Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence
Background: 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.Methods: A total of 142 subjects with a his...
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2021-01-01
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doaj-2925f8f2c43e4d75a6781b4465dd68252021-01-15T05:13:02ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402021-01-011110.3389/fpsyt.2020.599099599099Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine DependenceChang Qi0Xiaobing Fan1Sean Foxley2Qiuxia Wu3Jinsong Tang4Jinsong Tang5Wei Hao6An Xie7Jianbin Liu8Zhijuan Feng9Zhijuan Feng10Tieqiao Liu11Yanhui Liao12Yanhui Liao13Department of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Radiology, The University of Chicago, Chicago, IL, United StatesDepartment of Radiology, The University of Chicago, Chicago, IL, United StatesDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Psychiatry, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, ChinaKey Laboratory of Medical Neurobiology of Zhejiang Province, Hangzhou, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Radiology, The People's Hospital of Hunan Province, Changsha, ChinaDepartment of Radiology, The People's Hospital of Hunan Province, Changsha, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Psychiatry, Weifang Mental Health Center, Weifang, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Psychiatry, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, ChinaKey Laboratory of Medical Neurobiology of Zhejiang Province, Hangzhou, ChinaBackground: 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.Methods: 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.Results: By comparing whole-brain volume maps, this study found that subjects without craving (n = 64) had a significantly extensive decrease in IC GMV (family-wise error correction, p < 0.05) than subjects with craving group (n = 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.Conclusions: 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2020.599099/fullstructural imagingbiomarkerscravingrelapsemethamphetamine use |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chang Qi Xiaobing Fan Sean Foxley Qiuxia Wu Jinsong Tang Jinsong Tang Wei Hao An Xie Jianbin Liu Zhijuan Feng Zhijuan Feng Tieqiao Liu Yanhui Liao Yanhui Liao |
spellingShingle |
Chang Qi Xiaobing Fan Sean Foxley Qiuxia Wu Jinsong Tang Jinsong Tang Wei Hao An Xie Jianbin Liu Zhijuan Feng Zhijuan Feng Tieqiao Liu Yanhui Liao Yanhui Liao Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence Frontiers in Psychiatry structural imaging biomarkers craving relapse methamphetamine use |
author_facet |
Chang Qi Xiaobing Fan Sean Foxley Qiuxia Wu Jinsong Tang Jinsong Tang Wei Hao An Xie Jianbin Liu Zhijuan Feng Zhijuan Feng Tieqiao Liu Yanhui Liao Yanhui Liao |
author_sort |
Chang Qi |
title |
Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence |
title_short |
Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence |
title_full |
Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence |
title_fullStr |
Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence |
title_full_unstemmed |
Structural Imaging-Based Biomarkers for Detecting Craving and Predicting Relapse in Subjects With Methamphetamine Dependence |
title_sort |
structural imaging-based biomarkers for detecting craving and predicting relapse in subjects with methamphetamine dependence |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Psychiatry |
issn |
1664-0640 |
publishDate |
2021-01-01 |
description |
Background: 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.Methods: 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.Results: By comparing whole-brain volume maps, this study found that subjects without craving (n = 64) had a significantly extensive decrease in IC GMV (family-wise error correction, p < 0.05) than subjects with craving group (n = 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.Conclusions: 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. |
topic |
structural imaging biomarkers craving relapse methamphetamine use |
url |
https://www.frontiersin.org/articles/10.3389/fpsyt.2020.599099/full |
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