Multi-Volumetric Feature-Based Brain Age Prediction Using sMRI and Graph Neural Networks

There are noticeable studies where Graph Neural Networks (GNNs) have been utilized effectively in the field of neuroimaging, mostly on functional magnetic resonance imaging (fMRI), and their implementation on structural MRI (sMRI) has not been investigated much. GNNs perform better than traditional deep learning methods in capturing the intricate brain's anatomical attributes and the relationships between the brain regions of interest (ROIs). This is because of GNN's capacity to efficiently capture intricate information by leveraging the structural information of the underlying graph. Additionally, GNNs have relatively fewer message-passing stages, which makes them computationally efficient. This study presents a GNN-based framework for brain age prediction at the ROI level by utilizing volumetric information of grey matter (GM) and white matter (WM). The dataset comprises 232 T1-weighted sMRI scans (98 males, 134 females) of healthy controls obtained from ADNI. The brain was segmented into 56 ROIs using the LPBA40 brain atlas in CAT12. Subsequently, GM and WM volumes were extracted from these ROIs, and anatomical graphs were constructed based on GM volume information. These graphs served as inputs for different GNN models, including GCN, GraphSage, GAT, and GIN layers-based models. These models have been trained to predict brain age, and the model's effectiveness evaluation demonstrates that the GCN-based GNN model outperforms other models, with a Mean Absolute Error (MAE) value of 5.06 and Pearson's correlation coefficient (PCC) of 0.76.