A Novel Graph Neural Network Framework for Brain Age Prediction

Alzheimer's disease (AD) is a neurodegenerative disorder that causes cognitive decline, and early detection remains a challenge. Resting-state functional MRI (rs-fMRI) has shown potential for identifying early AD signs by analyzing brain connectivity. In this study, we propose a Hierarchical GCN-Transformer Network (HGTNet) for brain age prediction using rs-fMRI data. Through experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we demonstrate that, compared to traditional machine learning and deep learning methods, the combination of Graph Convolutional Networks (GCN) and Transformer architecture enhances the model's ability to capture complex brain interactions. Our model's more accurate brain age predictions provide a valuable step in identifying early neurodegenerative changes, aiding in the better intervention and management of Alzheimer's disease.