TY - JOUR
T1 - Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning
AU - Lee, Minji
AU - Sanz, Leandro R.D.
AU - Barra, Alice
AU - Wolff, Audrey
AU - Nieminen, Jaakko O.
AU - Boly, Melanie
AU - Rosanova, Mario
AU - Casarotto, Silvia
AU - Bodart, Olivier
AU - Annen, Jitka
AU - Thibaut, Aurore
AU - Panda, Rajanikant
AU - Bonhomme, Vincent
AU - Massimini, Marcello
AU - Tononi, Giulio
AU - Laureys, Steven
AU - Gosseries, Olivia
AU - Lee, Seong Whan
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85125515933
U2 - 10.1038/s41467-022-28451-0
DO - 10.1038/s41467-022-28451-0
M3 - Article
C2 - 35217645
AN - SCOPUS:85125515933
SN - 2041-1723
VL - 13
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 1064
ER -