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

Background 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. Methods 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. Results 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. Conclusion 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.

Automated summary

This study aimed to construct addiction-related brain networks for heroin dependent patients undergoing methadone maintenance treatment (MMT) and identify high-risk individuals for relapse. The study found that MMT patients can be classified into two subgroups with significantly different relapse rates based on distinct brain-network patterns. The group with high-relapse had wider DC changes in the cortical-striatal-thalamic circuit and reduced DC in the mesocorticolimbic circuit compared to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related to relapse.