Chemogenetic modulation of the parafacial respiratory group influences the recruitment of abdominal activity during REM sleep
Bibliographic record
Abstract
Current theories on respiratory control postulate that the respiratory rhythm is generated by oscillatory networks in the medulla: preBötzinger complex (preBötC) is the master oscillator responsible for generating inspiration, while parafacial respiratory group (pFRG) drives active expiration through recruitment of expiratory abdominal (ABD) muscle activity. Research addressing the role of pFRG in ventilation and rhythm generation across sleep states is limited. We recently reported the occurrence of ABD recruitment occurring despite the induction of muscle paralysis during REM sleep. This ABD recruitment was associated with increased tidal volume and regularization of the respiratory period in rats. As pFRG generates active expiration through the engagement of ABD muscles, we hypothesized that the expiratory oscillator is also responsible for the ABD recruitment observed during REM sleep. To test this hypothesis, we inhibited and activated pFRG using chemogenetics (i.e. designer receptors exclusively activated by designer drugs) while recording EEG and respiratory muscle EMG activities across sleep-wake cycles in male Sprague-Dawley rats. Our results suggest that inhibition of pFRG reduced the number of REM events expressing ABD recruitment, in addition to the intensity and prevalence of these events. Conversely, activation of pFRG resulted in an increase in the number of REM events in which ABD recruitment was observed, as well as the intensity and prevalence of ABD recruitment. Interestingly, modulation of pFRG activity did not affect ABD recruitment during NREM sleep or wakefulness. These results suggest that the occurrence of ABD recruitment during sleep is dependent on pFRG activity and is state dependent.
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 | 0.000 | 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".