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Sample records for brain state classification

  1. Stop state classification in intracortical brain-machine-interface.

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    Tze Hui Koh; Libedinsky, Camilo; Cuntai Guan; Kai Keng Ang; So, Rosa Q

    2017-07-01

    Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.

  2. 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.

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

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

    2007-10-01

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

  4. Idle state classification using spiking activity and local field potentials in a brain computer interface.

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    Williams, Jordan J; Tien, Rex N; Inoue, Yoh; Schwartz, Andrew B

    2016-08-01

    Previous studies of intracortical brain-computer interfaces (BCIs) have often focused on or compared the use of spiking activity and local field potentials (LFPs) for decoding kinematic movement parameters. Conversely, using these signals to detect the initial intention to use a neuroprosthetic device or not has remained a relatively understudied problem. In this study, we examined the relative performance of spiking activity and LFP signals in detecting discrete state changes in attention regarding a user's desire to actively control a BCI device. Preliminary offline results suggest that the beta and high gamma frequency bands of LFP activity demonstrated a capacity for discriminating idle/active BCI control states equal to or greater than firing rate activity on the same channel. Population classifier models using either signal modality demonstrated an indistinguishably high degree of accuracy in decoding rest periods from active BCI reach periods as well as other portions of active BCI task trials. These results suggest that either signal modality may be used to reliably detect discrete state changes on a fine time scale for the purpose of gating neural prosthetic movements.

  5. 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.

    2017-01-01

    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 Weiner 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 classifier 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

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

    Science.gov (United States)

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

    2016-02-01

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

  7. Pathological classification of brain tumors.

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    Pollo, B

    2012-04-01

    The tumors of the central nervous system are classified according to the last international classification published by World Health Organization. The Classification of Tumors of the Central Nervous System was done on 2007, based on morphological features, growth pattern and molecular profile of neoplastic cells, defining malignancy grade. The neuropathological diagnosis and the grading of each histotype are based on identification of histopathological criteria and immunohistochemical data. The histopathology, also consisting of findings with prognostic or predictive relevance, plays a critical role in the diagnosis and treatment of brain tumors. The recent progresses on radiological, pathological, immunohistochemical, molecular and genetic diagnosis improved the characterization of brain tumors. Molecular and genetic profiles may identify different tumor subtypes varying in biological and clinical behavior. To investigate new therapeutic approaches is important to study the molecular pathways that lead the processes of proliferation, invasion, angiogenesis, anaplastic transformation. Different molecular biomarkers were identified by genetic studies and some of these are used in neuro-oncology for the evaluation of glioma patients, in particular combined deletions of the chromosome arms 1p and 19q in oligodendroglial tumors, methylation status of the O-6 methylguanine- DNA methyltransferase gene promoter and alterations in the epidermal growth factor receptor pathway in adult malignant gliomas, isocitrate dehydrogenase 1 (IDH1) and IDH2 gene mutations in diffuse gliomas, as well as BRAF status in pilocytic astrocytomas. The prognostic evaluation and the therapeutic strategies for patients depend on synthesis of clinical, pathological and biological data: histological diagnosis, malignancy grade, gene-molecular profile, radiological pictures, surgical resection and clinical findings (age, tumor location, "performance status").

  8. Within-brain classification for brain tumor segmentation.

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    Havaei, Mohammad; Larochelle, Hugo; Poulin, Philippe; Jodoin, Pierre-Marc

    2016-05-01

    In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

  9. Heterogeneous data fusion for brain tumor classification.

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    Metsis, Vangelis; Huang, Heng; Andronesi, Ovidiu C; Makedon, Fillia; Tzika, Aria

    2012-10-01

    Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution magic angle spinning (HRMAS) proton (1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtained from intact brain tumor biopsies. Our experimental results show that our novel framework outperforms any analysis using individual dataset.

  10. Classification of Cortical Brain Malformations

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    J Gordon Millichap

    2008-03-01

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

  11. State Classification for Humanoid Robots

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    Jialun Yang

    2008-01-01

    Full Text Available In this paper, we decouple the motion-planning problem of humanoid robots into two sub-problems, namely topological state planning and detailed motion planning. The state classification plays a key role for the first sub-problem. We propose several basic states, including lying, sitting, standing and handstanding, abstracted from the daily exercises of human beings. Each basic state is classified further from the topological point of view. Furthermore, generalised function (GF set theory is applied with the aim of analysing the kinematic characteristics of the end effectors for each state, and meaningful names are assigned for each state. Finally a topological state-planning example is given to show the effectiveness of this methodology. The results show that the large amounts of states can be described using assigned names, which leads to systematic and universal description of the states for humanoid robots.

  12. Deep learning for brain tumor classification

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

    2017-03-01

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

  13. 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.

  14. Brain extraction based on locally linear representation-based classification.

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    Huang, Meiyan; Yang, Wei; Jiang, Jun; Wu, Yao; Zhang, Yu; Chen, Wufan; Feng, Qianjin

    2014-05-15

    Brain extraction is an important procedure in brain image analysis. Although numerous brain extraction methods have been presented, enhancing brain extraction methods remains challenging because brain MRI images exhibit complex characteristics, such as anatomical variability and intensity differences across different sequences and scanners. To address this problem, we present a Locally Linear Representation-based Classification (LLRC) method for brain extraction. A novel classification framework is derived by introducing the locally linear representation to the classical classification model. Under this classification framework, a common label fusion approach can be considered as a special case and thoroughly interpreted. Locality is important to calculate fusion weights for LLRC; this factor is also considered to determine that Local Anchor Embedding is more applicable in solving locally linear coefficients compared with other linear representation approaches. Moreover, LLRC supplies a way to learn the optimal classification scores of the training samples in the dictionary to obtain accurate classification. The International Consortium for Brain Mapping and the Alzheimer's Disease Neuroimaging Initiative databases were used to build a training dataset containing 70 scans. To evaluate the proposed method, we used four publicly available datasets (IBSR1, IBSR2, LPBA40, and ADNI3T, with a total of 241 scans). Experimental results demonstrate that the proposed method outperforms the four common brain extraction methods (BET, BSE, GCUT, and ROBEX), and is comparable to the performance of BEaST, while being more accurate on some datasets compared with BEaST. Copyright © 2014 Elsevier Inc. All rights reserved.

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

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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

  16. 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

    2015-01-01

    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

  17. Brain states and hypnosis research.

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    Posner, Michael I; Rothbart, Mary K

    2011-06-01

    Research in cognitive neuroscience now considers the state of the brain prior to the task an important aspect of performance. Hypnosis seems to alter the brain state in a way which allows external input to dominate over internal goals. We examine how normal development may illuminate the hypnotic state. Copyright © 2009 Elsevier Inc. All rights reserved.

  18. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Norberto Eiji Nawa

    2015-12-01

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

  1. Tissue Tracking: Applications for Brain MRI Classification

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    2007-01-01

    previous work on Bayesian classification algorithms. First, we present work by Haker et al.4 which outlines the general structure of Bayesian...groups have previously proposed Bayesian classification algorithms. Most relevant to our work is the work by Haker et al. on the tracking of objects in 2D...Segmentation,” Massachusetts Institute of Technology , 1999. 4. S. Haker , G. Sapiro, and A. Tannenbaum, “Knowledge-based segmentation of SAR data with

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. Entanglement classification of four-partite states under the SLOCC

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    Zangi, S. M.; Li, Jun-Li; Qiao, Cong-Feng

    2017-08-01

    We present a practical classification scheme for the four-partite entangled states under stochastic local operations and classical communication (SLOCC). By transforming a four-partite state into a triple-state set composed of two tripartite states and a bipartite state, the entanglement classification is reduced to the classification of tripartite and bipartite entanglements. This reduction method has the merit of involving only the linear constrains, and meanwhile provides an insight into the entanglement character of the subsystems.

  7. Error probability of intracranial brain computer interfaces under non-task elicited brain states.

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    Torres Valderrama, Aldemar; Paclik, Pavel; Vansteensel, Mariska J; Aarnoutse, Erik J; Ramsey, Nick F

    2012-12-01

    Intracranial brain computer interfaces (BCIs) can be connected to the user's cortex permanently. The interfaces response when fed with non-task elicited brain activity becomes important as design criterion: ideally intracranial BCIs should remain silent. We study their error probability in the form of false alarms. Using electrocorticograms recorded during task and non-task brain states, we compute false alarms, investigate their origin and introduce strategies to reduce them, using signal detection theory, classifier cascading and adaptation concepts. We show that the incessant dynamics of the brain is prone to spontaneously produce signals, the spectral and topographical characteristics of which can resemble those associated with common control tasks, generating brain state classification errors. In addition to hit and bit rates, response of BCIs to non-task brain states constitutes an important measure of BCI performance. Static classification cascading reduces considerably false positives during no-task brain states. False alarms in intracranial BCIs are undesirable and could have dangerous consequences for the users. Designs which effectively incorporate the error correction strategies discussed in this paper, could be more successful when taken from the laboratory or acute care setting and used in the real world. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Functional Brain Network Classification With Compact Representation of SICE Matrices.

    Science.gov (United States)

    Zhang, Jianjia; Zhou, Luping; Wang, Lei; Li, Wanqing

    2015-06-01

    Recently, a sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering 1) an SICE matrix resides on a Riemannian manifold of symmetric positive definiteness matrices, and 2) human brains share common patterns of connectivity across subjects. Therefore, we propose to employ manifold-based similarity measures and kernel-based PCA to extract principal connectivity components as a compact representation of brain network. Moreover, to cater for the requirement of both discrimination and interpretation in neuroimage analysis, we develop a novel preimage estimation algorithm to make the obtained connectivity components anatomically interpretable. To verify the efficacy of our method and gain insights into SICE-based brain networks, we conduct extensive experimental study on synthetic data and real rs-fMRI data from the ADNI dataset. Our method outperforms the comparable methods and improves the classification accuracy significantly.

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

    Science.gov (United States)

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

    2014-08-01

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

  10. Application of machine learning on brain cancer multiclass classification

    Science.gov (United States)

    Panca, V.; Rustam, Z.

    2017-07-01

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

  11. Topology-preserving tissue classification of magnetic resonance brain images.

    Science.gov (United States)

    Bazin, Pierre-Louis; Pham, Dzung L

    2007-04-01

    This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.

  12. Multi-fractal detrended texture feature for brain tumor classification

    Science.gov (United States)

    Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

    2015-03-01

    We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

  13. 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 ...

  14. 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

  15. Electroencephalogram signals processing for topographic brain mapping and epilepsies classification.

    Science.gov (United States)

    Arab, Mohammad Reza; Suratgar, Amir Abolfazl; Ashtiani, Alireza Rezaei

    2010-09-01

    In this study, topographic brain mapping and wavelet transform-neural network method are used for the classification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessing is included to remove artifacts occurred by blinking, wandering baseline (electrodes movement) and eyeball movement using the Discrete Wavelet Transformation (DWT). De-noising EEG signals from the AC power supply frequency with a suitable notch filter is another job of preprocessing. In experimental data, the preprocessing enhanced speed and accuracy of the processing stage (wavelet transform and neural network). The EEGs signals are categorized to normal and petit mal and clonic epilepsy by an expert neurologist. The categorization is confirmed by Fast Fourier Transform (FFT) analysis and brain mapping. The dataset includes waves such as sharp, spike and spike-slow wave. Through the Counties Wavelet Transform (CWT) of EEG records, transient features are accurately captured and separated and used as classifier input. We introduce a two-stage classifier based on the Learning Vector Quantization (LVQ) neural network location in both time and frequency contexts. The brain mapping used for finding the epilepsy locates in the brain. The simulation results are very promising and the accuracy of the proposed classifier in experimental clinical data is ∼80%. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Directed progression brain networks in Alzheimer's disease: properties and classification.

    Science.gov (United States)

    Friedman, Eric J; Young, Karl; Asif, Danial; Jutla, Inderjit; Liang, Michael; Wilson, Scott; Landsberg, Adam S; Schuff, Norbert

    2014-06-01

    This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties.

  17. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

  19. Predicting brain stimulation treatment outcomes of depressed patients through the classification of EEG oscillations.

    Science.gov (United States)

    Al-Kaysi, Alaa M; Al-Ani, Ahmed; Loo, Colleen K; Breakspear, Michael; Boonstra, Tjeerd W

    2016-08-01

    Major depressive disorder (MDD) is a mental disorder that is characterized by negative thoughts, mood and behavior. Transcranial direct current stimulation (tDCS) has recently emerged as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that run over a few weeks, however, not all participants respond to this type of treatment. This study aims to predict which patients improve in mood and cognition in response to tDCS treatment by analyzing electroencephalography (EEG) of MDD patients that was collected at the start of tDCS treatment. This is achieved through classifying power spectral density (PSD) of resting-state EEG using support vector machine (SVM), linear discriminate analysis (LDA) and extreme learning machine (ELM). Participants were labelled as improved/not improved based on the change in mood and cognitive scores. The obtained classification results of all channel pair combinations are used to identify the most relevant brain regions and channels for this classification task. We found the frontal channels to be particularly informative for the prediction of the clinical outcome of the tDCS treatment. Subject independent results reveal that our proposed method enables the correct identification of the treatment outcome for seven of the ten participants for mood improvement and nine of ten participants for cognitive improvement. This represents an encouraging sign that EEG-based classification may help to tailor the selection of patients for treatment with tDCS brain stimulation.

  20. 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.

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

    Science.gov (United States)

    Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

    2017-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    2010-12-01

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

  3. 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.

  4. 9 CFR 146.44 - Terminology and classification; States.

    Science.gov (United States)

    2010-01-01

    ... part; (ii) All turkey breeding flocks in production within the State are classified as U.S. H5/H7 Avian... Special Provisions for Meat-Type Turkey Slaughter Plants § 146.44 Terminology and classification; States. (a) U.S. H5/H7 Avian Influenza Monitored State, Turkeys. (1) A State will be declared a U.S. H5/H7...

  5. TOPICAL REVIEW: A review of classification algorithms for EEG-based brain computer interfaces

    Science.gov (United States)

    Lotte, F.; Congedo, M.; Lécuyer, A.; Lamarche, F.; Arnaldi, B.

    2007-06-01

    In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

  6. Extraction of Wavelet Based Features for Classification of T2-Weighted MRI Brain Images

    OpenAIRE

    Ms. Yogita K.Dubey; Mushrif, Milind M.

    2012-01-01

    Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, ...

  7. 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...

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

    Science.gov (United States)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  10. Stress Impact on Resting State Brain Networks.

    Science.gov (United States)

    Soares, José Miguel; Sampaio, Adriana; Ferreira, Luís Miguel; Santos, Nadine Correia; Marques, Paulo; Marques, Fernanda; Palha, Joana Almeida; Cerqueira, João José; Sousa, Nuno

    2013-01-01

    Resting state brain networks (RSNs) are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI) studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI) in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam), and respective gender- and age-matched controls (medical students under normal academic activities). Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN), dorsal attention (DAN), ventral attention (VAN), sensorimotor (SMN), and primary visual (VN) networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

  11. Quantum learning: asymptotically optimal classification of qubit states

    Science.gov (United States)

    Guţă, Mădălin; Kotłowski, Wojciech

    2010-12-01

    Pattern recognition is a central topic in learning theory, with numerous applications such as voice and text recognition, image analysis and computer diagnosis. The statistical setup in classification is the following: we are given an i.i.d. training set (X1, Y1), ... , (Xn, Yn), where Xi represents a feature and Yiin{0, 1} is a label attached to that feature. The underlying joint distribution of (X, Y) is unknown, but we can learn about it from the training set, and we aim at devising low error classifiers f: X→Y used to predict the label of new incoming features. In this paper, we solve a quantum analogue of this problem, namely the classification of two arbitrary unknown mixed qubit states. Given a number of 'training' copies from each of the states, we would like to 'learn' about them by performing a measurement on the training set. The outcome is then used to design measurements for the classification of future systems with unknown labels. We found the asymptotically optimal classification strategy and show that typically it performs strictly better than a plug-in strategy, which consists of estimating the states separately and then discriminating between them using the Helstrom measurement. The figure of merit is given by the excess risk equal to the difference between the probability of error and the probability of error of the optimal measurement for known states. We show that the excess risk scales as n-1 and compute the exact constant of the rate.

  12. Classification of Surface Water Sources in Imo State, for Irrigation ...

    African Journals Online (AJOL)

    This study determined the salinity and sodicity status of eleven rivers in Imo State with a view to classifying them according to their suitability for irrigation based on already existing standards for such classification. The result showed that seven rivers: Imo, Iyodo, Ogochie, Okitankwo, Oramiriukwa, Orashi, and Otamiri rivers ...

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

    Science.gov (United States)

    Hong, Keum-Shik; Khan, Muhammad Jawad

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  15. Stress Impact on Resting State Brain Networks.

    Directory of Open Access Journals (Sweden)

    José Miguel Soares

    Full Text Available Resting state brain networks (RSNs are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam, and respective gender- and age-matched controls (medical students under normal academic activities. Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN, dorsal attention (DAN, ventral attention (VAN, sensorimotor (SMN, and primary visual (VN networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

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

    Science.gov (United States)

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

    2017-07-01

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

  17. 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 ...... are found to be boat–chair, chair and boat–boat; in all the boat–chair cases, the phosphorus atoms appear connected by a bridging carbon atom....

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

  20. 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

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

    Science.gov (United States)

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

    2015-09-01

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

  2. State-dependencies of learning across brain scales

    Science.gov (United States)

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

    2015-01-01

    Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly. PMID:25767445

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

    Directory of Open Access Journals (Sweden)

    Tijn M. Schouten

    2016-01-01

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

  4. 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.

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

    Science.gov (United States)

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

    2015-11-01

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

  6. 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.

  7. 2016 Updates to the WHO Brain Tumor Classification System: What the Radiologist Needs to Know.

    Science.gov (United States)

    Johnson, Derek R; Guerin, Julie B; Giannini, Caterina; Morris, Jonathan M; Eckel, Lawrence J; Kaufmann, Timothy J

    2017-01-01

    Radiologists play a key role in brain tumor diagnosis and management and must stay abreast of developments in the field to advance patient care and communicate with other health care providers. In 2016, the World Health Organization (WHO) released an update to its brain tumor classification system that included numerous significant changes. Several previously recognized brain tumor diagnoses, such as oligoastrocytoma, primitive neuroectodermal tumor, and gliomatosis cerebri, were redefined or eliminated altogether. Conversely, multiple new entities were recognized, including diffuse leptomeningeal glioneuronal tumor and multinodular and vacuolating tumor of the cerebrum. The glioma category has been significantly reorganized, with several infiltrating gliomas in children and adults now defined by genetic features for the first time. These changes were driven by increased understanding of important genetic factors that directly impact tumorigenesis and influence patient care. The increased emphasis on genetic factors in brain tumor diagnosis has important implications for radiology, as we now have tools that allow us to evaluate some of these alterations directly, such as the identification of 2-hydroxyglutarate within infiltrating gliomas harboring mutations in the genes for the isocitrate dehydrogenases. For other tumors, such as medulloblastoma, imaging can demonstrate characteristic patterns that correlate with particular disease subtypes. The purpose of this article is to review the changes to the WHO brain tumor classification system that are most pertinent to radiologists. ©RSNA, 2017.

  8. Classification of Alzheimer's disease using regional saliency maps from brain MR volumes

    Science.gov (United States)

    Pulido, Andrea; Rueda, Andrea; Romero, Eduardo

    2013-02-01

    Accurate diagnosis of Alzheimer's disease (AD) from structural Magnetic Resonance (MR) images is difficult due to the complex alteration of patterns in brain anatomy that could indicate the presence or absence of the pathology. Currently, an effective approach that allows to interpret the disease in terms of global and local changes is not available in the clinical practice. In this paper, we propose an approach for classification of brain MR images, based on finding pathology-related patterns through the identification of regional structural changes. The approach combines a probabilistic Latent Semantic Analysis (pLSA) technique, which allows to identify image regions through latent topics inferred from the brain MR slices, with a bottom-up Graph-Based Visual Saliency (GBVS) model, which calculates maps of relevant information per region. Regional saliency maps are finally combined into a single map on each slice, obtaining a master saliency map of each brain volume. The proposed approach includes a one-to-one comparison of the saliency maps which feeds a Support Vector Machine (SVM) classifier, to group test subjects into normal or probable AD subjects. A set of 156 brain MR images from healthy (76) and pathological (80) subjects, splitted into a training set (10 non-demented and 10 demented subjects) and one testing set (136 subjects), was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 87.21%.

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

    Directory of Open Access Journals (Sweden)

    Vasily A Vakorin

    2016-12-01

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

  10. 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.

  11. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-10-01

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

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

    Science.gov (United States)

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

    1995-10-01

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

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Xiaolong Peng

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

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

    Science.gov (United States)

    Peng, Xiaolong; Lin, Pan; Zhang, Tongsheng; Wang, Jue

    2013-01-01

    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. 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. 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. 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.

  17. Brain states and hypnosis research✩,✩✩

    Science.gov (United States)

    Posner, Michael I.; Rothbart, Mary K.

    2011-01-01

    Research in cognitive neuroscience now considers the state of the brain prior to the task an important aspect of performance. Hypnosis seems to alter the brain state in a way which allows external input to dominate over internal goals. We examine how normal development may illuminate the hypnotic state. PMID:20060745

  18. Brain states and hypnosis research✩,✩✩

    OpenAIRE

    Posner, Michael I.; Rothbart, Mary K.

    2010-01-01

    Research in cognitive neuroscience now considers the state of the brain prior to the task an important aspect of performance. Hypnosis seems to alter the brain state in a way which allows external input to dominate over internal goals. We examine how normal development may illuminate the hypnotic state.

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

    Directory of Open Access Journals (Sweden)

    Ying Mei

    2017-06-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

  1. 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.

  2. 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.

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

    Science.gov (United States)

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

    2014-01-01

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

  4. 9 CFR 77.3 - Tuberculosis classifications of States and zones.

    Science.gov (United States)

    2010-01-01

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

  5. 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:…

  6. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

    Science.gov (United States)

    Kwak, No-Sang; Müller, Klaus-Robert

    2017-01-01

    The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN’s robust, accurate decoding abilities. PMID:28225827

  7. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.

    Science.gov (United States)

    Kwak, No-Sang; Müller, Klaus-Robert; Lee, Seong-Whan

    2017-01-01

    The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN's robust, accurate decoding abilities.

  8. 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…

  9. Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease.

    Science.gov (United States)

    Yuvaraj, R; Murugappan, M; Ibrahim, Norlinah Mohamed; Sundaraj, Kenneth; Omar, Mohd Iqbal; Mohamad, Khairiyah; Palaniappan, R

    2014-12-01

    In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Yaki Stern

    2016-12-01

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

  11. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

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

  12. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    NARCIS (Netherlands)

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

    2009-01-01

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

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

    NARCIS (Netherlands)

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

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

  16. 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.

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model. In this ......Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model...... encoding of the experiment. For a support vector machine, logistic regression and Fisher's discriminant analysis we demonstrate that selection of model regularization parameters has a strong but consistent impact on the generalizability and both the reproducibility and interpretable sparsity of the models....... This supports the view that the quality of spatial patterns extracted from models cannot be assessed purely by focusing on prediction accuracy. Our results instead suggest that model regularization parameters must be carefully selected, so that the model and its visualization enhance our ability to interpret...

  18. 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

  19. utomated real-time classification of functional states: the significance of individual tuning stage

    Directory of Open Access Journals (Sweden)

    Galatenko, Vladimir V.

    2013-09-01

    Full Text Available Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity. In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects.

  20. Voxel-based discriminant map classification on brain ventricles for Alzheimer's disease

    Science.gov (United States)

    Wang, Jingnan; de Haan, Gerard; Unay, Devrim; Soldea, Octavian; Ekin, Ahmet

    2009-02-01

    One major hallmark of the Alzheimer's disease (AD) is the loss of neurons in the brain. In many cases, medical experts use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular structure will directly lead to ventricle enlargement. For quantitative analysis, volume is the common choice. However, volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer's disease has been proposed. Training process is carried out to generate two probability maps for two training classes: healthy controls (HC) and AD patients. By subtracting the HC probability map from the AD probability map, we get a 3D ventricle discriminant map. Then a matching coefficient has been calculated between each training subject and the discriminant map. An adjustable cut-off point of the matching coefficients has been drawn for the two classes. Generally, the higher the cut-off point that has been drawn, the higher specificity can be achieved. However, it will result in relatively lower sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method.

  1. Online EEG Classification of Covert Speech for Brain-Computer Interfacing.

    Science.gov (United States)

    Sereshkeh, Alborz Rezazadeh; Trott, Robert; Bricout, Aurélien; Chau, Tom

    2017-12-01

    Brain-computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word "no" and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words "yes" and "no". Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

  2. Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Abeer Al-Nafjan

    2017-12-01

    Full Text Available Recent developments and studies in brain-computer interface (BCI technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants’ emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

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

  4. 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…

  5. 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.

  6. 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

  7. 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.

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

    Science.gov (United States)

    Ashari, Rehab Bahaaddin

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

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

    Directory of Open Access Journals (Sweden)

    Jenessa Lancaster

    2018-02-01

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

  10. Improved CSF classification and lesion detection in MR brain images with multiple sclerosis

    Science.gov (United States)

    Wolff, Yulian; Miron, Shmuel; Achiron, Anat; Greenspan, Hayit

    2007-03-01

    The study deals with the challenging task of automatic segmentation of MR brain images with multiple sclerosis lesions (MSL). Multi-Channel data is used, including "fast fluid attenuated inversion recovery" (fast FLAIR or FF), and statistical modeling tools are developed, in order to improve cerebrospinal fluid (CSF) classification and to detect MSL. Two new concepts are proposed for use within an EM framework. The first concept is the integration of prior knowledge as it relates to tissue behavior in different MRI modalities, with special attention given to the FF modality. The second concept deals with running the algorithm on a subset of the input that is most likely to be noise- and artifact-free data. This enables a more reliable learning of the Gaussian mixture model (GMM) parameters for brain tissue statistics. The proposed method focuses on the problematic CSF intensity distribution, which is a key to improved overall segmentation and lesion detection. A level-set based active contour stage is performed for lesion delineation, using gradient and shape properties combined with previously learned region intensity statistics. In the proposed scheme there is no need for preregistration of an atlas, a common characteristic in brain segmentation schemes. Experimental results on real data are presented.

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

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

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

    Science.gov (United States)

    Petti, Manuela; Toppi, Jlenia; Babiloni, Fabio; Cincotti, Febo; Mattia, Donatella; Astolfi, Laura

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Manuela Petti

    2016-01-01

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

  15. Variability of Brain Death Policies in the United States.

    Science.gov (United States)

    Greer, David M; Wang, Hilary H; Robinson, Jennifer D; Varelas, Panayiotis N; Henderson, Galen V; Wijdicks, Eelco F M

    2016-02-01

    Brain death is the irreversible cessation of function of the entire brain, and it is a medically and legally accepted mechanism of death in the United States and worldwide. Significant variability may exist in individual institutional policies regarding the determination of brain death. It is imperative that brain death be diagnosed accurately in every patient. The American Academy of Neurology (AAN) issued new guidelines in 2010 on the determination of brain death. To evaluate if institutions have adopted the new AAN guidelines on the determination of brain death, leading to policy changes. Fifty-two organ procurement organizations provided US hospital policies pertaining to the criteria for determining brain death. Organizations were instructed to procure protocols specific to brain death (ie, not cardiac death or organ donation procedures). Data analysis was conducted from June 26, 2012, to July 1, 2015. Policies were evaluated for summary statistics across the following 5 categories of data: who is qualified to perform the determination of brain death, what are the necessary prerequisites for testing, details of the clinical examination, details of apnea testing, and details of ancillary testing. We compared these data with the standards in the 2010 AAN update on practice parameters for brain death. A total of 508 unique hospital policies were obtained, representing the majority of hospitals in the United States that would be eligible and equipped to evaluate brain death in a patient. Of these, 492 provided adequate data for analysis. Although improvement with AAN practice parameters was readily apparent, there remained significant variability across all 5 categories of data, such as excluding the absence of hypotension (276 of 491 policies [56.2%]) and hypothermia (181 of 228 policies [79.4%]), specifying all aspects of the clinical examination and apnea testing, and specifying appropriate ancillary tests and how they were to be performed. Of the 492 policies

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

    Science.gov (United States)

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

    2017-04-15

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

  17. 78 FR 39765 - Notice of Proposed Classification of Public Lands/Minerals for State Indemnity Selection, Colorado

    Science.gov (United States)

    2013-07-02

    ... Bureau of Land Management Notice of Proposed Classification of Public Lands/Minerals for State Indemnity Selection, Colorado AGENCY: Bureau of Land Management, Interior. ACTION: Notice of Proposed Classification. SUMMARY: The Colorado State Board of Land Commissioners (State) has filed a petition for classification...

  18. 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

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

    Science.gov (United States)

    Farwell, Lawrence A.; Richardson, Drew C.; Richardson, Graham M.; Furedy, John J.

    2014-01-01

    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 three 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?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational

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

    Science.gov (United States)

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

    2012-10-01

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

  1. 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%.

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

    NARCIS (Netherlands)

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

    2013-01-01

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

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

    NARCIS (Netherlands)

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

    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

  4. Classification of archaeological sherds across the southeast United States based on variable selection from compositional fingerprints.

    Science.gov (United States)

    Pizarro, C; González-Sáiz, J M; Esteban-Díez, I; Rodríguez-Tecedor, S; Pérez-del-Notario, Nuria; Sáenz-González, C

    2009-07-30

    The transfer of advances in chemometrics into archaeometric research opens a wide range of new application possibilities in this rapidly developing field. The present research represents a feasibility study aimed at showing how the huge potential that multivariate analysis and feature selection techniques have demonstrated for classification purposes can be extrapolated to archaeological provenance studies, thus pursuing an enhancement of the resulting classification performance. The classification problem studied here was related to the discrimination of pottery sherds from different sources across the southeast of the United States from their compositional fingerprints. The sample elemental concentrations were analyzed using the stepwise linear discriminant analysis (SLDA) method, thus simultaneously performing feature selection and classification. Several approaches, more or less restrictive according to the geographical scope and the number of considered classes, were explored, following a hierarchical classification approach. In contrast to previous studies on the same data set, the reliable and unequivocal classification strategy presented here did not merely focus on developing a large-scale classification into broad geographical areas, but finer classifications were also successively obtained until samples were assigned into individual regions. The great discrimination ability and effectiveness of the classification methodology proposed are promising and encourage its application to new samples of unknown provenance and the feasibility of using similar approaches in other archaeological studies. The high quality results obtained were even more remarkable considering the relatively small number of discriminant variables selected in each case by the stepwise procedure.

  5. An algorithm for automatic segmentation and classification of magnetic resonance brain images.

    Science.gov (United States)

    Erickson, B J; Avula, R T

    1998-05-01

    In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.

  6. Effective enhancement of classification of respiratory states using ...

    Indian Academy of Sciences (India)

    and if unnoticed, it leads to heart failure, blood pressure (low and high), brain tumour, blood cancer and lung cancer. Besides, it poses a threat of increasing the hazards of diabetes, and also work-related and driving accidents. Conventional methods for detection of sleeping disorder rely on the recordings of Electrocar-.

  7. Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

    Science.gov (United States)

    Biller, A; Badde, S; Nagel, A; Neumann, J-O; Wick, W; Hertenstein, A; Bendszus, M; Sahm, F; Benkhedah, N; Kleesiek, J

    2016-01-01

    MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels. Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system. We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors. © 2016 by American Journal of Neuroradiology.

  8. 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....

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

    Science.gov (United States)

    2010-01-01

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

  10. 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.

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

    Science.gov (United States)

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

    2011-06-01

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

  12. 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.

  13. [State dependent modification of auditory brain stem potentials].

    Science.gov (United States)

    Maier, R; Rebentisch, E

    1984-11-01

    It is well known, amplitudes and latencies of auditory brain stem potentials are almost independent of viligance state. Contrary to that, simple cognitive requirements effect amplitude changes (enhancement of variance, amplitude reduction) in a part 2/3) of the subjects.

  14. Brain activity-based image classification from rapid serial visual presentation.

    Science.gov (United States)

    Bigdely-Shamlo, Nima; Vankov, Andrey; Ramirez, Rey R; Makeig, Scott

    2008-10-01

    We report the design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density (128-channel) electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task. Image clips were selected from a broad area image and presented in rapid succession (12/s) in 4.1-s bursts. Participants indicated by subsequent button press whether or not each burst of images included a target airplane feature. Image clip creation and search path selection were designed to maximize user comfort and maintain user awareness of spatial context. Independent component analysis (ICA) was used to extract a set of independent source time-courses and their minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from 128-channel EEG data recorded during clip burst presentations in a training session. The naive Bayes fusion of two Fisher discriminant classifiers, computed from the 100 most discriminative time and time-frequency features, respectively, was used to estimate the likelihood that each clip contained a target feature. This estimator was applied online in a subsequent test session. Across eight training/test session pairs from seven participants, median area under the receiver operator characteristic curve, by tenfold cross validation, was 0.97 for within-session and 0.87 for between-session estimates, and was nearly as high (0.83) for targets presented in bursts that participants mistakenly reported to include no target features.

  15. 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

  16. Computing the Social Brain Connectome Across Systems and States.

    Science.gov (United States)

    Alcalá-López, Daniel; Smallwood, Jonathan; Jefferies, Elizabeth; Van Overwalle, Frank; Vogeley, Kai; Mars, Rogier B; Turetsky, Bruce I; Laird, Angela R; Fox, Peter T; Eickhoff, Simon B; Bzdok, Danilo

    2017-05-18

    Social skills probably emerge from the interaction between different neural processing levels. However, social neuroscience is fragmented into highly specialized, rarely cross-referenced topics. The present study attempts a systematic reconciliation by deriving a social brain definition from neural activity meta-analyses on social-cognitive capacities. The social brain was characterized by meta-analytic connectivity modeling evaluating coactivation in task-focused brain states and physiological fluctuations evaluating correlations in task-free brain states. Network clustering proposed a functional segregation into (1) lower sensory, (2) limbic, (3) intermediate, and (4) high associative neural circuits that together mediate various social phenomena. Functional profiling suggested that no brain region or network is exclusively devoted to social processes. Finally, nodes of the putative mirror-neuron system were coherently cross-connected during tasks and more tightly coupled to embodied simulation systems rather than abstract emulation systems. These first steps may help reintegrate the specialized research agendas in the social and affective sciences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    N. Sauwen

    2016-01-01

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

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

    Science.gov (United States)

    Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

    2016-01-01

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

  1. Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations.

    Science.gov (United States)

    İşcan, Zafer; Nikulin, Vadim V

    2018-01-01

    Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The

  2. 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.

  3. Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers

    Directory of Open Access Journals (Sweden)

    M. Beyreuther

    2011-02-01

    Full Text Available Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms (earthquakes are often hard to detect and classify. Here, we propose to use models from speech synthesis which extend the double stochastic models from speech recognition by integrating a more realistic duration of the target waveforms. The method, which has general applicability, is applied to earthquake detection and classification. First, we generate characteristic functions from the time-series. The Hidden semi-Markov Models are estimated from the characteristic functions and Weighted Finite-State Transducers are constructed for the classification. We test our scheme on one month of continuous seismic data, which corresponds to 370 151 classifications, showing that incorporating the time dependency explicitly in the models significantly improves the results compared to Hidden Markov Models.

  4. Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers

    Science.gov (United States)

    Beyreuther, M.; Wassermann, J.

    2011-02-01

    Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms (earthquakes) are often hard to detect and classify. Here, we propose to use models from speech synthesis which extend the double stochastic models from speech recognition by integrating a more realistic duration of the target waveforms. The method, which has general applicability, is applied to earthquake detection and classification. First, we generate characteristic functions from the time-series. The Hidden semi-Markov Models are estimated from the characteristic functions and Weighted Finite-State Transducers are constructed for the classification. We test our scheme on one month of continuous seismic data, which corresponds to 370 151 classifications, showing that incorporating the time dependency explicitly in the models significantly improves the results compared to Hidden Markov Models.

  5. 9 CFR 145.44 - Terminology and classification; States.

    Science.gov (United States)

    2010-01-01

    .... gallisepticum is known to exist nor to have existed in turkey breeding flocks in production within the State during the preceding 12 months. (ii) All turkey breeding flocks in production are classified as U.S. M...) No Mycoplasma synoviae is known to exist nor to have existed in turkey breeding flocks in production...

  6. PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

    Science.gov (United States)

    Zhou, Zhen; Wang, Jian-Bao; Zang, Yu-Feng; Pan, Gang

    2018-01-01

    Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e

  7. Linear discriminant analysis of brain tumour (1)H MR spectra: a comparison of classification using whole spectra versus metabolite quantification.

    Science.gov (United States)

    Opstad, K S; Ladroue, C; Bell, B A; Griffiths, J R; Howe, F A

    2007-12-01

    (1)H MRS is an attractive choice for non-invasively diagnosing brain tumours. Many studies have been performed to create an objective decision support system, but there is not yet a consensus as to the best techniques of MRS acquisition or data processing to be used for optimum classification. In this study, we investigate whether LCModel analysis of short-TE (30 ms), single-voxel tumour spectra provide a better input for classification than the use of the original spectra. A total of 145 histologically diagnosed brain tumour spectra were acquired [14 astrocytoma grade II (AS2), 15 astrocytoma grade III (AS3), 42 glioblastoma (GBM), 41 metastases (MET) and 33 meningioma (MNG)], and linear discriminant analyses (LDA) were performed on the LCModel analysis of the spectra and the original spectra. The results consistently suggest improvement in classification when the LCModel concentrations are used. LDA of AS2, MNG and high-grade tumours (HG, comprising GBM and MET) correctly classified 94% using the LCModel dataset compared with 93% using the spectral dataset. The inclusion of AS3 reduced the accuracy to 82% and 78% for LCModel analysis and the original spectra, respectively, and further separating HG into GBM and MET gave 70% compared with 60%. Generally MNG spectra have profiles that are visually distinct from those of the other tumour types, but the classification accuracy was typically about 80%, with MNG with substantial lipid/macromolecule signals being classified as HG. Omission of the lipid/macromolecule concentrations in the LCModel dataset provided an improvement in classification of MNG (91% compared with 76%). In conclusion, there appears to be an advantage to performing pattern recognition on the quantitative analysis of tumour spectra rather than using the whole spectra. However, the results suggest that a two-step LDA process may help in classifying the five tumour groups to provide optimum classification of MNG with high lipid

  8. 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.

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

    Science.gov (United States)

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

    2014-02-01

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

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

    OpenAIRE

    Saad, N. M.; Syed A.R. Abu-Bakar; A.F. Muda; Sobri Muda; A. R. Syafeeza

    2014-01-01

    In this paper, a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed. Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke, solid tumor, chronic stroke, and necrosis are analyzed. The analysis is divided into four stages: pre-processing, segmentation, feature extraction, and classification. In the detection and segmentation stage, the image is divided into 8x8 macro-block regions. Adaptive thresholding t...

  11. 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.

  12. Description of a land classification system and its application to the management of Tennessee's state forests

    Science.gov (United States)

    Glendon W. Smalley; S. David Todd; K. Ward Tarkington

    2006-01-01

    The Tennessee Division of Forestry has adopted a land classification system developed by the senior author as the basic theme of information for the management of its 15 state forests (162,371 acres) with at least 1 in each of 8 physiographic provinces. This paper summarizes the application of the system to six forests on the Cumberland Plateau. Landtypes are the most...

  13. Classification, parameter estimation and state estimation: an engineering approach using matlab

    NARCIS (Netherlands)

    van der Heijden, Ferdinand; Duin, R.P.W.; de Ridder, D.; Tax, D.M.J.

    Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MatLab. "Prtools" is a powerful MatLab toolbox for pattern recognition and is

  14. Energy landscapes of resting-state brain networks

    Directory of Open Access Journals (Sweden)

    Takamitsu eWatanabe

    2014-02-01

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

  15. Governance typology: a consensus classification of state-local health department relationships.

    Science.gov (United States)

    Meit, Michael; Sellers, Katie; Kronstadt, Jessica; Lawhorn, Nikki; Brown, Alexa; Liss-Levinson, Rivka; Pearsol, Jim; Jarris, Paul E

    2012-11-01

    Public health practitioners and researchers often refer to state public health systems as being centralized, decentralized, shared, or mixed. These categories refer to governance of the local public health units within the state and whether they operate under the authority of the state government, local government, shared state and local governance, or a mix of governance structures within the state. This article describes the development of an objective method of classifying states as centralized, decentralized, shared, or mixed. We also discuss some initial analyses that have been conducted to identify how public health resources and activities vary across states with different classifications. Cross-sectional study. State health agencies. Survey respondents were organizational leaders from all 50 state health agencies. Total full-time equivalent employees, total health agency expenditures, expenditures on clinical services, and provision of clinical services. Centralized state health agencies employ more full-time equivalent employees, have higher total expenditures, and provide more clinical services than decentralized state health agencies. Although higher expenditures on clinical services were observed, these differences were not statistically significant. It is important to take governance classification into account when investigating variation in services, resources, or performance of governmental public health systems. As public health systems and services researchers seek to identify best practices in the organization of public health systems, consistent definition of different types of organization is critical. This system provides an objective and reliable system for classifying governance relationships that allows for comparisons that are meaningful to both practitioners and researchers.

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    Gemma Modinos

    2013-02-01

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

  18. Modulatory interactions of resting-state brain functional connectivity.

    Directory of Open Access Journals (Sweden)

    Xin Di

    Full Text Available The functional brain connectivity studies are generally based on the synchronization of the resting-state functional magnetic resonance imaging (fMRI signals. Functional connectivity measures usually assume a stable relationship over time; however, accumulating studies have reported time-varying properties of strength and spatial distribution of functional connectivity. The present study explored the modulation of functional connectivity between two regions by a third region using the physiophysiological interaction (PPI technique. We first identified eight brain networks and two regions of interest (ROIs representing each of the networks using a spatial independent component analysis. A voxel-wise analysis was conducted to identify regions that showed modulatory interactions (PPI with the two ROIs of each network. Mostly, positive modulatory interactions were observed within regions involved in the same system. For example, the two regions of the dorsal attention network revealed modulatory interactions with the regions related to attention, while the two regions of the extrastriate network revealed modulatory interactions with the regions in the visual cortex. In contrast, the two regions of the default mode network (DMN revealed negative modulatory interactions with the regions in the executive network, and vice versa, suggesting that the activities of one network may be associated with smaller within network connectivity of the competing network. These results validate the use of PPI analysis to study modulation of resting-state functional connectivity by a third region. The modulatory effects may provide a better understanding of complex brain functions.

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

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

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

  20. Tradeoff between User Experience and BCI Classification Accuracy with Frequency Modulated Steady-State Visual Evoked Potentials.

    Science.gov (United States)

    Dreyer, Alexander M; Herrmann, Christoph S; Rieger, Jochem W

    2017-01-01

    Steady-state visual evoked potentials (SSVEPs) have been widely employed for the control of brain-computer interfaces (BCIs) because they are very robust, lead to high performance, and allow for a high number of commands. However, such flickering stimuli often also cause user discomfort and fatigue, especially when several light sources are used simultaneously. Different variations of SSVEP driving signals have been proposed to increase user comfort. Here, we investigate the suitability of frequency modulation of a high frequency carrier for SSVEP-BCIs. We compared BCI performance and user experience between frequency modulated (FM) and traditional sinusoidal (SIN) SSVEPs in an offline classification paradigm with four independently flickering light-emitting diodes which were overtly attended (fixated). While classification performance was slightly reduced with the FM stimuli, the user comfort was significantly increased. Comparing the SSVEPs for covert attention to the stimuli (without fixation) was not possible, as no reliable SSVEPs were evoked. Our results reveal that several, simultaneously flickering, light emitting diodes can be used to generate FM-SSVEPs with different frequencies and the resulting occipital electroencephalography (EEG) signals can be classified with high accuracy. While the performance we report could be further improved with adjusted stimuli and algorithms, we argue that the increased comfort is an important result and suggest the use of FM stimuli for future SSVEP-BCI applications.

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

    Directory of Open Access Journals (Sweden)

    Bo Peng

    2017-05-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ling Zhu

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

  4. Brain state dependent activity in the cortex and thalamus.

    Science.gov (United States)

    McCormick, David A; McGinley, Matthew J; Salkoff, David B

    2015-04-01

    Cortical and thalamocortical activity is highly state dependent, varying between patterns that are conducive to accurate sensory-motor processing, to states in which the brain is largely off-line and generating internal rhythms irrespective of the outside world. The generation of rhythmic activity occurs through the interaction of stereotyped patterns of connectivity together with intrinsic membrane and synaptic properties. One common theme in the generation of rhythms is the interaction of a positive feedback loop (e.g., recurrent excitation) with negative feedback control (e.g., inhibition, adaptation, or synaptic depression). The operation of these state-dependent activities has wide ranging effects from enhancing or blocking sensory-motor processing to the generation of pathological rhythms associated with psychiatric or neurological disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2012-01-01

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

  6. Ancestral Circuits for the Coordinated Modulation of Brain State.

    Science.gov (United States)

    Lovett-Barron, Matthew; Andalman, Aaron S; Allen, William E; Vesuna, Sam; Kauvar, Isaac; Burns, Vanessa M; Deisseroth, Karl

    2017-11-30

    Internal states of the brain profoundly influence behavior. Fluctuating states such as alertness can be governed by neuromodulation, but the underlying mechanisms and cell types involved are not fully understood. We developed a method to globally screen for cell types involved in behavior by integrating brain-wide activity imaging with high-content molecular phenotyping and volume registration at cellular resolution. We used this method (MultiMAP) to record from 22 neuromodulatory cell types in behaving zebrafish during a reaction-time task that reports alertness. We identified multiple monoaminergic, cholinergic, and peptidergic cell types linked to alertness and found that activity in these cell types was mutually correlated during heightened alertness. We next recorded from and controlled homologous neuromodulatory cells in mice; alertness-related cell-type dynamics exhibited striking evolutionary conservation and modulated behavior similarly. These experiments establish a method for unbiased discovery of cellular elements underlying behavior and reveal an evolutionarily conserved set of diverse neuromodulatory systems that collectively govern internal state. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

    Science.gov (United States)

    Poletti, Enea; Veronese, Elisa; Calabrese, Massimiliano; Bertoldo, Alessandra; Grisan, Enrico

    2012-02-01

    The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images, due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present: the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks, we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. We propose here a supervised classification scheme that does not require any anatomical a priori information to identify the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2 original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel. From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2 expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of 99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification accuracy of 98.79%.

  8. 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.

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

    OpenAIRE

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

    2012-01-01

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

  10. A classification of multipartite states by degree of non-locality

    Directory of Open Access Journals (Sweden)

    Samson Abramsky

    2014-12-01

    Full Text Available We propose a novel form of classification of multipartite states, in terms of the maximum degree of non-locality they can exhibit under any choice of local observables. This uses the hierarchy of notions previously introduced by Abramsky and Brandenburger: strong contextuality, logical contextuality, and probabilistic contextuality. We study n-qubit pure states. We conjecture that for more than 2 parties, all entangled states are logically contextual. We prove a number of results in support of this conjecture: (1 We show that all permutation-symmetric states are logically non-local. (2 We study the class of balanced states with functional dependencies. These states are described by Boolean functions and have a rich structure, allowing a detailed analysis, which again confirms the conjecture in this case.

  11. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly; Kunt, Murat; Celka, Patrick

    2007-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

  12. Phase synchronization for classification of spontaneous EEG signals in brain-computer interfaces

    OpenAIRE

    Gysels, Elly

    2005-01-01

    By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs different mental tasks, that correspond to different output commands as recognized by the system. From the recorded brain signals (Electroencephalogram, EEG), features that characterize the mental tasks and allow their discrimination by a cla...

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

    NARCIS (Netherlands)

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

    2008-01-01

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

  14. 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

  15. Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

    Science.gov (United States)

    Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

    2017-04-15

    Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

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

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    Andrea Finke

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

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

    Directory of Open Access Journals (Sweden)

    A.V. Faria

    2011-02-01

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

  18. 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

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

    Science.gov (United States)

    Pernet, Cyril R; Poline, Jean Baptiste; Demonet, Jean François; Rousselet, Guillaume A

    2009-01-01

    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 subjects, so that lower and

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

    Kim, Yong-Ku; Na, Kyoung-Sae

    2018-01-03

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

  2. 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.

  3. Brain-Computer Interface-Based Communication in the Completely Locked-In State.

    Science.gov (United States)

    Chaudhary, Ujwal; Xia, Bin; Silvoni, Stefano; Cohen, Leonardo G; Birbaumer, Niels

    2017-01-01

    Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)-two of them in permanent CLIS and two entering the CLIS without reliable means of communication-learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a "yes" or "no" response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.

  4. 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.

  5. Emotional brain states carry over and enhance future memory formation.

    Science.gov (United States)

    Tambini, Arielle; Rimmele, Ulrike; Phelps, Elizabeth A; Davachi, Lila

    2017-02-01

    Emotional arousal can produce lasting, vivid memories for emotional experiences, but little is known about whether emotion can prospectively enhance memory formation for temporally distant information. One mechanism that may support prospective memory enhancements is the carry-over of emotional brain states that influence subsequent neutral experiences. Here we found that neutral stimuli encountered by human subjects 9-33 min after exposure to emotionally arousing stimuli had greater levels of recollection during delayed memory testing compared to those studied before emotional and after neutral stimulus exposure. Moreover, multiple measures of emotion-related brain activity showed evidence of reinstatement during subsequent periods of neutral stimulus encoding. Both slow neural fluctuations (low-frequency connectivity) and transient, stimulus-evoked activity predictive of trial-by-trial memory formation present during emotional encoding were reinstated during subsequent neutral encoding. These results indicate that neural measures of an emotional experience can persist in time and bias how new, unrelated information is encoded and recollected.

  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.

    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

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

    NARCIS (Netherlands)

    van der Knaap, M. S.; Smit, L. M.; Barth, P. G.; Catsman-Berrevoets, C. E.; Brouwer, O. F.; Begeer, J. H.; de Coo, I. F.; Valk, J.

    1997-01-01

    A survey was performed of magnetic resonance imaging (MRI) findings in 21 patients with congenital muscular dystrophy (CMD) 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

  8. Sequences of ground states and classification of frustration in odd-numbered antiferromagnetic rings

    Science.gov (United States)

    Florek, Wojciech; Antkowiak, Michał; Kamieniarz, Grzegorz

    2016-12-01

    The sequences of ground states in frustrated antiferromagnetic rings with odd number of local spins characterized by a single bond defect or by arbitrary uniform couplings to an additional spin located at the center are determined. The sequences provide firm constraints on the total ground-state quantum numbers, which are more stringent than those arising from the Lieb-Mattis theorem for bipartite quantum spin systems. Apart from their theoretical importance, they suggest the possibility of tailoring a given class of the molecular nanomagnets with desired ground-state properties by tuning the relevant couplings. In particular, they predict the spin S =1 /2 ground state for the centered rings composed of the half-integer spins with approximately uniform interactions. They confirm the applicability of the recent classification of spin frustration in both types of molecular nanomagnets. The classification is also discussed in the classical limit for the first class of the rings, providing a direct picture of frustration types. The Lieb-Mattis energy-level ordering and an analog of the Landé band, i.e., the energy spectra properties simplifying the characterization of the rings using the bulk magnetic or NMR measurements, are briefly discussed.

  9. 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. © 2015 John Wiley & Sons Ltd.

  10. Development of a School Nutrition-Environment State Policy Classification System (SNESPCS).

    Science.gov (United States)

    Mâsse, Louise C; Frosh, Marcy M; Chriqui, Jamie F; Yaroch, Amy L; Agurs-Collins, Tanya; Blanck, Heidi M; Atienza, Audie A; McKenna, Mary L; Igoe, James F

    2007-10-01

    As policy strategies are rapidly being developed to address childhood overweight, a system was developed to systematically and reliably classify state policies related to the school nutrition environment. This study describes the development process, the inter-rater reliability to code state policies enacted as of December 2003, and the variability in state policies related to the school nutrition environment. The development of the School Nutrition Environment State Policy Classification System (SNESPCS) included a comprehensive review of published literature, reports from government and nongovernmental sources, input from an expert panel, and select experts. Baseline statutes and regulations for each of the 50 states and the District of Columbia were retrieved from Westlaw (data retrieved in 2005-2006 and analyzed in 2006) and pilot testing of the system was conducted. SNESPCS included 11 policy areas that relate to a range of environmental and surveillance domains. At baseline, states had no (advertising/promotion and preferential pricing) or modest (school meal environment, reimbursable school meals, coordinating or advisory councils, body mass index screening) activities in many of the policy areas. As of 2003, 60% of the states had policies related to the sale of foods in school that compete with the school meal program. Evaluation of policies that affect the school-nutrition environment is in its earliest stage. SNESPCS provides a mechanism for assessing variation in state policies that can be incorporated in an evaluation framework aimed at elucidating the impact of state policies on the school environment, social norms, and children's dietary behaviors in schools.

  11. What should be the roles of conscious States and brain States in theories of mental activity?

    Science.gov (United States)

    Dulany, Donelson E

    2011-01-01

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

  12. Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface.

    Science.gov (United States)

    Kodama, Takumi; Makino, Shoji

    2017-07-01

    In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full- body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal- training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.

  13. 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.

  14. Classification of EEG recordings in auditory brain activity via a logistic functional linear regression model.

    OpenAIRE

    Gannaz, Irène

    2014-01-01

    International audience; We want to analyse EEG recordings in order to investigate the phonemic categorization at a very early stage of auditory processing. This problem can be modelled by a supervised classification of functional data. Discrimination is explored via a logistic functional linear model, using a wavelet representation of the data. Different procedures are investigated, based on penalized likelihood and principal component reduction or partial least squares reduction.

  15. A Bayesian state-space approach for damage detection and classification

    Science.gov (United States)

    Dzunic, Zoran; Chen, Justin G.; Mobahi, Hossein; Büyüköztürk, Oral; Fisher, John W.

    2017-11-01

    The problem of automatic damage detection in civil structures is complex and requires a system that can interpret collected sensor data into meaningful information. We apply our recently developed switching Bayesian model for dependency analysis to the problems of damage detection and classification. The model relies on a state-space approach that accounts for noisy measurement processes and missing data, which also infers the statistical temporal dependency between measurement locations signifying the potential flow of information within the structure. A Gibbs sampling algorithm is used to simultaneously infer the latent states, parameters of the state dynamics, the dependence graph, and any changes in behavior. By employing a fully Bayesian approach, we are able to characterize uncertainty in these variables via their posterior distribution and provide probabilistic estimates of the occurrence of damage or a specific damage scenario. We also implement a single class classification method which is more realistic for most real world situations where training data for a damaged structure is not available. We demonstrate the methodology with experimental test data from a laboratory model structure and accelerometer data from a real world structure during different environmental and excitation conditions.

  16. 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

  17. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

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

  18. Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

    Science.gov (United States)

    Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

    2017-03-30

    Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD. Copyright © 2016 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2017-06-01

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

  20. Nosologic imaging of the brain: segmentation and classification using MRI and MRSI.

    NARCIS (Netherlands)

    Luts, J.; Laudadio, T.; Idema, A.J.S.; Simonetti, A.W.; Heerschap, A.; Meulen, D. van der; Suykens, J.A.; Huffel, S. van

    2009-01-01

    A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel

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

    Science.gov (United States)

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

    2004-01-01

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

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

    Science.gov (United States)

    Soares, José M.; Sampaio, Adriana; Marques, Paulo; Ferreira, Luís M.; Santos, Nadine C.; Marques, Fernanda; Palha, Joana A.; Cerqueira, João J.; Sousa, Nuno

    2013-01-01

    Chronic stress has been widely reported to have deleterious impact in multiple biological systems. Specifically, structural and functional remodeling of several brain regions following prolonged stress exposure have been described; importantly, some of these changes are eventually reversible. Recently, we showed the impact of stress on resting state networks (RSNs), but nothing is known about the plasticity of RSNs after recovery from stress. Herein, we examined the “plasticity” of RSNs, both at functional and structural levels, by comparing the same individuals before and after recovery from the exposure to chronic stress; results were also contrasted with a control group. Here we show that the stressed individuals after recovery displayed a decreased resting functional connectivity in the default mode network (DMN), ventral attention network (VAN), and sensorimotor network (SMN) when compared to themselves immediately after stress; however, this functional plastic recovery was only partial as when compared with the control group, as there were still areas of increased connectivity in dorsal attention network (DAN), SMN and primary visual network (VN) in participants recovered from stress. Data also shows that participants after recovery from stress displayed increased deactivations in DMN, SMN, and auditory network (AN), to levels similar to those of controls, showing a normalization of the deactivation pattern in RSNs after recovery from stress. In contrast, structural changes (volumetry) of the brain areas involving these networks are absent after the recovery period. These results reveal plastic phenomena in specific RSNs and a functional remodeling of the activation-deactivation pattern following recovery from chronic-stress, which is not accompanied by significant structural plasticity. PMID:24416009

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

    Science.gov (United States)

    Salvaris, Mathew; Sepulveda, Francisco

    2010-10-01

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

  4. Resting-state fMRI: a window into human brain plasticity.

    Science.gov (United States)

    Guerra-Carrillo, Belén; Mackey, Allyson P; Bunge, Silvia A

    2014-10-01

    Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level-dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-dependent brain plasticity in humans. Here, we evaluate the hypothesis that resting-state functional connectivity reflects the repeated history of co-activation between brain regions. To this end, we review resting-state fMRI studies in the sensory, motor, and cognitive learning literature. This body of research provides evidence that the brain's resting-state functional architecture displays dynamic properties in young adulthood. © The Author(s) 2014.

  5. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging.

    Science.gov (United States)

    Chen, Gang; Ward, B Douglas; Xie, Chunming; Li, Wenjun; Wu, Zhilin; Jones, Jennifer L; Franczak, Malgorzata; Antuono, Piero; Li, Shi-Jiang

    2011-04-01

    To use large-scale network (LSN) analysis to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) subjects. The study was conducted with institutional review board approval and was in compliance with HIPAA regulations. Written informed consent was obtained from each participant. Resting-state functional magnetic resonance (MR) imaging was used to acquire the voxelwise time series in 55 subjects with clinically diagnosed AD (n = 20), aMCI (n =15), and normal cognitive function (n = 20). The brains were divided into 116 regions of interest (ROIs). The Pearson product moment correlation coefficients of pairwise ROIs were used to classify these subjects. Error estimation of the classifications was performed with the leave-one-out cross-validation method. Linear regression analysis was performed to analyze the relationship between changes in network connectivity strengths and behavioral scores. The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification. For differentiation between subjects with aMCI and CN subjects, AUC was 95%; sensitivity, 93%; and specificity, 90%. The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects. Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects. LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects. In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests. © RSNA, 2011.

  6. Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke.

    Science.gov (United States)

    Jiménez-Xarrié, Elena; Davila, Myriam; Candiota, Ana Paula; Delgado-Mederos, Raquel; Ortega-Martorell, Sandra; Julià-Sapé, Margarida; Arús, Carles; Martí-Fàbregas, Joan

    2017-01-13

    Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier. A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier ( http://gabrmn.uab.es/?q=sc ). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity). SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content).

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

    OpenAIRE

    Bach Cuadra, Meritxell; Thiran, Jean-Philippe

    2005-01-01

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

  8. 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…

  9. Brain networks involved in tactile speed classification of moving dot patterns: the effects of speed and dot periodicity.

    Science.gov (United States)

    Yang, Jiajia; Kitada, Ryo; Kochiyama, Takanori; Yu, Yinghua; Makita, Kai; Araki, Yuta; Wu, Jinglong; Sadato, Norihiro

    2017-02-01

    Humans are able to judge the speed of an object's motion by touch. Research has suggested that tactile judgment of speed is influenced by physical properties of the moving object, though the neural mechanisms underlying this process remain poorly understood. In the present study, functional magnetic resonance imaging was used to investigate brain networks that may be involved in tactile speed classification and how such networks may be affected by an object's texture. Participants were asked to classify the speed of 2-D raised dot patterns passing under their right middle finger. Activity in the parietal operculum, insula, and inferior and superior frontal gyri was positively related to the motion speed of dot patterns. Activity in the postcentral gyrus and superior parietal lobule was sensitive to dot periodicity. Psycho-physiological interaction (PPI) analysis revealed that dot periodicity modulated functional connectivity between the parietal operculum (related to speed) and postcentral gyrus (related to dot periodicity). These results suggest that texture-sensitive activity in the primary somatosensory cortex and superior parietal lobule influences brain networks associated with tactually-extracted motion speed. Such effects may be related to the influence of surface texture on tactile speed judgment.

  10. 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.

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

    Science.gov (United States)

    Combaz, Adrien; Van Hulle, Marc M

    2015-01-01

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

  12. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data.

    Science.gov (United States)

    Tsolaki, Evangelia; Svolos, Patricia; Kousi, Evanthia; Kapsalaki, Eftychia; Fezoulidis, Ioannis; Fountas, Konstantinos; Theodorou, Kyriaki; Kappas, Constantine; Tsougos, Ioannis

    2015-07-01

    A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

  13. Heart rate variability: an index of brain processing in vegetative state? An artificial intelligence, data mining study.

    Science.gov (United States)

    Riganello, F; Candelieri, A; Quintieri, M; Conforti, D; Dolce, G

    2010-12-01

    Brain processing at varying levels of functional complexity has been documented in vegetative state. In this study, data mining procedures are applied to identify significant changes in heart rate variability (an emerging objective descriptor of autonomic correlates of brain activation) in response to complex auditory stimuli with emotional value (music). The heart rate of subjects in vegetative state from brain damage (n=6) or spontaneous hemorrhage (n=3) and 16 healthy controls was recorded while they passively listened to four pre-selected music samples by different authors (mean recording time: 3m and 36s±24s). The parametric and non-parametric frequency spectra were computed on the heart rate, spectra were compared within/across subjects and music authors, and the spectra descriptors were entered into a 1-R rules data mining procedure (WEKA software Leave One Out and Ten Fold Cross validation). The procedure independently classified the heart rate spectral patterns of both patients and controls and the emotions reported by healthy subjects as "positive" or "negative". In both healthy controls and vegetative state subjects, the power spectra while passively listening to music differed from baseline when compared irrespective of the music authorship and from each other when compared across music samples. Data mining sorted the nu_LF (normalized parameter unit of the spectrum low frequency range) as the significant descriptor of heart rate variability in the conditions of the study. The nu_LF classification of the healthy controls' HRV changes in response to music replicated that based on subjective reports with 75-93.7% accuracy. Although preliminary, these findings suggest that autonomic changes with possible emotional value can be induced by complex stimuli also in vegetative state, with implications on the residual responsiveness of these subjects. Heart rate variability descriptors and data mining methods appear applicable to investigate brain function in

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

    Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born position. No relation was found between the median total duration of each pattern and injury on cUS and MRI or cognition at 2 and 5 years. Clear ictal discharges are rare in extremely preterm infants. PEDs are common but their significance is unclear. Rhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  17. Classification of Physiology Indicators for the Automatic Detection of Potentially Hazardous Physiological States

    Directory of Open Access Journals (Sweden)

    I. G. Damousis

    2011-01-01

    Full Text Available In EU-funded project HUMABIO, physiological signals are used as biometrics for security purposes. Data are collected via electrode sensors that are attached to the body of the subject and are obtrusive to some degree. In order to maximize the obtained information and the benefits from the use of obtrusive, physiological sensors, the collected data are processed to also detect abnormal physiology states that may endanger the subjects and those around them during critical operations. Three abnormal states are studied: drug and alcohol consumption and sleep deprivation. For the classification of the physiology, four state-of-the-art techniques were compared, support vector machines, fuzzy expert systems, neural networks, and Gaussian mixture models. The results reveal that there is significant potential on the automatic detection of potentially hazardous physiology states without the need for a human supervisor and that such a system could be included at installations such as nuclear factories to enhance safety by reducing the possibility of human operator related accidents.

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

    Science.gov (United States)

    Dulany, Donelson E.

    2011-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  20. 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.

  1. 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.

  2. 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. Copyright © 2015 Elsevier B.V. All rights

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

    Science.gov (United States)

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

    2013-01-01

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

  4. Classification and evaluation for forest sites on the Natchez Trace State Forest, State Resort Park, and Wildlife Management Area in West Tennessee

    Science.gov (United States)

    Glendon W. Smalley

    1991-01-01

    Presents comprehensive forest site classification system for the 45,084-acre Natchez Trace State Forest, State Resort park, and WIldlife Management Area in the highly dissected and predominantly hilly Upper Coastal Plain of west Tennessee. Twenty-five landtypes are identified. Each landtype is defined in terms of nine elements and evaluated on the baiss of...

  5. 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.

  6. 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

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

    Science.gov (United States)

    Koch, Henriette; Christensen, Julie A E; Frandsen, Rune; Zoetmulder, Marielle; Arvastson, Lars; Christensen, Soren R; Jennum, Poul; Sorensen, Helge B D

    2014-09-30

    The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects. 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 (% μ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD). Statistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes. The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Development of a Physical Education-Related State Policy Classification System (PERSPCS).

    Science.gov (United States)

    Mâsse, Louise C; Chriqui, Jamie F; Igoe, James F; Atienza, Audie A; Kruger, Judy; Kohl, Harold W; Frosh, Marcy M; Yaroch, Amy L

    2007-10-01

    As policy-based approaches are increasingly proposed to address childhood obesity, this paper seeks to: (1) present the development of a system to systematically and reliably assess the nature and extent of state physical education (PE) and recess-related policies; (2) determine the inter-rater agreement in using the system; and (3) report on the variability in state policies using a December 31, 2003 baseline. The PE and Recess State Policy Classification System (PERSPCS) was developed from a conceptual framework and was informed by reviewing the scientific and gray literatures and through consultations with an expert panel and key experts. Statutes and regulations enacted as of December 31, 2003 were retrieved from Westlaw (data retrieved and analyzed in 2004-2005). PERSPCS addresses five areas: PE time requirements, staffing requirements for PE, curriculum standards for PE, assessment of health-related fitness, and recess time (elementary schools only). The inter-rater agreement ranged from 0.876 (PE staffing requirements) to perfect agreement (recess time). Staffing requirements had more restrictive policies, followed in decreasing order by time requirements, curriculum standards, assessment, and recess time. Overall, state policies met minimal requirements across areas and grade levels as of December 2003. Extending PERSPCS to address other aspects of childhood obesity is a critical first step in understanding the range of state policy approaches in this area and their impact. PERSPCS should be examined in conjunction with school district-level policies to determine the overall effects of policies on school environmental and behavioral outcomes. PERSPCS is not designed to set policy guidelines.

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

    Science.gov (United States)

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

    2010-09-01

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

  10. 18F-FDG PET brain images as features for Alzheimer classification

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Directory of Open Access Journals (Sweden)

    John R. Pruett, Jr.

    2015-04-01

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

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

    Science.gov (United States)

    Pruett, John R; Kandala, Sridhar; Hoertel, Sarah; Snyder, Abraham Z; Elison, Jed T; Nishino, Tomoyuki; Feczko, Eric; Dosenbach, Nico U F; Nardos, Binyam; Power, Jonathan D; Adeyemo, Babatunde; Botteron, Kelly N; McKinstry, Robert C; Evans, Alan C; Hazlett, Heather C; Dager, Stephen R; Paterson, Sarah; Schultz, Robert T; Collins, D Louis; Fonov, Vladimir S; Styner, Martin; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Constantino, John N; Estes, Annette M; Petersen, Steven E; Schlaggar, Bradley L; Piven, Joseph

    2015-04-01

    Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2014-09-01

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

  17. Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.

    Science.gov (United States)

    Qureshi, Nauman Khalid; Naseer, Noman; Noori, Farzan Majeed; Nazeer, Hammad; Khan, Rayyan Azam; Saleem, Sajid

    2017-01-01

    In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher ( p  classification-performance fNIRS-BCI.

  18. 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.

  19. Brain activation by music in patients in a vegetative or minimally conscious state following diffuse brain injury.

    Science.gov (United States)

    Okumura, Yuka; Asano, Yoshitaka; Takenaka, Shunsuke; Fukuyama, Seisuke; Yonezawa, Shingo; Kasuya, Yukinori; Shinoda, Jun

    2014-01-01

    The aim of this study was to objectively evaluate the brain activity potential of patients with impaired consciousness in a chronic stage of diffuse brain injury (DBI) using functional MRI (fMRI) following music stimulation (MS). Two patients in a minimally conscious state (MCS) and five patients in a vegetative state (VS) due to severe DBI were enrolled along with 21 healthy adults. This study examined the brain regions activated by music and assessed topographical differences of the MS-activated brain among healthy adults and these patients. MS was shown to activate the bilateral superior temporal gyri (STG) of both healthy adults and patients in an MCS. In four of five patients in a VS, however, no significant activation in STG could be induced by the same MS. The remaining patient in a VS displayed the same MS-induced brain activation in STG as healthy adults and patients in an MCS and this patient's status also improved to an MCS 4 months after the study. The presence of STG activation by MS may predict a possible improvement of patients in a VS to MCS and fMRI employing MS may be a useful modality to objectively evaluate consciousness in these patients.

  20. Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI.

    Science.gov (United States)

    Challis, Edward; Hurley, Peter; Serra, Laura; Bozzali, Marco; Oliver, Seb; Cercignani, Mara

    2015-05-15

    Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating

  1. 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.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jared A Nielsen

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

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

    Science.gov (United States)

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

    2013-05-16

    Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

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

    Science.gov (United States)

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

    2015-01-01

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

  6. 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

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

    Science.gov (United States)

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

    2012-09-30

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

  8. 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.

  9. Variable Classification of Drug-Intoxication Suicides across US States: A Partial Artifact of Forensics?

    Directory of Open Access Journals (Sweden)

    Ian R H Rockett

    Full Text Available The 21st-century epidemic of pharmaceutical and other drug-intoxication deaths in the United States (US has likely precipitated an increase in misclassified, undercounted suicides. Drug-intoxication suicides are highly prone to be misclassified as accident or undetermined. Misclassification adversely impacts suicide and other injury mortality surveillance, etiologic understanding, prevention, and hence clinical and public health policy formation and practice.To evaluate whether observed variation in the relative magnitude of drug-intoxication suicides across US states is a partial artifact of the scope and quality of toxicological testing and type of medicolegal death investigation system.This was a national, state-based, ecological study of 111,583 drug-intoxication fatalities, whose manner of death was suicide, accident, or undetermined. The proportion of (nonhomicide drug-intoxication deaths classified by medical examiners and coroners as suicide was analyzed relative to the proportion of death certificates citing one or more specific drugs and two types of state death investigation systems. Our model incorporated five sociodemographic covariates. Data covered the period 2008-2010, and derived from NCHS's Multiple Cause-of-Death public use files.Across states, the proportion of drug-intoxication suicides ranged from 0.058 in Louisiana to 0.286 in South Dakota and the rate from 1 per 100,000 population in North Dakota to 4 in New Mexico. There was a low correlation between combined accident and undetermined drug-intoxication death rates and corresponding suicide rates (Spearman's rho = 0.38; p<0.01. Citation of 1 or more specific drugs on the death certificate was positively associated with the relative odds of a state classifying a nonhomicide drug-intoxication death as suicide rather than accident or undetermined, adjusting for region and type of state death investigation system (odds ratio, 1.062; 95% CI,1.016-1.110. Region, too, was a

  10. Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome

    Science.gov (United States)

    Haimovici, Ariel; Tagliazucchi, Enzo; Balenzuela, Pablo; Chialvo, Dante R.

    2013-04-01

    The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatiotemporal patterns of brain activity emerge from the empirically derived network of human brain neuroanatomical connections. The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

  11. Modulation of brain resting-state networks by sad mood induction

    National Research Council Canada - National Science Library

    Harrison, Ben J; Pujol, Jesus; Ortiz, Hector; Fornito, Alex; Pantelis, Christos; Yücel, Murat

    2008-01-01

    ...) signal observed in functional MRI resting-state studies. In humans, these slow BOLD variations are thought to reflect an underlying or intrinsic form of brain functional connectivity in discrete neuroanatomical systems...

  12. 78 FR 9929 - Current Traumatic Brain Injury State Implementation Partnership Grantees; Non-Competitive One...

    Science.gov (United States)

    2013-02-12

    ... HUMAN SERVICES Health Resources and Services Administration Current Traumatic Brain Injury State... State Departments of Health, Rehabilitation, Human Services, Education, Transportation, or Labor. This...,000 Oregon State Department of Education H21MC06769 OR 249,999 249,999 Texas Health & Human Services...

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

    OpenAIRE

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; LU, CHENGBIAO

    2016-01-01

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

  14. The Brain: Its Relationship to Learning, Emotional States, and Behavior

    Science.gov (United States)

    Armstrong, Terry

    1977-01-01

    Outlines findings of contemporary neuro-scientists studying the biological basis of human learning and behavior. Areas of research discussed are: (1) the center of human emotion--the limbic system; (2) brain rhythms; and (3) the molecular basis of learning. (CS)

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  18. 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.

  19. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine.

    Science.gov (United States)

    Song, Yuedong; Zhang, Jiaxiang

    2016-01-15

    Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings. Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach. The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins. Compared with traditional variance-based feature extraction, the proposed SampEn-based feature extraction method not only shows a significant improvement in the accuracy, but also has higher classification robustness and stability in terms of the much lower standard errors of classification accuracies across different evaluation periods. In addition, the proposed classification framework runs around 20 times faster than the support vector machine model during testing. The high accuracy and efficiency of the proposed method makes it feasible to extend it to the development of a real-time EEG-based brain monitoring system for epileptic seizure prediction. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

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

  2. A Survey of State-of-the-Art Methods on Question Classification

    NARCIS (Netherlands)

    Loni, B.

    2011-01-01

    The task of question classification (QC) is to predict the entity type of a question which is written in natural language. This is done by classifying the question to a category from a set of predefined categories. Question classification is an important component of question answering systems and

  3. Classification of atherosclerotic and non-atherosclerotic individuals using multiclass state vector machine.

    Science.gov (United States)

    Kumar, Paulraj Ranjith; Priya, Mohan

    2014-01-01

    Coronary artery disease due to atherosclerosis is an epidemic in India. An estimated 1.3 million Indians died from this in 2000. The projected death from coronary artery disease by 2016 is 2.98 million. To build an effective model which assorts the individuals, whether they belong to the normal group, risk group and pathologic group regarding atherosclerosis in real time by doing necessary preprocessing techniques and to compare the performance with other state-of-the-art machine learning techniques. In this work we have employed STULONG dataset. We have made a deep case study in selecting the attributes which contributes for higher accuracy in predicting the target. The selected attributes includes missing values. Initially our work includes imputation of missing values using Iterative Principal Component Analysis (IPCA). The second step includes selecting best features using Fast Correlation Based Filter (FCBF). Finally the classifier Multiclass Support Vector Machine (SVM) with kernel Radial Basis Function (RBF) is used for classification of atherosclerotic community. For the subjects belonging to the classes of normal, risk and pathologic, our methodology has outperformed with an accuracy of 99.85%, 99.80% and 99.46% respectively. The combined optimization methods such as Iterative Principal Component Analysis (IPCA) for missing value imputation, Multiclass SVM for classifying normal, risk and pathologic community in real time has performed with overall accuracy of about 98.97%. The essential pre-processing technique, Fast Correlation Based Filter (FCBF) was employed to further intensifying the target.

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

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    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.

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

    Science.gov (United States)

    Northoff, Georg

    2012-07-01

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

  6. Development of a Ternary Near-Infrared Spectroscopy Brain-Computer Interface: Online Classification of Verbal Fluency Task, Stroop Task and Rest.

    Science.gov (United States)

    Schudlo, Larissa C; Chau, Tom

    2017-10-26

    The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training. Additionally, samples were collected prior to each period of online classification to update the classifier. Using a continuous-wave spectrometer, measurements were collected from the prefrontal and parietal cortices while 11 able-bodied adult participants were cued to perform one of the two cognitive tasks or rests. Each task was used to indicate the desire to select a particular letter on a scanning interface, while rest avoided selection. Classification was performed using 25 iteration of bagging with a linear discriminant base classifier. Classifiers were trained on 10-dimensional feature sets. The BCI's classification decision was provided as feedback. An average online classification accuracy of [Formula: see text]% was achieved, representing an ITR of [Formula: see text] bits/min. The results demonstrate that online communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest. Our findings encourage continued efforts to enhance the ITR of NIRS-BCIs.

  7. Cyclic and sleep-like spontaneous alternations of brain state under urethane anaesthesia.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Clement

    Full Text Available BACKGROUND: Although the induction of behavioural unconsciousness during sleep and general anaesthesia has been shown to involve overlapping brain mechanisms, sleep involves cyclic fluctuations between different brain states known as active (paradoxical or rapid eye movement: REM and quiet (slow-wave or non-REM: nREM stages whereas commonly used general anaesthetics induce a unitary slow-wave brain state. METHODOLOGY/PRINCIPAL FINDINGS: Long-duration, multi-site forebrain field recordings were performed in urethane-anaesthetized rats. A spontaneous and rhythmic alternation of brain state between activated and deactivated electroencephalographic (EEG patterns was observed. Individual states and their transitions resembled the REM/nREM cycle of natural sleep in their EEG components, evolution, and time frame ( approximately 11 minute period. Other physiological variables such as muscular tone, respiration rate, and cardiac frequency also covaried with forebrain state in a manner identical to sleep. The brain mechanisms of state alternations under urethane also closely overlapped those of natural sleep in their sensitivity to cholinergic pharmacological agents and dependence upon activity in the basal forebrain nuclei that are the major source of forebrain acetylcholine. Lastly, stimulation of brainstem regions thought to pace state alternations in sleep transiently disrupted state alternations under urethane. CONCLUSIONS/SIGNIFICANCE: Our results suggest that urethane promotes a condition of behavioural unconsciousness that closely mimics the full spectrum of natural sleep. The use of urethane anaesthesia as a model system will facilitate mechanistic studies into sleep-like brain states and their alternations. In addition, it could also be exploited as a tool for the discovery of new molecular targets that are designed to promote sleep without compromising state alternations.

  8. Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine

    Science.gov (United States)

    Jansen, P.; Vergossen, D.; Renner, D.; John, W.; Götze, J.

    2015-11-01

    An alternative method for determining the state of charge (SOC) on lithium iron phosphate cells by impedance spectra classification is given. Methods based on the electric equivalent circuit diagram (ECD), such as the Kalman Filter, the extended Kalman Filter and the state space observer, for instance, have reached their limits for this cell chemistry. The new method resigns on the open circuit voltage curve and the parameters for the electric ECD. Impedance spectra classification is implemented by a Support Vector Machine (SVM). The classes for the SVM-algorithm are represented by all the impedance spectra that correspond to the SOC (the SOC classes) for defined temperature and aging states. A divide and conquer based search algorithm on a binary search tree makes it possible to grade measured impedances using the SVM method. Statistical analysis is used to verify the concept by grading every single impedance from each impedance spectrum corresponding to the SOC by class with different magnitudes of charged error.

  9. 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.

  10. 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

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

    Directory of Open Access Journals (Sweden)

    Justyna K Rzucidlo

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

    Fuchs, T

    2017-05-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

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

    Data.gov (United States)

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

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

    Science.gov (United States)

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

    2008-05-01

    In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain activity compared to healthy controls and that particularly global slowing correlates with neurocognitive dysfunction. Resting state MEG recordings were obtained from 17 LGG patients and 17 age-, sex-, and education-matched healthy controls. Relative spectral power was calculated in the delta, theta, upper and lower alpha, beta, and gamma frequency band. A battery of standardized neurocognitive tests measuring 6 neurocognitive domains was administered. LGG patients showed a slowing of the resting state brain activity when compared to healthy controls. Decrease in relative power was mainly found in the gamma frequency band in the bilateral frontocentral MEG regions, whereas an increase in relative power was found in the theta frequency band in the left parietal region. An increase of the relative power in the theta and lower alpha band correlated with impaired executive functioning, information processing, and working memory. LGG patients are characterized by global slowing of their resting state brain activity and this slowing phenomenon correlates with the observed neurocognitive deficits.

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

    Directory of Open Access Journals (Sweden)

    Anthony G Hudetz

    2014-12-01

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

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

    Science.gov (United States)

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

    2017-05-17

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

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

    Science.gov (United States)

    Faress, Ahmed; Chau, Tom

    2013-08-15

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

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

    Science.gov (United States)

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

    2017-04-15

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

  1. 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.

  2. 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.

  3. 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.

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

    Science.gov (United States)

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

    2016-08-01

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

  5. Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

    Directory of Open Access Journals (Sweden)

    Minkyung Kim

    2017-06-01

    Full Text Available How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious

  6. Bott periodicity for the topological classification of gapped states of matter with reflection symmetry

    Science.gov (United States)

    Trifunovic, Luka; Brouwer, Piet

    2017-11-01

    Using a dimensional reduction scheme based on scattering theory, we show that the classification tables for topological insulators and superconductors with reflection symmetry can be organized in two period-2 and four period-8 cycles, similar to the Bott periodicity found for topological insulators and superconductors without spatial symmetries. With the help of the dimensional reduction scheme, the classification in arbitrary dimensions d ≥1 can be obtained from the classification in one dimension, for which we present a derivation based on relative homotopy groups and exact sequences to classify one-dimensional insulators and superconductors with reflection symmetry. The resulting classification is fully consistent with a comprehensive classification obtained recently by Shiozaki and Sato [Phys. Rev. B 90, 165114 (2014), 10.1103/PhysRevB.90.165114]. The use of a scattering-matrix-inspired method allows us to address the second descendant Z2 phase, for which the topological nontrivial phase was previously reported to be vulnerable to perturbations that break translation symmetry.

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

    Science.gov (United States)

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

    2015-03-01

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

  8. Characterization of Task-free and Task-performance Brain States via Functional Connectome Patterns

    OpenAIRE

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

    2013-01-01

    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-...

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

    OpenAIRE

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

    2014-01-01

    There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to hot states which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A) that exists after a brief period of food re...

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

    Science.gov (United States)

    2015-12-22

    AFRL-AFOSR-VA-TR-2016-0037 An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States Adrian Lee UNIVERSITY OF WASHINGTON...FORM TO THE ABOVE ORGANIZATION . 1. REPORT DATE (DD-MM-YYYY)      06-01-2016 2. REPORT TYPE Final Performance 3. DATES COVERED (From - To) 15-09-2012...to 14-09-2015 4. TITLE AND SUBTITLE An Integrated Neuroscience and Engineering Approach to Classifying Human Brain- States 5a.  CONTRACT NUMBER 5b

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

    DEFF Research Database (Denmark)

    Kjaerby, Celia; Rasmussen, Rune; Andersen, Mie

    2017-01-01

    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...... 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....

  12. [State of the art of classification, diagnostics and therapy for cervicofacial hemangiomas and vascular malformations].

    Science.gov (United States)

    Werner, J A; Eivazi, B; Folz, B J; Dünne, A-A

    2006-12-01

    The successful treatment of vascular anomalies depends on profound knowledge of the biologic behavior of vascular lesions and their correct classification. On the base of the clinical course Mulliken and Glowacki developed a biologic classification that was accepted as official classification by the ISSVA (International Society for the Study of Vascular Anomalies). Based on an extended literature research, this manuscript will give an overview of different internationally accepted treatment concepts. Even if a wait-and-see strategy can be recommended in many cases of uneventful hemangiomas in infants the proliferative growth of such lesions requires an adequate treatment indication. Vascular malformations that persist lifelong require treatment in the majority of the cases, especially when clinical symptoms occur. Based on individual parameters such as the diameter, location or growth behavior, different therapeutic options as cryotherapy, corticosteroids, laser therapy, sclerotherapy, surgical intervention and/or embolisation can be performed successfully. None of those treatment concepts, however, represents the only treatment method of choice.

  13. A Study on the Effect of Electrical Stimulation as a User Stimuli for Motor Imagery Classification in Brain-Machine Interface.

    Science.gov (United States)

    Bhattacharyya, Saugat; Clerc, Maureen; Hayashibe, Mitsuhiro

    2016-06-13

    Functional Electrical Stimulation (FES) provides a neuroprosthetic interface to non-recovered muscle groups by stimulating the affected region of the human body. FES in combination with Brain-machine interfacing (BMI) has a wide scope in rehabilitation because this system directly links the cerebral motor intention of the users with its corresponding peripheral muscle activations. In this paper, we examine the effect of FES on the electroencephalography (EEG) during motor imagery (left- and right-hand movement) training of the users. Results suggest a significant improvement in the classification accuracy when the subject was induced with FES stimuli as compared to the standard visual one.

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

    Science.gov (United States)

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

    2015-07-01

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

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

    Science.gov (United States)

    Frank, Lawrence R; Galinsky, Vitaly L

    2016-09-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

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

    2017-03-07

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

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

    Directory of Open Access Journals (Sweden)

    Ann M Hermundstad

    2014-05-01

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

  20. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data. PMID:26005413

  1. A classification method of different motor imagery tasks based on fractal features for brain-machine interface.

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2009-03-01

    The objective of this study is to classify spontaneous electroencephalogram (EEG) signal on the basis of fractal concepts. Four motor imagery tasks (left hand movement, right hand movement, feet movement, and tongue movement) were investigated for each EEG recording session. Ten subjects volunteered to participate in this study. As we known, fractal geometry is a mathematical tool for dealing with complex systems like EEG signal. Therefore, we used the fractal dimension (FD) as feature for the application of brain-machine interface (BMI). Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded FD values between relaxing and imaging states of the recorded EEG signal. To show the pattern of FDs, we propose a windowing-based method or also called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. The K-L divergence and different expected values are employed as the input parameters of classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results show that the proposed method is more effective than the conventional methods.

  2. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

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

    Directory of Open Access Journals (Sweden)

    Katrin eArelin

    2015-02-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the laten...

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

    Directory of Open Access Journals (Sweden)

    Юрій Павлович РАК

    2016-02-01

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

  7. 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.

  8. Somatic survival and organ donation among brain-dead patients in the state of Qatar.

    Science.gov (United States)

    George, Saibu; Thomas, Merlin; Ibrahim, Wanis H; Abdussalam, Ahmed; Chandra, Prem; Ali, Husain Shabbir; Raza, Tasleem

    2016-10-31

    The Qatari law, as in many other countries, uses brain death as the main criteria for organ donation and cessation of medical support. By contrast, most of the public in Qatar do not agree with the limitation or withdrawal of medical care until the time of cardiac death. The current study aims to examine the duration of somatic survival after brain death, organ donation rate in brain-dead patients as well as review the underlying etiologies and level of support provided in the state of Qatar. This is a retrospective study of all patients diagnosed with brain death over a 10-year period conducted at the largest tertiary center in Qatar (Hamad General Hospital). Among the 53 patients who were diagnosed with brain death during the study period, the median and mean somatic survivals of brain-dead patients in the current study were 3 and 4.5 days respectively. The most common etiology was intracranial hemorrhage (45.3 %) followed by ischemic stroke (17 %). Ischemic stroke patients had a median survival of 11 days. Organ donation was accepted by only two families (6.6 %) of the 30 brain dead patients deemed suitable for organ donation. The average somatic survival of brain-dead patients is less than one week irrespective of supportive measures provided. Organ donation rate was extremely low among brain-dead patients in Qatar. Improved public education may lead to significant improvement in resource utilization as well as organ transplant donors and should be a major target area of future health care policies.

  9. The brain tryptophan hydroxylase activity in the sleep-like states in frog.

    Science.gov (United States)

    Kulikov, A V; Karmanova, I G; Kozlachkova, E Y; Voronova, I P; Popova, N K

    1994-10-01

    The activity of the rate-limiting enzyme of serotonin biosynthesis, tryptophan hydroxylase, was determined in the brain stem in active awake frogs, and frogs in three sleep-like states: with plastic muscle tone (SLS-1), with rigid muscle tone (SLS-2), and with relaxed muscle tone (SLS-3). Significant decrease in the enzyme activity has been found in frogs in SLS-1 and SLS-2 compared to awake animals. The development in frogs a cataleptic-like immobility after treating the animals with rhythmic lighting was accompanied with a decrease in the brain tryptophan hydroxylase activity. These results provide strong evidence for the involvement of the brain serotonin in frogs in the control of evolutionary ancient sleep-like states, probably by the regulation of muscle tone.

  10. Specific and Evolving Resting-State Network Alterations in Post-Concussion Syndrome Following Mild Traumatic Brain Injury

    OpenAIRE

    Arnaud Messé; Sophie Caplain; Mélanie Pélégrini-Issac; Sophie Blancho; Richard Lévy; Nozar Aghakhani; Michèle Montreuil; Habib Benali; Stéphane Lehéricy

    2013-01-01

    Post-concussion syndrome has been related to axonal damage in patients with mild traumatic brain injury, but little is known about the consequences of injury on brain networks. In the present study, our aim was to characterize changes in functional brain networks following mild traumatic brain injury in patients with post-concussion syndrome using resting-state functional magnetic resonance imaging data. We investigated 17 injured patients with persistent post-concussion syndrome (under the D...

  11. 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....

  12. Resting-State Functional MR Imaging: A New Window to the Brain

    NARCIS (Netherlands)

    Barkhof, F.; Haller, S.; Rombouts, S.A.R.B.

    2014-01-01

    Resting-state (RS) functional magnetic resonance (MR) imaging constitutes a novel paradigm that examines spontaneous brain function by using blood oxygen level-dependent contrast in the absence of a task. Spatially distributed networks of temporal synchronization can be detected that can

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Anne ePetzold

    2015-10-01

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

  15. 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

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

    Science.gov (United States)

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

    2015-01-01

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

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

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    Ali MOMENNEZHAD

    2014-06-01

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

  18. Carnosinase levels in aging brain: redox state induction and cellular stress response.

    Science.gov (United States)

    Bellia, Francesco; Calabrese, Vittorio; Guarino, Francesca; Cavallaro, Monia; Cornelius, Carolin; De Pinto, Vito; Rizzarelli, Enrico

    2009-11-01

    Carnosinase is a dipeptidase found almost exclusively in brain and serum. Its natural substrate carnosine, present at high concentration in the brain, has been proposed as an antioxidant in vivo. We investigated the role of carnosinase in brain aging to establish a possible correlation with age-related changes in cellular stress response and redox status. In addition, a stable HeLa cell line expressing recombinant human serum carnosinase CN1 was established. The enzyme was purified from transfected cells, and specific antibodies were produced against it. Brain expression of CN1, Hsp72, heme oxygenase-1, and thioredoxin reductase increased with age, with a maximal induction in hippocampus and substantia nigra, followed by cerebellum, cortex, septum, and striatum. Hsps induction was associated with significant changes in total SH groups, GSH redox state, carbonyls, and HNE levels. A positive correlation between decrease in GSH and increase in Hsp72 expression was observed in all brain regions examined during aging. Increased carnosinase activity in the brain can lead to decreased carnosine levels and GSH/GSSG ratio. These results, consistent with the current notion that oxidative stress and cellular damage are characteristic hallmarks of the aging process, sustain the critical role of cellular stress-response mechanisms as possible targets for novel antiaging strategies.

  19. 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.

  20. Altered resting-state brain activity at functional MRI during automatic memory consolidation of fear conditioning.

    Science.gov (United States)

    Feng, Tingyong; Feng, Pan; Chen, Zhencai

    2013-07-26

    Investigations of fear conditioning in rodents and humans have illuminated the neural mechanisms of fear acquisition and extinction. However, the neural mechanism of automatic memory consolidation of fear conditioning is still unclear. To address this question, we measured brain activity following fear acquisition using resting-state functional magnetic resonance imaging (rs-fMRI). In the current study, we used a marker of fMRI, amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to quantify the spontaneous brain activity. Brain activity correlated to fear memory consolidation was observed in parahippocampus, insula, and thalamus in resting-state. Furthermore, after acquired fear conditioning, compared with control group some brain areas showed ALFF increased in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) in the experimental group, whereas some brain areas showed decreased ALFF in striatal regions (caudate, putamen). Moreover, the change of ALFF in vmPFC was positively correlated with the subjective fear ratings. These findings suggest that the parahippocampus, insula, and thalamus are the neural substrates of fear memory consolidation. The difference in activity could be attributed to a homeostatic process in which the vmPFC and ACC were involved in the fear recovery process, and change of ALFF in vmPFC predicts subjective fear ratings. Copyright © 2013 Elsevier B.V. All rights reserved.

  1. Lateralized Resting-State Functional Brain Network Organization Changes in Heart Failure.

    Directory of Open Access Journals (Sweden)

    Bumhee Park

    Full Text Available Heart failure (HF patients show brain injury in autonomic, affective, and cognitive sites, which can change resting-state functional connectivity (FC, potentially altering overall functional brain network organization. However, the status of such connectivity or functional organization is unknown in HF. Determination of that status was the aim here, and we examined region-to-region FC and brain network topological properties across the whole-brain in 27 HF patients compared to 53 controls with resting-state functional MRI procedures. Decreased FC in HF appeared between the caudate and cerebellar regions, olfactory and cerebellar sites, vermis and medial frontal regions, and precentral gyri and cerebellar areas. However, increased FC emerged between the middle frontal gyrus and sensorimotor areas, superior parietal gyrus and orbito/medial frontal regions, inferior temporal gyrus and lingual gyrus/cerebellar lobe/pallidum, fusiform gyrus and superior orbitofrontal gyrus and cerebellar sites, and within vermis and cerebellar areas; these connections were largely in the right hemisphere (p<0.005; 10,000 permutations. The topology of functional integration and specialized characteristics in HF are significantly changed in regions showing altered FC, an outcome which would interfere with brain network organization (p<0.05; 10,000 permutations. Brain dysfunction in HF extends to resting conditions, and autonomic, cognitive, and affective deficits may stem from altered FC and brain network organization that may contribute to higher morbidity and mortality in the condition. Our findings likely result from the prominent axonal and nuclear structural changes reported earlier in HF; protecting neural tissue may improve FC integrity, and thus, increase quality of life and reduce morbidity and mortality.

  2. Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

    Science.gov (United States)

    Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

    2014-01-01

    Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

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

    Science.gov (United States)

    2013-03-01

    data were filtered using elliptical IIR filter banks with passbands consistent with the traditional EEG bands. The frequency ranges of the five...structured as an ERP design , but must instead rely on spectral features for classification. The assessment of reliability over time has most commonly been...procedure. The ANN was a feedforward backpropagation neural network (Widrow and Lehr, 1990; Lippmann, 1987) implemented via the MATLAB Neural Network

  4. 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.

  5. Spontaneous brain oscillations as neural fingerprints of working memory capacities: A resting-state MEG study.

    Science.gov (United States)

    Oswald, Victor; Zerouali, Younes; Boulet-Craig, Aubrée; Krajinovic, Maja; Laverdière, Caroline; Sinnett, Daniel; Jolicoeur, Pierre; Lippé, Sarah; Jerbi, Karim; Robaey, Philippe

    2017-12-01

    Short-term storage and mental information manipulation capacities in the human brain are key to healthy cognition. These brain processes collectively known as working memory (WM) are associated with modulations of rhythmic brain activity across multiple brain areas and frequencies. Yet, it is not clear whether - and, if so, how-intrinsic resting-state neuronal oscillations are related to individual WM capacities, as measured by standard neuropsychological tests. We addressed this question by probing the correlation between resting-state brain activity, recorded with magnetoencephalography (MEG), and verbal and visuo-spatial WM indices obtained from the standardized Wechsler Adult Intelligence Scale (WAIS-IV) and the Wechsler Memory Scale (WMS-IV). To this end, 5-min eyes-open resting-state MEG data were acquired in 28 healthy participants. Source-reconstructed spectral power estimates were then computed in standard frequency bands and their correlation with neuropsychological indices across individuals was assessed using Pearson correlation and cluster-level statistics. We found statistically significant positive correlations between spectral amplitudes measured at rest and standardized scores on both verbal and visuo-spatial WM performance. The correlation clusters primarily involved key medial and dorsolateral components within the parietal and prefrontal regions. In addition, while the correlation in some clusters was frequency selective (e.g., alpha-band oscillations), other areas showed correlations with WM across a wide range of frequencies reflecting a broadband effect. These results provide the first evidence for a positive correlation between neuromagnetic signals measured at rest and WM performance separately assessed by standardized neuropsychological tests. Our results advance our understanding of the link between WM capacities and intrinsic oscillatory dynamics networks. They also suggest that individual differences in baseline spectral power might

  6. Reducing traumatic brain injuries in youth sports: youth sports traumatic brain injury state laws, January 2009-December 2012.

    Science.gov (United States)

    Harvey, Hosea H

    2013-07-01

    I sought to describe current state-wide youth sports traumatic brain injury (TBI) laws and their relationship to prevailing scientific understandings of youth sports TBIs, and to facilitate further research by creating an open-source data set of current laws. I used Westlaw and LexisNexis databases to create a 50-state data set of youth sports TBI laws enacted between January 2009 and December 2012. I collected and coded the text and citations of each law and developed a protocol and codebook to facilitate future research. Forty-four states and Washington, DC, passed youth sports TBI laws between 2009 and 2012. No state's youth sports TBI law focuses on primary prevention. Instead, such laws focus on (1) increasing coaches' and parents' ability to identify and respond to TBIs and (2) reducing the immediate risk of multiple TBIs. Existing youth sports TBI laws were not designed to reduce initial TBIs. Evaluation is required to assess their effectiveness in reducing the risk and consequences of multiple TBIs. Continued research and evaluation of existing laws will be needed to develop a more comprehensive youth TBI-reduction solution.

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

    OpenAIRE

    Paolini, Brielle M.; Laurienti, Paul J.; Norris, James; Rejeski, W. Jack

    2014-01-01

    There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to “hot states” which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A) that exists after a brief period of food ...

  8. Can brain state be manipulated to emphasize individual differences in functional connectivity?

    Science.gov (United States)

    Finn, Emily S; Scheinost, Dustin; Finn, Daniel M; Shen, Xilin; Papademetris, Xenophon; Constable, R Todd

    2017-10-15

    While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Heike Leutheuser

    Full Text Available 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.

  10. 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.

  11. 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.

  12. 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.

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

    Science.gov (United States)

    Pagliardini, Silvia; Gosgnach, Simon; Dickson, Clayton T

    2013-01-01

    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.

  14. Motorcycle helmet effectiveness in reducing head, face and brain injuries by state and helmet law.

    Science.gov (United States)

    Olsen, Cody S; Thomas, Andrea M; Singleton, Michael; Gaichas, Anna M; Smith, Tracy J; Smith, Gary A; Peng, Justin; Bauer, Michael J; Qu, Ming; Yeager, Denise; Kerns, Timothy; Burch, Cynthia; Cook, Lawrence J

    2016-12-01

    Despite evidence that motorcycle helmets reduce morbidity and mortality, helmet laws and rates of helmet use vary by state in the U.S. We pooled data from eleven states: five with universal laws requiring all motorcyclists to wear a helmet, and six with partial laws requiring only a subset of motorcyclists to wear a helmet. Data were combined in the Crash Outcome Data Evaluation System's General Use Model and included motorcycle crash records probabilistically linked to emergency department and inpatient discharges for years 2005-2008. Medical outcomes were compared between partial and universal helmet law settings. We estimated adjusted relative risks (RR) and 95 % confidence intervals (CIs) for head, facial, traumatic brain, and moderate to severe head/facial injuries associated with helmet use within each helmet law setting using generalized log-binomial regression. Reported helmet use was higher in universal law states (88 % vs. 42 %). Median charges, adjusted for inflation and differences in state-incomes, were higher in partial law states (emergency department $1987 vs. $1443; inpatient $31,506 vs. $25,949). Injuries to the head and face, including traumatic brain injuries, were more common in partial law states. Effectiveness estimates of helmet use were higher in partial law states (adjusted-RR (CI) of head injury: 2.1 (1.9-2.2) partial law single vehicle; 1.4 (1.2, 1.6) universal law single vehicle; 1.8 (1.6-2.0) partial law multi-vehicle; 1.2 (1.1-1.4) universal law multi-vehicle). Medical charges and rates of head, facial, and brain injuries among motorcyclists were lower in universal law states. Helmets were effective in reducing injury in both helmet law settings; lower effectiveness estimates were observed in universal law states.

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

    Directory of Open Access Journals (Sweden)

    Robin Lester Carhart-Harris

    2014-02-01

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

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

    Science.gov (United States)

    Crook, J; Lovick, T

    2016-01-28

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

  17. A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity.

    Science.gov (United States)

    Huang, Lijie; Huang, Taicheng; Zhen, Zonglei; Liu, Jia

    2016-03-15

    We present a test-retest dataset for evaluation of long-term reliability of measures from structural and resting-state functional magnetic resonance imaging (sMRI and rfMRI) scans. The repeated scan dataset was collected from 61 healthy adults in two sessions using highly similar imaging parameters at an interval of 103-189 days. However, as the imaging parameters were not completely identical, the reliability estimated from this dataset shall reflect the lower bounds of the true reliability of sMRI/rfMRI measures. Furthermore, in conjunction with other test-retest datasets, our dataset may help explore the impact of different imaging parameters on reliability of sMRI/rfMRI measures, which is especially critical for assessing datasets collected from multiple centers. In addition, intelligence quotient (IQ) was measured for each participant using Raven's Advanced Progressive Matrices. The data can thus be used for purposes other than assessing reliability of sMRI/rfMRI alone. For example, data from each single session could be used to associate structural and functional measures of the brain with the IQ metrics to explore brain-IQ association.

  18. Hierarchy of graph-diagonal states based on quantum discord and entanglement classification

    Science.gov (United States)

    Jafarizadeh, Mohammad Ali; Karimi, Naser; Sahlan, Davood Amidi; Heshmati, Ahmad; Yahyavi, Marziyeh

    2017-10-01

    For the relative entropy-based measure of quantum discord the key idea is to find the closest classical state (CCS) for a given state ρ, which is in general a more complicated problem. In this work, we study three and four qubit graph-diagonal states and give the explicit expressions of CCS for these states. Using the CCS, we compute the quantum discord of graph-diagonal states of three and four qubit systems and show that there is a hierarchy for the quantum discord of graph-diagonal states of any three and four qubit systems. Then we classify the entanglement of graph-diagonal states of three and four qubit systems and draw the hierarchy of entanglement of these graph-diagonal states. Finally, we compare the hierarchy of quantum discord and quantum entanglement of the these graph-diagonal states and show that the hierarchy of quantum entanglement is at least in equivalence of quantum discord.

  19. Not in one metric: Neuroticism modulates different resting state metrics within distinctive brain regions.

    Science.gov (United States)

    Gentili, Claudio; Cristea, Ioana Alina; Ricciardi, Emiliano; Vanello, Nicola; Popita, Cristian; David, Daniel; Pietrini, Pietro

    2017-06-01

    Neuroticism is a complex personality trait encompassing diverse aspects. Notably, high levels of neuroticism are related to the onset of psychiatric conditions, including anxiety and mood disorders. Personality traits are stable individual features; therefore, they can be expected to be associated with stable neurobiological features, including the Brain Resting State (RS) activity as measured by fMRI. Several metrics have been used to describe RS properties, yielding rather inconsistent results. This inconsistency could be due to the fact that different metrics portray different RS signal properties and that these properties may be differently affected by neuroticism. To explore the distinct effects of neuroticism, we assessed several distinct metrics portraying different RS properties within the same population. Neuroticism was measured in 31 healthy subjects using the Zuckerman-Kuhlman Personality Questionnaire; RS was acquired by high-resolution fMRI. Using linear regression, we examined the modulatory effects of neuroticism on RS activity, as quantified by the Amplitude of low frequency fluctuations (ALFF, fALFF), regional homogeneity (REHO), Hurst Exponent (H), global connectivity (GC) and amygdalae functional connectivity. Neuroticism modulated the different metrics across a wide network of brain regions, including emotional regulatory, default mode and visual networks. Except for some similarities in key brain regions for emotional expression and regulation, neuroticism affected different metrics in different ways. Metrics more related to the measurement of regional intrinsic brain activity (fALFF, ALFF and REHO), or that provide a parsimonious index of integrated and segregated brain activity (HE), were more broadly modulated in regions related to emotions and their regulation. Metrics related to connectivity were modulated across a wider network of areas. Overall, these results show that neuroticism affects distinct aspects of brain resting state activity

  20. 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

  1. Reducing Traumatic Brain Injuries in Youth Sports: Youth Sports Traumatic Brain Injury State Laws, January 2009–December 2012

    Science.gov (United States)

    2013-01-01

    Objectives. I sought to describe current state-wide youth sports traumatic brain injury (TBI) laws and their relationship to prevailing scientific understandings of youth sports TBIs, and to facilitate further research by creating an open-source data set of current laws. Methods. I used Westlaw and LexisNexis databases to create a 50-state data set of youth sports TBI laws enacted between January 2009 and December 2012. I collected and coded the text and citations of each law and developed a protocol and codebook to facilitate future research. Results. Forty-four states and Washington, DC, passed youth sports TBI laws between 2009 and 2012. No state’s youth sports TBI law focuses on primary prevention. Instead, such laws focus on (1) increasing coaches’ and parents’ ability to identify and respond to TBIs and (2) reducing the immediate risk of multiple TBIs. Conclusions. Existing youth sports TBI laws were not designed to reduce initial TBIs. Evaluation is required to assess their effectiveness in reducing the risk and consequences of multiple TBIs. Continued research and evaluation of existing laws will be needed to develop a more comprehensive youth TBI-reduction solution. PMID:23678903

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

    Science.gov (United States)

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

    2013-12-30

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

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

    Directory of Open Access Journals (Sweden)

    Dong Guangheng

    2012-08-01

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

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

    Science.gov (United States)

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

    2015-09-30

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

  5. Hemispheric specialization varies with EEG brain resting states and phase of menstrual cycle.

    Science.gov (United States)

    Cacioppo, Stephanie; Bianchi-Demicheli, Francesco; Bischof, Paul; Deziegler, Dominique; Michel, Christoph M; Landis, Theodor

    2013-01-01

    A growing body of behavioral studies has demonstrated that women's hemispheric specialization varies as a function of their menstrual cycle, with hemispheric specialization enhanced during their menstruation period. Our recent high-density electroencephalogram (EEG) study with lateralized emotional versus neutral words extended these behavioral results by showing that hemispheric specialization in men, but not in women under birth-control, depends upon specific EEG resting brain states at stimulus arrival, suggesting that hemispheric specialization may be pre-determined at the moment of the stimulus onset. To investigate whether EEG brain resting state for hemispheric specialization could vary as a function of the menstrual phase, we tested 12 right-handed healthy women over different phases of their menstrual cycle combining high-density EEG recordings and the same lateralized lexical decision paradigm with emotional versus neutral words. Results showed the presence of specific EEG resting brain states, associated with hemispheric specialization for emotional words, at the moment of the stimulus onset during the menstruation period only. These results suggest that the pre-stimulus EEG pattern influencing hemispheric specialization is modulated by the hormonal state.

  6. Decreased activation of subcortical brain areas in the motor fatigue state: an fMRI study

    Directory of Open Access Journals (Sweden)

    Lijuan Hou

    2016-08-01

    Full Text Available One aspect of motor fatigue is the exercise-induced reduction of neural activity to voluntarily drive the muscle or muscle group. Functional magnetic resonance imaging provides access to investigate the neural activation on the whole brain level and studies observed changes of activation intensity after exercise-induced motor fatigue in the sensorimotor cortex. However, in human, little evidence exists to demonstrate the role of subcortical brain regions in motor fatigue, which is contradict to abundant researches in rodent indicating that during simple movement, the activity of the basal ganglia is modulated by the state of motor fatigue. Thus, in present study, we explored the effect of motor fatigue on subcortical areas in human. A series of fMRI data were collected from 11 healthy subjects while they were executing simple motor tasks in two conditions: before and under the motor fatigue state. The results showed that in both conditions, movements evoked activation volumes in the sensorimotor areas, SMA, cerebellum, thalamus and basal ganglia. Of primary importance are the results that the intensity and size of activation volumes in the subcortical areas (i.e. thalamus and basal ganglia areas are significantly decreased during the motor fatigue state, implying that motor fatigue disturbs the motor control processing in a way that both sensorimotor areas and subcortical brain areas are less active. Further study is needed to clarify how subcortical areas contribute to the overall decreased activity of CNS during motor fatigue state.

  7. 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.

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

    KAUST Repository

    Zhang, Chongsheng

    2017-04-05

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

  9. State of the science: molecular classifications of breast cancer for clinical diagnostics.

    Science.gov (United States)

    Robison, John E; Perreard, Laurent; Bernard, Philip S

    2004-07-01

    Over the past few years, the study of genomics has embarked on developing gene expression-based classifications for tumors-an initiative that promises to revolutionize cancer medicine. High-throughput genomic platforms, such as microarray and SAGE, have found gene expression signatures that correlate to important clinical parameters used in current staging and are providing additional information that will improve standard of care. Although implementing a molecular taxonomy for prognosis and treatment would likely benefit cancer patients, there remain significant obstacles to using these assays within the current diagnostic framework. Since most genomic assays are being performed from fresh tissue, there is a need to either change the practice of formalin-fixing and paraffin-embedding tissue or adapting the assays for use on degraded RNA specimens. To date, even the most mature data sets, such as molecular classifications for breast cancer, still fall short of the number of patients needed to generalize the results to treating large populations. To implement these assays in large scale, there will need to be standardization of sample procurement, preparation, and analysis. Certainly, the greatest improvements in patient care will come through tailored therapies as genomics is coupled with clinical trials that randomize cohorts to different treatments. This manuscript reviews the current standards of care, presents progress that is being made in the development of genomic assays for breast cancer and discusses options for implementing these new tests into the clinical setting.

  10. Infant homicide and accidental death in the United States, 1940-2005: ethics and epidemiological classification.

    Science.gov (United States)

    Riggs, Jack E; Hobbs, Gerald R

    2011-07-01

    Potential ethical issues can arise during the process of epidemiological classification. For example, unnatural infant deaths are classified as accidental deaths or homicides. Societal sensitivity to the physical abuse and neglect of children has increased over recent decades. This enhanced sensitivity could impact reported infant homicide rates. Infant homicide and accident mortality rates in boys and girls in the USA from 1940 to 2005 were analysed. In 1940, infant accident mortality rates were over 20 times greater than infant homicide rates in both boys and girls. After about 1980, when the ratio of infant accident mortality rates to infant homicide rates decreased to less than five, and the sum of infant accident and homicide rates became relatively constant, further decreases in infant accident mortality rates were associated with increases in reported infant homicide rates. These findings suggest that the dramatic decline of accidental infant mortality and recent increased societal sensitivity to child abuse may be related to the increased infant homicide rates observed in the USA since 1980 rather than an actual increase in societal violence directed against infants. Ethical consequences of epidemiological classification, involving the principles of beneficence, non-maleficence and justice, are suggested by observed patterns in infant accidental deaths and homicides in the USA from 1940 to 2005.

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

    Directory of Open Access Journals (Sweden)

    Ye Tu

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

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

    Science.gov (United States)

    Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

    2010-06-01

    The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBBdrug design and virtual screenings. Published by Elsevier Inc.

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

    Science.gov (United States)

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

    2015-07-30

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

  14. 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.

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

    Science.gov (United States)

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

    2016-12-12

    A new classification of hypovolemic shock based on the shock index (SI) was proposed in 2013. This classification contains four classes of shock and shows good correlation with acidosis, blood product need and mortality. Since their applicability was questioned, the aim of this study was to verify the validity of the new classification in multiple injured patients with traumatic brain injury. Between 2002 and 2013, data from 40 888 patients from the TraumaRegister DGU® were analysed. Patients were classified according to their initial SI at hospital admission (Class I: SI < 0.6, class II: SI ≥0.6 to <1.0, class III SI ≥1.0 to <1.4, class IV: SI ≥1.4). Patients with an additional severe TBI (AIS ≥ 3) were compared to patients without severe TBI. 16,760 multiple injured patients with TBI (AIShead ≥3) were compared to 24,128 patients without severe TBI. With worsening of SI class, mortality rate increased from 20 to 53% in TBI patients. Worsening SI classes were associated with decreased haemoglobin, platelet counts and Quick's values. The number of blood units transfused correlated with worsening of SI. Massive transfusion rates increased from 3% in class I to 46% in class IV. The accuracy for predicting transfusion requirements did not differ between TBI and Non TBI patients. The use of the SI based classification enables a quick assessment of patients in hypovolemic shock based on universally available parameters. Although the pathophysiology in TBI and Non TBI patients and early treatment methods such as the use of vasopressors differ, both groups showed an identical probability of recieving blood products within the respective SI class. Regardless of the presence of TBI, the classification of hypovolemic shock based on the SI enables a fast and reliable assessment of hypovolemic shock in the emergency department. Therefore, the presented study supports the SI as a feasible tool to assess patients at risk for blood product transfusions, even in

  16. Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data

    Science.gov (United States)

    Davis, Faith G.; Dolecek, Therese A.; McCarthy, Bridget J.; Villano, John L.

    2012-01-01

    Few population estimates of brain metastasis in the United States are available, prompting this study. Our objective was to estimate the expected number of metastatic brain tumors that would subsequently develop among incident cancer cases for 1 diagnosis year in the United States. Incidence proportions for primary cancer sites known to develop brain metastasis were applied to United States cancer incidence data for 2007 that were retrieved from accessible data sets through Centers for Disease Control and Prevention (CDC Wonder) and Surveillance, Epidemiology, and End Results (SEER) Program Web sites. Incidence proportions were identified for cancer sites, reflecting 80% of all cancers. It was conservatively estimated that almost 70 000 new brain metastases would occur over the remaining lifetime of individuals who received a diagnosis in 2007 of primary invasive cancer in the United States. That is, 6% of newly diagnosed cases of cancer during 2007 would be expected to develop brain metastasis as a progression of their original cancer diagnosis; the most frequent sites for metastases being lung and bronchus and breast cancers. The estimated numbers of brain metastasis will be expected to be higher among white individuals, female individuals, and older age groups. Changing patterns in the occurrence of primary cancers, trends in populations at risk, effectiveness of treatments on survival, and access to those treatments will influence the extent of brain tumor metastasis at the population level. These findings provide insight on the patterns of brain tumor metastasis and the future burden of this condition in the United States. PMID:22898372

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  18. Breathing and brain state: urethane anesthesia as a model for natural sleep.

    Science.gov (United States)

    Pagliardini, Silvia; Funk, Gregory D; Dickson, Clayton T

    2013-09-15

    Respiratory control differs dramatically across sleep stages. Indeed, along with rapid eye movements (REM), respiration was one of the first physiological variables shown to be modulated across sleep stages. The study of sleep stages, their physiological correlates, and neurobiological underpinnings present a challenge because of the fragility and unpredictability of individual stages, not to mention sleep itself. Although anesthesia has often substituted as a model for a unitary stage of slow-wave (non-REM) sleep, it is only recently that urethane anesthesia has been proposed to model the full spectrum of sleep given the presence of spontaneous brain state alternations and concurrent physiological correlates that appear remarkably similar to natural sleep. We describe this model, its parallels with natural sleep, and its power for studying modulation of respiration. Specifically, we report data on the EEG characteristics across brain states under urethane anesthesia, the dependence of brain alternations on neurotransmitter systems, and the observations on state dependent modulation of respiration. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. Brain-state dependent uncoupling of BOLD and local field potentials in laminar olfactory bulb.

    Science.gov (United States)

    Gong, Ling; Li, Bo; Wu, Ruiqi; Li, Anan; Xu, Fuqiang

    2014-09-19

    The neural activities of the olfactory bulb (OB) can be modulated significantly by internal brain states. While blood oxygenation level dependent functional MRI (BOLD-fMRI) has been extensively applied to study OB in small animals, the relationship between BOLD signals and electrophysiological signals remains to be elucidated. Our recent study has revealed a complex relationship between BOLD and local field potentials (LFP) signals in different OB layers during odor stimulation. However, no study has been performed to compare these two types of signals under global brain states. Here, the changes of BOLD and LFP signals in the glomerular, mitral cell, and granular cell layers of the OB under different brain states, which were induced by different concentrations of isoflurane, were sequentially acquired using electrode array and high-resolution MRI. It was found that under deeper anesthesia, the LFP powers in all layers were decreased but the BOLD signals were unexpectedly increased. Furthermore, the decreases of LFP powers were layer-independent, but the increases of BOLD signal were layer-specific, with the order of glomerular>mitral cell>granular cell layer. The results provide new evidence that the direct neural activity levels might not be correlated well with BOLD signals in some cases, and remind us that cautions should be taken to use BOLD signals as the index of neural activities. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  20. Distinction in coherent neural network between resting and working brain states.

    Science.gov (United States)

    Liu, Xiao; Zhu, Xiao-Hong; Chen, Wei

    2011-01-01

    The resting brain is not silent; rather, it is characterized by organized resting-state networks showing spontaneous and coherent neuronal activities, which can be mapped using the spatiotemporal correlation of blood oxygenation level-dependent (BOLD) signal fluctuations measured by functional magnetic resonance imaging (fMRI). However, it remains elusive whether the similar fMRI approach is able to image the coherent network in a working brain, and if yes, whether there is a distinction between the resting- and working-state coherent networks. This study aimed to address these questions in the human visual cortex with a desired activation paradigm using continuous, sustained visual stimuli. It was found that the resting-state coherent network covering the human visual cortex was spatially reorganized during the stimulation into two coherent networks with distinct temporal characteristics of BOLD fluctuations: one covering the activated visual cortical region and the other covering the remaining (nonactivated) visual cortex. The stimulus-specific reorganization of the coherent network observed in the present fMRI study in human is consistent with previous electrophysiological findings from animal studies, and may suggest an essential mechanism for brain functioning. Finally, a similar fMRI experiment was also conducted under brief, short stimulation to examine how the stimulation paradigm can affect the observations.

  1. Extraversion and Neuroticism relate to topological properties of resting-state brain networks

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    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.

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

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    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.

  3. Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research.

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    Brakowski, Janis; Spinelli, Simona; Dörig, Nadja; Bosch, Oliver Gero; Manoliu, Andrei; Holtforth, Martin Grosse; Seifritz, Erich

    2017-09-01

    The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, pharmaceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across

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

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    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.

  5. Deep brain stimulation of the subthalamic nucleus selectively improves learning of weakly associated cue combinations during probabilistic classification learning in Parkinson's disease.

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    Wilkinson, Leonora; Beigi, Mazda; Lagnado, David A; Jahanshahi, Marjan

    2011-05-01

    Evidence from functional imaging and clinical studies on patients with Parkinson's disease (PD) or Huntington's disease (HD) suggests that the basal ganglia play a crucial role in learning on the weather prediction task (WPT). Using deep brain stimulation (DBS) on versus off methodology, the aim of this study was to investigate the effect of altering the output from the basal ganglia to the prefrontal cortex on implicit probabilistic classification learning on the WPT by patients with PD. Eleven PD patients with bilateral DBS of the subthalamic nucleus (STN) and 13 matched controls completed 200 trials of the WPT on 2 separate occasions, with the patients tested with DBS of the STN on or off. DBS of the STN had no effect on overall WPT learning. However, STN DBS selectively improved implicit learning of cue combinations that were weakly (implicitly), rather than strongly (explicitly), associated with the WPT outcome. Results suggest that the STN plays a role in implicit probabilistic classification learning by altering basal ganglia output to the frontal cortex.

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

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    Naseer, Noman; Hong, Keum-Shik

    2013-10-11

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

  7. Transient brain activity explains the spectral content of steady-state visual evoked potentials.

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    Gaume, Antoine; Vialatte, François; Dreyfus, Gérard

    2014-01-01

    Steady-state visual evoked potentials (SSVEPs) are widely used in the design of brain-computer interfaces (BCIs). A lot of effort has therefore been devoted to find a fast and reliable way to detect SSVEPs. We study the link between transient and steady-state VEPs and show that it is possible to predict the spectral content of a subject's SSVEPs by simulating trains of transient VEPs. This could lead to a better understanding of evoked potentials as well as to better performances of SSVEP-based BCIs, by providing a tool to improve SSVEP detection algorithms.

  8. Frustrated resonating valence bond states in two dimensions: classification and short-range correlations.

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    Yang, Fan; Yao, Hong

    2012-10-05

    Resonating valence bond (RVB) states are of crucial importance in our intuitive understanding of quantum spin liquids in 2D. We systematically classify short-range bosonic RVB states into symmetric or nematic spin liquids by examining their flux patterns. We further map short-range bosonic RVB states into projected BCS wave functions, on which we perform large-scale Monte Carlo simulations without the minus sign problem. Our results clearly show that both spin and dimer correlations decay exponentially in all the short-range frustrated (nonbipartite or Z2) bosonic RVB states we studied, indicating that they are gapped Z2 quantum spin liquids. Generically, we conjecture that all short-range frustrated bosonic RVB states in 2D have only short-range correlations.

  9. Classification of Non-Time-Locked Rapid Serial Visual Presentation Events for Brain-Computer Interaction Using Deep Learning

    Science.gov (United States)

    2014-07-08

    raw data include 7.1 s EEG data epochs, each centered on a 4.1s RSVP burst. The data was fIrSt bandpass-filtered in the range 2-100Hz...using this data . Classification for RSVP data is usually performed in a ’time-locked’ fashion, by analyzing the spectrum or amplitude in the EEG ... EEG data including high dimensional feature spaces, temporal and spatial data correlation, and excessive noise will affect the imple mentation

  10. Cortical neurons and networks are dormant but fully responsive during isoelectric brain state.

    Science.gov (United States)

    Altwegg-Boussac, Tristan; Schramm, Adrien E; Ballestero, Jimena; Grosselin, Fanny; Chavez, Mario; Lecas, Sarah; Baulac, Michel; Naccache, Lionel; Demeret, Sophie; Navarro, Vincent; Mahon, Séverine; Charpier, Stéphane

    2017-09-01

    A continuous isoelectric electroencephalogram reflects an interruption of endogenously-generated activity in cortical networks and systematically results in a complete dissolution of conscious processes. This electro-cerebral inactivity occurs during various brain disorders, including hypothermia, drug intoxication, long-lasting anoxia and brain trauma. It can also be induced in a therapeutic context, following the administration of high doses of barbiturate-derived compounds, to interrupt a hyper-refractory status epilepticus. Although altered sensory responses can be occasionally observed on an isoelectric electroencephalogram, the electrical membrane properties and synaptic responses of individual neurons during this cerebral state remain largely unknown. The aim of the present study was to characterize the intracellular correlates of a barbiturate-induced isoelectric electroencephalogram and to analyse the sensory-evoked synaptic responses that can emerge from a brain deprived of spontaneous electrical activity. We first examined the sensory responsiveness from patients suffering from intractable status epilepticus and treated by administration of thiopental. Multimodal sensory responses could be evoked on the flat electroencephalogram, including visually-evoked potentials that were significantly amplified and delayed, with a high trial-to-trial reproducibility compared to awake healthy subjects. Using an analogous pharmacological procedure to induce prolonged electro-cerebral inactivity in the rat, we could describe its cortical and subcortical intracellular counterparts. Neocortical, hippocampal and thalamo-cortical neurons were all silent during the isoelectric state and displayed a flat membrane potential significantly hyperpolarized compared with spontaneously active control states. Nonetheless, all recorded neurons could fire action potentials in response to intracellularly injected depolarizing current pulses and their specific intrinsic

  11. Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

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    Chandler R. L. Mongerson

    2017-08-01

    Full Text Available Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1 present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA, such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2 review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3 discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

  12. Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity.

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    Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Liang, Zhu-Yuan; Chen, Xiao-Ping; Zheng, Dang; Tan, Cheng; Tian, Zhi-Qiang; Wang, Chun-Hui; Bai, Yan-Qiang; Chen, Shan-Guang; Li, Shu

    2014-01-01

    Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI) to study whether the functional architecture of the brain is altered after 45 days of -6° head-down tilt (HDT) bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of -6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC), to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC) analysis. We found decreased DC in two regions, the left anterior insula (aINS) and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies), in the male volunteers after 45 days of -6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS-MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function, and central neural activity.

  13. Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity

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    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.

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

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    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

  15. Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

    Science.gov (United States)

    Yuan, Han; He, Bin

    2014-01-01

    Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e. the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g. electroencephalography (EEG), and have demonstrated the capability of multi-dimensional prosthesis control. This article reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications are reviewed. Lastly, limitations of SMR-BCIs and future outlooks are also discussed. PMID:24759276

  16. Regional homogeneity of the resting-state brain activity correlates with individual intelligence.

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    Wang, Leiqiong; Song, Ming; Jiang, Tianzi; Zhang, Yunting; Yu, Chunshui

    2011-01-25

    Resting-state functional magnetic resonance imaging has confirmed that the strengths of the long distance functional connectivity between different brain areas are correlated with individual differences in intelligence. However, the association between the local connectivity within a specific brain region and intelligence during rest remains largely unknown. The aim of this study is to investigate the relationship between local connectivity and intelligence. Fifty-nine right-handed healthy adults participated in the study. The regional homogeneity (ReHo) was used to assess the strength of local connectivity. The associations between ReHo and full-scale intelligence quotient (FSIQ) scores were studied in a voxel-wise manner using partial correlation analysis controlling for age and sex. We found that the FSIQ scores were positively correlated with the ReHo values of the bilateral inferior parietal lobules, middle frontal, parahippocampal and inferior temporal gyri, the right thalamus, superior frontal and fusiform gyri, and the left superior parietal lobule. The main findings are consistent with the parieto-frontal integration theory (P-FIT) of intelligence, supporting the view that general intelligence involves multiple brain regions throughout the brain. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  17. Whole Brain Functional Connectivity Using Phase Locking Measures Of Resting State Magnetoencephalography

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    Benjamin T Schmidt

    2014-06-01

    Full Text Available The analysis of spontaneous functional connectivity reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG. Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we preformed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

  18. Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography.

    Science.gov (United States)

    Schmidt, Benjamin T; Ghuman, Avniel S; Huppert, Theodore J

    2014-01-01

    The analysis of spontaneous functional connectivity (sFC) reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG). Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality (EVC) scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we performed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

  19. NEUROREHABILITATION OF POST-TRAUMATIC STRESS AND DEPRESSIVE BEHAVIORS BY BRAIN STATE CONDITIONING

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    Vijendra K. SINGH

    2009-06-01

    Full Text Available Brain State Conditioning™ (BSC is an innovative technology that optimizes brainwaves in real-time to achieve balance and harmony of the human brain. Since the brain function is imbalanced in individuals with psychiatric disorders and neurological diseases, we explored the possibility of using this technology to help people with post-traumatic stress disorder (PTSD and depression. We conducted a pilot study of 8 adult subjects who had symptoms of PTSD, depression and anxiety problems. The severity of symptoms was evaluated by Objective survey and Beck’s inventory for depression and anxiety. After the initial assessment of brain maps, individuals were administered with highly personalized training sessions, for example 4-5 sessions over 4-5 days. After the administration of BSC, we found a consistent decline in Beck’s inventory scores, which implied alleviation of depressive and anxiety tendencies. All subjects in the study responded to BSC technology and showed noticeable improvement in the quality of their lives. Thus we suggest that BSC is a viable approach to brainwave optimization to help people overcome health problems due to PTSD and depression.

  20. Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

  1. Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

  2. Diverse effects of hypothermia therapy in patients with severe traumatic brain injury based on the computed tomography classification of the traumatic coma data bank.

    Science.gov (United States)

    Suehiro, Eiichi; Koizumi, Hiroyasu; Fujisawa, Hirosuke; Fujita, Motoki; Kaneko, Tadashi; Oda, Yasutaka; Yamashita, Susumu; Tsuruta, Ryosuke; Maekawa, Tsuyoshi; Suzuki, Michiyasu

    2015-03-01

    A multicenter randomized controlled trial of patients with severe traumatic brain injury who received therapeutic hypothermia or fever control was performed from 2002 to 2008 in Japan (BHYPO). There was no difference in the therapeutic effect on traumatic brain injury between the two groups. The efficacy of hypothermia treatment and the objective of the treatment were reexamined based on a secondary analysis of the BHYPO trial in 135 patients (88 treated with therapeutic hypothermia and 47 with fever control). This analysis was performed to examine clinical outcomes according to the CT classification of the Traumatic Coma Data Bank on admission. Clinical outcomes were evaluated with the Glasgow Outcome Scale and mortality at 6 months after injury. Good recovery and moderate disability were defined as favorable outcomes. Favorable outcomes in young patients (≤50 years old) with evacuated mass lesions significantly increased from 33.3% with fever control to 77.8% with therapeutic hypothermia. Patients with diffuse injury III who were treated with therapeutic hypothermia, however, had significantly higher mortality than patients treated with fever control. It was difficult to control intracranial pressure with hypothermia for patients with diffuse injury III, but hypothermia was effective for young patients with an evacuated mass lesion.

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

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    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.

  4. 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.

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

    Science.gov (United States)

    Schiff, Nicholas D

    2013-01-01

    This chapter considers the use of central thalamic deep brain stimulation (CT/DBS) to support arousal regulation mechanisms in the minimally conscious state (MCS). CT/DBS for selected patients in a MCS is first placed in the historical context of prior efforts to use thalamic electrical brain stimulation to treat the unconscious clinical conditions of coma and vegetative state. These previous studies and a proof of concept result from a single-subject study of a patient in a MCS are reviewed against the background of new population data providing benchmarks of the natural history of vegetative and MCSs. The conceptual foundations for CT/DBS in selected patients in a MCS are then presented with consideration of both circuit and cellular mechanisms underlying recovery of consciousness identified from empirical studies. Directions for developing future generalizable criteria for CT/DBS that focus on the integrity of necessary brain systems and behavioral profiles in patients in a MCS that may optimally response to support of arousal regulation mechanisms are proposed. © 2013 Elsevier B.V. All rights reserved.

  6. 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.

  7. 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.

  8. 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...... (NP), transient membrane permeabilization (TMP), and permanent membrane permeabilization (PMP), respectively. DW-MRI was acquired 5 minutes, 2 hours, 24 hours and 48 hours after EP. Histology was performed for validation of the permeabilization states. Tissue content of water, Na+, K+, Ca2......+, and extracellular volume were determined. The Kruskal-Wallis test was used to compare the DW-MRI parameters, apparent diffusion coefficient (ADC) and kurtosis, at different voltage levels. The two-sample Mann- Whitney test with Holm's Bonferroni correction was used to identify pairs of significantly different...

  9. 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. Copyright © 2015. Published by Elsevier Inc.

  10. Brain cancer incidence trends in relation to cellular telephone use in the United States

    Science.gov (United States)

    Inskip, Peter D.; Hoover, Robert N.; Devesa, Susan S.

    2010-01-01

    The use of cellular telephones has grown explosively during the past two decades, and there are now more than 279 million wireless subscribers in the United States. If cellular phone use causes brain cancer, as some suggest, the potential public health implications could be considerable. One might expect the effects of such a prevalent exposure to be reflected in general population incidence rates, unless the induction period is very long or confined to very long-term users. To address this issue, we examined temporal trends in brain cancer incidence rates in the United States, using data collected by the Surveillance, Epidemiology, and End Results (SEER) Program. Log-linear models were used to estimate the annual percent change in rates among whites. With the exception of the 20–29-year age group, the trends for 1992–2006 were downward or flat. Among those aged 20–29 years, there was a statistically significant increasing trend between 1992 and 2006 among females but not among males. The recent trend in 20–29-year-old women was driven by a rising incidence of frontal lobe cancers. No increases were apparent for temporal or parietal lobe cancers, or cancers of the cerebellum, which involve the parts of the brain that would be more highly exposed to radiofrequency radiation from cellular phones. Frontal lobe cancer rates also rose among 20–29-year-old males, but the increase began earlier than among females and before cell phone use was highly prevalent. Overall, these incidence data do not provide support to the view that cellular phone use causes brain cancer. PMID:20639214

  11. 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.

  12. Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

    Science.gov (United States)

    Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

    2017-01-01

    As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

  13. Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network

    Directory of Open Access Journals (Sweden)

    Shujie Geng

    2017-07-01

    Full Text Available As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS has attracted widespread attention for advancing resting-state functional connectivity (FC and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

  14. 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 pbrain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

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

    Science.gov (United States)

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

    2016-01-01

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

  16. State of charge classification for lithium-ion batteries using impedance based features

    Science.gov (United States)

    Patrik Felder, Marian; Götze, Jürgen

    2017-09-01

    Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV) to state of charge (SoC) characteristic of lithium ion batteries, methods employed in applications with other cell chemistries cannot be adapted. Exploiting the higher significance of the impedance for state estimation for that chemistry, new impedance based features are proposed by this work. To evaluate the suitability of these features, simulations have been conducted using a simplified on-board power supply net as excitation source. The simulation outcome has been investigated regarding the cross correlation factor rxy and in a polynomial regression scenario. The results of the simulations show a best case error below 1 % SoC, which is 3 percentage points lower than using terminal voltage and impedance. When increasing the measurement uncertainty, the difference remains around 2 percent points.

  17. Consequences of bulk odd-frequency superconducting states for the classification of Cooper pairs

    OpenAIRE

    Asano, Yasuhiro; Fominov, Yakov V.; Tanaka, Yukio

    2014-01-01

    We analyze symmetries and magnetic properties of Cooper pairs appearing as subdominant pairing correlations in inhomogeneous superconductors, on the basis of the quasiclassical Green-function theory. The frequency symmetry, parity, and the type of magnetic response of such subdominant correlations are opposite to those of the dominant pairing correlations in the bulk state. Our conclusion is valid even when we generalize the theory of superconductivity to recently proposed diamagnetic odd-fre...

  18. 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.

  19. Acute Effects of Modafinil on Brain Resting State Networks in Young Healthy Subjects

    Science.gov (United States)

    Pieramico, Valentina; Ferretti, Antonio; Macchia, Antonella; Tommasi, Marco; Saggino, Aristide; Ciavardelli, Domenico; Manna, Antonietta; Navarra, Riccardo; Cieri, Filippo; Stuppia, Liborio; Tartaro, Armando; Sensi, Stefano L.

    2013-01-01

    Background There is growing debate on the use of drugs that promote cognitive enhancement. Amphetamine-like drugs have been employed as cognitive enhancers, but they show important side effects and induce addiction. In this study, we investigated the use of modafinil which appears to have less side effects compared to other amphetamine-like drugs. We analyzed effects on cognitive performances and brain resting state network activity of 26 healthy young subjects. Methodology A single dose (100 mg) of modafinil was administered in a double-blind and placebo-controlled study. Both groups were tested for neuropsychological performances with the Raven’s Advanced Progressive Matrices II set (APM) before and three hours after administration of drug or placebo. Resting state functional magnetic resonance (rs-FMRI) was also used, before and after three hours, to investigate changes in the activity of resting state brain networks. Diffusion Tensor Imaging (DTI) was employed to evaluate differences in structural connectivity between the two groups. Protocol ID: Modrest_2011; NCT01684306; http://clinicaltrials.gov/ct2/show/NCT01684306. Principal Findings Results indicate that a single dose of modafinil improves cognitive performance as assessed by APM. Rs-fMRI showed that the drug produces a statistically significant increased activation of Frontal Parietal Control (FPC; pmodafinil has cognitive enhancing properties and provide functional connectivity data to support these effects. Trial Registration ClinicalTrials.gov NCT01684306 http://clinicaltrials.gov/ct2/show/NCT01684306. PMID:23935959

  20. 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

  1. [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.

  2. Examination of corticothalamic fiber projections in United States service members with mild traumatic brain injury

    Science.gov (United States)

    Rashid, Faisal M.; Dennis, Emily L.; Villalon-Reina, Julio E.; Jin, Yan; Lewis, Jeffrey D.; York, Gerald E.; Thompson, Paul M.; Tate, David F.

    2017-11-01

    Mild traumatic brain injury (mTBI) is characterized clinically by a closed head injury involving differential or rotational movement of the brain inside the skull. Over 3 million mTBIs occur annually in the United States alone. Many of the individuals who sustain an mTBI go on to recover fully, but around 20% experience persistent symptoms. These symptoms often last for many weeks to several months. The thalamus, a structure known to serve as a global networking or relay system for the rest of the brain, may play a critical role in neurorehabiliation and its integrity and connectivity after injury may also affect cognitive outcomes. To examine the thalamus, conventional tractography methods to map corticothalamic pathways with diffusion-weighted MRI (DWI) lead to sparse reconstructions that may contain false positive fibers that are anatomically inaccurate. Using a specialized method to zero in on corticothalamic pathways with greater robustness, we noninvasively examined corticothalamic fiber projections using DWI, in 68 service members. We found significantly lower fractional anisotropy (FA), a measure of white matter microstructural integrity, in pathways projecting to the left pre- and postcentral gyri - consistent with sensorimotor deficits often found post-mTBI. Mapping of neural circuitry in mTBI may help to further our understanding of mechanisms underlying recovery post-TBI.

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

    Directory of Open Access Journals (Sweden)

    Lu D

    2014-02-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

  5. Association between number of cell phone contracts and brain tumor incidence in nineteen U.S. States.

    Science.gov (United States)

    Lehrer, Steven; Green, Sheryl; Stock, Richard G

    2011-02-01

    Some concern has arisen about adverse health effects of cell phones, especially the possibility that the low power microwave-frequency signal transmitted by the antennas on handsets might cause brain tumors or accelerate the growth of subclinical tumors. We analyzed data from the Statistical Report: Primary Brain Tumors in the United States, 2000-2004 and 2007 cell phone subscription data from the Governing State and Local Sourcebook. There was a significant correlation between number of cell phone subscriptions and brain tumors in nineteen US states (r = 0.950, P numbers of both cell phone subscriptions and brain tumors could be due solely to the fact that some states, such as New York, have much larger populations than other states, such as North Dakota, multiple linear regression was performed with number of brain tumors as the dependent variable, cell phone subscriptions, population, mean family income and mean age as independent variables. The effect of cell phone subscriptions was significant (P = 0.017), and independent of the effect of mean family income (P = 0.894), population (P = 0.003) and age (0.499). The very linear relationship between cell phone usage and brain tumor incidence is disturbing and certainly needs further epidemiological evaluation. In the meantime, it would be prudent to limit exposure to all sources of electro-magnetic radiation.

  6. Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

    Directory of Open Access Journals (Sweden)

    Bin Wang

    2017-11-01

    Full Text Available Alzheimer's disease (AD is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs, 33 early MCI (EMCI, 32 late MCI (LMCI, and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ scores and global Clinical Dementia Rating (CDR scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE

  7. State of charge classification for lithium-ion batteries using impedance based features

    Directory of Open Access Journals (Sweden)

    M. P. Felder

    2017-09-01

    Full Text Available Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to a flat open circuit voltage (OCV to state of charge (SoC characteristic of lithium ion batteries, methods employed in applications with other cell chemistries cannot be adapted. Exploiting the higher significance of the impedance for state estimation for that chemistry, new impedance based features are proposed by this work. To evaluate the suitability of these features, simulations have been conducted using a simplified on-board power supply net as excitation source. The simulation outcome has been investigated regarding the cross correlation factor rxy and in a polynomial regression scenario. The results of the simulations show a best case error below 1 % SoC, which is 3 percentage points lower than using terminal voltage and impedance. When increasing the measurement uncertainty, the difference remains around 2 percent points.

  8. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Science.gov (United States)

    Wingfield, Cai; Su, Li; Liu, Xunying; Zhang, Chao; Woodland, Phil; Thwaites, Andrew; Fonteneau, Elisabeth; Marslen-Wilson, William D

    2017-09-01

    There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  9. Alcohol affects the brain's resting-state network in social drinkers.

    Directory of Open Access Journals (Sweden)

    Chrysa Lithari

    Full Text Available Acute alcohol intake is known to enhance inhibition through facilitation of GABA(A receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN. To test our hypothesis, electroencephalographic (EEG measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC on standardized Low Resolution Electromagnetic Tomography (sLORETA solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected in alpha, beta (eyes-open and theta bands (eyes-closed following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05. Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo. Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as

  10. 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.

  11. 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.

  12. Specific and evolving resting-state network alterations in post-concussion syndrome following mild traumatic brain injury.

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    Arnaud Messé

    Full Text Available Post-concussion syndrome has been related to axonal damage in patients with mild traumatic brain injury, but little is known about the consequences of injury on brain networks. In the present study, our aim was to characterize changes in functional brain networks following mild traumatic brain injury in patients with post-concussion syndrome using resting-state functional magnetic resonance imaging data. We investigated 17 injured patients with persistent post-concussion syndrome (under the DSM-IV criteria at 6 months post-injury compared with 38 mild traumatic brain injury patients with no post-concussion syndrome and 34 healthy controls. All patients underwent magnetic resonance imaging examinations at the subacute (1-3 weeks and late (6 months phases after injury. Group-wise differences in functional brain networks were analyzed using graph theory measures. Patterns of long-range functional networks alterations were found in all mild traumatic brain injury patients. Mild traumatic brain injury patients with post-concussion syndrome had greater alterations than patients without post-concussion syndrome. In patients with post-concussion syndrome, changes specifically affected temporal and thalamic regions predominantly at the subacute stage and frontal regions at the late phase. Our results suggest that the post-concussion syndrome is associated with specific abnormalities in functional brain network that may contribute to explain deficits typically observed in PCS patients.

  13. Where's the Psychology? A Commentary on "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art"

    Science.gov (United States)

    Leighton, Jacqueline P.

    2008-01-01

    In this commentary, the author asks the analogous question, "where's the psychology?" Not because the authors of the focus article "Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art" have not provided a solid review of the technical aspects of Diagnostic…

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

    Science.gov (United States)

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

    2016-01-01

    Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery. © 2016 Elsevier B.V. All rights reserved.

  15. 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.

  16. 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.

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

    Science.gov (United States)

    Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

    2014-01-01

    The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits 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.

  18. 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.

  19. 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.

  20. Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

    Science.gov (United States)

    Collow, Allie Marquardt; Bosilovich, Mike; Ullrich, Paul; Hoeck, Ian

    2017-01-01

    Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

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

    NARCIS (Netherlands)

    Zhang, Delong; Liang, Bishan; Wu, Xia; Wang, Zengjian; Xu, Pengfei; Chang, Song; Liu, Bo; Liu, Ming; Huang, Ruiwang

    2015-01-01

    The present study examined directional connections in the brain among resting-state networks (RSNs) when the participant had their eyes open (E0) or had their eyes closed (EC). The resting state fMRI data were collected from 20 healthy participants (9 males, 20.17 +/- 2.74 years) under the EO and EC

  2. The International Classification of Functioning (ICF) to evaluate deep brain stimulation neuromodulation in childhood dystonia-hyperkinesia informs future clinical & research priorities in a multidisciplinary model of care.

    Science.gov (United States)

    Gimeno, Hortensia; Lin, Jean-Pierre

    2017-01-01

    The multidisciplinary team (MDT) approach illustrates how motor classification systems, assessments and outcome measures currently available have been applied to a national cohort of children and young people with dystonia and other hyperkinetic movement disorders (HMD) particularly with a focus on dyskinetic cerebral palsy (CP). The paper is divided in 3 sections. Firstly, we describe the service model adopted by the Complex Motor Disorders Service (CMDS) at Evelina London Children's Hospital and King's College Hospital (ELCH-KCH) for deep brain stimulation. We describe lessons learnt from available dystonia studies and discuss/propose ways to measure DBS and other dystonia-related intervention outcomes. We aim to report on current available functional outcome measures as well as some impairment-based assessments that can encourage and generate discussion among movement disorders specialists of different backgrounds regarding choice of the most important areas to be measured after DBS and other interventions for dystonia management. Finally, some recommendations for multi-centre collaboration in regards to functional clinical outcomes and research methodologies for dystonia-related interventions are proposed. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

  3. Impact of asymptomatic nodavirus carrier state and intraperitoneal viral mimic injection on brain transcript expression in Atlantic cod (Gadus morhua).

    Science.gov (United States)

    Rise, Matthew L; Hall, Jennifer R; Rise, Marlies; Hori, Tiago S; Browne, Mitchell J; Gamperl, A Kurt; Hubert, Sophie; Kimball, Jennifer; Bowman, Sharen; Johnson, Stewart C

    2010-07-07

    Nodaviruses and other RNA viruses have a profoundly negative impact on the global aquaculture industry. Nodaviruses target nervous tissue causing viral nervous necrosis, a disease characterized by neurological damage, swimming abnormalities, and morbidity. This study used functional genomic techniques to study the Atlantic cod (Gadus morhua) brain transcript expression responses to asymptomatic high nodavirus carrier state and intraperitoneal injection of polyriboinosinic polyribocytidylic acid (pIC). Reciprocal suppression subtractive hybridization (SSH) cDNA libraries enriched for virus-responsive brain transcripts were constructed and characterized. We generated 1,938 expressed sequence tags (ESTs) from a forward brain SSH library (enriched for transcripts upregulated by nodavirus and/or pIC) and 1,980 ESTs from a reverse brain SSH library (enriched for transcripts downregulated by nodavirus and/or pIC). To examine the effect of nodavirus carrier state on individual brain gene expression in asymptomatic cod, 27 transcripts of interest were selected for quantitative reverse transcription-polymerase chain reaction (QPCR) studies. Transcripts found to be >10-fold upregulated in individuals with a high nodavirus carrier state relative to those in a no/low nodavirus carrier state were identified as ISG15, IL8, DHX58 (alias LGP2), ZNFX1, RSAD2 (alias viperin), and SACS (sacsin, alias spastic ataxia of Charlevoix-Saguenay). These and other SSH-identified transcripts were also found by QPCR to be significantly (P SSH library, including two putative ubiquitination pathway members (HERC4 and SUMO2), were found to be significantly (P < 0.05) downregulated in individuals with a high nodavirus carrier state. Our data shows that Atlantic cod brains have a strong interferon pathway response to asymptomatic high nodavirus carrier state and that many interferon pathway and other immune relevant transcripts are significantly induced in brain by both nodavirus and pIC.

  4. Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

    Science.gov (United States)

    Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

    2017-08-01

    Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

  5. Brain correlates of hypnotic paralysis-a resting-state fMRI study.

    Science.gov (United States)

    Pyka, M; Burgmer, M; Lenzen, T; Pioch, R; Dannlowski, U; Pfleiderer, B; Ewert, A W; Heuft, G; Arolt, V; Konrad, C

    2011-06-15

    Hypnotic paralysis has been used since the times of Charcot to study altered states of consciousness; however, the underlying neurobiological correlates are poorly understood. We investigated human brain function during hypnotic paralysis using resting-state functional magnetic resonance imaging (fMRI), focussing on two core regions of the default mode network and the representation of the paralysed hand in the primary motor cortex. Hypnotic suggestion induced an observable left-hand paralysis in 19 participants. Resting-state fMRI at 3T was performed in pseudo-randomised order awake and in the hypnotic condition. Functional connectivity analyses revealed increased connectivity of the precuneus with the right dorsolateral prefrontal cortex, angular gyrus, and a dorsal part of the precuneus. Functional connectivity of the medial frontal cortex and the primary motor cortex remained unchanged. Our results reveal that the precuneus plays a pivotal role during maintenance of an altered state of consciousness. The increased coupling of selective cortical areas with the precuneus supports the concept that hypnotic paralysis may be mediated by a modified representation of the self which impacts motor abilities. Copyright © 2011 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2016-07-01

    Trance is an absorptive state of consciousness characterized by narrowed awareness of external surroundings and has long been used-for example, by shamans-to gain insight. Shamans across cultures often induce trance by listening to rhythmic drumming. Using functional magnetic resonance imaging (fMRI), we examined the brain-network configuration associated with trance. Experienced shamanic practitioners (n = 15) listened to rhythmic drumming, and either entered a trance state or remained in a nontrance state during 8-min scans. We analyzed changes in network connectivity. Trance was associated with higher eigenvector centrality (i.e., stronger hubs) in 3 regions: posterior cingulate cortex (PCC), dorsal anterior cingulate cortex (dACC), and left insula/operculum. Seed-based analysis revealed increased coactivation of the PCC (a default network hub involved in internally oriented cognitive states) with the dACC and insula (control-network regions involved in maintaining relevant neural streams). This coactivation suggests that an internally oriented neural stream was amplified by the modulatory control network. Additionally, during trance, seeds within the auditory pathway were less connected, possibly indicating perceptual decoupling and suppression of the repetitive auditory stimuli. In sum, trance involved coactive default and control networks, and decoupled sensory processing. This network reconfiguration may promote an extended internal train of thought wherein integration and insight can occur. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art

    Science.gov (United States)

    Rupp, Andre A.; Templin, Jonathan L.

    2008-01-01

    "Diagnostic classification models" (DCM) are frequently promoted by psychometricians as important modelling alternatives for analyzing response data in situations where multivariate classifications of respondents are made on the basis of multiple postulated latent skills. In this review paper, a definitional boundary of the space of DCM…

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

    Directory of Open Access Journals (Sweden)

    Roberto Esposito

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

  9. Genotypic association of the DAOA gene with resting-state brain activity in major depression.

    Science.gov (United States)

    Chen, Jun; Xu, Yong; Zhang, Juan; Liu, Zhifen; Xu, Cheng; Zhang, Kerang; Shen, Yan; Xu, Qi

    2012-10-01

    Compelling evidence suggests that the glutamatergic system may contribute to the pathophysiology of major depression (MDD). While the D-amino acid oxidase activator (DAOA) gene can affect glutamatergic function, its genetic associations with MDD and abnormal resting-state brain activity have yet to be elucidated. A total of 488 patients with MDD and 480 controls were recruited to examine MDD association for the DAOA gene in a Chinese population, of whom 53 medication-free patients and 46 well-matched controls underwent resting-state functional magnetic resonance imaging for regional homogeneity (ReHo) analysis. The differences in ReHo between genotypes of interest were initially tested by the Student's t test, and the 2 × 2 (genotypes × disease status) ANOVA was then performed to identify the main effects of genotypes, disease status, and their interactions in MDD. Allelic association of the DAOA gene with MDD was observed for rs2391191, rs3918341, and rs778294 and haplotypic association for 2- and 3-SNP haplotypes. Six clusters in the cerebellum, right middle frontal gyrus and left middle temporal gyrus showed genotypic association between altered ReHo and rs2391191. The main effects of rs2391191 genotypes were found in the right culmen and right middle frontal gyrus. The left uvula and left middle temporal gyrus showed a genotypes × disease status interaction. Our results suggest that the DAOA gene may confer genetic risk of MDD. Genotypic effect of rs2391191 and its interaction with disease status may contribute to the altered ReHo in patients with MDD. Glutamatergic modulation may play an important role in alteration of the resting-state brain activities.

  10. 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.

  11. 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.

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

    Science.gov (United States)

    Liang, Peipeng; Li, Zhihao; Deshpande, Gopikrishna; Wang, Zhiqun; Hu, Xiaoping; Li, Kuncheng

    2014-01-01

    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.

  13. 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.

  14. Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

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    Lucas Eduardo de Oliveira Aparecido

    Full Text Available ABSTRACT Climate is the set of average atmospheric conditions that characterizes a region. It directly influences the majority of human activities, especially agriculture. Climate classification systems (CCSs are important tools in the study of agriculture, enabling knowledge of the climatic characteristics of a region. Thus, we aimed to perform the climatic characterization of the State of Paraná using the methods proposed by Köppen and Geiger (1928, modified by Trewartha (1954 (KT, Thornthwaite (1948 (TH and Camargo (1991 and modified by Maluf (2000 (CM, using data from the European Center for Medium-Range Weather Forecast (ECMWF model. The results of spatial interpolation (virtual stations were performed using the Kriging method in spherical shape with one neighbour and resolution of 0.25°. The CCSs displayed the ability to separate the warm and dry from cold and wet regions. The most predominant climates were Cfa (temperate humid with hot summers, C1rA'a' (sub-humid with little water deficiency, megathermal and ST-UMi (humid subtropical with dry winter, according to KT, TH and CM, respectively. CM is an intermediate CCS between KT and TH.

  15. Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization.

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    Melle J W van der Molen

    Full Text Available Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI, a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior and short-range (frontal-frontal and posterior-posterior clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

  16. Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

    Science.gov (United States)

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

    2017-10-01

    Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Ancestral state reconstruction reveals multiple independent evolution of diagnostic morphological characters in the "Higher Oribatida" (Acari), conflicting with current classification schemes

    Science.gov (United States)

    2010-01-01

    Background The use of molecular genetic data in phylogenetic systematics has revolutionized this field of research in that several taxonomic groupings defined by traditional taxonomic approaches have been rejected by molecular data. The taxonomic classification of the oribatid mite group Circumdehiscentiae ("Higher Oribatida") is largely based on morphological characters and several different classification schemes, all based upon the validity of diagnostic morphological characters, have been proposed by various authors. The aims of this study were to test the appropriateness of the current taxonomic classification schemes for the Circumdehiscentiae and to trace the evolution of the main diagnostic traits (the four nymphal traits scalps, centrodorsal setae, sclerits and wrinkled cuticle plus octotaxic system and pteromorphs both in adults) on the basis of a molecular phylogenetic hypothesis by means of parsimony, likelihood and Bayesian approaches. Results The molecular phylogeny based on three nuclear markers (28S rDNA, ef-1α, hsp82) revealed considerable discrepancies to the traditional classification of the five "circumdehiscent" subdivisions, suggesting paraphyly of the three families Scutoverticidae, Ameronothridae, Cymbaeremaeidae and also of the genus Achipteria. Ancestral state reconstructions of six common diagnostic characters and statistical evaluation of alternative phylogenetic hypotheses also partially rejected the current morphology-based classification and suggested multiple convergent evolution (both gain and loss) of some traits, after a period of rapid cladogenesis, rendering several subgroups paraphyletic. Conclusions Phylogenetic studies revealed non-monophyly of three families and one genus as a result of a lack of adequate synapomorphic morphological characters, calling for further detailed investigations in a framework of integrative taxonomy. Character histories of six morphological traits indicate that their evolution followed a rather

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

    Science.gov (United States)

    Song, Hongwen; Zou, Zhiling; Kou, Juan; Liu, Yang; Yang, Lizhuang; Zilverstand, Anna; d'Oleire Uquillas, Federico; Zhang, Xiaochu

    2015-01-01

    Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Functional magnetic resonance imaging (fMRI) studies have found activation increases in brain regions involved in the processing of reward, motivation and emotion regulation, when romantic lovers view photographs of their partners. However, not much is known about whether romantic love affects the brain's functional architecture during rest. In the present study, resting state functional magnetic resonance imaging (rsfMRI) data was collected to compare the regional homogeneity (ReHo) and functional connectivity (FC) across an "in-love" group (LG, N = 34, currently intensely in love), an "ended-love" group (ELG, N = 34, ended romantic relationship recently), and a "single" group (SG, N = 32, never fallen in love). Results show that: (1) ReHo of the left dorsal anterior cingulate cortex (dACC) was significantly increased in the LG (in comparison to the ELG and the SG); (2) ReHo of the left dACC was positively correlated with length of time in love in the LG, and negatively correlated with the lovelorn duration since breakup in the ELG; (3) FC within the reward, motivation, and emotion regulation network (dACC, insula, caudate, amygdala, and nucleus accumbens) as well as FC in the social cognition network [temporo-parietal junction (TPJ), posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal, precuneus, and temporal lobe] was significantly increased in the LG (in comparison to the ELG and SG); (4) in most regions within both networks FC was positively correlated with the duration of love in the LG but negatively correlated with the lovelorn duration of time since breakup in the ELG. This study provides first empirical evidence of love-related alterations in brain functional architecture. Furthermore, the results shed light on the underlying neural mechanisms of romantic love, and demonstrate the

  19. Deep brain stimulation for the early treatment of the minimally conscious state and vegetative state: experience in 14 patients.

    Science.gov (United States)

    Chudy, Darko; Deletis, Vedran; Almahariq, Fadi; Marčinković, Petar; Škrlin, Jasenka; Paradžik, Veronika

    2017-06-16

    OBJECTIVE An effective treatment of patients in a minimally conscious state (MCS) or vegetative state (VS) caused by hypoxic encephalopathy or traumatic brain injury (TBI) is not yet available. Deep brain stimulation (DBS) of the thalamic reticular nuclei has been attempted as a therapeutic procedure mainly in patients with TBI. The purpose of this study was to investigate the therapeutic use of DBS for patients in VS or MCS. METHODS Fourteen of 49 patients in VS or MCS qualified for inclusion in this study and underwent DBS. Of these 14 patients, 4 were in MCS and 10 were in VS. The etiology of VS or MCS was TBI in 4 cases and hypoxic encephalopathy due to cardiac arrest in 10. The selection criteria for DBS, evaluating the status of the cerebral cortex and thalamocortical reticular formation, included: neurological evaluation, electrophysiological evaluation, and the results of positron emission tomography (PET) and MRI examinations. The target for DBS was the centromedian-parafascicular (CM-pf) complex. The duration of follow-up ranged from 38 to 60 months. RESULTS Two MCS patients regained consciousness and regained their ability to walk, speak fluently, and live independently. One MCS patient reached the level of consciousness, but was still in a wheelchair at the time the article was written. One VS patient (who had suffered a cerebral ischemic lesion) improved to the level of consciousness and currently responds to simple commands. Three VS patients died of respiratory infection, sepsis, or cerebrovascular insult (1 of each). The other 7 patients remained without substantial improvement of consciousness. CONCLUSIONS Spontaneous recovery from MCS/VS to the level of consciousness with no or minimal need for assistance in everyday life is very rare. Therefore, if a patient in VS or MCS fulfills the selection criteria (presence of somatosensory evoked potentials from upper extremities, motor and brainstem auditory evoked potentials, with cerebral glucose

  20. 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. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2013-01-01

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

  2. Moral competence and brain connectivity: a resting-state fMRI study

    Science.gov (United States)

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

    2016-01-01

    Moral competence (MC) refers to the ability to apply certain moral orientations in a consistent and differentiated manner when judging moral issues. People greatly differ in terms of MC, however, little is known about how these differences are implemented in the brain. To investigate this question, we used functional magnetic resonance imaging and examined resting-state functional connectivity (RSFC) in n=31 individuals with MC scores in the highest 15% of the population and n=33 individuals with MC scores in the lowest 15%, selected from a large sample of 730 Master of Business Administration (MBA) students. Compared to individuals with lower MC, individuals with higher MC showed greater amygdala-ventromedial prefrontal connectivity, which may reflect better ability to cope with emotional conflicts elicited by moral dilemmas. Moreover, individuals with higher MC showed less inter-network connectivity between the amygdalar and fronto-parietal networks, suggesting a more independent operation of these networks. Our findings provide novel insights into how individual differences in moral judgment are associated with RSFC in brain circuits related to emotion processing and cognitive control. PMID:27456537

  3. 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

  4. The spectral diversity of resting-state fluctuations in the human brain.

    Directory of Open Access Journals (Sweden)

    Klaudius Kalcher

    Full Text Available In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms and compared to 20 resting-state datasets from standard, high-TR (1800 ms EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1-0.25 Hz; 0.25-0.75 Hz; 0.75-1.4 Hz was computed for both the low-TR and (for the two lower-frequency ranges the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low

  5. The Spectral Diversity of Resting-State Fluctuations in the Human Brain

    Science.gov (United States)

    Huf, Wolfgang; Bartova, Lucie; Kronnerwetter, Claudia; Derntl, Birgit; Pezawas, Lukas; Filzmoser, Peter; Nasel, Christian; Moser, Ewald

    2014-01-01

    In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1–0.25 Hz; 0.25–0.75 Hz; 0.75–1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI

  6. 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.

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

    DEFF Research Database (Denmark)

    Stender, Johan; Kupers, Ron; Rodell, Anders

    2015-01-01

    of these patients. However, no quantitative comparisons of cerebral glucose metabolism in VS/UWS and MCS have yet been reported. We calculated the regional and whole-brain CMRglc of 41 patients in the states of VS/UWS (n=14), MCS (n=21) or emergence from MCS (EMCS, n=6), and healthy volunteers (n=29). Global...... cortical CMRglc in VS/UWS and MCS averaged 42% and 55% of normal, respectively. Differences between VS/UWS and MCS were most pronounced in the frontoparietal cortex, at 42% and 60% of normal. In brainstem and thalamus, metabolism declined equally in the two conditions. In EMCS, metabolic rates were...... indistinguishable from those of MCS. Ordinal logistic regression predicted that patients are likely to emerge into MCS at CMRglc above 45% of normal. Receiver-operating characteristics showed that patients in MCS and VS/UWS can be differentiated with 82% accuracy, based on cortical metabolism. Together...

  8. Steady State Visual Evoked Potential Based Brain-Computer Interface for Cognitive Assessment

    DEFF Research Database (Denmark)

    Westergren, Nicolai; Bendtsen, Rasmus L.; Kjær, Troels W.

    2016-01-01

    decline is important. Cognitive decline may be detected using fullyautomated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer...... interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% ± 0%. The test...... subjects achieved an information transfer rate (ITR) of 14:64 bits/min ± 7:63 bits=min and a subject test performance of 47:22% ± 34:10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system...

  9. Transfection of rat brain endothelium in a primary culture model of the blood-brain barrier at different states of barrier maturity

    DEFF Research Database (Denmark)

    Larsen, Annette Burkhart; Thomsen, Louiza Bohn; Lichota, Jacek

    approaches have been developed. Gene therapy could be a promising and novel approach to overcome the restricting properties of the BBB to polypeptides and proteins. Gene therapy is based on the delivery of genetic material into brain capillary endothelial cells (BCECs), which, theoretically, will result...... in expression and secretion of the recombinant protein from the BCECs and into the brain, thus turning BCECs into small recombinant protein factories. In this study, the possibility of using BCECs as small factories for recombinant protein production was investigated. To mimic the in-vivo situation as closely...... transfection is positively correlated with cell division, with dividing cells being more sensitive to gene transfection. Therefore, by using this model, the transfection efficiency of the rat endothelium was investigated in an immature state and mature state of the BBB. During the transfection period...

  10. The use of dopamine enhancing medications with children in low response states following brain injury.

    Science.gov (United States)

    Patrick, P D; Buck, M L; Conaway, M R; Blackman, J A

    2003-06-01

    The study examines the possible relationship between dopamine-enhancing medications and improvement of arousal and awareness in children during persistent low response states (Rancho Los Amigos Levels I, II and III). A retrospective review was conducted of 10 children enrolled in an existing clinical protocol. The Kluge Children's Rehabilitation Center (KCRC) low response protocol provides a double baseline serial measure (A, A, B, B, B) design. Scores on the Western NeuroSensory Stimulation Profile (WNSSP) are the dependent variable. Ten children, mean age of 13.7 years low response state (30 days or more) who were treated with dopamine agonists. Co-morbid or iatrogenic influences were addressed or ruled out. Seven children had traumatic brain injury, one cerebral vascular accident, one anoxia and one encephalitis. On average, dopamine medications were started 52.9 days post-event. Paired t-test of WNSSP scores before medications and on medications were significant at p = 0.03 (paired t-test). Also, the distributions of the slopes (rates of change of WNSSP scores over time) were significantly different in the pre-medication and medication phases (Paired T-test, p = 0.02). Random coefficient model comparison of individuals during pre- and medication phase response variability on WNSSP yielded F-test at p = 0.02. These results suggest a promising relationship between acceleration of recovery for some children in a low response state and administration of dopamine-enhancing medications.

  11. Resting state brain connectivity patterns before eventual relapse into cocaine abuse.

    Science.gov (United States)

    Berlingeri, M; Losasso, D; Girolo, A; Cozzolino, E; Masullo, T; Scotto, M; Sberna, M; Bottini, G; Paulesu, E

    2017-06-01

    According to recent theories, drug addicted patients suffer of an impaired response inhibition and salience attribution (I-RISA) together with a perturbed connectivity between the nuclei accumbens (NAcs) and the orbito-prefrontal (oPFC) and dorsal prefrontal (dPFC) cortices, brain regions associated with motivation and cognitive control. To empirically test these assumptions, we evaluated the (neuro)psychological trait and the functional organization of the resting state brain networks associated with the NAcs in 18 former cocaine abusers (FCAs), while being in drug abstinence since 5 months. The psychological data were grouped into three empirical variables related with emotion regulation, emotion awareness and strategic and controlled behaviour. Comparison of the resting state patterns between the entire sample of FCAs and 19 controls revealed a reduction of functional connectivity between the NAcs and the dPFC and enhanced connectivity between the NAcs and the dorsal-striatum. In the 8 FCAs who relapsed into cocaine use after 3 months, the level of functional connectivity between the NAcs and dPFC was lower than the functional connectivity estimated in the group of patients that did not relapsed. Finally, in the entire sample of FCAs, the higher the connectivity between the NAc and the oPFC the lower was the level of strategic and controlled behaviour. Taken together, these results are compatible with models of the interactions between the NAcs, the dorsal striatum and frontal cortices in the I-RISA syndrome, showing that such interactions are particularly perturbed in patients at greater risk of relapse into cocaine abuse. Copyright © 2017 Elsevier B.V. All rights reserved.

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

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    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.

  13. Classification of Flotation Frothers

    Directory of Open Access Journals (Sweden)

    Jan Drzymala

    2018-02-01

    Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

  14. Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy

    Directory of Open Access Journals (Sweden)

    Gülsüm Akdeniz

    2017-01-01

    Full Text Available Objective. Complexity analysis of functional brain structure data represents a new multidisciplinary approach to examining complex, living structures. I aimed to construct a connectivity map of visual brain activities using resting-state functional magnetic resonance imaging (fMRI data and to characterize the level of complexity of functional brain activity using these connectivity data. Methods. A total of 25 healthy controls and 20 patients with attention deficit hyperactivity disorder (ADHD participated. fMRI preprocessing analysis was performed that included head motion correction, temporal filtering, and spatial smoothing process. Brain entropy (BEN was calculated using the Shannon entropy equation. Results. My findings demonstrated that patients exhibited reduced brain complexity in visual brain areas compared to controls. The mean entropy value of the ADHD group was 0.56±0.14, compared to 0.64±0.11 in the control group. Conclusion. My study adds an important novel result to the growing literature pertaining to abnormal visual processing in ADHD that my ADHD patients had lower BEN values, indicating more-regular functional brain structure and abnormal visual information processing.

  15. The epidemiology of childhood brain injury in the state of Selangor and Federal Territory of Kuala Lumpur, Malaysia.

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    Tay, Ee Lin; Lee, Shaun Wen Huey; Jamaluddin, Sabariah Faizah; Tam, Cai Lian; Wong, Chee Piau

    2016-04-27

    There are limited studies describing the epidemiology of childhood brain injury, especially in developing countries. This study analyses data from the Malaysian National Trauma Database (NTrD) registry to estimate the incidence of childhood brain injury among various demographic groups within the state of Selangor and Federal Territory of Kuala Lumpur. This study analysed all traumatic brain injury cases for children ages 0-19 included in the 2010 NTrD report. A total of 5,836 paediatric patients were admitted to emergency departments (ED) of reporting hospitals for trauma. Of these, 742 patients (12.7 %) suffered from brain injuries. Among those with brain injuries, the mortality rate was 11.9 and 71.2 % were aged between 15 and 19. Traffic accidents were the most common mode of injury (95.4 %). Out of the total for traffic accidents, 80.2 % of brain injuries were incurred in motorcycle accidents. Severity of injury was higher among males and patients who were transferred or referred to the reporting centres from other clinics. Glasgow Coma Scale (GCS) total score and type of admission were found to be statistically significant, χ (2) (5, N = 178) = 66.53, p Malaysia. Most brain injuries occurred among older male children, with traffic, specifically motorcycle-related, accidents being the main mode of injury. These findings point to risk factors that could be targeted for future injury prevention programs.

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

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    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 asse