Multimodal brain features at preschool age and the relationship with pre-reading measures one year later: an exploratory study
Bibliographic record
Abstract
Abstract Pre-reading language skills develop rapidly in early childhood and are related to brain structure and function in young children prior to formal education. However, the early neurobiological development that support these skills is not well understood and has not been assessed longitudinally using multiple imaging approaches. Here we acquired anatomical, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) from 35 children at 3.5 years of age. Children were assessed for pre-reading abilities using the NEPSY-II subtests one year later (4.5 years). We applied a data-driven linked independent component analysis to explore the shared co-variation of grey and white matter measures. Two sources of structural variation at 3.5 years of age demonstrated a weak relationship with Speeded Naming scores at 4.5 years of age. The first imaging component involved volumetric variability in reading-related cortical regions alongside microstructural features of the superior longitudinal fasciculus. The second component was dominated by cortical volumetric variations within the cerebellum and visual association area. In a subset of children with rs-fMRI data, we evaluated the inter-network functional connectivity of the left-lateralized fronto-parietal language (FPL) network and its relationship with pre-reading measures. Higher functional connectivity between the FPL functional network and the default mode and visual networks at 3.5 years predicted better Phonological Processing scores at 4.5 years. Together, these results suggest that the integration of functional networks, as well as the co-development of white and grey matter brain structures in early childhood, may support the emergence of pre-reading measures in preschool 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".