  We propose a mesh-based technique to aid in the classification of Alzheimer's
disease dementia (ADD) using mesh representations of the cortex and subcortical
structures. Deep learning methods for classification tasks that utilize
structural neuroimaging often require extensive learning parameters to
optimize. Frequently, these approaches for automated medical diagnosis also
lack visual interpretability for areas in the brain involved in making a
diagnosis. This work: (a) analyzes brain shape using surface information of the
cortex and subcortical structures, (b) proposes a residual learning framework
for state-of-the-art graph convolutional networks which offer a significant
reduction in learnable parameters, and (c) offers visual interpretability of
the network via class-specific gradient information that localizes important
regions of interest in our inputs. With our proposed method leveraging the use
of cortical and subcortical surface information, we outperform other machine
learning methods with a 96.35% testing accuracy for the ADD vs. healthy control
problem. We confirm the validity of our model by observing its performance in a
25-trial Monte Carlo cross-validation. The generated visualization maps in our
study show correspondences with current knowledge regarding the structural
localization of pathological changes in the brain associated to dementia of the
Alzheimer's type.
