  Graph neural networks have emerged as a promising approach for the analysis
of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays
an important role for modelling anatomical structures, and shape classification
can be used in computer aided diagnosis and disease detection. However, with a
plethora of options, the best architectural choices for medical shape analysis
using GNNs remain unclear. We conduct a comparative analysis to provide
practitioners with an overview of the current state-of-the-art in geometric
deep learning for shape classification in neuroimaging. Using biological sex
classification as a proof-of-concept task, we find that using FPFH as node
features substantially improves GNN performance and generalisation to
out-of-distribution data; we compare the performance of three alternative
convolutional layers; and we reinforce the importance of data augmentation for
graph based learning. We then confirm these results hold for a clinically
relevant task, using the classification of Alzheimer's disease.
