Synchrony of neuromodulatory systems during NREM sleep
Bibliographic record
Abstract
Neuromodulatory systems play an essential role in regulating brain states and functions. The canonical view is that the release of monoamines including norepinephrine (NE) and serotonin (5-HT) is high during wakefulness and attenuated during sleep, particularly during rapid eye movement (REM) sleep, whereas the cholinergic system is active during both wakefulness and REM sleep and quiescent during non-REM (NREM) sleep. Recent studies have revealed a slow and rhythmic release pattern of neuromodulators during NREM sleep that drives infraslow oscillation (ISO) (0.02 to 0.03 Hz) in the brain. A key question is whether/how the release of different neuromodulators during sleep is coordinated. In this study, we combined 2-site fiber photometry with electroencephalogram/electromyography recording to monitor the release of NE, 5-HT, and acetylcholine in the cortex and hippocampus during sleep and wake cycles. We found that the ISO of these neuromodulators is synchronized during NREM sleep. The synchrony between neuromodulatory systems increases in the oscillatory cycles leading to arousal. Furthermore, pharmacological inhibition of either the 5-HT or NE system eliminates the oscillation of other neuromodulators during NREM sleep. Optogenetic activation of 5-HT or NE neurons during NREM sleep induces the release of other neuromodulators in the absence of sleep-to-wake transitions. These results suggest that the rhythmic neuromodulator releases are highly coordinated in the brain. The synchrony among multiple neuromodulatory systems across brain regions provides a powerful neural mechanism to orchestrate sleep architecture and sleep-related neural processes.
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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".