Mapping Brain Growth and Sex Differences Across Prenatal to Postnatal Development
Bibliographic record
Abstract
Abstract The perinatal period, encompassing both prenatal and early postnatal stages, is a highly dynamic and foundational phase of brain development. Despite its significance, limited work has tracked brain growth continuously across prenatal to postnatal development. In this study, we analysed one of the largest perinatal MRI datasets from the Developing Human Connectome Project (798 scans from 699 unique individuals: 263 prenatal and 535 neonatal; 380 males and 319 females) to model age-related changes and sex differences in brain volumes from 21 to 45 weeks postconceptional age. We found that total brain volume grew at an increasing rate, with white matter dominating mid-gestational growth and gray matter dominating late-gestational and postnatal growth. Subcortical gray matter structures showed distinct trajectories and earlier peak growth rates compared to cortical gray matter structures. Additionally, sex differences in brain growth patterns were observed, with males showing greater volumetric increases with age compared with females. The findings demonstrate the evolving structural dynamics of perinatal brain development as well as the importance of integrating prenatal and postnatal neuroimaging to map continuous early brain growth trajectories.
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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 | 0.001 | 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 | 0.000 |
| 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".