Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.

Journal: Nature communications
PMID: 35217645

Abstract

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

Authors

  • Minji Lee
  • Leandro R D Sanz
    Coma Science Group, GIGA-Consciousness, GIGA Research Center, University of Liège, Liège, Belgium.
  • Alice Barra
    Coma Science Group, GIGA-Consciousness, GIGA Research Center, University of Liège, Liège, Belgium.
  • Audrey Wolff
    Coma Science Group, GIGA-Consciousness, GIGA Research Center, University of Liège, Liège, Belgium.
  • Jaakko O Nieminen
    Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA.
  • Melanie Boly
    Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA.
  • Mario Rosanova
    Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy.
  • Silvia Casarotto
    Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy.
  • Olivier Bodart
    Coma Science Group, GIGA-Consciousness, GIGA Research Center, University of Liège, Liège, Belgium.
  • Jitka Annen
    Coma Science Group, GIGA-Consciousness, GIGA Research Center, University of Liège, Liège, Belgium.
  • Aurore Thibaut
    Coma Science Group, GIGA Research, Université et Centre Hospitalier Universitaire de Liège, Liège, Belgium.
  • Rajanikant Panda
    Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka 560029, India; Advanced Brain Imaging Facility, Cognitive Neuroscience Centre, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka 560029, India.
  • Vincent Bonhomme
    GIGA - Consciousness, Anesthesia and Intensive Care Medicine Laboratory, University and CHU University Hospital of Liège, Liège, Belgium; University Department of Anesthesia and Intensive Care Medicine, CHR Citadelle and CHU Liege, Liège, Belgium.
  • Marcello Massimini
    Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy.
  • Giulio Tononi
    Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA.
  • Steven Laureys
    Coma Science Group, University Hospital of Liege, Liege, Belgium.
  • Olivia Gosseries
    Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium.
  • Seong-Whan Lee

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