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Details of Grant 

EPSRC Reference: EP/S021507/1
Title: Determining cerebrovascular reactivity from the pupil flash response
Principal Investigator: Bulte, Professor DP
Other Investigators:
Spitschan, Dr M Chappell, Professor MA Namburete, Dr AIL
Researcher Co-Investigators:
Project Partners:
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 01 June 2019 Ends: 31 May 2026 Value (£): 1,074,229
EPSRC Research Topic Classifications:
Artificial Intelligence Biomedical neuroscience
Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
31 Jan 2019 HT Investigator-led Panel Meeting - Jan 2019 Announced
Summary on Grant Application Form
For most neurological disorders a diagnosis and intervention is only possible once significant progression has occurred and symptoms are present by which time the prognosis may be poor. It would be preferable to be able to make an early and accurate identification of those people who would be likely to develop such a disease later in life, allowing sufficiently early intervention to prevent it.

The premise is that with the identification of reliable biomarkers of conditions such as Alzheimer's disease, multiple sclerosis, Parkinson's disease and stroke, early identification of individuals predisposed to developing these conditions could be identified from regular screening within routine healthcare activities. The identification of a suitable biomarker has been the focus of much research.

One feature of these diseases which has the indications of being a good biomarker is a measure of how good the blood vessels in the brain are at responding to changes in demand or in response to an external stimulus. This effect is called the cerebrovascular reactivity or CVR. CVR is known to be impaired in the majority of brain diseases, and appears to be one of the earliest detectable symptoms that something is wrong. CVR is however quite complex and difficult to measure, requiring specialist, expensive equipment, and so it has not been widely studied in clinical trials of diseases or treatments. It has also been suggested that there are two different forms of CVR dysfunction, one due to a person's intrinsic biology and another due to their lifestyle, with each requiring different treatments.

The impact of lifestyle on CVR can be estimated from simple questions and physiological measurements but there is currently no simple means of determining the level of intrinsic CVR function. Because of this there is the potential that trials of new treatments could target only one cause but include patients with both types (intrinsic and lifestyle) and therefore be ineffective in a majority of subjects and be incorrectly deemed to be of no use. Being able to readily and cost-effectively determine which of these causes is operative would be highly beneficial to both research and, ultimately, clinical environments.

The hypothesis of this project is that intrinsic CVR dysfunction is caused by a general smooth muscle disorder; as smooth muscle is wrapped around blood vessels to control the flow. One of the few other places that smooth muscle occurs in the body is controlling the iris of the eye. It has been shown that groups known to have impaired CVR also tend to have a higher risk of developing neurological disorders, and the same groups have also been shown to have an impaired response of the pupil to a brief flash of light. As the pupil flash response (PFR) is potentially very cheap, quick and easy to assess it would make an excellent means of testing for intrinsic smooth muscle impairments as an indicator of impaired CVR.

By determining a range of simple physiological measurements for subjects, along with their responses to a lifestyle questionnaire and a measurement of the pupil flash response, the necessary data could be obtained. By then applying analytical machine learning techniques to the results, we propose that this will allow the development of a protocol to enable an accurate assessment of CVR, and its likely type, to be determined. This measurement could then form part of a risk assessment for a host of neurological disorders and enable early interventions to be implemented or discovered.

Key Findings
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Organisation Website: http://www.ox.ac.uk