Transfer function analysis of dynamic cerebral autoregulation: A white paper from the International Cerebral Autoregulation Research Network

Abstract

Cerebral autoregulation is the intrinsic ability of the brain to maintain adequate cerebral perfusion in the presence of blood pressure changes. A large number of methods to assess the quality of cerebral autoregulation have been proposed over the last 30 years. However, no single method has been universally accepted as a gold standard. Therefore, the choice of which method to employ to quantify cerebral autoregulation remains a matter of personal choice. Nevertheless, given the concept that cerebral autoregulation represents the dynamic relationship between blood pressure (stimulus or input) and cerebral blood flow (response or output), transfer function analysis became the most popular approach adopted in studies based on spontaneous fluctuations of blood pressure. Despite its sound theoretical background, the literature shows considerable variation in implementation of transfer function analysis in practice, which has limited comparisons between studies and hindered progress towards clinical application. Therefore, the purpose of the present white paper is to improve standardisation of parameters and settings adopted for application of transfer function analysis in studies of dynamic cerebral autoregulation. The development of these recommendations was initiated by (but not confined to) theCerebral Autoregulation Research Network(CARNet -www.car-net.org).

Keywords: Cerebral autoregulation; cerebral blood flow; gold standard; transfer function analysis; white paper.

Figure 1.

Main stages of transfer function analysis (TFA). In the time–domain, mean values of blood pressure (BP) and cerebral blood flow-velocity (CBFV) are obtained for each cardiac cycle and the spectral analysis algorithm (FFT: Fast Fourier Transform) is used to obtain spectral estimates in the frequency domain. The auto- and cross-spectrum are then used to obtain estimates of the coherence function, amplitude (gain) and phase frequency responses. Courtesy of JD Smirl ‘The relationship between arterial blood pressure and cerebral blood flow: insights into aging, altitude and exercise’, PhD Thesis, The University of British Columbia (Okanagan), June 2015. MAP: mean arterial pressure; MCAv: cerebral blood flow-velocity in the middle cerebral artery.

Figure 2.

Representative recordings of blood pressure (BP) and cerebral blood flow-velocity (CBFV) in a healthy adult subject. (a) Good quality recording showing reduced presence of noise and absence of artefacts with clear visualisation of each waveform. (b) Inadequate data quality with considerable amount of noise and frequent occurrence of artefacts that distort the BP and CBFV tracings.

Figure 3.

Critical values for coherence estimates at the α = 0.01, 0.05 and 0.1 significance level for 3–15 windows. Solid lines: Monte Carlo simulation from 1000 pairs of independent white Gaussian noise using Hanning windows with 50% overlap and spectral smoothing. The dotted lines give the critical values without spectral smoothing and with non-overlapping windows, calculated from theory.

Figure 4.

Occurrence of phase ‘wrap-around’ as indicated by the relatively large negative values of phase (continuous line), compared to the consistently positive values more often observed (dashed line) for frequencies below 0.1 Hz.

Figure 5.

Schematic representation of the process of interpolation and resampling following calculation of beat-to-beat values of mean blood pressure (BP) or cerebral blood flow-velocity (CBFV) for each cardiac cycle. This procedure leads to signals with a uniform time base, thus removing the influence of heart rate variability.