Comparison of Static and Dynamic Cerebral Autoregulation Measurements

Tiecks, Frank P.; Lam, Arthur M.; Aaslid, Rune; Newell, David W.

doi:10.1161/01.str.26.6.1014

Cited by 796 publications

(1,104 citation statements)

References 32 publications

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“…Cerebral autoregulation dynamics are usually evaluated by the changes in BFV and BP during the interventions that induce rapid BP reductions or increases such as the Valsalva maneuver, thigh cuff deflation and the head-up tilt [2,9,50,[60][61][62][63]. The conventional approach is valuable, because it allows assessments of the autoregulation responses during rapid variations in systemic pressure under the stressed conditions.…”

Section: Ivc Assessment Of Cerebral Autoregulation From Spontaneousmentioning

confidence: 99%

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Peng

Huang

et al. 2008

Physica A: Statistical Mechanics and its Applications

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Cerebral autoregulation (CA) is an important mechanism that involves dilation and constriction in arterioles to maintain relatively s cerebral blood flow in response to changes of systemic blood pressure. Traditional assessments of CA focus on the changes of cerebral blood flow velocity in response to large blood pressure fluctuations induced by interventions. This approach is not feasible for patients with impaired autoregulation or cardiovascular regulation. Here we propose a newly developed technique-the multimodal pressure-flow (MMPF) analysis, which assesses CA by quantifying nonlinear phase interactions between spontaneous oscillations in blood pressure and flow velocity during resting conditions. We show that CA in healthy subjects can be characterized by specific phase shifts between spontaneous blood pressure and flow velocity oscillations, and the phase shifts are significantly reduced in diabetic subjects. Smaller phase shifts between oscillations in the two variables indicate more passive dependence of blood flow velocity on blood pressure, thus suggesting impaired cerebral autoregulation. Moreover, the reduction of the phase shifts in diabetes is observed not only in previously-recognized effective region of CA (<0.1Hz), but also over the higher frequency range from ~0.1 to 0.4Hz. These findings indicate that Type 2 diabetes alters cerebral blood flow regulation over a wide frequency range and that this alteration can be reliably assessed from spontaneous oscillations in blood pressure and blood flow velocity during resting conditions. We also show that the MMPF method has better performance than traditional approaches based on Fourier transform, and is more sui for the quantification of nonlinear phase interactions between nonstationary biological signals such as blood pressure and blood flow.

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Section: Ivc Assessment Of Cerebral Autoregulation From Spontaneousmentioning

confidence: 99%

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Peng

Huang

et al. 2008

Physica A: Statistical Mechanics and its Applications

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“…The final value of the models response (both linear and non linear) after applying an idealised step (see Figure 2A) is a commonly used approach [6,8,26] to assess CA , and will be denoted as FVS (Final Value from the Step); in absent CA, FVS remains elevated [15]. Also, the amplitude at 3 seconds was calculated and expressed as a percentage change from the initial segment of the response (amplitude at 1 second) -denoted PCS (Percentage Change from the Step), as this was shown to be more robust than FVS [6].…”

Section: Selection Of Autoregulatory Parametersmentioning

confidence: 99%

“…Figure 2B and 3A show the expected left-shift (phase lead) in the PPR [7] resulting from the high-pass characteristics of the autoregulatory response [5,14]. Results from preliminary work [13] based on simulations using Tiecks model [15] and recorded data showed that the PPR at 1.5 seconds (A1.5) and the amplitude at 7 seconds (A7) (as shown in Fig. 3A) provide good separation of different levels of CA and are thus selected for inclusion in the current study.…”

Section: Selection Of Autoregulatory Parametersmentioning

confidence: 99%

“…Using the correlation method [18], Mx was estimated as the average Pearson's correlation coefficient of four equal segments of the %ABP and %CBFV time series. The Autoregulatory Index ARI was calculated by evaluating the set of models proposed in [15] using the parameter values given by the authors. In each recording, the model was applied to the %ABP signal and the model leading to the highest correlation coefficient between the model generated velocity and the measured %CBFV determined the ARI.…”

Section: Selection Of Autoregulatory Parametersmentioning

confidence: 99%

“…slopes or amplitudes at selected points [9,14]. An alternative method is provided by the autoregulatory index ARI [15]: this varies from 0 (absent CA) to 9 (excellent CA), with each corresponding to a specific linear filter. The filter that best fits the data determines the ARI for the recording, and this can be applied to spontaneous as well as induced blood pressure variations [16,17] .…”

Section: Introductionmentioning

confidence: 99%

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Optimising the assessment of cerebral autoregulation from black box models

Angarita-Jaimes

Kouchakpour

Liu

et al. 2014

Medical Engineering & Physics

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Cerebral autoregulation (CA) mechanisms maintain blood flow approximately stable despite changes in arterial blood pressure. Mathematical models that characterise this system have been used extensively in the quantitative assessment of function/impairment of CA. Using spontaneous fluctuations in arterial blood pressure (ABP) as input and cerebral blood flow velocity (CBFV) as output, the autoregulatory mechanism can be modelled using linear and nonlinear approaches, from which indexes can be extracted to provide an overall assessment of CA. Previous studies have considered a single -or at most a couple of measures, making it difficult to compare the performance of different CA parameters. We compare the performance of established autoregulatory parameters and propose novel measures. The key objective is to identify which model and index can best distinguish between normal and impaired CA. To this end twenty-six recordings of ABP and CBFV from normocapnia and hypercapnia (which temporarily impairs CA) in 13 healthy adults were analysed. In the absence of a 'gold' standard for the study of dynamic CA, lower inter-and intra-subject variability of the parameters in relation to the difference between normo-and hypercapnia were considered as criteria for identifying improved measures of CA. Significantly improved performance compared to some conventional approaches was achieved, with the simplest method emerging as probably the most promising for future studies.

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References

2004

Nonlinear Dynamic Modeling of Physiological Systems

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Comparison of Static and Dynamic Cerebral Autoregulation Measurements

Abstract: These data show that in normal human subjects measurement of dynamic autoregulation yields similar results as static testing of intact and pharmacologically impaired autoregulation.

Cited by 796 publications

References 32 publications

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Optimising the assessment of cerebral autoregulation from black box models

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