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

Abstract Background 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. Method We proposed a spectral GCN approach with CCA loss function (GCN‐CCA) to extract feature representations from imaging and genetics data. Briefly, the graph embeddings on the graph nodes were filtered in the Fourier domain. We used two hidden layers with 64 hidden units for extracting imaging and genetic data. ReLU activations were used after each graph convolution layer. A canonical correlation loss function was optimized based on Adam optimizer. We compared our model with the deep CCA model (DCCA) for AD classification. Result We downloaded data for 310 participants (103 AD and 207 Cognitive Normal [CN]) including neuroimaging and genetic data from the ADNI database. We used average structural connectivity based on the AAL atlas as the graph in our GCN model, three imaging measurements (VBM, FDG, FBR) as initial attributes on the graph nodes. We selected 2,644 candidate SNPs from the GWAS catalog ( https://www.ebi.ac.uk/gwas/ ). The proposed model obtained 82.25 % test accuracy for the AD/CN classification, outperforming the DCCA model (77.41%). For interpretation, we generated the saliency maps using guided gradient backpropagation (Figs 1 and 2). We observed the imaging phenotypes from left middle temporal gyrus, superior temporal, frontal inferior triangularis, putamen, paracentral lobule, frontal medial orbital, and pallidum, and right posterior cingulum, and genetic markers from ABCA13 (rs2163935, rs6955132, rs4024044) and APOE (rs429358) contributed to the AD outcome prediction. Conclusion Here, we demonstrated the utility of GCN‐CCA model and its interpretability. The GCN‐CCA not only obtained higher prediction performance but also highlighted important regions for AD classification. We plan to apply our algorithm to other AD cohorts to see if our algorithm generalizes to independent data sets.