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International Journal of Neural Systems

Piotr Suffczynski
No abstract text is available yet for this article.
December 26, 2018: International Journal of Neural Systems
José Manuel Ferrández, Diego Andina, Eduardo Fernández
No abstract text is available yet for this article.
December 19, 2018: International Journal of Neural Systems
Vesna Vuksanović, Roger T Staff, Trevor Ahearn, Alison D Murray, Claude M Wischik
Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer's disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort...
November 14, 2018: International Journal of Neural Systems
Richard J Duro, Jose A Becerra, Juan Monroy, Francisco Bellas
In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call "automation" of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure...
November 14, 2018: International Journal of Neural Systems
Magdalena Zieleniewska, Anna Duszyk, Piotr Różański, Marcin Pietrzak, Marta Bogotko, Piotr Durka
We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging...
October 29, 2018: International Journal of Neural Systems
Elżbieta Gajewska-Dendek, Andrzej Wróbel, Marek Bekisz, Piotr Suffczynski
We have previously shown that during top-down attentional modulation (stimulus expectation) correlations of the beta signals across the primary visual cortex were uniform, while during bottom-up attentional processing (visual stimulation) their values were heterogeneous. These different patterns of attentional beta modulation may be caused by feed-forward lateral inhibitory interactions in the visual cortex, activated solely during stimulus processing. To test this hypothesis, we developed a large-scale computational model of the cortical network...
October 29, 2018: International Journal of Neural Systems
Marian Dovgialo, Anna Chabuda, Anna Duszyk, Magdalena Zieleniewska, Marcin Pietrzak, Piotr Różański, Piotr Durka
Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. These problems are especially pronounced in pediatric patients. We present the first study on the assessment of pediatric DOC patients by means of command-following procedures and involving long-latency cognitive event-related potentials...
October 29, 2018: International Journal of Neural Systems
Maciej Labecki, Maria Malgorzata Nowicka, Piotr Suffczynski
Electroencephalographic responses to periodic stimulation are termed steady-state visual evoked potentials (SSVEP). Their characteristics in terms of amplitude, frequency and phase are commonly assumed to be stationary. In this work, we tested this assumption in 30 healthy participants submitted to 50 trials of 60 <mml:math xmlns:mml=""> <mml:mspace/> </mml:math> s flicker stimulation at 15 <mml:math xmlns:mml=""> <mml:mspace/> </mml:math> Hz frequency...
October 29, 2018: International Journal of Neural Systems
Christos Koutlis, Vasilios K Kimiskidis, Dimitris Kugiumtzis
The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality...
October 29, 2018: International Journal of Neural Systems
Juan A Barios, Santiago Ezquerro, Arturo Bertomeu-Motos, Marius Nann, Fco Javier Badesa, Eduardo Fernandez, Surjo R Soekadar, Nicolas Garcia-Aracil
Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain-Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals...
October 24, 2018: International Journal of Neural Systems
Antonio Lozano, Cristina Soto-Sánchez, Javier Garrigós, J Javier Martínez, J Manuel Ferrández, Eduardo Fernández
Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional Neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells...
December 2018: International Journal of Neural Systems
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No abstract text is available yet for this article.
December 2018: International Journal of Neural Systems
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...
November 2018: International Journal of Neural Systems
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...
November 2018: International Journal of Neural Systems
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...
November 2018: International Journal of Neural Systems
Khalid B Mirza, Andrea Alenda, Amir Eftekhar, Nir Grossman, Konstantin Nikolic, Stephen R Bloom, Christofer Toumazou
OBJECTIVE: Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for treating obesity. The objective of this study was to characterize the left ventral subdiaphragmatic gastric trunk of vagus nerve (SubDiaGVN) and to analyze the influence of intravenous injection of gut hormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observed on the same branch, with the aim of understanding the impact of hormones on VNS and incorporating the methods and results into closed loop implant design...
November 2018: International Journal of Neural Systems
Francisco Zamora-Martinez, Maria Jose Castro-Bleda
Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality...
November 2018: International Journal of Neural Systems
Pawel Trajdos, Marek Kurzynski
In the paper, the problem of multi-label (ML) classification using the label-pairwise (LPW) scheme is addressed. For this approach, the method of correction of binary classifiers which constitute the LPW ensemble is proposed. The correction is based on a probabilistic (randomized) model of a classifier that assesses the local class-specific probabilities of correct classification and misclassification. These probabilities are determined using the original concepts of a randomized reference classifier (RRC) and a local soft confusion matrix...
November 2018: International Journal of Neural Systems
Maciej Kaminski, Aneta Brzezicka, Jan Kaminski, Katarzyna J Blinowska
Transmission of EEG activity during a visual and auditory version of the working memory task based on the paradigm of linear syllogism was investigated. Our aim was to find possible similarities and differences in the synchronization patterns between brain structures during the same mental activity performed on different modality stimuli. The EEG activity transmission was evaluated by means of full frequency Directed Transfer Function (ffDTF) and short-time Directed Transfer Function (SDTF). SDTF provided information on dynamical propagation of EEG activity...
October 1, 2018: International Journal of Neural Systems
Chen Fang, Chunfei Li, Mercedes Cabrerizo, Armando Barreto, Jean Andrian, Naphtali Rishe, David Loewenstein, Ranjan Duara, Malek Adjouadi
Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group...
October 2018: International Journal of Neural Systems
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