FMD-IMHF: Foundation Model Distillation and Hypergraph Fusion for Neurodegenerative Disease Diagnosis with Incomplete Multimodal Data

Accurate diagnosis of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) requires integrating complementary information from multiple modalities, including structural MRI (sMRI), PET, and clinical data. However, a limited number of labeled samples and frequently missing modalities in clinical data pose challenges to representation learning and multimodal fusion. Medical foundation models offer powerful pretrained representations for modality-specific learning, but jointly using independently pretrained models incurs substantial computational costs and leads to misaligned representation spaces. Meanwhile, existing incomplete multimodal learning approaches remain limited in exploiting complementary information from available modalities to compensate for missing information. To address these challenges, we propose FMD-IMHF, a framework that distills modality-specific foundation models into lightweight encoders and aligns their representations in a shared diagnostic semantic space. A patient-modality hypergraph further models cross-modal complementarity and inter-patient similarity, enabling incomplete multimodal fusion without reconstructing missing modalities. Experiments on ADNI demonstrate the effectiveness of FMD-IMHF for AD diagnosis and MCI progression prediction, as well as its robustness under varying missing-modality rates. Evaluations on AIBL, SCAN, and PPMI further demonstrate its generalizability. Distillation reduces parameter counts, inference time, and peak GPU memory, while ablation studies validate the key components of FMD-IMHF.