Cloud-Based Big Neuroimaging Data Resource for Harmonized Research on Neuropsychiatric Symptoms in Alzheimer's Disease

超过600万美国人患有阿尔茨海默病（AD），其中最严重的是老年痴呆症（AD）。 常见的与年龄相关的神经退行性痴呆80%的AD患者还表现出神经精神障碍 症状（抑郁症），包括抑郁、焦虑、激动、攻击性等。抗抑郁药AD反应不佳 常规治疗，并可能导致严重的功能障碍，增加照顾者的负担， 制度化。AD患者的神经回路中存在严重的疾病相关变性，这可能是AD患者的一个重要原因。 AD患者的临床病程和治疗耐药性的机制。我们的首要目标是 一项由父母资助的研究旨在确定脑卒中和AD的神经回路之间的关系 神经回路退化，最终可能导致AD伴痴呆的结局更差。为了探测这些 关系，我们正在进行NIMH研究领域标准（RDoC）相关的二次分析 来自人类连接组项目（HCP）年轻成人和衰老数据集的测量，以及连接组 与人类疾病相关（CRHD）对难治性抑郁症，焦虑症，阿尔茨海默病， 和脑老化与痴呆症我们将计算方法应用于大数据分析， RDoC的原则，它将痴呆和AD视为大脑行为正常值的极端 映射。我们通过这六个组合数据集的大样本量（N~ 3，500）最大限度地提高科学严谨性， 通过采用ReproNim的做法，设计用于可重复的神经成像研究。本行政 补充请求，我们建议创建一个基于云的神经影像大数据资源， 协调对云计算和广告的研究，使研究人员能够利用基于云的资源 用于他们的研究。我们的数据资源包括：1）母研究中使用的四个CRHD数据集， 包括与HCP YA和老化协调的完全预处理的成像数据以及我们的新图像- 衍生表型，将在亚马逊网络服务（AWS）上开放访问，以及2）容器化软件 一种工具，利用AWS云计算完成新数据集的从头到尾的处理， 研究人员可以将他们的数据集与HCP/CRHD协调，计算我们的或他们自己的新成像特征， 并将我们的规范模型应用于他们的患者数据。这一管理支持将使我们能够保持整体 我们的研究的影响，增加我们的父母奖的整体影响，发挥持续的高影响力的影响力， AD研究，并增加我们的成果对研究界和NIH资助的研究的好处。

PROJECT SUMMARY ABSTRACT More than 6 million Americans suffer from Alzheimer’s disease (AD), the most common age-associated, neurodegenerative dementia. 80% of AD patients also exhibit neuropsychiatric symptoms (NPS), including depression, anxiety, agitation, aggression, and others. NPS in AD respond poorly to conventional treatments and can lead to severe functional impairment, increased caregiver burden, and institutionalization. There is profound disease-related degeneration in neurocircuitry in AD that may be a mechanism for the clinical course and treatment resistance of NPS in AD. The overarching goal of our funded parent grant is to identify relationships between the neurocircuitry underlying NPS and AD neurocircuit degeneration that ultimately may drive worse outcomes in AD with NPS. To probe these relationships, we are conducting secondary analyses of NIMH Research Domain Criteria (RDoC)-related measures from Human Connectome Project (HCP) Young Adult and Aging datasets, and the Connectomes Related to Human Disease (CRHD) on Treatment Resistant Depression, Anxious Misery, Alzheimer’s Disease, and Brain Aging and Dementia. We apply computational methods for big data analysis that inherently embody the principles of RDoC, which treats NPS and AD as extremes from normal values of brain – behavior mappings. We maximize scientific rigor via large sample size of these six combined datasets (N~3,500), and by adopting ReproNim practices designed for reproducible neuroimaging research. In this Administrative Supplement request, we propose to create a cloud-based big neuroimaging data resource for harmonized research on NPS and AD that will allow investigators to leverage cloud-based resources for their research. Our data resource is comprised of: 1) four CRHD datasets used in the parent study, including the fully pre-processed imaging data harmonized with HCP YA and Aging and our novel image- derived phenotypes that will be open access on Amazon Web Service (AWS), and 2) a containerized software tool that leverages AWS cloud computing for complete start-to-finish processing of new datasets so researchers can harmonize their datasets with HCP/CRHD, compute ours or their own novel imaging features, and apply our normative models to their patient data. This Admin Supp will allow us to preserve the overall impact of our study, increase the overall impact of our parent award, exert a sustained high impact influence on AD research, and increase the benefits of our outcomes to the research community and NIH-funded research.