  The structure and variability of the brain's connections can be investigated
via prediction of non-imaging phenotypes using neural networks. However, known
neuroanatomical relationships between input features are generally ignored in
network design. We propose TractGraphCNN, a novel, anatomically informed graph
CNN framework for machine learning tasks using diffusion MRI tractography. An
EdgeConv module aggregates features from anatomically similar white matter
connections indicated by graph edges, and an attention module enables
interpretation of predictive white matter tracts. Results in a sex prediction
testbed task demonstrate strong performance of TractGraphCNN in two large
datasets (HCP and ABCD). Graphs informed by white matter geometry demonstrate
higher performance than graphs informed by gray matter connectivity. Overall,
the bilateral cingulum and left middle longitudinal fasciculus are consistently
highly predictive of sex. This work shows the potential of incorporating
anatomical information, especially known anatomical similarities between input
features, to guide convolutions in neural networks.
