BrainGenFlow: Modeling Longitudinal Progression via Residual Flow Matching and Gaussian Imputation

Forecasting individualized brain trajectories from a single baseline scan is essential for prognosis and early intervention in neurodegenerative diseases, yet existing methods either operate on composite biomarkers or require sequences of follow-up visits at inference. We introduce BrainGenFlow, a two-stage framework that produces month-resolved, multi-region brain trajectories from a single scan and clinical covariates. The central design addresses cohort sparsity through Bayesian distillation: a deep kernel Gaussian process performs exact temporal imputation per region of interest, converting sparse irregular visits into dense monthly supervisory trajectories. We utilize these trajectories to train an autoregressive flow matching model through a new parameterization technique called Residual Flow Matching, to model the changes in the biomarkers directly. At inference, BrainGenFlow forecasts decade-long trajectories across 145 brain regions using only the baseline scan, without requiring access to longitudinal data. On long-horizon AD biomarker forecasting, our method achieves state-of-the-art performance with improvements that hold under leave-one-study-out evaluation. Furthermore, predicted regional rate-of-change align with established Alzheimer's disease progression pattern.