Diagnosis of Multiple Sclerosis Using Graph-Theoretic Measures of Cognitive-Task-Based Functional Connectivity Networks

Graph theory allows us to gain better insight concerning changes in brain functional architecture associated with cognitive impairments in the early stages of multiple sclerosis (MS). In the present study, we employed a machine-learning system based on graph measures from functional networks constructed by cognitive-task-related functional magnetic resonance imaging (fMRI) data. We used a predefined atlas to define the brain regions and Pearson’s correlation to describe the connectivity strength between the regions. Then, several graph metrics were extracted for each subject. After that, the most efficient subsets of features were selected through the Wilcoxon rank-sum test, and the linear support vector machine (SVM) classifier was employed to distinguish between MS and healthy subjects. The node degree, subgraph centrality, K-coreness, and PageRank centralities measured in the left fusiform, hippocampus, and parahippocampal gyri regions demonstrated an accuracy of 85% through the combination of all local measures. Two optimal global measures, modularity and small-worldness index, and individual betweenness centrality feature improved the identification of MS patients with a sensitivity of 81.25%. Our results indicated the potential of the proposed system to identify cognitive changes in early MS for diagnostic purposes.