Identification of a Novel Form of Noradrenergic-Dependent Respiratory Motor Plasticity Triggered by Vagal Feedback
Bibliographic record
Abstract
The respiratory control system is not just reflexive, it is smart, it learns, and, in fact, it has a memory. The respiratory system listens to and carefully remembers how previous stimuli affect breathing. Respiratory memory is laid down by adjusting synaptic strength between respiratory neurons. For example, repeated hypoxic bouts trigger a form of respiratory memory that functions to strengthen the ability of respiratory motoneurons to trigger contraction of breathing muscles. This type of respiratory plasticity is known as long-term facilitation (LTF). Although chemical feedback, such as hypoxia, initiates LTF, it is unknown whether natural modulation of mechanical feedback (from vagal inputs) also causes motor plasticity. Here, we used reverse microdialysis, electrophysiology, neuropharmacology, and histology to determine whether episodic modulation of vagally mediated mechanical feedback is able to induce respiratory LTF in anesthetized adult rats. We show that repeated obstructive apneas disrupt vagal feedback and trigger LTF of hypoglossal motoneuron activity and genioglossus muscle tone. This same stimulus does not cause LTF of diaphragm activity. Hypoxic episodes do not cause apnea-induced LTF; instead, LTF is triggered by modulation of vagal feedback. Unlike hypoxia-induced respiratory plasticity, vagus-induced LTF does not require 5-HT(2) receptors but instead relies on activation of α1-adrenergic receptors on hypoglossal motoneurons. In summary, we identify a novel form of hypoxia- and 5-HT-independent respiratory motor plasticity that is triggered by physiological modulation of vagal feedback and is mediated by α1-adrenergic receptor activation on (or near) hypoglossal motoneurons.
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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".