Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification. (arXiv:2107.12838v2 [q-bio.NC] UPDATED)

Brain functional connectivity (FC) reveals biomarkers for identification of
various neuropsychiatric disorders. Recent application of deep neural networks
(DNNs) to connectome-based classification mostly relies on traditional
convolutional neural networks using input connectivity matrices on a regular
Euclidean grid. We propose a graph deep learning framework to incorporate the
non-Euclidean information about graph structure for classifying functional
magnetic resonance imaging (fMRI)-derived brain networks in major depressive
disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on
the graph convolutional …

arxiv bio brain embedding graph identification learning major networks