Project Summary Children with severe Early Caregiving Adversities (ECAs) are the most vulnerable to psychopathology as a result of prolonged neglect, abuse, and care disruptions that impact neurodevelopment. It is currently estimated that addressing ECAs would lead to a 29.8% reduction in worldwide psychiatric illness. Existing research, including findings from the original grant of this renewal, has demonstrated that there is a very strong link between ECA exposures and increased risk for psychopathology and altered neurodevelopment at the population level; and yet, given the heterogeneity in ECA populations, there is a critical gap in knowledge regarding how ECAs increase any specific risks to an individual child. The proposed research addresses this significant mental health problem by combining sophisitcated data-analysis methods that use experiential and phenotypic heterogeneity together with longitudinal neuroimaging and behavioral assessments in school-age children. This approach will increase precision when linking ECAs and child outcomes associated with the Research Domain Criteria constructs of Negative Valence and Cognitive Control Systems (NVS/CCS). The overarching goal of the present work is to create an explanatory model for the heterogeneous impact of ECAs on neurodevelopmental trajectories of NVS/CCS. This project's premise is that children exposed to ECAs have highly heterogeneous developmental histories as well as heterogeneous outcomes; therefore, prediction of ECA outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data-driven approaches will 1) more precisely define NVS/CCS outcomes for school-aged children with ECAs, and 2) provide a more robust explanatory model for links between ECAs and NVS/CCS trajectories. Aim 1A subtypes children with a history of ECAs based on 2.5-year developmental trajectories of NVS/CCS. 300 6-8 year old children (250 sampled from previous institutional and foster care; 50 community comparisons) will provide neuroimaging, behavioral, and self/caregiver reports every 15 months for 2.5 years. Biclustering methods will be applied to the baseline and follow-up data to identify homogeneous NVS/CCS final outcome clusters of children. Aim 1B develops an explanatory model to predict developmental trajectory subtypes for children with ECAs, from early life profiles and brain/behavior phenotypes at the time of enrollment. Machine learning methods applied to early life profiles, baseline NVS/CCS profiles, and sex, will predict developmental trajectory subtypes. Aim 2 identifies adverse and protective life events during the 2.5-year assessment period that are predictive of 2.5-year follow-up outcomes for children with ECAs. The inclusion of child-sex and current life- events will identify potential divergence in pathways across middle childhood. This prospective design of children exposed to various ECAs is designed to develop predictive models for ECA trajectory subtypes a...