  Brain aging is a widely studied longitudinal process throughout which the
brain undergoes considerable morphological changes and various machine learning
approaches have been proposed to analyze it. Within this context, brain age
prediction from structural MR images and age-specific brain morphology template
generation are two problems that have attracted much attention. While most
approaches tackle these tasks independently, we assume that they are inverse
directions of the same functional bidirectional relationship between a brain's
morphology and an age variable. In this paper, we propose to model this
relationship with a single conditional normalizing flow, which unifies brain
age prediction and age-conditioned generative modeling in a novel way. In an
initial evaluation of this idea, we show that our normalizing flow brain aging
model can accurately predict brain age while also being able to generate
age-specific brain morphology templates that realistically represent the
typical aging trend in a healthy population. This work is a step towards
unified modeling of functional relationships between 3D brain morphology and
clinical variables of interest with powerful normalizing flows.
