Resting-state functional connectivity and reading subskills in children
Bibliographic record
Abstract
Individual differences in reading ability have been linked to characteristics of functional connectivity in the brain in both children and adults. However, many previous studies have used single or composite measures of reading, leading to difficulty characterizing the role of functional connectivity in discrete subskills of reading. The present study addresses this issue using resting-state fMRI to examine how resting-state functional connectivity (RSFC) related to individual differences in children's reading subskills, including decoding, sight word reading, reading comprehension, and rapid automatized naming (RAN). Findings showed both positive and negative RSFC-behaviour relationships that diverged across different reading subskills. Positive relationships included increasing RSFC among left dorsal and anterior regions with increasing decoding proficiency, and increasing RSFC between the left thalamus and right fusiform gyrus with increasing sight word reading, RAN, and reading comprehension abilities. In contrast, negative relationships suggested greater functional segregation of attentional and reading networks with improved performance on RAN, decoding, and reading comprehension tasks. Importantly, the results suggest that although reading subskills rely to some extent on shared functional networks, there are also distinct functional connections supporting different components of reading ability 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 | 0.000 |
| 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".