Investigating the shifting architectures of brain and cognition in older adulthood

研究老年人大脑和认知结构的变化

• 批准号: RGPIN-2019-06692
• 负责人: Turner, Gary
• 金额: $ 3.42万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689513
• 关键词: Investigating; shifting; architectures; brain; cognition

Psychological and neuroscience studies of cognitive aging are disproving the fallacy that cognitive aging equals cognitive decline. The true story of cognitive aging is one of both gains as well as losses. With age comes an expanding repertoire of knowledge and life experience that can help navigate the routines and the surprises of daily life. When and how past knowledge is accessed to guide behavior can influence how successfully older adults are able to reason, problem-solve, and plan in their everyday lives. Within the brain, complex cognitive abilities such as reasoning and problem-solving engage frontal cortices and functionally connected regions that together form an executive network'. However, accessing one's store of prior knowledge, including representations of oneself and the world engages a different set of regions known as the default network'.*** It is well known that complex cognitive abilities decline over the adult lifespan. However, evidence is beginning to suggest that older adults may be able to leverage their relatively larger store of knowledge and experiences to mitigate these declines. But how this happens in the brain is not well understood. My work has shown that one potential neural mechanism that may support the engagement of prior knowledge during complex cognitive tasks is the interaction of brain networks supporting complex cognition (i.e. the executive network) and activation of stored knowledge representations (i.e. the default network). In other words, increased defaultexecutive coupling may serve as a neural conduit allowing prior knowledge to influence cognitive functioning in later life. My colleague and I have labeled this the Default-Executive Coupling Hypothesis of Aging (DECHA). The goal of this research proposal is to directly test the DECHA model using functional neuroimaging (fMRI) and neurostimulation (transcranial magnetic stimulation TMS) methods. *** We will examine patterns of default-executive coupling associated with performance on tasks where prior knowledge is known to either (i) facilitate or (ii) hinder task performance. We predict that neural markers of DECHA (derived from fMRI and neurostimulation-based studies) will be associated with better task performance for older adults when prior knowledge is relevant, and worse performance when it is irrelevant, to task goals. This proposal will interrogate a novel theory of neurocognitive aging that may serve to reconcile patterns of age-related brain and behavioural change. If the hypotheses are realized, this research will identify the DECHA as a candidate neural mechanism underlying greater reliance on prior knowledge to support cognitive functioning in older adulthood. Further, these findings would provide the first network neuroscience model to predict the full spectrum of neurocognitive aging, encompassing both cognitive gains and losses.**

心理学和神经科学对认知老化的研究正在驳斥认知老化等同于认知衰退的谬误。认知老化的真实情况是既有收获也有损失。随着年龄的增长，知识和生活经验的不断扩大，可以帮助驾驭日常生活中的例行公事和惊喜。何时以及如何获取过去的知识来指导行为可以影响老年人在日常生活中推理、解决问题和计划的成功程度。在大脑内部，复杂的认知能力，如推理和解决问题，涉及额叶皮质和功能相连的区域，共同形成一个执行网络。然而，访问一个人的先验知识，包括对自己和世界的表征，需要一组不同的区域，称为默认网络。*众所周知，复杂的认知能力会随着成年人的寿命而下降。然而，证据开始表明，老年人或许能够利用他们相对较大的知识和经验来缓解这些下降。但这是如何在大脑中发生的还没有被很好地理解。我的工作表明，在复杂认知任务中，一种可能支持先前知识参与的潜在神经机制是支持复杂认知的大脑网络(即执行网络)和存储的知识表征的激活(即默认网络)的相互作用。换句话说，更多的默认-执行耦合可能作为一条神经管道，允许先前的知识影响以后生活中的认知功能。我和我的同事将此称为老龄化的默认-高管耦合假说(Decha)。这项研究提案的目标是使用功能神经成像(FMRI)和神经刺激(经颅磁刺激TMS)方法直接测试Decha模型。*我们将考察与任务绩效相关的默认-执行耦合模式，在这些任务中，已知先验知识可以(I)促进或(Ii)阻碍任务绩效。我们预测，当先验知识与任务目标相关时，Decha的神经标记物(来自功能磁共振成像和基于神经刺激的研究)将与老年人更好的任务表现相关，而当与任务目标无关时，老年人的任务表现更差。这一提议将质疑一种新的神经认知老化理论，该理论可能有助于调和与年龄相关的大脑和行为变化的模式。如果假设成真，这项研究将确定Decha作为一种候选神经机制，在更大程度上依赖先前的知识来支持老年人的认知功能。此外，这些发现将提供第一个网络神经科学模型来预测神经认知老化的全范围，包括认知收益和认知损失。