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International Journal of Neural Systems

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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
#1
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/28076982/from-structure-to-activity-using-centrality-measures-to-predict-neuronal-activity
#2
Jack McKay Fletcher, Thomas Wennekers
It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes...
November 16, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28043201/physiological-ripples-associated-with-sleep-spindles-differ-in-waveform-morphology-from-epileptic-ripples
#3
Jonas C Bruder, Matthias Dümpelmann, Daniel Lachner Piza, Malenka Mader, Andreas Schulze-Bonhage, Julia Jacobs-Le Van
High frequency oscillations (HFOs, 80-500[Formula: see text]Hz) serve as novel electroencephalography (EEG) markers of epileptic tissue. The differentiation of physiological and epileptic HFO is an important challenge and is complicated by the fact that both types are generated in mesiotemporal structures. This study aimed to identify oscillation features that serve to distinguish physiological ripples associated with sleep spindles and epileptic ripples. We studied 19 patients with chronic intracranial EEG(iEEG) with mesiotemporal implantation and simultaneous scalp EEG...
November 2, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28030999/seizure-control-in-a-computational-model-using-a-reinforcement-learning-stimulation-paradigm
#4
Vivek Nagaraj, Andrew Lamperski, Theoden I Netoff
Neuromodulation technologies such as vagus nerve stimulation and deep brain stimulation, have shown some efficacy in controlling seizures in medically intractable patients. However, inherent patient-to-patient variability of seizure disorders leads to a wide range of therapeutic efficacy. A patient specific approach to determining stimulation parameters may lead to increased therapeutic efficacy while minimizing stimulation energy and side effects. This paper presents a reinforcement learning algorithm that optimizes stimulation frequency for controlling seizures with minimum stimulation energy...
November 2, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27596928/towards-tunable-consensus-clustering-for-studying-functional-brain-connectivity-during-affective-processing
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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/27701931/author-index-volume-26-2016
#19
(no author information available yet)
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
#20
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
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