Brain Size and Cortical Structure in the Adult Human Brain
Bibliographic record
Abstract
We investigated the scale relationship between size and cortical structure of human brains in a large sample of magnetic resonance imaging data. Cortical structure was estimated with several measures (cortical volume, surface area, and thickness, sulcal depth, and absolute mean curvature in sulcal regions and sulcal walls) using three-dimensional surface-based methods in 148 normal subjects (n [men/women]: 83/65, age [mean +/- standard deviation]: 25.0 +/- 4.9 years). We found significantly larger scaling exponents than geometrically predicted for cortical surface area, absolute mean curvature in sulcal regions and in sulcal walls, and smaller ones for cortical volume and thickness. As brain size increases, the cortex thickens only slightly, but the degree of sulcal convolution increases dramatically, indicating that human cortices are not simply scaled versions of one another. Our results are consistent with previous hypotheses that greater local clustering of interneuronal connections would be required in a larger brain, and fiber tension between local cortical areas would induce cortical folds. We suggest that sex effects are explained by brain size effects in cortical structure at a macroscopic and lobar regional level, and that it is necessary to consider true relationships between cortical measures and brain size due to the limitations of linear stereotaxic normalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".