Cerebral autoregulation refers to the physiological mechanism that aims to maintain blood flow to the brain approximately constant when blood pressure changes. Impairment of this protective mechanism has been linked to a number of serious clinical conditions, including carotid stenosis, head trauma, subarachnoid haemorrhage and stroke. While the concept and experimental evidence is well established, methods for the assessment of autoregulation in individual patients remains an open challenge, with no gold-standard having emerged. In the current review paper, we will outline some of the basic concepts of autoregulation, as a foundation for experimental protocols and signal analysis methods used to extract indexes of cerebral autoregulation. Measurement methods for blood flow and pressure are discussed, followed by an outline of signal pre-processing steps. An outline of the data analysis methods is then provided, linking the different approaches through their underlying principles and rationale. The methods cover correlation based approaches (e.g. Mx) through Transfer Function Analysis to non-linear, multivariate and time-variant approaches. Challenges in choosing which method may be ‘best’ and some directions for ongoing and future research conclude this work.

脑自动调节是指旨在在血压变化时保持流向大脑的血流大致恒定的生理机制。这种保护机制的损害与许多严重的临床疾病有关，包括颈动脉狭窄、头部外伤、蛛网膜下腔出血和中风。虽然概念和实验证据已经确立，但评估个体患者自动调节的方法仍然是一个开放的挑战，没有出现金标准。在当前的综述文章中，我们将概述自动调节的一些基本概念，作为用于提取脑自动调节指标的实验方案和信号分析方法的基础。讨论了血流和压力的测量方法，其次是信号预处理步骤的概述。然后提供数据分析方法的概述，通过其基本原理和基本原理将不同方法联系起来。这些方法涵盖了通过传递函数分析到非线性、多变量和时变方法的基于相关性的方法（例如 Mx）。选择哪种方法可能是“最佳”的挑战以及正在进行和未来研究的一些方向总结了这项工作。