  Given the wide success of convolutional neural networks (CNNs) applied to
natural images, researchers have begun to apply them to neuroimaging data. To
date, however, exploration of novel CNN architectures tailored to neuroimaging
data has been limited. Several recent works fail to leverage the 3D structure
of the brain, instead treating the brain as a set of independent 2D slices.
Approaches that do utilize 3D convolutions rely on architectures developed for
object recognition tasks in natural 2D images. Such architectures make
assumptions about the input that may not hold for neuroimaging. For example,
existing architectures assume that patterns in the brain exhibit translation
invariance. However, a pattern in the brain may have different meaning
depending on where in the brain it is located. There is a need to explore novel
architectures that are tailored to brain images. We present two simple
modifications to existing CNN architectures based on brain image structure.
Applied to the task of brain age prediction, our network achieves a mean
absolute error (MAE) of 1.4 years and trains 30% faster than a CNN baseline
that achieves a MAE of 1.6 years. Our results suggest that lessons learned from
developing models on natural images may not directly transfer to neuroimaging
tasks. Instead, there remains a large space of unexplored questions regarding
model development in this area, whose answers may differ from conventional
wisdom.
