Sleep electroencephalographic characteristics of the <scp>C</scp>ynomolgus monkey measured by telemetry
Bibliographic record
Abstract
Cynomolgus monkeys are widely used as models of diseases and in pre-clinical studies to assess the impact of new pharmacotherapies on brain function and behaviour. However, the time course of electroencephalographic delta activity during sleep, which represents the main marker of sleep intensity associated with recovery during sleep, has never been described in this non-human primate. In this study, telemetry implants were used to record one spontaneous 24-h sleep-wake cycle in four freely-moving Cynomolgus monkeys, and to quantify the time course of electroencephalographic activity during sleep using spectral analysis. Animals presented a diurnal activity pattern interrupted by short naps. During the dark period, most of the time was spent in sleep with non-rapid eye movement sleep/rapid eye movement sleep alternations and sleep consolidation profiles intermediate between rodents and humans. Deep non-rapid eye movement sleep showed a typical predominance at the beginning of the night with decreased propensity in the course of the night, which was accompanied by a progressive increase in rapid eye movement sleep duration. Spectral profiles showed characteristic changes between vigilance states as reported in other mammalian species. Importantly, delta activity also followed the expected time course of variation, showing a build-up with wakefulness duration and dissipation across the night. Thus, Cynomolgus monkeys present typical characteristics of sleep architecture and spectral structure as those observed in other mammalian species including humans, validating the use of telemetry in this non-human primate model for translational sleep studies.
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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".