Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning
Nature Communications February 25, 2022 Minji Lee, Leandro Sanz, Alice Barra et al. 120 citations
A 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.