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.
@article{arxiv.2603.19307,
title = {Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning},
author = {Siyu Liu and Guangqi Wen and Peng Cao and Jinzhu Yang and Xiaoli Liu and Fei Wang and Osmar R. Zaiane},
journal= {arXiv preprint arXiv:2603.19307},
year = {2026}
}