Interactions Between Dyspnea and the Brain Processing of Nociceptive Stimuli: Experimental Air Hunger Attenuates Laser-Evoked Brain Potentials in Humans
Bibliographic record
Abstract
Dyspnea and pain share several characteristics and certain neural networks and interact with each other. Dyspnea-pain counter-irritation consists of attenuation of preexisting pain by intercurrent dyspnea and has been shown to have neurophysiological correlates in the form of inhibition of the nociceptive spinal reflex RIII and laser-evoked potentials (LEPs). Experimentally induced exertional dyspnea inhibits RIII and LEPs, while "air hunger" dyspnea does not inhibit RIII despite its documented analgesic effects. We hypothesized that air hunger may act centrally and inhibit LEPs. LEPs were obtained in 12 healthy volunteers (age: 21-29) during spontaneous breathing (FB), ventilator-controlled breathing (VC) tailored to FB, after inducing air hunger by increasing the inspired fraction of carbon dioxide -FiCO2- (VCCO2), and during ventilator-controlled breathing recovery (VCR). VCCO2 induced intense dyspnea (visual analog scale = 63% ± 6% of full scale, p < 0.001 vs. VC), predominantly of the air hunger type. VC alone reduced the amplitude of the N2-P2 component of LEPs (Δ = 24.0% ± 21.1%, p < 0.05, effect-size = 0.74) predominantly through a reduction in P2, and the amplitude of this inhibition was further reduced by inducting air hunger (Δ = 22.6% ± 17.9%, p < 0.05, effect-size = 0.53), predominantly through a reduction in N2. Somatosensory-evoked potentials (SEPs) were not affected by VC or VCCO2, suggesting that the observed effects are specific to pain transmission. We conclude that air hunger interferes with the cortical mechanisms responsible for the cortical response to painful laser skin stimulation, which provides a neurophysiological substrate to the central nature of its otherwise documented analgesic effects.
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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 | 0.001 |
| 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".