自动调节
时间常数
脑自动调节
常量(计算机编程)
脑血流
机制(生物学)
神经科学
时域
计算机科学
血压
控制理论(社会学)
生物系统
医学
心脏病学
物理
人工智能
心理学
工程类
内科学
生物
量子力学
电气工程
计算机视觉
程序设计语言
控制(管理)
摘要
Abstract Dynamic cerebral autoregulation (dCA) is the mechanism that describes how the brain maintains cerebral blood flow approximately constant in response to short‐term changes in arterial blood pressure. This is known to be impaired in many different pathological conditions, including ischaemic and haemorrhagic stroke, dementia and traumatic brain injury. Many different approaches have thus been used both to analyse and to quantify this mechanism in a range of healthy and diseased subjects, including data‐driven models (in both the time and the frequency domain) and biophysical models. However, despite the substantial body of work on both biophysical models and data‐driven models of dCA, there remains little work that links the two together. One of the reasons for this is proposed to be the discrepancies between the time constants that govern dCA in models and in experimental data. In this study, the processes that govern dCA are examined and it is proposed that the application of biophysical models remains limited due to a lack of understanding about the physical processes that are being modelled, partly due to the specific model formulation that has been most widely used (the equivalent electrical circuit). Based on the analysis presented here, it is proposed that the two most important time constants are arterial transit time and feedback time constant. It is therefore time to revisit equivalent electrical circuit models of dCA and to develop a more physiologically realistic alternative, one that can more easily be related to experimental data. image Key points Dynamic cerebral autoregulation is governed by two time constants. The first time constant is the arterial transit time, rather than the traditional ‘RC’ time constant widely used in previous models. This arterial transit time is approximately 1 s in the brain. The second time constant is the feedback time constant, which is less accurately known, although it is somewhat larger than the arterial transit time. The equivalent electrical circuit model of dynamic cerebral autoregulation should be replaced with a more physiologically representative model.
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