DeMorph: A Decoupled Deformation Model with Anatomical Guidance for Predicting Longitudinal Brain Atrophy

Modeling the longitudinal progression of brain atrophy in Alzheimer's Disease (AD) is critical for early diagnosis and prognosis, but remains challenging for existing generative models. Current methods either fail to preserve anatomical integrity or treat progression as an entangled process, where changes from normal aging are inseparably fused with AD pathology. This fundamentally limits their scientific utility. To address this, we propose DeMorph, a novel deformation-based framework that explicitly disentangles and accurately predicts longitudinal brain MRI changes. Our transformer-based architecture features two key innovations: (1) a Decoupled Feed-Forward Network (D-FFN) for separate aging and pathology deformation pathways; and (2) a Multi-Scale Anatomical Guidance Cross-Attention (MS-AGCA) module, which explicitly injects anatomical priors to ensure spatially precise and biologically plausible disentanglement. Extensive experiments on four datasets (ADNI, OASIS-3, AIBL, and an inhouse dataset) demonstrate that DeMorph achieves stateof- the-art performance in both image similarity and anatomical accuracy. Crucially, we validate the success of our framework through robust bidirectional counterfactual synthesis, demonstrating the ability to controllably induce and remove a quantitatively plausible, AD-specific pathological effect.