Stochastic nature of neural inertia

UnCheol Lee

Stochastic nature of neural inertia

UnCheol Lee

2018, British Journal of Anaesthesia

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A randomized trial comparing the Ambu (R) Aura-i with the air-Q intubating laryngeal airway as conduits for tracheal intubation in children.

Editorials 19. Jagannathan N, Ramsey MA, White MC, Sohn L. An update on newer pediatric supraglottic airways with recommendations for clinical use. Pediatr Anesth 2015; 25: 334e45 20. Jagannathan N, Sohn LE, Sawardekar A, et al. A randomized trial comparing the Ambu (R) Aura-i with the air-Q intubating laryngeal airway as conduits for - 7 tracheal intubation in children. Pediatr Anesth 2012; 22: 1197e204 21. Jagannathan N, Sequera-Ramos L, Sohn L, Wallis B, Shertzer A, Schaldenbrand K. Elective use of supraglottic airway devices for primary airway management in children with difficult airways. Br J Anaesth 2014; 112: 742e8 British Journal of Anaesthesia, 121 (1): 7e8 (2018) doi: 10.1016/j.bja.2018.04.018 Advance Access Publication Date: 18 May 2018 © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. Stochastic nature of neural inertia U. Lee* and G. A. Mashour Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA *Corresponding author. E-mail: [email protected] For the past decade, neuroscientists studying anaesthetic state transitions have shifted their attention from what is happening in the brain during the induction of general anaesthesia to what is happening in the brain during emergence from general anaesthesia. Several lines of evidence suggest that the hysteresis of these two processes (i.e. the distinction in the forward and reverse pathways of anaesthetic-induced unconsciousness) might not be trivially reducible to pharmacokinetics.1,2 Although controversy exists,3,4 the possibility of an intrinsic property of resistance to behavioural state changes has been formalised as the concept of neural inertia. But, what governs this ostensible resistance, which is manifested as the requirement of higher anaesthetic concentrations to induce unconsciousness compared with those required to maintain unconsciousness? Although there could potentially be a distinct neurobiology between induction and emergence, in this issue of the British Journal of Anaesthesia, Proekt and Hudson5 suggest that the appearance of neural inertia is driven by the degree of randomness in the system. Using a model system, the authors tested the hypothesis that neural inertia is a consequence of the stochastic switching between the waking and anesthetised states. This approach was motivated by empirical observations and the well-known fact that bistable systems are generally associated with the phenomenon of hysteresis. In terms of dynamics, bistability means the system has two stable states of equilibrium. In terms of potential energy, bistable systems have two local minima separated by a peak. At rest, the system will be in one of the minimum equilibrium positions, which corresponds to the state of lowest energy. A system can transition from one state of minimal energy to the other if it is given enough activation energy (or noise) to cross over the barrier. In some parameter range, the two states coexist, which has been referred to as alternative stable states. Bistability is widely used in engineering to store binary data in digital electronic devices. The bistable model has been studied extensively in the context of multistable perception, a form DOI of original article: doi: 10.1016/j.bja.2018.02.035. of perceptual phenomena, in which there are unpredictable sequences of spontaneous subjective changes in visual, auditory, and olfactory perceptions.6 For instance, when observers are presented with an ambiguous stimulus that has two distinct interpretations (e.g. a Necker cube), their perception alternates over time between the different possible percepts in an irregular manner. Bistability has also been used to explain the neural regulation of sleep and wake state transitions.7 The sleepewake state transition model, often referred to as a ‘flip-flop’ switch, consists of mutually inhibitory synaptic projections between sleep- and wake-promoting neuronal populations. It has been proposed that this toggling property is why the brain cannot be both asleep and awake. The authors leveraged this flip-flop switch framework and modified it for the study of stochastic transitions between two stable states: wakefulness and general anaesthesia. To model the stochastic transition between two states, the authors used an energy landscape model, specifically, a potential energy function adapted from Moreno-Bote and colleagues.6 In the energy landscape, the two minima correspond to activated wake-promoting neurons or activated sleeppromoting neurons. To study the effect of noise, which plays an important role that enables the system to cross over from one attractor state to another, the authors used Brownian motion on an energy landscape and generalised it with Markov processes. The authors took the Markov process for the sleepewake state transitions and cleverly modified it as an anaesthesiaewake state transition model, replacing the probability of remaining in the waking and sleep states with a standard Hill equation applicable to anaesthetics. With this modification, they were able to modulate the anaesthetic concentration as a control parameter and, thus, study numerous features of neural inertia in terms of anaesthesiaewake state transitions. The authors demonstrated that anaesthetic state transitions, modelled through a diffusion process on a two-well energy landscape, give rise to hysteresis. Furthermore, the hysteresis can be diminished by increasing the noise in the system on a timescale that suggests it is independent of pharmacokinetics. Rather, it originates from the bistability of the 8 - Editorials wake and anesthetised states, and the neuronal populations that mutually inhibit them. The investigators found that the timescale is a function of noise, with noisier systems associated with a shorter collapse time for hysteresis. The extension of the Markov process to 10 states (three representing awake and seven representing unconsciousness) explains the shallower and left-shifted doseeresponse curve for emergence relative to induction, and the large variability of emergence. The results provide an important implication about hysteresis and anaesthetic state transitions. Complex transitions between consciousness and unconsciousness could potentially be described with a simple and minimally modified bistable model. The findings point to a common underlying neural mechanism that governs various bistable dynamics, such as bistable perceptions, sleepewake switch, anaesthesiaewake transitions, etc., even though their timescales may be different. Bistable systems have been studied in the field of physics and mathematics under the auspices of phase transition for the past three decades.8,9 The fundamental mechanism of complex state transitions and their regulation has been investigated as a simplified, coupled two-pendulum problem. In that sense, the bistable model that Proekt and Hudson5 propose may be developed further to control the anaesthesiaewake state transition, for example, by applying well-established bifurcation and control theories derived from the study of phase transitions. For example, instead of adding an arbitrary number of states to represent anaesthesia, the system could be designed to fit empirical data with different types of instability. The finding of a specific timescale beyond which neural inertia is diminished is an important achievement of this study. However, the model prediction has yet to be tested empirically, giving rise to questions about how to measure the timescale empirically, how different the timescales are amongst anaesthetics, and how to acquire enough transitions between the states of wakefulness and anaesthesia to be statistically robust. Further work must be done before the implications of this elegant study make their way to the operating room. Declaration of interest The authors declare that they have no conflicts of interest. References 1. Friedman EB, Sun Y, Moore JT, et al. A conserved behavioral state barrier impedes transitions between anestheticinduced unconsciousness and wakefulness: evidence for neural inertia. PLoS One 2010; 5: e11903 2. Joiner WJ, Friedman EB, Hung HT, et al. Genetic and anatomical basis of the barrier separating wakefulness and anesthetic-induced unresponsiveness. PLoS Genet 2013; 9: e1003605 3. Kuizenga MH, Colin PJ, Reyntjens KMEM, et al. Test of neural inertia in humans during general anaesthesia. Br J Anaesth 2018; 120: 525e36 € dinger’s cat: anaesthetised and not! 4. Proekt A, Kelz M. Schro Br J Anaesth 2018; 120: 424e8 5. Proekt A, Hudson AE. A stochastic basis for neural inertia in emergence from general anesthesia. Br J Anaesth 2018; 121: 86e94 6. Moreno-Bote R, Rinzel J, Rubin N. Noise-induced alternations in an attractor network model of perceptual bistability. J Neurophysiol 2007; 98: 1125e39 7. Lu J, Sherman D, Devor M, Saper CB. A putative flip-flop switch for control of REM sleep. Nature 2006; 441: 589e94 8. Haken H, Kelso JAS, Bunz H. A theoretical model of phase transitions in human hand movements. Biol Cybern 1985; 51: 347e56 9. Kelso JAS. Dynamic patterns: the self organization of brain and behavior. Cambridge, MA: MIT Press; 1997 British Journal of Anaesthesia, 121 (1): 8e12 (2018) doi: 10.1016/j.bja.2018.03.003 Advance Access Publication Date: 13 April 2018 © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. Sugammadex-induced bradycardia and asystole: how great is the risk? J. M. Hunter1,* and M. Naguib2 1 Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK and 2Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA *Corresponding author. E-mail: [email protected] The molecular design of a cyclodextrin compound for the reversal of aminosteroidal neuromuscular blocking drugs by Bom and colleagues1 in 2002 was a completely new approach to anaesthetic practice. The structure of sugammadex, consisting of eight concentric glucopyranoside molecules, DOI of original article: doi: 10.1016/j.bja.2018.02.036. suggested an inert molecule albeit with eight negatively charged tails. These charged tails attract positively charged aminosteroidal neuromuscular blocking drugs to the molecule before the encapsulation of the muscle relaxant into the central core of sugammadex, where it is irreversibly fixed.2,3 As it is not acting to inhibit acetylcholinesterase, the concomitant use of an anticholinergic drug, such as glycopyrronium, is considered unnecessary with the use of sugammadex.2,3

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