Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network
Bibliographic record
Abstract
BACKGROUND: Substantial evidence acknowledges the complex gene-environment interplay impacting brain development and learning. Intergenerational neuroimaging allows the assessment of familial transfer effects on brain structure, function and behavior by investigating neural similarity in caregiver-child dyads. METHODS: Neural similarity in the human reading network was assessed through well-used measures of brain structure (i.e., surface area (SA), gyrification (lG), sulcal morphology, gray matter volume (GMV) and cortical thickness (CT)) in 69 mother-child dyads (children's age~11 y). Regions of interest for the reading network included left-hemispheric inferior frontal gyrus, inferior parietal lobe and fusiform gyrus. Mother-child similarity was quantified by correlation coefficients and familial specificity was tested by comparison to random adult-child dyads. Sulcal morphology analyses focused on occipitotemporal sulcus interruptions and similarity was assessed by chi-square goodness of fit. RESULTS: Significant structural brain similarity was observed for mother-child dyads in the reading network for lG, SA and GMV (r = 0.349/0.534/0.542, respectively), but not CT. Sulcal morphology associations were non-significant. Structural brain similarity in lG, SA and GMV were specific to mother-child pairs. Furthermore, structural brain similarity for SA and GMV was higher compared to CT. CONCLUSION: Intergenerational neuroimaging techniques promise to enhance our knowledge of familial transfer effects on brain development and disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".