MuST-GCN: Multiscale and Hybrid Spatial Temporal Graph Convolutional Network for Accurate Identification of Alcohol Abuse and Alcohol Dependence Within Alcohol Use Disorder.

Abstract

Alcohol Use Disorder (AUD), encompassing Alcohol Abuse (AA) and Alcohol Dependence (AD), is a chronic, relapsing brain condition that can cause mental and physical issues. Confusion between AA and AD can lead to ineffective or even excessively aggressive treatments, but distinguishing them is difficult due to similar symptoms and physiological indicators. Fortunately, from a psychological perspective, AA is closely linked to poor behavioral control, while AD is associated with affective lability. These psychological mechanisms are differently featured in the activity of patients' brain regions. Inspired by this, we propose a model called Multiscale and Hybrid Spatial Temporal Graph Convolutional Network (MuST-GCN) that enables to extract the features of electroencephalogram (EEG) signals for accurate identification of AA and AD within AUD. It includes two modules: Multiscale Feature Extraction (MSFE) module with GCN concurrently analyzes inter-regional and intra-regional connectivity of brain regions to extract EEG features, exploring functional connectivity differences in AA and AD patients; Hybrid Spatial Temporal Memory (HSTM) module integrates spatial and temporal attention mechanisms to refine the significant features extracted from MSFE, targeting the key brain regions and temporal dynamics. The HSTM module yields a refined feature representation reducing overfitting and improving multiclass classification accuracy. MuST-GCN is evaluated using five-fold cross-validation on two datasets, achieving classification accuracies of 90.74% and 99.99%, respectively, and demonstrating superior performance in identifying AA, AD, and AUD compared to existing methods.