  We present a proof-of-concept, deep learning (DL) based, differentiable
biomechanical model of realistic brain deformations. Using prescribed maps of
local atrophy and growth as input, the network learns to deform images
according to a Neo-Hookean model of tissue deformation. The tool is validated
using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) dataset, and we demonstrate that the trained model is capable
of rapidly simulating new brain deformations with minimal residuals. This
method has the potential to be used in data augmentation or for the exploration
of different causal hypotheses reflecting brain growth and atrophy.
