Mapping the Causal Genetic-Imaging-Clinical Pathway for Alzheimer's Disease

项目概要/摘要 阿尔茨海默病（AD）是一个巨大的公共卫生负担，同时导致显著的发病率和 mortality.近年来，人们做了大量的工作来收集和分析各种病理生理学的数据， 逻辑水平和不同的实验范式，但很难从风险因素转移到因果关系， 这可能导致减缓或阻止AD的新疗法。根据NOT-MH-21-175， 使用人类连接体数据进行二次分析，我们将绘制因果遗传-成像-临床 通过联合协调和分析遗传、成像和临床数据， 在多个生物医学研究中。为了实现这一目标，我们将开发目标1：一个强大的功能连接， 托姆分析（RFCA）框架，以揭示人类大脑功能的遗传结构，通过提取强大的 不同层次的功能连接指标;目标2：发现AD因果通路的CGIC框架 从基因到AD进展敏感的图像特征到AD认知测量和诊断;以及 目的3：通过使用成像遗传数据整合的CGIC预测（CGIC-P）框架进行AD风险预测。 是的。此外，在目标4中，我们将通过广泛的调查， Monte Carlo模拟并通过分析八个大规模生物医学来解决具有临床意义的问题， cal研究。这些研究包括英国生物库（UKB）研究、青少年大脑认知发展研究、 （ABCD）研究，人类连接组计划（HCP），IMAGEN研究，阿尔茨海默病神经成像， ing Initiative（ADNI）研究、A4研究、开放获取系列成像研究（OASIS）和威斯康星州 阿尔茨海默病预防登记处（WRAP）研究。多类型数据的联合分析，包括成像数据， 遗传学/基因组学数据，健康记录和认知信息，从这些研究将提供深入了解 AD进展和健康老龄化的病理学。配套软件（将提供许多需要的 用于分析成像和遗传数据的分析工具）和知识门户网站， 通过我们集团BIG-S2的网站和NITRC的科学社区。各种成像基因组研究， 神经精神障碍、神经变性疾病和物质使用障碍，以及正常脑 开发，将贝内于我们新颖的分析方法和框架。除了AD，更好的下- 大脑功能及其基因组机制的地位，以及用于AD的疗法，可能会激发新的 以及迫切需要的预防、诊断和治疗其他脑部疾病的方法。

Project Summary/Abstract Alzheimer's disease (AD) presents a massive public health burden, while resulting in significant morbidity and mortality. Recently, tremendous efforts have been taken to collect and analyze data from various pathophysio- logical levels and in different experimental paradigms, but it has been difficult to move from risk factors to causal mechanisms, which may lead to new treatments for slowing or stopping AD. In response to NOT-MH-21-175 on the use of human connectome data for secondary analysis, we will map the Causal Genetic-Imaging-Clinical (CGIC) pathway for AD through jointly harmonizing and analyzing genetic, imaging, and clinical data across multiple biomedical studies. To achieve this goal, we will develop Aim 1: a Robust Functional Connec- tome Analysis (RFCA) framework to uncover the genetic architecture of human brain function by extracting robust functional connectivity metrics at different levels; Aim 2: a CGIC framework to discover AD's causal pathways starting from genes to AD progression-sensitive image features to AD cognitive measures and diagnosis; and Aim 3: a CGIC prediction (CGIC-P) framework for AD risk prediction by using imaging genetic data integra- tion. Furthermore, in Aim 4, we will verify the efficacy of the newly developed statistical tools through extensive Monte Carlo simulations and solve problems with clinical significance by analyzing eight large-scale biomedi- cal studies. These studies include the UK Biobank (UKB) study, the Adolescent Brain Cognitive Development (ABCD) study, the Human Connectome Project (HCP), the IMAGEN study, the Alzheimer's Disease Neuroimag- ing Initiative (ADNI) study, the A4 study, the Open Access Series of Imaging Studies (OASIS) and the Wisconsin Registry for Alzheimer's Prevention (WRAP) study. The joint analysis of multi-type data, including imaging data, genetics/genomics data, health records, and cognitive information, from these studies will provide insight into the pathology of AD progression and healthy aging. The companion software (which will provide many needed analytic tools for the analysis of imaging and genetic data) and the knowledge portal will be disseminated to scientific community through our group BIG-S2's websites and NITRC. A variety of imaging genomic studies of neuropsychiatric disorders, neurodegenerative diseases, and substance use disorders, as well as normal brain development, will benefit from our novel analytical methods and framework. In addition to AD, a better under- standing of brain function and its genomic mechanisms, as well as the therapies used for AD, may inspire new and urgently required approaches to prevention, diagnosis, and treatment of other brain disorders as well.