Literature DB >> 15382831

Cortical entropy changes with general anaesthesia: theory and experiment.

J W Sleigh1, D A Steyn-Ross, M L Steyn-Ross, C Grant, G Ludbrook.   

Abstract

Commonly used general anaesthetics cause a decrease in the spectral entropy of the electroencephalogram as the patient transits from the conscious to the unconscious state. Although the spectral entropy is a configurational entropy, it is plausible that the spectral entropy may be acting as a reliable indicator of real changes in cortical neuronal interactions. Using a mean field theory, the activity of the cerebral cortex may be modelled as fluctuations in mean soma potential around equilibrium states. In the adiabatic limit, the stochastic differential equations take the form of an Ornstein-Uhlenbeck process. It can be shown that spectral entropy is a logarithmic measure of the rate of synaptic interaction. This model predicts that the spectral entropy should decrease abruptly from values approximately 1.0 to values of approximately 0.7 as the patient becomes unconscious during induction of general anaesthesia, and then not decrease significantly on further deepening of anaesthesia. These predictions were compared with experimental results in which electrocorticograms and brain concentrations of propofol were recorded in seven sheep during induction of anaesthesia with intravenous propofol. The observed changes in spectral entropy agreed with the theoretical predictions. We conclude that spectral entropy may be a sensitive monitor of the consciousness-unconsciousness transition, rather than a progressive indicator of anaesthetic drug effect.

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Year:  2004        PMID: 15382831     DOI: 10.1088/0967-3334/25/4/011

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


  14 in total

1.  Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables.

Authors:  R Shalbaf; H Behnam; H Jelveh Moghadam
Journal:  Cogn Neurodyn       Date:  2014-05-09       Impact factor: 5.082

2.  The electrocortical effects of enflurane: experiment and theory.

Authors:  James W Sleigh; Jeannette A Vizuete; Logan Voss; Alistair Steyn-Ross; Moira Steyn-Ross; Charles J Marcuccilli; Anthony G Hudetz
Journal:  Anesth Analg       Date:  2009-10       Impact factor: 5.108

3.  Entropy and Complexity Analyses in Alzheimer's Disease: An MEG Study.

Authors:  Carlos Gómez; Roberto Hornero
Journal:  Open Biomed Eng J       Date:  2010-10-10

4.  Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents.

Authors:  Rosalyn J Moran; Fabienne Jung; Tetsuya Kumagai; Heike Endepols; Rudolf Graf; Raymond J Dolan; Karl J Friston; Klaas E Stephan; Marc Tittgemeyer
Journal:  PLoS One       Date:  2011-08-02       Impact factor: 3.240

5.  Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

Authors:  Zhixian Yang; Yinghua Wang; Gaoxiang Ouyang
Journal:  ScientificWorldJournal       Date:  2014-03-25

6.  EEG feature comparison and classification of simple and compound limb motor imagery.

Authors:  Weibo Yi; Shuang Qiu; Hongzhi Qi; Lixin Zhang; Baikun Wan; Dong Ming
Journal:  J Neuroeng Rehabil       Date:  2013-10-12       Impact factor: 4.262

7.  The right thalamus may play an important role in anesthesia-awakening regulation in frogs.

Authors:  Yanzhu Fan; Xizi Yue; Fei Xue; Steven E Brauth; Yezhong Tang; Guangzhan Fang
Journal:  PeerJ       Date:  2018-03-15       Impact factor: 2.984

8.  EEG Characterization of the Alzheimer's Disease Continuum by Means of Multiscale Entropies.

Authors:  Aarón Maturana-Candelas; Carlos Gómez; Jesús Poza; Nadia Pinto; Roberto Hornero
Journal:  Entropy (Basel)       Date:  2019-05-28       Impact factor: 2.524

9.  Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer's Disease-Related Pathology: Computer Modeling and Simulations.

Authors:  Dariusz Świetlik; Jacek Białowąs; Janusz Moryś; Ilona Klejbor; Aida Kusiak
Journal:  Entropy (Basel)       Date:  2019-06-13       Impact factor: 2.524

10.  Improving clustering by imposing network information.

Authors:  Susanne Gerber; Illia Horenko
Journal:  Sci Adv       Date:  2015-08-07       Impact factor: 14.136

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