# Predicting Heterogeneous Neurodevelopmental Outcomes in School-age Children with Early Caregiving Adversities

> **NIH NIH R01** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2020 · $698,273

## Abstract

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...

## Key facts

- **NIH application ID:** 9964904
- **Project number:** 5R01MH091864-09
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Michael Peter Milham
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $698,273
- **Award type:** 5
- **Project period:** 2010-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964904

## Citation

> US National Institutes of Health, RePORTER application 9964904, Predicting Heterogeneous Neurodevelopmental Outcomes in School-age Children with Early Caregiving Adversities (5R01MH091864-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9964904. Licensed CC0.

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