Expiratory load compensation is associated with electroencephalographic premotor potentials in humans
Bibliographic record
Abstract
In normal humans during quiet breathing, expiration is mostly driven by elastic recoil of the lungs. Expiration becomes active when ventilation must be increased to meet augmented metabolic demands, or in response to expiratory loading, be it experimental or disease-related. The response to expiratory loading is considered to be mediated by both reflex and cortical mechanisms, but the latter phenomenon have not been neurophysiologically characterized. We recorded the EEG in 20 healthy volunteers (9 men, 11 women, age: 22 to 50 yr) during unloaded breathing, voluntary expirations, and in response to 50 cmH2O·l(-1)·s expiratory resistive load (ERL), 20 cmH2O expiratory threshold load (high ETL), and 10 cmH2O expiratory threshold load (low ETL). EEGs were processed by ensemble averaging expiratory time-locked segments and examined for pre-expiratory potentials, defined as a slow negative shift from the baseline signal preceding expiration, and suggestive of cortical preparation of expiration involving the supplementary motor area. Four subjects were excluded because of technical EEG problems. Pre-expiratory potentials were present in one subject at baseline and in all subjects during voluntary expirations. They were present in eight subjects during low ETL, in 15 subjects during high ETL, and in 13 subjets during ERL (control vs. low ETL, P = 0.008; control vs. high ETL, P < 0.001; and control vs. ERL, P < 0.001). Respiratory discomfort was more intense in the presence of pre-expiratory potentials (P < 0.001). These results provide a neurophysiological substrate to a cortical component of the physiological response to experimental expiratory loads in humans.
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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 | 0.001 |
| 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".