Abstract: Alzheimer’s Disease (AD), a prevalent neurodegenerative disorder characterized by memory loss and cognitive decline, poses significant challenges for individuals and society. Multimodal data fusion has emerged as a promising approach for AD diagnosis, with Graph Convolutional Networks (GCNs) effectively capturing irregular brain information. However, traditional GCN methods face limitations in representing and integrating multimodal data, often resulting in feature mismatch. In this study, we propose a novel Kolmogorov-Arnold Graph Attention Network (KAGAN) model to address this issue through semantic-level alignment. KAGAN incorporates a Multimodal Feature Construction method (MuStaF) to extract structural and functional features from T1- and T2-weighted images, and a Multimodal Graph Adjacency Matrix Construction method (MuGAC) to integrate clinical information, modeling intricate relationships across modalities. Experiments conducted on the ADNI dataset demonstrate the superiority of KAGAN in AD/CN/MCI classification, achieving an accuracy of 98.29 ± 1.21%. This highlights KAGAN’s potential for early AD diagnosis by enabling interactive learning and fusion of multimodal features at the semantic level. The source code of our proposed model and the related datasets are available at https://github.com/sheeprra/KAGAN.

Key words: Alzheimer’s disease diagnosis')">Alzheimer’s disease diagnosis, Multimodal data fusion, Feature mismatch problem, GAT, KAN