Quantifying cerebral blood arrival times using hypoxia-mediated arterial BOLD contrast
Bibliographic record
Abstract
Cerebral blood arrival and tissue transit times are sensitive measures of the efficiency of tissue perfusion and can provide clinically meaningful information on collateral blood flow status. We exploit the arterial blood oxygen level dependent (BOLD) signal contrast established by precisely decreasing, and then increasing, arterial hemoglobin saturation using respiratory re-oxygenation challenges to quantify arterial blood arrival times throughout the brain. We term this approach the Step Hemoglobin re-Oxygenation Contrast Stimulus (SHOCS). Carpet plot analysis yielded measures of signal onset (blood arrival), global transit time (gTT) and calculations of relative total blood volume. Onset times averaged across 12 healthy subjects were 1.1 ± 0.4 and 1.9 ± 0.6 for cortical gray and deep white matter, respectively. The average whole brain gTT was 4.5 ± 0.9 s. The SHOCS response was 1.7 fold higher in grey versus white matter; in line with known differences in tissue-specific blood volume fraction. SHOCS was also applied in a patient with unilateral carotid artery occlusion revealing ipsilateral prolonged signal onset with normal perfusion in the unaffected hemisphere. We anticipate that SHOCS will further inform on the extent of collateral blood flow in patients with upstream steno-occlusive vascular disease, including those already known to manifest reductions in vasodilatory reserve capacity or vascular steal.
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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 | 0.001 |
| 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".