Evaluating the association between steroid hormones and filtering of sensory information in healthy women
Bibliographic record
Abstract
Sensori (motor) gating is an integral part of information processing. Stress may affect information processing and is a potent risk factor for the onset and worsening of mental disorders. Sensori (motor) gating is affected by sex hormone rhythms, e.g. changes over the menstrual cycle. Here, we investigated if steroid hormones, i.e. cortisol and ovarian sex hormones, influence sensori (motor) gating. Data from 53 naturally cycling healthy women were analyzed in a cross-sectional design. Associations between prepulse inhibition (PPI) and P50 suppression variables and endogenous estradiol, progesterone, testosterone, total daily cortisol, and the cortisol awakening response were evaluated using multiple linear regression and Spearman's correlation. Total daily cortisol was negatively associated with PPI. Estradiol was positively associated with the acoustic startle response in the PPI paradigm and the P50 amplitude to the conditioning stimulus in the P50 paradigm. Steroid hormone levels were not associated with P50 suppression. In conclusion, estrogen and cortisol levels in healthy, naturally cycling women are associated with key brain measures of basic information processing. This finding may stimulate further studies into the role of stress hormones and estrogen transitions in modulating information filtering, which may be critical for risk and resilience to mental disorders.
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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".