Abstract

Understanding how the brain recovers from unconsciousness can inform neurobiological theories of consciousness and guide clinical investigation. To address this question, we conducted a multicenter study of 60 healthy humans, half of whom received general anesthesia for three hours and half of whom served as awake controls. We administered a battery of neurocognitive tests and recorded electroencephalography to assess cortical dynamics. We hypothesized that recovery of consciousness and cognition is an extended process, with differential recovery of cognitive functions that would commence with return of responsiveness and end with return of executive function, mediated by prefrontal cortex. We found that, just prior to the recovery of consciousness, frontal-parietal dynamics returned to baseline. Consistent with our hypothesis, cognitive reconstitution after anesthesia evolved over time. Contrary to our hypothesis, executive function returned first. Early engagement of prefrontal cortex in recovery of consciousness and cognition is consistent with global neuronal workspace theory.

Data availability

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.

Article and author information

Author details

  1. George A Mashour

    Anesthesiology; Neuroscience, University of Michigan, Ann Arbor, United States
    For correspondence
    gmashour@umich.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5457-5932
  2. Ben JA Palanca

    Anesthesiology, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Mathias Basner

    Psychiatry, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Duan Li

    Anesthesiology, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Wei Wang

    Statistics, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Stephanie Blain-Moraes

    Occupational Therapy; Biomedical Engineering, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  7. Nan Lin

    Statistics, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Kaitlyn Maier

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Maxwell Muench

    Anesthesiology, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Vijay Tarnal

    Anesthesiology, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Giancarlo Vanini

    Anesthesiology; Neuroscience, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. E Andrew Ochroch

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Rosemary Hogg

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Marlon Schwartz

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Hannah Maybrier

    Anesthesiology, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Randall Hardie

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Ellen Janke

    Anesthesiology, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. Goodarz Golmirzaie

    Anesthesiology, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  19. Paul Picton

    Anesthesiology, University of Michigan, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Andrew R McKinstry-Wu

    Anesthesiology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7078-4603
  21. Michael S Avidan

    Anesthesiology, Washington University, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Max B Kelz

    Bioengineering, Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2803-6078

Funding

James S. McDonnell Foundation (Understanding Human Cognition)

  • George A Mashour

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Redmond G O'Connell, Trinity College Dublin, Ireland

Ethics

Human subjects: The study received ethics committee approval from the University of Michigan, Washington University, and the University of Pennsylvania; written informed consent was obtained after careful discussion with each participant.

Version history

  1. Received: June 1, 2020
  2. Accepted: May 6, 2021
  3. Accepted Manuscript published: May 10, 2021 (version 1)
  4. Version of Record published: May 28, 2021 (version 2)

Copyright

© 2021, Mashour et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 11,677
    views
  • 1,247
    downloads
  • 52
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. George A Mashour
  2. Ben JA Palanca
  3. Mathias Basner
  4. Duan Li
  5. Wei Wang
  6. Stephanie Blain-Moraes
  7. Nan Lin
  8. Kaitlyn Maier
  9. Maxwell Muench
  10. Vijay Tarnal
  11. Giancarlo Vanini
  12. E Andrew Ochroch
  13. Rosemary Hogg
  14. Marlon Schwartz
  15. Hannah Maybrier
  16. Randall Hardie
  17. Ellen Janke
  18. Goodarz Golmirzaie
  19. Paul Picton
  20. Andrew R McKinstry-Wu
  21. Michael S Avidan
  22. Max B Kelz
(2021)
Recovery of consciousness and cognition after general anesthesia in humans
eLife 10:e59525.
https://doi.org/10.7554/eLife.59525

Share this article

https://doi.org/10.7554/eLife.59525

Further reading

    1. Medicine
    Peigen Chen, Haicheng Chen ... Xing Yang
    Research Article

    Caesarean section scar diverticulum (CSD) is a significant cause of infertility among women who have previously had a Caesarean section, primarily due to persistent inflammatory exudation associated with this condition. Even though abnormal bacterial composition is identified as a critical factor leading to this chronic inflammation, clinical data suggest that a long-term cure is often unattainable with antibiotic treatment alone. In our study, we employed metagenomic analysis and mass spectrometry techniques to investigate the fungal composition in CSD and its interaction with bacteria. We discovered that local fungal abnormalities in CSD can disrupt the stability of the bacterial population and the entire microbial community by altering bacterial abundance via specific metabolites. For instance, Lachnellula suecica reduces the abundance of several Lactobacillus spp., such as Lactobacillus jensenii, by diminishing the production of metabolites like Goyaglycoside A and Janthitrem E. Concurrently, Clavispora lusitaniae and Ophiocordyceps australis can synergistically impact the abundance of Lactobacillus spp. by modulating metabolite abundance. Our findings underscore that abnormal fungal composition and activity are key drivers of local bacterial dysbiosis in CSD.

    1. Medicine
    2. Neuroscience
    Matthew F Wipperman, Allen Z Lin ... Olivier Harari
    Tools and Resources

    Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.