Modeling Resilience to Alzheimer's Disease Pathology in Cognitively Healthy Older Adults

项目摘要 老年人的认知能力下降很常见，但并不普遍。事实上，一些老年人有正常的认知能力， 尽管有证据表明他们的大脑中存在阿尔茨海默病（AD）病理学，但他们的表现仍然很差。恢复力，或成功 处理病理学，是一个新的概念，提供了一个框架，研究变异的认知 衰老大脑病理变化的后果。关键的观点是， 只有当负担很高时，才会因病理而出现认知能力下降。相比之下， 可能会导致低弹性个体的认知变化。这项建议解决了一个重大挑战， 复原力的研究：发展一种准确的、定量的现象测量方法。使用 用于结合新的定量生物学的病理学的体内测量的神经成像技术 测量和统计方法，本提案中描述的研究代表了一种前沿的 概念和工具的结合。具体而言，将计算空间和时间耦合措施 将tau正电子发射断层扫描（PET）成像与个体水平的神经变性或萎缩相关联。 AD病理级联中这些步骤的耦合可能有助于更好地定义弹性，这可能是 与级联的动力学有关。因此，这种方法的另一个关键的新颖方面是， 将复原力概念化为反映不同变量之间关系的生物现象 认知能力下降的因果关系空间耦合将使用体素空间相关性来测量 而潜在差异评分模型将用于估计tau-PET的变化预测 萎缩接下来，线性模型将用于预测四个领域的基线认知：情景记忆，工作记忆， 记忆、执行功能和处理速度。空间和时间耦合指标将包括在 这些线性模型。来自预测认知的线性模型的抵抗力，这是一种衡量认知能力的指标。 将提取并用于创建一般复原力系数。简而言之，偏最小二乘 路径模型将被用来定义三个潜在的因素：认知弹性，认知储备和大脑储备。 一般弹性因子将从这三个测量模型潜在因子中确定。作为验证 第一步，将提取一般弹性因子得分，并用于预测认知和措施的变化 认知参与。人们期望，一个准确的、定量的弹性指标将是一个有用的工具。 预测未来认知结果。来自ADNI的单独数据集将用作复制样本， 确保这种方法的可重复性及其对更大的多中心队列的适用性。最终， AD的发病，即使只是几年，也会大大降低该疾病的总体患病率。研究 韧性，促进健康的认知功能更长的时间，可能揭示关键因素，可能会延迟 症状发作。

Project Summary Cognitive decline in aging is common but not universal. In fact, some older adults have normal cognitive performance despite evidence of Alzheimer’s disease (AD) pathology in their brains. Resilience, or successfully coping with pathology, is a new concept that provides a framework for studying variability in the cognitive consequences of pathological changes in the aging brain. The critical idea is that individuals with high resilience will only experience cognitive decline as a result of pathology when the burden is high. In contrast, less pathology may cause changes in cognition in individuals with low resilience. This proposal addresses a major challenge to the study of resilience: the development of an accurate, quantitative measurement of the phenomenon. Using neuroimaging techniques for in vivo measurement of pathology combined with novel quantitative biological measurements and statistical approaches, the research described in this proposal represents a cutting-edge combination of concepts and tools. Specifically, spatial and temporal coupling measures will be calculated relating tau positron emission tomography (PET) imaging to neurodegeneration or atrophy at the individual level. The coupling of these steps in the AD pathological cascade may help to better define resilience, which is likely related to the dynamics of that cascade. Thus, another crucial novel aspect of this approach is the conceptualization of resilience as a biological phenomenon reflecting relationships between different variables on the causal pathway to cognitive decline. Spatial coupling will be measured using voxelwise spatial correlations while latent difference score models will be used to estimate the extent to which change in tau-PET predicts atrophy. Next, linear models will be used to predict baseline cognition in four domains: episodic memory, working memory, executive functioning and processing speed. Spatial and temporal coupling metrics will be included in these linear models. Residuals from linear models predicting cognition, which are a measure of cognitive resilience, will be extracted and used in the creation of the general resilience factor. Briefly, a partial least squares path model will be used to define three latent factors: cognitive resilience, cognitive reserve and brain reserve. A general resilience factor will be determined from these three measurement model latent factors. As a validation step, general resilience factor scores will be extracted and used to predict changes in cognition and measures of cognitive engagement. The expectation is that an accurate, quantitative resilience measure will be a useful predictor of future cognitive outcomes. A separate dataset from ADNI will be used as a replication sample to ensure reproducibility of this approach and its applicability to a larger multi-site cohort. Ultimately, a delay in the onset of AD, even just by several years, would greatly decrease the overall prevalence of the disease. Studying resilience, which promotes healthy cognitive functioning for longer, may reveal critical factors that could delay symptom onset.