The role of ultradian rhythms in post-deprivation rebounds and diurnal rhythms of sleep and wakefulness in rats
Bibliographic record
Abstract
Abstract The temporal organization of ultradian rhythms in sleep and wakefulness during post-sleep deprivation (TSD) rebound were investigated in 15 rats under contant bright light (LL). Following baseline recordings, rats were subjected to TSD using gentle manual stimulation. Post-TSD rebounds in cumulative wakefulness (WAKE), rapid eye movement sleep (REM) and non-REM sleep (NREM) were analyzed in WAKE-dominant (υ w ) and sleep-dominant (υ s ) ultradian phases. Rebounds in WAKE and NREM were present only when data were analyzed on a full ultradian cycle basis, and were absent in υ s and υ w phases alone. These rebounds were approximately 50% complete and not proportional to TSD excess/deficit. Rebounds in REM were present in full ultradian cycles and partially expressed in υ s but absent in υ w . REM rebounds fully compensated for REM deficit. Rebounds were mediated mainly by a reduction in the duration of the υ w ultradian phase, and by decreased probability of arousal in the υ s ultradian phase. These mechanisms were also found to partially mediate diurnal rhythms in 10 rats under a 12:12 h LD cycle. This study implicates an ultradian timing mechanism in the control of post-TSD rebounds and suggests that rebounds in all three states are mainly mediated by post-TSD adjustments in WAKE-promoting mechanisms. Ultradian rhythms should be taken into account to avoid errors in data analysis. Highlights Sleep-wake state exhibits circadian rhythms and ultradian rhythms. These rhythms interact with rebounds after sleep deprivation. Circadian amplitude and sleep rebound are partially mediated by ultradian timing. Arousal-related processes control these sleep-wake patterns in both states. Measuring ultradian rhythms is necessary for accurate analysis of data.
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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 | 0.000 | 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".