Neurovascular de‐coupling underlies dual‐task cost across cognitive abilities
Bibliographic record
Abstract
Abstract INTRODUCTION We tested the hypothesis that increased middle cerebral artery velocity (MCA velocity) during complex motor (overground walking) and cognitive tasks (e.g., dual task) is associated with cognitive performance in older adults with varying levels of cognitive ability. METHODS Fifty‐six participants (19 females, 75 ± 7 years old) completed a seated single task that assessed working memory performance; a walking single task, assessing overground walking gait speed; and a dual task, combining both. Continuous MCA velocity was collected, and participants completed a Montreal Cognitive Assessment (MoCA). RESULTS Higher MCA velocity was associated with faster gait speed, better working memory performance, and greater MoCA scores (all p < 0.05). Participants with lower MoCA scores had lower MCA velocity ( p = 0.052), slower gait speed ( p = 0.035), and lower working memory performance ( p = 0.016) than people with higher MoCA scores. The hyperemic response of MCA velocity from single task walking to the dual task with increased cognitive load significantly contributed to MoCA scores ( p = 0.017). DISCUSSION The functional response of cerebral blood flow with these tests suggests vascular properties may be considered a biomarker indicative of subclinical cognitive function during walking tasks. Highlights Mobile devices simultaneously assessed neurovascular coupling and dual‐task cost. Middle cerebral artery velocity (MCA velocity) is negatively associated with dual‐task cost. MCA velocity is associated with gait speed, working memory, and Montreal Cognitive Assessment scores. MCA velocity decreased from controls to mild cognitive impairment to dementia. Novel methodological approach to utilize MCA velocity during overground walking, single‐tasks, and dual‐tasks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".