The transcriptomic architecture of the human cerebral cortex
Bibliographic record
Abstract
Summary For over a century, scientists have been attempting to map the human cerebral cortex, however, they have not taken into account the complex molecular structure of the cortex, which is only beginning to be understood. Here, we parcellate the human cerebral cortex using a machine learning (ML) approach to define its transcriptomic architecture, revealing a multi-resolution organization across individuals. The transcriptomically-derived spatial patterns of gene expression separate the cortex into three major regions, frontal, temporal and parietooccipital, with smaller subregions appearing at lower levels of the transcriptomic hierarchy. The core regions, which remain stable across different hierarchical levels, are physiologically associated with language, emotion regulation, social cognition, motor and visuospatial processing and planning. Importantly, some core regions cross structural and anatomical boundaries identified in previous parcellations of the cortex, revealing that the transcriptomic architecture of the cortex is closely linked to human-specific higher cognitive function.
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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 | 0.000 |
| 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 | 0.000 |
| 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".