Electroencephalogram signatures of ketamine anesthesia-induced unconsciousness
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nihms-779861.pdf
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Author(s)
Akeju, Oluwaseun
Pavone, Kara J.
Brown, Emery Neal
Song, Andrew H.
Hamilos, Allison E.
Flores Plaza, Francisco Javier
Purdon, Patrick Lee
Date Issued
March 2016
Journal
Clinical Neurophysiology
Publisher
Elsevier
Citation
Akeju, Oluwaseun, et al. “Electroencephalogram Signatures of Ketamine Anesthesia-Induced Unconsciousness.” Clinical Neurophysiology 127, 6 (June 2016): 2414–2422 © 2016 International Federation of Clinical Neurophysiology
Version
Author's final manuscript
Abstract
Objectives: Ketamine is an N-methyl-. d-aspartate (NMDA) receptor antagonist commonly administered as a general anesthetic. However, neural circuit mechanisms to explain ketamine anesthesia-induced unconsciousness in humans are yet to be clearly defined. Disruption of frontal-parietal network connectivity has been proposed as a mechanism to explain this brain state. However, this mechanism was recently demonstrated at subanesthetic doses of ketamine in awake-patients. Therefore, we investigated whether there is an electroencephalogram (EEG) signature specific for ketamine anesthesia-induced unconsciousness. Methods: We retrospectively studied the EEG in 12 patients who received ketamine for the induction of general anesthesia. We analyzed the EEG dynamics using power spectral and coherence methods. Results: Following the administration of a bolus dose of ketamine to induce unconsciousness, we observed a "gamma burst" EEG pattern that consisted of alternating slow-delta (0.1-4 Hz) and gamma (~27-40 Hz) oscillations. This pattern was also associated with increased theta oscillations (~4-8 Hz) and decreased alpha/beta oscillations (~10-24 Hz). Conclusions: Ketamine anesthesia-induced unconsciousness is associated with a gamma burst EEG pattern. Significance: The EEG signature of ketamine anesthesia-induced unconsciousness may offer new insights into NMDA circuit mechanisms for unconsciousness.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.CLINPH.2016.03.005
