Distinct cortical haemodynamics during squat-stand and continuous aerobic exercise do not influence the magnitude of a postexercise executive function benefit
Bibliographic record
Abstract
A single bout of aerobic exercise benefits executive function (EF). A potential mechanism for this benefit is an exercise-mediated increase in cerebral blood flow (CBF) that elicits vascular endothelial shear-stress improving EF efficiency. Moderate intensity continuous aerobic exercise (MCE) asymptotically increases CBF, whereas continuous body weight squat-stand exercise (SSE) provides a large amplitude oscillatory response. Some work has proposed that an increase in CBF oscillation amplitude provides the optimal shear-stress for improving EF and brain health. We examined whether a large amplitude oscillatory CBF response associated with a single bout of SSE imparts a larger postexercise EF benefit than an MCE cycle ergometer protocol. Exercise changes in middle cerebral artery velocity (MCAv) were measured via transcranial Doppler ultrasound to estimate CBF, and pre- and postexercise EF was assessed via the antisaccade task. MCE produced a steady state increase in MCAv, whereas SSE produced a large amplitude MCAv oscillation. Both conditions produced a postexercise EF benefit that null hypothesis and equivalence tests showed to be comparable in magnitude. Accordingly, we provide a first demonstration that a single bout of SSE benefits EF; however, the condition's oscillatory CBF response does not impart a larger benefit than a time- and intensity-matched MCE protocol.
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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) | 0.002 | 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".