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Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance treatment

Published online by Cambridge University Press:  27 October 2021

Lei Wang
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an 710061, P.R. China Department of Nuclear Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Feng Hu
Department of Radiology, The Hospital of Shaanxi Provincial Geology and Mineral Resources Bureau, Xi'an, P.R. China
Wei Li
Department of Radiology, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Qiang Li
Department of Radiology, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Yongbin Li
Department of Radiology, The Second Hospital of Xi'an Medical University, Xi'an, P.R. China
Jia Zhu
Department of Radiology, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Xuan Wei
Department of Radiology, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Jian Yang
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an 710061, P.R. China
Jianxin Guo
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an 710061, P.R. China
Yue Qin
Department of Radiology, Xi'an Daxing Hospital, Xi'an, P.R. China
Hong Shi
Department of Radiology, Xi'an No.1 Hospital, Xi'an, P.R. China
Wei Wang*
Department of Radiology, Tangdu Hospital, Air Force Military Medical University, Xi'an, P.R. China
Yarong Wang*
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an 710061, P.R. China
Authors for correspondence: Yarong Wang, E-mail:; Wei Wang, E-mail:
Authors for correspondence: Yarong Wang, E-mail:; Wei Wang, E-mail:



Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.


Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.


Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.


MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.

Original Article
Copyright © The Author(s), 2021. Published by Cambridge University Press

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These two authors contributed to this work equally.


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Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance treatment
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Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance treatment
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