Normative Sex Differences in Cognition and Morphometric Brain Connectivity: Evidence from 30,000+ UK Biobank Participants
Bibliographic record
Abstract
Abstract There is robust evidence for sex differences in domain-specific cognitive performance in the general population, where females typically show an advantage for verbal memory (VM), while males tend to perform better on tasks of spatial memory (SM). Sex differences in brain structure and connectivity are also well-documented and may provide insight into sex differences in cognition. In this study, we examined sex differences in cognition and morphometric brain connectivity of a large healthy sample (N = 31,180) from the UK Biobank dataset. Using T1-weighted magnetic resonance imaging (MRI) scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of morphometric brain connectivity, hypothesizing that sex-related differences in brain network global efficiency, or overall connectivity, would underlie observed cognitive differences. As predicted, females demonstrated better VM performance and males showed an advantage in SM. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males tended to have higher global efficiency, as well as higher regional connectivity (nodal strengths) in both the left and right hemispheres relative to females. Furthermore, higher global efficiency in males was found to mediate observed sex differences in cognition, predicting poorer verbal memory performance, better spatial memory, and slower processing speed in males. These findings contribute to an improved understanding of the way biological sex and differences in cognitive performance are related to morphometric brain 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 | 0.009 |
| 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) | 0.005 | 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".