Resting brain networks and implicit learning in healthy aging

描述（由申请人提供）：这个项目的总体目标是测试我们的假设，即在没有任何外部目标或任务要求（即，在静息状态）的情况下，神经系统的内在通信预测了内隐概率序列学习（IL）的个体和年龄差异。IL是一种无意识的学习，对许多基本生活技能至关重要，例如适应不断变化的技术和环境。研究与年龄相关的IL缺陷背后的神经机制是很重要的，这种缺陷通常是在老年人与年轻人进行比较时观察到的，因为能够以这种方式学习可能对保持独立性和生活质量有影响。大脑各区域在静息状态下（即心理活动不受约束时）如何相互交流，最近被认为是认知功能中个体和年龄差异的重要神经机制。静息状态通信的一种测量方法，称为静息状态功能连接（rsFC），甚至被发现可以预测未来认知表现的个体差异，这表明rsFC可能会促进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）表现的预测价值的研究还很有限。这项提议的研究将首次检验rsFC是否可以预测一种内隐学习的个体和成人年龄差异，在这种学习中，人们必须对微妙的概率模式变得敏感。目的1将在健康的年轻人和老年人完成IL测量三胞胎学习任务（TLT）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（已知在IL期间共同激活的两个大脑区域）之间的rsFC阳性程度越高，就预示着年轻人和老年人的IL水平越高，并起到调节作用

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.