Shared and Unique Connectivity Signatures of Reading and Language Deficits
Bibliographic record
Abstract
Reading ability depends on multiple cognitive skills, including decoding and language comprehension, which can vary widely across individuals-even among those with similarly low reading performance. To better understand the brain basis of this variability, we used connectome-based predictive modeling (CPM) to identify large-scale functional connectivity patterns associated with reading and language skills in a population-based sample. Cross-sectional CPM models were trained using functional connectivity data from the Adolescent Brain and Cognitive Development study (n = 6894) and tested in two independent cohorts: the New Haven Lexinome Project and the Genes, Reading, and Dyslexia study (combined n = 136). Functional connectivity measures included both resting- and task-based scans. Reading and language were measured with psychometric tests of word reading and vocabulary, respectively. CPM models significantly predicted reading (r = .24) and language (r = .28) scores in the discovery sample and generalized to an external sample (rs = .23 and .19). Anatomically, the reading and language models showed significant overlap, with the medial frontal network emerging as most predictive in both. However, these models exhibited distinct generalization patterns to children with decoding versus language comprehension difficulties-classified using 20th percentile cutoffs-highlighting their neural specificity. Reading and language models included distinct connectivity signatures and generalized differently to children with decoding versus language comprehension difficulties. These findings demonstrate that although reading and language abilities are behaviorally related, they are supported by partially distinct neural architectures. Integrating behavioral and neuroimaging data may clarify specific brain-behavior relationships and inform more tailored interventions for children with reading and language 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 | 0.002 |
| 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 | 0.001 |
| 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".