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Computational Modelling of Ageing Brains: Virtual Populations for In Silico Clinical Trials


   Faculty of Health and Life Science

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  Dr Wahbi El-Bouri, Dr Navjot Kukreja, Prof GYH Lip  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Training

The University of Liverpool has set up a Doctoral Training Network in Technologies for Healthy Ageing to train the next generation of physical scientists and engineers to develop novel technologies and devices to address the challenges faced by older people and our clinical colleagues who work with them. All students will have the opportunity to network and learn from the other students in the cohort in conjunction with their research project.

Background

The UK’s population is ageing. In 2019, one-fifth of the UK population was aged 65 or over, with this projected to rise to 24% by 2043. The fastest increase in this population is expected in the over 85s with their population expected to double by 2041. Ageing and disease are closely linked, with older populations more likely to have multiple chronic diseases as well as increased risk of stroke, dementia, and atrial fibrillation. This project will aim to develop models of the ageing cerebrovasculature for in silico clinical trial use and medical device development.

Objectives

This project will develop validated models of cerebrovascular ageing to generate an ageing virtual population that can be used in in silico clinical trials of stroke and its treatment. The models of ageing will be developed from the microcirculation to the large blood vessels and applied to the in silico trials for treatment of acute ischaemic stroke (INSIST) framework [1]. This project will primarily focus on using readily available data to infer patient parameters hence creating a “digital twin” of that patient. It will be part of a wider research project aiming to develop virtual populations of ageing brains for clinical trial use. The research will broadly be split into 3 work packages:

WP1 Participant data collection

Physiological signals, such as cerebral blood flow and blood pressure, will be collected for healthy participants of different ages. There is scope to do this both prospectively and retrospectively in collaboration with our partners at Liverpool John Moores University. Further data will be collected on stroke patients including patient characteristics, imaging post-stroke and post-treatment, as well as outcomes. These stroke patients will be used to validate the ageing model. Physiological age and actual age will be determined from patient characteristics.

WP2 Personalised in silico model of ageing

Using the data gathered in WP1, the candidate will develop a simple 1-dimensional model of blood flow in the large blood vessels of the brain. Using the data and the model, an inverse problem will be solved to personalise the mathematical model to a given individual of a certain age. Physics informed neural networks will be used to constrain the parameters of the model to fit the observed data [2].

WP3 Generating ageing virtual brains

The model developed in WP2 will be combined with a microcirculation model of the whole brain to simulate strokes in brains of different ages. These simulations will be compared quantitatively to our database of stroke patients in the Merseyside region, focussing on simulations of different age ranges. A sensitivity analysis will be conducted to determine the impact of the microcirculation on the outcomes [3].

Outcome

We will have a validated model of cerebrovascular ageing that is able to predict stroke outcome based on physiological age. This is a step towards realistic virtual populations for the benefit of stroke research through medical device and drug development. The virtual populations can be used both in stroke treatment development as well as to investigate other cerebral diseases linked to ageing such as dementia and Alzheimer’s disease.

For any enquiries please contact Dr. Wahbi El-Bouri, University of Liverpool on: [Email Address Removed]

To apply for this opportunity, please send a CV and supporting statement/cover letter to Dr. Wahbi El-Bouri on: [Email Address Removed]


Funding Notes

This is an EPSRC funded studentship with an annual stiped of £16,062 (2022/23 rate) and includes UK tuition fees and £3,500 bench fees. Non UK applicants may have to contribute to the higher non-UK tuition fees.
Due to a recent change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.

References

[1] Konduri, P.R. et al., In-Silico Trials for Treatment of Acute Ischemic Stroke, Front. Neurol., 2020
[2] Sarabian, M. et al., Physics-informed neural networks for brain hemodynamic predictions using medical imaging, IEEE Trans. Med. Imaging, 2022
[3] Graff, B. J. et al., The Ageing Brain: Investigating the Role of Age in Changes to the Human Cerebral Microvasculature With an in silico Model, Front. Ageing Neurosci. 2021
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