Imaging genetics based dementia risk prediction using deep survival neural network

Abstract

AbstractBackgroundDementia has a large impact on people’s lives and is one of the leading causes of death. Therefore, accurate risk assessment is essential for improving early diagnostics and intervention. Previous studies have proposed various models for dementia risk prediction, based on imaging biomarkers and genetic factors. However, most of them have linear, unimodality or low‐dimensional nature due to model limitations and computational challenges of integrating such heterogeneous and high‐dimensional domains as images and genotype data. Recent development of Artificial Intelligence, specifically neural networks, enabled such integration, yet focused on classification problems, not risk prediction. We present a deep survival neural network to meet the purpose.MethodData were obtained from the Rotterdam Study. Individuals with complete data and available brain MRI before dementia diagnosis were included. MRI scans were pre‐processed using voxel‐based morphometry according to Good et al. into gray matter images. We included ApoE‐ε4 and 76 top‐risk SNPs based on most recently published GWAS (Bellenguez et al.). We developed the model combining Convolutional Neural Network and Survival Analysis, with the maximum‐likelihood estimator and C‐index of cox proportional hazard (Cox) model as the loss function and metric. To evaluate model performances, we used Cox model as comparisons: one baseline model with gender, age, genetic inputs and one extended model with additional extracted brain measurements. We performed 10 cross‐validation by stratified sampling to reveal the true differences.ResultTable1 shows little performance differences among the models, as expected age is already the strongest predictor. However, we see that raw gray matter and genetics inputs contribute to prediction additionally in all models. Furthermore, using the method from Tsang et al. we show strong interactions among image, age and genetic features (Fig.1). Fig.2 visualizes the most important brain regions for prediction on 3D image using GRAD‐CAM.ConclusionWe developed a deep survival model for dementia risk prediction which do not outperform classical model with brain measurements at present. However, our model is able to integrate high‐dimensional MRI and genetic data, detect and visualize the importance and interactions among input data, which may give additional insight for dementia prediction and etiologic research.