Article (Scientific journals)
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.
Lee, Minji; Sanz, Leandro; Barra, Alice et al.
2022In Nature Communications, 13 (1), p. 1064
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Keywords :
Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Physics and Astronomy (all); General Physics and Astronomy; General Biochemistry, Genetics and Molecular Biology; General Chemistry
Abstract :
[en] 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.
Disciplines :
Neurology
Author, co-author :
Lee, Minji ;  Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
Sanz, Leandro  ;  Université de Liège - ULiège > Département des sciences cliniques
Barra, Alice  ;  Université de Liège - ULiège > GIGA
WOLFF, Audrey ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de médecine de l'appareil locomoteur
Nieminen, Jaakko O ;  Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA ; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
Boly, Mélanie  ;  Université de Liège - ULiège > Département des sciences cliniques > Neurologie ; Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA ; Department of Neurology, University of Wisconsin, Madison, WI, USA
Rosanova, Mario ;  Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy ; Fondazione Europea di Ricerca Biomedica, FERB Onlus, Milan, Italy
Casarotto, Silvia ;  Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy ; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
BODART, Olivier  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie
Annen, Jitka  ;  Université de Liège - ULiège > Département des sciences cliniques
Thibaut, Aurore ;  Université de Liège - ULiège > Département des sciences cliniques
Panda, Rajanikant  ;  Université de Liège - ULiège > Département des sciences cliniques
BONHOMME, Vincent  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service d'anesthésie - réanimation
Massimini, Marcello;  Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy ; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
Tononi, Giulio ;  Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin, Madison, USA
LAUREYS, Steven  ;  Centre Hospitalier Universitaire de Liège - CHU > > Centre du Cerveau²
Gosseries, Olivia  ;  Université de Liège - ULiège > Département des sciences cliniques
Lee, Seong-Whan ;  Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea. sw.lee@korea.ac.kr
More authors (8 more) Less
Language :
English
Title :
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.
Publication date :
25 February 2022
Journal title :
Nature Communications
eISSN :
2041-1723
Publisher :
Nature Research, England
Volume :
13
Issue :
1
Pages :
1064
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
MSIP | Institute for Information and communications Technology Promotion
Funding text :
This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean government (Nos. 2017-0-00451; 2017-0-01779; 2019-0-00079; 2019-0-01371; and 2021-0-02068), the University and University Hospital of Liège, Belgian National Fund for Scientific Research (F.R.S-FNRS), the Italian Ministry of Health, GR-2016–02361494 (to S.C.), the Canadian Institute for Advanced Research (CIFAR) (to M.M.), European Union’s Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement (No. 945539, Human Brain Project SGA3) (to M.M. and S.L.), BIAL Foundation, AstraZeneca Foundation, Fund Generate, King Baudouin Foundation, DOCMA project [EU-H2020-MSCA-RISE-–778234], James McDonnell Foundation, Mind Science Foundation, Fondazione Europea di Ricerca Biomedica, National Institutes of Health (No. R01MH064498), Academy of Finland (Nos. 265680 and 294625), Tiny Blue Dot Foundation (to M.M.), and grant EraPerMed JTC 2019 “PerBrain” (to M.R.). L.R.D.S. and R.P. are PhD fellows, O.G. and A. T. are research associates, and S.L. is research director at the F.R.S.–FNRS. We thank S. Lapuschkin for sharing the code; further, we thank all the healthy participants, patients, and their families who participated in this study.
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