Ramp protocol for non-linear cerebrovascular reactivity with transcranial doppler ultrasound
Bibliographic record
Abstract
Cerebrovascular reactivity (CVR) reflects the ability of cerebral blood vessels to adjust their diameter in response to vasoactive stimuli, which is crucial for maintaining brain health. Traditional CVR assessments commonly use a two-point measurement, assuming a linear relationship between cerebral blood flow (CBF) and arterial CO 2 . However, this approach fails to capture non-linear characteristics, particularly the plateaus at extreme CO 2 levels. This study introduces a cost-effective, ramp-based end-tidal CO 2 (PETCO 2 ) protocol to assess non-linear aspects of CVR. Using transcranial Doppler ultrasound, we monitored blood velocity responses to progressive increases in arterial CO 2 levels in eleven healthy adults, covering a spectrum from hypocapnia to hypercapnia. All eleven participants successfully completed the protocol, with an average PETCO 2 range of 26 ± 4 mmHg and blood velocity changes from −29 % to + 50 % relative to baseline. Non-linear CVR characteristics were observed in all subjects. Sigmoid models provided significantly better fits to the CVR data than linear models, while Bayesian approaches followed expected physiological ranges more accurately than least squares regression methods. Unlike traditional CVR methods, this ramp protocol captures the full, non-linear CVR profile. The sigmoid modeling approach offers a more accurate representation of cerebrovascular dynamics, particularly at CO 2 extremes. The PETCO 2 ramp protocol with non-linear CVR modeling shows promise as an accessible and reliable tool for assessing CBF dynamics. With high completion rates, straightforward implementation, and low equipment cost, this approach holds significant potential for clinical applications in cerebrovascular health evaluation. • A PETCO 2 ramp protocol is used to assess non-linear cerebrovascular reactivity. • Sigmoid models outperform linear models in cerebrovascular reactivity assessment. • Transcranial Doppler ultrasound tracks blood velocity across a CO 2 spectrum. • Bayesian fits incorporate physiological priors in cerebrovascular reactivity models. • The protocol is cost-effective, with potential for clinical cerebrovascular applications.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".