Altered gray matter development in pre‐reading children with a family history of reading disorder
Bibliographic record
Abstract
Reading disorders are common in children and can impact academic success, mental health, and career prospects. Reading is supported by network of interconnected left hemisphere brain regions, including temporo-parietal, occipito-temporal, and inferior-frontal circuits. Poor readers often show hypoactivation and reduced gray matter volumes in this reading network, with hyperactivation and increased volumes in the posterior right hemisphere. We assessed gray matter development longitudinally in pre-reading children aged 2-5 years using magnetic resonance imaging (MRI) (N = 32, 110 MRI scans; mean age: 4.40 ± 0.77 years), half of whom had a family history of reading disorder. The family history group showed slower proportional growth (relative to total brain volume) in the left supramarginal and inferior frontal gyri, and faster proportional growth in the right angular, right fusiform, and bilateral lingual gyri. This suggests delayed development of left hemisphere reading areas in children with a family history of dyslexia, along with faster growth in right homologues. This alternate development pattern may predispose the brain to later reading difficulties and may later manifest as the commonly noted compensatory mechanisms. The results of this study further shows our understanding of structural brain alterations that may form the neurological basis of reading difficulties.
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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".