The Reproducibility of Cerebrovascular Reactivity Across MRI Scanners
Bibliographic record
Abstract
Cerebrovascular reactivity (CVR) is defined as the ratio of the cerebral blood flow (CBF) response to an increase in a vasoactive stimulus. We used changes in blood oxygenation level-dependent (BOLD) MRI as surrogates for changes of CBF, and standardized quantitative changes in arterial partial pressure of carbon dioxide as the stimulus. Despite uniform stimulus and test conditions, differences in voxel-wise BOLD changes between testing sites may remain, attributable to physiologic and machine variability. We generated a reference atlas of normal CVR metrics (voxel-wise mean and SD) for each of two sites. We hypothesized that there would be no significant differences in CVR between the two atlases enabling each atlas to be used at any site. A total of 69 healthy subjects were tested to create site-specific atlases, with 20 of those individuals tested at both sites. 38 subjects were scanned at Site 1 (17F, 37.5 ± 16.8 y) and 51 subjects were tested at Site 2 (22F, 40.9 ± 17.4 y). MRI platforms were: Site 1, 3T Magnetom Skyra Siemens scanner with 20-channel head and neck coil; and Site 2, 3T HDx Signa GE scanner with 8-channel head coil. To construct the atlases, test results of individual subjects were co-registered into a standard space and voxel-wise mean and SD CVR metrics were calculated. Map comparisons of z scores found no significant differences between white matter or gray matter in the 20 subjects scanned at both sites when analyzed with either atlas. We conclude that individual CVR testing, and atlas generation are compatible across sites provided that standardized respiratory stimuli and BOLD MRI scan parameters are used. This enables the use of a single atlas to score the normality of CVR metrics across multiple sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".