Multimodal Multi-Graph Fusion Learning for Alzheimer's Disease Diagnosis

Alzheimer's Disease (AD) is a prevalent and severe neurodegenerative disorder, and early diagnosis is essential for managing disease progression. Recently, multimodal graph learning has demonstrated significant potential in integrating both medical imaging and non-imaging data, as well as uncovering relationships between patients. However, the high-dimensional nature of multimodal medical data poses significant challenges for constructing and learning modality graph structures. Moreover, existing methods are often imprecise in modeling graph structures for continuous data. To address these issues, this paper introduces a novel multimodal multi-graph fusion learning method for Alzheimer's disease diagnosis. Specifically, multimodal state space networks (multimodal SSNs) are proposed to capture the dependencies between multimodal and high-dimensional features. Furthermore, a novel graph structure learning (KGSL) based on an initial K-nearest neighbors graph is proposed to separately construct graph structures for each modality. This method is particularly suitable for modeling the graph structures of Euclidean data. Finally, multimodal graph fusion integrates various modal graph structures into a single graph, leading to enhanced multimodal integration. In addition, this paper uses a learnable Chebyshev Graph Convolutional Network for the classification network, which enables end-to-end optimization. Experimental results demonstrate that our approach achieves excellent performance on public datasets.