Comparable Blood Velocity Changes in Middle and Posterior Cerebral Arteries During and Following Acute High‐Intensity Exercise in Young Fit Women
Bibliographic record
Abstract
The influence of high‐intensity exercise training (HIIT) on cerebral blood flow (CBF) regulation remains unclear. HIIT induces surges in mean arterial pressure (MAP), which could be transmitted to the brain, especially early after exercise onset. The aims of this study were to 1) describe regional CBF changes during and following 30 s of high‐intensity exercise and; 2) examine whether dynamic cerebral autoregulation (dCA) is associated with CBF changes. Ten women (age: 26 ± 6 yrs; VO 2max : 48.6 ± 3.8 ml×kg×min −1 ) cycled for 30 s at the workload reached at VO 2max followed by 3 min of passive recovery. dCA was characterized using transfer function analysis of forced oscillations induced by repeated squat‐stands (0.05 and 0.10 Hz). Middle (MCAv mean ) and posterior cerebral artery mean blood velocities (PCAv mean ; transcranial Doppler), MAP (finger photoplethysmography) and end‐tidal carbon dioxide partial pressure (P ET CO 2 ; gaz analyzer) were measured . MCAv mean (+19 ± 10%) and PCAv mean (+21 ± 14%) increased early after exercise onset, returning toward baseline values afterwards. MAP increased throughout exercise (p<0.0001). P ET CO 2 initially decreased by 3 ±2 mmHg (p<0.0001) before returning to baseline values at end‐exercise. During recovery, MCAv mean (+43 ±15%), PCAv mean (+42 ± 15%) and P ET CO 2 (+11 ± 3 mmHg; p<0.0001) increased. TFA gain was higher in the MCA (p < 0.0001). Other dCA metrics were comparable between arteries and unrelated to exercise‐induced cerebral blood velocity changes. In young fit women, blood velocity changes during and following a 30‐s high‐intensity exercise are comparable between MCA and PCA and unrelated to dCA. Support or Funding Information L.L. and S.I. are supported by a doctoral training scholarship from the Fonds de recherche du Québec – Santé (FRQS).
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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".