CBT-GCL: Leveraging Connectional Brain Templates for Enhanced ASD Diagnosis via Graph Contrastive Learning

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with a rising global prevalence, making its early and precise diagnosis crucial. Although deep learning has shown great potential in the diagnosis of ASD with Functional Connectivity (FC) data, it still suffers from challenges, including substantial noise in raw FC data, redundant connections, and high inter-individual heterogeneity. In facing these challenges, the Connectional Brain Template (CBT), which represents a group of brain networks, can effectively mitigate them, thereby providing prior guidance for diagnosis. To this end, we propose a CBT-driven graph contrastive learning model, called CBT-GCL, which utilizes CBT as prior knowledge to assist ASD diagnosis. Specifically, our model concurrently trains two encoders to extract multi-level features from both the CBT and individual-specific FC data, subsequently fusing and enhancing these features. A perceptual loss function aligns hierarchical features between the two groups, while a contrastive loss function pulls features of positive samples closer and pushes features of negative samples apart. This effectively extracts critical connections from FC data, thereby enhancing ASD diagnostic performance. Experiments on the Autism Brain Imaging Data Exchange (ABIDE) I and II datasets demonstrate that CBT-GCL outperforms state-of-the-art models in multiple evaluation metrics. Ablation studies further confirm the effectiveness of each component of CBT-GCL.