Hierarchical Hypergraph Learning with Functional Alignment for Brain Disorder Diagnosis

Graph Neural Networks (GNNs) have shown promise in brain disorder diagnosis by modeling neurophysiological signals as graphs, such as Electroencephalogram (EEG) and functional Near-Infrared Spectroscopy (fNIRS). However, existing brain graph learning methods remain limited by hierarchical anatomical heterogeneity, manifested as inconsistencies for alterations caused by brain disorders across different anatomical levels. Specifically, this heterogeneity can be analyzed from two perspectives: (1) Brain High-order interaction knowledge hierarchy heterogeneity, referring to inconsistencies in interactions among brain elements across anatomical levels. The inadequate capture of level-specific knowledge leads to unsuitable modeling. (2) Brain element functional role hierarchy heterogeneity, referring to inconsistency in the functional roles of the same element when viewed from different levels. This inconsistency leads to functional knowledge conflict, thereby impairing GNN cross-level generalizability. To address these challenges, we propose **HHRA**, a framework that addresses dual hierarchical heterogeneity. First, it introduces **H**ierarchical **H**igh-order Knowledge Capturing (HHKC), which enables the joint learning on multiple anatomical levels. Specifically, by constructing both pairwise and hypergraph relations across levels, HHKC effectively enables capturing level-specific high-order interaction knowledge. Second, it introduces Hierarchical Functional **R**ole **A**lignment (HFRA), which promotes the coordination across multiple anatomical levels. Specifically, it estimates and aligns the functional importance of brain elements across levels for consistent knowledge and a cross-level generalizable GNN. Extensive experiments on EEG and fNIRS datasets validate the superiority of HHRA. The code is available at <https://anonymous.4open.science/r/HHRA-6656/>.