Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Takashi Tarumi Ph.D.; Rong Zhang

doi:10.1177/0271678X231182245

Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Takashi Tarumi Ph.D., Rong Zhang

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) is based on linear system theory to examine the relationship between changes in blood pressure and cerebral blood flow. With TFA, dCA is characterized as a frequency-dependent phenomenon quantified by gain, phase, and coherence in the distinctive frequency bands. These frequency bands likely reflect the underlying regulatory mechanisms of the cerebral vasculature. In addition, obtaining TFA metrics over a specific frequency band facilitates reliable spectral estimation and statistical data analysis to reduce random noise. This commentary discusses the benefits and cautions of banding TFA parameters in dCA studies.

Original language	English (US)
Pages (from-to)	1625-1627
Number of pages	3
Journal	Journal of Cerebral Blood Flow and Metabolism
Volume	43
Issue number	9
DOIs	https://doi.org/10.1177/0271678X231182245
State	Published - Sep 2023

Keywords

Autoregulation
cerebrovascular regulatory mechanisms
frequency band
spectral smoothing
transfer function analysis

ASJC Scopus subject areas

Neurology
Clinical Neurology
Cardiology and Cardiovascular Medicine

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10.1177/0271678X231182245

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title = "Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?",

abstract = "Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) is based on linear system theory to examine the relationship between changes in blood pressure and cerebral blood flow. With TFA, dCA is characterized as a frequency-dependent phenomenon quantified by gain, phase, and coherence in the distinctive frequency bands. These frequency bands likely reflect the underlying regulatory mechanisms of the cerebral vasculature. In addition, obtaining TFA metrics over a specific frequency band facilitates reliable spectral estimation and statistical data analysis to reduce random noise. This commentary discusses the benefits and cautions of banding TFA parameters in dCA studies.",

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PY - 2023/9

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N2 - Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) is based on linear system theory to examine the relationship between changes in blood pressure and cerebral blood flow. With TFA, dCA is characterized as a frequency-dependent phenomenon quantified by gain, phase, and coherence in the distinctive frequency bands. These frequency bands likely reflect the underlying regulatory mechanisms of the cerebral vasculature. In addition, obtaining TFA metrics over a specific frequency band facilitates reliable spectral estimation and statistical data analysis to reduce random noise. This commentary discusses the benefits and cautions of banding TFA parameters in dCA studies.

AB - Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) is based on linear system theory to examine the relationship between changes in blood pressure and cerebral blood flow. With TFA, dCA is characterized as a frequency-dependent phenomenon quantified by gain, phase, and coherence in the distinctive frequency bands. These frequency bands likely reflect the underlying regulatory mechanisms of the cerebral vasculature. In addition, obtaining TFA metrics over a specific frequency band facilitates reliable spectral estimation and statistical data analysis to reduce random noise. This commentary discusses the benefits and cautions of banding TFA parameters in dCA studies.

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Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Abstract

Keywords

ASJC Scopus subject areas

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