Brain Network Mechanisms of Aging-Related Cognitive Decline

项目摘要/摘要 与衰老相关的认知功能下降与静息状态功能的改变独立相关 连接(RSFC)和使用功能磁共振成像(FMRI)的认知任务激活，但它现在已经很好地建立了 在RSFC和任务激活之间存在着很强的统计关系。这是尽管有这些 完全不同的衡量标准：RSFC是根据分布的大脑活动时间序列之间的相关性来计算的 休息，而任务激活是大脑活动幅度在主动任务执行过程中的局部性变化。这个 RSFC-激活关系背后的网络机制尚不清楚，但要理解这一点 这种关系将阐明年龄如何改变RSFC和认知任务的激活。对于RSFC，链接到 认知任务的激活可能有助于解释为什么RSFC与认知过程有关，尽管 独立于测试这些流程的任务进行测量。对于认知任务激活，链接到RSFC可以 以统一的网络为基础，全面了解各种与老化相关的激活变化 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。在没有这样的知识的情况下，获得统一的 不太可能理解与衰老相关的认知衰退的神经基础。这样做的总体目标是 建议确定可以解释RSFC和认知变化的网络机制 认知控制能力随年龄增长而下降的任务激活(18-28岁至65-75岁) 在健康的老年人中。这种对认知控制的关注反映了它对适应性的、目标导向的重要性。 日常生活中的行为。此外，认知控制网络(CCN)区域具有RSFC网络的“中枢”属性 非常适合调节一般认知能力。认知控制与衰老特别相关，认知控制是 受健康衰老和阿尔茨海默病影响最大的能力，它在长期记忆中发挥作用 缺陷(阿尔茨海默病的一个主要特征)。这项提议的中心假设是，与衰老相关的 RSFC的改变反映了影响任务期间大脑激活的内在网络路径的变化 表现，从而调节认知控制能力的中断。将使用三种方法 横跨三个目标来检验我们的中心假设。简而言之，第一个将利用一种创新的方法来预测 健康老年人中基于RSFC异常的激活异常。第二种是利用个人 差异，以确定CCN中枢中断对衰老相关认知功能下降的贡献。第三个遗嘱 利用认知训练对RSFC的既定影响来研究功能网络的作用 增龄相关认知衰退中的可塑性。这一项目预计将显著提高对 健康衰老导致认知能力下降的脑网络基础，以及提高对 认知背后的大范围网络机制。

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.