Rapid pressure-to-flow dynamics of cerebral autoregulation induced by instantaneous changes of arterial CO2

Liu, Jia; Simpson, David M.; Kouchakpour, H.; Panerai, Ronney B.; Chen, Jie; Gao, Shan; Zhang, Pandeng; Wu, Xinyu

doi:10.1016/j.medengphy.2014.09.005

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…Indeed, over half of the variation in the difference in the increase in falling slope was explained by LBP-related changes in systemic haemodynamic variables (R 2 = 0.53) ( Table 3). The available literature supports an effect of CO 2 on autoregulation (Panerai et al 1999;Maggio et al 2013;Liu et al 2014;Minhas et al 2016). This is consistent with the apparent effect of CO 2 on falling slope.…”

Section: Discussionsupporting

confidence: 83%

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations

Hamner

Ishibashi

Tan

2019

The Journal of Physiology

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Key points Cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03 Hz (∼30 s). This suggests that frequency bands for characterizing cerebral autoregulation should be redefined Low cross‐spectral coherence below 0.03 Hz highlights the limitations of transfer function approaches Haemodynamic changes induced by lower body pressure could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible. Abstract There is currently little empirical basis for time scales that are considered to be most significant in cerebrovascular counter‐regulation of changes in arterial pressure. Although it is well established that cerebral autoregulation behaves as a ‘high‐pass’ filter, recommended frequency bands have been largely arbitrarily determined. To test effectiveness of cerebral autoregulation, we refined oscillatory lower body pressure (LBP) to augment resting pressure fluctuations below 0.1 Hz by a factor of two in 13 young male volunteers, and thoroughly characterized the time and frequency responses of cerebral autoregulation. We observed that despite a threefold increase in arterial pressure power <0.03 Hz with oscillatory LBP, there was no change in cerebral blood flow power, indicating near perfect counter‐regulation. By contrast, in the range 0.03–0.10 Hz, both cerebral blood flow and arterial pressure power more than doubled. Our data demonstrate that cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03 Hz (∼30 s). This suggests that frequency bands of interest should be redefined and recording length should be increased considerably to account for this. Furthermore, low cross‐spectral coherence below 0.03 Hz, even when pressure fluctuations were augmented, highlights the uncertainty in transfer function approaches and the need to either report precision or use non‐linear approaches. Finally, haemodynamic changes induced by LBP could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible.

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Section: Discussionsupporting

confidence: 83%

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations

Hamner

Ishibashi

Tan

2019

The Journal of Physiology

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“…Several groups have attempted to apply the Laguerre-Volterra expansion of kernels to model the dynamics of CA with non-linearity considered 33. Additionally, multivariate models were designed to model the influence of covariates, for example, CO 2 , on CBF velocity, and more recently non-stationary property was investigated by using moving windows or adaptive methods 34. Analytical techniques to measure CA should include projection pursuit regression.…”

Section: Methodologies Of the Assessment Of Camentioning

confidence: 99%

Impaired cerebral autoregulation: measurement and application to stroke

Xiong

Liu

Shang

et al. 2017

J Neurol Neurosurg Psychiatry

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Cerebral autoregulation (CA) is a protective mechanism that maintains cerebral blood flow at a relatively constant level despite fluctuations of cerebral perfusion pressure or arterial blood pressure. It is a universal physiological mechanism that may involve myogenic, neural control as well as metabolic regulations of cerebral vasculature in response to changes in pressure or cerebral blood flow. Traditionally, CA has been represented by a sigmoid curve with a wide plateau between about 50 mm Hg and 170 mm Hg of steady-state changes in mean arterial pressure, defined as static CA. With the advent of transcranial Doppler, measurement of cerebral blood flow in response to transient changes in arterial pressure has been used to assess dynamic CA. However, a gold standard for measuring CA is not currently available. Stroke has been the leading cause of long-term adult disability throughout the world. A better understanding of CA and its response to pathological derangements can help assess the severity of stroke, guide management decisions, assess response to interventions and provide prognostic information. The objective of this review is to provide a comprehensive insight about physiology of autoregulation, measurement methodologies and clinical applications in stroke to help build a consensus for what should be included in an internationally agreed protocol for CA testing and monitoring, and to promote its translation into clinical bedside practice for stroke management.

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“…In this study T-F analysis was performed using ZAMD distribution belonging to the Cohen class, as it allows to achieve high resolution both in frequency and time domain while keeping cross-terms relatively reduced [ 32 ]. There have already been a number of studies on CA using wavelet-based approaches [ 52 – 54 ], autoregressive moving-average (ARMA) [ 55 – 57 ], recursive least squares [ 58 , 59 ], and Volterra series [ 25 – 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…CA has been also modeled using recursive least squares adaptive filter [ 58 , 59 ] with Hilbert transform employed to calculate instantaneous phase shift between ABP and CBFV [ 59 ]. To focus the analysis on a particular frequency band, corresponding band pass filter should be applied prior to the system identification and performing transform.…”

Section: Discussionmentioning

confidence: 99%

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Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia

et al. 2017

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ObjectiveClassic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment.MethodsContinuous recording of CBFV, ABP, ECG, and end-tidal CO2 were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time—frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02–0.07 Hz (VLF), 0.07–0.20 Hz (LF), and 0.20–0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed.ResultsThe hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF.ConclusionThe time—frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

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Rapid pressure-to-flow dynamics of cerebral autoregulation induced by instantaneous changes of arterial CO2

Cited by 9 publications

References 38 publications

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations

Impaired cerebral autoregulation: measurement and application to stroke

Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia

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