The objective of this study is to identify the heroin dependents undertaking stable methadone maintenance treatment (MMT patients) at high risk for opioid relapse prospectively. First, a self-defined addiction-related brain network was constructed with 10 hubs of several circuits associated with addiction and their degree centrality. Next, sixty male MMT patients was classified into different subgroups through grouping their addiction-related network into distinct neuronal activity patterns by K-means clustering algorithm. By comparing relapse rate between subgroups with distinct network pattern, the one at high risk for relapse was identified. This finding implicated a novel strategy for improving MMT therapeutic effect.