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, UK.
ArXiv. 2024 Nov 13:arXiv:2411.09046v1.
Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the involved cellular processes are mechanically driven, the quantification of haemodynamic forces in in-vivo settings remains extremely difficult and uncertain. In this work, we propose a novel computational framework 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 is built on contractile (myogenically active) vascular wall mechanics and blood flow dynamics models, which can be numerically coupled in either a weak or strong way. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The robustness of the model was assessed by considering different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. For the vessel size and boundary conditions considered, weak coupling ensured accurate results with a lower computational cost. To complete the analysis, we evaluated the effect of an upstream pressure surge on the haemodynamics of the vascular network. This provided a clear quantitative picture of how pressure and flow are redistributed 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。尽管所涉及的细胞过程是由机械驱动的，但在体内环境中对血流动力学力进行量化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框架，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（和灌注压力）并限制血管壁内应力。所引入的框架基于收缩性（肌源性活动）血管壁力学和血流动力学模型，它们可以以弱耦合或强耦合的方式进行数值耦合。我们在单血管和网络层面研究了血管壁对压力变化响应的时间依赖性。通过在由大脑中动脉及其三代分支组成的理想化血管网络中考虑不同类型的入口信号和数值设置，评估了模型的稳健性。对于所考虑的血管尺寸和边界条件，弱耦合以较低的计算成本确保了准确的结果。为了完成分析，我们评估了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，展示了在入口压力变化时压力和血流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。
