Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural Circuits.

Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and network-level analyses. However, most studies have focused on single substances in isolation, making it difficult to directly compare shared and distinct network computations and limiting their generalizability, given that many individuals engage in polysubstance use. Here, we introduce an explainable, connectivity-based multiclass classification framework that models distributed brain network organization to delineate both shared and substance-specific mechanisms of addiction. This design enables direct comparison among cannabis users, alcohol users, and controls, revealing network computations that uniquely differentiate each group. Using functional connectivity features from cue-induced craving tasks, we classified cannabis users (n=166), alcohol users (n=101), and healthy controls (n=238), achieving high out-of-sample accuracy (87% for cannabis, 69% for alcohol, and 73% for controls). Functional connectivity-based models consistently outperformed activation-based models, highlighting the importance of inter-regional network properties for biomarker development. Network analysis further revealed dorsal prefrontal, cingulate, and precuneus hubs consistent with meta-analytic craving networks and substance-specific connectivity motifs enhanced prefrontal coupling in cannabis users and greater insular-striatal integration in alcohol users. These results demonstrate that distinct network configurations define different substance-use profiles, advancing interpretable biomarkers for addiction neuroscience.