MT-BAAN: Multi-View Topological Bilinear Aggregation Attention Network Model for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are common cognitive disorders. Research has shown that cognitive decline is closely related to abnormal connections between different functional areas of the brain. However, research on brain functional network (BFN) has mainly focused on individual topological structures, seldom considering the sparsity of the BFNs and the complexity of multi-level interactions among brain regions. To tackle this problem, in this article, we propose a multi-view topological bilinear aggregation attention network model (MT-BAAN) for disease diagnosis and brain network analysis. Based on rs-fMRI data, the model mainly includes a multi-view graph construction module (MVGC), a feature enhancement module (FEM), a dual-level attention module (DLAM), and a graph relation convolution network module (GRCN). MVGC module uses two sparse methods to construct high-view and low-view graphs and retains fully connected BFN topology as the full-view, aiming at capturing multi-scale topological features. FEM and DLAM utilize bilinear aggregation and attention mechanisms, respectively, to learn topological features and obtain weight coefficients that reflect the importance of different network views. The GRCN module employs two convolutional operators to learn the BFN topology information at the node and network levels and completes the classification. The experimental results indicate that the complementary learning of multi-view topologies can effectively improve model performance. Across binary classification tasks and ternary classification tasks, MT-BAAN shows superior performance compared to other experimental methods, which is valuable for research and clinical diagnosis of attention deficit disorder AD and MCI.