Cerebrovascular reactivity differences in healthy cerebral gray and white matter
Bibliographic record
Abstract
PURPOSE: To quantify the speed and magnitude of cerebrovascular reactivity (CVR) metrics in hemispheric gray and white matter. METHODS: A standardized isoxic hypercapnic cerebrovascular stimulus was administered using an automated arterial gas targeting system while monitoring blood oxygen-level dependent MRI. The correlation between the blood oxygen-level dependent signal and end-tidal partial pressure of carbon dioxide were measured over time, enabling calculation of CVR metrics including the magnitude and speed (TAU) of the vascular response. The average CVR magnitude and TAU metrics were obtained from 50 healthy participants in the following regions: anterior, middle, and posterior cerebral artery (ACA, MCA, PCA) territory gray matter, the striatum, the thalamus, and hemispheric white matter. RESULTS: The average MCA CVR is 17.39% greater than ACA CVR (95% confidence interval [CI]: 3.50, 31.28), and the average PCA CVR is 43.03% (95% CI: 29.13, 56.91) and 21.84% (95% CI: 10.00, 33.68) greater than ACA CVR and MCA CVR, respectively. The average TAU in the six regions were ACA = 29.5 ± 9.7 s, MCA = 29.4 ± 8.7 s, PCA = 28.6 ± 10.2 s, striatum = 30.5 ± 8.9 s, thalamus = 25.7 ± 10.0 s, and white matter = 46.3 ± 6.9 s. TAU was similar among all regions investigated except for the white matter, which was approximately 60% slower than the other regions (p < 0.0001). CONCLUSION: In healthy individuals, there are significant differences in CVR metrics among the ACA, MCA, PCA gray-matter vascular territories, thalamus, striatum, and hemispheric white matter. Future investigations of CVR should consider the presence of regional variability in CVR metrics when comparing healthy and diseased populations.
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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 | 0.001 | 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".