Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network

ArXi:2604.06469v1 Announce Type: new Alzheimer's disease (AD) is a neurodegenerative disorder that affects than seven million people in the United States alone. AD currently has no cure, but there are ways to potentially slow its progression if caught early enough. In this study, we propose a graph neural network (GNN)-based model for predicting whether a subject will transition to a severe stage of cognitive impairment at their next clinical visit. We consider three stages of cognitive impairment in order of severity: cognitively normal (CN), mild cognitive impairment (MCI), and AD.