A generalized procedure for calibrated MRI incorporating hyperoxia and hypercapnia
Bibliographic record
Abstract
Calibrated MRI techniques use the changes in cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) signal evoked by a respiratory manipulation to extrapolate the total BOLD signal attributable to deoxyhemoglobin at rest (M). This parameter can then be used to estimate changes in the cerebral metabolic rate of oxygen consumption (CMRO(2)) based on task-induced BOLD and CBF signals. Different approaches have been described previously, including addition of inspired CO(2) (hypercapnia) or supplemental O(2) (hyperoxia). We present here a generalized BOLD signal model that reduces under appropriate conditions to previous models derived for hypercapnia or hyperoxia alone, and is suitable for use during hybrid breathing manipulations including simultaneous hypercapnia and hyperoxia. This new approach yields robust and accurate M maps, in turn allowing more reliable estimation of CMRO(2) changes evoked during a visual task. The generalized model is valid for arbitrary flow changes during hyperoxia, thus benefiting from the larger total oxygenation changes produced by increased blood O(2) content from hyperoxia combined with increases in flow from hypercapnia. This in turn reduces the degree of extrapolation required to estimate M. The new procedure yielded M estimates that were generally higher (7.6 ± 2.6) than those obtained through hypercapnia (5.6 ± 1.8) or hyperoxia alone (4.5 ± 1.5) in visual areas. These M values and their spatial distribution represent a more accurate and robust depiction of the underlying distribution of tissue deoxyhemoglobin at rest, resulting in more accurate estimates of evoked CMRO(2) changes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".