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

Abstract

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.

Glymphatic-System Function Is Associated with Addiction and Relapse in Heroin Dependents Undergoing Methadone Maintenance Treatment

“…The robust Poisson regression method has been enhanced to address potential outliers and influential points while diminishing reliance on the assumption of a Poisson distribution, thereby strengthening the rigor of the analytic results [ 35 , 36 , 37 ]. In the regression model, the normalized divergence of bilateral DTI-ALPS was defined as the variable of interest, age as the covariates that influence both the relapse behavior [ 12 ] and brain glymphatic function [ 38 , 39 ], and relapse count from the 24-month follow-up as the dependent variable.…”

“…Furthermore, white-matter microstructure indices in the posterior limb of the internal capsule were addressed for their association with relapse to heroin use in MMT [ 11 ]. Recently, a data-driven method was used to identify the poor and good MMT responder’s prognosis [ 12 ] based on the network graph measures. The above studies mainly focused on brain parenchyma; however, little research has been done on the role of the brain interstitium in addiction and relapse, and the mechanisms involved are not well understood.…”