Brain Plasticity in Later Life: Improving cognitive-motor function

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.