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Abstract
Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R2 = 0.38; Michigan alcoholism screening test, MAST, R2 = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R2 = 0.17; MAST, R2 = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.
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Details
1 University of Science and Technology of China, Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, Hefei, China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); Hefei Normal University, Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei, China (GRID:grid.462326.7) (ISNI:0000 0004 1761 5124); Anhui Jianzhu University, The Institute of Linguistics and Applied Linguistics, Hefei, China (GRID:grid.440647.5) (ISNI:0000 0004 1757 4764)
2 University of Science and Technology of China, Department of Psychology, School of Humanities and Social Science, Hefei, China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639)
3 University of Science and Technology of China, Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, Hefei, China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639)
4 Hefei Fourth People’s Hospital, Department of Substance-Related Disorders, Hefei, China (GRID:grid.477985.0) (ISNI:0000 0004 1757 6137)
5 University of Science and Technology of China, Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, Hefei, China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); University of Science and Technology of China, Department of Psychology, School of Humanities and Social Science, Hefei, China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); Bengbu Medical College, School of Mental Health, Bengbu, China (GRID:grid.252957.e) (ISNI:0000 0001 1484 5512); Shanghai International Studies University, Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai, China (GRID:grid.412515.6) (ISNI:0000 0001 1702 5894); Hefei Comprehensive Science Center, Institute of Health and Medicine, Hefei, China (GRID:grid.412515.6)




