Project Summary/Abstract Decoding-based reading disorder (RD, also known as dyslexia), a neurodevelopmental disorder with difficulties in reading and spelling, affects approximately 3-10% of all children. Because RD leads to negative consequences beyond academic achievement such as poor mental health outcomes, identifying effective reading interventions is a high priority for researchers and educators. Currently, evidence-based multi-componential interventions that explicitly and systematically target phonics (PHON, focused on letter-sound matching) are considered most effective for remediating RD. However, up to 30% of RD children continue to struggle, warranting broader examination of interventions that goes beyond PHON. Emerging research suggests that morphology-based intervention (MORPH, focused on the understanding and identification of the structure of a word, such as word bases, prefixes, and affixes) may serve as an alternative and complementary strategy. This proposed project, leveraging a funded reading intervention program, will examine event-related potential (ERP)-based neural signals in relation to responsiveness to interventions (RTI) by drawing insights from a computational model of reading (i.e., the connectionist triangle model). Based on this model, we predict that PHON intervention primarily targets the orthography-phonology [O-P] pathway, and MORPH intervention primarily targets the orthography- semantics [O-S] pathway. We also predict that MORPH intervention promotes greater reading improvements for individuals who primarily rely on O-S (possibly compensatory) mechanisms to overcome their weakness in phonological processing. Specifically, this longitudinal project aims to address (1) whether children with RD who have enhanced O-S processes at baseline are more responsive to PHON and MORPH interventions, and whether such responsiveness will be greater if the approach emphasizes both O-S and O-P pathways as compared to the O-P pathway alone; (2) whether the MORPH intervention strengthens the representation of O- P and O-S pathways. We will examine ERP-based neural signals from a lexical task, collected 4 times over the course of 5-week intensive interventions (MORPH, PHON, Executive function [EF], and Math interventions). We will use machine learning and an individual difference approach to assess O-P and O-S neural signals and their trajectories over the course of intervention, and to understand the relationship between these neural signals and children’s RTI. This proposal will help us better understand factors that predict children’s RTI, and the mechanism of different reading interventions. This fellowship will provide crucial research and professional training opportunities to become an independent translational researcher. This includes gaining experience in designing and conducting randomized controlled trials (RCTs), and methodological training in EEG/ERP including machine learning and multilevel mixed modeling analyti...