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Progress in Modeling EEG Effects of General Anesthesia: Biphasic Response and Hysteresis

  • D. A. Steyn-RossEmail author
  • M. L. Steyn-Ross
  • J. W. Sleigh
  • M. T. Wilson
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 15)

Abstract

It is a well established clinical observation that, at low concentrations, most anesthetic agents produce a surge in brain activity that occurs around the time of loss of consciousness. At higher concentrations, brain activity slows, and eventually tends towards electrical silence. A second surge in EEG power occurs during the return to consciousness. These induction and recovery biphasic power surges were first explained in terms of a first-order switching transition between distinct active and quiescent neural states, but subsequent modeling by other researchers has demonstrated that biphasic surges can also be generated by a smooth, graduated transition between normal and suppressed levels of cortical activity. In this chapter we examine the contrasting predictions of the switching model versus the continuous model for anesthetic induction. If the continuous non-switching picture is correct, then the return path to recovery will retrace the trajectory for induction, so the biphasic peaks should occur at identical drug concentrations. In contrast, the switching model predicts that there must be a hysteresis separation between the entry and recovery EEG power maxima, and that the patient will awaken at a lower drug concentration than that required to put her to sleep.

Keywords

Firing Rate Anesthetic Concentration Fluctuation Spectrum Homogeneous Steady State Inhibitory Postsynaptic Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bojak I, Liley DTJ (2005) Modeling the effects of anesthesia on the electroencephalogram. Phys Rev E 71:041902 CrossRefGoogle Scholar
  2. Chaturvedi S, Gardiner CW, Matheson IS, Walls DF (1977) Stochastic analysis of a chemical reaction with spatial and temporal structures. J Stat Phys 17:469–489 CrossRefGoogle Scholar
  3. Eilers PHC (1994) Smoothing and interpolation with finite differences. In: Heckbert PS (ed) Graphic gems IV. Academic Press, San Diego, pp 241–250 Google Scholar
  4. Foster BL, Bojak I, Liley DTJ (2008) Population based models of cortical drug response: insights from anaesthesia. Cog Neurodyn 2(4):283–296 CrossRefGoogle Scholar
  5. Franks NP, Lieb WR (1994) Molecular and cellular mechanisms of general anaesthesia. Nature 367:607–613 PubMedCrossRefGoogle Scholar
  6. Franks NP, Dickenson R, de Sousa SLM, Hall AC, Lieb WR (1998) How does xenon produce anaesthesia? Nature 396:324 PubMedCrossRefGoogle Scholar
  7. Fulcher BD, Phillips AJK, Robinson PA (2008) Modeling the impact of impulsive stimuli on sleep-wake dynamics. Phys Rev E 78(5):051920 CrossRefGoogle Scholar
  8. Guedel AE (1937) Inhalational anesthesia: a fundamental guide. Macmillan, New York Google Scholar
  9. Hutt A, Longtin A (2010) Effects of the anesthetic agent propofol on neural populations. Cog Neurodyn 4(1):37–59 CrossRefGoogle Scholar
  10. Hutt A, Schimansky-Geier L (2008) Anesthetic-induced transitions by propofol modeled by nonlocal neural populations involving two neuron types. J Biol Phys 34(3–4):433–440 PubMedCrossRefGoogle Scholar
  11. John ER, Prichep LS, Kox W, Valdés-Sosa P, Bosch-Bayard J, Aubert E, Tom M, diMichele F, Guginoi LD (2001) Invariant reversible QEEG effects of anesthetics. Conscious Cogn 10:165–183 PubMedCrossRefGoogle Scholar
  12. Kelly DD (1991) Sleep and dreaming. In: Kandel ER, Schwartz JH, Jessell TM (eds) Principles of neural science, 3rd edn. Prentice-Hall, Toronto, pp 792–804 Google Scholar
  13. Kitamura A, Marszalec W, Yeh JZ, Narahashi T (2002) Effects of halothane and propofol on excitatory and inhibitory synaptic transmission in rat cortical neurons. J Pharmacol 304(1):162–171 Google Scholar
  14. Kuizenga K, Kalkman CJ, Hennis PJ (1998) Quantitative electroencephalographic analysis of the biphasic concentration–effect relationship of propofol in surgical patients during extradural analgesia. Br J Anaesth 80:725–732 PubMedGoogle Scholar
  15. Kuizenga K, Proost JH, Wierda JMKH, Kalkman CJ (2001a) Predictability of processed electroencephalography effects on the basis of pharmacokinetic–pharmacodynamic modeling during repeated propofol infusions in patients with extradural analgesia. Anesthesiology 95:607–615 PubMedCrossRefGoogle Scholar
  16. Kuizenga K, Wierda JMKH, Kalkman CJ (2001b) Biphasic EEG changes in relation to loss of consciousness during induction with thiopental, propofol, etomidate, midazolam or sevoflurane. Br J Anaesth 86:354–360 PubMedCrossRefGoogle Scholar
  17. Liley DTJ, Bojak I (2005) Understanding the transition to seizure by modeling the epileptiform activity of general anesthetic agents. Clin Neurophysiol 22(5):300–313 Google Scholar
  18. Liley DTJ, Cadusch PJ, Wright JJ (1999) A continuum theory of electro-cortical activity. Neurocomputing 26–27:795–800 CrossRefGoogle Scholar
  19. Ludbrook GL, Upton RN, Grant C, Gray EC (1996) Brain and blood concentrations of propofol after rapid intravenous injection in sheep and their relationships to cerebral effects. Anaesth Intensive Care 24(4):445–452 PubMedGoogle Scholar
  20. Ludbrook GL, Upton RN, Grant C, Martinez A (1999) Prolonged dysequilibrium between blood and brain concentrations of propofol during infusions in sheep. Acta Anaesthesiol Scand 43(2):206–211 PubMedCrossRefGoogle Scholar
  21. Molaee-Ardekani B, Senhadji L, Shamsollahi MB, Vosoughi-Vahdat B, Wodey E (2007) Brain activity modeling in general anesthesia: enhancing local mean-field models using a slow adaptive firing rate. Phys Rev E 76(4):041911 CrossRefGoogle Scholar
  22. Olofsen E, Sleigh JW, Dahan A (2008) Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. Br J Anaesth 101(6):810–821 PubMedCrossRefGoogle Scholar
  23. Phillips AJK, Robinson PA (2007) A quantitative model of sleep-wake dynamics based on the physiology of the brainstem ascending arousal system. J Biol Rhythms 22(2):167–179 PubMedCrossRefGoogle Scholar
  24. Rennie CJ, Wright JJ, Robinson PA (2000) Mechanisms for cortical electrical activity and emergence of gamma rhythm. J Theor Biol 205:17–35 PubMedCrossRefGoogle Scholar
  25. Robinson PA, Rennie CJ, Wright JJ (1997) Propagation and stability of waves of electrical activity in the cerebral cortex. Phys Rev E 56:826–840 CrossRefGoogle Scholar
  26. Robinson PA, Rennie CJ, Wright JJ, Bourke PD (1998) Steady states and global dynamics of electrical activity in the cerebral cortex. Phys Rev E 58:3557–3571 CrossRefGoogle Scholar
  27. Robinson P, Rennie C, Phillips A, Kim J, Roberts J (2010) Phase transitions in physiologically-based multiscale mean-field brain models. In: Steyn-Ross DA, Steyn-Ross ML (eds) Modeling phase transitions in the brain. Springer series in computational neuroscience, vol 4. Springer, Berlin, pp 179–201 CrossRefGoogle Scholar
  28. Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Liley DTJ (1999) Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: evidence for a general anesthetic-induced phase transition. Phys Rev E 60:7299–7311 CrossRefGoogle Scholar
  29. Steyn-Ross DA, Steyn-Ross ML, Wilcocks LC, Sleigh JW (2001a) Toward a theory of the general anesthetic-induced phase transition of the cerebral cortex: II. Stochastic numerical simulations, spectral entropy, and correlations. Phys Rev E 64:011918 CrossRefGoogle Scholar
  30. Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Wilcocks LC (2001b) Toward a theory of the general anesthetic-induced phase transition of the cerebral cortex: I. A statistical mechanics analogy. Phys Rev E 64:011917 CrossRefGoogle Scholar
  31. Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Whiting DR (2003) Theoretical predictions for spatial covariance of the EEG signal during the anesthetic-induced phase transition: increased correlation length and emergence of self-organization. Phys Rev E 68:021902 CrossRefGoogle Scholar
  32. Steyn-Ross ML, Steyn-Ross DA, Sleigh JW (2004) Modelling general anaesthesia as a first-order phase transition in the cortex. Prog Biophys Mol Biol 85:369–385 PubMedCrossRefGoogle Scholar
  33. Steyn-Ross DA, Steyn-Ross ML, Sleigh JW, Wilson MT, Gillies IP, Wright JJ (2005) The sleep cycle modelled as a cortical phase transition. J Biol Phys 31:547–569 CrossRefGoogle Scholar
  34. Steyn-Ross DA, Steyn-Ross ML, Wilson MT, Sleigh JW (2006) White-noise susceptibility and critical slowing in neurons near spiking threshold. Phys Rev E 74:051920 CrossRefGoogle Scholar
  35. Steyn-Ross ML, Steyn-Ross DA, Wilson MT, Sleigh JW (2007) Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise. Phys Rev E 76:011916 CrossRefGoogle Scholar
  36. Steyn-Ross ML, Steyn-Ross DA, Wilson MT, Sleigh JW (2009) Modeling brain activation patterns for the default and cognitive states. NeuroImage 45:298–311 PubMedCrossRefGoogle Scholar
  37. Steyn-Ross DA, Steyn-Ross ML, Wilson MT, Sleigh JW (2010a) Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles. In: Steyn-Ross DA, Steyn-Ross ML (eds) Modeling phase transitions in the brain. Springer series in computational neuroscience, vol 4. Springer, Berlin, pp 1–26 CrossRefGoogle Scholar
  38. Steyn-Ross ML, Steyn-Ross DA, Wilson MT, Sleigh JW (2010b) Cortical patterns and gamma genesis are modulated by reversal potentials and gap-junction diffusion. In: Steyn-Ross DA, Steyn-Ross ML (eds) Modeling phase transitions in the brain. Springer series in computational neuroscience, vol 4. Springer, Berlin, pp 271–299 CrossRefGoogle Scholar
  39. Strogatz SH (2000) Nonlinear dynamics and chaos. Westview Press, Cambridge Google Scholar
  40. Upton RN, Mather LE, Runciman WB, Nancarrow C, Carapetis RJ (1988) The use of mass balance principles to describe regional drug distribution and elimination. J Pharmacokinet Biopharm 16(1):13–29 PubMedCrossRefGoogle Scholar
  41. Voss LJ, Ludbrook G, Grant C, Upton R, Sleigh JW (2007) A comparison of pharmacokinetic/pharmacodynamic versus mass-balance measurement of brain concentrations of intravenous anesthetics in sheep. Anesth Analg 104(6):1440–1446 PubMedCrossRefGoogle Scholar
  42. Wilson MT, Steyn-Ross ML, Steyn-Ross DA, Sleigh JW (2005) Predictions and simulations of cortical dynamics during natural sleep using a continuum approach. Phys Rev E 72:051910 CrossRefGoogle Scholar
  43. Wilson MT, Sleigh JW, Steyn-Ross DA, Steyn-Ross ML (2006) General anesthetic-induced seizures can be explained by a mean-field model of cortical dynamics. Anesthesiology 104:588–593 PubMedCrossRefGoogle Scholar
  44. Wilson MT, Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Gillies IP, Hailstone DJ (2010) What can a mean-field model tell us about the dynamics of the cortex. In: Steyn-Ross DA, Steyn-Ross ML (eds) Modeling phase transitions in the brain. Springer series in computational neuroscience, vol 4. Springer, Berlin, pp 223–242 CrossRefGoogle Scholar
  45. Wright JJ, Robinson PA, Rennie CJ, Gordon E, Bourke PD, Chapman CL, Hawthorn N, Lees GJ, Alexander D (2001) Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability. Biosystems 73:71–88 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • D. A. Steyn-Ross
    • 1
    Email author
  • M. L. Steyn-Ross
    • 1
  • J. W. Sleigh
    • 2
  • M. T. Wilson
    • 1
  1. 1.Department of EngineeringUniversity of WaikatoHamiltonNew Zealand
  2. 2.Waikato Clinical SchoolUniversity of Auckland, Waikato HospitalHamiltonNew Zealand

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