Activation of the periaqueductal gray controls respiratory output through a distributed brain network
Bibliographic record
Abstract
Introduction: The periaqueductal gray (PAG) has been previously established to play a key role in producing the vital changes in respiration occurring in response to threat. However, it is not fully understood how PAG activation alters the ongoing respiratory output, nor it is understood which pathways mediate these effects, as several regions have been previously identified to influence respiratory activity. Methods: We used optogenetic tools in conjunction with EMG recordings of inspiratory and expiratory musculature to determine how PAG activation on short (250 ms) and longer (10-15 s) timescales alters respiratory muscle activity. Through cFOS mapping, we also identified key downstream brain regions which were likely modulated by PAG activation including the preBötzinger Complex (preBötC) and the lateral parafacial area (pFL). We then stimulated PAG terminals in those regions to determine whether their activity can account for the observed effects of PAG stimulation. Results: Directly stimulating the PAG resulted in prominent changes to all recorded muscle activities and reset the breathing rhythm in either a phase-independent or phase-dependent manner. In contrast, stimulating PAG terminals in either preBötC or pFL with long or shorter timescale stimuli could not completely replicate the effects of direct PAG stimulation and also did not produce any respiratory reset. Conclusions: Our results show that the effects of PAG activity on respiration are not mediated solely by PAG inputs to either the preBötC or pFL and more likely involve integration across a larger network of brainstem areas.
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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".