Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning
Minji Lee, Leandro 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
Nature Communications February 25, 2022 DOI: 10.1038/s41467-022-28451-0 via OpenAlex
Summary
AI-generated from the abstractA deep-learning-based explainable consciousness indicator (ECI) uses EEG responses to transcranial magnetic stimulation and resting-state EEG to separately quantify arousal and awareness. Tested during sleep (n=6), general anesthesia (n=16), and severe brain injury (n=34), ECI distinguishes states such as ketamine-induced anesthesia and rapid eye movement sleep, which combine low arousal with high awareness. Parietal brain regions are most relevant for these measurements. The indicator offers a way to disentangle the two components of consciousness across physiological, pharmacological, and pathological conditions.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 71 |
| Population | Healthy individuals during sleep, individuals under general anesthesia, and patients with severe brain injury |
| Keywords | Arousal Consciousness Altered state Computer science Cognitive psychology |
| Citations | 120 |
| Key finding | The explainable consciousness indicator simultaneously quantifies arousal and awareness, clearly distinguishing states with low arousal and high awareness, such as ketamine-induced anesthesia and REM sleep, from other states. |
Abstract
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.