Unlocking shared-specific features of multi-modal brain graphs for accurate psychiatric diagnosis

Brain graphs constructed from diverse neuroimaging modalities offer complementary perspectives for characterizing structural and functional connectivity patterns within the human brain. Although multi-modal integration has significantly advanced psychiatric diagnosis, existing fusion methods encounter a critical limitation: they tend to prioritize either modality-specific characteristics or shared complementary information, rather than leverage both synergistically. To address this issue, we propose a Shared-Specific Graph Learning (S²GL) framework, designed to comprehensively explore multi-modal features for improving psychiatric diagnosis. Specifically, we develop two modality-specific graph learning modules to extract multi-level representations from structural and functional brain graphs, respectively. In parallel, a customized structural–functional shared graph learning module captures cross-modal correlations in the feature embedding space, and generates shared representations for high-order feature extraction. The resulting specific and shared representations are then integrated by a shared-specific graph feature aggregation module through multi-modal graph pooling for final diagnosis. Extensive experiments on two psychiatric disorders demonstrate that S²GL consistently outperforms state-of-the-art diagnostic methods. Furthermore, neuroscientific analysis reveals that the identified brain regions align with established clinical findings, highlighting the interpretability and clinical relevance of S²GL for distinguishing complex psychiatric conditions.