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

TL;DR

This paper introduces KD-Brain, a graph learning framework that incorporates prior knowledge to better model subnetwork interactions in brain networks, improving disorder diagnosis and biomarker identification.

Contribution

The paper proposes a novel Prior-Informed Graph Learning framework with semantic-conditioned interaction and pathology-consistent constraints for brain network analysis.

Findings

Achieves state-of-the-art performance on disorder diagnosis tasks.

Identifies interpretable biomarkers aligned with psychiatric pathophysiology.

Effectively incorporates prior knowledge to guide subnetwork interaction modeling.

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.