Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants
Bibliographic record
Abstract
There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".