Differential classification of states of consciousness using envelope- and phase-based functional connectivity
Catherine Duclos,
Charlotte Maschke,
Yacine Mahdid,
Kathleen Berkun,
Jason da Silva Castanheira,
Vijay Tarnal,
Paul Picton,
Giancarlo Vanini,
Goodarz Golmirzaie,
Ellen Janke,
Michael S. Avidan,
Max B. Kelz,
Lucrezia Liuzzi,
Matthew J. Brookes,
George A. Mashour,
Stefanie Blain-Moraes
Affiliations
Catherine Duclos
Montreal General Hospital, McGill University Health Centre, 1650 Cedar Ave, Montreal, QC, Canada; School of Physical and Occupational Therapy, McGill University, 3654 Promenade Sir-William-Osler Montreal, Quebec H3G 1Y5, Canada
Charlotte Maschke
Montreal General Hospital, McGill University Health Centre, 1650 Cedar Ave, Montreal, QC, Canada; Integrated Program in Neuroscience, McGill University, 3801 University Street, Montreal Quebec H3A 2B4, Canada
Yacine Mahdid
Montreal General Hospital, McGill University Health Centre, 1650 Cedar Ave, Montreal, QC, Canada; Integrated Program in Neuroscience, McGill University, 3801 University Street, Montreal Quebec H3A 2B4, Canada
Kathleen Berkun
Section on Behavioral Neuroscience, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 United States
Jason da Silva Castanheira
School of Physical and Occupational Therapy, McGill University, 3654 Promenade Sir-William-Osler Montreal, Quebec H3G 1Y5, Canada; Integrated Program in Neuroscience, McGill University, 3801 University Street, Montreal Quebec H3A 2B4, Canada
Vijay Tarnal
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Paul Picton
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Giancarlo Vanini
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Goodarz Golmirzaie
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Ellen Janke
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Michael S. Avidan
Department of Anesthesiology, Washington University School of Medicine, 660 S. Euclid Ave. St. Louis, MO 63110 United States
Max B. Kelz
Department of Anesthesiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 United States
Lucrezia Liuzzi
Mood Brain and Development Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 United States
Matthew J. Brookes
Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD United Kingdom
George A. Mashour
Center for Consciousness Science and Department of Anesthesiology, 1301 Catherine Street, 4102 Medical Science 1, University of Michigan Medical School, Ann Arbor, MI 48109 United States
Stefanie Blain-Moraes
Montreal General Hospital, McGill University Health Centre, 1650 Cedar Ave, Montreal, QC, Canada; School of Physical and Occupational Therapy, McGill University, 3654 Promenade Sir-William-Osler Montreal, Quebec H3G 1Y5, Canada; Corresponding author at: Montreal General Hospital, Room L3-317, 1650 Cedar Ave, Montréal, QC H3G 1A4 Canada.
The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8–13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious (“deep” unconsciousness) vs. Pre-ROC (“light” unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., “deep” from “light” unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.
