Rationale
Dementia is a progressive neurodegenerative disease in which patients encounter frequent delays in diagnosis, leading to increased morbidity. There is a major need of biomarkers for the early prediction as acknowledged by the Alzheimer’s Drug Discovery Foundation. The eye is often seen as the ‘window to the brain’ with much effort dedicated recently to predicting disease such as mild cognitive impairment (MCI) and dementia through images of the retinal microcirculation. The eye is closely coupled to the brain through its circulation, with the eye being perfused through a branch off the internal carotid artery that then continues on to the brain. Furthermore, the eye is coupled to the brain neuronally through projection of the CNS into the retina (neuroretina). As such, it is believed that retinal imaging can be used to monitor changes in the brain which has been demonstrated by recent published data. However, whilst the eye is easily imageable down to the micro-scale, the skull around the brain makes imaging much more difficult. Computational models, on the other hand, can simulate the brain circulation and pressure so we know exactly what is happening in the brain in silico. Whilst utilising artificial intelligence (AI) models retinal imaging can help in the diagnosis and prediction of MCI and dementia.
Aim: To develop a model of the circulation in the brain coupled to the eye, observing retinal circulatory alterations with concomitant change in the brain and development of AI models for diagnosis (detection) and prediction of future disease.
Utilising data in the UK Biobank, his work will entail:
1. Coupling circulation models of the brain and eye (In Silico/digital twin model)
2. Synthetic imaging of the eye and brain pipeline simulation (In Silico/digital twin model)
3. Development of AI model of MCI/dementia detection and prediction of dementia and other diseases