Tong Zheng, Catherall Mark, Payne Stephen J
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, UK.
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, UK.
Med Eng Phys. 2021 Sep;95:51-63. doi: 10.1016/j.medengphy.2021.08.003. Epub 2021 Aug 12.
The mechanism of cerebral autoregulation ensures a continuous and sufficient blood supply to the brain to maintain normal function in the presence of changes in blood pressure. Impaired cerebral autoregulation is implicated in a range of brain diseases. We thus present here a multiscale model of cerebral autoregulation to provide a more detailed basis for a better understanding of the mechanisms behind impaired autoregulation. This model is built around a model of single arteriole, which includes a model of Nitric Oxide (NO) transport, the myogenic response, and a 4-state kinetic model coupled to a mechanical model of the vessel wall. In particular, the NO component of the model is added here to better understand the interaction mode between NO and the myogenic response, since the role of NO, the recognized effective vasodilator, is poorly understood in this context. This vessel model is then integrated within a model of the full-brain vasculature. The model is validated using a range of experimental data from the literature, both steady-state and dynamic. The model is able to predict the response of the arteriole to changes in both driving pressure and baseline pressure, indicating that the model captures well the balance between the myogenic and metabolic mechanisms. We next plan to examine the ways in which impaired autoregulation is manifested in different patient groups, potentially leading to improved therapy.
脑自动调节机制可确保在血压变化时持续且充足地为大脑供血，以维持正常功能。脑自动调节功能受损与一系列脑部疾病有关。因此，我们在此提出一种脑自动调节的多尺度模型，以便为更好地理解自动调节受损背后的机制提供更详细的依据。该模型围绕单个小动脉模型构建，其中包括一氧化氮（NO）运输模型、肌源性反应模型以及与血管壁力学模型耦合的四态动力学模型。特别是，在此添加了模型中的NO成分，以更好地理解NO与肌源性反应之间的相互作用模式，因为在这种情况下，公认的有效血管舒张剂NO的作用尚不清楚。然后将该血管模型整合到全脑血管系统模型中。使用文献中的一系列稳态和动态实验数据对该模型进行了验证。该模型能够预测小动脉对驱动压力和基线压力变化的反应，表明该模型很好地捕捉到了肌源性和代谢机制之间的平衡。接下来，我们计划研究自动调节受损在不同患者群体中的表现方式，这可能会带来更好的治疗方法。
