Altered sleep EEG spectral dynamics across the menstrual cycle in premenstrual dysphoric disorder
Bibliographic record
Abstract
INTRODUCTION: Premenstrual Dysphoric Disorder (PMDD) is frequently associated with luteal-phase (LP) insomnia. Although the mechanisms underlying insomnia remain unclear, prior evidence suggests paradoxically increased N3 sleep in PMDD across the menstrual cycle. This study investigated potential alterations in sleep microarchitecture in women with PMDD. METHODS: Six women diagnosed with PMDD (32.0 ± 5.7years), along with five healthy controls (30.4 ± 8.2 years) completed polysomnographic sleep recordings (PSGs) every third night throughout one menstrual cycle. EEG spectral analysis (C3-A2) was performed on NREM sleep using Fast Fourier Transform with a 4-s Hamming window and 50 % overlap, and a sleep spindle detector was used. Within-group changes were reported relative to the follicular phase (set at 100 %), and between-group differences relative to controls (set at 100 %), with 95 % confidence intervals for significance. Mixed-model ANOVA was used for spindles parameters. Correlations examined the relationships among EEG frequency bands, core body temperature (Tcore), urinary 6-sulfatoxymelatonin (aMT6), and self-reported mood and sleep quality. RESULTS: Relative to their FP, the PMDD group showed decreased theta and increased spindle frequency activity (SFA) in the LP. Between-group comparisons indicated higher SFA (∼60 %) and spindle density in PMDD across both phases, as well as lower slow wave activity (SWA; ∼40 %). Moreover, SWA correlated with aMT6 and subjective sleep quality, while elevated SFA was associated with Tcore and mood symptoms. CONCLUSIONS: This study revealed alterations in sleep microarchitecture in PMDD, particularly regarding spindle activity and SWA. The previously observed increase in N3 sleep may reflect a compensatory mechanism in response to disrupted homeostatic processes, warranting further investigation into targeted interventions for insomnia in PMDD.
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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".