Literature DB >> 22821992

Assessment of cerebral autoregulation: the quandary of quantification.

Y C Tzeng1, P N Ainslie, W H Cooke, K C Peebles, C K Willie, B A MacRae, J D Smirl, H M Horsman, C A Rickards.   

Abstract

We assessed the convergent validity of commonly applied metrics of cerebral autoregulation (CA) to determine the extent to which the metrics can be used interchangeably. To examine between-subject relationships among low-frequency (LF; 0.07-0.2 Hz) and very-low-frequency (VLF; 0.02-0.07 Hz) transfer function coherence, phase, gain, and normalized gain, we performed retrospective transfer function analysis on spontaneous blood pressure and middle cerebral artery blood velocity recordings from 105 individuals. We characterized the relationships (n = 29) among spontaneous transfer function metrics and the rate of regulation index and autoregulatory index derived from bilateral thigh-cuff deflation tests. In addition, we analyzed data from subjects (n = 29) who underwent a repeated squat-to-stand protocol to determine the relationships between transfer function metrics during forced blood pressure fluctuations. Finally, data from subjects (n = 16) who underwent step changes in end-tidal P(CO2) (P(ET)(CO2) were analyzed to determine whether transfer function metrics could reliably track the modulation of CA within individuals. CA metrics were generally unrelated or showed only weak to moderate correlations. Changes in P(ET)(CO2) were positively related to coherence [LF: β = 0.0065 arbitrary units (AU)/mmHg and VLF: β = 0.011 AU/mmHg, both P < 0.01] and inversely related to phase (LF: β = -0.026 rad/mmHg and VLF: β = -0.018 rad/mmHg, both P < 0.01) and normalized gain (LF: β = -0.042%/mmHg(2) and VLF: β = -0.013%/mmHg(2), both P < 0.01). However, Pet(CO(2)) was positively associated with gain (LF: β = 0.0070 cm·s(-1)·mmHg(-2), P < 0.05; and VLF: β = 0.014 cm·s(-1)·mmHg(-2), P < 0.01). Thus, during changes in P(ET)(CO2), LF phase was inversely related to LF gain (β = -0.29 cm·s(-1)·mmHg(-1)·rad(-1), P < 0.01) but positively related to LF normalized gain (β = 1.3% mmHg(-1)/rad, P < 0.01). These findings collectively suggest that only select CA metrics can be used interchangeably and that interpretation of these measures should be done cautiously.

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Year:  2012        PMID: 22821992     DOI: 10.1152/ajpheart.00328.2012

Source DB:  PubMed          Journal:  Am J Physiol Heart Circ Physiol        ISSN: 0363-6135            Impact factor:   4.733


  63 in total

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Review 2.  Transfer function analysis of dynamic cerebral autoregulation: A white paper from the International Cerebral Autoregulation Research Network.

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Journal:  J Cereb Blood Flow Metab       Date:  2016-01-18       Impact factor: 6.200

3.  Cerebral pressure-flow relationship in lowlanders and natives at high altitude.

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4.  Revisiting human cerebral blood flow responses to augmented blood pressure oscillations.

Authors:  J W Hamner; Keita Ishibashi; Can Ozan Tan
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5.  Methodological comparison of active- and passive-driven oscillations in blood pressure; implications for the assessment of cerebral pressure-flow relationships.

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6.  Perturbed and spontaneous regional cerebral blood flow responses to changes in blood pressure after high-level spinal cord injury: the effect of midodrine.

Authors:  Aaron A Phillips; Andrei V Krassioukov; Philip N Ainslie; Darren E R Warburton
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7.  Dynamic Autoregulatory Response After Aneurysmal Subarachnoid Hemorrhage and Its Relation to Angiographic Vasospasm and Clinical Outcome.

Authors:  Johann Fontana; Julius Moratin; Gregory Ehrlich; Johann Scharf; Christel Weiß; Kirsten Schmieder; Martin Barth
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8.  CrossTalk proposal: dynamic cerebral autoregulation should be quantified using spontaneous blood pressure fluctuations.

Authors:  Y C Tzeng; R B Panerai
Journal:  J Physiol       Date:  2017-12-05       Impact factor: 5.182

9.  Rebuttal from Y. C. Tzeng and R. B. Panerai.

Authors:  Y C Tzeng; R B Panerai
Journal:  J Physiol       Date:  2017-12-05       Impact factor: 5.182

Review 10.  Integrative physiological and computational approaches to understand autonomic control of cerebral autoregulation.

Authors:  Can Ozan Tan; J Andrew Taylor
Journal:  Exp Physiol       Date:  2013-10-04       Impact factor: 2.969

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