# Brain Disorder Diagnosis

The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.

The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.

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.

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.