Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies extracted invaluable information from the underlying substrate of the brain. However, Cognitive deficiency has insufficiently been assessed by researchers in Multiple Sclerosis (MS). Therefore, extracting the brain network differences among Relapsing-Remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information (KMI) based on AAL atlas. Subsequently, a statistical test was carried out to determine the variation of brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with fisher score as a hybrid feature selection method. The results were then classified using multiple machine learning methods. Some regions, namely the Hippocampus, Para Hippocampal, Cuneus, Pallidum, and two segments of Cerebellum were considered to be discriminative between these groups. We achieved an accuracy of 95% using the support vector machine. The results of classification performance metrics showed that constructing a brain network using a novel non-linear connectivity measure in task-fMRI outperforms linear connectivity measures in terms of classification. The outcome of the Wilcoxon rank-sum test also demonstrated a superior result for clinical applications.

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