Problems and solutions in quantifying cerebrovascular reactivity using BOLD-MRI
Bibliographic record
Abstract
Abstract Cerebrovascular reactivity (CVR) imaging is used to assess the vasodilatory capacity of cerebral blood vessels. While blood flow (CVRCBF), blood velocity (CVRv), and preferably blood volume changes (CVRCBV) are used to represent physiological CVR, quantifying these measures is fraught with acquisition challenges in humans. Consequently, blood oxygenation level-dependent (BOLD)-MRI CVR (CVRBOLD) is the most widely used MRI-based CVR method, even though it arguably provides the most indirect estimation of CVR. In this paper, we sought to holistically address the quantitative capacity and shortcomings of CVRBOLD. To do so, we developed a CVRBOLD simulation framework and, together with data from the CVRBOLD literature, addressed whether and to what extent CVRBOLD accurately reflects CVR, and with which parameters CVRBOLD varies most. In short, we show the following: CVRBOLD does not necessarily correspond to physiological measures of CVR and depends on physiological (e.g., hematocrit) and acquisition (e.g., field strength) parameters; CVRBOLD is dependent on the stimulus protocol (e.g., breath-holding vs. controlled hypercapnia) chosen to elicit a vasoactive response; resting-state CVRBOLD does not necessarily reflect breath-hold CVRBOLD, likely due to confounding neuronal activity; in stenotic disease and steal physiology, CVRBOLD results from a combination of factors which do not necessarily reflect the underlying CVR. We are confident that this work will provide researchers and clinicians with invaluable insights and advance the field of cerebrovascular imaging by enabling more accurate quantification of CVR in both health and disease.
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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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".