Multi-hypergraph Fusion Optimization for Alzheimer's Disease Auxiliary Diagnosis

In the method of constructing hypergraphs for Alzheimer’s disease (AD) classification using the average blood oxygen level dependent (BOLD) sequences, there exists a problem where hypergraphs constructed based on a limited number of time points lead to the loss of critical details in the regions of interest (ROI) of the subjects’ brains, a multi-hypergraph fusion optimization model for AD classification is proposed. The model employs a sliding window approach on BOLD sequences to sequentially extract nonlinear high-order relationships between various brain regions within the window to construct multiple hypergraphs, considering the subtle differences in feature vectors of hyperedges across window dimensions, extract and fuse hypergraph features based on the functional connectivity and similarity relationships between hyperedges, and build a fMRI hypergraph attention neural network (FHyperGAT) that incorporates attention mechanisms to identify the functional connectivity features between brain regions within the fused hypergraph data. Experimental results demonstrate that the method proposed in this research has improved the classification performance on the AD/normal control (NC) classification task by 10 percentage points compared with the hypergraph convolutional network model (HyperGCN) , proving the effectiveness of the model.