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

Author

Listed:
  • Minji Lee

    (Korea University)

  • Leandro R. D. Sanz

    (University of Liège
    University Hospital of Liège)

  • Alice Barra

    (University of Liège
    University Hospital of Liège)

  • Audrey Wolff

    (University of Liège
    University Hospital of Liège)

  • Jaakko O. Nieminen

    (University of Wisconsin
    Aalto University School of Science)

  • Melanie Boly

    (University of Wisconsin
    University of Wisconsin)

  • Mario Rosanova

    (University of Milan
    Fondazione Europea di Ricerca Biomedica, FERB Onlus)

  • Silvia Casarotto

    (University of Milan
    IRCCS Fondazione Don Carlo Gnocchi ONLUS)

  • Olivier Bodart

    (University of Liège)

  • Jitka Annen

    (University of Liège
    University Hospital of Liège)

  • Aurore Thibaut

    (University of Liège
    University Hospital of Liège)

  • Rajanikant Panda

    (University of Liège
    University Hospital of Liège)

  • Vincent Bonhomme

    (University Hospital of Liège
    University Department of Anesthesia and Intensive Care Medicine, CHR Citadelle
    Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Research Center, University of Liège)

  • Marcello Massimini

    (University of Milan
    IRCCS Fondazione Don Carlo Gnocchi ONLUS)

  • Giulio Tononi

    (University of Wisconsin)

  • Steven Laureys

    (University of Liège
    University Hospital of Liège)

  • Olivia Gosseries

    (University of Liège
    University Hospital of Liège
    University of Wisconsin
    University of Wisconsin)

  • Seong-Whan Lee

    (Korea University)

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.

Suggested Citation

  • Minji Lee & Leandro R. D. Sanz & Alice Barra & Audrey Wolff & Jaakko O. Nieminen & Melanie Boly & Mario Rosanova & Silvia Casarotto & Olivier Bodart & Jitka Annen & Aurore Thibaut & Rajanikant Panda &, 2022. "Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28451-0
    DOI: 10.1038/s41467-022-28451-0
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    1. Andrea I. Luppi & Michael M. Craig & Ioannis Pappas & Paola Finoia & Guy B. Williams & Judith Allanson & John D. Pickard & Adrian M. Owen & Lorina Naci & David K. Menon & Emmanuel A. Stamatakis, 2019. "Consciousness-specific dynamic interactions of brain integration and functional diversity," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    2. Kristjan Korjus & Martin N Hebart & Raul Vicente, 2016. "An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    3. Sarah Webb, 2018. "Deep learning for biology," Nature, Nature, vol. 554(7693), pages 555-557, February.
    4. M. Rosanova & M. Fecchio & S. Casarotto & S. Sarasso & A. G. Casali & A. Pigorini & A. Comanducci & F. Seregni & G. Devalle & G. Citerio & O. Bodart & M. Boly & O. Gosseries & S. Laureys & M. Massimin, 2018. "Sleep-like cortical OFF-periods disrupt causality and complexity in the brain of unresponsive wakefulness syndrome patients," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    5. Sebastian Lapuschkin & Stephan Wäldchen & Alexander Binder & Grégoire Montavon & Wojciech Samek & Klaus-Robert Müller, 2019. "Unmasking Clever Hans predictors and assessing what machines really learn," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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