  We tackle classification based on brain connectivity derived from diffusion
magnetic resonance images. We propose a machine-learning model inspired by
graph convolutional networks (GCNs), which takes a brain connectivity input
graph and processes the data separately through a parallel GCN mechanism with
multiple heads. The proposed network is a simple design that employs different
heads involving graph convolutions focused on edges and nodes, capturing
representations from the input data thoroughly. To test the ability of our
model to extract complementary and representative features from brain
connectivity data, we chose the task of sex classification. This quantifies the
degree to which the connectome varies depending on the sex, which is important
for improving our understanding of health and disease in both sexes. We show
experiments on two publicly available datasets: PREVENT-AD (347 subjects) and
OASIS3 (771 subjects). The proposed model demonstrates the highest performance
compared to the existing machine-learning algorithms we tested, including
classical methods and (graph and non-graph) deep learning. We provide a
detailed analysis of each component of our model.
