Artificial Intelligence Predictive Modelling for Exploring the Link Between Brain and Heart Ageing: Uncovering the Role of Cardiometabolic Disease in Dementia
Supervisors
Dr Michele Syanera, College of Medical, Veterinary & Life Sciences, University of Glasgow
Dr Frederick Ho, Collage of Medical, Veterinary & Life Sciences, University of Glasgow
Dr Donald Lyall, Collage of Medial, Veterinary & Life Sciences, University of Glasgow
Dr Katie Gallacher, School of Health and Wellbeing, University of Glasgow
Summary
Cardiometabolic disease (CMD) is a known risk for dementia, including risk factors like hypertension, “bad” cholesterol, and diabetes, suggesting an intricate connection between heart and brain health. There is a poor understanding of what structural brain differences underlie the association between CMD and brain health and how much heart and brain ageing explicitly correlate.
The student will use the latest advancements in predictive and generative AI to compute a deep learning predictive model from brain and cardiovascular magnetic resonance image data. Leveraging data from the UK Biobank, a large-scale biomedical database with over 70,000 participants, the project aims to identify individuals at higher risk for Alzheimer’s, vascular dementia, and coronary artery disease long before symptoms appear. This early detection would enable tailored interventions to slow disease progression, improve patient outcomes, and advance personalised medicine.
The studentship will develop transferable skills in deep learning, computational neuroscience, and neuroanatomy. The student will join a vibrant research community at the University of Glasgow's Centre for Cognitive Neuroimaging, benefiting from regular seminars and journal clubs. The ideal candidate will have prior experience in Machine Learning or a related field.