Depressive Disorders Recognition by Functional Connectivity Using Graph Convolutional Network Based on EEG Microstates

The exploration of electroencephalogram (EEG) microstates and functional connectivity shows promising potential for both predicting and investigating the neural mechanisms of depression. However, the performance of depression evaluation based on physiological metrics remains unsatisfactory. In this study, we propose a Specific-General Functional Graph Convolutional Network (SGFGCN) to explore biomarkers related to the functional connectivity properties of different EEG microstates. Five microstate topographies, labeled as microstate class A to E, are obtained to describe depressive EEG dynamics, which is highly consistent with the findings of previous studies. Then, incorporating microstate class A to E, our SGFGCN model constructs specific adaptive functional connectivity and general dynamic functional connectivity for each microstate class sequence. It also extracts specific features and general features for each sample using graph convolutional network (GCN). The experimental results, which fuse specific and general depression-related features from resting-state EEG data in the MODMA dataset, demonstrate the superiority of the SGFGCN in identifying depression across various EEG microstate classes compared to previous models. Additionally, based on the recognition results and statistical analysis of the microstates, we confirm that there is a strong correlation between microstate C and depression. These findings suggest the viability of assessing depressive disorders using functional connectivity under certain microstates, offering fresh insights for exploring depression biomarkers.