Sex Hormones and Cognition: Neuroendocrine Influences on Memory and Learning
Bibliographic record
Abstract
Sex differences in neurological disease exist in incidence, severity, progression, and symptoms and may ultimately influence treatment. Cognitive disturbances are frequent in neuropsychiatric disease with men showing greater cognitive impairment in schizophrenia, but women showing more severe dementia and cognitive decline with Alzheimer's disease. Although there are no overall differences in intelligence between the sexes, men, and women demonstrate slight but consistent differences in a number of cognitive domains. These include a male advantage, on average, in some types of spatial abilities and a female advantage on some measures of verbal fluency and memory. Sex differences in traits or behaviors generally indicate the involvement of sex hormones, such as androgens and estrogens. We review the literature on whether adult levels of testosterone and estradiol influence spatial ability in both males and females from rodent models to humans. We also include information on estrogens and their ability to modulate verbal memory in men and women. Estrone and progestins are common components of hormone therapies, and we also review the existing literature concerning their effects on cognition. We also review the sex differences in the hippocampus and prefrontal cortex as they relate to cognitive performance in both rodents and humans. There has been greater recognition in the scientific literature that it is important to study both sexes and also to analyze study findings with sex as a variable. Only by examining these sex differences can we progress to finding treatments that will improve the cognitive health of both men and women. © 2016 American Physiological Society. Compr Physiol 6:1295-1337, 2016.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".