An increasing number of studies employ resting-state fMRI to investigate brain alteration in alcoholism and drug addiction. Regional homogeneity (ReHo) is a prevalent method for analyzing resting-state fMRI data, which quantifies the temporal synchronization of nearest neighbors’ time series and can be utilized to map local spontaneous neural activity, rendering it a valuable tool for detecting alterations in cerebral function [11]. Previous research has suggested that chronic alcohol use may result in aberrations in localized connectivity within executive control, mood regulation, decision-making, memory, motor coordination, and reward processing systems as evidenced by alterations in ReHo [2, 12–14]. These findings indicate disrupted localized functional integration associated with AUD using traditional group-level analysis. However, group-level analysis is limited in its ability to detect subtle differences among individuals, thereby hindering the diagnostic decision-making process for individual patients. Machine-learning algorithms offer alternative tools, such as multivariate pattern analysis (MVPA), which can examine multivariate patterns in data and aid in clinical diagnoses of relevant mental disorders [15, 16]. MVPA methods have the potential to identify clinical biomarkers associated with AUD based on ReHo, However, no MVPA study to date has explored ReHo in distinguishing AUD subjects from a control group.
