海洛因
美沙酮维持
美沙酮
上瘾
中心性
精神科
医学
联想(心理学)
心理学
临床心理学
内科学
神经科学
药品
心理治疗师
数学
组合数学
作者
Lei Wang,Feng Hu,Wěi Li,Qiang Li,Yongbin Li,Jiang Zhu,Wei Xuan,Jian Yang,Jianxin Guo,Yue Qin,Hong Shi,Wei Wang,Yarong Wang
标识
DOI:10.1017/s0033291721003937
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI