Multi-Head Graph Convolutional Network for Structural Connectome Classification
This work addresses brain connectome classification for sex differences, which is important for health and disease understanding, but it is incremental as it builds on existing graph convolutional networks.
The authors tackled sex classification from brain connectivity data using a multi-head graph convolutional network, achieving the highest performance on two datasets (PREVENT-AD with 347 subjects and OASIS3 with 771 subjects) compared to existing methods.
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.