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Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects information processing in the nervous system and the procedure of natural brain evolution. Therefore, the processing and analysis of the brain function of these patients have captivated the attention of many researchers. Qualitative electroencephalography (EEG) is an evaluation method for determining functional brain abnormalities that is different from quantitative EEG. Chaotic tools are used in qualitative EEG whereas linear and nonlinear methods are applied in quantitative EEG. The purpose of the present study is to compare qualitative EEG findings of healthy and autistic subjects. Materials and Methods: In this study, 19 channels of brain signals (Cz, C4, F4, Fz, F3, C3, P3, Pz, P4, T4, F8, Fp2, Fp1, F7, T3, T5, O1, O2, T6) of 6 healthy and 5 autistic subjects were evaluated in two phases. At first 5-minutes with closed eyes and then 5-minutes with opened eyes, the subject's EEG was recorded. After removing the artifacts, the correlation dimension of the signals was calculated, and brain maps were plotted to analyze the changes of correlation dimension on the scalp surface. Results: By comparison of the brain maps of the healthy and autistic groups between the opened and closed eyes periods, we found there was a difference between the brain function of the groups, especially in the T3 and T4 regions of the temporal regions as well as frontal and posterior areas. Conclusion: Using brain maps, correlation dimension mapping on the brain surface provides a better understanding of brain dynamics in autistic subjects.

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Introduction: Recognition of mental activities in brain-computer interface systems based on motor imagery has attracted the attention of many researchers. A visibility graph is a powerful method for analyzing the function and connectivity of different areas of the brain. The aim of this study is to improve and develop the visibility graph method for analyzing brain behavior and detecting motor imagery. Materials and Methods: First, brain signals including four motor imagery classes of left-handed, right-handed, foot, and tongue were transformed into three types of visibility graphs, and important features of these graphs were extracted. Then, to reduce features, the method of analysis of variance was used. To classify the motor imagery classes, the support vector machine was used. In most investigations, graph degree distribution has been used to extract information and graph weighting. In the present study, amplitude difference distribution has been used so shorter time series are required. To analyze the function and connectivity of different areas of the brain and to obtain the direction of information flow, a new method called weighted horizontal visibility graph-transfer entropy has been proposed. Results: Increasing the kappa value compared to other studies showed that a weighted horizontal visibility graph is a suitable method for processing brain signals based on motor imagery. A comparison of brain graphs and the direction of information flow in the four classes of motor imagery showed a significant difference between them. Conclusion: Temporal networks provide a better understanding of brain dynamics in brain-computer interface systems based on motor imagery.

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Introduction: Computational modeling plays a pivotal role in bridging the gap between cognitive neuroscience and clinical neurology, particularly in the context of neurodegenerative diseases like Alzheimer's disease (AD). This study explores the application of computational models to understand cognitive systems and the pathological processes leading to cognitive decline in AD. Materials and Methods: We proposed a set of computational approaches, including neural networks and dynamical systems modeling, to simulate neural activity, synaptic plasticity, and interactions between genetic and environmental factors. Data integration from neuroimaging, genomics, and behavioral studies was crucial in enhancing the accuracy and predictive capabilities of these models. Results: The computational models provided significant insights into the mechanisms of cognition, memory formation, and their deterioration in AD. Our models identified potential biomarkers and informed strategies for therapeutic intervention, demonstrating the importance of a multi-disciplinary approach to understanding and treating cognitive decline. Conclusion: Computational modeling is essential for promoting our understanding of AD and other cognitive disorders. Future research should focus on refining these models and fostering greater interdisciplinary collaboration to develop more accurate and comprehensive simulations.