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

项目概要/摘要 阿尔茨海默氏病（AD）给公众健康带来了巨大的负担，同时导致显着的发病率和 死亡率。最近，人们付出了巨大的努力来收集和分析来自各种病理生理学的数据 逻辑层面和不同的实验范式中，但很难从风险因素转向因果因素 机制，这可能会带来减缓或阻止 AD 的新疗法。回应 NOT-MH-21-175 利用人类连接组数据进行二次分析，我们将绘制因果遗传-成像-临床图 (CGIC) 通过联合协调和分析遗传、影像和临床数据来治疗 AD 的途径 跨越多项生物医学研究。为了实现这一目标，我们将制定目标 1：强大的功能连接 tome Analysis (RFCA) 框架通过提取稳健的数据来揭示人脑功能的遗传结构 不同级别的功能连接指标；目标 2：发现 AD 因果路径的 CGIC 框架 从基因到 AD 进展敏感的图像特征，再到 AD 认知测量和诊断；和 目标 3：通过使用成像遗传数据集成来预测 AD 风险的 CGIC 预测 (CGIC-P) 框架 。此外，在目标 4 中，我们将通过广泛的研究来验证新开发的统计工具的有效性。 蒙特卡罗模拟并通过分析八个大规模生物医学解决具有临床意义的问题 加州研究。这些研究包括英国生物银行 (UKB) 研究、青少年大脑认知发展 (ABCD) 研究、人类连接组计划 (HCP)、IMAGEN 研究、阿尔茨海默病 Neuroimag- ing Initiative (ADNI) 研究、A4 研究、开放获取系列影像研究 (OASIS) 和威斯康星州 阿尔茨海默病预防 (WRAP) 研究登记处。多类型数据联合分析，包括影像数据、 这些研究中的遗传学/基因组数据、健康记录和认知信息将提供深入了解 AD 进展和健康老龄化的病理学。配套软件（它将提供许多所需的 用于分析成像和遗传数据的分析工具）和知识门户将被传播给 通过我们集团 BIG-S2 的网站和 NITRC 与科学界进行交流。各种影像基因组研究 神经精神疾病、神经退行性疾病和物质使用障碍以及正常大脑 的发展，将受益于我们新颖的分析方法和框架。除了 AD 之外，还有一个更好的方案： 大脑功能及其基因组机制的现状以及用于 AD 的疗法可能会激发新的灵感 以及其他脑部疾病的迫切需要的预防、诊断和治疗方法。

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.