Adaptive Integration of Incomplete Multimodal 3D Neuroimaging for Alzheimer's Prediction and Biomarker Discovery

Alzheimer's disease (AD) diagnosis requires analysis of diverse data types to capture the heterogeneous factors underlying its development and progression. Magnetic resonance imaging (MRI) and positron emission tomography (PET) noninvasively measure brain structure and neuronal activity, respectively, and can serve as early indicators of AD onset and future progression. We propose V3D-MMoE, an interpretable framework to adaptively integrate incomplete multimodal 3D neuroimaging for AD diagnosis prediction and biomarker discovery. It goes beyond prior approaches by leveraging (1) a sparse mixture-of-experts formulation to account for variation in the importance of different modality combinations, (2) modality alignment to enhance cross-modal learning, and (3) cross-encoders to dynamically handle missing modalities. When applied to MRI and PET scans to predict two-year AD diagnosis, V3D-MMoE outperformed state-of-the-art multimodal 3D neuroimaging methods. Interpretability analyses revealed subject-specific MRI and PET biomarkers consistent with the known biology of AD. Ablation experiments demonstrated the benefit of leveraging multimodal neuroimaging.