# Project I: Definition, Classification, and Risk Prediction > **NIH NIH P50** · FLORIDA STATE UNIVERSITY · 2020 · $338,503 ## Abstract ABSTRACT Project 1 Project I: Definition, Classification, and Prediction of Risk Word-level reading disability (i.e., dyslexia) and specific reading comprehension disability (SRCD) are two important public health problems, with estimates of prevalence ranging from 3 to 20 percent for reading disability and 8 to 10 percent for SRCD. The long-term objective of this project is to substantially increase replicable knowledge about the nature of these learning disabilities and to implement this knowledge in tools that potentially can improve the outcomes of individuals with learning disabilities and their families. Existing definitions of reading disability that prioritize a single indicator (e.g., poor decoding, inadequate response to instruction/intervention) show poor levels of agreement and longitudinal stability. However, an operational definition derived from a multivariate model of reading disability shows substantially better performance by combining multiple indicators. Specific aim 1 is to implement a multivariate model of reading disability as a tool that can be used at the level of the individual to predict risk, aid in identification, and estimate probabilities about functionally-significant outcomes such as the likely value of using assistive technology. Model-based meta-analysis, simulation, and application to new data will be used to generate and test prediction models, including models derived from artificial intelligence and Bayesian inference. Specific aim 2 is to identify neurobiological and behavioral leading indicators of dyslexia that may have value for predicting risk, aiding identification, or in predicting functionally significant outcomes. Although established relations exist between brain-based constructs (both structural and functional) and language and literacy constructs, it is largely unknown whether the brain-based constructs are best conceptualized as causes, consequences, or mere correlates of the language and literacy constructs. Latent change score modeling, a form of dynamic systems modeling, is a state-of-the-science approach to test alternative models that posit leading, lagging, or no direct relations between two constructs beyond their mere correlated development. Specific aim 3 is to further understanding about the nature of specific reading comprehension disability and how it best can be predicted and identified. We intend to further explore the nature of this phenomenon and to use the approach we will have outlined in specific aim 1 to develop a model for predicting risk of specific reading comprehension disability. Finally specific aim 4 is to recruit, study, analyze, and report disaggregated results where possible for historically understudied and underserved populations (e.g., English Language Learners, families living in poverty, males and females, racial/ethnic identity) as we work to achieve the previous three specific aims. ## Key facts - **NIH application ID:** 10003050 - **Project number:** 5P50HD052120-14 - **Recipient organization:** FLORIDA STATE UNIVERSITY - **Principal Investigator:** RICHARD K WAGNER - **Activity code:** P50 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $338,503 - **Award type:** 5 - **Project period:** 2006-07-01 → 2022-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10003050 ## Citation > US National Institutes of Health, RePORTER application 10003050, Project I: Definition, Classification, and Risk Prediction (5P50HD052120-14). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10003050. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*