KD-Brain guides subnetwork interaction modeling with semantic priors and a pathology-consistent constraint, beating 12 baselines on ASD, BD, and MDD diagnosis while yielding interpretable functional pathways
The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.