Transcranial <scp>Doppler</scp> ultrasound validation of <scp>BOLD</scp> ‐ <scp>fMRI</scp> cerebral blood flow relationship
Bibliographic record
Abstract
Abstract Purpose A precise understanding of the interplay between cerebral blood flow (CBF) and blood oxygen level‐dependent (BOLD) fMRI signals is essential for advancing cerebrovascular research. Although calibrated BOLD approaches often rely on arterial spin labelling (ASL) to estimate CBF, alternative validation using transcranial Doppler ultrasound (TCD) has not been explored. This study aims to determine whether a simplified hemodynamic model and linear regression can accurately characterize the relationship between TCD‐derived CBF velocity and BOLD‐fMRI responses during a ramp CO 2 stimulus. We hypothesized that both models would provide robust fits within the moderate partial pressure of end‐tidal carbon dioxide (PETCO 2 ) and BOLD signal ranges tested. Methods Twenty‐five healthy participants underwent two sessions. In session 1, right middle cerebral artery velocity (MCAv) was acquired using clinical TCD. In session 2, 3 T BOLD‐fMRI data were collected. Both sessions used a ramp PETCO 2 protocol with deep breaths followed by 5% and 10% CO 2 . Data processing included motion correction, spatial smoothing, fieldmap correction, high‐pass filtering, and PETCO 2 alignment with smoothed MCAv (MCA ) and BOLD signals from the right parietal lobe. A simplified hemodynamic model and linear regression were applied to assess the MCA ‐BOLD relationship, with model performance evaluated by R 2 . Results Final analysis included 21 participants. The hemodynamic model produced consistent fits (R 2 ≥ 0.69). Linear regression showed strong agreement between MCA and BOLD (R 2 = 0.759). Conclusion Both modeling approaches successfully linked TCD‐derived MCA and BOLD‐fMRI responses during hypercapnia. These findings support the use of TCD as a complementary surrogate for CBF in BOLD calibration and cerebrovascular research.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".