Mechanisms of REM sleep in health and disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Our understanding of rapid eye movement (REM) sleep and how it is generated remains a topic of debate. Understanding REM sleep mechanisms is important because several sleep disorders result from disturbances in the neural circuits that control REM sleep and its characteristics. This review highlights recent work concerning how the central nervous system regulates REM sleep, and how the make up and breakdown of these REM sleep-generating circuits contribute to narcolepsy, REM sleep behaviour disorder and sleep apnea. RECENT FINDINGS: A complex interaction between brainstem REM sleep core circuits and forebrain and hypothalamic structures is necessary to generate REM sleep. Cholinergic activation and GABAergic inhibition trigger the activation of subcoeruleus neurons, which form the core of the REM sleep circuit. SUMMARY: Untimely activation of REM sleep circuits leads to cataplexy - involuntary muscle weakness or paralysis - a major symptom of narcolepsy. Degeneration of the REM circuit is associated with excessive muscle activation in REM sleep behaviour disorder. Inappropriate arousal from sleep during obstructive sleep apnea repeatedly disturbs the activity of sleep circuits, particularly the REM sleep circuit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".