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

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects more than 7 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 more 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 Alzheimer’s disease (AD). We use functional connectivity (FC) graphs obtained from resting-state functional magnetic resonance imaging (rs-fMRI) scans from 303 subject visit histories. Our GNN-based model incorporates a recurrent neural network (RNN) block, enabling it to process data from the subject’s entire visit history. It can also work with irregular time gaps between visits by incorporating visit distance information in our input features. Our model demonstrates robust predictive performance, even with missing visits in the subjects’ visit histories. It achieves an accuracy of 82.9%, with an especially impressive accuracy of 68.8% on CN to MCI conversions – a task that poses a substantial challenge in the field. Our results emphasize the effectiveness of using rs-fMRI scans in predicting the onset of MCI or AD and offer a viable method enabling timely interventions to slow the progression of cognitive impairment.