Spatiotemporal patterns of theta-band activity during rapid-eye movement sleep: a magnetoencephalography analysis
Bibliographic record
Abstract
Theta oscillations (4-8 Hz) in frontal cortical regions are present to different degrees across states of consciousness. In sleep, theta is prominent in periods of rapid eye-movement (REM) sleep. Theta has been linked to processes of memory consolidation; however, its mechanistic contribution specifically during REM sleep is not well understood. Interestingly, in the wake state, frontal theta activity increases during effortful cognitive tasks involving executive functions such as working memory, hinting at similarities in circuitry, and potentially, function. The aim of the present work is to create a spatially resolved, whole-brain characterisation of REM oscillatory activity in healthy human subjects, distinguishing theta from neighbouring frequency bands, differentiating substages of REM sleep (phasic and tonic REM), and comparing REM theta to that which is evoked during a working memory task. To that end, we analysed magneto- and electroencephalography (M/EEG) data recorded during overnight sleep in 10 healthy subjects, and similar data from 17 healthy subjects who performed a working memory task, using a novel whole-brain, source-localised MEG approach. Our results show that (i) theta activity has a frontal midline topography that is distinct from those of other prominent frequency bands in REM (delta, alpha, beta), (ii) theta activity in frontal midline regions is best observed within a focused 5-7 Hz range, separating it from occipital alpha activity, (iii) REM theta is dominant over the frontal midline but is also observed in several sub-cortical areas, (iv) theta is more widespread in tonic than phasic REM sleep, and (v) the focused frontal midline theta pattern observed in REM phasic sleep is the most similar of all observed sleep substages to theta evoked by a working memory task. These results enhance our understanding of theta physiology in REM sleep and suggest future targets for research into REM's role in learning and memory.
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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 | 0.000 |
| 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".