# 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 · $1,253,126

## Abstract

PROJECT SUMMARY: Internalizing/externalizing psychopathologies are identifiable by age 3, with
neurodevelopmental risk markers evident in infants. Despite powerful implications for prevention, clinical
impact has been minimal. We use innovative computational and epidemiologic data science methods to
accelerate clinical translation of neurodevelopmental discovery during infancy towards generalizable risk
prediction for preschool psychopathology. Our main objective is generating a pragmatic clinical risk calculator
for public health use, the Mental Health Risk Calculator for Young Children (MHRiskCalc-YC). To achieve
necessary power and precision, we create the Mental Health, Earlier Synthetic Cohort (MHESC), pooling
multiple extramural cohorts at Washington University and Northwestern University to form the first clinically-
enriched “synthetic” neuroimaging cohort for generation of neurodevelopmentally-based clinical risk algorithms
(N=1,020, followed from birth-54 mos.). To maximize the risk calculator's clinical and research utility and cost
effectiveness, we will generate a series of risk algorithms tailored to envisioned end-users, incorporating input
from clinical stakeholders. Algorithms will also establish added value of pre-postnatal environmental factors in
risk prediction, a crucial but understudied RDoC element. Aim 1 optimizes clinical feasibility and cost
effectiveness by generating an MHRiskCalc-YC algorithm derived solely from commonly used survey data to
optimize feasibility for future use in primary care settings. Aim 2 optimizes precision of prediction by
establishing statistical and clinical incremental utility of more intensive assessment for future use in mental
health specialty settings. This algorithm sequentially tests the added predictive value of methods of
intermediate-high intensity (from direct assessments to EEG to MRI) for most precise, least burdensome risk
prediction. The Aim 3 algorithm is optimized for future clinical research use in neurodevelopmental consortia,
modeling the added value of MRI data to the Aim 1 algorithm. This mirrors “common” protocols of
neuroimaging consortia and will also generate an empirically-derived best practices guide for consortia to
optimize timing/ number of neuroimaging assessments. External validity will be established in the Baby
Connectome Project (BCP). The MHESC capitalizes on an unprecedented, time-sensitive opportunity to
accelerate scientific and clinical impact of multiple extramural activities that have been extensively pre-aligned.
The public health impact of an infancy-based clinical risk prediction tool for preschool psychopathology has
transformative potential for altering standard of care in early identification and prevention of mental disorders.

## Key facts

- **NIH application ID:** 10794237
- **Project number:** 5R01MH121877-05
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** JOAN L. LUBY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,253,126
- **Award type:** 5
- **Project period:** 2020-05-12 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10794237

## Citation

> US National Institutes of Health, RePORTER application 10794237, Optimizing prediction of preschool psychopathology from brain: behavior markers of emotion dysregulation from birth: A computational, developmental cognitive neuroscience approach (5R01MH121877-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10794237. Licensed CC0.

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