Literature DB >> 10484432

Linear and nonlinear analysis of human dynamic cerebral autoregulation.

R B Panerai1, S L Dawson, J F Potter.   

Abstract

The linear dynamic relationship between systemic arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) was studied by time- and frequency-domain analysis methods. A nonlinear moving-average approach was also implemented using Volterra-Wiener kernels. In 47 normal subjects, ABP was measured with Finapres and CBFV was recorded with Doppler ultrasound in both middle cerebral arteries at rest in the supine position and also during ABP drops induced by the sudden deflation of thigh cuffs. Impulse response functions estimated by Fourier transfer function analysis, a second-order mathematical model proposed by Tiecks, and the linear kernel of the Volterra-Wiener moving-average representation provided reconstructed velocity model responses, for the same segment of data, with significant correlations to CBFV recordings corresponding to r = 0.52 +/- 0.19, 0.53 +/- 0.16, and 0.67 +/- 0.12 (mean +/- SD), respectively. The correlation coefficient for the linear plus quadratic kernels was 0.82 +/- 0.08, significantly superior to that for the linear models (P < 10(-6)). The supine linear impulse responses were also used to predict the velocity transient of a different baseline segment of data and of the thigh cuff velocity response with significant correlations. In both cases, the three linear methods provided equivalent model performances, but the correlation coefficient for the nonlinear model dropped to 0.26 +/- 0.25 for the baseline test set of data and to 0.21 +/- 0.42 for the thigh cuff data. Whereas it is possible to model dynamic cerebral autoregulation in humans with different linear methods, in the supine position a second-order nonlinear component contributes significantly to improve model accuracy for the same segment of data used to estimate model parameters, but it cannot be automatically extended to represent the nonlinear component of velocity responses of different segments of data or transient changes induced by the thigh cuff test.

Entities:  

Mesh:

Year:  1999        PMID: 10484432     DOI: 10.1152/ajpheart.1999.277.3.H1089

Source DB:  PubMed          Journal:  Am J Physiol        ISSN: 0002-9513


  55 in total

1.  Spectral indices of human cerebral blood flow control: responses to augmented blood pressure oscillations.

Authors:  J W Hamner; Michael A Cohen; Seiji Mukai; Lewis A Lipsitz; J Andrew Taylor
Journal:  J Physiol       Date:  2004-07-14       Impact factor: 5.182

Review 2.  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

3.  Cross-correlation of instantaneous phase increments in pressure-flow fluctuations: applications to cerebral autoregulation.

Authors:  Zhi Chen; Kun Hu; H Eugene Stanley; Vera Novak; Plamen Ch Ivanov
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2006-03-15

4.  Noninvasive intracranial pressure assessment based on a data-mining approach using a nonlinear mapping function.

Authors:  Sunghan Kim; Fabien Scalzo; Marvin Bergsneider; Paul Vespa; Neil Martin; Xiao Hu
Journal:  IEEE Trans Biomed Eng       Date:  2010-11-22       Impact factor: 4.538

5.  Dynamic cerebral autoregulation assessment using chaotic analysis in diabetic autonomic neuropathy.

Authors:  Ben-Yi Liau; Shoou-Jeng Yeh; Chuang-Chien Chiu; Yu-Chou Tsai
Journal:  Med Biol Eng Comput       Date:  2007-09-14       Impact factor: 2.602

6.  Dynamic cerebral autoregulation: different signal processing methods without influence on results and reproducibility.

Authors:  Erik D Gommer; Eri Shijaku; Werner H Mess; Jos P H Reulen
Journal:  Med Biol Eng Comput       Date:  2010-11-04       Impact factor: 2.602

7.  Closed-loop dynamic modeling of cerebral hemodynamics.

Authors:  V Z Marmarelis; D C Shin; M E Orme; R Zhang
Journal:  Ann Biomed Eng       Date:  2013-01-05       Impact factor: 3.934

Review 8.  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

9.  A data mining framework for time series estimation.

Authors:  Xiao Hu; Peng Xu; Shaozhi Wu; Shadnaz Asgari; Marvin Bergsneider
Journal:  J Biomed Inform       Date:  2009-11-10       Impact factor: 6.317

10.  Effects of autoregulation and CO2 reactivity on cerebral oxygen transport.

Authors:  S J Payne; J Selb; D A Boas
Journal:  Ann Biomed Eng       Date:  2009-07-24       Impact factor: 3.934

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