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Investigating spatial variations in dynamic cerebral autoregulation through a computational model of stenosis

Abstract:
Objective. Dynamic cerebral autoregulation (dCA) is a well-established mechanism that acts to maintain cerebral blood flow (CBF) reasonably constant in response to short-term fluctuations in blood pressure. It is known to be impaired in many clinical conditions, including stenosis, which is also a major risk factor for ischaemic stroke. However, it is not yet well understood whether impairment in dCA in one brain region is independent or not on dCA impairment in other brain regions, for example, whether there are spatial effects of stenosis on dCA. This is due to the complex blood flow environment and the lack of physiological experiments. Approach. We thus establish and apply a novel computational stenosis model including the circle of Willis to investigate and to quantify the degree of dCA impairment and CBF patterns as a function of stenosis fraction, measured in different configurations of the cerebral vasculature. Main results. We find some evidence for dependence between dCA in different brain regions, although this is very preliminary and much more experimental data will be required to answer this question fully. Significance. Our study has provided a first attempt to consider the effect of stenosis in various arteries on cerebral autoregulation to investigate spatial variations in dCA. This has potential applications in the treatment of cerebrovascular diseases where the control of cerebral perfusion is critical but where measurements are scarce.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/1361-6579/acca5d

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
ORCID:
0000-0002-5921-6282
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Role:
Author
ORCID:
0000-0003-1156-2810


Publisher:
IOP Publishing
Journal:
Physiological Measurement More from this journal
Volume:
44
Issue:
4
Pages:
044003
Article number:
044003
Publication date:
2023-04-19
Acceptance date:
2023-04-04
DOI:
EISSN:
1361-6579
ISSN:
0967-3334


Language:
English
Keywords:
Source identifiers:
4048702
Deposit date:
2026-05-14
ARK identifier:
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