# Computational examination of RDoC threat and reward constructs in a representative, predominantly low-income, longitudinal sample at increased risk for internalizing disorders (Admin Supplement)

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $311,501

## Abstract

ABSTRACT
Depression and anxiety are prevalent, debilitating, and poorly understood disorders. RDoC charts the nature of
these conditions across multiple units, but the domains are based on expert consensus, permitting bias and
missed opportunities. Moreover, little is known about how adversity affects RDoC constructs and contributes to
psychopathology. Thus, there is a critical need to rigorously evaluate RDoC domains in developmental samples
from diverse backgrounds at increased risk for exposure to adversity and later psychopathology. We will use
data-driven analytics to design, apply and validate multilevel-multimodal models of Threat and Reward
constructs in an existing longitudinal cohort at risk for psychopathology. To predict internalizing symptoms, we
will identify biotypes cross-sectionally and examine the longitudinal plasticity of RDoC-informed biotypes. Harsh
social-ecological conditions will be deeply assessed and used to forecast the onset/intensification of internalizing
symptoms at multiple units. We will assess 500 young adults from The Future of Families and Child Wellbeing
Study (FFCWS), an ongoing study of children born to predominantly low-income families. Attributes of the
FFCWS are: 1) children were assessed at birth, 1, 3, 5, 9, 15 years; 2) the sample is representative of people
born in cities and, thus, unlike almost all other neuroimaging research, findings are generalizable; 3) Although a
full range of incomes and race/ethnicities are represented, there is substantial representation of low-income
families and Black/African-American families, populations often under-represented in research; and 4)
participants are entering early adulthood, a period of heightened risk for psychopathology. We will assess Threat
and Reward at four units of analysis: symptoms, task-based behaviors, and brain and link these units to exposure
to adversity. The central hypothesis is that the RDoC Threat and Reward constructs will each cluster across
individuals and units, are distinct from each other, and have specific socio-ecological predictors. We will examine
multisource/multimodal data structure in 500 participants longitudinally at two timepoints, ages 22 and 24. Our
transdisciplinary team of experts positions us well to elucidate the structure of the Threat and Reward constructs
and map risk for internalizing biotypes. However, due to 18+ months of COVID restrictions and impacts on
participation, as well as increased costs for participation, recruitment requires more resources than pre-COVID,
particularly to recover recruitment milestones after 18+ lost months of recruitment time. To achieve our
recruitment goals and obtain necessary statistical power, the current supplement will add three more recruiters
and three more staff members for data collection to increase our recruitment and visits capacity dramatically. By
deeply phenotyping a large cohort enriched for low income and African American participants, we will determine
the validi...

## Key facts

- **NIH application ID:** 10978276
- **Project number:** 3R01MH121079-05S1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Luke Williamson Hyde
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $311,501
- **Award type:** 3
- **Project period:** 2019-08-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978276

## Citation

> US National Institutes of Health, RePORTER application 10978276, Computational examination of RDoC threat and reward constructs in a representative, predominantly low-income, longitudinal sample at increased risk for internalizing disorders (Admin Supplement) (3R01MH121079-05S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10978276. Licensed CC0.

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