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Abstract #3430

Abnormal brain white matter volume underlying methamphetamine abusers: A machine learning approach

Wentao Lai1, Mei Yang2, Zhifeng Zhou1, Wentao Jiang1, Xia Liu1, Gangqiang Hou1, Long Qian3, Zhi Kong2, and Haiyan Run2

1Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, China, 2Department of Drug Dependence, Shenzhen Kangning Hospital, Medicine Division of Shenzhen University, Shenzhen, China, 3GE Healthcare, MR Research China, Beijing, China

The disruption of specific microstructural features of white-matter (WM) has been observed in methamphetamine (MA) abusers. However, it remains unknown whether WM volume is abnormal in MA abusers. To address this issue, a machine learning approach was applied in this study to differentiate between 21 MA abusers and 13 age- and gender- healthy controls. Our results showed that a linear support vector machine classifier achieved an accuracy of 73.53% using the white matter volume as input features. Particularly, the most discriminative WM regions included pontine crossing tract, motor system and the reading related network.

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