Robust estimation of dynamic cerebrovascular reactivity using breath-holding fMRI: application in diabetes and hypertension
Bibliographic record
Abstract
Abstract Breath-holding (BH) tasks during functional magnetic resonance imaging (fMRI) acquisitions are gaining popularity for non-invasive mapping of carbon-dioxide (CO 2 ) driven cerebrovascular reactivity (CVR), which is a valuable clinical marker of vascular function. However, compliance to BH tasks is often unclear, and the ability to record end-tidal CO 2 often limited, rendering the optimal analysis of BH fMRI data a challenge. In this work, we demonstrate an adaptive data-driven approach for estimating CVR from BH fMRI data that minimizes errors due to subject non-compliance and regional CVR time delay variability. Building on previous work, we propose a frequency-domain-based approach for CVR estimation without the need for end-tidal CO 2 (PETCO 2 ) recordings. CVR amplitude is estimated in units of %ΔBOLD directly from the data-driven BH frequency. Serious deviations from the designed task paradigm were suppressed and thus did not bias the estimated CVR values. We demonstrate our method in detecting regional CVR amplitude and time-lag differences in a group of 56 individuals, consisting of healthy (CTL), hypertensive (HT) and diabetic-hypertensive (DM+HT) groups of similar ages and sex ratios. The CVR amplitude was lowest in HT+DM, and HT had a lower CVR amplitude than CTL regionally but the voxelwise comparison did not yield statistical significance. Notably, we demonstrate that the voxelwise CVR time delay estimated in Fourier domain is a more sensitive marker of vascular dysfunction than CVR amplitude. While HT+DM seems to confer longer CVR delays, HT seems to confer shorter delays than CTL. These are the first MRI-based observations of CVR time delay differences between diabetic-hypertensive patients and healthy controls. These results demonstrate the feasibility of extracting CVR amplitude and CVR time delay using BH challenges without PETCO 2 recordings, and the unique clinical value of CVR time-delay information.
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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 | 0.000 | 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) | 0.000 | 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 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".