Precise control of end‐tidal carbon dioxide and oxygen improves BOLD and ASL cerebrovascular reactivity measures
Bibliographic record
Abstract
In-depth investigation of cerebrovascular blood flow and MR mechanisms underlying the blood oxygenation level dependent signal requires precise manipulation of the arterial partial pressure of carbon dioxide and oxygen, measured by their noninvasive surrogates, the end-tidal values. The traditional methodology consists of administering a fixed fractional concentration of inspired CO(2), but this causes a variable ventilatory response across subjects, resulting in different values of end-tidal partial pressures of CO(2) and O(2). In this study, we investigated whether fine control of these end-tidal partial pressures would improve stability and predictability of blood oxygenation level dependent and arterial spin labeling signals for studying cerebrovascular reactivity. In 11 healthy volunteers, we compared the MR signals generated by the traditional fixed fractional concentration of inspired CO(2) method to those of an automated feed-forward system, a simpler, safer, and more compact alternative to dynamic end-tidal forcing systems, designed to target constant end-tidal partial pressures of CO(2) and O(2). We found that near square-wave changes in end-tidal partial pressure of CO(2) of 5, 7.5, and 10 mm Hg (+/-1.01 mm Hg within two to three breaths) and constrained changes in the end-tidal partial pressure of O(2) (<10 mm Hg) induced cerebral vascular reactivity measurements with faster transitions, together with improved stability and gradation, than those achieved with the traditional fixed fractional concentration of inspired CO(2) method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".