# Longitudinal and Predictive Modeling to Relate Self-Evaluation and Depression in Adolescent Girls

> **NIH NIH F31** · UNIVERSITY OF OREGON · 2022 · $21,459

## Abstract

PROJECT SUMMARY
Depression–which has increased rates of onset during adolescence–is a leading cause of disability worldwide,
particularly among adolescent girls. Self-development is a key task of adolescence, but individuals with
depression exhibit biases in self-perception. Of note, both self-evaluation and depression have neural
foundations that overlap, including in the ventromedial prefrontal cortex (vmPFC). However, how self-
evaluative processes and depression relate over time is still unclear. Longitudinal designs that track the
interrelations between neural and behavioral indices of self-evaluative processing and depression are needed
to understand the development of this disorder. Additionally, prospective prediction of depression prior to
depression onset–which is important to minimize disease burden and associated healthcare costs–has thus far
proved challenging. Longitudinal datasets capturing individuals prior to depression onset, combined with novel
analytic approaches, may prove fruitful for tracking transactional, longitudinal relations between self-evaluation
and depression and for prospectively predicting depression. This project will achieve these objectives using
three waves of data from a longitudinal study of adolescent females (initial N = 174, initial ages 10-13, 18
months between waves; at least 31% of sample has a clinical depressive disorder at waves 2 or 3). The
specific aims are: 1) characterize the cross-sectional and prospective associations between neural and
behavioral indices of self-evaluation and depression across three waves of data; 2a) predict depression
diagnosis prospectively via a machine learning classifier approach built on self-evaluative behavior; and 2b)
predict depression diagnosis prospectively via multivariate pattern analyses of brain activity during self-
evaluation. Completion of these aims will result in a body of knowledge that tracks developmental interrelations
between self-evaluation and depression and predicts depression onset using a clinically meaningful target with
high potential for translation. This project has four training goals to help me achieve the project objective and to
prepare me for a career as an independent clinical developmental scientist: develop expertise in 1) open and
reproducible science; 2) modeling longitudinal transactional relations between behavior, brain development,
and mental health; 3) predictive analysis techniques using machine-learning methods and multivariate
approaches; and 4) inclusive research, professional development, and science communication. The University
of Oregon’s state-of-the-art Center for Translational Neuroscience provides the ideal environment for
completion of these training goals. Dr. Jennifer Pfeifer will be the primary training mentor overseeing the
project due to her extensive knowledge of adolescent self-evaluative development and developmental
neuroimaging. Dr. Nicholas Allen will serve as the clinical expert providing insights o...

## Key facts

- **NIH application ID:** 10465940
- **Project number:** 1F31MH130138-01
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** Victoria Guazzelli Williamson
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $21,459
- **Award type:** 1
- **Project period:** 2022-03-16 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465940

## Citation

> US National Institutes of Health, RePORTER application 10465940, Longitudinal and Predictive Modeling to Relate Self-Evaluation and Depression in Adolescent Girls (1F31MH130138-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10465940. Licensed CC0.

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