The present study applied MVPA and spectral DCM analysis methods to investigate the neurobiological substrates of AUD during the resting state. Our results show that image-based machine-learning techniques can be used to distinguish AUD. Compared to ALFF, fALFF, DCpb, and DCpw, ReHo showed the highest accuracy in classifying AUD from HCs (classification accuracy: 98.57%). The most informative brain regions for the classification are left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These brain regions are involved in executive control, decision-making, and reward/loss processing and might provide a novel perspective for the clinical diagnosis of AUD. These findings were validated using an independent data set, achieving a validation accuracy of 91.67%. Our results demonstrate the potential of image-based machine-learning techniques in predicting addiction severity (MAST and AUDIT scores) among patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. This finding was validated in an independent data set. Moreover, this study represents the first endeavor to employ spectral DCM to identify impaired causal interactions among
