HyperEvo: History-Aware Discrete Hypergraph Topology Evolution for Enhanced Brain Disease Classification

Functional brain networks exhibit complex high-order interactions and temporally evolving patterns, and their abnormal reorganization may provide important information for brain disease classification. However, existing methods often rely on predefined or fixed topologies, which may fail to capture disease-associated reorganization of dynamic node-hyperedge relationships across temporal states. Moreover, historical functional states are rarely exploited to guide topology evolution, limiting the ability to characterize the temporal evolution of functional brain networks. To address these problems, we propose HyperEvo, a history-aware discrete hypergraph topology Evolution framework that improves brain disease classification by modeling dynamic functional brain networks. First, we construct window-level sparse hypergraphs from resting-state functional magnetic resonance imaging (rs-fMRI) signals and learn high-order functional representations through hypergraph convolution. Then, we aggregate preceding-window information through a history-aware topology evidence mechanism and perform budget-constrained discrete topology evolution by explicitly adding and deleting node-hyperedge memberships. Finally, we employ a multi-window kernel attention mechanism to integrate functional representations across temporal states and capture discriminative spatiotemporal patterns in dynamic functional brain networks. Extensive experiments on the ADNI and PPMI datasets demonstrate that HyperEvo achieves superior performance across multiple brain disease classification tasks. Further analyses show that the learned dynamic hypergraphs highlight interpretable brain regions and high-order functional connectivity patterns associated with disease-related functional alterations.