Statistical method for neural mechanism mediating and moderating cognitive system in Alzheimer's disease and aging research.

阿尔茨海默病和衰老研究中介导和调节认知系统的神经机制的统计方法。

• 批准号: 10320002
• 负责人: SEONJOO LEE
• 金额: $ 40.97万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320002
• 关键词: Accounting; Address; Adult; Aging; Algorithms; Alzheimer' s Disease; Alzheimer’s disease biomarker; Amyloid; Biometry; Brain; Brain imaging; Clinical; Cognition; Cognitive; Cognitive aging; Computer software; Cross-Sectional Studies; Data; Dementia; Development; Dimensions; Disease; Early Diagnosis; Education; Elderly; Functional Magnetic Resonance Imaging; Functional disorder; Image; Image Analysis; Impaired cognition; Individual; Joints; Magnetic Resonance Imaging; Measures; Mediating; Mediation; Mediator of activation protein; Medical Imaging; Methods; Monitor; Multicenter Studies; Multimodal Imaging; Nerve Degeneration; Neurology; Pathology; Pattern; Persons; Population; Positron-Emission Tomography; Predisposition; Proxy; Regression Analysis; Research; Research Personnel; Rest; Role; Software Tools; Source Code; Statistical Data Interpretation; Statistical Methods; System; Testing; Thick; cognitive ability; cognitive change; cognitive neuroscience; cognitive performance; cognitive reserve; cognitive system; cognitive task; design; experience; feature selection; follow-up; high dimensionality; imaging biomarker; imaging study; improved; individual patient; longitudinal analysis; morphometry; multidimensional data; multimodal neuroimaging; multimodality; neural network; neuroimaging; neuromechanism; normal aging; novel; open source; pre-clinical; relating to nervous system; tool

Alzheimer's disease (AD), as well as normal aging, are associated with a wide range of brain and cognitive changes. In investigating cognitive changes, it has been observed that some people can sustain more brain changes or pathology than others, and this differential susceptibility is related to measures such as IQ, education, vocational experiences etc. This observation is the basis for the cognitive reserve (CR) hypothesis, where CR moderates the effects of brain changes on cognition. Recent developments in medical imaging, particularly multimodal neuroimaging, can provide better understanding of neural mechanisms that underlie both cognitive changes and the role of CR. However, existing statistical methods were not designed to accommodate large-scale multi-dimensional data, particularly for incorporating high-dimensional moderators and mediators. To address these issues, we propose to develop, validate, and apply software tools for the cross-sectional and longitudinal analysis of multimodal MR brain images and cognitive data acquired from individuals with normal cognitive aging, preclinical AD and AD from two independent studies of aging and Alzheimer's disease: the Reference Ability Neural Networks (RANN) (Yaakov Stern, PI) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We will demonstrate that the developed statistical methods offer improved accuracy and robustness over current tools. First, we will develop tools for identifying robust relationships between neurodegeneration or pathology markers and brain function (network expression measured by task fMRI) in the presence of CR as a moderator. Second, we will derive neural substrate of CR using resting-state functional MRI and task fMRI and then develop statistical tools to test the moderation effect of the imaging CR proxies. Third, we will develop the sparse moderated mediation methods for high-dimensional predictors and mediators accounting for moderation. to test whether network expression during cognitive tasks mediates the effect of brain changes (measured via multimodal structural MRI) on cognitive performance, cognitive decline and dementia transition, and whether the derived neural substrate of CR moderates the mediation.

阿尔茨海默病 (AD) 以及正常衰老与多种大脑功能有关 和认知变化。在研究认知变化时，观察到一些 人们比其他人能够承受更多的大脑变化或病理，并且这种差异 易感性与智商、教育、职业经验等指标有关。 观察是认知储备（CR）假说的基础，其中 CR 调节 大脑变化对认知的影响。医学成像的最新发展，特别是 多模式神经影像，可以更好地理解背后的神经机制 认知变化和 CR 的作用。但现有的统计方法并不能 旨在容纳大规模多维数据，特别是合并 高维调节者和中介者。为了解决这些问题，我们建议开发， 验证并应用软件工具进行多模态的横截面和纵向分析 从具有正常认知老化的个体获取的 MR 大脑图像和认知数据， 临床前 AD 和 AD 来自两项关于衰老和阿尔茨海默病的独立研究： 参考能力神经网络 (RANN)（Yaakov Stern，PI）和阿尔茨海默病 神经影像倡议（ADNI）。我们将证明所开发的统计方法提供 与当前工具相比，提高了准确性和稳健性。首先，我们将开发识别工具 神经退行性变或病理标志物与大脑功能之间的牢固关系 （通过任务 fMRI 测量的网络表达）在 CR 作为调节器的情况下。第二， 我们将使用静息态功能 MRI 和任务 fMRI 推导 CR 的神经基质，然后 开发统计工具来测试成像 CR 代理的调节效果。第三，我们将 开发高维预测变量的稀疏调节中介方法 调解员负责调节。测试认知任务期间是否存在网络表达 介导大脑变化（通过多模式结构 MRI 测量）对认知的影响 表现、认知能力下降和痴呆转变，以及衍生的神经是否 CR 的底物调节中介作用。