CO <sub>2</sub> cerebrovascular reactivity measured with CBF-MRI in older individuals: Association with cognition, physical function, amyloid and tau proteins
Bibliographic record
Abstract
Vascular pathology is the second leading cause of cognitive impairment and represents a major contributing factor in mixed dementia. However, biomarkers for vascular cognitive impairment and dementia (VCID) are under-developed. Here we aimed to investigate the potential role of CO2 Cerebrovascular Reactivity (CVR) measured with phase-contrast quantitative flow MRI in cognitive impairment and dementia. Forty-five (69 ± 7 years) impaired (37 mild-cognitive-impairment and 8 mild-dementia by syndromic diagnosis) and 22 cognitively-healthy-control (HC) participants were recruited and scanned on a 3 T MRI. Biomarkers of AD pathology were measured in cerebrospinal fluid. We found that CBF-CVR was lower (p = 0.027) in the impaired (mean±SE, 3.70 ± 0.15%/mmHg) relative to HC (4.28 ± 0.21%/mmHg). After adjusting for AD pathological markers (Aβ42/40, total tau, and Aβ42/p-tau181), higher CBF-CVR was associated with better cognitive performance, including Montreal Cognitive Assessment, MoCA (p = 0.001), composite cognitive score (p = 0.047), and language (p = 0.004). Higher CBF-CVR was also associated with better physical function, including gait-speed (p = 0.006) and time for five chair-stands (p = 0.049). CBF-CVR was additionally related to the Clinical-Dementia-Rating, CDR, including global CDR (p = 0.026) and CDR Sum-of-Boxes (p = 0.015). CBF-CVR was inversely associated with hemoglobin A1C level (p = 0.017). In summary, CBF-CVR measured with phase-contrast MRI shows associations with cognitive performance, physical function, and disease-severity, independent of AD pathological markers.
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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) | 0.001 | 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".