Cross-Modal Dynamic Hypergraph Computation via Functional-Structural Brain Network for Brain Disorder Diagnosis

Abstract

Cross-modal brain networks characterize the complex connections between different brain regions from both functional and structural perspectives, which is of significant importance for brain network analysis and the diagnosis of brain diseases. However, existing methods have failed to fully exploit the complementary information between functional and structural brain networks, neglecting the guiding role of topology in the transmission of functional modal information, as well as the potential associations of cross-modal high-order information. To address these challenges, this paper proposes a cross-modal dynamic hypergraph computing (CDHGC) framework for brain disease diagnosis and an in-depth analysis of coupled functional-structural brain networks. The CDHGC comprises two key modules: the topology-guided dynamic hypergraph generation module, which combines the topology-constrained KNN and topology-guided centroid sampling methods to uncover high-order correlations among brain regions across modalities based on topological structure. This module dynamically optimizes the hypergraph structure during training to explore potential associations of cross-modal information. The cross-modal hypergraph convolution module efficiently aggregates information from functional-structural brain networks through a cross-modal attention mechanism during message-passing, resulting in high-order joint representations. Experiments conducted on the ADNI and ABIDE datasets demonstrate that the proposed method outperforms current state-of-the-art approaches. Interpretability analysis reveals that the proposed method can discover multi-modal biomarkers associated with brain diseases.