Phonological decoding ability is associated with fiber density of the left arcuate fasciculus longitudinally across reading development
Bibliographic record
Abstract
Abstract Numerous studies have linked reading ability to white matter microstructure using diffusion tensor imaging, but recent large studies have failed to show consistency. Fiber-specific diffusion-weighted magnetic resonance imaging (dMRI) models offer enhanced precision to measure specific features of white matter structure and may be more sensitive to individual differences in reading skills. However, fiber-specific models have not yet been applied to examine associations between reading ability and white matter microstructure over the course of reading acquisition. In this accelerated longitudinal study, we applied constrained spherical deconvolution (CSD) and fiber-specific modelling to characterize developmental changes in fiber density of key white matter tracts of the reading network bilaterally, and investigated associations between tract-wise fiber density and children’s phonological decoding abilities. Fiber density was measured from ages 2-13 years, and decoding ability (pseudoword reading) was assessed at ages 6 years and older. Higher decoding ability was associated with greater fiber density in the left arcuate fasciculus, and effects remained consistent over time. Follow-up analysis revealed that asymmetry changes in the arcuate fasciculus were moderated by decoding ability: good decoders showed leftward asymmetry from early childhood onward, while poorer decoders shifted toward leftward asymmetry over time. These results suggest that densely organized fibers in the left arcuate fasciculus serve as a foundation for the development of reading skills from the pre-reading stage through fluent reading. Ongoing developmental changes in fiber density and microstructural asymmetry throughout childhood may reflect a reciprocal relationship in which reading experience continues to refine and strengthen these pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".