Problems and solutions in quantifying cerebrovascular reactivity using BOLD-MRI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it