Personalised healthy ageing: AI-based approach to predicting changes in self-perception and the relationship to cognitive decline

The world is experiencing a demographic shift with more people ageing than ever before. Ageing is a critical focus area in health because the ageing process is often accompanied with physical adversity and cognitive capacities [1]. Recent research has shown that self-reference facilitates cognitive performance across the lifespan, and it links to specific neural circuits [2]. This raises the issue of whether self-reference can be used to remediate cognitive decline. To help promote health, tracking the ageing process and understanding the relationship to self-reference could highlight when the healthcare system is needed.
To address this challenge it is essential to understand whether self-reference acts as a buffer against the deterioration of cognitive decline in older people, whether performance boosted by self-reference is maintained in older people by the same processes as found in young people, or whether there is recruitment of additional, compensatory processes. Neuropsychological and clinical examinations have revealed different levels of cognitive decline, e.g., reductions in memory, executive functions, and processing speed. In this project, we will focus on the first of these using behavioural and Electroencephalography (EEG) techniques: understanding the rules of self-reference in bolstering age-related memory.
The prospective student will use the new experimental procedures we recently developed in combination with cutting-edge AI approaches [3,4] for a dataset with multiple-scales, (i) testing if enhanced memory in healthy older adults stems from ultra-fast neural responses to self-related stimuli, and (ii) training offline EEG data based on these neural circuits for driving self-referential memory, and then using this classifier to decode real-time EEG data for the prediction of cognitive decline. The latter will have a vast potential to support the growing need for healthcare monitoring in an ageing population by harnessing AI to give a readout of health status.
The project is inherently interdisciplinary and involves strong integration between behavioural neuroscience and computing science. The student will be supported throughout the PhD by the excellent collaborative team of researchers in the schools of Psychology and NCS at the University of Aberdeen. The project includes a clear opportunity for the student to flourish within the unique strengths of our interdisciplinary AI and healthy ageing environment.
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