate gyrus appeared to correlate with the bilateral functional connectivity increase in the hippocampus. These findings revealed tissue structural modifications in the whole brain during pregnancy, and that the hippocampus was structurally and functionally remodeled in a more marked manner.

  16. Predicting 14-day mortality after severe traumatic brain injury: application of the IMPACT models in the brain trauma foundation TBI-trac® New York State database.

    Science.gov (United States)

    Roozenbeek, Bob; Chiu, Ya-Lin; Lingsma, Hester F; Gerber, Linda M; Steyerberg, Ewout W; Ghajar, Jamshid; Maas, Andrew I R

    2012-05-01

    Prognostic models for outcome prediction in patients with traumatic brain injury (TBI) are important instruments in both clinical practice and research. To remain current a continuous process of model validation is necessary. We aimed to investigate the performance of the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic models in predicting mortality in a contemporary New York State TBI registry developed and maintained by the Brain Trauma Foundation. The Brain Trauma Foundation (BTF) TBI-trac® database contains data on 3125 patients who sustained severe TBI (Glasgow Coma Scale [GCS] score ≤ 8) in New York State between 2000 and 2009. The outcome measure was 14-day mortality. To predict 14-day mortality with admission data, we adapted the IMPACT Core and Extended models. Performance of the models was assessed by determining calibration (agreement between observed and predicted outcomes), and discrimination (separation of those patients who die from those who survive). Calibration was explored graphically with calibration plots. Discrimination was expressed by the area under the receiver operating characteristic (ROC) curve (AUC). A total of 2513 out of 3125 patients in the BTF database met the inclusion criteria. The 14-day mortality rate was 23%. The models showed excellent calibration. Mean predicted probabilities were 20% for the Core model and 24% for the Extended model. Both models showed good discrimination with AUCs of 0.79 (Core) and 0.83 (Extended). We conclude that the IMPACT models validly predict 14-day mortality in the BTF database, confirming generalizability of these models for outcome prediction in TBI patients.

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

    Science.gov (United States)

    Yu, Qingbao; Sui, Jing; Rachakonda, Srinivas; He, Hao; Gruner, William; Pearlson, Godfrey; Kiehl, Kent A; Calhoun, Vince D

    2011-01-01

    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.

  18. 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.

  19. 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

  20. 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.

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

    Science.gov (United States)

    Priesemann, Viola; Wibral, Michael; Valderrama, Mario; Pröpper, Robert; Le Van Quyen, Michel; Geisel, Theo; Triesch, Jochen; Nikolić, Danko; Munk, Matthias H J

    2014-01-01

    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, anesthetized 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.

  2. 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.

  3. 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。

  4. Solid lipid nanoparticles as vehicles of drugs to the brain: current state of the art.

    Science.gov (United States)

    Gastaldi, Lucia; Battaglia, Luigi; Peira, Elena; Chirio, Daniela; Muntoni, Elisabetta; Solazzi, Ilaria; Gallarate, Marina; Dosio, Franco

    2014-08-01

    Central nervous system disorders are already prevalent and steadily increasing among populations worldwide. However, most of the pharmaceuticals present on world markets are ineffective in treating cerebral diseases, because they cannot effectively cross the blood brain barrier (BBB). Solid lipid nanoparticles (SLN) are nanospheres made from biocompatible solid lipids, with unique advantages among drug carriers: they can be used as vehicles to cross the BBB. This review examines the main aspects surrounding brain delivery with SLN, and illustrates the principal mechanisms used to enhance brain uptake of the delivered drug.

  5. 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.

  6. 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.

  7. Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression

    OpenAIRE

    Tadayonnejad, Reza; Yang, Shaolin; Kumar, Anand; Ajilore, Olusola

    2014-01-01

    The pervasive and persistent nature of depressive symptoms has made resting-state functional magnetic resonance imaging (rs-fMRI) an appropriate approach for understanding the underlying mechanisms of major depressive disorder. The majority of rs-fMRI research has focused on depression-related alterations in the interregional coordination of brain baseline low frequency oscillations (LFOs). However, alteration of the regional amplitude of LFOs in depression, particularly its clinical, cogniti...

  8. 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.

  9. 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.

  10. Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play.

    Science.gov (United States)

    Savitz, J B; Rauch, S L; Drevets, W C

    2013-05-01

    In response to queries about whether brain imaging technology has reached the point where it is useful for making a clinical diagnosis and for helping to guide treatment selection, the American Psychiatric Association (APA) has recently written a position paper on the Clinical Application of Brain Imaging in Psychiatry. The following perspective piece is based on our contribution to this APA position paper, which specifically emphasized the application of neuroimaging in mood disorders. We present an introductory overview of the challenges faced by researchers in developing valid and reliable biomarkers for psychiatric disorders, followed by a synopsis of the extant neuroimaging findings in mood disorders, and an evidence-based review of the current research on brain imaging biomarkers in adult mood disorders. Although there are a number of promising results, by the standards proposed below, we argue that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders.

  11. Regional Homogeneity of Resting-State Brain Activity Suppresses the Effect of Dopamine-Related Genes on Sensory Processing Sensitivity.

    Directory of Open Access Journals (Sweden)

    Chunhui Chen

    Full Text Available Sensory processing sensitivity (SPS is an intrinsic personality trait whose genetic and neural bases have recently been studied. The current study used a neural mediation model to explore whether resting-state brain functions mediated the effects of dopamine-related genes on SPS. 298 healthy Chinese college students (96 males, mean age = 20.42 years, SD = 0.89 were scanned with magnetic resonance imaging during resting state, genotyped for 98 loci within the dopamine system, and administered the Highly Sensitive Person Scale. We extracted a "gene score" that summarized the genetic variations representing the 10 loci that were significantly linked to SPS, and then used path analysis to search for brain regions whose resting-state data would help explain the gene-behavior association. Mediation analysis revealed that temporal homogeneity of regional spontaneous activity (ReHo in the precuneus actually suppressed the effect of dopamine-related genes on SPS. The path model explained 16% of the variance of SPS. This study represents the first attempt at using a multi-gene voxel-based neural mediation model to explore the complex relations among genes, brain, and personality.

  12. 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.

  13. 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.

  14. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks

    Science.gov (United States)

    Triggiani, Antonio I.; Bevilacqua, Vitoantonio; Brunetti, Antonio; Lizio, Roberta; Tattoli, Giacomo; Cassano, Fabio; Soricelli, Andrea; Ferri, Raffaele; Nobili, Flavio; Gesualdo, Loreto; Barulli, Maria R.; Tortelli, Rosanna; Cardinali, Valentina; Giannini, Antonio; Spagnolo, Pantaleo; Armenise, Silvia; Stocchi, Fabrizio; Buenza, Grazia; Scianatico, Gaetano; Logroscino, Giancarlo; Lacidogna, Giordano; Orzi, Francesco; Buttinelli, Carla; Giubilei, Franco; Del Percio, Claudio; Frisoni, Giovanni B.; Babiloni, Claudio

    2017-01-01

    Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification. PMID:28184183

  15. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.

    Science.gov (United States)

    Triggiani, Antonio I; Bevilacqua, Vitoantonio; Brunetti, Antonio; Lizio, Roberta; Tattoli, Giacomo; Cassano, Fabio; Soricelli, Andrea; Ferri, Raffaele; Nobili, Flavio; Gesualdo, Loreto; Barulli, Maria R; Tortelli, Rosanna; Cardinali, Valentina; Giannini, Antonio; Spagnolo, Pantaleo; Armenise, Silvia; Stocchi, Fabrizio; Buenza, Grazia; Scianatico, Gaetano; Logroscino, Giancarlo; Lacidogna, Giordano; Orzi, Francesco; Buttinelli, Carla; Giubilei, Franco; Del Percio, Claudio; Frisoni, Giovanni B; Babiloni, Claudio

    2016-01-01

    Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz Hz), alpha1 (8-10.5 Hz), and alpha2 (10.5-13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

  16. Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf.

    Science.gov (United States)

    Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; Nguyen, Thien An; Alfano, Robert R

    2014-06-01

    Two-photon (2P) excitation of the second singlet (S₂) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S₂ state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S₂ state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

  17. Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations.

    Science.gov (United States)

    Wig, Gagan S; Laumann, Timothy O; Cohen, Alexander L; Power, Jonathan D; Nelson, Steven M; Glasser, Matthew F; Miezin, Francis M; Snyder, Abraham Z; Schlaggar, Bradley L; Petersen, Steven E

    2014-08-01

    We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability-reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units.

  18. 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.

  19. 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...

  20. 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

  1. 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

  2. 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.

  3. Completion of the classification

    CERN Document Server

    Strade, Helmut

    2012-01-01

    This is the last of three volumes about ""Simple Lie Algebras over Fields of Positive Characteristic""by Helmut Strade, presenting the state of the art of the structure and classification of Lie algebras over fields of positive characteristic. In this monograph the proof of the Classification Theorem presented in the first volumeis concluded.Itcollects all the important results on the topic whichcan be found only in scatteredscientific literaturso far.

  4. Expected Classification Accuracy

    Directory of Open Access Journals (Sweden)

    Lawrence M. Rudner

    2005-08-01

    Full Text Available Every time we make a classification based on a test score, we should expect some number..of misclassifications. Some examinees whose true ability is within a score range will have..observed scores outside of that range. A procedure for providing a classification table of..true and expected scores is developed for polytomously scored items under item response..theory and applied to state assessment data. A simplified procedure for estimating the..table entries is also presented.

  5. 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...

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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.

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

    NARCIS (Netherlands)

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

    2016-01-01

    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

  16. Visual processing during recovery from vegetative state to consciousness: Comparing behavioral indices to brain responses

    NARCIS (Netherlands)

    Wijnen, V.J.; Eilander, H.J.; Gelder, B. de; Boxtel, G.J. Van

    2014-01-01

    BACKGROUND: Auditory stimulation is often used to evoke responses in unresponsive patients who have suffered severe brain injury. In order to investigate visual responses, we examined visual evoked potentials (VEPs) and behavioral responses to visual stimuli in vegetative patients during recovery to

  17. Effects of hunger state on food-related brain responses across the lifespan

    NARCIS (Netherlands)

    Charbonnier, L.

    2016-01-01

    Thesis aims The studies conducted in this thesis were part of the Full4Health project. The aims of the Full4Health project were to assess the differences in the brain responses to food presentation and food choice and how these responses are modulated by hunger and gut signals in lean and obese subj

  18. Intraoperative fluorescence imaging for personalized brain tumor resection: Current state and future directions

    Directory of Open Access Journals (Sweden)

    Evgenii Belykh

    2016-10-01

    Full Text Available Introduction: Fluorescence-guided surgery is one of the rapidly emerging methods of surgical theranostics. In this review, we summarize current fluorescence techniques used in neurosurgical practice for brain tumor patients, as well as future applications of recent laboratory and translational studies.Methods: Review of the literature.Results: A wide spectrum of fluorophores that have been tested for brain surgery is reviewed. Beginning with a fluorescein sodium application in 1948 by Moore, fluorescence guided brain tumor surgery is either routinely applied in some centers or is under active study in clinical trials. Besides the trinity of commonly used drugs (fluorescein sodium, 5-ALA and ICG, less studied fluorescent stains, such as tetracyclines, cancer-selective alkylphosphocholine analogs, cresyl violet, acridine orange, and acriflavine can be used for rapid tumor detection and pathological tissue examination. Other emerging agents such as activity-based probes and targeted molecular probes that can provide biomolecular specificity for surgical visualization and treatment are reviewed. Furthermore, we review available engineering and optical solutions for fluorescent surgical visualization. Instruments for fluorescent-guided surgery are divided into wide-field imaging systems and hand-held probes. Recent advancements in quantitative fluorescence-guided surgery are discussed.Conclusion: We are standing on the doorstep of the era of marker-assisted tumor management. Innovations in the fields of surgical optics, computer image analysis, and molecular bioengineering are advancing fluorescence-guided tumor resection paradigms, leading to cell-level approaches to visualization and resection of brain tumors.

  19. The Protective Effects of Sufentanil Pretreatment on Rat Brains under the State of Cardiopulmonary Bypass.

    Science.gov (United States)

    Zhang, Kun; Li, Man; Peng, Xiao-Chun; Wang, Li-Shen; Dong, Ai-Ping; Shen, Shu-Wei; Wang, Rong

    2015-01-01

    This study aimed to observe the protective effects of sufentanil pretreatment on rat cerebral injury during cardiopulmonary bypass (CPB) and to explore the underlying mechanism. Twenty-four male adult Sprague Dawley (SD) rats were divided into 4 groups. Then, the rat CPB model was established. A 14G trocar was inserted into the atrium dextrum. For rats in S1 and S5 groups, sufentanil (1 µgKg(-1) and 5 µgKg(-1)) were applied before CPB process. After the operation, rat brain samples were harvested for measurement of the water content of the brains, total calcium in brain tissue and the level of serum S100β. Compared with the Sham group, the water content and the total calcium of the brain tissue, and the expression of S100β in serum were significantly increased in the CPB group (PCPB group, sufentanil treatment significantly reduced the water content of the brains, the total calcium and S100β expression (PCPB, S1, and S5 compared with Sham group during CPB. Compared with the Sham group, the levels of pH and blood lactate in other groups were decreased and increased, respectively, in the post-CPB period. During the CPB and post-CPB periods, the hematocrit levels were significantly down-regulated in groups CPB, S1, and S5 compared with Sham group. In conclusion, sufentanil pretreatment was effective in reducing the cerebral injury during CPB. Reduction in calcium overload may be a potential mechanism in such process.

  20. Risperidone Effects on Brain Dynamic Connectivity—A Prospective Resting-State fMRI Study in Schizophrenia

    Science.gov (United States)

    Lottman, Kristin K.; Kraguljac, Nina V.; White, David M.; Morgan, Charity J.; Calhoun, Vince D.; Butt, Allison; Lahti, Adrienne C.

    2017-01-01

    Resting-state functional connectivity studies in schizophrenia evaluating average connectivity over the entire experiment have reported aberrant network integration, but findings are variable. Examining time-varying (dynamic) functional connectivity may help explain some inconsistencies. We assessed dynamic network connectivity using resting-state functional MRI in patients with schizophrenia, while unmedicated (n = 34), after 1 week (n = 29) and 6 weeks of treatment with risperidone (n = 24), as well as matched controls at baseline (n = 35) and after 6 weeks (n = 19). After identifying 41 independent components (ICs) comprising resting-state networks, sliding window analysis was performed on IC timecourses using an optimal window size validated with linear support vector machines. Windowed correlation matrices were then clustered into three discrete connectivity states (a relatively sparsely connected state, a relatively abundantly connected state, and an intermediately connected state). In unmedicated patients, static connectivity was increased between five pairs of ICs and decreased between two pairs of ICs when compared to controls, dynamic connectivity showed increased connectivity between the thalamus and somatomotor network in one of the three states. State statistics indicated that, in comparison to controls, unmedicated patients had shorter mean dwell times and fraction of time spent in the sparsely connected state, and longer dwell times and fraction of time spent in the intermediately connected state. Risperidone appeared to normalize mean dwell times after 6 weeks, but not fraction of time. Results suggest that static connectivity abnormalities in schizophrenia may partly be related to altered brain network temporal dynamics rather than consistent dysconnectivity within and between functional networks and demonstrate the importance of implementing complementary data analysis techniques. PMID:28220083

  1. 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.

  2. 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.

  3. Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: Case studies.

    Science.gov (United States)

    Hwang, Han-Jeong; Han, Chang-Hee; Lim, Jeong-Hwan; Kim, Yong-Wook; Choi, Soo-In; An, Kwang-Ok; Lee, Jun-Hak; Cha, Ho-Seung; Hyun Kim, Seung; Im, Chang-Hwan

    2017-03-01

    Although the feasibility of brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) has been extensively investigated, only a few studies have evaluated its clinical feasibility in patients with locked-in syndrome (LIS), who are the main targets of BCI technology. The main objective of this case report was to share our experiences of SSVEP-based BCI experiments involving five patients with LIS, thereby providing researchers with useful information that can potentially help them to design BCI experiments for patients with LIS. In our experiments, a four-class online SSVEP-based BCI system was implemented and applied to four of five patients repeatedly on multiple days to investigate its test-retest reliability. In the last experiments with two of the four patients, the practical usability of our BCI system was tested using a questionnaire survey. All five patients showed clear and distinct SSVEP responses at all four fundamental stimulation frequencies (6, 6.66, 7.5, 10 Hz), and responses at harmonic frequencies were also observed in three patients. Mean classification accuracy was 76.99% (chance level = 25%). The test-retest reliability experiments demonstrated stable performance of our BCI system over different days even when the initial experimental settings (e.g., electrode configuration, fixation time, visual angle) used in the first experiment were used without significant modifications. Our results suggest that SSVEP-based BCI paradigms might be successfully used to implement clinically feasible BCI systems for severely paralyzed patients.

  4. Repetition Priming Influences Distinct Brain Systems: Evidence From Task-Evoked Data and Resting-State Correlations

    Science.gov (United States)

    Wig, Gagan S.; Buckner, Randy L.; Schacter, Daniel L.

    2009-01-01

    Behavioral dissociations suggest that a single experience can separately influence multiple processing components. Here we used a repetition priming functional magnetic resonance imaging paradigm that directly contrasted the effects of stimulus and decision changes to identify the underlying brain systems. Direct repetition of stimulus features caused marked reductions in posterior regions of the inferior temporal lobe that were insensitive to whether the decision was held constant or changed between study and test. By contrast, prefrontal cortex showed repetition effects that were sensitive to the exact stimulus-to-decision mapping. Analysis of resting-state functional connectivity revealed that the dissociated repetition effects are embedded within distinct brain systems. Regions that were sensitive to changes in the stimulus correlated with perceptual cortices, whereas the decision changes attenuated activity in regions correlated with middle-temporal regions and a frontoparietal control system. These results thus explain the long-known dissociation between perceptual and conceptual components of priming by revealing how a single experience can separately influence distinct, concurrently active brain systems. PMID:19225167

  5. Phosphorylation and oligomerization states of native pig brain HSP90 studied by mass spectrometry

    DEFF Research Database (Denmark)

    Garnier, C.; Lafitte, D.; Jorgensen, T.J.;

    2001-01-01

    HSP90 purified from pig brain. The two protein isoforms were clearly distinguished by ESI-MS, the alpha isoform being approximately six times more abundant than the beta isoform. ESI-MS in combination with lambda phosphatase treatment provided direct evidence of the existence of four phosphorylated...... such as actin-microfilament, tubulin-microtubule and intermediate filaments, and also exhibits conventional chaperone functions. This protein exists in two isoforms alpha-HSP90 and beta-HSP90, and it forms dimers which are crucial species for its biological activity. PAGE, ESI-MS and MALDI-MS were used to study...... forms of native pig brain alpha-HSP90, with the diphosphorylated form being the most abundant. For the beta isoform, the di-phosphorylated was also the most abundant. MALDI mass spectra of HSP90 samples after chemical cross-linking showed a high percentage of alpha-alpha homodimers. In addition...

  6. Is lactate a volume transmitter of metabolic states of the brain?

    DEFF Research Database (Denmark)

    Bergersen, Linda H; Gjedde, Albert

    2012-01-01

    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 role of lactate as mediator...... of metabolic information rather than metabolic substrate answers a number of questions raised by the controversial oxidativeness of astrocytic metabolism and its contribution to neuronal function....

  7. 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

  8. 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.

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

    Science.gov (United States)

    2015-12-22

    that it is difficult to target common patterns of brain activity, or t