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Neural Signatures of Conscious Experience During Sleep: A Serial Awakening Study Using High-Density EEG

Identifying neural signatures of consciousness remains a central challenge in neuroscience. Sleep offers a tractable model for comparing brain activity in the presence or absence of subjective experience while minimizing behavioral responsiveness confounds. Using overnight 256 electrode high-density EEG in 140 participants and a serial-awakening paradigm, we analyzed 699 non-rapid eye movement…

Unraveling the neural signatures of consciousness during sleep has long been a perplexing question in neuroscience. Sleep provides an ideal setting to examine brain activity when experiencing or not experiencing subjective awareness while avoiding behavioral response complications. By employing 256 electrode high-density EEG on 140 participants and implementing a serial-awakening method, researchers examined 699 non-rapid eye movement (NREM) sleep stage 2 and 3 awakenings, categorizing them into 351 instances of dreaming experiences and 348 periods without dreams.

Key features observed in the 60 seconds leading up to each awakening consisted of regional spectral power, lagged-coherence connectivity, graph-theoretic metrics, and gamma-to-alpha power ratios. Participants experiencing dreams displayed shifts in posterior spectral balance, featuring decreased alpha and delta power and heightened gamma-related indicators, alongside altered large-scale network organization compared to those who did not experience dreams.

Using cross-validated machine-learning analyses on a participant-level basis, all classifiers outperformed random chance, with the most effective ensemble model achieving an ROC-AUC of 0.80 and an average precision of 0.80. The findings illuminate reproducible posterior electrophysiological and network-level markers of conscious states during NREM sleep, offering valuable insights into the neural underpinnings of consciousness.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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