Structural and pragmatic language skills in school-age children relate to resting state functional connectivity
Bibliographic record
Abstract
Language difficulties are common in school-age children but their etiology is often unknown. Although neural underpinnings of language have been well-studied in neurotypical individuals, functional connectivity differences between children with language difficulties and their typically-developing peers have not. There is little evidence regarding patterns of neural connectivity for children with language difficulties. Differences in neural networks related to degree of language difficulties and subtype of language skills (structural or pragmatic) are unclear. We examined expressive and receptive language networks, and an executive function network, in school-age children (8-12 years, n = 81) relative to their caregiver-reported language skills. We hypothesized that children with poorer structural and pragmatic language skills would have decreased connectivity in these networks. Participants were separated into groups by structural and pragmatic language scores: those with structural language difficulties (SLD), pragmatic language difficulties (PLD), and combined language difficulties (CLD, consisting of some participants in both SLD and PLD). The remainder of participants were in the typical language (TL) group. Results showed trends toward increased cross-hemispheric connectivity in age-matched controls relative to those with poorer language skills. Specifically, connectivity between bilateral inferior frontal gyri and areas including bilateral supplementary motor areas, cerebellar regions, and bilateral frontal gyri was associated with higher structural and pragmatic language scores. Connectivity among additional regions including bilateral superior temporal gyri and Heschl's gyrus showed both positive and negative correlation with both language scores. This suggests that reduced connectivity between regions involved in language processing may contribute to language difficulties in school-age children.
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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 | 0.002 |
| 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".