Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

Spiking Neural P Systems with Communication on Request.

Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5 % followed by the PNN (91.6 %), k-NN (90.8 %) and CT (90.2 %). The EPNN exhibited an accuracy of 98.6 % when classifying healthy control (HC) versus PD, higher than any previous studies.

Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network

Neuroimaging studies have documented that ageing can disrupt certain higher cognitive systems such as the default mode network (DMN), the salience network (SN) and the central executive network (CEN). The effect of cognitive training on higher cognitive systems remains unclear. This study used a one-year longitudinal design to explore the cognitive training effect on three higher cognitive networks in healthy older adults. The community-living healthy older adults were divided into two groups: the multi-domain cognitive training group (24 sessions of cognitive training over a three-month period) and the wait-list control group. All subjects underwent cognitive measurements and resting-state functional magnetic resonance imaging (fMRI) scanning at baseline and at one year after the training ended. We examined training-related changes in functional connectivity (FC) within and between three networks. Compared with the baseline, we observed maintained or increased FC within all three networks after training. The scans after training also showed maintained anti-correlation of FC between the DMN and CEN compared to the baseline. These findings demonstrated that cognitive training maintained or improved the functional integration within networks and the coupling between the DMN and CEN in older adults. Our findings suggested that multi-domain cognitive training can mitigate the ageing-related dysfunction of higher cognitive networks.

/pdf/effects-of-cognitive-training-on-resting-state-functional-1twjyvz6l9.pdf

Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks

SummaryObjective
It is now well appreciated that benign epilepsy with centrotemporal spikes (BECTS, or more recently, ECTS) is associated with a range of cognitive and behavioral disturbances. Despite our improved understanding of cognitive functioning in ECTS, there have been to date no efforts to quantitatively synthesize the available literature within a comprehensive cognitive framework.

Methods
The present systematic review and meta-analysis was conducted according to PRISMA guidelines. Forty-two case–control samples met eligibility criteria comprising a total of 1,237 children with ECTS and 1,137 healthy control children. Univariate, random-effects meta-analyses were conducted on eight cognitive factors in accordance with the Cattell–Horn–Carroll model of intelligence.

Results
Overall, children with ECTS demonstrated significantly lower scores on neuropsychological tests across all cognitive factors compared to healthy controls. Observed effects ranged from 0.42 to 0.81 pooled standard deviation units, with the largest effect for long-term storage and retrieval and the smallest effect for visual processing.

Significance
The results of the present meta-analysis provide the first clear evidence that children with ECTS display a profile of pervasive cognitive difficulties and thus challenge current conceptions of ECTS as a benign disease or of limited specific or localized cognitive effect.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.13865

Cognitive functioning in children with self‐limited epilepsy with centrotemporal spikes: A systematic review and meta‐analysis

Our results indicate a hierarchy of brain damage in patients with schizophrenia, indicating an important role of the thalamus and frontal lobe in the pathologic progression of the disease.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2017171832

Progressive Reduction in Gray Matter in Patients with Schizophrenia Assessed with MR Imaging by Using Causal Network Analysis

Benign epilepsy with centrotemporal spikes (BECT) is the most common form of childhood idiopathic focal epilepsy syndrome. We investigated quantitative evidence regarding brain morphology and functional connectivity features to provide insight into the neuroanatomical foundation of this disorder, using high resolution T1-weighted magnetic resonance imaging (MRI) and resting state functional MRI in 21 patients with BECT and in 20 healthy children. The functional connectivity analysis, seeded at the regions with altered gray-matter (GM) volume in voxel-based morphometry (VBM) analysis, was further performed. Then, the observed structural and functional alteration were investigated for their association with the clinical and behavior manifestations. The increased GM volume in the striatum and fronto-temporo-parietal cortex (striato-cortical circuit) was observed in BECT. The decreased connections were found among the motor network and frontostriatal loop, and between the default mode network (DMN) and language regions. Additionally, the GM of striatum was negatively correlated with age at epilepsy onset. The current observations may contribute to the understanding of the altered structural and functional feature of striato-cortical circuit in patients with BECT. The findings also implied alterations of the motor network and DMN, which were associated with the epileptic activity in patients with BECT. This further suggested that the onset of BECT might have enduring structural and functional effects on brain maturation.

Altered Structural and Functional Feature of Striato-Cortical Circuit in Benign Epilepsy with Centrotemporal Spikes

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes.

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Altered Structural and Functional Feature of Striato-Cortical Circuit in Benign Epilepsy with Centrotemporal Spikes

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes.