Access provided by Autonomous University of Puebla. Download reference work entry PDF
Definition
General anesthesia is a reversible, drug-induced state of unconsciousness characterized by lack of awareness of surroundings, lack of responsiveness to painful stimuli (nociception), and inability to form memories (amnesia). The change in brain state from wakeful to unconscious produces alterations in cortical electrical activity that can be monitored with electrodes placed on the scalp (electroencephalogram (EEG)) or on the surface of the cortex (electrocorticogram (ECoG)). The goal of neural modelers is to develop equations that describe the gross behavior of spatially averaged populations of neurons during both induction of and recovery from general anesthesia.
Detailed Description
Classes of General Anesthesia
There are two broad classes of anesthetic drugs: inductive agents (such as propofol, etomidate, isoflurane) that produce a slowed sleeplike EEG and dissociative agents (e.g., ketamine, nitrous oxide) that induce a dissociated state with an activated EEG similar to that of REM sleep.
Most commonly used intravenous and volatile agents – such as propofol or sevoflurane – boost inhibition by increasing the influx of chloride ions at gamma-aminobutyric acid (GABA) receptors on postsynaptic membranes (Weir 2006), causing the postsynaptic neuron to become hyperpolarized. In contrast, dissociative drugs are believed to disrupt excitatory synaptic transmission. In both cases, the excitatory – inhibitory balance required for normal brain function has been shifted to favor inhibition.
The Induction: Recovery Trajectory
At low concentrations, most GABAergic agents (e.g., propofol, sevoflurane, etomidate) cause a paradoxical boost in cortical activity (called the “biphasic effect”) across most EEG frequency bands (Kuizenga et al. 2001), with the biphasic peak appearing first in the high beta frequencies (24–28 Hz), then sliding smoothly towards lower frequencies in time (e.g., see Fig. 3 of Koskinen et al. (2005)). With further increase in concentration, the EEG slows as large-amplitude delta-band oscillations (1–4 Hz) become dominant, then changes to an intermittent burst–suppression pattern (bursting activity alternating with relative silence), and finally collapses into a flat-line trace at the deepest levels of comatose anesthesia.
This sequence is reversed as the anesthetic drug is eliminated naturally from the body, allowing the patient to return to consciousness. However, the fact that the recovery of responsiveness generally occurs at a lower drug concentration (as measured in the blood) than that required to induce unresponsiveness suggests a hysteresis separation between induction and recovery trajectories. Part of this hysteresis can be explained in terms of the time required for the drug to diffuse across the blood–brain barrier (Voss et al. 2007) and so can be compensated using pharmacokinetics models (Roberts 2007), but such compensations are typically only partially successful (Ludbrook et al. 1999; Coppens et al. 2010). The remaining hysteresis may be a consequence of a recently proposed “neural inertia” that resists transitions between conscious and unconscious states (Friedman et al. 2010); such distinct induction/recovery paths arise naturally if the brain has access to multiple steady states as suggested by the modeling of Steyn-Ross et al. (1999, 2004).
Cellular Effects of General Anesthetic Drugs
Studies of propofol, halothane, and isoflurane have shown that, at drug concentrations rendering human subjects unresponsive, cerebral blood flow and metabolism are reduced by about 50% (Antkowiak 2002) as a result of global reductions in cortical activity. This is consistent both with in vivo investigations in rat cortex – where sedative-level concentrations were found to suppress neural firing rates by 50–70% (Gaese and Ostwald 2001) – and with cultured brain-slice studies in which low concentrations of general anesthetics (GABAergic agonists propofol, halothane, isoflurane, enflurane, sevoflurane, etomidate, ethanol, and pentobarbital and the non-GABAergic agent ketamine) significantly decreased mean firing rates (Antkowiak 2002).
All anesthetic drugs influence cellular function in a number of different ways, but the major mechanism for GABAergic suppression of firing rates is believed to be the prolongation of the opening of chloride channels on the postsynaptic neuron, thus causing a substantial increase in negative charge transfer (by a factor of 2–4 times control at clinically relevant concentrations (Kitamura et al. 2003; Banks and Pearce 1999)) during the inhibitory postsynaptic current (IPSC) pulse.
Dissociative drugs reduce excitatory transmission by blocking N-methyl-d-aspartate (NMDA) glutamate channels, which probably has a significant role in producing the characteristic dissociated anesthetic state (Petrenko et al. 2013); however, these drugs also have other effects such as inhibition of hyperpolarization-activated cyclic nucleotide-gated (HCN1) channels (Chen et al. 2009) or increased potassium channel opening (Gruss et al. 2004).
Modeling Anesthetic Effects
The challenge for anesthesia modelers is to bridge the scales from the microscopic cellular drug effects to the consequent macroscopic population behaviors detected with scalp or cortical electrodes. By considering spatially averaged (“mean-field”) properties of cortical tissue, we can avoid the need (and computational expense) of attempting to explicitly represent myriads of individual neurons (as is done in neural networks). There is a steadily growing interest in applying mean-field methods to the challenge of understanding anesthesia; see Foster et al. (2008) and Steyn-Ross et al. (2011) for reviews.
The notion of neural fields dates from foundation work by Wilson and Cowan (1972) that modeled the brain as homogenous populations of excitatory and inhibitory neurons. The first attempt at modeling propofol anesthesia by Steyn-Ross et al. (1999) incorporated prolongation of inhibitory response into the mean-field neural model of Liley et al. (1999); it predicted the possibility of multiple steady states with distinct first-order phase transitions between activated (“conscious”) and inactivated (“unconscious”) states and provided a possible explanation for the hysteretically separated biphasic power surges observed at loss and recovery of consciousness (Kuizenga et al. 2001).
Subsequent work by Bojak and Liley (2005) on isoflurane anesthesia showed that, for suitable choices of cortical parameters, a smooth descent into unconsciousness can also generate a biphasic drug response. Using an alternative mean-field model, Hutt and colleagues (Hutt and Schimansky-Geier 2008; Hutt and Longtin 2010) predicted that biphasic power surges can be expected for both the bistable (jump transition) and monostable (smooth) inductions of anesthesia.
General anesthetic agents are widely used to treat seizures, but paradoxically, some anesthetics (e.g., enflurane) can also provoke cortical seizures when the patient is deeply anesthetized. Liley and Bojak (2005) and Wilson et al. (2006) used mean-field modeling to show that subtle changes in the shape and duration of the drug-induced inhibitory postsynaptic response can explain why enflurane, but not isoflurane, is seizurogenic.
An important part of general anesthesia is the suppression of noxious stimuli. A practical index of antinociception has been developed from a mean-field model (Liley et al. 2010) that informed construction of an autoregressive – moving-average (ARMA) noise-driven filter whose output approximates the scalp-recorded EEG. The mean filter frequency tracks the level of propofol-induced hypnosis (“cortical state”), while the decrease in required noise intensity (“cortical input”) tracks the concentration of a coadministered analgesic agent (remifentanil). This computed “cortical input” signal is presumed to be a measure of cortical stimulus, both noxious and normal, entering from the thalamus, and potentially allows differentiation between hypnotic and analgesic drug effects.
The unconscious state of anesthesia and of deepest natural sleep are both characterized by large-amplitude, slow (0.5–4 Hz) delta waves of EEG activity. The source of these slow waves is unknown but is generally supposed to originate from gradual alternations in depolarizing and hyperpolarizing ionic currents. By introducing a slow ionic gating variable into a mean-field model for desflurane anesthesia, Molaee-Ardekani et al. (2007) demonstrated emergence of realistic slow waves. A quite different slow-wave mechanism has been proposed by Steyn-Ross et al. (2013): if inhibitory gap junctions are included in the two-dimensional cortical sheet, then a Turing (pattern-forming) instability can interact with a weakly damped low-frequency Hopf instability to produce turbulent slow-wave activity across the cortex. Anesthetic-induced closure of inhibitory gap junctions (Wentlandt et al. 2006) is predicted to weaken the Turing instability in favor of the Hopf oscillation.
There has been interest in modeling some of the specific details of EEG spectral changes caused by various general anesthetic drugs, for example, the displacement in alpha peak frequency induced by ketamine (Bojak et al. 2013) or propofol (Hindriks and van Putten 2012; Hutt 2013) and the burst–suppression pattern of deep anesthesia (Liley and Walsh 2013).
Increasingly there has been a realization that general anesthesia may disrupt neuronal networks in an anatomically specific fashion (Kuhlmann et al. 2013; Lee et al. 2013) and that the current homogenous and isotropic neuronal population models might need to include aspects of network topology. This has led to attempts to link EEG patterns probabilistically with underlying anesthetic effects on inhibitory and excitatory neuronal groups – this should provide a quantitative basis for the estimation of model parameters. At an abstract level, dynamic causal-modeling methods have been employed (Moran et al. 2011; Boly et al. 2012), but a more direct Bayesian approach – which has been used for natural sleep (Dadok et al. 2013) – could be applied to anesthesia EEG.
Cross-References
References
Antkowiak B (2002) In vitro networks: cortical mechanisms of anaesthetic action. Br J Anaesth 89(1):102–111
Banks MI, Pearce RA (1999) Dual actions of volatile anesthetics on GABA(a) IPSCs: dissociation of blocking and prolonging effects. Anesthesiology 90(1):120–134
Bojak I, Liley DT (2005) Modeling the effects of anesthesia on the electroencephalogram. Phys Rev E 71(4 Pt 1):041902. http://www.ncbi.nlm.nih.gov/pubmed/15903696
Bojak I, Day HC, Liley DT (2013) Ketamine, propofol, and the EEG: a neural field analysis of HCN1-mediated interactions. Front Comput Neurosci 7:22. https://doi.org/10.3389/fncom.2013.00022. http://www.ncbi.nlm.nih.gov/pubmed/23576979
Boly M, Moran R, Murphy M, Boveroux P, Bruno MA, Noirhomme Q, Ledoux D, Bonhomme V, Brichant JF, Tononi G, Laureys S, Friston K (2012) Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness. J Neurosci 32(20):7082–7090. https://doi.org/10.1523/JNEUROSCI.3769-11.2012
Chen X, Shu S, Bayliss DA (2009) HCN1 channel subunits are a molecular substrate for hypnotic actions of ketamine. J Neurosci 29(3):600–609. https://doi.org/10.1523/JNEUROSCI.3481-08.2009
Coppens M, Van Limmen JGM, Schnider T, Wyler B, Bonte S, Dewaele F, Struys MMRF, Vereecke HEM (2010) Study of the time course of the clinical effect of propofol compared with the time course of the predicted effect-site concentration: performance of three pharmacokinetic-dynamic models. Br J Anaesth 104(4):452–458. https://doi.org/10.1093/bja/aeq028
Dadok VM, Kirsch HE, Sleigh JW, Lopour BA, Szeri AJ (2013) A probabilistic framework for a physiological representation of dynamically evolving sleep state. J Comput Neurosci. https://doi.org/10.1007/s10827-013-0489-x
Foster BL, Bojak I, Liley DTJ (2008) Population based models of cortical drug response: insights from anaesthesia. Cogn Neurodyn 2(4):283–296. https://doi.org/10.1007/s11571-008-9063-z
Friedman EB, Sun Y, Moore JT, Hung HT, Meng QC, Perera P, Joiner WJ, Thomas SA, Eckenho RG, Sehgal A, Kelz MB (2010) A conserved behavioral state barrier impedes transitions between anesthetic-induced unconsciousness and wakefulness: evidence for neural inertia. PLoS One 5(7):e11903. https://doi.org/10.1371/journal.pone.0011903
Gaese BH, Ostwald J (2001) Anesthesia changes frequency tuning of neurons in the rat primary auditory cortex. J Neurophysiol 86(2):1062–1066
Gruss M, Bushell TJ, Bright DP, Lieb WR, Mathie A, Franks NP (2004) Two-pore-domain K+ channels are a novel target for the anesthetic gases xenon, nitrous oxide, and cyclopropane. Mol Pharmacol 65(2):443–452. https://doi.org/10.1124/mol.65.2.443
Hindriks R, van Putten MJAM (2012) Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms. NeuroImage 60(4):2323–2334. https://doi.org/10.1016/j.neuroimage.2012.02.042
Hutt A (2013) The anesthetic propofol shifts the frequency of maximum spectral power in EEG during general anesthesia: analytical insights from a linear model. Front Comput Neurosci 7:2. https://doi.org/10.3389/fncom.2013.00002. http://www.ncbi.nlm.nih.gov/pubmed/23386826
Hutt A, Longtin A (2010) Effects of the anesthetic agent propofol on neural populations. Cogn Neurodyn 4(1):37–59. https://doi.org/10.1007/s11571-009-9092-2
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. https://doi.org/10.1007/s10867-008-9065-4
Kitamura A, Marszalec W, Yeh JZ, Narahashi T (2003) Effects of halothane and propofol on excitatory and inhibitory synaptic transmission in rat cortical neurons. J Pharmacol Exp Ther 304(1):162–171. https://doi.org/10.1124/jpet.102.043273
Koskinen M, Mustola S, Seppänen T (2005) Relation of EEG spectrum progression to loss of responsiveness during induction of anesthesia with propofol. Clin Neurophysiol 116(9):2069–2076. https://doi.org/10.1016/j.clinph.2005.06.004
Kuhlmann L, Foster BL, Liley DT (2013) Modulation of functional EEG networks by the NMDA antagonist nitrous oxide. PLoS One 8(2):e56434. https://doi.org/10.1371/journal.pone.0056434. http://www.ncbi.nlm.nih.gov/pubmed/23457568
Kuizenga K, Wierda JM, Kalkman CJ (2001) Biphasic EEG changes in relation to loss of consciousness during induction with thiopental, propofol, etomidate, midazolam or sevoflurane. Br J Anaesth 86(3):354–360
Lee U, Ku S, Noh G, Baek S, Choi B, Mashour GA (2013) Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane. Anesthesiology 118(6):1264–1275. https://doi.org/10.1097/ALN.0b013e31829103f5
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
Liley DT, Walsh M (2013) The mesoscopic modeling of burst suppression during anesthesia. Front Comput Neurosci 7:46. https://doi.org/10.3389/fncom.2013.00046. http://www.ncbi.nlm.nih.gov/pubmed/23641211
Liley DTJ, Cadusch PJ, Wright JJ (1999) A continuum theory of electro-cortical activity. Neurocomputing 26–27:795–800
Liley DT, Sinclair NC, Lipping T, Heyse B, Vereecke HE, Struys MM (2010) Propofol and remifentanil differentially modulate frontal electroencephalographic activity. Anesthesiology 113(2):292–304. https://doi.org/10.1097/ALN.0b013e3181e3d8a6
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
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 Pt 1):041911. http://www.ncbi.nlm.nih.gov/pubmed/17995030
Moran RJ, Jung F, Kumagai T, Endepols H, Graf R, Dolan RJ, Friston KJ, Stephan KE, Tittge-meyer M (2011) Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents. PLoS One 6(8):e22790. https://doi.org/10.1371/journal.pone.0022790. http://www.ncbi.nlm.nih.gov/pubmed/21829652
Petrenko AB, Yamakura T, Sakimura K, Baba H (2013) Defining the role of NMDA receptors in anesthesia: are we there yet? Eur J Pharmacol 723C:29–37. https://doi.org/10.1016/j.ejphar.2013.11.039
Roberts F (2007) Pharmacokinetics and anaesthesia. Contin Educ Anaesth Crit Care Pain 7(1):25–29. https://doi.org/10.1093/bjaceaccp/mkl058
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(6 Pt B):7299–7311. http://www.ncbi.nlm.nih.gov/pubmed/11970675
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(2–3):369–385. https://doi.org/10.1016/j.pbiomolbio.2004.02.001
Steyn-Ross DA, Steyn-Ross ML, Sleigh JW, Wilson MT (2011) Progress in modeling EEG effects of general anesthesia: biphasic response and hysteresis. In: Hutt A (ed) Sleep and anesthesia: neural correlates in theory and experiment, chapter 8, Springer series in computational neuroscience, vol 15. Springer, New York, pp 167–194. https://doi.org/10.1007/978-1-4614-0173-5n8
Steyn-Ross ML, Steyn-Ross DA, Sleigh JW (2013) Interacting Turing-Hopf instabilities drive symmetry-breaking transitions in a mean-field model of the cortex: a mechanism for the slow oscillation. Phys Rev X 3(2):021005. https://doi.org/10.1103/PhysRevX.3.021005. http://link.aps.org/doi/10.1103/PhysRevX.3.021005
Voss LJ, Ludbrook G, Grant C, Upton R, Sleigh JW (2007) A comparison of pharmacokinetic/pharmacodynamic versus mass-balance measurement of brain concentrations of intra-venous anesthetics in sheep. Anesth Analg 104(6):1440–1446. https://doi.org/10.1213/01.ane.0000263274.62303.1a
Weir CJ (2006) The molecular mechanisms of general anaesthesia: dissecting the GABAA receptor. Contin Educ Anaesth Crit Care Pain 6(2):49–53. https://doi.org/10.1093/bjaceaccp/mki068
Wentlandt K, Samoilova M, Carlen PL, El Beheiry H (2006) General anesthetics inhibit gap junction communication in cultured organotypic hippocampal slices. Anesth Analg 102(6):1692–1698. https://doi.org/10.1213/01.ane.0000202472.41103.78
Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1–24
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Steyn-Ross, D.A., Steyn-Ross, M., Sleigh, J. (2022). Anesthesia, Neural Population Models of. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-1006-0_52
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1006-0_52
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-0716-1004-6
Online ISBN: 978-1-0716-1006-0
eBook Packages: Biomedical and Life SciencesReference Module Biomedical and Life Sciences