Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease

利用神经影像遗传数据进行深度学习治疗阿尔茨海默病

• 批准号: 10647797
• 负责人: Wei Pan
• 金额: $ 66.78万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647797
• 关键词: Advocate; Affect; Alzheimer' s Disease; Architecture; Atlases; Biological; Brain; Classification; Communities; Complex; Computer software; Data; Data Analyses; Development; Disease; Documentation; Early Diagnosis; Entropy; Environment; Etiology; Genetic; Genotype; Image; Individual; Investigation; Knowledge; Learning; Magnetic Resonance Imaging; Manuals; Methodology; Methods; Modeling; Modification; Nature; Noise; Outcome; Performance; Phenotype; Prevention; Process; Proxy; Psychological reinforcement; Public Domains; Publishing; Pythons; Relaxation; Research; Speed; Statistical Computing; Statistical Methods; Testing; Writing; biobank; combinatorial; convolutional neural network; cost; cost effective; deep learning; deep learning model; endophenotype; feature extraction; feature selection; genetic association; genetic variant; genome wide association study; genomic locus; high dimensionality; improved; insight; interest; method development; model building; neural; neural network architecture; neuroimaging; novel; novel strategies; predictive modeling; programs; software development; success; theories; therapeutic development; tool; trait; web site

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.

总结 阿尔茨海默氏病（AD）影响全球超过4400万人，并且该数字预计将增加两倍 到2050年然而，目前还没有治愈AD的方法。该项目旨在开发和应用新的统计 方法，特别是深度学习，以推进AD的神经成像遗传学。它涉及新的方法论 在目标1-4的发展，成本效益的应用，以大规模的英国生物银行神经影像遗传数据， AD（目标5）和软件开发（目标6）。方法开发的所有四个目标都是为了解决新兴的重要问题， 深度学习的主题及其在AD神经成像遗传学中的应用;尽管其他三个目标 处理独立的主题与他们自己的其他广泛的应用，他们反过来服务于目标1：1）目标1适用 手动搜索深度学习模型，用于从神经图像中自动提取特征/表型， 随后的全基因组关联研究（GWAS）的统计功效和生物学解释 2）Aim 2采用（自动）神经架构搜索（NAS）， 识别更好的深度学习模型，然后将其应用于目标1，以增强特征提取/表型分析 从而增强GWAS的力量; 3）目标3专注于可解释的深度学习，提供生物学见解 通过本地化和突出显示深度学习模型提取的最重要的特征， 目标1; 4）目标4为深度学习开发了一种新的推理理论，然后应用于严格测试 目标1中使用的任何选定/突出显示特征的统计显著性。在目标5中，这些新方法将 应用于英国生物库神经影像和GWAS数据，以确定新的遗传位点和神经影像特征 对于AD。作为一个副产品，我们将开发和分发实现目标6中提出的方法的软件。