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.
Daucourt et al. (Mon,) studied this question.