An Explainable Connectome Convolutional Transformer for Multimodal Autism Spectrum Disorder Classification

The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration often introduces variability, hindering the achievement of accurate and interpretable results. This study presents the Connectome Convolutional Transformer (CCTF), a multimodal deep learning framework that integrates functional and structural brain connectivity information from fMRI and sMRI modalities. The CCTF enriches feature representation by incorporating diverse functional connectivity metrics and structural covariance networks based on multiple morphological properties. It employs a connectome convolutional embedding module and transformer encoder to capture and refine brain connectivity patterns. In addition, a node-to-graph pooling layer facilitates the identification of potential ASD biomarkers. Evaluation on the multi-site ABIDE dataset demonstrated that CCTF outperformed state-of-the-art methods, achieving accuracies of [Formula: see text] for fMRI, [Formula: see text] for sMRI, and [Formula: see text] for the ensemble fMRI+sMRI model in intra-site cross-validation. In the inter-site leave-one-site-out cross-validation, the CCTF maintained its superiority, with the ensemble model reaching [Formula: see text] accuracy, underscoring its robustness and generalizability across different sites. The identified brain regions are consistent with established ASD neurobiology, underscoring CCTF's potential to advance the understanding of the neural mechanisms underlying this complex disorder.