Structural MRI–Informed Multimodal Fusion for Robust Alzheimer’s Disease Prediction

Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder, and accurate prediction remains a critical challenge. Multiple modalities can inform AD prediction, with public resources such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing access to diverse datasets. However, prior studies often rely on single modalities, limiting clinical applicability, or struggle to integrate multimodal data effectively. In this work, we introduce MOIRA (Multi-Omics Integration with Robustness to Absent modalities), a predictive framework that leverages the strong discriminative power of structural Magnetic Resonance Imaging (sMRI) while flexibly incorporating additional modalities to boost performance. MOIRA achieves 0.91 accuracy, substantially surpassing existing approaches. Notably, we show that our model trained with sMRI can still improve prediction without sMRI data at inference, supporting potentially cost-efficient diagnostic strategies in clinical settings. Our findings highlight the value of sMRI-informed multimodal integration for advancing robust, translational AD prediction.