Deep Phenotyping of Dyslexia Subtypes
- Institution
- Stanford University, CA
- Principal investigator
- Jason D Yeatman
- Funding
- $662K
- Duration
- 2025–2029
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Abstract
PROJECT SUMMARY Learning to read depends on a confluence of foundational skills. Some skills, such as phonological awareness, are known to be critical. However, the role of other factors – such as differences in visual processing and executive functions (EFs) – are still debated . The overarching goal of this proposal is to characterize consistency and heterogeneity in mechanisms associated with word reading difficulties (i.e., developmental dyslexia) using a deep phenotyping approach. Here we take an innovative approach involving a) development of new, open-source technology and b) large-scale data collection in a diverse sample spanning over 100 schools across 16 states to answer three significant questions regarding the mechanisms of word reading difficulties such as dyslexia: Do individual differences in visual processing and executive functions (EFs) explain additional variance in reading abilities above and beyond phonological awareness? Within the domains of visual processing and EF, does one factor or multiple independent factors contribute to differences in reading ability? Are measures of visual processing and EF useful additions to conventional dyslexia screening in kindergarten and first grade? These questions represent long-standing scientific challenges with significant implications for dyslexia screening and intervention. We address these significant questions through an innovative methodology: ● Open-source assessment platform: We have developed and validated an online platform for reading assessment - the Rapid Online Assessment of Reading (ROAR) - that allows us to collect reliable and valid measures of reading ability, phonological processing, rapid automatized naming, visual processing, and executive functions at scale. Our team has a long history of developing open-source software to support rigorous, robust, and reproducible science and will openly distribute the tools developed here to support clinical research and practice. ● Research Practice Partnerships: Through partnerships with a) schools serving children with learning disabilities, b) charter schools, and c) public schools, we will collect the first large-scale, longitudinal dataset with detailed measures of visual processing and EFs alongside measures of reading ability in a large and diverse sample of children with dyslexia and typical readers. Significance and innovation: Deep phenotyping is the foundation of precision medicine, but has seen little attention in reading and dyslexia research. This proposal will resolve long-lasting controversies regarding dyslexia phenotypes and create the next generation of open-source tools to revolutionize phenotyping of developmental disorders. Ultimately, this proposal will inspire future work linking phenotypes to interventions and catalyze new approaches in clinical practice.
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