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.

项目摘要 进行性衰老相关的认知能力下降与自我护理和功能障碍有关。 独立，身体健康恶化，即将痴呆和死亡，即使在其他方面， 健康.识别和理解认知老化背后的神经退行性过程是 开发干预措施以预防或改善认知能力下降。关于衰老的分离理论 暗示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， 但是缺乏严格检验这些理论所需的扩散MRI数据和连接组学方法。到 加快理解人类结构连接体中与衰老相关的变化如何与衰老相关的 认知能力下降，我们将应用最新的连接组学和多变量数据分析方法， 现有的数据来自两个非常独特的数据集：（1）英国生物银行，一个横截面样本， 约10，000名40-75岁的成年人接受了弥散MRI扫描， 多项认知测试，并提供了广泛的社会人口和医疗信息;以及 (2)1936年的洛锡安出生队列，一个老年人的窄年龄队列（基线年龄= 73岁; N = 10）。 731)接受过弥散磁共振成像扫描的人，接受过多种认知测试， 并提供了广泛的社会人口和医疗信息， 每一次，相隔三年。使用最近开发的图论模型，我们将 为每个参与者在每个波的弥散MRI数据构建结构性脑连接体网络， 提取反映几个特定的感兴趣网络（NOI）内的网络拓扑结构的指数， 赌注我们还将确定拓扑中心枢纽区域，这些区域不成比例地管理每个区域内的效率 个体的连接体网络。我们将应用横截面和纵向结构方程模型， 检查网络指数中与年龄相关的变化，检查并发和纵向耦合 网络指数和认知能力之间的关系，以及网络指数水平和变化的测试预测因素 和认知能力。这将使我们能够对比所选NOI对认知老化的预测效用 并确定参与认知老化的网络结构的具体特征， 认知老化的人口统计学、医学和生活方式危险因素。此外，我们还提供机器- 学习方法，以从网络指标估计认知老化预测的上限，并识别 网络拓扑的新特征作为认知能力下降的候选机制。推出两款 独特的大型和特征良好的数据集将使我们能够确保研究结果是严格的， 使用样本内（保留）和样本间交叉验证可重现。为了所有的目标，我们将 相当重视测试网络指数相对于传统的 结构神经解剖学测量和网络完整性的拓扑学简单总结指数。