Neurodevelopmental Trajectories of the Human Cerebral Cortex
Bibliographic record
Abstract
Understanding the organization of the cerebral cortex remains a central focus of neuroscience. Cortical maps have relied almost exclusively on the examination of postmortem tissue to construct structural, architectonic maps. These maps have invariably distinguished between areas with fewer discernable layers, which have a less complex overall pattern of lamination and lack an internal granular layer, and those with more complex laminar architecture. The former includes several agranular limbic areas, and the latter includes the homotypical and granular areas of association and sensory cortex. Here, we relate these traditional maps to developmental data from noninvasive neuroimaging. Changes in cortical thickness were determined in vivo from 764 neuroanatomic magnetic resonance images acquired longitudinally from 375 typically developing children and young adults. We find differing levels of complexity of cortical growth across the cerebrum, which align closely with established architectonic maps. Cortical regions with simple laminar architecture, including most limbic areas, predominantly show simpler growth trajectories. These areas have clearly identified homologues in all mammalian brains and thus likely evolved in early mammals. In contrast, polysensory and high-order association areas of cortex, the most complex areas in terms of their laminar architecture, also have the most complex developmental trajectories. Some of these areas are unique to, or dramatically expanded in primates, lending an evolutionary significance to the findings. Furthermore, by mapping a key characteristic of these development trajectories (the age of attaining peak cortical thickness) we document the dynamic, heterochronous maturation of the cerebral cortex through time lapse sequences ("movies").
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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 | 0.001 |
| 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".