The influence of menstrual phase on synaptic plasticity induced via intermittent theta-burst stimulation
Bibliographic record
Abstract
BACKGROUND: Ovarian hormones influence the propensity for short-term plasticity induced by repetitive transcranial magnetic stimulation (rTMS). Estradiol appears to enhance the propensity for neural plasticity. It is currently unknown how progesterone influences short-term plasticity induced by rTMS. OBJECTIVE: The present research investigates whether the luteal versus follicular phase of the menstrual cycle influence short-term plasticity induced by intermittent theta-burst stimulation (iTBS). We tested the hypothesis that iTBS would increase motor evoked potentials (MEPs) during the follicular phase. Further, we explored the effects of the luteal phase on iTBS-induced neural plasticity. METHOD: Twenty-nine adult females participated in a placebo-controlled study that delivered real and sham iTBS to the left primary motor cortex in separate sessions corresponding to the follicular phase (real iTBS), luteal phase (real iTBS), and a randomly selected day (sham iTBS). Outcomes included corticospinal excitability as measured by the amplitude of MEPs and short-interval intracortical inhibition (SICI) recorded from the right first dorsal interosseous muscle before and following iTBS (612 pulses). RESULTS: MEP amplitude was increased following real iTBS during the follicular condition. No significant changes in MEP amplitude were observed during the luteal or sham visits. SICI was unchanged by iTBS irrespective of menstrual phase. CONCLUSION: These findings suggest women experience a variable propensity for iTBS-induced short-term plasticity across the menstrual cycle. This information is important for designing studies aiming to induce plasticity via rTMS in women.
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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".