Brain Plasticity in Later Life: Improving cognitive-motor function

目前英国有1200万65岁或以上的人，因此有必要了解老龄化过程并制定支持健康老龄化的干预措施。高龄与认知运动技能的下降有关。这表现为行动减慢和选择适当行动的不足，这对日常生活活动产生了负面影响。这些认知运动控制力的下降与大脑的变化同步发生。例如，不同大脑区域相互作用的方式发生了改变：一些区域是新参与的，而另一些区域则变得更加脱节。在这里，这位博士生将使用最先进的技术，允许参与者可视化并控制他们自己的大脑活动，称为fMRI神经反馈。学生将把它与一项涉及计划和选择适当动作的任务结合起来。这项任务对年龄相关的变化特别敏感，因为它既涉及认知过程，也涉及运动过程。当在核磁共振扫描仪中完成这项任务时，参与者将尝试增强不同大脑区域之间的相互作用。在这一干预之后，学生将测试各种其他行为任务的改善情况，包括日常生活活动问卷。这将使学生能够确定哪些大脑连接对认知运动能力和老年独立生活非常重要。这名学生还将测试参与者是否能够使用策略来增强大脑连接，即使在没有神经反馈的情况下也是如此。为此，学生将使用定量和定性的方法来确定可能在家里使用的行为策略。在这个项目中，博士生将接受高级神经成像数据采集和分析、行为测试、高级统计方法(例如排列测试、线性混合模型、多元线性回归等)方面的培训。这项计划将加深我们对衰老过程的了解，并为支持老年人健康老龄化和独立生活的尖端干预工具提供概念证明。

There are currently 12 million people aged 65 or over in the UK, so there is a fundamental need to understand the ageing process and develop interventions supporting healthy ageing. Advanced age is associated with deterioration of cognitive-motor skills. This manifests as a slowing of movements and deficits in the selection of appropriate actions, which negatively impacts the daily life activities. These declines in cognitive-motor control occur in parallel to changes in the brain. For instance, the way different brain areas interact with each other is altered: some areas are newly engaged, while others become more disconnected. Here, the PhD student will employ state-of-the-art technology that allows participants to visualise and control their own brain activity, termed fMRI neurofeedback. The student will combine it with a task involving planning and selection of the appropriate movement. This task is particularly sensitive to age-related changes as it engages both cognitive and motor processes. While doing this task inside a MRI scanner, participants will attempt to enhance the interaction between different brain areas. After this intervention, the student will test for improvements on a variety of other behavioural tasks, including a daily life activity questionnaire. This will allow the student to identify which brain connections are important for cognitive-motor performance and the independent living in older age. The student will also test whether participants can use strategies to enhance brain connections even in the absence of neurofeedback. To this end, the student will use quantitative and qualitative methods to identify behavioural strategies that can potentially be used at home. During this project the PhD student will be trained in advanced neuroimaging data acquisition and analysis, behavioural testing, advanced statistical methods (e.g. permutation testing, linear mixed models, multiple linear regression, etc.) and basic programming (e.g. Python, Matlab, R etc.).This project will further our understanding of the ageing process and provide proof of concept for a cutting-edge intervention tool to support healthy ageing and the independent living of the older person.