Coccarelli Alberto, Polydoros Ioannis, Drysdale Alex, Harraz Osama F, Kadapa Chennakesava
Zienkiewicz Institute for Modelling, Data and AI, Faculty of Science and Engineering, Swansea University, Swansea, UK.
Department of Mechanical Engineering, Faculty of Science and Engineering, Swansea University, Swansea University Bay Campus, Fabian Way, Crymlyn Burrows, Skewen, Swansea, SA1 8EN, UK.
Biomech Model Mechanobiol. 2025 Jun;24(3):1123-1140. doi: 10.1007/s10237-025-01958-3. Epub 2025 May 9.
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.
脑自动调节通过在压力波动时限制血流变化发挥关键的生理作用。尽管潜在的血管细胞过程是由化学机械驱动的，但在体内估计相关的血流动力学力仍然极其困难且不确定。在这项工作中，我们提出了一种新颖的计算方法，用于评估跨肌源性活动脑动脉网络的血流动力学，这些动脉可以调节其肌肉张力以稳定血流（和灌注压）并限制血管壁内应力。引入的框架将基于连续介质力学、具有生物学动机的大鼠血管壁模型与一维血流动力学相结合。我们在单血管和网络水平上研究血管壁对压力变化响应的时间依赖性。针对不同的压力方案和条件（对照和无细胞外液）验证了血管壁力学模型的动力学性能。在由大脑中动脉及其三代分支组成的理想化血管网络中，使用不同类型的入口信号和数值设置评估了集成流固相互作用框架的稳健性。所提出的计算机模拟方法旨在量化上游管腔压力的急性变化如何在大鼠脑动脉网络中传播并影响血流。弱耦合确保了在所考虑的血管大小和边界条件下以较低的计算成本获得准确结果。为了完成分析，我们评估了在存在和不存在肌源性张力的情况下上游压力激增对血管网络血流动力学的影响。这提供了一幅清晰的定量图景，展示了入口压力变化时压力、血流和血管收缩如何在每一代血管中重新分布。这项工作为未来旨在解读脑自动调节的联合实验 - 计算研究铺平了道路。
