Copy number variants and the tangential expansion of the cerebral cortex
Bibliographic record
Abstract
The tangential expansion of the human cerebral cortex, indexed by its surface area (SA), occurs mainly during prenatal and early postnatal periods, and is influenced by genetic factors. Here we investigate the role of rare copy number variants (CNVs) in shaping SA, and the underlying mechanisms, by aggregating CNVs across the genome in community-based cohorts (N = 39,015). We reveal that genome-wide CNV deletions and duplications are associated with smaller SA. Subsequent analyses with gene expression in fetal cortex suggest that CNVs influence SA by interrupting the proliferation of neural progenitor cells during fetal development. Notably, the deletion of genes with strong (but not weak) coexpression with neural progenitor genes is associated with smaller SA. Follow up analyses reveal similar mechanisms at play in three clinical CNVs, 1q21.1, 16p11.2 and 22q11.2. Together, this study of rare CNVs expands our knowledge about genetic architecture of human cerebral cortex. Variation in cortical surface area in adults can reflect developmental events occurring during prenatal and early postnatal periods. Here, the authors find rare copy number variants associated with cortical surface area, which are also found to disrupt neural progenitor proliferation during fetal development.
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 | 0.001 |
| 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) | 0.002 | 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".