# A computational model for how risk for anxiety and depression influences affective neurodevelopment

> **NIH NIH DP5** · WASHINGTON UNIVERSITY · 2024 · $388,750

## Abstract

PROJECT SUMMARY
Depression and anxiety are associated with alterations in brain networks supporting emotion processing and
regulation, including attention (dorsal attention [DAN], ventral attention [VAN]), arousal and focus (cingulo-
opercular [CON], saliency [SAL]), and self-regulation (default [DMN], frontoparietal [FPN]) networks. These
networks undergo substantial development during the preschool years, a critical stage for developing self-
regulation. Developing self-regulation involves learning how to distinguish specific negative emotions (e.g.,
sadness, fear, and anger). How caregivers react when their child expresses negative affect theoretically
shapes how the neural representations of negative emotions develop, however this has not been formally
tested. Further, a separate body of work shows that increased negative affect during the preschool period is a
risk factor for later depression and anxiety. Together, this suggests a reinforcement model linking these two
risk factors: increased expression of negative affect in the child elicits a caregiver response, which either
scaffolds emotion learning or not. If the caregiving response is consistently negative, this results in the brain
failing to develop separable concepts for distinct emotion categories, resulting in increased distress when
faced with these emotions in the future. The principal goal of this study is to test this reinforcement model
of how negative affect and caregiving interact to alter brain and emotional development, conferring
risk for depression and anxiety. In Aim 1, I will characterize emotion processing development (activation
and gaze to negative emotions) across the preschool period. In Aim 2, I will fit a reinforcement learning model,
testing negative caregiving as a moderator of developing distinct negative emotion concepts. Finally in Aim 3, I
will test if frequency of negative affect expression influences these associations. The department of Psychiatry
is highly supportive of my transition to faculty and has offered a faculty position—Assistant Professor on the
Investigator (tenure) Track—that is not contingent on the receipt of the DP5 grant and with 95% of my effort
dedicated to research and a startup package tailored to my needs. I am also more than prepared for early
independence. I have spent my entire research career independently studying the neurobiological etiology of
anxiety and depression in collaboration with my mentors. Each of my first author papers builds on what was
learned in the previous paper, innovating either conceptually or methodologically. I also have formal training
and extensive experience in mentoring, training, management, grant-writing and data collection have all the
necessary skills to run the complex and collaborative projects that comprise my independent program of
research. In sum, my scientific and technical expertise, my accomplishments, and my leadership, mentorship,
and management skills are perfectly suited to launch my...

## Key facts

- **NIH application ID:** 10923319
- **Project number:** 1DP5OD037370-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Maria Catalina Camacho
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $388,750
- **Award type:** 1
- **Project period:** 2024-09-19 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923319

## Citation

> US National Institutes of Health, RePORTER application 10923319, A computational model for how risk for anxiety and depression influences affective neurodevelopment (1DP5OD037370-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10923319. Licensed CC0.

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