Functional connectivity in the developing language network in 4‐year‐old children predicts future reading ability
Bibliographic record
Abstract
Understanding how pre-literate children's language abilities and neural function relate to future reading ability is important for identifying children who may be at-risk for reading problems. Pre-literate children are already proficient users of spoken language and their developing brain networks for language become highly overlapping with brain networks that emerge during literacy acquisition. In the present longitudinal study, we examined language abilities, and neural activation and connectivity within the language network in pre-literate children (mean age = 4.2 years). We tested how language abilities, brain activation, and connectivity predict children's reading abilities 1 year later (mean age = 5.2 years). At Time 1, children (n = 37) participated in a functional near infrared spectroscopy (fNIRS) experiment of speech processing (listening to words and pseudowords) and completed a standardized battery of language and cognitive assessments. At Time 2, children (n = 28) completed standardized reading assessments. Using psychophysiological interaction (PPI) analyses, we observed significant connectivity between the left IFG and right STG in pre-literate children, which was modulated by task (i.e., listening to words). Neural activation in left IFG and STG and increased task-modulated connectivity between the left IFG and right STG was predictive of multiple reading outcomes. Increased connectivity was associated later with increased reading ability.
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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 | 0.000 |
| 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".