Sex Differences in Cortical Morphometry during Ageing: Examining the Interplay between Lifestyle and Reproductive Factors
Bibliographic record
Abstract
Abstract Sex differences in neurodegenerative disorder prevalence have been attributed to life expectancy, modifiable risk factors related to lifestyle and the impact of changes in sex hormones and the reproductive system. Although these factors are known to interact with one another, they are often studied in isolation. Here, we used a multivariate approach to investigate how lifestyle, along with menopause and the number of children, interacts with cortical thickness (CT) in healthy adults. Using CT measures from T1-weighted scans (MPRAGE, 1 mm 3 voxels; 124 participants; 67 females; 40-70 years old) from the Cam-CAN dataset. Using a partial least squares decomposition, we identified patterns of covariance between CT and lifestyle factors, menopause and the number of children. In women, we identified significant patterns that linked education, socioeconomic status, social contact and length of reproductive period to CT in the left prefrontal cortex, as well as alcohol consumption, physical activity and menopausal status to CT in the frontal poles. Contrastingly, the results in men were driven by education and anxiety, and involved increased CT in the temporal poles. Our findings suggests that sex differences in cortical anatomy during brain ageing might be driven by interactions between contrasting lifestyles and the female-specific endocrine environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".