Global-Local Dual-Masked Region-Aware Graph Transformer for Autism Spectrum Disorder Diagnosis

Abstract

Functional brain network analysis is pivotal for understanding the neurological basis of Autism Spectrum Disorder (ASD). However, functional connectivity (FC) suffers from inherent noise, making it challenging to accurately capture its global and local information while mitigating noise interference. To address this limitation, we propose a Region-Aware Graph Transformer with Global-Local Dual Attention Mask (RAGT-DM). Specifically, the global attention mask retains nodes with larger attention scores, which captures long-range global topological patterns while reducing noise interference. Meanwhile, the local attention mask selects adjacent brain nodes based on Euclidean distance to characterize local structural connections, strengthening the model’s perception of local information. Experiments on the ABIDE I and ABIDE II datasets demonstrate that RAGT-DM achieves 73.04% and 71.49% accuracy, outperforming state-of-the-art methods.