观点对垒：动态脑自动调节的传递函数分析：带通滤波还是不带通滤波？
Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?
机构信息
Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan.
出版信息
J Cereb Blood Flow Metab. 2023 Sep;43(9):1625-1627. doi: 10.1177/0271678X231182245. Epub 2023 Jun 11.
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.
脑血流自动调节的传递函数分析（TFA）基于线性系统理论，用以检测血压变化与脑血流之间的关系。通过 TFA，脑血流自动调节被描述为一种频率依赖性现象，其特征在于在不同的特征频率带中以增益、相位和相干性来量化。这些频率带可能反映了脑血管调节的潜在机制。此外，在特定的频率带内获取 TFA 指标有助于可靠的频谱估计和统计数据分析，从而减少随机噪声。本评论讨论了在脑血流自动调节研究中对 TFA 参数进行带通滤波的优点和注意事项。
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