Identification of Major Depressive Disorder Using Multiple Functional Connection Patterns

Major depressive disorder (MDD) poses a significant challenge to global mental health, highlighting the urgent need for effective diagnostic tools to enable early and accurate detection. Resting-state functional magnetic resonance imaging (rs-fMRI) has garnered considerable attention for evaluating MDD through functional connectivity networks (FCNs). While graph convolutional networks (GCNs) have advanced FCN analysis by capturing complex interregional connection patterns, existing GCN-based approaches have predominantly focused on a single connection pattern, thereby neglecting the multifaceted information encoded in diverse connection profiles.

We propose a Multiple Functional Connection Patterns Graph Convolutional Network (MFCP) that integrates three distinct connection patterns-sparse representation, Pearson correlation, and Granger causality mapping-to leverage their complementary information. The proposed framework employs multiple graph convolutional modules to integrate diverse connectivity information, thereby enhancing MDD-related diagnostic features extracted from FCNs. We conducted primary experiments on the REST-MDD dataset Site 20, comprising 533 subjects, to evaluate the proposed MFCP.

The MFCP framework achieved superior diagnostic performance with an accuracy of 87.74%, precision of 86.21%, recall of 90.91%, F1-score of 88.50%, and an area under the ROC curve (AUC) of 0.9326. Comparative analysis revealed that integrating three connection patterns significantly outperformed single-pattern approaches (accuracy range: 72.64%-81.13%) and two-pattern combinations (accuracy range: 77.36%-83.02%). t-SNE visualization confirmed enhanced class separability with increasing pattern integration, while Grad-CAM analysis identified distinct discriminative brain regions across different connectivity patterns.

The proposed MFCP framework effectively integrates multiple functional connection patterns to enhance diagnostic feature extraction, demonstrating strong effectiveness for MDD identification. These findings suggest that leveraging complementary connectivity information through multi-pattern integration represents a promising approach for improving automated MDD diagnosis based on rs-fMRI functional connectivity analysis.