Brain Size and Folding of the Human Cerebral Cortex
Bibliographic record
Abstract
During evolution, the mammalian cerebral cortex has expanded disproportionately to brain volume. As a consequence, most mammals with large brains have profusely convoluted cortices. The human cortex is a good example of this trend, however, given the large variability in human brain size, it is not clear how cortical folding varies from the smallest to the largest brains. We analyzed cortical folding in a large cohort of human subjects exhibiting a 1.7-fold variation in brain volume. We show that the same disproportionate increase of cortical surface relative to brain volume observed across species can be also observed across human brains: the largest brains can have up to 20% more surface than a scaled-up small brain. We introduce next a novel local measure of cortical folding, and we show that the correlation between cortical folding and size varies along a rostro-caudal gradient, being especially significant in the prefrontal cortex. The expansion of the cerebral cortex, and in particular that of its prefrontal region, is a major evolutionary landmark in the emergence of human cognition. Our results suggest that this may be, at least in part, a natural outcome of increasing brain size.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".