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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