التفاصيل البيبلوغرافية
العنوان:
Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning
المؤلفون:
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 , Seong-Whan Lee
المساهمون:
Korea University, University of Liege, Department of Neuroscience and Biomedical Engineering, University of Wisconsin-Madison, University of Milano, Aalto-yliopisto, Aalto University
المصدر:
Nature Communications, Vol. 13, No. 1
سنة النشر:
2022
مصطلحات موضوعية:
Multidisciplinary , Deep Learning , Consciousness , Brain Injuries , General Physics and Astronomy , Humans , Electroencephalography , General Chemistry , Anesthesia, General , Wakefulness , Arousal , General Biochemistry, Genetics and Molecular Biology
الوصف:
Funding Information: 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. Publisher Copyright: © 2022, The Author(s). 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.
وصف الملف:
application/pdf
تدمد:
2041-1723
DOI:
10.1038/s41467-022-28451-0
URL الوصول:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34dd53dc2d08b8f6e643eddb73350664
حقوق:
OPEN
رقم الأكسشن:
edsair.doi.dedup.....34dd53dc2d08b8f6e643eddb73350664
قاعدة البيانات:
OpenAIRE
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