# Early Academic Achievement and Intervention Response: Role of Executive Function

> **NIH NIH R37** · VANDERBILT UNIVERSITY · 2022 · $628,782

## Abstract

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...

## Key facts

- **NIH application ID:** 10246836
- **Project number:** 5R37HD095519-04
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Laurie E Cutting
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $628,782
- **Award type:** 5
- **Project period:** 2018-09-20 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246836

## Citation

> US National Institutes of Health, RePORTER application 10246836, Early Academic Achievement and Intervention Response: Role of Executive Function (5R37HD095519-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246836. Licensed CC0.

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