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Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging modality to study the changes of functional brain networks in schizophrenic patients. Several representations have been proposed to capture the essential features of these networks. In particular, graph-theoretic representations can be effectively used to discriminate healthy subjects from schizophrenic patients. In this paper, we propose a machine-learning system based on a graph-theoretic approach to investigate and differentiate the brain network alterations. The fMRI data samples are first preprocessed to reduce noise and normalize the images. The automated anatomical labeling (AAL) atlas is then used to parcellate the brain into 90 regions and construct a region connectivity matrix. A weighted undirected graph is hence constructed and graph measures are computed for each subject. These graph measures include …