Resting brain networks and implicit learning in healthy aging

描述(由申请人提供)：本项目的总体目标是检验我们的假设，即神经系统在没有任何外部目标或任务要求的情况下(即处于休息状态)的内在沟通可以预测个体和年龄在内隐概率序列学习(IL)中的差异。IL是一种在没有意识或意图的情况下发生的学习，是许多基本生活技能的核心，例如适应不断变化的技术和环境。重要的是要研究与年龄相关的IL缺陷背后的神经机制，这些机制通常在将老年人与年轻人进行比较时观察到，因为能够以这种方式学习可以对保持独立性和生活质量产生影响。大脑区域如何在休息状态下相互交流--当精神活动不受限制时--最近已经成为个体和年龄认知功能差异的重要神经机制。一种衡量静息状态交流的指标，称为静息状态功能连接性(RsFC)，甚至被发现可以预测未来个体在认知表现上的差异，这表明rsFC可能促进了神经系统执行认知任务的准备。然而，到目前为止，关于rsFC对特定认知过程(如学习)的预测价值的研究还很有限。这项拟议的研究将首次检验rsFC是否可以在一种内隐学习类型中预测个体和成人的年龄差异，在内隐学习中，人们必须对微妙的概率模式变得敏感。目标1将在健康的年轻人和老年人完成IL的测量之前测量rsFC，并将评估任务相关区域之间rsFC的个体差异是否预测TLT中的IL。据推测，尾状核和MTL之间的rsFC更阳性，这两个已知在IL期间被共同激活的大脑区域，将预测年轻人和老年人更好的IL，并也起到调节作用 IL的年龄差异。这些假设是基于对这一提议的初步研究，该研究发现了年轻人尾状核-MTL连接与IL之间的预测关系，以及之前的fMRI研究表明，IL的年龄差异与这些区域的任务诱发活动有关。目标2将评估在TLT之前测量的rsFC如何与TLT期间的连通性模式相关，以及休息状态和任务状态之间的连通性变化如何与IL相关。我们假设(1)年轻人从休息到任务的连接性比老年人表现出更大的变化，(2)从休息到任务尾状核和MTL之间的连接性的更大变化将与两个年龄组更好的IL相关。这些假说的基础是，有研究表明，大脑区域之间的功能连接会根据认知状态发生变化，也有研究表明，随着年龄的增长，与状态相关的功能连接的适应性会下降。确定IL背后的神经系统的特征将有助于更好地理解健康衰老过程中发生的基本行为功能的变化。

DESCRIPTION (provided by applicant): The overall aim of this project is to test our hypothesis that the intrinsic communication of neural systems in the absence of any external goals or task demands (i.e., in a resting state), predicts individual and age differences in implict probabilistic sequence learning (IL). IL is a type of learning that occurs without conscious awareness or intent and is central to many fundamental life skills, such as adapting to ever-changing technologies and environments. It is important to examine the neural mechanisms underlying the age-related deficits in IL that are typically observed when older adults are compared to younger ones because being able to learn in this way can have implications for maintaining independence and quality of life. How brain regions communicate with each other in a resting state--when mental activity is unconstrained--has recently emerged as an important neural mechanism underlying individual and age differences in cognitive functioning. A measure of resting state communication, called resting state functional connectivity (rsFC) has even been found to predict future individual differences in cognitive performance, suggesting that rsFC may be promoting a neural system's "readiness" to perform cognitive tasks. So far, however, research on the predictive value of rsFC for performance on specific cognitive processes, such as learning, is limited. The proposed study will be the first to examine whether rsFC can predict individual and adult age differences in a type of implicit learning in which people must become sensitive to subtle probabilistic patterns. Aim 1 will measure rsFC before healthy young and older adults complete a measure of IL, the Triplets Learning Task (TLT), and will assess whether individual differences in rsFC between task-relevant regions predict IL in the TLT. It is hypothesized that more positive rsFC between the caudate and MTL, two brain regions known to be coactivated during IL, will predict better IL in both young and older adults and also mediate age differences in IL. These hypotheses are based on the preliminary study for this proposal, which found the predicted relationship between caudate-MTL connectivity and IL in younger adults, and on previous fMRI studies showing that age differences in IL are related to the task-evoked activity of these regions. Aim 2 will assess how rsFC, measured prior to the TLT, relates to patterns of connectivity during the TLT, and how changes in connectivity between the rest and task states relate to IL. We hypothesize that (1) young adults will show greater changes in connectivity from rest to task than older adults and (2) Greater changes in connectivity between the caudate and MTL from rest to task will be related to better IL for both age groups. These hypotheses are based on studies showing that functional connections between brain regions change based on cognitive state, and on studies showing declines in the state-related adaptability of functional connections with age. Characterizing the neural systems underlying IL will foster a better understanding of the changes in essential behavioral functions that occur in healthy aging.