Evaluating Longitudinal Changes in the Human Structural Connectome in Relation to Cognitive Aging

评估与认知衰老相关的人体结构连接组的纵向变化

• 批准号: 9385440
• 负责人: Elliot Max Tucker-Drob
• 金额: $ 50.25万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9385440
• 关键词: 3 year old; Adult; Age; Aging; Algorithms; Birth; Brain; Brain region; Cognitive; Cognitive aging; Coupled; Coupling; Data; Data Analyses; Data Collection; Data Set; Dementia; Diffusion Magnetic Resonance Imaging; Elderly; Ensure; Equation; Genetic; Graph; Health; Human; Image; Impaired cognition; Individual; Intervention; Left; Life Style; Link; Longevity; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Measures; Mediating; Medical; Memory; Methods; Modeling; Nerve Degeneration; Outcome; Participant; Physiological; Process; Property; Quality of life; Reaction Time; Reproducibility; Research; Risk Factors; Sampling; Scanning; Self Care; Speed; Structure; System; Testing; Theoretical model; Time; Validation; Variant; Visuospatial; Work; aged; biobank; cognitive ability; cognitive change; cognitive performance; cognitive testing; cohort; connectome; genetic predictors; improved; indexing; interest; learning strategy; longitudinal analysis; mortality; network architecture; novel; physical conditioning; prevent; social; theories; tool

PROJECT SUMMARY Progressive aging-related cognitive declines are associated with limitations in self-care and functional independence, deteriorating physical health, and impending dementia and mortality, even among the otherwise healthy. Identifying and understanding the neurodegenerative processes that underlie cognitive aging is key to developing interventions to prevent or ameliorate cognitive decline. Disconnection theories of aging specifically implicate weakening of structural brain connectivity as a key mechanism of cognitive decline, but until recently, diffusion MRI data and connectomic methods needed to rigorously test such theories have been lacking. To expedite understanding how aging-related changes in the human structural connectome relate to aging-related cognitive declines, we will apply the latest connectomic and multivariate data analysis methods to existing data from two highly unique datasets: (1) The UK Biobank, a cross-sectional sample of ~10,000 40-75 year old adults, who have undergone diffusion MRI scanning, have been measured with multiple cognitive tests, and have provided extensive sociodemographic and medical information; and (2) The Lothian Birth Cohort of 1936, a narrow-age cohort of older adults (baseline age = 73 years; N = 731) who have undergone diffusion MRI scanning, have been measured with multiple cognitive tests, and have provided extensive sociodemographic and medical information on each of three separate occasions, each separated by three years. Using recently developed graph-theoretic models, we will construct structural brain connectome networks for each participant's diffusion MRI data at each wave and extract indices reflective of network topology within several specific networks of interest (NOIs) identified ex ante. We will also identify topologically central hub regions that disproportionately govern efficiency within each individual's connectome network. We will apply cross-sectional and longitudinal structural equation models to examine aging-related transformations in network indices, examine concurrent and longitudinal coupling between network indices and cognitive abilities, and test predictors of levels and changes in network indices and cognitive abilities. This will allow us to contrast the predictive utility of the selected NOIs for cognitive aging and to identify specific features of network architecture involved in cognitive aging and mediate the effects of demographic, medical, and lifestyle risk factors for cognitive aging. We additionally implement machine- learning methods to estimate an upper bound of prediction of cognitive aging from network indices, and identify novel features of network topology as candidate mechanisms of cognitive decline. The availability of two uniquely large and well-characterized datasets will allow us to ensure that findings are rigorous and reproducible using within sample (holdout) and between sample cross-validation. For all aims, we will place considerable emphasis on testing for incremental validity of network indices relative to both conventional structural neuroanatomical measures and topologically naïve summary indices of network integrity.

项目概要 与衰老相关的进行性认知能力下降与自我护理和功能限制有关 独立性、身体健康状况恶化以及即将发生的痴呆症和死亡，甚至在其他方面也是如此 识别和理解认知衰老背后的神经退行性过程是健康的关键。 制定干预措施来预防或改善衰老的认知衰退理论。 意味着大脑结构连接的减弱是认知能力下降的一个关键机制，但直到最近， 一直缺乏严格检验此类理论所需的扩散磁共振成像数据和连接组学方法。 加快了解人体结构连接组中与衰老相关的变化如何与衰老相关 认知能力下降，我们将应用最新的连接组和多元数据分析方法来 来自两个高度独特的数据集的现有数据：（1）英国生物银行，一个横截面样本 约 10,000 名 40-75 岁的成年人接受了扩散 MRI 扫描，并使用 多项认知测试，并提供了广泛的社会人口统计和医疗信息；以及 (2) 1936 年洛锡安出生队列，一个狭窄年龄的老年人队列（基线年龄 = 73 岁；N = 第731章 731） 并提供了关于三个独立的每一个的广泛的社会人口统计和医疗信息 使用最近开发的图论模型，每次相隔三年。 为每个参与者的每个波的扩散 MRI 数据构建结构性脑连接组网络，并且 提取反映几个特定感兴趣网络（NOI）中的网络拓扑的索引 我们还将确定拓扑中心枢纽区域，这些区域在每个区域内不成比例地控制效率。 我们将应用横截面纵向和结构方程模型来分析个体的连接组网络。 检查网络指数中与老化相关的转换，检查并发和纵向耦合 网络指数和认知能力之间的关系，并测试网络指数水平和变化的预测因子 这将使我们能够比较所选 NOI 对认知衰老的预测效用。 并识别与认知老化有关的网络架构的具体特征并调节认知老化的影响 我们还实施了机器认知老化的人口、医疗和生活方式风险因素。 根据网络指数估计认知老化预测上限的学习方法，并识别 网络拓扑的新特征作为认知衰退的候选机制 两种可用性。 独特的大型且特征明确的数据集将使我们能够确保研究结果严谨且 对于所有目标，我们将在样本内（保留）和样本之间进行可重复的交叉验证。 相当重视测试网络相对指数相对于传统指数的增量有效性 结构神经解剖学测量和网络完整性的拓扑朴素总结指数。