Determining Significant Within‐Subject Changes of BOLD MRI Cerebrovascular Reactivity to CO <sub>2</sub>
Bibliographic record
Abstract
Cerebrovascular reactivity (CVR) is the ratio of the cerebral blood flow (CBF) response to an increase in a vasoactive stimulus. Current measurement of CVR uses the BOLD MRI signal as an indicator of CBF in response to standardized changes in CO 2 . Despite uniform test conditions, there are still test‐to‐test differences in CVR due to variations in physiology and technology over time. Thus performing longitudinal studies requires the quantification of abnormal differences in CVR over time. We therefore generated a database of normal CVR test‐retest variability as a normal reference to statistically score differences in CVR. Two standardized CVR tests were conducted in fifteen healthy males (36.7 ± 16.1 y) within a three week period. All scans were co‐registered into standard space and a normal difference CVR map was calculated from the two time points for all voxels for each of the 15 subjects consisting of the mean and standard deviation of the differences to generate an interval test difference (ID) atlas. We illustrate our concept by generating ID z‐maps for patients with steno‐occlusive disease who had CVR tests done before and after surgical intervention. Differences in CVR values for the patients were compared to the ID atlas and scored as z‐values, the fractional standard deviation from the corresponding voxel of the atlas. The ID z‐maps confirmed improvement brought about by surgical intervention, and gave an indication of the extent and distribution of changes in CVR. The application of ID z‐maps will enable study of the natural history of cerebrovascular disease and response to treatment.
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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 | 0.001 | 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".