# Interventions in math learning disabilities: cognitive and neural correlates

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $672,205

## Abstract

Project Abstract
Mathematical learning disabilities (MLD) impact up to 14% of school-aged children, and are linked to high rates
of morbidity and poorer health outcomes making it a significant public health concern requiring
extensive health resources. Designing effective interventions to remediate MLD and identifying the
cognitive and neurobiological features underlying their efficacy are critical steps for addressing the public
health burdens of innumeracy and learning disabilities more broadly. Leveraging a productive, innovative, and
high-impact line of research, we propose to investigate neurocognitive mechanisms underlying response to
intervention (RTI) aimed at remediating core and persistent cognitive impairments in children with MLD.
To achieve this goal, we will use a theoretically-motivated integrated symbolic/non-symbolic (iSNS)
intervention with a randomized controlled design to enhance cross-format mapping between symbolic and
non-symbolic representations of quantities. We will develop innovative computational models to investigate
individual differences in latent cognitive processes, including evidence accumulation, sensitivity to item
difficulty, and performance monitoring, that underlie learning and brain plasticity in children with MLD. Our
central hypotheses are that (1) iSNS will remediate numerical problem-solving deficits and strengthen latent
cognitive processes in children with MLD, and that (2) plasticity of neural representations and
reconfiguration of functional brain circuits and networks will contribute to learning, retention, and transfer
in children with MLD. Crucially, building on innovative systems neuroscience approaches, we will leverage
novel computational tools and quantitative network analysis of functional brain circuits linking visuospatial
attention, cognitive control, and memory formation systems to advance foundational knowledge of the
neurocognitive mechanisms underlying RTI in children with MLD. Findings from our novel approach and
neurocognitive models will have major implications for informing the etiology of MLD, the neurobiology of
learning disabilities more generally, and the neurocognitive basis of individual differences in RTI.
Findings will also provide new insights into individual differences in learning, with broad consequences for
optimizing learning in all children. More broadly, our proposed studies will provide a deeper understanding of
dynamic neurocognitive processes underlying learning, retention and transfer (generalization) in children
with learning disabilities.

## Key facts

- **NIH application ID:** 10296524
- **Project number:** 2R01HD059205-11A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Vinod Menon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $672,205
- **Award type:** 2
- **Project period:** 2008-12-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10296524

## Citation

> US National Institutes of Health, RePORTER application 10296524, Interventions in math learning disabilities: cognitive and neural correlates (2R01HD059205-11A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10296524. Licensed CC0.

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