Age-related sex differences in language lateralization: A magnetoencephalography study in children.
Bibliographic record
Abstract
It is well supported by behavioral and neuroimaging studies that typical language function is lateralized to the left hemisphere in the adult brain and this laterality is less well defined in children. The behavioral literature suggests there maybe be sex differences in language development, but this has not been examined systematically with neuroimaging. In this study, magnetoencephalography was used to investigate the spatiotemporal patterns of language lateralization as a function of age and sex. Eighty typically developing children (46 female, 34 male; 4-18 years) participated in an overt visual verb generation task. An analysis method called differential beamforming was used to analyze language-related changes in oscillatory activity referred to as low-gamma event-related desynchrony (ERD). The proportion of ERD over language areas relative to total ERD was calculated. We found different patterns of laterality between boys and girls. Boys showed left-hemisphere lateralization in the frontal and temporal language-related areas across age groups, whereas girls showed a more bilateral pattern, particularly in frontal language-related areas. Differences in patterns of ERD were most striking between boys and girls in the younger age groups, and these patterns became more similar with increasing age, specifically in the preteen years. Our findings show sex differences in language lateralization during childhood; however, these differences do not seem to persist into adulthood. We present possible explanations for these differences. We also discuss the implications of these findings for presurgical language mapping in children and highlight the importance of examining the question of sex-related language differences across 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".