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，了解老龄化过程并制定支持健康老龄化的干预措施是一项基本需求。高龄与认知运动技能的退化有关。这表现为在选择适当的行动时动作缓慢和缺陷，这对日常生活活动产生了负面影响。这些认知运动控制的下降与大脑的变化同时发生。例如，不同的大脑区域相互作用的方式被改变：一些区域被新激活，而另一些区域变得更加分离。在这里，博士生将采用最先进的技术，允许参与者可视化和控制自己的大脑活动，称为功能磁共振成像神经反馈。学生将联合收割机与一个涉及规划和选择适当运动的任务相结合。这项任务对年龄相关的变化特别敏感，因为它涉及认知和运动过程。当在MRI扫描仪内完成这项任务时，参与者将尝试增强不同大脑区域之间的相互作用。在这种干预之后，学生将测试各种其他行为任务的改善，包括日常生活活动问卷。这将使学生能够识别哪些大脑连接对认知运动表现和老年独立生活很重要。学生还将测试参与者是否可以使用策略来增强大脑连接，即使在没有神经反馈的情况下。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影像数据采集和分析，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）的培训。基本编程（如Python、Matlab、R等）。该项目将进一步加深我们对老龄化过程的理解，并为支持老年人健康老龄化和独立生活的先进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.