Dynamic cerebral autoregulation during brain activation paradigms

Panerai, Ronney B.; Moody, Michelle; Eames, Penelope J.; Potter, John F.

doi:10.1152/ajpheart.00115.2005

Cited by 40 publications

(43 citation statements)

References 40 publications

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“…This is the usual assumption made in estimating confidence limits in system identification e.g. [31] but requires further investigation following on from some recent studies on the dynamics of CA [23,32]. In the current work the estimated intra-subject variability can however be interpreted as the dispersion of estimates to be expected if the source of errors were only added noise rather than less tractable physiological variation.…”

Section: Intra-subject Variabilitymentioning

confidence: 88%

“…Previous analyses that concentrated on the performance of the ARI [25,32,33] have shown large variations over time and sudden drops to zero of this index. Although rigorous studies have not been performed to understand the source of this variability, this may be due to the constraint that the Aaslid's model [15] imposes on the index as it only uses a set of ten filters [25].…”

Section: Intra-subject Variabilitymentioning

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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Section: Intra-subject Variabilitymentioning

confidence: 88%

Section: Intra-subject Variabilitymentioning

confidence: 99%

Optimising the assessment of cerebral autoregulation from black box models

Angarita-Jaimes

Kouchakpour

Liu

et al. 2014

Medical Engineering & Physics

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“…It has been reported that systemic fluctuations related to blood pressure or heart rate-in particular, because of dynamic cerebral autoregulation 63,64 or veins on the brain surface 34 -can influence the NIRS signal. 65,66 In these cases, to extract cerebral blood including systemic signal, we need to use a signal discrimination method that considers the dependence of signal amplitude on S-D distance instead of just the waveform characteristics of the signal.…”

Section: Future Workmentioning

confidence: 99%

Dynamic phantom with two stage-driven absorbers for mimicking hemoglobin changes in superficial and deep tissues

Funane¹,

Atsumori

Kiguchi

et al. 2012

J. Biomed. Opt.

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Abstract. In near-infrared spectroscopy (NIRS) for monitoring brain activity and cerebral functional connectivity, the effect of superficial tissue on NIRS signals needs to be considered. Although some methods for determining the effect of scalp and brain have been proposed, direct validation of the methods has been difficult because the actual absorption changes cannot be known. In response to this problem, we developed a dynamic phantom that mimics hemoglobin changes in superficial and deep tissues, thus allowing us to experimentally validate the methods. Two absorber layers are independently driven with two one-axis automatic stages. We can use the phantom to design any type of waveform (e.g., brain activity or systemic fluctuation) of absorption change, which can then be reproducibly measured. To determine the effectiveness of the phantom, we used it for a multiple source-detector distance measurement. We also investigated the performance of a subtraction method with a short-distance regressor. The most accurate lower-layer change was obtained when a shortest-distance channel was used. Furthermore, when an independent component analysis was applied to the same data, the extracted components were in good agreement with the actual signals. These results demonstrate that the proposed phantom can be used for evaluating methods of discriminating the effects of superficial tissue.

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“…These results are consistent with studies investigating the dynamic properties of the myogenic component of autoregulation of blood flow in different species and/or other vascular beds. Using transfer function analysis between BP and cerebral blood flow (transcranial Doppler), dynamic cerebral autoregulation was found to operate at frequencies below 0.07 Hz in humans [16][17][18]. The myogenic component of renal blood flow autoregulation has been identified between 0.1 and 0.2 Hz in rats [19][20][21] and between 0.06 and 0.15 Hz in dogs [22] and in the mesenteric circulation of rats, autoregulation operates in a similar frequency range and is most effective at 0.13 Hz [21].…”

Section: Introductionmentioning

confidence: 99%