Brain network mechanisms of aging-related cognitive decline

项目总结/摘要 与衰老相关的认知能力下降与静息状态功能的改变独立相关， 连接（RSFC）和认知任务激活功能磁共振成像（fMRI），但它现在已经很好地建立 RSFC和任务激活之间存在很强的统计关系。尽管这些被 相当不同的措施：RSFC计算为分布式大脑活动时间序列之间的相关性， 而任务激活是在主动任务执行过程中局部脑活动幅度的变化。的 RSFC激活关系背后的网络机制尚不清楚，但要了解这一点 关系将阐明衰老如何改变RSFC和认知任务激活。对于RSFC，链接到 认知任务激活可能有助于解释为什么RSFC与认知过程相关，尽管RSFC是 独立于测试这些过程的任务进行测量。对于认知任务激活，与RSFC的链接可能 对不同年龄段的激活变化进行统一的基于网络的理解 许多大脑区域和任务。因此，迫切需要确定潜在的网络机制， RSFC与认知任务激活的关系。如果没有这样的知识， 不太可能理解与衰老有关的认知能力下降的神经基础。本报告的总体目标 一项建议是确定可以解释RSFC和认知功能改变的网络机制。 与年龄相关的认知控制能力下降（从18 - 28岁到65 - 75岁）发生的任务激活 健康的老年人。这种对认知控制的关注反映了它对适应性、目标导向性 日常生活中的行为。此外，认知控制网络（CCN）区域具有RSFC网络"枢纽"属性 非常适合调节一般认知能力。与衰老特别相关的是，认知控制是其中一个 这种能力受健康老龄化和阿尔茨海默病的影响最大，它在长期记忆中发挥作用 缺陷（阿尔茨海默病的一个关键特征）。这项提议的核心假设是， RSFC的改变反映了影响任务期间大脑激活的内在网络通路的变化 表现，并因此介导认知控制能力的中断。将采用三种方法 三个目标来检验我们的中心假设。简单地说，第一个将利用创新的方法来预测 基于健康老年人RSFC异常的激活异常。第二种是利用个人 差异，以确定CCN枢纽破坏对衰老相关认知下降的贡献。第三次重计费用将 利用认知训练对RSFC的既定影响来研究功能网络的作用 可塑性与衰老有关的认知能力下降。预计该项目将显著提高对 健康老龄化认知能力下降的大脑网络基础，除了提高对 认知背后的大规模网络机制。

PROJECT SUMMARY/ABSTRACT Aging-related cognitive decline has been independently linked to alterations in resting-state functional connectivity (RSFC) and cognitive task activations using functional MRI (fMRI), yet it is now well established that there is a strong statistical relationship between RSFC and task activations. This is despite these being quite distinct measures: RSFC is calculated as correlations among distributed brain activity time series during rest, while task activations are localized brain activity amplitude changes during active task performance. The network mechanisms underlying the RSFC-activation relationship are unknown, yet understanding this relationship would clarify how aging alters both RSFC and cognitive task activations. For RSFC, linking to cognitive task activations may help explain why RSFC is associated with cognitive processes despite being measured independently of tasks testing those processes. For cognitive task activations, linking to RSFC may bring a unified network-based understanding to the varied aging-related activation changes identified across many brain regions and tasks. Thus, there is a critical need to determine the network mechanisms underlying the relationship between RSFC and cognitive task activations. Without such knowledge, obtaining a unified understanding of the neural basis of aging-related cognitive decline is unlikely. The overall objective of this proposal is to identify network mechanisms that can account for the alterations in both RSFC and cognitive task activations that occur with aging-related decline (from ages 18-28 to 65-75) of cognitive control abilities among healthy older adults. This focus on cognitive control reflects its importance to adaptive, goal-directed behavior in daily life. Further, cognitive control network (CCN) regions have RSFC network “hub” properties well suited to regulate general cognitive ability. Of particular relevance to aging, cognitive control is one of the abilities most affected by both healthy aging and Alzheimer’s disease, and it plays a role in long-term memory deficits (a key feature of Alzheimer’s disease). This proposal’s central hypothesis is that aging-related alterations in RSFC reflect changes in intrinsic network pathways that influence brain activations during task performance, and hence mediate disruption of cognitive control abilities. Three approaches will be utilized across three aims to test our central hypothesis. Briefly, the first will utilize an innovative approach to predict activation abnormalities based on RSFC abnormalities in healthy older adults. The second will utilize individual differences to determine the contribution of CCN hub disruption to aging-related cognitive decline. The third will utilize the established influence of cognitive training on RSFC to investigate the role of functional network plasticity in aging-related cognitive decline. This project is expected to markedly improve understanding of the brain network basis of cognitive decline from healthy aging, in addition to improving general understanding of the large-scale network mechanisms underlying cognition.