BrainIPAgent: Multi-Agent Interpretable Structural Priors for Multimodal Brain Network Learning

The effective integration of structural connectivity (SC) and functional connectivity (FC) is crucial for brain disorder diagnosis. However, existing methods remain predominantly data-driven and fail to effectively incorporate interpretable priors, leading to limited circuit-level interpretability. Static multimodal fusion strategies further limit the adaptive reliance on structural priors. In this paper, we propose BrainIPAgent, a multimodal brain network learning framework with multi-agent interpretable structural priors and RL-guided alignment. BrainIPAgent maps structural patterns into an attention bias in the logit space, enabling soft control over information flow in functional representation learning. To preserve structural consistency across hierarchies, we introduce a mask propagation operator that projects structural constraints through soft pooling. Multimodal fusion is formulated as a bilevel optimization problem, where contrastive alignment models the distributional discrepancy between textual embeddings derived from iteratively refined clinical reports and continuous functional representations. An RL-guided controller then adaptively regulates the strengths of structural guidance and contrastive alignment throughout training. Experiments on ADNI and real-world datasets demonstrate that BrainIPAgent achieves superior classification performance while providing interpretable insights. The code is available at https://anonymous.4open.science/r/BrainIPAgent.