Fractionation of sex differences in human cortical anatomy
Bibliographic record
Abstract
Humans show reproducible sex differences in regional cortical volume (CV), but it remains unclear how these arise from underlying sex-biases in the two biologically dissociable determinants of CV: surface area (SA) and cortical thickness (CT). Moreover, limited access to experimental methods in humans has hindered direct studies of the causal drivers of regional sex differences in the human cortex, although rodent models have argued for both chromosomal and gonadal contributions to sex-biased mammalian cortical development. Here, we first use structural neuroimaging data in two independent human cohorts (combined N=1,754; 967 females) to quantify and spatially resolve the differential contributions of SA and CT to observed sex differences in CV. These dissociable facets of sex-biased cortical organization are highly reproducible and align with distinct functional networks and histo-molecular signatures. We then leverage complementary neuroimaging data in clinical case-control cohorts (combined N=313) featuring variations in X and Y chromosome dosage (sex chromosome aneuploidies) and testicular hormone production (isolated GnRH deficiency) to establish that regions of sex-biased CV, SA and CT in humans are enriched for congruent anatomical effects of X-chromosome dosage (e.g., primary sensory and insular cortices) and gonadal hormones (e.g. dorsomedial frontal and temporo-parietal-occipital regions). Taken together, these findings substantially advance both the breadth and granularity of our understanding regarding sex-biased cortical organization in humans - disambiguating sex effects on regional CV, SA and CT and nominating their potential genetic and endocrine causes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".