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Abstract #3513

Aberrant white matter networks in methamphetamine-dependent patients and its application in support vector machine-based classification

Yadi Li1, Ping Cheng1, Pu-Yeh Wu2, Wenwen Shen3, Huifen Liu4, Jianbing Zhang4, Haibo Dong1, and Wenhua Zhou 3
1Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, Ningbo, China, 2GE Healthcare,Beijing,China, Beijing, China, 3Laboratory of Behavioral Neuroscience, Ningbo Addiction Research and Treatment Center, Ningbo, China, Ningbo, China, 4Ningbo Addiction Research and Treatment Center, Ningbo, China, Ningbo, China

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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