Early Academic Achievement and Intervention Response: Role of Executive Function

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PROJECT SUMMARY In 2015, over 30% of 4th graders did not show proficiency in reading and math. Given the importance of these academic skills for success in school and employment, understanding more about the mechanisms underlying academic success/failure is a key public health issue. While early reading and math growth each correlate with distinct cognitive skills (phonological awareness for reading and symbolic magnitude processing for math), they also substantially overlap, as seen by: (1) the comorbidity between reading and math difficulties; (2) overlap in genetic variance for reading and math; and (3) the fact that several similar cognitive processes, including executive functions (EFs), are important cognitive correlates of reading and math. Considerable theoretical and empirical evidence also supports the importance of EF to reading and math. For example, while fMRI tasks elicit skill-specific areas [reading: left occipito-temporal; math: intraparietal sulcus], EF regions also are engaged. Although the predictive relations between EF and intervention response are negligible in in school age children who are struggling academically, early EF (in preschool/Kindergarten) significantly predicts later academic success. However, despite these observed relations, there is little understanding of the neural mechanisms by which EF-academic linkages develop, and how distinct neural networks may relate to response to intervention. While brain networks supporting reading, math, and EF have been investigated separately, their integration has not been studied within a developmental and intervention context. Central to the current proposal, our recent work strongly supports a role for EF brain networks in academics: we find that EF neural networks facilitate connections between skill-specific nodes in the brain. We also find that the way EF brain regions interact with reading regions predicts poor readers’ response to reading intervention with 95% accuracy. In the current study, we leverage our team’s expertise in longitudinal multimodal neuroimaging to examine how the neural networks supporting EF and skill-specific regions develop and interact. Specifically, we follow 260 children from Kindergarten through 1st grade, and examine how EF and skill-specific neural network interactions predict general academic growth. Then, we drill down further to examine how EF-skill specific network interactions predict responsiveness to (reading) intervention in poor readers. We hypothesize that the interaction between EF and skill-specific neural networks, not the individual networks themselves, will be highly predictive of: (a) reading and math growth from Kindergarten through 1st grade (Aim 1) and (b) response to reading intervention in 1st grade poor readers (Aim 2). In sum, our proposal aims to elucidate how EF influences early academic growth, specifically whether interactions between networks (vs individual networks) are core driving factors in EF-academic links intervention response. Given the commonality of EF deficits across many developmental disorders, the proposed work has high