Disrupted Subcortical-Cortical Connections in a Phonological but Not Semantic Task in Chinese Children With Dyslexia
Bibliographic record
Abstract
Reading disability has been considered as a disconnection syndrome. Recently, an increasing number of studies have emphasized the role of subcortical regions in reading. However, the majority of research on reading disability has focused on the connections amongst brain regions within the classic cortical reading network. Here, we used graph theoretical analysis to investigate whether subcortical regions serve as hubs (regions highly connected with other brain regions) during reading both in Chinese children with reading disability (N = 15, age ranging from 11.03 to 13.08 years) and in age-matched typically developing children (N = 16, age ranging from 11.17 to 12.75 years) using a visual rhyming judgment task and a visual meaning judgment task. We found that the bilateral thalami were the unique hubs for typically developing children across both tasks. Additionally, subcortical regions (right putamen, left pallidum) were also unique hubs for typically developing children but only in the rhyming task. Among these subcortical hub regions, the left pallidum showed reduced connectivity with inferior frontal regions in the rhyming judgment but not semantic task in reading disabled compared with typically developing children. These results suggest that subcortical-cortical disconnection, which may be particularly relevant to the phonological and phonology-related learning process, may be associated with Chinese reading disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".