Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

• URL: http://arxiv.org/abs/2603.19307v1
• Date: Fri, 13 Mar 2026 12:04:23 GMT
• Title: Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
• Abstract summary: KD-Brain is a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process.<n> KD-Brain leads to state-of-the-art performance on a wide range of disorder diagnosis and identifies interpretable biomarkers.
• Score: 13.2346150249448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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