Lack of correlation between cerebral vasomotor reactivity and dynamic cerebral autoregulation during stepwise increases in inspired CO<sub>2</sub> concentration
Bibliographic record
Abstract
Cerebral vasomotor reactivity (CVMR) and dynamic cerebral autoregulation (CA) are measured extensively in clinical and research studies. However, the relationship between these measurements of cerebrovascular function is not well understood. In this study, we measured changes in cerebral blood flow velocity (CBFV) and arterial blood pressure (BP) in response to stepwise increases in inspired CO2 concentrations of 3 and 6% to assess CVMR and dynamic CA in 13 healthy young adults [2 women, 32 ± 9 (SD) yr]. CVMR was assessed as percentage changes in CBFV (CVMRCBFV) or cerebrovascular conductance index (CVCi, CVMRCVCi) in response to hypercapnia. Dynamic CA was estimated by performing transfer function analysis between spontaneous oscillations in BP and CBFV. Steady-state CBFV and CVCi both increased exponentially during hypercapnia; CVMRCBFV and CVMRCVCi were greater at 6% (3.85 ± 0.90 and 2.45 ± 0.79%/mmHg) than at 3% CO2 (2.09 ± 1.47 and 0.21 ± 1.56%/mmHg, P = 0.009 and 0.005, respectively). Furthermore, CVMRCBFV was greater than CVMRCVCi during either 3 or 6% CO2 (P = 0.017 and P < 0.001, respectively). Transfer function gain and coherence increased in the very low frequency range (0.02-0.07 Hz), and phase decreased in the low-frequency range (0.07-0.20 Hz) when breathing 6%, but not 3% CO2 There were no correlations between the measurements of CVMR and dynamic CA. These findings demonstrated influences of inspired CO2 concentrations on assessment of CVMR and dynamic CA. The lack of correlation between CVMR and dynamic CA suggests that cerebrovascular responses to changes in arterial CO2 and BP are mediated by distinct regulatory mechanisms.
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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 | 0.001 | 0.003 |
| 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".