  Synthetic longitudinal brain MRI simulates brain aging and would enable more
efficient research on neurodevelopmental and neurodegenerative conditions.
Synthetically generated, age-adjusted brain images could serve as valuable
alternatives to costly longitudinal imaging acquisitions, serve as internal
controls for studies looking at the effects of environmental or therapeutic
modifiers on brain development, and allow data augmentation for diverse
populations. In this paper, we present a diffusion-based approach called
SynthBrainGrow for synthetic brain aging with a two-year step. To validate the
feasibility of using synthetically-generated data on downstream tasks, we
compared structural volumetrics of two-year-aged brains against
synthetically-aged brain MRI. Results show that SynthBrainGrow can accurately
capture substructure volumetrics and simulate structural changes such as
ventricle enlargement and cortical thinning. Our approach provides a novel way
to generate longitudinal brain datasets from cross-sectional data to enable
augmented training and benchmarking of computational tools for analyzing
lifespan trajectories. This work signifies an important advance in generative
modeling to synthesize realistic longitudinal data with limited lifelong MRI
scans. The code is available at XXX.
