# COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disorders

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $155,961

## Abstract

Abstract
The novel Coronavirus (COVID-19) pandemic has had a far-reaching impact on the US population, and has
disproportionately affected race-ethnic minorities, individuals living in or near poverty, and those living in large
urban areas. Based on past literature we expect COVID-19 related stressors to have large negative effects on
mental health, but we do not yet understand how social and economic factors might moderate those effects.
The current supplement would add time-sensitive data by collecting real-time online surveys of both target
young adults and their parents (n=1,200) in a representative sample of a disadvantaged population. To better
understand how major stressors, like the COVID-19 pandemic, are moderated, we must assess these social,
occupational, economic, health, and mental health impacts as they happen, across different cities/states (with
different pandemic policies) and in those most at-risk for poor outcomes: low-income and minority families and
young adults who are showing disparities in infection and mortality. Thus, by adding this critical, time-sensitive
assessment, we will be even better positioned to understand how adversity shapes the ongoing development
of RDoC threat and reward circuits, as well as a broader assessment of how COVID-19 is impacting mental
health in marginalized, low-income, minority populations. Moreover, we will document the way in which
resilience factors including social support, economic policies, and family resources moderate the negative
effect of COVID-19 related stressors on mental health. This builds on the parent grant's focus to use data-
driven analytics and hypothesis testing to validate multilevel-multimodal models of Threat and Reward
constructs in an existing representative longitudinal cohort at risk for psychopathology and to delineate how a
history of exposure to adversity links to these domains. The parent grant is assessing 600 young adults twice
(at age 20 and 24) from The Fragile Families and Child Wellbeing Study (FFCWS), an ongoing study of 4900
children born 1998-200 in large US cities. Attributes of the FFCWS are: 1) parents and children were surveyed
and assessed at birth, 1, 3, 5, 9, 15 years; 2) the sample is nationally representative; 3) Substantial enrichment
for low-income (median income to needs ratio=1.4) and minority families (66%), populations are often under-
represented in research; and 4) participants are entering early adulthood, a period of heightened risk for
psychopathology. Because of the unique social distancing required by COVID-19, having data from multiple
family members will be particularly powerful in understanding the economic and social, and in turn, mental
health consequences of COVID-19. To predict internalizing symptoms, we will identify biotypes cross-
sectionally and longitudinally. Socio-ecological conditions will be deeply assessed (including COVID-19-related
adversity, prior public assistance, COVID-19 related public assistance and polic...

## Key facts

- **NIH application ID:** 10152843
- **Project number:** 3R01MH121079-02S1
- **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:** 2020
- **Award amount:** $155,961
- **Award type:** 3
- **Project period:** 2019-08-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10152843

## Citation

> US National Institutes of Health, RePORTER application 10152843, COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disorders (3R01MH121079-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10152843. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*