Context-Conditioned Anomalous Subgraph Representation Learning for Brain Disease Detection

Existing approaches for brain network analysis typically perform whole-graph classification or rely on dynamic functional connectivity sequences, overlooking the fact that healthy brains exhibit a stable contextual regularity where local subnetworks are consistently constrained by their specific functional contexts. We discover that this fundamental organizational principle is largely preserved in neuropsychiatric disorders, with disease-related alterations manifesting primarily as subtle perturbations to its concrete instantiation. Context-conditioned anomalous subgraph representation construction module is proposed to localize abnormal brain subnetwork. Role-aware brain subnetwork guidance encoder is introduced to encode multi-modal deviation directions, while anomaly-guided signed brain graph network is designed to propagate subgraph-level signals across the whole brain. Theoretically, we establish controlled false-positive bounds, abnormal detectability conditions, and region recovery guarantees via total-variation-regularized residual fields, showing that CCA-SRL provably distinguishes normal from abnormal brains when local subnetworks exhibit sufficiently strong conditional-representation shifts. Empirically, our model achieves new state-of-the-art performance on three public datasets—ADHD-200, OpenNeuro-ds002424, and ABIDE-II—outperforming 13 baselines.