Impaired dynamic cerebrovascular response to hypercapnia predicts development of white matter hyperintensities
Bibliographic record
Abstract
PURPOSE: To evaluate the relationship between both dynamic and steady-state measures of cerebrovascular reactivity (CVR) and the progression of age-related white matter disease. METHODS: Blood oxygen level-dependent (BOLD) MRI CVR scans were acquired from forty-five subjects (age range: 50-90 years, 25 males) with moderate to severe white matter disease, at baseline and one-year follow-up. To calculate the dynamic (τ) and steady-state (ssCVR) components of the BOLD signal response, the PETCO2 signal waveform was convolved with an exponential decay function. The τ corresponding to the best fit between the convolved PETCO2 and BOLD signal defined the speed of response, and the slope of the regression between the convolved PETCO2 and BOLD signal defined ssCVR. ssCVR and τ were compared between normal-appearing white matter (NAWM) that remains stable over time and NAWM that progresses to white matter hyperintensities (WMHs). RESULTS: In comparison to contralateral NAWM, NAWM that progressed to WMH had significantly lower ssCVR values by mean (SD) 46.5 (7.6)%, and higher τ values by 31.9 (9.6)% (both P < 0.01). CONCLUSIONS: Vascular impairment in regions of NAWM that progresses to WMH consists not only of decreased magnitude of ssCVR, but also a pathological decrease in the speed of vascular response. These findings support the association between cerebrovascular dysregulation and the development of WMH.
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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 | 0.002 |
| 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".