Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)
Journal
Journal of Cerebral Blood Flow and Metabolism
ISSN
0271-678X
1559-7016
Date Issued
2024-04-30
Author(s)
Kyriaki Kostoglou
Felipe Bello-Robles
Patrice Brassard
Max Chacon
Jurgen AHR Claassen
Marek Czosnyka
Jan-Willem Elting
Kun Hu
Lawrence Labrecque
Jia Liu
Vasilis Z Marmarelis
Dae Cheol Shin
David Simpson
Jonathan Smirl
Ronney B Panerai
Georgios D Mitsis
Abstract
Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.
Subjects
CARNet
cerebral autoregulation
cerebral blood flow
time-domain methods
white paper
Publisher
SAGE Publications
Type
journal article