MT-DBDAE: a multi-task dual-branch denoising autoencoder for major depressive disorder diagnosis

Major depressive disorder (MDD) is a globally prevalent mental illness. Functional connectivity (FC) analysis based on resting-state functional magnetic resonance imaging resting-state fMRI (rs-fMRI) provides new opportunities for neurobiological mechanism research and objective diagnosis of MDD. However, existing methods are prone to overfitting when handling high-dimensional FC data and predominantly rely on single-feature learning strategies, which limits their ability to fully leverage synergistic information between local connectivity patterns and global network features. To address these challenges, this study proposes a multi-task dual-branch denoising autoencoder (MT-DBDAE) framework for automatic MDD diagnosis. The framework uses a heterogeneous dual-pathway module (HDM) that integrates FC transformer and graph attention networks (GATv2) to achieve multi-scale feature extraction, applies hidden space mixup technology for data augmentation in latent space and finally jointly optimizes three objectives – classification, reconstruction and contrastive learning – through a multi-task learning strategy. Nested cross-validation experiments were conducted on the large-scale multi-center resting-state (REST)-meta-MDD data set, achieving favorable classification performance on both the CC200 and anatomical automatic labeling atlases. This effectively alleviates issues of high-dimensional FC data and medical data scarcity, providing an effective tool for objective MDD diagnosis.