This is a pilot study of the weighted white matter (WM) network in MA-dependent patients. By combining DTI-based probabilistic tractography and graph theory, the WM networks of MA-dependent patients presented small-worldness, and these networks tend to be random networks. The network metrics, that presented inter-group differences were used to construct a support vector machine, that achieved an excellent performance in discriminating MA-dependent patients from normal controls. Overall, the current study demonstrated that MA dependence is associated with abnormal network metrics, and these metrics can be promising features to train a classifier which need further verification with a larger sample size.
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