Exercise and Hypercapnia Differentially Modify Ratios of Extracranial and Intracranial Pulsatility
Bibliographic record
Abstract
INTRODUCTION Damping of pulsatile flow between extracranial and intracranial cerebral arteries is an essential allostatic mechanism protecting cerebral microvessels from recalcitrant hemodynamics. The ratio of pulsatility between proximal and distal cerebral arteries may provide a measure of cerebrovascular hemodynamic damping. This might prove useful as an evaluation of cerebrovascular regulation in response to pulsatile perturbations. PURPOSE To characterize cerebral pulsatile damping between extracranial and intracranial environments in response to perturbations eliciting matched shear stress, such as exercise and hypercapnia. METHODS Participants (n=10) completed two 30‐min experimental conditions aimed at matching cerebral artery shear stress, each separated by 48 hrs: (1) mild hypercapnia (CO 2 ; F I CO 2 :0.045) and (2) submaximal cycling (EX; 60%HRreserve). Cerebral pulsatility index (PI: (systolic velocity‐diastolic velocity)/mean velocity)) was assessed at baseline, during, and following each condition in the internal carotid artery (ICA) and middle cerebral artery (MCA) using Doppler ultrasound. Heart rate (HR) and blood pressure (BP) were assessed continuously using ECG and photoplethysmography, respectively. Cerebral pulsatile damping was calculated: (ICA PI / MCA PI) to investigate ratios of cerebral pulsatile hemodynamics between extracranial and intracranial arteries. RESULTS Cerebral pulsatile damping was greater during CO 2 (1.66 ± 0.31) than EX (1.22 ± 0.35) (time*condition effect, p =0.002). The change in cerebral pulsatile damping was related to the change in heart rate (r = ‐0.70, p = 0.04), but not BP between baseline and the experimental conditions. CONCLUSIONS As evidenced by the response in cerebral pulsatile damping, exercise and hypercapnia result in different ratios of extracranial to intracranial pulsatility despite inducing similar vasodilation when shear stress was matched. This might in part be explained by differences in HR between conditions. Further research is important to elucidate the mechanisms behind the deviance in hemodynamic responses.
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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) | 0.002 | 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".