Dynamic cerebral autoregulation across the cardiac cycle during 8 hr of recovery from acute exercise
Bibliographic record
Abstract
Current protocols examining cerebral autoregulation (CA) parameters require participants to refrain from exercise for 12-24 hr, however there is sparse objective evidence examining the recovery trajectory of these measures following exercise across the cardiac cycle (diastole, mean, and systole). Therefore, this study sought to determine the duration acute exercise impacts CA and the within-day reproducibility of these measures. Nine participants performed squat-stand maneuvers at 0.05 and 0.10 Hz at baseline before three interventions: 45-min moderate-continuous exercise (at 50% heart-rate reserve), 30-min high-intensity intervals (ten, 1-min at 85% heart-rate reserve), and a control day (30-min quiet rest). Squat-stands were repeated at hours zero, one, two, four, six, and eight after each condition. Transcranial doppler ultrasound of the middle cerebral artery (MCA) and the posterior cerebral artery (PCA) was used to characterize CA parameters across the cardiac cycle. At baseline, the systolic CA parameters were different than mean and diastolic components (ps < 0.015), however following both exercise protocols in both frequencies this disappeared until hour four within the MCA (ps > 0.079). In the PCA, phase values were affected only following high-intensity intervals until hour four (ps > 0.055). Normalized gain in all cardiac cycle domains remained different following both exercise protocols (ps < 0.005) and across the control day (p < .050). All systolic differences returned by hour six across all measures (ps < 0.034). Future CA studies may use squat-stand maneuvers to assess the cerebral pressure-flow relationship 6 hr after exercise. Finally, CA measures under this paradigm appear to have negligible within-day variation, allowing for reproducible interpretations to be drawn.
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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".