# Optimizing prediction of preschool psychopathology from brain: behavior markers of emotion dysregulation from birth: A computational, developmental cognitive neuroscience approach

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2024 · $304,155

## Abstract

PROJECT SUMMARY
 Late talkers are at risk for language impairment and have high rates of co-occurring mental health
impairments, estimated to range from 30-70%. Our group has shown that as early as 12 months, children’s
dimensional irritability is related to their vocabulary size, and late talkers have significantly more frequent and
severe temper tantrums than typical talkers. Despite the frequency of late talking, language impairment and
internalizing and externalizing (INT/EXT) mental health impairments, few studies have examined the
multifaceted trajectories that lead to them. Further, few studies examine multi-faceted predictors of outcome or
promotive factors which may help increase reliability of outcome prediction for children from under-resourced
and historically marginalized environments, who experience inequities in early identification. Via our group’s
expertise and currently funded supplement parent study, the Mental Health, Earlier Synthetic Cohort (MHESC,
NIMH R01MH121877, PIs Wakschlag, Luby, Rogers) whose feeder studies are Northwestern’s irritability- and
late talker-enriched W2W samples, and WashU’s adversity-enriched eLABE sample), we are poised to
deepen and accelerate the aims of the MHESC, via supplementary study of the precursors and
consequences of late talking. To do so, we enrich consideration of language development and ecological
environment within the MHESC risk models designed to predict preschool psychopathology, using
computational approaches, and employing a health equity lens. We pursue the following supplementary
aims, within scope of MHESC grant’s Aim 2a: Supp Aim 1a. Apply the MHE-RiskCalc algorithm with deeper
phenotyping measures of child language at 24 months (e.g., language sample analysis) and joint consideration
of language and irritability measures to improve precision of prediction (beyond the base model) for three
outcomes at age 4+ years: a) INT/EXT, b) language, and c) co-occurring language and INT/EXT impairments.
1b. Test whether a RiskCalc based on 12-month measures can predict 24-month late talking status. 1c. Test
whether a RiskCalc based on 12-month measures can improve prediction of (a) INT/EXT (b) language & (c)
INT/EXT+language impairments at age 4+. Supp Aim 2. Test whether adding promotive factors, including
observed measures of dyadic language environment (caregiver responsiveness to child communication,
language input quality) and child social-emotional competence improves RiskCalc precision and reduces bias
of language and INT/EXT prediction, especially increasing accuracy for children from historically
marginalized/under-resourced communities. Supp Aim 3. Use computational machine learning methods to
determine data-driven indicators of risk of language and mental health impairments in the MHESC cohort, with
emphasis on bias reduction and equity for diverse demographic sub-groups. In sum, we are poised to advance
the objectives of the MHESC to accelerate pace of translation and equita...

## Key facts

- **NIH application ID:** 10840528
- **Project number:** 3R01MH121877-05S1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** JOAN L. LUBY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $304,155
- **Award type:** 3
- **Project period:** 2020-05-12 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840528

## Citation

> US National Institutes of Health, RePORTER application 10840528, Optimizing prediction of preschool psychopathology from brain: behavior markers of emotion dysregulation from birth: A computational, developmental cognitive neuroscience approach (3R01MH121877-05S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10840528. Licensed CC0.

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