Prediction of network‐based neurodegeneration using deep graph recurrent neural network

Alzheimer's disease (AD) is a neurodegenerative disease characterized by the progressive loss of neurons. To predict such progression, deep neural network models with longitudinal data have been used recently. However, there is a limit that they didn't consider graph topology of brain network, while it is well‐known that neurodegeneration progresses along the large‐scale brain network. In this study, we proposed a graph neural network (GNN) based deep recurrent model to predict longitudinal progression of neurodegeneration in mild cognitive impairment (MCI) patients.