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

项目摘要/摘要 阿尔茨海默病(AD)是一个巨大的公共卫生负担，同时也导致显著的fi发病率和 死亡率。最近，人们已经做出了巨大的努力来收集和分析来自不同病理生理的数据。 逻辑水平和不同的实验范式，但fi邪教很难从风险因素转移到因果关系 机制，这可能导致减缓或阻止阿尔茨海默病的新疗法。回应NOT-MH-21-175 ON 利用人类连接组数据进行二次分析，我们将绘制出因果遗传-成像-临床 (CGIC)通过联合协调和分析遗传、成像和临床数据来治疗AD 进行了多项生物医学研究。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- Tome分析(RFCA)框架通过提取健壮性来揭示人脑功能的遗传结构 不同级别的功能连接性度量；目标2：一个发现AD因果路径的CGIC框架 从基因到AD进展敏感的影像特征，到AD的认知测量和诊断； 目的3：利用成像遗传数据集成预测AD风险的CGIC预测框架(CGIC-P) 提顿。此外，在目标4中，我们将通过广泛的扩展来验证新开发的统计工具的EFfi准确性 通过对8个大型生物医学的分析，进行蒙特卡罗模拟并解决具有临床意义的fi问题。 加州大学的研究。这些研究包括英国生物库(UKB)的研究，青少年大脑认知发展 (ABCD)研究，人类连接组计划(HCP)，IMAGEN研究，阿尔茨海默病神经成像- ING倡议(ADNI)研究、A4研究、开放获取成像研究系列(OASIS)和威斯康星州 阿尔茨海默病预防(WRAP)研究注册。多种类型数据的联合分析，包括成像数据， 来自这些研究的遗传学/基因组学数据、健康记录和认知信息将提供对 阿尔茨海默病进展和健康衰老的病理学。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)和知识门户将传播到 科学fic社区通过我们的小组BIG-S2‘S网站和NITRC。各种成像基因组学研究 神经精神障碍、神经退行性疾病、物质使用障碍以及正常大脑 开发，将使fi受益于我们新的分析方法和框架。除了AD，更好的Under- 对大脑功能及其基因组机制的认识，以及对阿尔茨海默病的治疗，可能会启发新的 也迫切需要预防、诊断和治疗其他大脑疾病的方法。

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.