Based on functional connectivity (FC) matrices derived from resting-state functional magnetic resonance imaging (rs-fMRI) data, graph neural networks (GNNs), as an advanced deep learning technique, have been widely applied in major depressive disorder (MDD) diagnosis. However, conventional GNNs suffer from a critical limitation in preserving the spatial specificity of brain regions, which is attributed to their intrinsic node permutation invariance that ignores the unique order and specific roles of brain regions in neural circuits. To address this limitation, in this paper, we propose a novel deep learning framework, Graph Contrastive Learning based on Edge Convolution (EC-GCL), to analyze resting-state fMRI data from 1,160 participants, including 597 patients with MDD and 563 healthy controls. This framework integrates an edge convolution encoder, specifically designed to preserve the spatial specificity of brain regions, with a learnable graph augmentation module into an adversarial graph contrastive learning, thereby enhancing the extraction of discriminative MDD-related FC features and improving diagnostic classification accuracy. Compared with conventional machine learning and GNN models, our proposed EC-GCL achieved superior performance (AUC = 71.2%) and improved interpretability. In particular, the framework identified several key brain regions, including the dorsolateral superior frontal gyrus, thalamus, and insula, that are closely linked to the pathophysiology of MDD, which is consistent with the findings of prior neuroimaging studies. This study demonstrates that combining edge convolution with contrastive learning provides a robust and explainable method for MDD diagnosis. This provides new insights into depression and may support improvements in clinical practice.
Long et al. (Tue,) studied this question.