Menstrual cycle status does not impact exercise-based changes in cerebral blood flow or executive function benefits
Bibliographic record
Abstract
A single bout of exercise enhances executive function (EF) and may relate to an increase in cerebral blood flow (CBF). A limitation in the current literature is that biologically female participants are underrepresented given some evidence that changes in hormone levels across the menstrual cycle impact physiological and psychological variables. Here, biologically female participants completed separate single bouts of moderate intensity exercise (80% of estimated lactate threshold) during the follicular (FOL) and luteal (LUT) phases of their menstrual cycle. In addition, biologically male participants completed a same duration/intensity exercise session. Middle cerebral artery velocity (MCAv) was used to estimate CBF and pre- and postexercise EF was assessed via the antisaccade task. Results showed that resting MCAv was larger in the LUT than FOL phase; however, the exercise-mediated increase in MCAv was equivalent between menstrual cycle phases, and between female and male participants. Antisaccade reaction times reliably decreased from pre- to postexercise and frequentist and non-frequentist statistics demonstrated that the magnitude of the decrease was equivalent across FOL and LUT phases, and between female and male participants. Thus, results evince that menstrual cycle status should not serve as a basis limiting biologically female participants’ inclusion in research examining exercise and EF.
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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 | 0.002 |
| 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) | 0.005 | 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".