Developmental trajectories of white matter structure in children with and without reading impairments
Bibliographic record
Abstract
Left temporal-parietal white matter structure is consistently associated with reading abilities in children. A small number of longitudinal studies show that development of this area over time is altered in children with impaired reading. However, it remains unclear how brain developmental patterns relate to specific reading skills such as fluency, which is a critical part of reading comprehension. Here, we examined white matter development trajectories in children with dysfluent reading (20 dysfluent and inaccurate readers, 36 dysfluent and accurate readers) compared to non-impaired readers (n = 14) over 18 months. We found typical age-related increases of fractional anisotropy (FA) in bilateral temporal-parietal areas in non-impaired readers, but a lack of similar changes in dysfluent readers. We also found steeper decreases of mean diffusivity (MD) in the right corona radiata and left uncinate fasciculus in dysfluent inaccurate readers compared to dysfluent accurate readers. Changes in diffusion parameters were correlated with changes in reading scores over time. These results suggest delayed white matter development in dysfluent readers, and show maturational differences between children with different types of reading impairment. Overall, these results highlight the importance of considering developmental trajectories, and demonstrate that the window of plasticity may be different for different 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 | 0.001 | 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".