Cerebrovascular Reactivity Mapping: An Evolving Standard for Clinical Functional Imaging
Bibliographic record
Abstract
SUMMARY: This review article explains the methodology of breath-hold cerebrovascular reactivity mapping, both in terms of acquisition and analysis, and reviews applications of this method to presurgical mapping, particularly with respect to blood oxygen level-dependent fMRI. Its main application in clinical fMRI is for the assessment of neurovascular uncoupling potential. Neurovascular uncoupling is potentially a major limitation of clinical fMRI, particularly in the setting of mass lesions in the brain such as brain tumors and intracranial vascular malformations that are associated with alterations in regional hemodynamics on either an acquired or congenital basis. As such, breath-hold cerebrovascular reactivity mapping constitutes an essential component of quality control analysis in clinical fMRI, particularly when performed for presurgical mapping of eloquent cortex. Exogenous carbon dioxide challenges used for cerebrovascular reactivity mapping will also be discussed, and their applications to the evaluation of cerebrovascular reserve and cerebrovascular disease will be described.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".