Genetics Encoded Joint Embedding of Multimodal Connectomes with Explainable Graph Neural Network for Schizophrenia Classification

Badhan Mazumder, Lei Wu, Vince D. Calhoun, Dong Hye Ye

Abstract: Investigating structural and functional brain connectivity is crucial for understanding neuropsychiatric disorders like schizophrenia (SZ), where genetic markers such as SNPs (single nucleotide polymorphisms) also play a significant role. Graph Neural Networks (GNNs) offer powerful tools for learning complex patterns from connectome data, yet their use across multimodal connectomes remains under-explored. In this study, we developed an explainable multiview GNN framework for SZ classification, integrating genetic markers with connectome features by leveraging message-passing scheme of GNN with Deep Canonical Correlation Analysis (DCCA) for multimodal fusion. Experiments on clinical dataset confirmed the robustness of the framework, highlighting potential clinical biomarkers.

External IDs:dblp:conf/isbi/MazumderWCY25