Predicting structural brain trajectories with discrete optimal transport normalizing flows

We propose to use discrete optimal transport normalizing flows (OT-NF) for the simultaneous synthesis of brain images through years, and the explicit control of such progression. The OT-NF formulation, based on the minimization of the sliced-Wasserstein distance, allows to infer such trajectories in the absence of longitudinal data. The proposed framework could allow the imputation of brain images conditioned on non-constant insults or stimuli.