Resistance exercise acutely elevates dynamic cerebral autoregulation gain
Bibliographic record
Abstract
Dynamic cerebral autoregulation (dCA) describes the regulation of cerebral blood flow (CBF) in response to fluctuations in systemic blood pressure (BP). Heavy resistance exercise is known to induce large transient elevations in BP, which are translated into perturbations of CBF, and may alter dCA in the immediate aftermath. This study aimed to better quantify the time course of any acute alterations in dCA after resistance exercise. Following familiarisation to all procedures, 22 (14 male) healthy young adults (22 ± 2 years) completed an experimental trial and resting control trial, in a counterbalanced order. Repeated squat-stand manoeuvres (SSM) at 0.05 and 0.10 Hz were used to quantify dCA before, and 10 and 45 min after four sets of ten repetition back squats at 70% of one repetition maximum, or time matched seated rest (control). Diastolic, mean and systolic dCA were quantified by transfer function analysis of BP (finger plethysmography) and middle cerebral artery blood velocity (transcranial Doppler ultrasound). Mean gain (p = 0.02; d = 0.36) systolic gain (p = 0.01; d = 0.55), mean normalised gain (p = 0.02; d = 0.28) and systolic normalised gain (p = 0.01; d = 0.67) were significantly elevated above baseline during 0.10 Hz SSM 10-min post resistance exercise. This alteration was not present 45 min post-exercise, and dCA indices were never altered during SSM at 0.05 Hz. dCA metrics were acutely altered 10 min post resistance exercise at the 0.10 Hz frequency only, which indicate changes in the sympathetic regulation of CBF. These alterations recovered 45 min post-exercise.
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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".