Neurovascular coupling during dynamic upper body resistance exercise in healthy individuals
Bibliographic record
Abstract
Abstract During unilateral static and rhythmic handgrip exercise, middle cerebral artery blood velocity (MCAv) increases in the contralateral side to the exercising limb. However, whether this neurovascular coupling‐mediated increase in contralateral MCAv is apparent against a background of fluctuating perfusion pressure produced by dynamic resistance exercise (RE) is unclear. We examined the cerebral haemodynamic response to unilateral dynamic RE in 30 healthy individuals (female = 16, mean ± SD: age, 26 ± 6 years; height, 175 ± 10 cm; weight, 74 ± 15 kg; body mass index, 24 ± 5 kg m−2). Participants completed four sets of 10 paced repetitions (15 repetitions min−1) of unilateral bicep curl exercise at 60% of the predicted one‐repetition maximum (7 ± 3 kg). Beat‐to‐beat blood pressure, bilateral MCAv and end‐tidal carbon dioxide were measured throughout. One‐way ANOVA was used to analyse cardiovascular variables and two‐way ANOVA to analyse dependent cerebrovascular variables (side × sets, 2 × 5). A linear mixed model analysis was also performed to investigate the effects of end‐tidal carbon dioxide and mean arterial blood pressure on MCAv. In comparison to baseline, within‐exercise mean arterial blood pressure increased (P < 0.001) across the sets, whereas bilateral MCAv decreased (P < 0.001). However, no significant interaction effect was observed for any dependent variables (all P > 0.787). The linear mixed model revealed that end‐tidal carbon dioxide had the greatest effect on MCAv (estimate = 1.019, t = 8.490, P < 0.001). No differences were seen in contralateral and ipsilateral MCAv during dynamic RE, suggesting that neurovascular coupling contributions during dynamic RE might be masked by other regulators, such as blood pressure.
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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".