Association of Cerebrovascular Reactivity and Cognitive Performance in a Racially Diverse Cohort
Bibliographic record
Abstract
Abstract Background Cerebrovascular reactivity (CVR) to increased carbon dioxide (CO2) reflects cerebral microvascular function and is impaired in hypertension and stroke. Limited studies have looked at its relationship with cognitive performance in African Americans and Whites. Methods We used data from the Brain‐Stress Hypertension and Aging Program cohort (B‐SHARP) consisting of racially diverse individuals with normal cognition or mild cognitive impairment (MCI). CVR was measured using advanced signal modeling approach developed by BH/IH during blood oxygenation level dependent (BOLD) resting state ‐fMRI. Participants underwent cognitive assessment using global and domain‐specific tests such as Montreal Cognitive Assessment (MoCA), Boston Naming Test (BNT), Hopkins Verbal Learning Test‐Revised (HVLT‐R), and Trail Making Test. We used multivariate regression models to assess the relationship of CVR and cognitive domains and tested interaction of CVR with race. Results Successful CVR data were completed on 219 participants with mean age= 65.3 years (SD 7.8), 46% African American and 65% women. An increased resting whole brain CVR was associated with a higher MoCA score (β = 6.46, SE 2.43, p‐value 0.008), higher BNT score (β =2.15, SE 0.93 p‐value 0.02), and shorter time on Trail B‐A, (β= ‐113.4, SE 42.0, p‐value 0.007) after adjusting for mean arterial pressure. After adjusting for age, sex, and race, increased resting whole brain CVR was associated with higher MoCA (β =4.4, SE 2.26, p‐value 0.05), BNT score (β =1.46, SE 0.89, p‐value 0.1) and Trail B‐A score (β =‐86.7, SE 40.3, p‐value 0.03). There was no association with delayed memory based on HVLT‐R. The results were similar for individuals with normal cognition or MCI. There was a significant interaction by race (p<0.01) such that whole brain CVR was associated with better performance on all cognitive tests for African Americans but not for Whites. Conclusions Higher CVR, reflected by resting state BOLD‐fMRI, was associated with higher overall cognitive status and specific domains including language and executive functioning in individuals with normal cognition or MCI. The differential effect in African Americans vs Whites could suggest a greater contribution of microvascular dysfunction for the higher prevalence of cognitive impairment in African Americans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".