Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence

• Published in: Frontiers in psychiatry Vol. 11; p. 599099
• Main Authors: Qi, Chang, Fan, Xiaobing, Foxley, Sean, Wu, Qiuxia, Tang, Jinsong, Hao, Wei, Xie, An, Liu, Jianbin, Feng, Zhijuan, Liu, Tieqiao, Liao, Yanhui
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 15.01.2021
• Subjects: biomarkers; craving; methamphetamine use; Psychiatry; relapse; structural imaging; craving; structural imaging; methamphetamine use; relapse; biomarkers
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2020.599099

Craving is the predictor of relapse, and insula cortex (IC) is a critical neural substrate for craving and drug seeking. This study investigated whether IC abnormalities among MA users can detect craving state and predict relapse susceptibility. A total of 142 subjects with a history of MA dependence completed structural MRI (sMRI) scans, and 30 subjects (10 subjects relapsed) completed 4-month follow-up scans. MA craving was measured by the Visual Analog Scale for Craving. Abnormalities of IC gray matter volume (GMV) between the subjects with and without craving were investigated by voxel-based morphometry (VBM). The receiver operating characteristic (ROC) analysis was performed for the region-of-interest (ROI) of IC GMV to assess the diagnostic accuracy. By comparing whole-brain volume maps, this study found that subjects without craving ( = 64) had a significantly extensive decrease in IC GMV (family-wise error correction, < 0.05) than subjects with craving group ( = 78). The ROI of IC GMV had a significantly positive correlation with the craving scores reported by MA users. The ROC analysis showed a good discrimination (area under curve is 0.82/0.80 left/right) for IC GMV between the subjects with and without craving. By selecting Youden index cut-off point from whole model group, calculated sensitivity/specificity was equal to 78/70% and 70/75% for left and right IC, respectively. By applying the above optimal cut-off values to 30 follow-up subjects as validations, the results showed a similar sensitivity (73-80%) and specificity (73-80%) for detecting craving state as model group. For predicting relapse susceptibility, the sensitivity (50-55%) was low and the specificity (80-90%) was high. Our study provides the first evidence that sMRI may be used to diagnosis the craving state in MA users based on optimal cut-off values, which could be served as MRI bio-markers and an objective measure of craving state.