Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
J Cereb Blood Flow Metab. 2023 Sep;43(9):1628-1630. doi: 10.1177/0271678X221098448. Epub 2022 May 5.
Transfer function analysis (TFA) is the most frequently adopted method for assessing dynamic cerebral autoregulation (CA) with continuously recorded arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). Conventionally, values of autoregulatory metrics (e.g., gain and phase) derived from TFA are averaged within three frequency bands separated by cut-off frequencies at 0.07 Hz and 0.20 Hz, respectively, to represent the efficiency of dynamic CA. However, this is of increasing concerns, as there remains no solid evidence for choosing these specific cut-off frequencies, and the rigid adoption of these bands can stifle further developments in TFA of dynamic CA. In this 'Point-Counterpoint' mini-review, we provide evidence against the fixed banding, indicate possible alternatives, and call for awareness of the risk of the 'one-size-fits-all' banding becoming dogmatic. We conclude that we need to remain open to the multiple possibilities offered by TFA to realize its full potential in studies of human dynamic CA.
传递函数分析（TFA）是评估连续记录的动脉血压（ABP）和脑血流速度（CBFV）的动态脑自动调节（CA）的最常用方法。传统上，从 TFA 得出的自动调节指标（例如增益和相位）的值在通过截止频率分别为 0.07 Hz 和 0.20 Hz 分隔的三个频带内平均，以表示动态 CA 的效率。然而，这引起了越来越多的关注，因为选择这些特定截止频率尚无确凿的证据，并且严格采用这些频带可能会阻碍 TFA 对动态 CA 的进一步发展。在这个“观点交锋”的小型评论中，我们提供了反对固定频带的证据，指出了可能的替代方案，并呼吁注意“一刀切”频带的风险变得教条化。我们的结论是，我们需要对 TFA 提供的多种可能性持开放态度，以在人类动态 CA 的研究中充分发挥其潜力。
