Literature DB >> 11556667

A new mathematical model of dynamic cerebral autoregulation based on a flow dependent feedback mechanism.

S K Kirkham1, R E Craine, A A Birch.   

Abstract

A new mathematical model representing dynamic cerebral autoregulation as a flow dependent feedback mechanism is presented. Two modelling parameters are introduced, lambda, the rate of restoration, and tau, a time delay. Velocity profiles are found for a general arterial blood pressure, allowing the model to be applied to any experiment that uses changes in arterial blood pressure to assess dynamic cerebral autoregulation. Two such techniques, thigh cuffs and a lower body negative pressure box, which produce step changes and oscillatory variations in arterial blood pressure respectively, are investigated. Results derived using the mathematical model are compared with data from the two experiments. The comparisons yield similar estimates for lambda and tau, suggesting these parameters are independent of the pressure change stimulus and depend only on the main features of the dynamic cerebral autoregulation process. The modelling also indicates that for imposed oscillatory variations in arterial blood pressure a small phase difference between pressure and velocity waveforms does not necessarily imply impaired autoregulation. It is shown that the ratio between the variation in maximum velocity and pressure variation can be used, along with the phase difference, to indicate the nature of the autoregulatory response.

Mesh:

Year:  2001        PMID: 11556667     DOI: 10.1088/0967-3334/22/3/305

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  5 in total

1.  Cerebral blood flow and autoregulation: current measurement techniques and prospects for noninvasive optical methods.

Authors:  Sergio Fantini; Angelo Sassaroli; Kristen T Tgavalekos; Joshua Kornbluth
Journal:  Neurophotonics       Date:  2016-06-21       Impact factor: 3.593

2.  Analysis of dynamic cerebral autoregulation using an ARX model based on arterial blood pressure and middle cerebral artery velocity simulation.

Authors:  Y Liu; R Allen
Journal:  Med Biol Eng Comput       Date:  2002-09       Impact factor: 2.602

3.  The use of automated pupillometry to assess cerebral autoregulation: a retrospective study.

Authors:  Armin Quispe Cornejo; Carla Sofía Fernandes Vilarinho; Ilaria Alice Crippa; Lorenzo Peluso; Lorenzo Calabrò; Jean-Louis Vincent; Jacques Creteur; Fabio Silvio Taccone
Journal:  J Intensive Care       Date:  2020-07-31

4.  Cerebral Autoregulation Real-Time Monitoring.

Authors:  Adi Tsalach; Eliahu Ratner; Stas Lokshin; Zmira Silman; Ilan Breskin; Nahum Budin; Moshe Kamar
Journal:  PLoS One       Date:  2016-08-29       Impact factor: 3.240

5.  A stochastic delay differential model of cerebral autoregulation.

Authors:  Simona Panunzi; Laura D'Orsi; Daniela Iacoviello; Andrea De Gaetano
Journal:  PLoS One       Date:  2015-04-01       Impact factor: 3.240

  5 in total

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