Brain Functional Residual Temporal Convolution Network for Major Depressive Disorder Recognition

Major depressive disorder (MDD) is the most common psychological disorder that affects mental and physical health. To narrow the gap in real world mental healthcare and improve the effectiveness of MDD treatment, an increasing number of artificial intelligence (AI) methods have been proposed to explore electroencephalography (EEG) features, including traditional signal features and measures of brain functional connectivity network (BFCN), for the recognition of depression-related patterns. However, these methods fail to capture long-term dependencies and limit the modeling ability of information transmission dependencies in MDD brain regions. To address these issues, we propose a novel brain functional residual temporal convolution network (BFRTCN) method for MDD recognition. On one hand, this model directly focuses on the connectivity weights of BFCNs to model the information transmission between brain regions, allowing for better differentiation of the differences in information transmission patterns between MDD and normal control (NC). On the other hand, we introduce a residual temporal convolution network (ResiTCN) that utilizes temporal convolution layers to capture short-term changes in brain regions and establish residual connections to help maintain long-term dependencies for improving ability to capture disease variations. Experimental results on benchmark datasets validate the superior performance and time complexity of BFRTCN. Analysis shows that the Beta band MDD transmission mode is relatively stable. There are defects in the brain functional connections between the frontal and right temporal (RT) regions on Alpha and Gamma bands, which can serve as potential biomarkers for MDD recognition.