  The cerebral cortex performs higher-order brain functions and is thus
implicated in a range of cognitive disorders. Current analysis of cortical
variation is typically performed by fitting surface mesh models to inner and
outer cortical boundaries and investigating metrics such as surface area and
cortical curvature or thickness. These, however, take a long time to run, and
are sensitive to motion and image and surface resolution, which can prohibit
their use in clinical settings. In this paper, we instead propose a machine
learning solution, training a novel architecture to predict cortical thickness
and curvature metrics from T2 MRI images, while additionally returning metrics
of prediction uncertainty. Our proposed model is tested on a clinical cohort
(Down Syndrome) for which surface-based modelling often fails. Results suggest
that deep convolutional neural networks are a viable option to predict cortical
metrics across a range of brain development stages and pathologies.
