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International Journal of Neural Systems

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https://www.readbyqxmd.com/read/28178853/clinical-vagus-nerve-stimulation-paradigms-induce-pronounced-brain-and-body-hypothermia-in-rats
#1
Lars Emil Larsen, Wouter Van Lysebettens, Charlotte Germonpré, Sofie Carrette, Sofie Daelemans, Mathieu Sprengers, Lisa Thyrion, Wytse Jan Wadman, Evelien Carrette, Jean Delbeke, Paul Boon, Kristl Vonck, Robrecht Raedt
Vagus nerve stimulation (VNS) is a widely used neuromodulation technique that is currently used or being investigated as therapy for a wide array of human diseases such as epilepsy, depression, Alzheimer's disease, tinnitus, inflammatory diseases, pain, heart failure and many others. Here, we report a pronounced decrease in brain and core temperature during VNS in freely moving rats. Two hours of rapid cycle VNS (7s on/18s off) decreased brain temperature by around [Formula: see text]C, while standard cycle VNS (30[Formula: see text]s on/300[Formula: see text]s off) was associated with a decrease of around [Formula: see text]C...
December 22, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28093049/efficient-variational-approach-to-multimodal-registration-of%C3%A2-anatomical%C3%A2-and%C3%A2-functional-intra-patient-tumorous-brain-data
#2
Alvar-Ginés Legaz-Aparicio, Rafael Verdú-Monedero, Jorge Larrey-Ruiz, Juan Morales-Sánchez, Fernando López-Mir, Valery Naranjo, Ángela Bernabéu
This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain...
November 29, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27596928/towards-tunable-consensus-clustering-for-studying-functional-brain-connectivity-during-affective-processing
#3
Chao Liu, Basel Abu-Jamous, Elvira Brattico, Asoke K Nandi
In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27440466/stress-detection-using-wearable-physiological-and-sociometric-sensors
#4
COMPARATIVE STUDY
Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Radu Dobrescu, Jose Manuel Ferrandez
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and [Formula: see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST)...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27440465/deep-learning-representation-from-electroencephalography-of-early-stage-creutzfeldt-jakob-disease-and-features-for-differentiation-from-rapidly-progressive-dementia
#5
MULTICENTER STUDY
Francesco Carlo Morabito, Maurizio Campolo, Nadia Mammone, Mario Versaci, Silvana Franceschetti, Fabrizio Tagliavini, Vito Sofia, Daniela Fatuzzo, Antonio Gambardella, Angelo Labate, Laura Mumoli, Giovanbattista Gaspare Tripodi, Sara Gasparini, Vittoria Cianci, Chiara Sueri, Edoardo Ferlazzo, Umberto Aguglia
A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27377661/sparse-bayesian-learning-for-obtaining-sparsity-of-eeg-frequency-bands-based-feature-vectors-in-motor-imagery-classification
#6
Yu Zhang, Yu Wang, Jing Jin, Xingyu Wang
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/26906455/superlinear-summation-of-information-in-premotor-neuron-pairs
#7
Fernando Montani, Andriy Oliynyk, Luciano Fadiga
Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27776439/introduction
#8
Levin Kuhlmann, David B Grayden, Mark J Cook
No abstract text is available yet for this article.
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27776437/statistical-performance-analysis-of-data-driven-neural-models
#9
Dean R Freestone, Kelvin J Layton, Levin Kuhlmann, Mark J Cook
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27718767/neural-activity-elicited-by-a-cognitive-task-can-be-detected-in-single-trials-with-simultaneous-intracerebral-eeg-fmri-recordings
#10
Mani Saignavongs, Carolina Ciumas, Mathilde Petton, Romain Bouet, Sébastien Boulogne, Sylvain Rheims, David W Carmichael, Jean-Philippe Lachaux, Philippe Ryvlin
Recent studies have shown that it is feasible to record simultaneously intracerebral EEG (icEEG) and functional magnetic resonance imaging (fMRI) in patients with epilepsy. While it has mainly been used to explore the hemodynamic changes associated with epileptic spikes, this approach could also provide new insight into human cognition. However, the first step is to ensure that cognitive EEG components, that have lower amplitudes than epileptic spikes, can be appropriately detected under fMRI. We compared the high frequency activities (HFA, 50-150[Formula: see text]Hz) elicited by a reading task in icEEG-only and subsequent icEEG-fMRI in the same patients ([Formula: see text]), implanted with depth electrodes...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27712456/emergence-of-narrowband-high-frequency-oscillations-from-asynchronous-uncoupled-neural-firing
#11
Stephen V Gliske, William C Stacey, Eugene Lim, Katherine A Holman, Christian G Fink
Previous experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrowband oscillations if each neuron fires quasi-periodically, without requiring any intrinsic oscillatory cells or feedback inhibition...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27596927/probing-to-observe-neural-dynamics-investigated-with-networked-kuramoto-oscillators
#12
Elma O'Sullivan-Greene, Levin Kuhlmann, Ewan S Nurse, Dean R Freestone, David B Grayden, Mark Cook, Anthony Burkitt, Iven Mareels
The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27464854/seizure-forecasting-and-the-preictal-state-in-canine-epilepsy
#13
Yogatheesan Varatharajah, Ravishankar K Iyer, Brent M Berry, Gregory A Worrell, Benjamin H Brinkmann
The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27389004/multi-biosignal-analysis-for-epileptic-seizure-monitoring
#14
Diana Cogan, Javad Birjandtalab, Mehrdad Nourani, Jay Harvey, Venkatesh Nagaraddi
Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient's physician...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27377662/which-brain-regions-are-important-for-seizure-dynamics-in-epileptic-networks-influence-of-link-identification-and-eeg-recording-montage-on-node-centralities
#15
Christian Geier, Klaus Lehnertz
Nodes in large-scale epileptic networks that are crucial for seizure facilitation and termination can be regarded as potential targets for individualized focal therapies. Graph-theoretical approaches based on centrality concepts can help to identify such important nodes, however, they may be influenced by the way networks are derived from empirical data. Here we investigate evolving functional epileptic brain networks during 82 focal seizures with different anatomical onset locations that we derive from multichannel intracranial electroencephalographic recordings from 51 patients...
February 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27701932/introduction
#16
Sharon Shmuely, Roland D Thijs, Stiliyan N Kalitzin, Josemir W Sander
No abstract text is available yet for this article.
December 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27633895/seizures-start-without-common-signatures-of-critical-transition
#17
REVIEW
Piotr Milanowski, Piotr Suffczynski
Complex dynamical systems may exhibit sudden autonomous changes from one state to another. Such changes that occur rapidly in comparison to the regular dynamics have been termed critical transitions. Examples of such phenomena can be found in many complex systems: changes in climate and ocean circulation, changes in wildlife populations, financial crashes, as well as in medical conditions like asthma attacks and depression. It has been recognized that critical transitions, even if they arise in completely different contexts and situations, share several common attributes and also generic early-warning signals that indicate that a critical transition is approaching...
December 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27464853/extracellular-potassium-and-seizures-excitation-inhibition-and-the-role-of-ih
#18
Lihua Wang, Suzie Dufour, Taufik A Valiante, Peter L Carlen
Seizure activity leads to increases in extracellular potassium concentration ([K[Formula: see text]]o), which can result in changes in neuronal passive and active membrane properties as well as in population activities. In this study, we examined how extracellular potassium modulates seizure activities using an acute 4-AP induced seizure model in the neocortex, both in vivo and in vitro. Moderately elevated [K[Formula: see text]]o up to 9[Formula: see text]mM prolonged seizure durations and shortened interictal intervals as well as depolarized the neuronal resting membrane potential (RMP)...
December 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27440467/nonlinear-connectivity-in-the-human-stretch-reflex-assessed-by-cross-frequency-phase-coupling
#19
Yuan Yang, Teodoro Solis-Escalante, Jun Yao, Frans C T van der Helm, Julius P A Dewald, Alfred C Schouten
Communication between neuronal populations is facilitated by synchronization of their oscillatory activity. Although nonlinearity has been observed in the sensorimotor system, its nonlinear connectivity has not been widely investigated yet. This study investigates nonlinear connectivity during the human stretch reflex based on neuronal synchronization. Healthy participants generated isotonic wrist flexion while receiving a periodic mechanical perturbation to the wrist. Using a novel cross-frequency phase coupling metric, we estimate directional nonlinear connectivity, including time delay, from the perturbation to brain and to muscle, as well as from brain to muscle...
December 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27389003/preventive-and-abortive-strategies-for-stimulation-based-control-of-epilepsy-a-computational-model-study
#20
Marc Koppert, Stiliyan Kalitzin, Demetrios Velis, Fernando Lopes Da Silva, Max A Viergever
Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons...
December 2016: International Journal of Neural Systems
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