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We tackle classification based on brain connectivity derived from diffusionmagnetic resonance images. We propose a machine-learning model inspired bygraph convolutional networks (GCNs), which takes a brain connectivity inputgraph and processes the data separately through a parallel GCN mechanism withmultiple heads. The proposed network is a simple design that employs differentheads involving graph convolutions focused on edges and nodes, capturingrepresentations from the input data thoroughly. To test the ability of ourmodel to extract complementary and representative features from brainconnectivity data, we chose the task of sex classification. This quantifies thedegree to which the connectome varies depending on the sex, which is importantfor improving our understanding of health and disease in both sexes. We showexperiments on two publicly available datasets: PREVENT-AD (347 subjects) andOASIS3 (771 subjects). The proposed model demonstrates the highest performancecompared to the existing machine-learning algorithms we tested, includingclassical methods and (graph and non-graph) deep learning. We provide adetailed analysis of each component of our model.

Multi-Head Graph Convolutional Network for Structural Connectome Classification