Reading Disability

Despite the fact that a substantial number of school age children struggle with both reading and math acquisition, the brain mechanisms of the overlapping aspects of reading and math skills, thought in part to be linked via executive functions (EF), have not been unpacked. This project will use a longitudinal design, following children from Kindergarten through 1st grade, to understand how the brain networks associated with reading, math, and EF interact to predict academic outcomes and, in those who struggle academically, intervention response.

The goal of this project is to address the urgent need for effective, scalable adult literacy interventions by integrating breakthroughs in two separate fields: 1.) the brain network science of resilience to reading disorders and 2.) high-definition non-invasive brain network stimulation. This study will first establish the efficacy of a novel, noninvasive stimulation protocol on reading behavior and brain metrics; then will determine how stimulation-induced effects interact with baseline reading comprehension ability; and lastly, will identify whether stimulation-induced effects are more clinically-beneficial than canonical behavioral interventions. Results may change the foundation for how we treat low adult literacy, and have the potential for wider reaching impacts on non-invasive stimulation protocols for other clinical disorders.

This project will (a) examine the relationship between knowledge retrieval and inferencing; (b) determine the effectiveness of an intervention that improves knowledge retrieval and inferencing among struggling readers; and (c) expand research opportunities for undergraduates. The research design uses 316 struggling readers in grades 4-6 of diverse backgrounds. The effects of knowledge retrieval (accuracy and speed) on inferencing will be modeled without dichotomizing the distribution. Linear mixed effect models will be fit to determine whether reader characteristics make unique contributions to inferencing across the posttest and follow-up data collection time points. First, several structural models will be considered as students may be nested in teachers, schools, and tutors. Unconditional models will estimate the intraclass correlation for each level of the study design. If significant interclass correlations emerge, multilevel models will be fit to evaluate the effectiveness of the intervention while controlling for covariates such as pre-test performance on inference-related measures and child-attributes such as English learner status. The primary analysis plan assumes an intent-to-treat model in which the efficacy of two intact conditions will be tested. Effect sizes will be estimated to report the magnitude of difference between the two conditions. Expected outcomes include (a) the identification of a method that effectively facilitates knowledge retrieval and the application of relevant knowledge to form inferences among elementary struggling readers from diverse backgrounds; (b) the validation of an intervention that teaches struggling readers how to activate, retrieve, and interweave relevant knowledge with information in the text and accurately form inferences while reading that can be broadly implemented in general education classrooms; and (c) expansion of undergraduate research opportunities, particularly among students from diverse backgrounds who have been historically underserved.