Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis - Li, Wenchao; Zhao, Jiaqi; Shen, Chenyu; Zhang, Jingwen; Hu, Ji; Xiao, Mang; Zhang, Jiyong; Chen, Minghan (Frontiers in Neuroinformatics, 2022)
Alzheimer's disease (AD) has raised extensive concern in health care and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this paper, we build a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural MRI (magnetic resonance imaging), DWI (diffusion-weighted imaging), and amyloid PET (positron emission tomography). Based on some existing research results, we apply statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on left and right hemisphere. In the light of advances in graph convolutional networks for graph classifications, and summarized characteristics of brain networks and AD pathologies, we propose a Regional Brain Fusion-Graph Convolutional Network (RBF-GCN), which is constructed with an RBF framework mainly including three sub-modules: hemispheric network generation module, multi-channel GCN module, and feature fusion module. In the multi-channel GCN module, the improved GCN by our proposed Adaptive Native Node Attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit, but also achieved competitive results in multiple sets of AD stages classification tasks using hundreds of experiments over the ADNI clinical dataset.