Neurovascular Coupling Across the Menstrual Cycle
Bibliographic record
Abstract
Vascular function is modulated in response to the changing hormonal patterns of the menstrual cycle. Sex hormones may also act on non‐vascular cells regulating blood flow, including astrocytes and neurons, which may alter cerebral blood flow responses. However, the impact of menstrual phase on cerebral neurovascular coupling has not been investigated. We hypothesized that the visually evoked cerebral blood flow response would be higher in the mid‐luteal phase of the menstrual cycle, corresponding to elevated estrogen levels. Nine normally menstruating women (Age = 28 ± 6 yrs, BMI = 25 ± 4kg/m 2 ) underwent a visual stimulation test in the early follicular (EF) and mid‐luteal (ML) phase of the menstrual cycle. Beat‐by‐beat mean arterial pressure (MAP) was derived using photoplethysmography (Finometer) and posterior cerebral artery blood flow velocity (PCAVP) was measured using transcranial Doppler ultrasound (Multigon). At rest, MAP (EF: 86.8 ± 7.4mmHg, ML: 87.7 ± 6.2mmHg, p=0.78) and cerebral blood flow (EF: 36.5 ± 5.5cm/s; ML: 37.6 ±11.3cm/s, p=0.80) were similar between phases. Further, the magnitude (EF: 25.0 ± 6.0%, ML: 26.5 ±8.5%, p=0.38) and the timing (EF: 12.0 ± 1.4s, ML: 11.8 ± 1.9s, p=0.78) of the cerebral blood flow response to visual stimulation was similar between phases. These data suggest that contrary to our hypothesis, neurovascular coupling assessed by visual stimulation was not different across the menstrual cycle. Supported by the University of Alberta ‐ Human Performance Scholarship Fund.
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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) | 0.002 | 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".