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International Journal of Neural Systems

Pablo Martínez-Cañada, Christian Morillas, Francisco Pelayo
Color plays a key role in human vision but the neural machinery that underlies the transformation from stimulus to perception is not well understood. Here, we implemented a two-dimensional network model of the first stages in the primate parvocellular pathway (retina, lateral geniculate nucleus and layer 4C[Formula: see text] in V1) consisting of conductance-based point neurons. Model parameters were tuned based on physiological and anatomical data from the primate foveal and parafoveal vision, the most relevant visual field areas for color vision...
July 30, 2018: International Journal of Neural Systems
Francisco J Martinez-Murcia, Juan M Górriz, Javier Ramírez, Andres Ortiz
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them...
July 26, 2018: International Journal of Neural Systems
Wei Li, Mengfan Li, Huihui Zhou, Genshe Chen, Jing Jin, Feng Duan
Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23...
July 26, 2018: International Journal of Neural Systems
P Lachert, J Zygierewicz, D Janusek, P Pulawski, P Sawosz, M Kacprzak, A Liebert, K J Blinowska
The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals...
July 26, 2018: International Journal of Neural Systems
Yuchao Jiang, Mingjun Duan, Xi Chen, Xingxing Zhang, Jinnan Gong, Debo Dong, Hui Li, Qizhong Yi, Shuya Wang, Jijun Wang, Cheng Luo, Dezhong Yao
Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV)...
July 16, 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
Xiaogang Chen, Bing Zhao, Yijun Wang, Shengpu Xu, Xiaorong Gao
Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system...
October 2018: International Journal of Neural Systems
Tingfang Wu, Florin-Daniel Bîlbîe, Andrei Păun, Linqiang Pan, Ferrante Neri
Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike...
October 2018: International Journal of Neural Systems
Qi Yuan, Weidong Zhou, Fangzhou Xu, Yan Leng, Dongmei Wei
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals...
October 2018: International Journal of Neural Systems
Andreas Antoniades, Loukianos Spyrou, David Martin-Lopez, Antonio Valentin, Gonzalo Alarcon, Saeid Sanei, Clive Cheong Took
Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance...
October 2018: International Journal of Neural Systems
Alireza Ghahari, Sumit R Kumar, Tudor C Badea
An important goal in visual neuroscience is to understand how neuronal population coding in vertebrate retina mediates the broad range of visual functions. Microelectrode arrays interface on isolated retina registers a collective measure of the spiking dynamics of retinal ganglion cells (RGCs) by probing them simultaneously and in large numbers. The recorded data stream is then processed to identify spike trains of individual RGCs by efficient and scalable spike detection and sorting routines. Most spike sorting software packages, available either commercially or as freeware, combine automated steps with judgment calls by the investigator to verify the quality of sorted spikes...
October 2018: International Journal of Neural Systems
Jason K Eshraghian, Seungbum Baek, Jun-Ho Kim, Nicolangelo Iannella, Kyoungrok Cho, Yong Sook Goo, Herbert H C Iu, Sung-Mo Kang, Kamran Eshraghian
Existing computational models of the retina often compromise between the biophysical accuracy and a hardware-adaptable methodology of implementation. When compared to the current modes of vision restoration, algorithmic models often contain a greater correlation between stimuli and the affected neural network, but lack physical hardware practicality. Thus, if the present processing methods are adapted to complement very-large-scale circuit design techniques, it is anticipated that it will engender a more feasible approach to the physical construction of the artificial retina...
September 2018: International Journal of Neural Systems
Yang Li, Weigang Cui, Meilin Luo, Ke Li, Lina Wang
The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics...
September 2018: International Journal of Neural Systems
Lin Cheng, Yang Zhu, Junfeng Sun, Lifu Deng, Naying He, Yang Yang, Huawei Ling, Hasan Ayaz, Yi Fu, Shanbao Tong
Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns...
September 2018: International Journal of Neural Systems
Lucia Rita Quitadamo, Roberto Mai, Francesca Gozzo, Veronica Pelliccia, Francesco Cardinale, Stefano Seri
Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data...
September 2018: International Journal of Neural Systems
Eduardo López-Larraz, Jaime Ibáñez, Fernando Trincado-Alonso, Esther Monge-Pereira, José Luis Pons, Luis Montesano
Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity...
September 2018: International Journal of Neural Systems
Jianyong Wang, Lei Zhang, Yuanyuan Chen, Zhang Yi
Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters...
August 2018: International Journal of Neural Systems
Gonzalo Alarcón, Diego Jiménez-Jiménez, Antonio Valentín, David Martín-López
OBJECTIVES: To model cortical connections in order to characterize their oscillatory behavior and role in the generation of spontaneous electroencephalogram (EEG). METHODS: We studied averaged responses to single pulse electrical stimulation (SPES) from the non-epileptogenic hemisphere of five patients assessed with intracranial EEG who became seizure free after contralateral temporal lobectomy. Second-order control system equations were modified to characterize the systems generating a given response...
August 2018: International Journal of Neural Systems
Zhan Li, David Guiraud, David Andreu, Anthony Gelis, Charles Fattal, Mitsuhiro Hayashibe
Functional electrical stimulation (FES) is a neuroprosthetic technique to help restore motor function of spinal cord-injured (SCI) patients. Through delivery of electrical pulses to muscles of motor-impaired subjects, FES is able to artificially induce their muscle contractions. Evoked electromyography (eEMG) is used to record such FES-induced electrical muscle activity and presents a form of [Formula: see text]-wave. In order to monitor electrical muscle activity under stimulation and ensure safe stimulation configurations, closed-loop FES control with eEMG feedback is needed to be developed for SCI patients who lose their voluntary muscle contraction ability...
August 2018: International Journal of Neural Systems
Antonino Naro, Alessia Bramanti, Antonino Leo, Placido Bramanti, Rocco Salvatore Calabrò
The extent of cortical reorganization after brain injury in patients with Vegetative State/Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) depends on the residual capability of modulating synaptic plasticity. Neuroplasticity is largely abnormal in patients with UWS, although the fragments of cortical activity may exist, while patients MCS show a better cortical organization. The aim of this study was to evaluate cortical excitability in patients with disorders of consciousness (DoC) using a transcranial direct current stimulation (TDCS) metaplasticity protocol...
August 2018: International Journal of Neural Systems
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