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

Medicine and Health

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

M. Lee, L. R. D. Sanz, et al.

This groundbreaking research introduces the explainable consciousness indicator (ECI), utilizing deep learning to differentiate between arousal and awareness in various altered states of consciousness. By analyzing EEG responses in patients undergoing sleep, anesthesia, and severe brain injuries, the ECI reveals fascinating insights into states like ketamine-induced anesthesia, revealing the complexity of human consciousness. Discover the work of leading experts, including Minji Lee and Steven Laureys, in this innovative study.

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Playback language: English
Abstract
This paper introduces an explainable consciousness indicator (ECI) using deep learning to distinguish between arousal and awareness in altered states of consciousness. Electroencephalographic (EEG) responses to transcranial magnetic stimulation (TMS) were used in sleep, general anesthesia, and severe brain injury patients. The ECI effectively quantifies both arousal and awareness, distinguishing states like ketamine-induced anesthesia (low arousal, high awareness) from others. Parietal regions showed the most relevance in quantifying both components.
Publisher
Nature Communications
Published On
Feb 25, 2022
Authors
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
Tags
explainable consciousness indicator
deep learning
arousal
awareness
EEG responses
altered states of consciousness
transcranial magnetic stimulation

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