No Cover Image

Journal article 422 views 75 downloads

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

  • 69355.VoR.pdf

    PDF | Version of Record

    © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License.

    Download (1.94MB)

Check full text

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...

Full description

Published in: Biomechanics and Modeling in Mechanobiology
ISSN: 1617-7959 1617-7940
Published: Springer Nature 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69355
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 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.
Keywords: Autoregulation; Cerebral arterial networks; Myogenic response;1D blood fow dynamics; Biologicallymotivated model; Fluid-structure interaction
College: Faculty of Science and Engineering
Funders: 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.
Issue: 3
Start Page: 1123
End Page: 1140

Similar Items