Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease

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Summary Alzheimer's disease (AD) affects over 44 million individuals worldwide, and the number is projected to triple by 2050. However, currently there is no cure for AD. This project aims to develop and apply novel statistical methods, especially deep learning, to advance neuroimaging genetics for AD. It involves novel methodological developments in Aims 1-4, cost-effective applications to the large-scale UK Biobank neuroimaging genetic data for AD (Aim 5), and software development (Aim 6). All four Aims for the methods development tackle emerging impor- tant topics in deep learning with their applications to neuroimaging genetics for AD; although the other three Aims deal with independent topics with their own other broad applications, they in turn serve for Aim 1: 1) Aim 1 applies manually searched deep learning models for automatic feature extraction/phenotyping from neuroimages, by which both the statistical power and biological interpretation of subsequent genome-wide association studies (GWAS) are expected to be enhanced; 2) Aim 2 employs (automatic) neural architecture search (NAS) to more efficiently identify better deep learning models, which are then applied to Aim 1 for enhancing feature extraction/phenotyping and thus boosting the power of GWAS; 3) Aim 3 focuses on explainable deep learning, offering biological insights by localizing and highlighting the most important features extracted by deep learning models that can be used for Aim 1; 4) Aim 4 develops a novel inferential theory for deep learning, which is then applied to rigorously test for the statistical significance of any selected/highlighted features used in Aim 1. In Aim 5, these new methods will be applied to the UK Biobank neuroimaging and GWAS data to identify novel genetic loci and neuroimaging features for AD. As a byproduct, we will develop and distribute software implementing the proposed methods in Aim 6.