Literature DB >> 16489099

Multiple coherence of cerebral blood flow velocity in humans.

Ronney B Panerai1, Penelope J Eames, John F Potter.   

Abstract

The coherence function has been used in transfer function analysis of dynamic cerebral autoregulation to assess the statistical significance of spectral estimates of gain and phase frequency response. Interpretation of the coherence function and choice of confidence limits has not taken into account the intrinsic nonlinearity represented by changes in cerebrovascular resistance due to vasomotor activity. For small spontaneous changes in arterial blood pressure (ABP), the relationship between ABP and cerebral blood flow velocity (CBFV) can be linearized, showing that corresponding changes in cerebrovascular resistance should be included as a second input variable. In this case, the standard univariate coherence function needs to be replaced by the multiple coherence, which takes into account the contribution of both inputs to explain CBFV variability. With the use of two different indicators of cerebrovascular resistance index [CVRI = ABP/CBFV and the resistance-area product (RAP)], multiple coherences were calculated for 42 healthy control subjects, aged 20 to 40 yr (28 +/- 4.6 yr, mean +/- SD), at rest in the supine position. CBFV was measured in both middle cerebral arteries, and ABP was recorded noninvasively by finger photoplethysmography. Results for the ABP + RAP inputs show that the multiple coherence of CBFV for frequencies <0.05 Hz is significantly higher than the corresponding values obtained for univariate coherence (P < 10(-5)). Corresponding results for the ABP + CVRI inputs confirm the principle of multiple coherence but are less useful due to the interdependence between CVRI, ABP, and CBFV. The main conclusion is that values of univariate coherence between ABP and CBFV should not be used to reject spectral estimates of gain and phase, derived from small fluctuations in ABP, because the true explained power of CBFV in healthy subjects is much higher than what has been usually predicted by the univariate coherence functions.

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Year:  2006        PMID: 16489099     DOI: 10.1152/ajpheart.01348.2005

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


  19 in total

Review 1.  Transfer function analysis of dynamic cerebral autoregulation: A white paper from the International Cerebral Autoregulation Research Network.

Authors:  Jurgen A H R Claassen; Aisha S S Meel-van den Abeelen; David M Simpson; Ronney B Panerai
Journal:  J Cereb Blood Flow Metab       Date:  2016-01-18       Impact factor: 6.200

Review 2.  The relationship between blood pressure and cognitive function.

Authors:  Vera Novak; Ihab Hajjar
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Review 3.  Transcranial Doppler for evaluation of cerebral autoregulation.

Authors:  Ronney B Panerai
Journal:  Clin Auton Res       Date:  2009-04-16       Impact factor: 4.435

4.  Dynamic cerebral autoregulation during passive heat stress in humans.

Authors:  David A Low; Jonathan E Wingo; David M Keller; Scott L Davis; Jian Cui; Rong Zhang; Craig G Crandall
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2009-03-11       Impact factor: 3.619

5.  Cognition and Hemodynamics.

Authors:  Vera Novak
Journal:  Curr Cardiovasc Risk Rep       Date:  2012-10

6.  Effects of continuous positive airway pressure and isocapnic-hypoxia on cerebral autoregulation in patients with obstructive sleep apnoea.

Authors:  Xavier Waltz; Andrew E Beaudin; Patrick J Hanly; Georgios D Mitsis; Marc J Poulin
Journal:  J Physiol       Date:  2016-12-01       Impact factor: 5.182

7.  Assessment of dynamic cerebral autoregulation and cerebral carbon dioxide reactivity during normothermic cardiopulmonary bypass.

Authors:  Ervin E Ševerdija; Erik D Gommer; Patrick W Weerwind; Jos P H Reulen; Werner H Mess; Jos G Maessen
Journal:  Med Biol Eng Comput       Date:  2014-11-21       Impact factor: 2.602

8.  Decreased upright cerebral blood flow and cerebral autoregulation in normocapnic postural tachycardia syndrome.

Authors:  Anthony J Ocon; Marvin S Medow; Indu Taneja; Debbie Clarke; Julian M Stewart
Journal:  Am J Physiol Heart Circ Physiol       Date:  2009-06-05       Impact factor: 4.733

9.  Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements.

Authors:  V Z Marmarelis; G D Mitsis; D C Shin; R Zhang
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2016-05-13       Impact factor: 4.226

10.  Altered Phase Interactions between Spontaneous Blood Pressure and Flow Fluctuations in Type 2 Diabetes Mellitus: Nonlinear Assessment of Cerebral Autoregulation.

Authors:  Kun Hu; C K Peng; Norden E Huang; Zhaohua Wu; Lewis A Lipsitz; Jerry Cavallerano; Vera Novak
Journal:  Physica A       Date:  2008-04-01       Impact factor: 3.263

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