BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset

Graph Neural Networks (GNNs), a deep learning model for non-Euclidean data structures, have shown significant improvement in brain-related intelligent tasks (neuroimaging, brain clustering). A graph constructed from brain atlas is input of GNNs for various tasks. However, the choice of node connectivity (edges) receives little attention in current works, the performance thereby degrades when dealing with noisy dataset. In this paper, we propose an end-to-end framework to boost the performance robustness on noisy functional Magnetic Resonance Imaging (fMRI) dataset, using a Variational Graph Auto-Encoders (VGAE)-based edge predictor.