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A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

Alberto Coccarelli Orcid Logo, Yannis Polydoros, Alexander Drysdale, Osama F. Harraz, Chennakesava Kadapa

Biomechanics and Modeling in Mechanobiology, Volume: 24, Issue: 3, Pages: 1123 - 1140

Swansea University Authors: Alberto Coccarelli Orcid Logo, Yannis Polydoros, Alexander Drysdale

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DOI (Published version): 10.1007/s10237-025-01958-3

Abstract

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncerta...

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Published in: Biomechanics and Modeling in Mechanobiology
ISSN: 1617-7959 1617-7940
Published: Springer Nature 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69355
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Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular ). 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spelling 2025-07-30T14:08:38.5976852 v2 69355 2025-04-25 A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks 06fd3332e5eb3cf4bb4e75a24f49149d 0000-0003-1511-9015 Alberto Coccarelli Alberto Coccarelli true false 93fcefc8f0de1a0efad45402322bfc45 Yannis Polydoros Yannis Polydoros true false 59f357e91ed91f03597ac28978e6bc30 Alexander Drysdale Alexander Drysdale true false 2025-04-25 ACEM Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular ). The robustness of the integrated fluid–structure interaction framework was assessed using different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. The proposed in-silico methodology aims to quantify how acute changes in upstream luminal pressure propagate and influence blood flow across a network of rat cerebral arteries. Weak coupling ensured accurate results with a lower computational cost for the vessel size and boundary conditions considered. To complete the analysis, we evaluated the effect of an upstream pressure surge on vascular network haemodynamics in the presence and absence of myogenic tone. This provided a clear quantitative picture of how pressure, flow and vascular constriction are re-distributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to decipher cerebral autoregulation. Journal Article Biomechanics and Modeling in Mechanobiology 24 3 1123 1140 Springer Nature 1617-7959 1617-7940 Autoregulation; Cerebral arterial networks; Myogenic response;1D blood fow dynamics; Biologicallymotivated model; Fluid-structure interaction 1 6 2025 2025-06-01 10.1007/s10237-025-01958-3 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) A.C. and I.P. acknowledge the support by Swansea University through the College of Engineering Zienkiewicz/Centenary scholarship. O.F.H was supported by the NIH National Heart, Lung, and Blood Institute (R01HL169681), the National Institute on Aging (R21AG082193), the National Institute of General Medical Sciences (P20GM135007), the Bloomfield Early Career Professorship in Cardiovascular Research, the Totman Medical Research Trust, the Cardiovascular Research Institute of Vermont, and a grant (2024-338506) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. 2025-07-30T14:08:38.5976852 2025-04-25T09:48:13.5611784 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Alberto Coccarelli 0000-0003-1511-9015 1 Yannis Polydoros 2 Alexander Drysdale 3 Osama F. Harraz 4 Chennakesava Kadapa 5 69355__34282__254ed93ad2074f59b4b0f52577b77af9.pdf 69355.VoR.pdf 2025-05-15T10:28:35.2010195 Output 2030205 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
spellingShingle A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
Alberto Coccarelli
Yannis Polydoros
Alexander Drysdale
title_short A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
title_full A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
title_fullStr A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
title_full_unstemmed A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
title_sort A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks
author_id_str_mv 06fd3332e5eb3cf4bb4e75a24f49149d
93fcefc8f0de1a0efad45402322bfc45
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author_id_fullname_str_mv 06fd3332e5eb3cf4bb4e75a24f49149d_***_Alberto Coccarelli
93fcefc8f0de1a0efad45402322bfc45_***_Yannis Polydoros
59f357e91ed91f03597ac28978e6bc30_***_Alexander Drysdale
author Alberto Coccarelli
Yannis Polydoros
Alexander Drysdale
author2 Alberto Coccarelli
Yannis Polydoros
Alexander Drysdale
Osama F. Harraz
Chennakesava Kadapa
format Journal article
container_title Biomechanics and Modeling in Mechanobiology
container_volume 24
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container_start_page 1123
publishDate 2025
institution Swansea University
issn 1617-7959
1617-7940
doi_str_mv 10.1007/s10237-025-01958-3
publisher Springer Nature
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
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description Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular ). The robustness of the integrated fluid–structure interaction framework was assessed using different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. The proposed in-silico methodology aims to quantify how acute changes in upstream luminal pressure propagate and influence blood flow across a network of rat cerebral arteries. Weak coupling ensured accurate results with a lower computational cost for the vessel size and boundary conditions considered. To complete the analysis, we evaluated the effect of an upstream pressure surge on vascular network haemodynamics in the presence and absence of myogenic tone. This provided a clear quantitative picture of how pressure, flow and vascular constriction are re-distributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to decipher cerebral autoregulation.
published_date 2025-06-01T05:23:42Z
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