Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
This work addresses the problem of limited training samples in brain network analysis for mental disorder diagnosis, offering a domain-specific solution with incremental improvements.
The paper tackles the challenge of modeling subnetwork interactions in brain networks for mental disorder diagnosis by proposing KD-Brain, a prior-informed graph learning framework that incorporates semantic and clinical priors, achieving state-of-the-art performance on disorder diagnosis tasks and identifying interpretable biomarkers.
Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetworks remains a significant challenge for existing Transformer-based methods due to the limited number of training samples. To address these challenges, we propose KD-Brain, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process. Specifically, we design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functional identities. Furthermore, we introduce a Pathology-Consistent Constraint, which regularizes the model optimization by aligning the learned interaction distributions with clinical priors. Additionally, KD-Brain leads to state-of-the-art performance on a wide range of disorder diagnosis tasks and identifies interpretable biomarkers consistent with psychiatric pathophysiology. Our code is available at https://anonymous.4open.science/r/KDBrain.