4.1 The time-course recovery of cerebrovascular parameters following acute exercise
Bibliographic record
Abstract
Objective To quantify the extent and duration exercise impacts cerebral blood flow (CBF) regulatory mechanisms: dynamic cerebral autoregulation (dCA), neurovascular coupling (NVC), and cerebrovascular reactivity (CVR). Design Randomized crossover. Setting Controlled lab environment with university students. Participants Using a convenience sample, nine healthy subjects were recruited (age: 26 ± 5 years, BMI: 25 ± 4 kg/cm2). Interventions All individuals performed three conditions: 25-minutes high-intensity intervals (HIIT) (ten, one-minute intervals at ~85–90% heart-rate reserve), 45-minutes of moderate-intensity exercise (MICT) (at ~50–60% heart-rate reserve), and a control condition (30-minutes quiet rest). Outcome Measures Squat-stand maneuvers were utilized to measure dCA; a modified rebreathing technique and controlled step-wise hyperventilation quantified the hypercapnic and hypocapnic slopes of CVR, respectively; and a complex visual scene search paradigm indexed NVC. The aforementioned tests were completed at baseline and zero, one, two, four, six, and eight-hours following the three conditions. Main Results The known cardiac cycle disparity in CBF regulation disappeared following both HIIT and MICT until hour six (p>0.06), indicative dCA was altered following exercise. The CVR hypercapnic slope was attenuated following both exercise conditions until hour two (p<0.02), whereas the hypocapnic slope was not affected (p>0.31). Finally, only HIIT resulted in a reduced activation of the NVC response (all p<0.05). There were no differences across the day within all control metrics (p>0.13). Conclusions These findings inform exercise-induced disruptions to CBF metrics, which highlights the time-courses required for future studies to consider when investigating CBF regulatory mechanisms immediately following sport-related concussions.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".