Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their ability to capture complex brain patterns. This paper develops a unified pipeline that utilizes Vision Transformers (ViTs) for extracting 3D region embeddings from sMRI data and Graph Neural Network (GNN) for classification. We explore two strategies for defining regions: (1) an atlas-based approach u…

Conclusion In this study, we proposed a unified atlas-based framework integrating 3D ViT with GAT for MDD classification using sMRI data. This model efficiently captures intra- and inter-regional relationships by using predefined brain atlases to extract ROIs, thereby enhancing its ability to identify complex brain changes associated with MDD. Results indicated that most atlas-based models (AAL, Dose, and CK) outperformed the Cube-based model, emphasizing the importance of anatomical priors for guiding the extraction of meaningful regional features. This process assisted in the precise identification of disease-relevant brain regions, improving classification performance for MDD detection. In future work, we plan to extend the proposed framework in several directions. First, we aim to investigate multimodal learning by incorporating resting-state fMRI and clinical features with sMRI. This integration is expected to provide complementary information that improves feature representations, optimizing classification accuracy and robustness. Second, leave-one-site-out cross-validation will be conducted to provide a more accurate evaluation of cross-site generalization and to improve the…

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