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

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, Vincent Bonhomme, Marcello Massimini, Giulio Tononi, Steven Laureys, Olivia Gosseries () and Seong-Whan Lee ()
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Minji Lee: Korea University
Leandro R. D. Sanz: University of Liège
Alice Barra: University of Liège
Audrey Wolff: University of Liège
Jaakko O. Nieminen: University of Wisconsin
Melanie Boly: University of Wisconsin
Mario Rosanova: University of Milan
Silvia Casarotto: University of Milan
Olivier Bodart: University of Liège
Jitka Annen: University of Liège
Aurore Thibaut: University of Liège
Rajanikant Panda: University of Liège
Vincent Bonhomme: University Hospital of Liège
Marcello Massimini: University of Milan
Giulio Tononi: University of Wisconsin
Steven Laureys: University of Liège
Olivia Gosseries: University of Liège
Seong-Whan Lee: Korea University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28451-0

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