Cross-Template-Based Hypergraph Transformer

Single-template-based brain functional network analysis methods can provide limited functional connectivity information, which constrains the performance of brain disease diagnosis. Previous works have explored multi-template functional network analysis but failed to integrate the high-order correlation information within templates and the complementary information between templates into a unified relationship strength between nodes, and we extract the high-order correlation information within each template through hypergraph convolution. Secondly, for the analysis of functional connectivity between templates, we propose a cross-template Transformer to capture long-range dependencies between templates. A cross-template mask is applied to focus the model’s attention on important connections between templates, thereby enhancing model robustness. Finally, we progressively fuse the high-order information captured within templates with the global information across templates for downstream classification tasks. The proposed method has been validated on the public ABIDE dataset, and it outperforms existing methods in the ASD diagnosis task.