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

阿尔茨海默病（AD）以及正常衰老与广泛的脑损害有关， 和认知变化。在研究认知变化时，已经观察到， 人们可以比其他人承受更多的大脑变化或病理，这种差异 易感性与智商、教育、职业经历等指标有关。 观察是认知储备（CR）假设的基础，其中CR调节了 大脑变化对认知的影响。医学成像领域的最新发展，特别是 多模态神经成像，可以提供更好的理解神经机制， 认知变化和CR的作用。然而，现有的统计方法并不 设计用于容纳大规模多维数据，特别是用于合并 高维度的调节者和调解者。为了解决这些问题，我们建议制定， 验证，并应用软件工具的横截面和纵向分析多模态 从具有正常认知老化的个体获得的MR脑图像和认知数据， 临床前AD和AD来自两项独立的衰老和阿尔茨海默病研究： 参考能力神经网络（RANN）（Yaakov Stern，PI）和阿尔茨海默病 神经影像学倡议（ADNI）。我们将证明，发达的统计方法提供 与现有工具相比，提高了精度和鲁棒性。首先，我们将开发工具， 神经变性或病理学标志物与脑功能之间的稳健关系 （通过任务fMRI测量的网络表达）在CR作为主持人的存在下。第二、 我们将利用静息态功能磁共振成像和任务功能磁共振成像推导出CR的神经基质， 开发统计工具来测试成像CR代理的调节效果。三是 为高维预测因子开发稀疏调节调解方法， 调解人占温和。为了测试在认知任务中网络表达是否 介导大脑变化（通过多模态结构MRI测量）对认知功能的影响 表现，认知能力下降和痴呆症的过渡，以及是否衍生的神经 CR的底物调节了这种中介作用。

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.