Intracerebral dynamics of sleep arousals: a combined scalp-intracranial EEG study
Bibliographic record
Abstract
As an intrinsic component of sleep architecture, sleep arousals represent an intermediate state between sleep and wakefulness and play an important role in sleep-wake regulation. They have been defined in an all-or-none manner, whereas they actually present a wide range of scalp-electroencephalography (EEG) activity patterns. It is poorly understood how these arousals differ in their mechanisms. Stereo-EEG (SEEG) provides the unique opportunity to record intracranial activities in superficial and deep structures in humans. Using combined polysomnography and SEEG, we quantitatively categorized arousals in non-rapid eye movement sleep into slow wave (SW) and Non-SW arousals based on whether they co-occurred with a scalp-EEG SW event. We then investigated their intracranial correlates in up to 26 brain regions from 26 patients (12 females). Across both arousal types, intracranial theta, alpha, sigma, and beta activities increased in up to 25 regions (p<0.05,d=0.06-0.63), while gamma and high frequency (HF) activities decreased in up to 18 regions across five brain lobes (p<0.05,d=0.06-0.44). Intracranial delta power widely increased across five lobes during SW arousals (p<0.05 in 22 regions,d=0.10-0.39), while it widely decreased during Non-SW arousals (p<0.05 in 19 regions,d=0.10-0.30). Despite these main patterns, unique activity was observed locally in some regions such as the hippocampus and middle cingulate cortex, indicating spatial heterogeneity of arousal responses. Our results suggest that Non-SW arousals correspond to a higher level of brain activation than SW arousals. The decrease in HF activities could potentially explain the absence of awareness and recollection during arousals. Significance StatementIntrinsic to sleep architecture, sleep arousals play an important role in sleep-wake regulation. They are defined in an all-or-none manner, whereas they actually present various scalp electroencephalography (EEG) patterns. Using simultaneous scalp and intracranial EEG in humans, we analyzed the intracranial activity during two types of arousals marked on scalp EEG, quantitatively categorized by whether they co-occurred with a scalp-EEG slow wave (SW). Non-SW arousals present prevalent low-voltage fast activity, while SW arousals exhibit high-voltage slow waves alongside fast activities. This work represents the first intracranial study of different types of NREM sleep arousals and provides a comprehensive description of local brain activities during both arousal types, serving as a foundation for future studies investigating regional behaviors during sleep-wake transition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".