The INfoMATAS project: Methods for assessing cerebral autoregulation in stroke.

David M Simpson, Stephen J Payne, Ronney B Panerai
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引用次数: 6

Abstract

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.

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INfoMATAS项目:评估脑卒中脑自动调节的方法。
脑自动调节是指当血压发生变化时,维持流向大脑的血流量大致恒定的生理机制。这种保护机制的损害与一些严重的临床状况有关,包括颈动脉狭窄、头部创伤、蛛网膜下腔出血和中风。虽然概念和实验证据已经很好地建立起来,但评估个体患者自我调节的方法仍然是一个公开的挑战,没有出现黄金标准。在本文中,我们将概述自调节的一些基本概念,作为提取大脑自调节指标的实验方案和信号分析方法的基础。讨论了血流和血压的测量方法,然后概述了信号预处理步骤。然后提供了数据分析方法的大纲,通过其基本原理和基本原理将不同的方法联系起来。这些方法包括基于相关性的方法(例如Mx),通过传递函数分析到非线性,多变量和时变方法。选择哪种方法可能是“最好”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。
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