Assessment of cerebral autoregulation indices - a modelling perspective.

ABSTRACT: Various methodologies to assess cerebral autoregulation (CA) have been developed, including model - based methods (e.g. autoregulation index, ARI), correlation coefficient - based methods (e.g. mean flow index, Mx), and frequency domain - based methods (e.g. transfer function analysis, TF). Our understanding of relationships among CA indices remains limited, partly due to disagreement of different studies by using real physiological signals, which introduce confounding factors. The influence of exogenous noise on CA parameters needs further investigation. Using a set of artificial cerebral blood flow velocities (CBFV) generated from a well-known CA model, this study aims to cross-validate the relationship among CA indices in a more controlled environment. Real arterial blood pressure (ABP) measurements from 34 traumatic brain injury patients were applied to create artificial CBFVs. Each ABP recording was used to create 10 CBFVs corresponding to 10 CA levels (ARI from 0 to 9). Mx, TF phase, gain and coherence in low frequency (LF) and very low frequency (VLF) were calculated. The influence of exogenous noise was investigated by adding three levels of colored noise to the artificial CBFVs. The result showed a significant negative relationship between Mx and ARI (r?=?-0.95, p?

SUBMITTER: Liu X 

PROVIDER: S-EPMC7295753 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature