Anesthesia is a state of reversable, controlled unconsciousness. It has enabled countless medical procedures. But it also serves as a tool for scientists to study how the brain regains consciousness after disruptions such as sleep, coma or medical procedures requiring general anesthesia.

It is still unclear how exactly the brain regains consciousness, and less so, why some patients do not recover normally after general anesthesia or fail to recover from brain injury. To find out more, Mashour et al. studied the patterns of reemerging consciousness and cognitive function in 30 healthy adults who underwent general anesthesia for three hours.

While the volunteers were under anesthesia, their brain activity was measured with an EEG; and their sleep-wake activity was measured before and after the experiment. Each participant took part in a series of cognitive tests designed to measure the reaction speed, memory and other functions before receiving anesthesia, right after the return of consciousness, and then every 30 minutes thereafter. Thirty healthy volunteers who did not have anesthesia also completed the scans and tests as a comparison group.

The experiments showed that certain normal EEG patterns resumed just before a person wakes up from anesthesia. The return of thinking abilities was an extended, multistep process, but volunteers recovered their cognitive abilities to nearly the same level as the volunteers within three hours of being deeply anesthetized. Mashour et al. also unexpectedly found that abstract problem-solving resumes early in the process, while other functions such as reaction time and attention took longer to recover. This makes sense from an evolutionary perspective. Sleep leaves individuals vulnerable. Quick evaluation and decision-making skills would be key to respond to a threat upon waking.

The experiments confirm that the front of the brain, which handles thinking and decision-making, was especially active around the time of recovery. This suggests that therapies targeting this part of the brain may help people who experience loss of consciousness after a brain injury or have difficulties waking up after anesthesia. Moreover, disorders of cognition, such as delirium, in the days following surgery may be caused by factors other than the lingering effects of anesthetic drugs on the brain.

The recovery of neurocognitive function after brain network perturbations such as sleep, general anesthesia, or disorders of consciousness is of both scientific and clinical importance. Scientifically, characterizing recovery processes after such perturbations might provide insight into the more general mechanisms by which consciousness and cognition are reconstituted after major network disruptions. The ability to recover cognitive function quickly after sleep, for example, likely confers a natural selection advantage. Moreover, understanding which brain functions are most resilient to perturbation could inform evolutionary neurobiology (Mashour and Alkire, 2013; Kelz and Mashour, 2019). Clinically, understanding the specific recovery patterns after pathologic states of unconsciousness could inform prognosis or therapeutic strategies. However, it is challenging to characterize differential cognitive recovery after sleep because of the rapidity of the process, whereas it can be impossible in pathologic states because of the unpredictable recovery. General anesthesia, by contrast, represents a controlled and reproducible method by which to perturb consciousness and cognition that is also amenable to systematic observations of the recovery process. Studying recovery of cognition after general anesthesia in humans is also of particular importance because animal studies suggest that general anesthetics have the potential to immediately and persistently impair cognition in the post-anesthetic period (Culley et al., 2004; Valentim et al., 2008; Carr et al., 2011; Callaway et al., 2012; Zurek et al., 2012; Jevtovic-Todorovic et al., 2013; Zurek et al., 2014; Avidan and Evers, 2016; Jiang et al., 2017), creating a potential public health concern for the hundreds of millions of surgical patients undergoing general anesthesia each year (Weiser et al., 2015).

To improve scientific understanding of recovery of consciousness and cognition after anesthetic-induced unconsciousness, we studied 30 healthy volunteers at three centers who were administered deep general anesthesia using isoflurane for 3 hr, with cognitive testing conducted at pre-anesthetic baseline as well as every 30 min for 3 hr after return of consciousness (Figure 1). We hypothesized that post-anesthetic recovery would be an extended process rather than a single point, commencing with return of responsiveness and concluding with return of executive function. We hypothesized that executive function would be the last to recover because there is evidence that neurologic recovery from general anesthesia occurs in a caudal-to-rostral direction (Långsjö et al., 2012; Reshef et al., 2019), suggesting that anterior structures mediating higher cognition would have the most prolonged recovery. To assess differential return of cognitive functions after the major perturbation of deep anesthesia, we assessed a neurocognitive battery of tests (including the Psychomotor Vigilance Test (PVT), Motor Praxis (MP), Digit Symbol Substitution Test (DSST), fractal 2-Back (NBCK), Visual Object Learning Test (VOLT), and Abstract Matching (AM); Table 1) at baseline and at multiple time points after the 3-hr period of anesthetic exposure. Isoflurane anesthesia was chosen because of its heterogeneous molecular targets, which affect multiple neural systems, and because its slower offset compared to other anesthetics would allow us to observe differential recovery of function (Hemmings et al., 2019). A halogenated ether was chosen instead of propofol because of the greater diversity of molecular targets, which would be predicted to have a more profound effect on neural dynamics through multiple neurotransmitter receptor and channel systems. Clinical observations as well as clinical research comparing recovery from isoflurane vs. propofol support this interpretation (Pollard et al., 1994; Geng et al., 2017). The 3-hr duration of anesthesia was chosen based on clinical data related to recovery of surgical patients, the pharmacokinetics of isoflurane, and practical considerations for volunteers participating in day-long experiments.

Figure 1

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To control for the learning effects of repeated cognitive testing (Basner et al., 2018), we also recruited 30 healthy volunteers who, instead of receiving anesthesia, were engaged in wakeful behavior for 3 hr and then underwent equivalent cognitive testing at time points corresponding to the cohort that underwent general anesthesia (Figure 1). All participants received actigraphy watches to monitor sleep-wake activity before and after anesthesia or the control condition, and all participants had electroencephalographic recording throughout the experiment to assess cortical dynamics of relevance to consciousness and cognition, with techniques that have been used to assess information processing in specific brain regions (permutation entropy) as well as more complex spatiotemporal patterns across the cortex (Lempel-Ziv complexity). With the control group serving as a reference, the aims of the study were: (1) to determine whether emergence and cognitive recovery occurred at a point or, as we hypothesized, through a process; (2) assess the sequence of cognitive recovery following emergence from a prolonged state of unconsciousness with serial neurobehavioral assessments to test the hypothesis that higher executive functions reconstitute only after more primary functions; and (3) to measure correlated changes in cortical dynamics that might account for the hypothesized differential recovery of cognitive function.

Full methods of the Reconstructing Consciousness and Cognition (ReCCognition) study (clinicaltrials.gov NCT01911195) have been published and are freely available (Maier et al., 2017). The published protocol includes details related to participants, electroencephalography, and neurocognitive testing.

This is the most comprehensive and controlled study assessing cognitive recovery from the anesthetized state in healthy humans. Although there have been numerous studies of recovery from anesthetic-induced unconsciousness (Långsjö et al., 2012; Purdon et al., 2013; Chennu et al., 2016; Kim et al., 2018; Banks et al., 2020; Scheinin et al., 2021), our investigation was unique in that the length and depth of general anesthesia was consistent with surgical conditions but the experimental paradigm did not include any surgical intervention. This allowed us to study cognitive reconstitution after a major perturbation of arousal state while also informing the ongoing controversy regarding the effects of general anesthesia on human cognition. We have demonstrated that reconstitution of cognition after general anesthesia is a process that unfolds over time rather than a discrete, singular event. Cognitive recovery also follows a counterintuitive sequence. Executive function was found to recover early, whereas tests of processing speed, attention, and reaction time had a more prolonged recovery. Furthermore, EEG-based measures of cortical dynamics return to baseline just prior to the recovery of consciousness after general anesthesia, with entropy in frontal cortex statistically significantly higher than posterior cortex just after emergence. There were not, however, strong predictors of cognitive recovery based on EEG dynamics. Finally, sleep-wake activity patterns are essentially unperturbed after anesthetic exposure. These findings are summarized in Figure 6.

Figure 6

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As described in the introduction, our hypothesis was motivated by preliminary evidence in the literature that neurocognitive recovery after general anesthesia proceeds in a caudal-to-rostral direction (Långsjö et al., 2012; Reshef et al., 2019), which would imply that executive functions mediated by anterior cortical structures would be the last to return. However, the finding that performance on the abstract matching test displayed more robust recovery than other cognitive functions indicates that there is early engagement of prefrontal cortex after recovery of consciousness. Furthermore, EEG analysis demonstrated that both anterior and posterior cortices were active in association with recovery of consciousness, although there was no statistically significant relationship between neurophysiologic complexity in prefrontal cortex and subsequent executive function performance. Collectively, our results are aligned with the theoretical framework of global neuronal workspace theory, which attempts to describe the neurocognitive mechanisms of conscious access (Dehaene et al., 1998; Dehaene and Changeux, 2011; Mashour et al., 2020). Access consciousness refers to the broad cognitive availability of sensory information, which often is measured based on report or task performance (as in this study), and is distinct from phenomenal consciousness, which has been defined as experience itself and hypothesized to be independent of post-perceptual processing in anterior cortex (Block, 2005; Raccah et al., 2021). According to workspace theory, a reverberant network that includes both prefrontal and posterior parietal cortex serves to sustain, amplify, and make available neural representations, with a ‘broadcasting’ effect that allows access by otherwise non-conscious cognitive processors (Mashour et al., 2020). The active frontal-parietal networks at return of consciousness and early recovery of a cognitive test associated with dorsolateral prefrontal cortex are consistent with at least a partially reintegrated global neuronal workspace. However, a fully intact workspace requires exquisite temporal coordination. We hypothesize that the differential temporal recovery of cognitive functions is a manifestation of differential temporal dynamics in the neural structures and cognitive processors across the workspace, which appear to return to, or be asymptotically close to, baseline approximately 3 hr after recovery of consciousness. It is important to note that the first evidence of conscious access during recovery was hand squeezing to command, which is a simple, binary task unlike the more challenging cognitive battery with continuous performance measures such as accuracy and speed. Thus, hand squeezing to command would not be robust evidence for a full reintegration of the global neuronal workspace. Further work is needed to determine if global neuronal workspace theory is sufficient to describe the reconstitution of consciousness and cognition after a major perturbation such as a general anesthesia, or whether other cognitive theories need to be employed.

This study is consistent with our prior analysis of source-localized alpha oscillations and graph-theoretical variables such as global efficiency and modularity, which were identified in a subset of participants who had high-density EEG and which also recovered within 3 hr after emergence from general anesthesia (Blain-Moraes et al., 2017). From a clinical perspective, it is remarkable that within 3 hr of recovery after a prolonged and deep general anesthetic, participants were performing a variety of complex cognitive tasks with similar accuracy and speed in comparison to participants who had not been anesthetized. On a shorter timescale, both local and global dynamic measures of cortical activity returned to baseline levels just prior to the return of responsiveness after general anesthesia. On a longer timescale, there was no evidence of disrupted sleep in the days following anesthetic exposure compared to non-anesthetized controls. This latter finding is striking considering that actigraphy measures of sleep are sensitive to stress, low alcohol exposure, and even sedative effects of non-alcoholic beer (Mezick et al., 2009; Geoghegan et al., 2012; Franco et al., 2012). The data suggest that, in healthy humans, higher cognition and arousal states recover uneventfully after deep general anesthesia.

The recovery of cortical dynamics in anterior and posterior cortical areas just prior to the return of consciousness in our experimental paradigm should be considered in light of prior studies focused on emergence from general anesthesia. One study of healthy volunteers anesthetized with either propofol or dexmedetomidine and imaged using positron emission tomography found that those responsive to command exhibited activation of subcortical arousal centers with only limited frontal-parietal cortex involvement (Långsjö et al., 2012) a recent study with a more sophisticated pharmacologic dosing strategy has largely confirmed these findings (Scheinin et al., 2021). These data are consistent with a study of functional magnetic resonance imaging that demonstrated a transient activation of brainstem loci upon emergence from propofol sedation (Nir et al., 2019) as well as a positron emission tomography study revealing subcortical and posterior cortical activation in association with the reversal of propofol sedation using the acetylcholinesterase inhibitor physostigmine (Xie et al., 2011). However, it is important to note that all of these studies involved experimental conditions in which there was ongoing exposure to a sedative-hypnotic, coupled with pharmacological or behavioral stimulation. By contrast, our paradigm reflects spontaneous emergence with residual isoflurane levels predicted to be 1 to 4 orders of magnitude below those required for hypnosis, which likely accounts for evidence of the robust return of cortical dynamics. However, it is worth noting that—in addition to the subcortical sites identified in human and animal studies (Kelz et al., 2019) —the prefrontal cortex might play a critical role in the control of arousal states of relevance to general anesthesia. One animal study demonstrated that cholinergic stimulation of medial prefrontal cortex, but not two sites in posterior parietal cortex, was sufficient to reverse the anesthetized state despite continuous administration of clinically relevant concentrations of sevoflurane (Pal et al., 2018). Thus, the prefrontal cortex might play a critical role in recovery of both consciousness (medial prefrontal) and cognition (dorsolateral prefrontal).

Strengths of this study include: (1) surgically relevant anesthetic concentrations and duration of anesthetic exposure, in the absence of the confound of surgery itself; (2) multicenter design with substantially more participants than are typically found in studies of anesthetic-induced unconsciousness; (3) a non-anesthetized population to control for learning effects of cognitive assessment as well as comparison of sleep-wake patterns; and (4) complement of neurophysiological, cognitive, and behavioral measures on different time scales. Limitations of this study that constrain interpretation include: (1) young healthy population that precludes extrapolation of findings to older, younger, or sicker surgical patients encountered in clinical care; (2) only one anesthetic regimen tested, albeit a clinically common one, with unclear relevance to other perturbations such as sleep or pathological disorders of consciousness; (3) inability to blind participants to anesthetized and non-anesthetized conditions, which could potentially influence results; (4) relatively sparse EEG channels that were found to be a reliable sources of data across all participants; (5) no assessment of sleep macroarchitecture (i.e. rapid eye movement sleep vs. slow-wave sleep); (6) no longer term cognitive or behavioral assessment beyond the first three days of post-anesthetic actigraphy; and (7) inability to assess source-localized brain regions, subcortical regions, or resting-state networks.

In conclusion, this study establishes neurophysiologic, cognitive, and behavioral recovery patterns after a surgically relevant general anesthetic in human volunteers. The rapid recovery of cortical dynamics just prior to recovering consciousness, the restored accuracy of executive function and multiple cognitive functions within 3 hr of emergence, and the normal sleep-wake patterns in the days following the experiment provide compelling evidence that the healthy brain is resilient to the effects of even deep general anesthesia. The findings also suggest that the immediate and persistent cognitive dysfunction identified after general anesthesia in healthy animals (Culley et al., 2004; Valentim et al., 2008; Carr et al., 2011; Callaway et al., 2012; Zurek et al., 2012; Jevtovic-Todorovic et al., 2013; Zurek et al., 2014; Jiang et al., 2017) does not necessarily translate to healthy humans and that postoperative neurocognitive disorders might relate to factors other than general anesthesia, such as surgery or patient comorbidity (Wildes et al., 2019; Krause et al., 2019). Finally, our data are consistent with the global neuronal workspace theory of conscious access.

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data have been provided for Figures 2-5.

1. 1. Abásolo D
   2. Simons S
   3. Morgado da Silva R
   4. Tononi G
   5. Vyazovskiy VV

   (2015) Lempel-Ziv complexity of cortical activity during sleep and waking in rats

   Journal of Neurophysiology 113:2742–2752.

   https://doi.org/10.1152/jn.00575.2014

   • PubMed
   • Google Scholar
2. 1. Avidan MS
   2. Evers AS

   (2016) The Fallacy of Persistent Postoperative Cognitive Decline

   Anesthesiology 124:255–258.

   https://doi.org/10.1097/ALN.0000000000000958

   • PubMed
   • Google Scholar
3. 1. Bandt C
   2. Pompe B

   (2002) Permutation entropy: a natural complexity measure for time series

   Physical Review Letters 88:174102.

   https://doi.org/10.1103/PhysRevLett.88.174102

   • PubMed
   • Google Scholar
4. 1. Banks MI
   2. Krause BM
   3. Endemann CM
   4. Campbell DI
   5. Kovach CK
   6. Dyken ME
   7. Kawasaki H
   8. Nourski KV

   (2020) Cortical functional connectivity indexes arousal state during sleep and anesthesia

   NeuroImage 211:116627.

   https://doi.org/10.1016/j.neuroimage.2020.116627

   • PubMed
   • Google Scholar
5. 1. Basner M
   2. Mollicone D
   3. Dinges DF

   (2011) Validity and Sensitivity of a Brief Psychomotor Vigilance Test (PVT-B) to Total and Partial Sleep Deprivation

   Acta Astronautica 69:949–959.

   https://doi.org/10.1016/j.actaastro.2011.07.015

   • PubMed
   • Google Scholar
6. 1. Basner M
   2. Savitt A
   3. Moore TM
   4. Port AM
   5. McGuire S
   6. Ecker AJ
   7. Nasrini J
   8. Mollicone DJ
   9. Mott CM
   10. McCann T
   11. Dinges DF
   12. Gur RC

   (2015) Development and validation of the cognition test battery for spaceflight

   Aerospace Medicine and Human Performance 86:942–952.

   https://doi.org/10.3357/AMHP.4343.2015

   • PubMed
   • Google Scholar
7. 1. Basner M
   2. Hermosillo E
   3. Nasrini J
   4. McGuire S
   5. Saxena S
   6. Moore TM
   7. Gur RC
   8. Dinges DF

   (2018) Repeated administration effects on psychomotor vigilance test performance

   Sleep 41:zsx187.

   https://doi.org/10.1093/sleep/zsx187

   • Google Scholar
8. 1. Berman KF
   2. Ostrem JL
   3. Randolph C
   4. Gold J
   5. Goldberg TE
   6. Coppola R
   7. Carson RE
   8. Herscovitch P
   9. Weinberger DR

   (1995) Physiological activation of a cortical network during performance of the Wisconsin Card Sorting Test: a positron emission tomography study

   Neuropsychologia 33:1027–1046.

   https://doi.org/10.1016/0028-3932(95)00035-2

   • PubMed
   • Google Scholar
9. 1. Blain-Moraes S
   2. Tarnal V
   3. Vanini G
   4. Bel-Behar T
   5. Janke E
   6. Picton P
   7. Golmirzaie G
   8. Palanca BJA
   9. Avidan MS
   10. Kelz MB
   11. Mashour GA

   (2017) Network efficiency and posterior alpha patterns are markers of recovery from general anesthesia: a High-Density electroencephalography study in healthy volunteers

   Frontiers in Human Neuroscience 11:328.

   https://doi.org/10.3389/fnhum.2017.00328

   • PubMed
   • Google Scholar
10. 1. Block N

    (2005) Two neural correlates of consciousness

    Trends in Cognitive Sciences 9:46–52.

    https://doi.org/10.1016/j.tics.2004.12.006

    • PubMed
    • Google Scholar
11. 1. Callaway JK
    2. Jones NC
    3. Royse CF

    (2012) Isoflurane induces cognitive deficits in the morris water maze task in rats

    European Journal of Anaesthesiology 29:239–245.

    https://doi.org/10.1097/EJA.0b013e32835103c1

    • PubMed
    • Google Scholar
12. 1. Carr ZJ
    2. Torjman MC
    3. Manu K
    4. Dy G
    5. Goldberg ME

    (2011) Spatial memory using active allothetic place avoidance in adult rats after isoflurane anesthesia: a potential model for postoperative cognitive dysfunction

    Journal of Neurosurgical Anesthesiology 23:138–145.

    https://doi.org/10.1097/ANA.0b013e3182049f19

    • PubMed
    • Google Scholar
13. 1. Casali AG
    2. Gosseries O
    3. Rosanova M
    4. Boly M
    5. Sarasso S
    6. Casali KR
    7. Casarotto S
    8. Bruno M-A
    9. Laureys S
    10. Tononi G
    11. Massimini M

    (2013) A theoretically based index of consciousness independent of sensory processing and behavior

    Science Translational Medicine 5:198ra105.

    https://doi.org/10.1126/scitranslmed.3006294

    • Google Scholar
14. 1. Chennu S
    2. O'Connor S
    3. Adapa R
    4. Menon DK
    5. Bekinschtein TA

    (2016) Brain connectivity dissociates responsiveness from drug exposure during Propofol-Induced transitions of consciousness

    PLOS Computational Biology 12:e1004669.

    https://doi.org/10.1371/journal.pcbi.1004669

    • PubMed
    • Google Scholar
15. 1. Cole RJ
    2. Kripke DF
    3. Gruen W
    4. Mullaney DJ
    5. Gillin JC

    (1992) Automatic sleep/wake identification from wrist activity

    Sleep 15:461–469.

    https://doi.org/10.1093/sleep/15.5.461

    • PubMed
    • Google Scholar
16. 1. Culley DJ
    2. Baxter MG
    3. Yukhananov R
    4. Crosby G

    (2004) Long-term impairment of acquisition of a spatial memory task following isoflurane-nitrous oxide anesthesia in rats

    Anesthesiology 100:309–314.

    https://doi.org/10.1097/00000542-200402000-00020

    • PubMed
    • Google Scholar
17. 1. Dehaene S
    2. Kerszberg M
    3. Changeux JP

    (1998) A neuronal model of a global workspace in effortful cognitive tasks

    PNAS 95:14529–14534.

    https://doi.org/10.1073/pnas.95.24.14529

    • PubMed
    • Google Scholar
18. 1. Dehaene S
    2. Changeux JP

    (2011) Experimental and theoretical approaches to conscious processing

    Neuron 70:200–227.

    https://doi.org/10.1016/j.neuron.2011.03.018

    • PubMed
    • Google Scholar
19. 1. Delorme A
    2. Makeig S

    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Journal of Neuroscience Methods 134:9–21.

    https://doi.org/10.1016/j.jneumeth.2003.10.009

    • PubMed
    • Google Scholar
20. 1. Drummond SP
    2. Bischoff-Grethe A
    3. Dinges DF
    4. Ayalon L
    5. Mednick SC
    6. Meloy MJ

    (2005) The neural basis of the psychomotor vigilance task

    Sleep 28:1059–1068.

    https://doi.org/10.1093/sleep/28.9.1059

    • PubMed
    • Google Scholar
21. 1. Eger EI
    2. Koblin DD
    3. Harris RA
    4. Kendig JJ
    5. Pohorille A
    6. Halsey MJ
    7. Trudell JR

    (1997) Hypothesis: inhaled anesthetics produce immobility and amnesia by different mechanisms at different sites

    Anesthesia and Analgesia 84:915–918.

    https://doi.org/10.1097/00000539-199704000-00039

    • PubMed
    • Google Scholar
22. 1. Eger EI
    2. Gong D
    3. Koblin DD
    4. Bowland T
    5. Ionescu P
    6. Laster MJ
    7. Weiskopf RB

    (1998) The effect of anesthetic duration on kinetic and recovery characteristics of desflurane versus Sevoflurane, and on the kinetic characteristics of compound A, in volunteers

    Anesthesia and Analgesia 86:414–421.

    https://doi.org/10.1097/00000539-199802000-00037

    • PubMed
    • Google Scholar
23. 1. Franco L
    2. Sánchez C
    3. Bravo R
    4. Rodríguez AB
    5. Barriga C
    6. Romero E
    7. Cubero J

    (2012) The sedative effect of non-alcoholic beer in healthy female nurses

    PLOS ONE 7:e37290.

    https://doi.org/10.1371/journal.pone.0037290

    • PubMed
    • Google Scholar
24. 1. Geng Y
    2. Wu Q
    3. Zhang R

    (2017) Effect of propofol, sevoflurane, and isoflurane on postoperative cognitive dysfunction following laparoscopic cholecystectomy in elderly patients: a randomized controlled trial

    Journal of Clinical Anesthesia 38:165–171.

    https://doi.org/10.1016/j.jclinane.2017.02.007

    • Google Scholar
25. 1. Geoghegan P
    2. O'Donovan MT
    3. Lawlor BA

    (2012) Investigation of the effects of alcohol on sleep using actigraphy

    Alcohol and Alcoholism 47:538–544.

    https://doi.org/10.1093/alcalc/ags054

    • PubMed
    • Google Scholar
26. 1. Glahn DC
    2. Gur RC
    3. Ragland JD
    4. Censits DM
    5. Gur RE

    (1997) Reliability, performance characteristics, construct validity, and an initial clinical application of a visual object learning test (VOLT)

    Neuropsychology 11:602–612.

    https://doi.org/10.1037//0894-4105.11.4.602

    • PubMed
    • Google Scholar
27. 1. Glahn DC
    2. Cannon TD
    3. Gur RE
    4. Ragland JD
    5. Gur RC

    (2000) Working memory constrains abstraction in schizophrenia

    Biological Psychiatry 47:34–42.

    https://doi.org/10.1016/S0006-3223(99)00187-0

    • PubMed
    • Google Scholar
28. 1. Gur RC
    2. Ragland JD
    3. Moberg PJ
    4. Turner TH
    5. Bilker WB
    6. Kohler C
    7. Siegel SJ
    8. Gur RE

    (2001) Computerized neurocognitive scanning: I. methodology and validation in healthy people

    Neuropsychopharmacology 25:766–776.

    https://doi.org/10.1016/S0893-133X(01)00278-0

    • PubMed
    • Google Scholar
29. 1. Gur RC
    2. Richard J
    3. Hughett P
    4. Calkins ME
    5. Macy L
    6. Bilker WB
    7. Brensinger C
    8. Gur RE

    (2010) A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation

    Journal of Neuroscience Methods 187:254–262.

    https://doi.org/10.1016/j.jneumeth.2009.11.017

    • PubMed
    • Google Scholar
30. 1. Hemmings HC
    2. Riegelhaupt PM
    3. Kelz MB
    4. Solt K
    5. Eckenhoff RG
    6. Orser BA
    7. Goldstein PA

    (2019) Towards a comprehensive understanding of anesthetic mechanisms of action: a decade of discovery

    Trends in Pharmacological Sciences 40:464–481.

    https://doi.org/10.1016/j.tips.2019.05.001

    • PubMed
    • Google Scholar
31. 1. Hudetz AG
    2. Liu X
    3. Pillay S
    4. Boly M
    5. Tononi G

    (2016) Propofol anesthesia reduces Lempel-Ziv complexity of spontaneous brain activity in rats

    Neuroscience Letters 628:132–135.

    https://doi.org/10.1016/j.neulet.2016.06.017

    • PubMed
    • Google Scholar
32. 1. Jackson O
    2. Schacter DL

    (2004) Encoding activity in anterior medial temporal lobe supports subsequent associative recognition

    NeuroImage 21:456–462.

    https://doi.org/10.1016/j.neuroimage.2003.09.050

    • PubMed
    • Google Scholar
33. 1. Jasper I
    2. Häussler A
    3. Marquardt C
    4. Hermsdörfer J

    (2009) Circadian rhythm in handwriting

    Journal of Sleep Research 18:264–271.

    https://doi.org/10.1111/j.1365-2869.2008.00727.x

    • PubMed
    • Google Scholar
34. 1. Jevtovic-Todorovic V
    2. Absalom AR
    3. Blomgren K
    4. Brambrink A
    5. Crosby G
    6. Culley DJ
    7. Fiskum G
    8. Giffard RG
    9. Herold KF
    10. Loepke AW
    11. Ma D
    12. Orser BA
    13. Planel E
    14. Slikker W
    15. Soriano SG
    16. Stratmann G
    17. Vutskits L
    18. Xie Z
    19. Hemmings HC

    (2013) Anaesthetic neurotoxicity and neuroplasticity: an expert group report and statement based on the BJA salzburg seminar

    British Journal of Anaesthesia 111:143–151.

    https://doi.org/10.1093/bja/aet177

    • PubMed
    • Google Scholar
35. 1. Jiang S
    2. Miao B
    3. Chen Y

    (2017) Prolonged duration of isoflurane anesthesia impairs spatial recognition memory through the activation of JNK1/2 in the hippocampus of mice

    Neuroreport 28:386–390.

    https://doi.org/10.1097/WNR.0000000000000760

    • PubMed
    • Google Scholar
36. 1. Jordan D
    2. Ilg R
    3. Riedl V
    4. Schorer A
    5. Grimberg S
    6. Neufang S
    7. Omerovic A
    8. Berger S
    9. Untergehrer G
    10. Preibisch C
    11. Schulz E
    12. Schuster T
    13. Schröter M
    14. Spoormaker V
    15. Zimmer C
    16. Hemmer B
    17. Wohlschläger A
    18. Kochs EF
    19. Schneider G

    (2013) Simultaneous electroencephalographic and functional magnetic resonance imaging indicate impaired cortical top-down processing in association with anesthetic-induced unconsciousness

    Anesthesiology 119:1031–1042.

    https://doi.org/10.1097/ALN.0b013e3182a7ca92

    • PubMed
    • Google Scholar
37. 1. Kelz MB
    2. García PS
    3. Mashour GA
    4. Solt K

    (2019) Escape from oblivion: neural mechanisms of emergence from general anesthesia

    Anesthesia and Analgesia 128:726–736.

    https://doi.org/10.1213/ANE.0000000000004006

    • PubMed
    • Google Scholar
38. 1. Kelz MB
    2. Mashour GA

    (2019) The Biology of General Anesthesia from Paramecium to Primate

    Current Biology: CB 29:R1199–R210.

    https://doi.org/10.1016/j.cub.2019.09.071

    • PubMed
    • Google Scholar
39. 1. Kim H
    2. Moon JY
    3. Mashour GA
    4. Lee U

    (2018) Mechanisms of hysteresis in human brain networks during transitions of consciousness and unconsciousness: Theoretical principles and empirical evidence

    PLOS Computational Biology 14:e1006424.

    https://doi.org/10.1371/journal.pcbi.1006424

    • PubMed
    • Google Scholar
40. 1. Krause BM
    2. Sabia S
    3. Manning HJ
    4. Singh-Manoux A
    5. Sanders RD

    (2019) Association between major surgical admissions and the cognitive trajectory: 19 year follow-up of Whitehall II cohort study

    BMJ 366:l4466.

    https://doi.org/10.1136/bmj.l4466

    • PubMed
    • Google Scholar
41. 1. Långsjö JW
    2. Alkire MT
    3. Kaskinoro K
    4. Hayama H
    5. Maksimow A
    6. Kaisti KK
    7. Aalto S
    8. Aantaa R
    9. Jääskeläinen SK
    10. Revonsuo A
    11. Scheinin H

    (2012) Returning from oblivion: imaging the neural core of consciousness

    Journal of Neuroscience 32:4935–4943.

    https://doi.org/10.1523/JNEUROSCI.4962-11.2012

    • PubMed
    • Google Scholar
42. 1. Lempel A
    2. Ziv J

    (1976) On the complexity of finite sequences

    IEEE Transactions on Information Theory 22:75–81.

    https://doi.org/10.1109/TIT.1976.1055501

    • Google Scholar
43. 1. Li X
    2. Cui S
    3. Voss LJ

    (2008) Using permutation entropy to measure the electroencephalographic effects of sevoflurane

    Anesthesiology 109:448–456.

    https://doi.org/10.1097/ALN.0b013e318182a91b

    • PubMed
    • Google Scholar
44. 1. Maier KL
    2. McKinstry-Wu AR
    3. Palanca BJA
    4. Tarnal V
    5. Blain-Moraes S
    6. Basner M
    7. Avidan MS
    8. Mashour GA
    9. Kelz MB

    (2017) Protocol for the reconstructing consciousness and cognition (ReCCognition) Study

    Frontiers in Human Neuroscience 11:284.

    https://doi.org/10.3389/fnhum.2017.00284

    • PubMed
    • Google Scholar
45. 1. Mashour GA
    2. Roelfsema P
    3. Changeux JP
    4. Dehaene S

    (2020) Conscious Processing and the Global Neuronal Workspace Hypothesis

    Neuron 105:776–798.

    https://doi.org/10.1016/j.neuron.2020.01.026

    • PubMed
    • Google Scholar
46. 1. Mashour GA
    2. Alkire MT

    (2013) Evolution of consciousness: phylogeny, ontogeny, and emergence from general anesthesia

    PNAS 110 Suppl 2:10357–10364.

    https://doi.org/10.1073/pnas.1301188110

    • PubMed
    • Google Scholar
47. 1. McLeod DR
    2. Griffiths RR
    3. Bigelow GE
    4. Yingling J

    (1982) An automated version of the digit symbol substitution test (DSST)

    Behavior Research Methods & Instrumentation 14:463–466.

    https://doi.org/10.3758/BF03203313

    • Google Scholar
48. 1. Mezick EJ
    2. Matthews KA
    3. Hall M
    4. Kamarck TW
    5. Buysse DJ
    6. Owens JF
    7. Reis SE

    (2009) Intra-individual variability in sleep duration and fragmentation: associations with stress

    Psychoneuroendocrinology 34:1346–1354.

    https://doi.org/10.1016/j.psyneuen.2009.04.005

    • PubMed
    • Google Scholar
49. 1. Neves MC
    2. Albuquerque MR
    3. Neves FS
    4. Lage GM
    5. Malloy-Diniz L
    6. Nicolato R
    7. Corrêa H

    (2014) Sensorimotor performance in euthymic bipolar disorder: the MPraxis (PennCNP) analysis

    Revista Brasileira de Psiquiatria 36:248–250.

    https://doi.org/10.1590/1516-4446-2013-1243

    • PubMed
    • Google Scholar
50. 1. Nickalls RW
    2. Mapleson WW

    (2003) Age-related iso-MAC charts for isoflurane, sevoflurane and desflurane in man

    British Journal of Anaesthesia 91:170–174.

    https://doi.org/10.1093/bja/aeg132

    • PubMed
    • Google Scholar
51. 1. Nir T
    2. Or-Borichev A
    3. Izraitel E
    4. Hendler T
    5. Lerner Y
    6. Matot I

    (2019) Transient subcortical functional connectivity upon emergence from propofol sedation in human male volunteers: evidence for active emergence

    British Journal of Anaesthesia 123:298–308.

    https://doi.org/10.1016/j.bja.2019.05.038

    • PubMed
    • Google Scholar
52. 1. Olofsen E
    2. Sleigh JW
    3. Dahan A

    (2008) Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect

    British Journal of Anaesthesia 101:810–821.

    https://doi.org/10.1093/bja/aen290

    • PubMed
    • Google Scholar
53. 1. Pal D
    2. Dean JG
    3. Liu T
    4. Li D
    5. Watson CJ
    6. Hudetz AG
    7. Mashour GA

    (2018) Differential role of prefrontal and parietal cortices in controlling level of consciousness

    Current Biology 28:2145–2152.

    https://doi.org/10.1016/j.cub.2018.05.025

    • Google Scholar
54. 1. Pollard BJ
    2. Bryan A
    3. Bennett D
    4. Faragher EB
    5. Un EN
    6. Keegan M
    7. Wilson A
    8. Burkill M
    9. Beatty PC
    10. Stollery BT

    (1994) Recovery after oral surgery with halothane, enflurane, isoflurane or propofol anaesthesia

    British Journal of Anaesthesia 72:559–566.

    https://doi.org/10.1093/bja/72.5.559

    • PubMed
    • Google Scholar
55. 1. Purdon PL
    2. Pierce ET
    3. Mukamel EA
    4. Prerau MJ
    5. Walsh JL
    6. Wong KF
    7. Salazar-Gomez AF
    8. Harrell PG
    9. Sampson AL
    10. Cimenser A
    11. Ching S
    12. Kopell NJ
    13. Tavares-Stoeckel C
    14. Habeeb K
    15. Merhar R
    16. Brown EN

    (2013) Electroencephalogram signatures of loss and recovery of consciousness from propofol

    PNAS 110:E1142–E1151.

    https://doi.org/10.1073/pnas.1221180110

    • PubMed
    • Google Scholar
56. 1. Raccah O
    2. Block N
    3. Fox KCR
    4. Kcr F

    (2021) Does the Prefrontal Cortex Play an Essential Role in Consciousness? Insights from Intracranial Electrical Stimulation of the Human Brain

    Journal of Neuroscience 41:2076–2087.

    https://doi.org/10.1523/JNEUROSCI.1141-20.2020

    • PubMed
    • Google Scholar
57. 1. Ragland JD
    2. Turetsky BI
    3. Gur RC
    4. Gunning-Dixon F
    5. Turner T
    6. Schroeder L
    7. Chan R
    8. Gur RE

    (2002) Working memory for complex figures: an fMRI comparison of letter and fractal n-back tasks

    Neuropsychology 16:370–379.

    https://doi.org/10.1037/0894-4105.16.3.370

    • PubMed
    • Google Scholar
58. 1. Ranft A
    2. Golkowski D
    3. Kiel T
    4. Riedl V
    5. Kohl P
    6. Rohrer G
    7. Pientka J
    8. Berger S
    9. Thul A
    10. Maurer M
    11. Preibisch C
    12. Zimmer C
    13. Mashour GA
    14. Kochs EF
    15. Jordan D
    16. Ilg R

    (2016) Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography

    Anesthesiology 125:861–872.

    https://doi.org/10.1097/ALN.0000000000001322

    • PubMed
    • Google Scholar
59. 1. Reshef ER
    2. Schiff ND
    3. Brown EN

    (2019) A Neurologic Examination for Anesthesiologists: Assessing Arousal Level during Induction, Maintenance, and Emergence

    Anesthesiology 130:462–471.

    https://doi.org/10.1097/ALN.0000000000002559

    • PubMed
    • Google Scholar
60. 1. Schartner M
    2. Seth A
    3. Noirhomme Q
    4. Boly M
    5. Bruno MA
    6. Laureys S
    7. Barrett A

    (2015) Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia

    PLOS ONE 10:e0133532.

    https://doi.org/10.1371/journal.pone.0133532

    • PubMed
    • Google Scholar
61. 1. Schartner MM
    2. Carhart-Harris RL
    3. Barrett AB
    4. Seth AK
    5. Muthukumaraswamy SD

    (2017) Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin

    Scientific Reports 7:46421.

    https://doi.org/10.1038/srep46421

    • PubMed
    • Google Scholar
62. 1. Scheinin A
    2. Kantonen O
    3. Alkire M
    4. Långsjö J
    5. Kallionpää RE
    6. Kaisti K
    7. Radek L
    8. Johansson J
    9. Sandman N
    10. Nyman M
    11. Scheinin M
    12. Vahlberg T
    13. Revonsuo A
    14. Valli K
    15. Scheinin H

    (2021) Foundations of Human Consciousness: Imaging the Twilight Zone

    Journal of Neuroscience 41:1769–1778.

    https://doi.org/10.1523/JNEUROSCI.0775-20.2020

    • PubMed
    • Google Scholar
63. 1. Shortal BP
    2. Hickman LB
    3. Mak-McCully RA
    4. Wang W
    5. Brennan C
    6. Ung H
    7. Litt B
    8. Tarnal V
    9. Janke E
    10. Picton P
    11. Blain-Moraes S
    12. Maybrier HR
    13. Muench MR
    14. Lin N
    15. Avidan MS
    16. Mashour GA
    17. McKinstry-Wu AR
    18. Kelz MB
    19. Palanca BJ
    20. Proekt A
    21. ReCCognition Study Group

    (2019) Duration of EEG suppression does not predict recovery time or degree of cognitive impairment after general anaesthesia in human volunteers

    British Journal of Anaesthesia 123:206–218.

    https://doi.org/10.1016/j.bja.2019.03.046

    • PubMed
    • Google Scholar
64. 1. Tucker AM
    2. Whitney P
    3. Belenky G
    4. Hinson JM
    5. Van Dongen HP

    (2010) Effects of sleep deprivation on dissociated components of executive functioning

    Sleep 33:47–57.

    https://doi.org/10.1093/sleep/33.1.47

    • PubMed
    • Google Scholar
65. 1. Usui N
    2. Haji T
    3. Maruyama M
    4. Katsuyama N
    5. Uchida S
    6. Hozawa A
    7. Omori K
    8. Tsuji I
    9. Kawashima R
    10. Taira M

    (2009) Cortical areas related to performance of WAIS Digit Symbol Test: a functional imaging study

    Neuroscience Letters 463:1–5.

    https://doi.org/10.1016/j.neulet.2009.07.048

    • PubMed
    • Google Scholar
66. 1. Valentim AM
    2. Alves HC
    3. Olsson IA
    4. Antunes LM

    (2008)

    The effects of depth of isoflurane anesthesia on the performance of mice in a simple spatial learning task

    Journal of the American Association for Laboratory Animal Science: JAALAS 47:16–19.

    • PubMed
    • Google Scholar
67. 1. Van Dongen HP
    2. Maislin G
    3. Mullington JM
    4. Dinges DF

    (2003) The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation

    Sleep 26:117–126.

    https://doi.org/10.1093/sleep/26.2.117

    • PubMed
    • Google Scholar
68. 1. Van Dongen HPA
    2. Dinges DF

    (2005) Sleep, circadian rhythms, and psychomotor vigilance

    Clinics in Sports Medicine 24:237–249.

    https://doi.org/10.1016/j.csm.2004.12.007

    • Google Scholar
69. 1. Weiser TG
    2. Haynes AB
    3. Molina G
    4. Lipsitz SR
    5. Esquivel MM
    6. Uribe-Leitz T
    7. Fu R
    8. Azad T
    9. Chao TE
    10. Berry WR
    11. Gawande AA

    (2015) Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes

    Lancet 385 Suppl 2:S11.

    https://doi.org/10.1016/S0140-6736(15)60806-6

    • PubMed
    • Google Scholar
70. 1. Wildes TS
    2. Mickle AM
    3. Ben Abdallah A
    4. Maybrier HR
    5. Oberhaus J
    6. Budelier TP
    7. Kronzer A
    8. McKinnon SL
    9. Park D
    10. Torres BA
    11. Graetz TJ
    12. Emmert DA
    13. Palanca BJ
    14. Goswami S
    15. Jordan K
    16. Lin N
    17. Fritz BA
    18. Stevens TW
    19. Jacobsohn E
    20. Schmitt EM
    21. Inouye SK
    22. Stark S
    23. Lenze EJ
    24. Avidan MS
    25. ENGAGES Research Group

    (2019) Effect of Electroencephalography-Guided Anesthetic Administration on Postoperative Delirium Among Older Adults Undergoing Major Surgery: The ENGAGES Randomized Clinical Trial

    JAMA 321:473–483.

    https://doi.org/10.1001/jama.2018.22005

    • PubMed
    • Google Scholar
71. 1. Xie G
    2. Deschamps A
    3. Backman SB
    4. Fiset P
    5. Chartrand D
    6. Dagher A
    7. Plourde G

    (2011) Critical involvement of the thalamus and precuneus during restoration of consciousness with physostigmine in humans during propofol anaesthesia: a positron emission tomography study

    British Journal of Anaesthesia 106:548–557.

    https://doi.org/10.1093/bja/aeq415

    • PubMed
    • Google Scholar
72. 1. Zurek AA
    2. Bridgwater EM
    3. Orser BA

    (2012) Inhibition of α5 γ-Aminobutyric acid type A receptors restores recognition memory after general anesthesia

    Anesthesia and Analgesia 114:845–855.

    https://doi.org/10.1213/ANE.0b013e31824720da

    • PubMed
    • Google Scholar
73. 1. Zurek AA
    2. Yu J
    3. Wang DS
    4. Haffey SC
    5. Bridgwater EM
    6. Penna A
    7. Lecker I
    8. Lei G
    9. Chang T
    10. Salter EW
    11. Orser BA

    (2014) Sustained increase in α5GABAA receptor function impairs memory after anesthesia

    The Journal of Clinical Investigation 124:5437–5441.

    https://doi.org/10.1172/JCI76669

    • PubMed
    • Google Scholar

Acceptance summary:

This paper is concisely written with a conceptual structure that is easy to follow.

The study is well controlled, and uses a wide range of neurocognitive tests to assess different aspects of cognition. The main findings, that executive function recovers before other potentially more basic aspects of cognition, supported by a similarly early return of frontal cortical dynamics offer important insights into the recovery of cognition after pharmacologically induced unconsciousness. These findings are novel and, although cannot be generalised beyond anaesthetic agent isoflurane, will be of interest to clinical anaesthesiologists, healthy individuals undergoing isoflurane-based general anaesthesia, and researchers investigating the relationship between consciousness and cognition.

Decision letter after peer review:

Thank you for submitting your article "Recovery of consciousness and cognition after general anesthesia in humans" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Redmond G O’Connell as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard Ivry as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Bekinschtein Tristan (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that significant revisions are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary

The three reviewers agreed that the paper reports results that are important as they appear to offer novel insights into the dynamics of cognitive recovery following loss of consciousness; an area that has been relatively under-investigated to date. However all three reviewers also highlighted some significant concerns regarding aspects of the study rationale and methodology. A consolidated list of major points is provided below:

Essential Revisions:

1. The authors do not provide any rationale for their starting hypotheses regarding the sequence in which cognitive functions are expected to recover. For example, the prediction that vigilant attention should recover first is not intuitive and no basis or prior literature is provided for this prediction.

2. In the Introduction the authors use terms that are difficult to identify with established cognitive constructs e.g. top of page 5 it is stated that 'attention' should recover first but this is an extremely loose term that incorporates a range of different subcomponents (vigilant/selective/spatial etc). Similarly, 'scanning and tracking' does not refer to a cognitively- or psychologically-motivated distinct function (top of page 5). The authors should also clarify which tests are being used to measure which functions because it is currently confusing that 5 functions are being linked to 6 behavioural tests. The descriptions in the methods section do not help to clarify the relationships; e.g., the Motor Praxis Task (MP) task linked to complex scanning and visual tracking in the introduction, is described to measure sensorimotor speed.

3. In general, the three reviewers agreed that many aspects of the methodology are too vaguely described and require clarification so that the study can be understood by the reader and potentially replicated by other labs in the future. Some critical examples are highlighted in the comments below but more generally, if the authors prefer to position the Results before the Methods then they should ensure that there is sufficient detail in the Results to allow the reader to understand the experiment. For example, the reader should not have to wait to reach the Methods to be informed that there were two separate groups and that the control group exercised prior to cognitive testing. For example, the results in 2nd paragraph of page 7 are very scantly described, and a summary table with full disclosure of test statistic values is needed. Figures 2 and 3 lack signposting of statistical significance, a missed opportunity given the rich information provided. E.g., it's impossible to visualise when performance in each task recovers. Similarly, the sentence "The PE demonstrated significant differences associated with behavioural states (F9, 86 = 42.423, p<0.001) ,brain regions (F1, 257 = 4.275, p=0.040) and the interaction between them (F9, 85 = 2.750, p=0.007)." does not tell the reader what test was run or what factors were included in the statistical model and so it is impossible for the reader to fully appreciate the meaning of this result. In general, there is a quite a lot of work to be done throughout the manuscript to render the description of analyses and results sufficiently transparent, and comprehensive.

4. The critical term 'recovery', on which the key results hinge, is never clearly defined or operationalised. A Bayesian regression approach is vaguely described in the Methods section but the information provided does not explain how recovery is actually defined or established. Currently, the study appears to rely heavily on null-hypothesis significance testing to draw its conclusions but p>0.05 does not mean that we should embrace the null hypothesis. The authors should consider including tests (e.g. Bayes Factor) to determine the degree of statistical confidence that post-sedation test scores no longer differ reliably from those at baseline or else provide a strong justification for their current approach.

5. As the authors themselves note, the potential for practice effects to confound any recovery estimates is a critical concern here one that needs to be addressed in a revision. Relatedly, there is the concern that these cognitive tests may differ quite markedly in their difficulty for potentially trivial reasons and this could impact on estimates of recovery. For example, if performance on some tasks is close to ceiling then recovery may be deemed to occur earlier. Can the authors provide new analyses that would address the possibility that some of these tasks may simply be more sensitive to cognitive perturbations than others?

6. The EEG analyses are potentially interesting but the authors do not provide any rationale for focussing in on these particular metrics out of the many that they could have considered (e.g. connectivity, power). Additional text discussing these metrics should be provided along with hypotheses regarding their post-sedation dynamics. In addition, the fact that the EEG trends are never linked to the cognitive ones limits the conclusions that can be drawn here. Did the authors consider examining the degree to which these EEG changes are predictive of cognitive recovery across subjects? Did they consider examining how levels of complexity before and/or during sedation might influence the degree or speed of recovery?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Recovery of consciousness and cognition after general anesthesia in humans" for further consideration by eLife. Your revised article has been reviewed by two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Richard Ivry as the Senior Editor.

The manuscript has been substantially improved but there are some remaining issues that need to be addressed, as outlined below. In particular, there is a need to flesh out the study rationale, to clarify why these particular EEG metrics were considered, to further clarify the theoretical background to the study and to interpret the results with respect to extant cognitive theories. The two reviewers have provided guidance on each of these points. I think these further changes, which would really help to ensure that the paper reaches and resonates with the widest possible audience.

The specific comments made by the two reviewers are provided below.

Reviewer #1:

I find the manuscript much improved. In particular I think placing the Methods ahead of the Results works well in this case and the authors have provided improved clarity regarding the study goals. I do have some comments regarding the written presentation:

The Abstract opens on a very vague note. I don't think it will be apparent to many readers what the authors mean when they say that understanding the re-emergence of cognition is important 'neurobiologically'. This is clarified in the Introduction but I think the Abstract needs to provide a more clear statement regarding the study rationale in order to ensure that the article will be read by a wide readership. For example, the question of whether recovery is progressive and whether certain cognitive functions recover before others could be stated in the second sentence. Extremely vague opening line. Add the hypotheses here.

I also take issue with the following statement in the Abstract: 'Contrary to our hypothesis, executive function returned first, with frontal cortical dynamics recovering faster than posterior cortical dynamics.' This phrasing implies that a correlation between the EF recovery and posterior cortical dynamics has been established in the author's analyses which in fact is not the case. The authors should rephrase this sentence to avoid giving that impression e.g. deal with the cognitive and EEG results in separate sentences.

I think the authors need to provide the reader with more information regarding the types of EEG metrics that the authors have chosen to analyse. Why these particular metrics and not others? Despite being an EEG researcher myself I found the term 'local and global dynamics' very vague and unclear. Is the idea to establish whether a particular brain area has recovered normal levels of activity? Some clear statement regarding the functional importance of the chosen metrics is necessary.

Reviewer #2:

Thanks for amending the manuscript, I think that there is still some work to do in the depiction of results front, make them easy to read and interpretable; on the theoretical framework, making a bit more explicit during the interpretation what theoretical tools are you using to discuss the results; to interpret the correlations with caution.

I think that the information of the cognitive domains that are tested with the tasks is key and forms an important aspect of the paper and the authors should clearly frame the initial hypotheses and results in theoretical terms for the cognitive neuroscience colleagues who will be reading the paper. It will also help understanding better the aims of this work outside the clinical description. From a cognitive perspective, what is recovery from unconsciousness? Can we frame different cognitive recovery dynamics in terms of the information integration that networks regain? How is that measured or conceptualized? Is this theory-driven account captured in any shape or form by the complexity measures?

It has been shown that primary cortices (auditory, visual, motor) recover faster or are more resilient to consciousness challenges (sleep, sedation). Does this inform the expected recovery dynamics or helps interpret it?

Can the tasks be ordered in terms of the cognitive demands they pose to the system (the brain)? Does this help to frame the recovery results found?

Do the authors think of these results in light of cognitive demands, information processing, integration of features, perceptual and cognitive modules? Please think in the interpretation of your results in light of a theoretical framework and whether it contributes or not to theory itself. Is this experimental model of cognitive recovery complementary to classic cognitive interference, non-conscious drug challenges, Transcranial magnetic or electrical stimulation?

In short, I do not think it is enough to restrict the introduction or discussion to the cognitive test and its putative neural correlates for the reasons and guiding questions above.

The other comment I have is the depiction of the results. A graph overlapping (even if simplified) the PP speed distributions and the accuracy distributions would make a great figure that to be used for other authors to reference this work in talks and to be used in teaching. In its current form it requires a lot of explanation and it does not paint in one look the findings. This is a preference and nice to have so the work can be featured better in the future.

For the correlations results, there are clearly weak or no associations and given the number of participants (low for correlations) and high number of comparison the likelihood of chance association is dangerously high. A word of caution in the paper about this will help and also please check if the even the correction for multiple correlations also renders this apparent significant associations void.

Will the behavioural and EEG data become available soon and if so in which platform.

Essential Revisions:

1. The authors do not provide any rationale for their starting hypotheses regarding the sequence in which cognitive functions are expected to recover. For example, the prediction that vigilant attention should recover first is not intuitive and no basis or prior literature is provided for this prediction.

Our primary hypothesis, described a priori in a published protocol, is that emergence occurs through a process, not at a point. Importantly, this hypothesis is independent of predicting the specific sequence of recovery. We have now stated this primary hypothesis more clearly in the introduction. In the revision, we have been more conservative in formulating the sequence of recovery, stating that – pursuant to our hypothesis regarding process – arousal and recovery of consciousness would occur first and return of higher executive function would return last. This latter point is defensible because there is neurologic examination and neuroimaging evidence to suggest that recovery from anesthesia occurs in a caudal to rostral manner (e.g., Langsjo et al., J Neuroscience, 2012;32(14):4935-43, and Reshef et al., Anesthesiology, 2019;130(3):462-471). In the revised version, we state that executive function – measured using a test that is known to engage the rostral structure dorsolateral prefrontal cortex – was predicted to recover last. This is perfectly consistent with our original formulation and statistical testing but avoids less defensible granularity in the recovery sequence of cognitive functions.

2. In the Introduction the authors use terms that are difficult to identify with established cognitive constructs e.g. top of page 5 it is stated that 'attention' should recover first but this is an extremely loose term that incorporates a range of different subcomponents (vigilant/selective/spatial etc). Similarly, 'scanning and tracking' does not refer to a cognitively- or psychologically-motivated distinct function (top of page 5). The authors should also clarify which tests are being used to measure which functions because it is currently confusing that 5 functions are being linked to 6 behavioural tests. The descriptions in the methods section do not help to clarify the relationships; e.g., the Motor Praxis Task (MP) task linked to complex scanning and visual tracking in the introduction, is described to measure sensorimotor speed.

We have restricted our description in the introduction to the cognitive tests rather than the cognitive domains, in order to avoid imprecision in the description of the cognitive function or mapping to the actual cognitive test. This is defensible and grounded in the use of this particular neurocognitive battery for assessment of cognitive deficits related to perturbations of other arousal states (i.e., recovery from sleep deprivation). We have also included a table (Author response table 1) that gives an overview of the tests, associated cognitive domain, and associated brain region thought to support the domain.

3. In general, the three reviewers agreed that many aspects of the methodology are too vaguely described and require clarification so that the study can be understood by the reader and potentially replicated by other labs in the future. Some critical examples are highlighted in the comments below but more generally, if the authors prefer to position the Results before the Methods then they should ensure that there is sufficient detail in the Results to allow the reader to understand the experiment. For example, the reader should not have to wait to reach the Methods to be informed that there were two separate groups and that the control group exercised prior to cognitive testing. For example, the results in 2nd paragraph of page 7 are very scantly described, and a summary table with full disclosure of test statistic values is needed. Figures 2 and 3 lack signposting of statistical significance, a missed opportunity given the rich information provided. E.g., it's impossible to visualise when performance in each task recovers. Similarly, the sentence "The PE demonstrated significant differences associated with behavioural states (F9, 86 = 42.423, p<0.001) ,brain regions (F1, 257 = 4.275, p=0.040) and the interaction between them (F9, 85 = 2.750, p=0.007)." does not tell the reader what test was run or what factors were included in the statistical model and so it is impossible for the reader to fully appreciate the meaning of this result. In general, there is a quite a lot of work to be done throughout the manuscript to render the description of analyses and results sufficiently transparent, and comprehensive.

We have, in response to this helpful suggestion, repositioned the Methods section before the Results section. We have also reviewed the manuscript critically for areas in which methodology and the ensuing explanation of our results could be made more explicit. The first line of the Materials and methods section highlights our published protocol, which contains more details and is open access.

4. The critical term 'recovery', on which the key results hinge, is never clearly defined or operationalised. A Bayesian regression approach is vaguely described in the Methods section but the information provided does not explain how recovery is actually defined or established. Currently, the study appears to rely heavily on null-hypothesis significance testing to draw its conclusions but p>0.05 does not mean that we should embrace the null hypothesis. The authors should consider including tests (e.g. Bayes Factor) to determine the degree of statistical confidence that post-sedation test scores no longer differ reliably from those at baseline or else provide a strong justification for their current approach.

We thank the reviewers for this critically important point. The recovery time involves both a performance speed and performance accuracy. Both processes are defined by the time for anesthetized subjects to have the test score back to their respective speed/accuracy baseline scores. We have revised the methods and results to include the following information/analysis.

Since our model was ${\displaystyle y_t=y_{baseline}+\alpha +\beta \ast e^t,}$the recovery time was calculated by ${\displaystyle T=\log \left(-\frac{\alpha }{\beta }\right)/.}$Author response image 1 shows the histogram of recovery time (measured in hours) for speed in each testing domain based on 10000 Markov Chain Monte Carlo samples.

Author response image 1

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As indicated both by the Y-axis values and by the simulated distribution, the recovery of NBCK (as measured by speed to task completion) is strongly impaired. Most samples did not recover over the experimental time course – a fact mirrored in the simulations.For the other 5 tests, the posterior probability that:

a. Recovery of MP speed occurred more than 0.5 hour before AM, PVT and DSST are 54%, 91% and 83%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-1.31, 0.24), (-3.95, -0.02) and (-1.64, -0.24);

b. Recovery of VOLT speed occurred more than 0.5 hour before MP, AM, PVT and DSST are 82%, 98%, 100% and 100%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-1.97, -0.12), (-2.35, -0.79), (-4.84, -1.17) and (-2.66, -1.33);

c. Recovery of AM speed occurred more than 0.5 hour before PVT and DSST are 79% and 40%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-3.49, 0.10) and (-0.92, 0.10);

d. Recovery of DSST occurred more than 0.5 hour before PVT is 64%; the 90% corresponding credible interval of the difference in recovery time is (-3.02, 0.47).

Author response image 2 shows the histogram of recovery time in hour for accuracy in each testing domain based on 10000 Markov Chain Monte Carlo samples.

Author response image 2

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The accuracy in PVT is strongly impaired as most samples cannot recover.For the other 5 tests, the posterior probability that

a. Recovery of MP occurred more than 0.5 hour before VOLT, NBCK and DSST are 93%, 97% and 99%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-2.82, -0.27), (-3.6, -0.77) and (-0.15, 0.78);

b. Recovery of VOLT occurred more than 0.5 hour before NBCK and DSST are 55% and 50%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-2.38, 1.32) and (-1.69, 0.74);

c. Recovery of AM occurred more than 0.5 hour before VOLT, NBCK and DSST are 98%, 99% and 100%, respectively; the 90% corresponding credible intervals of the difference in recovery time are (-2.92, -0.47), (-3.66, -0.91) and (-2.47, -1.15);

d. Recovery of DSST occurred more than 0.5 hour before NBCK is 42%; the 90% corresponding credible interval of the difference in recovery time is (-1.55, 1.09).

5. As the authors themselves note, the potential for practice effects to confound any recovery estimates is a critical concern here one that needs to be addressed in a revision. Relatedly, there is the concern that these cognitive tests may differ quite markedly in their difficulty for potentially trivial reasons and this could impact on estimates of recovery. For example, if performance on some tasks is close to ceiling then recovery may be deemed to occur earlier. Can the authors provide new analyses that would address the possibility that some of these tasks may simply be more sensitive to cognitive perturbations than others?

We agree that it is critical for the interpretation of the results to carefully take practice effects into account. All six cognitive tests with the exception of PVT are known to have protracted practice effects (Basner et al., J Clin Exp Neuropsychol, 2020;42(5): 516-29). Therefore, having an equally sized non-anesthetized control group is a major strength of this study. Without this control group, we would not have been able to establish a “return to normal,” as the “normal” would be drifting to faster/more accurate performance with repeated administration.

In Author response table 2, when corrected for multiple comparisons, our adjusted p-value cutoff considered to be significant is p < 0.05/12 = 0.0042. Hence, no baseline significant differences exist between the experimental and the control group. Furthermore, after only two administrations, neither group hit a ceiling (i.e., accuracy is below 100%) or a floor. When testing the PVT and DSST, accuracy is always close to 100% in alert and motivated subjects. Consequently, the focus of these tests is speed and cognitive throughput. This does not mean the tests are easy and they certainly did not recover first. The reviewers are correct that it is possible that some of the tasks may be more sensitive to cognitive perturbations than others. However, this relationship is not straightforward. For example, even when considering a single test – the MP test shows the most profound drop in performance at the point of emergence, falling by more than 10 z-scores from baseline pre-anesthesia performance. Nevertheless, accuracy on this test drops minimally immediately upon emergence from anesthesia, but fully recovers within 30 minutes unlike MP speed.

6. The EEG analyses are potentially interesting but the authors do not provide any rationale for focussing in on these particular metrics out of the many that they could have considered (e.g. connectivity, power). Additional text discussing these metrics should be provided along with hypotheses regarding their post-sedation dynamics. In addition, the fact that the EEG trends are never linked to the cognitive ones limits the conclusions that can be drawn here. Did the authors consider examining the degree to which these EEG changes are predictive of cognitive recovery across subjects? Did they consider examining how levels of complexity before and/or during sedation might influence the degree or speed of recovery?

We have provided justification in the revised manuscript for the choice of permutation entropy and Lempel-Ziv complexity. Furthermore, to explore whether the EEG measures are predictive of cognitive recovery in the post-anesthetic period, we assessed the associations of EEG measures during the pre-anesthetic baseline (EC1), the intra-anesthetic period (Maintenance) and the period immediately preceding recovery of responsiveness (pre-ROC) with the impairment of cognitive performance at emergence (just after recovery of consciousness). Spearman’s rank correlation analysis showed no association in EEG measures during EC1, but weak correlations were found between the LZC during Maintenance and the impairment of response time for MP (Motor Praxis) (r=0.522, p=0.004) and DSST (Digit Symbol Substitution Test) tasks (r=0.446, p=0.016), as well as between frontal PE and LZC during pre-ROC and the impairment of accuracy for DSST task (r=0.381, p=0.046 and r=0.479, p=0.011). We have provided these results in Figure 4—figure supplements 4-6.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #1:

I find the manuscript much improved. In particular I think placing the Methods ahead of the Results works well in this case and the authors have provided improved clarity regarding the study goals. I do have some comments regarding the written presentation:

The Abstract opens on a very vague note. I don't think it will be apparent to many readers what the authors mean when they say that understanding the re-emergence of cognition is important 'neurobiologically'. This is clarified in the Introduction but I think the Abstract needs to provide a more clear statement regarding the study rationale in order to ensure that the article will be read by a wide readership. For example, the question of whether recovery is progressive and whether certain cognitive functions recover before others could be stated in the second sentence. Extremely vague opening line. Add the hypotheses here.

Thank you for highlighting these opportunities for improvement. We have revised the abstract, also addressing comments regarding theoretical framework from Reviewer 2:

“Understanding how the brain recovers from unconsciousness can inform neurobiological theories of consciousness and guide clinical investigation. […] Early engagement of prefrontal cortex in recovery of consciousness and cognition is consistent with global neuronal workspace theory.”

I also take issue with the following statement in the Abstract: 'Contrary to our hypothesis, executive function returned first, with frontal cortical dynamics recovering faster than posterior cortical dynamics.' This phrasing implies that a correlation between the EF recovery and posterior cortical dynamics has been established in the author's analyses which in fact is not the case. The authors should rephrase this sentence to avoid giving that impression e.g. deal with the cognitive and EEG results in separate sentences.

We agree with the reviewer’s interpretation and (as can be seen in the abstract copied above) have separated the concepts in two sentences.

I think the authors need to provide the reader with more information regarding the types of EEG metrics that the authors have chosen to analyse. Why these particular metrics and not others? Despite being an EEG researcher myself I found the term 'local and global dynamics' very vague and unclear. Is the idea to establish whether a particular brain area has recovered normal levels of activity? Some clear statement regarding the functional importance of the chosen metrics is necessary.

In the revised manuscript we have avoided the phrase “local and global dynamics” in the introduction and instead state:

“…all participants had electroencephalographic recording throughout the experiment to assess cortical dynamics of relevance to consciousness and cognition, with techniques that have been used to assess information processing in specific brain regions (permutation entropy) as well as more complex spatiotemporal patterns across the cortex (Lempel-Ziv complexity).”

Furthermore, in the last revision, we added rationale for the selected analytic techniques and highlighted why we chose not to employ phase-based connectivity measures or graph-theoretical variables.

Reviewer #2:

Thanks for amending the manuscript, I think that there is still some work to do in the depiction of results front, make them easy to read and interpretable; on the theoretical framework, making a bit more explicit during the interpretation what theoretical tools are you using to discuss the results; to interpret the correlations with caution.

I think that the information of the cognitive domains that are tested with the tasks is key and forms an important aspect of the paper and the authors should clearly frame the initial hypotheses and results in theoretical terms for the cognitive neuroscience colleagues who will be reading the paper. It will also help understanding better the aims of this work outside the clinical description. From a cognitive perspective, what is recovery from unconsciousness? Can we frame different cognitive recovery dynamics in terms of the information integration that networks regain? How is that measured or conceptualized? Is this theory-driven account captured in any shape or form by the complexity measures?

It has been shown that primary cortices (auditory, visual, motor) recover faster or are more resilient to consciousness challenges (sleep, sedation). Does this inform the expected recovery dynamics or helps interpret it?

Can the tasks be ordered in terms of the cognitive demands they pose to the system (the brain)? Does this help to frame the recovery results found?

Do the authors think of these results in light of cognitive demands, information processing, integration of features, perceptual and cognitive modules? Please think in the interpretation of your results in light of a theoretical framework and whether it contributes or not to theory itself. Is this experimental model of cognitive recovery complementary to classic cognitive interference, non-conscious drug challenges, Transcranial magnetic or electrical stimulation?

In short, I do not think it is enough to restrict the introduction or discussion to the cognitive test and its putative neural correlates for the reasons and guiding questions above.

This was an important recommendation and we appreciate the opportunity to broaden the theoretical framework. In response, we have added more than a page to the Discussion to frame the interpretation of our results using the Global Neuronal Workspace (GNW) theory. We acknowledge that there are a number of options for theoretical frameworks but we have chosen this framework because our study focused on both consciousness and cognition, and also suggested a role for prefrontal cortex in recovery of consciousness and cognition, so it aligns with GNW as a cognitive theory of conscious access. Because our initial hypothesis was motivated by empirical rather than theoretical considerations, we restricted the application of GNW theory to a discussion of the results. The following appears after the first paragraph in the Discussion:

“As described in the introduction, our hypothesis was motivated by preliminary evidence in the literature that neurocognitive recovery after general anesthesia proceeds in a caudal-to-rostral direction Långsjö et al., 2012; Reshef et al., 2019(; ), which would imply that executive functions mediated by anterior cortical structures would be the last to return. […] Further work is needed to determine if global neuronal workspace theory is sufficient to describe the reconstitution of consciousness and cognition after a major perturbation such as a general anesthesia, or whether other cognitive theories need to be employed.”

The other comment I have is the depiction of the results. A graph overlapping (even if simplified) the PP speed distributions and the accuracy distributions would make a great figure that to be used for other authors to reference this work in talks and to be used in teaching. In its current form it requires a lot of explanation and it does not paint in one look the findings. This is a preference and nice to have so the work can be featured better in the future.

Thank you for this excellent suggestion. In response, we have created an additional figure (Figure 6; referenced in the Discussion) that summarizes the main findings regarding the recovery of consciousness, recovery of cognition, and recovery of sleep-wake patterns.

For the correlations results, there are clearly weak or no associations and given the number of participants (low for correlations) and high number of comparison the likelihood of chance association is dangerously high. A word of caution in the paper about this will help and also please check if the even the correction for multiple correlations also renders this apparent significant associations void.

We agree. We have added a note of caution regarding these findings:

“These data must be interpreted with caution given the multiple statistical comparisons and weak correlations.”

Will the behavioural and EEG data become available soon and if so in which platform.

In version 2/revision 1 of the manuscript, we included formatted data sets that support each figure.

Author details

1. George A Mashour

   Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Conceptualization, Data curation, Funding acquisition, Investigation, Writing - original draft, Project administration

   For correspondence

   gmashour@umich.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5457-5932

2. Ben JA Palanca

   Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Mathias Basner

   Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Formal analysis

   Competing interests

   No competing interests declared

4. Duan Li

   Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Wei Wang

   Department of Mathematics and Statistics, Washington University, St. Louis, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

6. Stefanie Blain-Moraes

   Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Nan Lin

   Department of Mathematics and Statistics, Washington University, St. Louis, United States

   Contribution

   Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Kaitlyn Maier

   Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Project administration

   Competing interests

   No competing interests declared

9. Maxwell Muench

   Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Project administration

   Competing interests

   No competing interests declared

10. Vijay Tarnal

    Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

11. Giancarlo Vanini

    Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

12. E Andrew Ochroch

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

13. Rosemary Hogg

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

14. Marlon Schwartz

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

15. Hannah Maybrier

    Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States

    Contribution

    Project administration

    Competing interests

    No competing interests declared

16. Randall Hardie

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

17. Ellen Janke

    Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

18. Goodarz Golmirzaie

    Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

19. Paul Picton

    Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    Data curation, Writing - review and editing

    Competing interests

    No competing interests declared

20. Andrew R McKinstry-Wu

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Data curation, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7078-4603

21. Michael S Avidan

    Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Investigation, Writing - original draft

    Contributed equally with

    Max B Kelz

    Competing interests

    No competing interests declared

22. Max B Kelz

    Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing - original draft

    Contributed equally with

    Michael S Avidan

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2803-6078

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