The role of sympathetic activation and carbon dioxide tension in human neurovascular coupling
Bibliographic record
Abstract
Neurovascular coupling describes a series of processes that serve to match blood supply to neuronal metabolism within the central nervous system. We know that the sympathetic nervous system plays a role in several cerebrovascular control pathways, and preclinical non‐human work demonstrates that the sympathetic nervous system may play a role in neurovascular coupling. These preclinical models utilize isolated cell preparations lacking key inputs to the neurovascular unit such as that of the autonomic nervous system, or utilize anesthetics which blunt cerebrovascular regulatory pathways. Here, our objective was to provide preliminary insight into the role of sympathetic nervous system activation in neurovascular coupling by using our non‐anesthetized, non‐stressed, human, truly in vivo model. We utilized moderate lower body negative pressure (−40 mmHg) to elevate sympathetic activity without affecting mean arterial pressure. Beat‐by‐beat blood pressure was recorded via finger photoplethysmography, while cerebral blood velocity in the middle and posterior cerebral arteries was measured via transcranial Doppler. Neurovascular coupling was elicited using our standardized visual stimulus protocol and data was analyzed using our custom software. As lower body negative pressure leads to sympatho‐excitation and hyperventilation, in a subset of individuals we maintained end‐tidal carbon dioxide levels identical to baseline during lower body negative pressure. Absolute cerebral blood velocity was influenced by arterial carbon dioxide levels. Neurovascular coupling was preserved during lower body negative pressure induced sympatho‐excitation and reduced arterial carbon dioxide levels. Support or Funding Information Natural Sciences and Engineering Research Council of Canada, Libin Cardiovascular Institute, Hotchkiss Brain Institute, Compute Canada
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".