13 HIGHLIGHTED PUBLICATION 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. Researchers of the Coma Science Group propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. They employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep, general anesthesia, and severe brain injury. They also test their framework using resting-state EEG under general anesthesia and severe brain injury. 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. QUANTIFYING AROUSAL AND AWARENESS IN ALTERED STATES OF CONSCIOUSNESS USING INTERPRETABLE DEEP LEARNING Lee M, Sanz LRD, Barra A, Wolff A, Nieminen JO, Boly M, Rosanova M, Casarotto S, Bodart O, Annen J, Thibaut A, Panda R, Bonhomme V, Massimini M, Tononi G, Laureys S, Gosseries O, Lee SW. Nat Commun. 2022 Feb 25;13(1):1064.
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