BOLD‐based cerebrovascular reactivity vascular transfer function isolates amplitude and timing responses to better characterize cerebral small vessel disease
Bibliographic record
Abstract
Cerebrovascular reactivity (CVR) is a dynamic measure of the cerebral blood vessel response to vasoactive stimulus. Conventional CVR measures amplitude changes in the blood‐oxygenation‐level‐dependent (BOLD) signal per unit change in end‐tidal CO 2 ( P ET CO 2 ), effectively discarding potential timing information. This study proposes a deconvolution procedure to characterize CVR responses based on a vascular transfer function (VTF) that separates amplitude and timing CVR effects. We implemented the CVR‐VTF to primarily evaluate normal‐appearing white matter (WM) responses in those with a range of small vessel disease. Comparisons between simulations of P ET CO 2 input models revealed that boxcar and ramp hypercapnia paradigms had the lowest relative deconvolution error. We used a T 2 * BOLD‐MRI sequence on a 3 T MRI scanner, with a boxcar delivery model of CO 2 , to test the CVR‐VTF approach in 18 healthy adults and three white matter hyperintensity (WMH) groups: 20 adults with moderate WMH, 12 adults with severe WMH, and 10 adults with genetic WMH (CADASIL). A subset of participants performed a second CVR session at a one‐year follow‐up. Conventional CVR, area under the curve of VTF (VTF‐AUC), and VTF time‐to‐peak (VTF‐TTP) were assessed in WM and grey matter (GM) at baseline and one‐year follow‐up. WMH groups had lower WM VTF‐AUC compared with the healthy group ( p < 0.0001), whereas GM CVR did not differ between groups ( p > 0.1). WM VTF‐TTP of the healthy group was less than that in the moderate WMH group ( p = 0.016). Baseline VTF‐AUC was lower than follow‐up VTF‐AUC in WM ( p = 0.013) and GM ( p = 0.026). The intraclass correlation for VTF‐AUC in WM was 0.39 and coefficient of repeatability was 0.08 [%BOLD/mm Hg]. This study assessed CVR timing and amplitude information without applying model assumptions to the CVR response; this approach may be useful in the development of robust clinical biomarkers of CSVD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".