Associations between maternal pre-pregnancy BMI and mean diffusivity of the hippocampus and amygdala in infants
Bibliographic record
Abstract
BACKGROUND: Maternal pre-pregnancy obesity may negatively affect offspring outcomes, including neurodevelopment. This study examined the relationship between maternal pre-pregnancy body mass index (MBMI) and the microstructure of the hippocampus and amygdala in neonates. METHODS: Diffusion tensor imaging was used to assess mean diffusivity (MD) in these brain regions in 122 infants (mean gestational age: 39.9 weeks, mean age at scan: 24.8 days) from the FinnBrain Birth Cohort Study ( www.finnbrain.fi ). Linear regression was applied to explore associations between MBMI and MD at the regional level, while non-parametric permutation analysis was used for voxelwise investigations. RESULTS: A positive association was found between MBMI and hippocampal MD, particularly in the right hippocampus. Voxelwise analyses showed stronger associations in distinct areas: posterior for the right hippocampus and anterior for the left. No significant association was found between MBMI and amygdala MD. CONCLUSION: These findings suggest that in utero exposure to high MBMI may influence hippocampal microstructure in infants, underscoring the need for further research on the intergenerational effects of maternal obesity on early brain development.
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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 | 0.000 | 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".