Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data. (May 2018) - Algunaid, Rami F.; Algumaei, Ali H.; Rushdi, Muhammad A.; Yassine, Inas A.
Highlights: We present a machine learning approach to discriminate schizophrenic and normal subjects. Schizophrenia and other mental disorders can be characterized by changes in brain connectivity. We extract graph-theoretic features from resting-state functional magnetic resonance images. We compare several feature selection techniques and evaluate the performance on a large dataset. We show the best reported performance among graph-theoretic methods for schizophrenia detection. Abstract: 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 betweenness centrality, characteristic path length, degree, clustering coefficient, local efficiency, global efficiency, participation coefficient and small-worldness. After that, feature selection algorithms are used to choose the most discriminant features. Finally, a SVM classifier is trained and tested on discriminant graph features. Experiments were performed on a large Rs-fMRI dataset formed of 70 schizophrenic patients and 70 healthy subjects. The performance was evaluated using nested-loop 10-fold cross-validation. The best detection results were found using the feature selection methods of Welch's t -test (82.85%), l 0 -norm (91.43%), and feature selection via concave minimization (FSV) (95.00%). Our results outperform those of recent state-of-the-art graph-theoretic methods.