Influence of high‐intensity interval training to exhaustion on the directional sensitivity of the cerebral pressure‐flow relationship in young endurance‐trained men
Bibliographic record
Abstract
We previously reported subtle dynamic cerebral autoregulation (dCA) alterations following 6 weeks of high-intensity interval training (HIIT) to exhaustion using transfer function analysis (TFA) on forced mean arterial pressure (MAP) oscillations in young endurance-trained men. However, accumulating evidence suggests the cerebrovasculature better buffers cerebral blood flow changes when MAP acutely increases compared to when MAP acutely decreases. Whether HIIT affects the directional sensitivity of the cerebral pressure-flow relationship in these athletes is unknown. In 18 endurance-trained men (age: 27 ± 6 years, VO2max: 55.5 ± 4.7 ml·kg−1·min−1), we evaluated the impact of 6 weeks of HIIT to exhaustion on dCA directionality using induced MAP oscillations during 5-min 0.05 and 0.10 Hz repeated squat-stands. We calculated time-adjusted changes in middle cerebral artery mean blood velocity (MCAv) per change in MAP (ΔMCAvT/ΔMAPT) for each squat transition. Then, we compared averaged ΔMCAvT/ΔMAPT during MAP increases and decreases. Before HIIT, ΔMCAvT/ΔMAPT was comparable between MAP increases and decreases during 0.05 Hz repeated squat-stands (p = 0.518). During 0.10 Hz repeated squat-stands, ΔMCAvT/ΔMAPT was lower during MAP increases versus decreases (0.87 ± 0.17 vs. 0.99 ± 0.23 cm·s−1·mmHg−1, p = 0.030). Following HIIT, ΔMCAvT/ΔMAPT was superior during MAP increases over decreases during 0.05 Hz repeated squat-stands (0.97 ± 0.38 vs. 0.77 ± 0.35 cm·s−1·mmHg−1, p = 0.002). During 0.10 Hz repeated squat-stands, dCA directional sensitivity disappeared (p = 0.359). These results suggest the potential for HIIT to influence the directional sensitivity of the cerebral pressure-flow relationship in young endurance-trained men.
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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 | 0.000 |
| 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".