  The neonatal cortical surface is known to be affected by preterm birth, and
the subsequent changes to cortical organisation have been associated with
poorer neurodevelopmental outcomes. Deep Generative models have the potential
to lead to clinically interpretable models of disease, but developing these on
the cortical surface is challenging since established techniques for learning
convolutional filters are inappropriate on non-flat topologies. To close this
gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to
translate sphericalised neonatal cortical surface features (curvature and
T1w/T2w cortical myelin) between different stages of cortical maturity. Results
show our method is able to reliably predict changes in individual patterns of
cortical organisation at later stages of gestation, validated by comparison to
longitudinal data; and translate appearance between preterm and term gestation
(> 37 weeks gestation), validated through comparison with a trained
term/preterm classifier. Simulated differences in cortical maturation are
consistent with observations in the literature.
