Hippocampal volume and affect in response to fluctuating estrogens in menstrual cycle irregularity: a longitudinal single-subject study
Bibliographic record
Abstract
Abstract Irregular menstrual cycles, affecting approximately 30% of women in their reproductive years, are often overlooked in research, limiting our insights into the broader spectrum of hormonal interactions. Understanding the relationship between endogenous hormone fluctuations and brain function across the menstrual cycle, particularly beyond regular menstrual cycles, is essential for comprehending mental disorders prevalent in women. To this aim, a healthy female with an irregular menstrual cycle underwent dense sampling for 5 consecutive weeks, primarily covering the follicular phase and ovulation. Blood draws provided measurements of estradiol, estrone, and progesterone. T1-weighted MRI scans assessed bilateral hippocampal volumes. Positive and negative affect were collected at each session. Statistical analyses included cubic regression curves, Spearman correlations, and mediation regression models to explore hormonal associations with hippocampal morphology and affect. Significant fluctuations in hormonal concentrations, hippocampal volume, and affect were observed across the 25 testing days. Estradiol and estrone significantly correlated with hippocampal volume, while progesterone showed no significant association. Increasing concentrations of estrogens were linked to decreasing positive affect, mediated by hippocampal volume fluctuations. Increasing concentrations of estrogens were further associated with increasing negative affect, however, independently of hippocampal changes. Our findings suggest potential roles of estrogens in affect regulation and brain function in a participant with an irregular menstrual cycle. This research serves as a blueprint for future investigations into the complex interplay between sex hormones and structural brain dynamics beyond regular menstrual cycles and establishes a fundamental framework for the advancement of sex-specific precision medicine.
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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 | 0.001 | 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 | 0.001 | 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".