Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities
Bibliographic record
Abstract
We examined how thalamocortical connectivity structure reflects children's reading performance. Diffusion-weighted MRI at 3 T and a series of reading measures were collected from 64 children (33 girls) ages 8-14 years with and without dyslexia. The topological properties of the left and right thalamus were computed based on the whole-brain white matter network and a hub-attached reading network, and were correlated with scores on several tests of children's reading and reading-related abilities. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Local efficiency was negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of network efficiency scores; follow-up analyses further demonstrated that this effect was specific to the pulvinar and mediodorsal nuclei of the left thalamus. We validated these results using an independent dataset and demonstrated that that the relationship between thalamic connectivity and phonemic decoding was specifically robust. Overall, the results highlight the role of the left thalamus and thalamocortical network in understanding the neurocognitive bases of skilled reading and dyslexia in 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 | 0.001 |
| 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) | 0.001 | 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".