ADWM: A Latent World Model for Modeling Alzheimer’s Disease Progression from Functional Imaging

Understanding dynamic changes in brain function is essential for characterizing disease progression in Alzheimer’s disease (AD). As a volumetric functional imaging modality, positron emission tomography (PET) imaging provides a direct view of cerebral metabolism and can capture disease-related metabolic alterations across brain regions. To model these evolving patterns, we introduce ADWM, a latent world model that learns the dynamics of dementia progression from sparse longitudinal observations. ADWM represents the PET modality and standardized uptake value ratio (SUVR) values in an aligned latent space, organized by a weakly supervised disease progression index (DPI). A shared neural ordinary differential equation (ODE) models population-level latent evolution along the DPI, while a separate nonnegative progression rate links disease stage to chronological time under longitudinal supervision. Conditioned on the initial state, ODE prediction, patient context, and visit-level treatment information, a residual diffusion model learns the distribution of individual latent changes beyond the shared progression trajectory. By combining a population-level progression prior with individual-level stochastic refinement, ADWN enables conditional simulation of latent disease states and regional metabolic profiles from sparse observations. This framework provides a functional-imaging-based world model for characterizing both shared and individualized AD progression dynamics, offering a unified approach to simulating disease evolution and its associated metabolic changes.