How does brain signal variability relate to plasticity during perceptual learning for younger, middle-aged, and older adults?

在确定大脑功能在健康衰老过程中如何改变的神经影像学标记以及这些改变与认知弹性的关系方面，已经投入了相当大的努力。最近的研究表明，大脑信号变异性是大脑处理完整性和灵活性的重要标志。可变性促进了从一个网络配置到另一个网络配置的移动，并使探索多个网络能够产生许多行为。在大脑衰老的背景下，变异性研究表明，年轻人和老年人处理信息的方式不同。随着年龄的增长，对局部信息处理的依赖增加，与远端大脑区域的远程互动减少。然而，目前尚不清楚，这些处理过程的变化是否仅仅是大脑对结构和功能衰退的反应，或者它们是否具有补偿性，因为它们支持更好的表现。重要的是，如果大脑信号的可变性能够探索多个网络并产生新的行为，那么它可能是驱动可塑性（大脑响应经验而改变的能力）的机制。年轻人和老年人不同的可变性特征应该不同地驱动学习相关的可塑性。对于老年人来说，增加对局部处理的依赖应该有利于支持行为的大脑区域兴奋性的局部改变。对于年轻人来说，对远程处理的依赖增加应该有利于大脑区域之间连接的整体改变，或者在支持行为的网络中增加新的区域。在这里，我们将研究视觉感知学习过程中成人生命周期的可塑性，其中区分简单刺激属性差异的能力随着实践逐渐提高。知觉学习被认为是可塑性的一种表现。它非常适合于研究与年龄相关的可塑性变化，因为支持学习的神经基质是明确的，并且尽管视觉和注意力功能与年龄相关，但学习带来的挑战水平可以调整以匹配个体之间的任务难度。在目标1中，我们将研究随着年龄增长而发生的结构衰退如何影响可塑性和大脑信号变异性，并使用纵向设计将这些发现与认知弹性联系起来。在目标2中，我们将研究哪种类型的大脑变化在支持新学习方面最有效，以及这是如何受到成人一生中局部和远程处理之间平衡转变的影响的。在目标3中，我们测试了可变性作为促进可塑性的假定机制。我们的研究结果将为老年人保持认知弹性的环境提供更精确的预测，并为可能驱动可塑性的特定机制提供新的见解。

Considerable effort has been invested in identifying neuroimaging markers of how brain function is altered during healthy aging, and how these alterations relate to cognitive resilience. Recent work suggests that brain signal variability is an important marker of processing integrity and flexibility in the brain. Variability facilitates movement from one network configuration to another, and enables the exploration many networks to generate many behaviours. In the context of brain aging, variability research suggests that young and older adults process information differently. Increasing age is associated with an increased reliance on local information processing and decreased long-range interactions with distant brain regions. It remains unclear, however, if these processing changes are merely the brain's response to structural and functional decline, or if they are compensatory in that they support better performance. Importantly, if brain signal variability enables the exploration of multiple networks and the generation of new behaviours, then it may be the mechanism that drives plasticity (the brain's ability to change in response to experience). The differing variability profiles for young and older adults should drive learning-related plasticity differently. For older adults, increased reliance on local processing should favour local alterations in the excitability of brain regions that support behaviour. For younger adults, the increased reliance on long-range processing should favour global alterations in connectivity between brain regions, or the addition of new regions to the network that supports behaviour. Here, we will examine plasticity across the adult lifespan during visual perceptual learning, where the ability to discriminate differences in simple stimulus attributes progressively improves with practice. Perceptual learning is considered a manifestation of plasticity. It is is ideally suited for examining age-associated changes in plasticity because the neural substrates that support learning are well defined, and the level of challenge posed by learning can be adjusted to match task difficulty across individuals despite age-associated differences in visual and attentional function. In Objective 1, we will examine how plasticity and brain signal variability are influenced by structural decline that occurs with age, and link these findings to cognitive resilience using a longitudinal design. In Objective 2, we will examine what type of brain changes are most effective in supporting new learning, and how this is influenced by the shifting in the balance between local and long-range processing across the adult lifespan. In Objective 3, we test variability as a putative mechanism for promoting plasticity. Our findings will afford more precise predictions about the circumstances under which older adults can maintain cognitive resilience, and provide new insights into a specific mechanism that may drive plasticity.