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Identifying imaging genetics biomarkers in Alzheimer’s disease via integrating graph convolutional neural network and canonical correlation analysis

Brain imaging genetics is an emerging research topic in the study of Alzheimer’s disease (AD). The conventional approach, such as canonical correlation analysis (CCA), has been widely used to identify imaging genetic associations. A deep learning model has recently been proposed to better understand the roots of the complex association between imaging and genetic measures. We propose a graph convolutional neural network (GCN) with CCA loss function to integrate and identify the complex imaging genetics associations in AD.