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International Journal of Neural Systems

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https://www.readbyqxmd.com/read/30001641/the-relationship-between-ictal-multi-unit-activity-and-the-electrocorticogram
#1
Tahra L Eissa, Catherine A Schevon, Ronald G Emerson, Guy M Mckhann, Robert R Goodman, Wim Van Drongelen
During neocortical seizures in patients with epilepsy, microelectrode array recordings from the ictal core show a strong correlation between the fast, cellular spiking activities and the low-frequency component of the potential field, reflected in the electrocorticogram (ECoG). Here, we model the relationship between the cellular spike activity and this low-frequency component as the input and output signals of a linear time invariant system. Our approach is based on the observation that this relationship can be characterized by a so-called sinc function, the unit impulse response of an ideal (brick-wall) filter...
June 18, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29954235/extracting-and-classifying-spatial-muscle-activation-patterns-in-forearm-flexor-muscles-using-high-density-electromyogram-recordings
#2
Chenyun Dai, Xiaogang Hu
The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns...
June 3, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29938549/differential-effects-of-simulated-cortical-network-lesions-on-synchrony-and-eeg-complexity
#3
Antonio José Ibáñez-Molina, Sergio Iglesias-Parro, Javier Escudero
Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity...
May 15, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29914312/performance-prediction-for-a-near-infrared-spectroscopy-brain-computer-interface-using-resting-state-functional-connectivity-of-the-prefrontal-cortex
#4
Jaeyoung Shin, Chang-Hwan Im
One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment...
May 11, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29914313/multi-objective-genetic-algorithms-to-find-most-relevant-volumes-of-the-brain-related-to-alzheimer-s-disease-and-mild-cognitive-impairment
#5
Olga Valenzuela, Xiaoyi Jiang, Antonio Carrillo, Ignacio Rojas
Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI...
May 7, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29886807/model-and-analysis-for-the-onset-of-parkinsonian-firing-patterns-in-a-simplified-basal-ganglia
#6
Michael Caiola, Mark H Holmes
Parkinson's disease (PD) is a degenerative neurological disease that disrupts the movement cycle in the basal ganglia. As the disease progresses, dopamine depletion leads to changes to how the basal ganglia functions as well as the appearance of abnormal beta oscillations. There is much debate on just exactly how these connection strengths change and just how the oscillations emerge. One leading hypothesis claims that the oscillations develop in the globus pallidus external, subthalamic nucleus, and globus pallidus internal loop...
May 3, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29914314/dynamic-redistribution-of-plasticity-in-a-cerebellar-spiking-neural-network-reproducing-an-associative-learning-task-perturbed-by-tms
#7
Alberto Antonietti, Jessica Monaco, Egidio D'Angelo, Alessandra Pedrocchi, Claudia Casellato
During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under behavioral feedback and should involve changes distributed across multiple synaptic sites. In eyeblink classical conditioning (EBCC), the cerebellum learns to predict the precise timing between two stimuli, hence EBCC represents an elementary yet meaningful paradigm to investigate the cerebellar network functioning. We have simulated EBCC mechanisms by reconstructing a realistic cerebellar microcircuit model and embedding multiple plasticity rules imitating those revealed experimentally...
April 24, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29909721/two-source-validation-of-progressive-fastica-peel-off-for-automatic-surface-emg-decomposition-in-human-first-dorsal-interosseous-muscle
#8
Maoqi Chen, Xu Zhang, Zhiyuan Lu, Xiaoyan Li, Ping Zhou
This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG...
April 24, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29936900/announcement-the-2018-hojjat-adeli-award-for-outstanding-contributions-in-neural-systems
#9
(no author information available yet)
No abstract text is available yet for this article.
August 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29673276/introduction
#10
Diego Andina, Kunihiko Fukushima, Javier Ropero Peláez, Duc Truong Pham
No abstract text is available yet for this article.
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29298521/neural-correlates-of-phrase-quadrature-perception-in-harmonic-rhythm-an-eeg-study-using-a-brain-computer-interface
#11
Alicia Fernández-Soto, Arturo Martínez-Rodrigo, José Moncho-Bogani, José Miguel Latorre, Antonio Fernández-Caballero
For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297265/short-term-effects-of-binaural-beats-on-eeg-power-functional-connectivity-cognition-gait-and-anxiety-in-parkinson-s-disease
#12
Gerardo Gálvez, Manuel Recuero, Leonides Canuet, Francisco Del-Pozo
We applied rhythmic binaural sound to Parkinson's Disease (PD) patients to investigate its influence on several symptoms of this disease and on Electrophysiology (Electrocardiography and Electroencephalography (EEG)). We conducted a double-blind, randomized controlled study in which rhythmic binaural beats and control were administered over two randomized and counterbalanced sessions (within-subjects repeated-measures design). Patients ([Formula: see text], age [Formula: see text], stage I-III Hoehn & Yahr scale) participated in two sessions of sound stimulation for 10[Formula: see text]min separated by a minimum of 7 days...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297264/mirror-neurons-modeled-through-spike-timing-dependent-plasticity-are-affected-by-channelopathies-associated-with-autism-spectrum-disorder
#13
Gabriela Antunes, Samuel F Faria da Silva, Fabio M Simoes de Souza
Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297263/foreground-detection-by-competitive-learning-for-varying-input-distributions
#14
Ezequiel López-Rubio, Miguel A Molina-Cabello, Rafael Marcos Luque-Baena, Enrique Domínguez
One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28359221/structured-pyramidal-neural-networks
#15
Alessandra M Soares, Bruno J T Fernandes, Carmelo J A Bastos-Filho
The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28264639/a-multiple-plasticity-spiking-neural-network-embedded-in-a-closed-loop-control-system-to-model-cerebellar-pathologies
#16
Alice Geminiani, Claudia Casellato, Alberto Antonietti, Egidio D'Angelo, Alessandra Pedrocchi
The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient neuronal and plastic properties of the cerebellum and operating in closed-loop. To this aim, we have exploited a realistic spiking computational model of the cerebellum to analyze the network correlates of cerebellar impairment...
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297262/tracking-the-reorganization-of-module-structure-in-time-varying-weighted-brain-functional-connectivity-networks
#17
Christoph Schmidt, Diana Piper, Britta Pester, Andreas Mierau, Herbert Witte
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297261/multi-step-time-series-forecasting-with-an-ensemble-of-varied-length-mixture-models
#18
Yicun Ouyang, Hujun Yin
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29281922/development-of-a-ternary-near-infrared-spectroscopy-brain-computer-interface-online-classification-of-verbal-fluency-task-stroop-task-and-rest
#19
Larissa C Schudlo, Tom Chau
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...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29022403/developmental-approach-for-behavior-learning-using-primitive-motion-skills
#20
Farhan Dawood, Chu Kiong Loo
Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration...
May 2018: International Journal of Neural Systems
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