Literature DB >> 12086006

Modeling of nonlinear physiological systems with fast and slow dynamics. II. Application to cerebral autoregulation.

G D Mitsis1, R Zhang, B D Levine, V Z Marmarelis.   

Abstract

Dynamic autoregulation of cerebral hemodynamics in healthy humans is studied using the novel methodology of the Laguerre-Volterra network for systems with fast and slow dynamics (Mitsis, G. D., and V. Z. Marmarelis, Ann. Biomed. Eng. 30:272-281, 2002). Since cerebral autoregulation is mediated by various physiological mechanisms with significantly different time constants, it is used to demonstrate the efficacy of the new method. Results are presented in the time and frequency domains and reveal that cerebral autoregulation is a nonlinear and dynamic (frequency-dependent) system with considerable nonstationarities. Quantification of the latter reveals greater variability in specific frequency bands for each subject in the low and middle frequency range (below 0.1 Hz). The nonlinear dynamics are prominent also in the low and middle frequency ranges, where the frequency response of the system exhibits reduced gain.

Entities:  

Keywords:  Non-programmatic

Mesh:

Year:  2002        PMID: 12086006     DOI: 10.1114/1.1477448

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  28 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

2.  A systematic study of linear dynamic modeling of intracranial pressure dynamics.

Authors:  Sunghan Kim; Marvin Bergsneider; Xiao Hu
Journal:  Physiol Meas       Date:  2011-02-01       Impact factor: 2.833

3.  Methodology of Recurrent Laguerre-Volterra Network for Modeling Nonlinear Dynamic Systems.

Authors:  Kunling Geng; Vasilis Z Marmarelis
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2016-06-24       Impact factor: 10.451

4.  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 5.  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

6.  Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy (CHS).

Authors:  Sergio Fantini
Journal:  Neuroimage       Date:  2013-04-10       Impact factor: 6.556

7.  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

8.  Model-based quantification of cerebral hemodynamics as a physiomarker for Alzheimer's disease?

Authors:  V Z Marmarelis; D C Shin; M E Orme; R Zhang
Journal:  Ann Biomed Eng       Date:  2013-06-15       Impact factor: 3.934

9.  Autonomic neural control of cerebral hemodynamics.

Authors:  Georgios D Mitsis; Rong Zhang; Benjamin D Levine; Efthalia Tzanalaridou; Demosthenes G Katritsis; Vasilis Z Marmarelis
Journal:  IEEE Eng Med Biol Mag       Date:  2009 Nov-Dec

10.  Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment.

Authors:  V Z Marmarelis; D C Shin; M E Orme; R Zhang
Journal:  Med Eng Phys       Date:  2014-03-31       Impact factor: 2.242

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