Abstract
Previous studies have demonstrated that the brain has an intrinsic resistance to changes in arousal state. This resistance is most easily measured at the population level in the setting of general anesthesia and has been termed neural inertia. To date, no study has attempted to determine neural inertia in individuals. We hypothesize that individuals with markedly increased or decreased neural inertia might be at increased risk for complications related to state transitions, from awareness under anesthesia, to delayed emergence or confusion/impairment after emergence. Hence, an improved theoretical and practical understanding of neural inertia may have the potential to identify individuals at increased risk for these complications. This study was designed to explicitly measure neural inertia in individuals and empirically test the stochastic model of neural inertia using spectral analysis of the murine EEG. EEG was measured after induction of and emergence from isoflurane administered near the EC50 dose for loss of righting in genetically inbred mice on a timescale that minimizes pharmacokinetic confounds. Neural inertia was assessed by employing classifiers constructed using linear discriminant or supervised machine learning methods to determine if features of EEG spectra reliably demonstrate path dependence at steady-state anesthesia. We also report the existence of neural inertia at the individual level, as well as the population level, and that neural inertia decreases over time, providing direct empirical evidence supporting the predictions of the stochastic model of neural inertia.
Highlights
The factors determining whether an individual experiences complications during transitions between the awake and anesthetized states, including awareness under general anesthesia, delayed emergence, or delirium after anesthetic emergence, remain poorly understood
Using a novel approach at steady-state population EC50 for isoflurane, we show that differences in EEG spectra solely dependent on a history of awake or deeply anesthetized states, consistent with neural inertia, occur but are present on a timescale unrelated to potential confounding effects of pharmacokinetics
We demonstrate that classifier efficacy decreases over time, consistent with a decrease in neural inertia, providing the first experimental evidence in support of the stochastic model of neural inertia proposed by Proekt and Hudson
Summary
The factors determining whether an individual experiences complications during transitions between the awake and anesthetized states, including awareness under general anesthesia, delayed emergence, or delirium after anesthetic emergence, remain poorly understood. Neural inertia has been shown to be subject to genetic control (Joiner et al, 2013), making it a promising avenue for investigation into interindividual variation in anesthetic response Despite this potential for individual differences in neural inertia contributing to variable anesthetic effects, many of the investigations of neural inertia have focused on the population level, leaving the question of interindividual variation incompletely explored. It is critical to demonstrate and analyze neural inertia in the absence of pharmacokinetic effects Such confounders have been the subject of some controversy (Colin et al, 2018; Proekt and Kelz, 2018, 2021; Sepúlveda et al, 2019; McKinstry-Wu et al, 2020); an experimental model employing extended steady state anesthesia may circumvent those complications. By approaching the population EC50 either from a more anesthetized or awake state, we present a novel method to evaluate neural inertia that minimizes potential confounding effects of both pharmacokinetics and drug-concentration specific effects, as the only difference between induction and emergence will be the history of the initial condition, rather than the final drug concentration
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