Personalized Structure Preservation Based Graph Neural Network via Connection Interaction and Refinement for Autism Spectrum Disorder Diagnosis

Graph Neural Networks (GNNs) have garnered widespread recognition in the identification of Autism Spectrum Disorder (ASD) owing to their remarkable adaptability to irregular patterns of Functional Brain Networks (FBNs). However, current methods for constructing FBNs generally employ a uniform modeling strategy to process neuroimaging data from different subjects, which fail to consider the heterogeneity of functional connectivity patterns among individuals adequately. In addition, existing methods tend to excessively focus on directly connected brain Regions of Interest (ROIs) when analyzing brain networks, underestimat\ing the importance of indirectly connected brain ROIs. At the same time, conventional approaches for identifying crucial brain regions may miss vital regions due to rigid threshold constraints. To address these issues, we propose Personalized Structure Preservation based GNN (PSP-GNN) for ASD diagnosis, which incorporates three aspects: 1) A personalized structure preservation strategy that constructs individualized brain networks by accounting for subject-specific variations; 2) A connection interaction-aware module designed to characterize interactions between directly and indirectly connected brain regions, providing comprehensive brain network representations; 3) A flexible brain region refinement technique based on Bernoulli sampling, which identifies salient brain regions without relying on pre-defined thresholds. Experimental results demonstrate the effectiveness of PSP-GNN in ASD diagnosis, highlighting its potential as a robust tool for future ASD diagnosis applications that combine FBNs and GNNs. Notably, the critical brain regions identified by PSP-GNN are consistent with established medical knowledge, suggesting their utility as potential biomarkers for clinical ASD diagnosis.