# Predicting the onset of depression in at-risk adolescents from endophenotype profiles

> **NIH NIH R01** · MCLEAN HOSPITAL · 2020 · $563,046

## Abstract

Project Summary/Abstract
 Major depressive disorder (MDD) is uncommon in childhood, but becomes increasingly prevalent
during adolescence. By the age of 18, about 15% of adolescents will have experienced at least one episode of
MDD, with females twice as likely than males to have suffered an episode. This developmental surge in
depression is especially high among teens who have a parent with a history of MDD, with close to half
developing the disorder by the end of adolescence. Despite these epidemiological findings, and the range of
negative downstream consequences linked to MDD, there are strikingly little data on the neural and behavioral
abnormalities that confer risk for future depression onset in youth. The ability to prospectively predict MDD
prior to its onset would have important clinical implications for the early identification of – and targeted
deployment of interventions for – at-risk youth, which is strongly aligned with the NIMH Strategic Plan.
 To address these gaps, adolescents ages 12-15 at increased risk of MDD onset by virtue of a parental
history of MDD, as well as a control sample with no parental history of depression, will complete baseline
neural (fMRI) and behavioral assessments of replicated endophenotypes of MDD (neuroticism, anhedonia,
cognitive control deficits). Growing evidence and our preliminary data suggest that these endophenotypes are
relatively stable trait-like risk markers, have non-overlapping neural substrates, and precede and prospectively
predict depression onset. The project has three aims. First, we will evaluate the neural correlates of these
three endophenotypes in an adolescent sample (n = 148), half of whom are at elevated risk of MDD (Aim 1).
Second, during a 24-month follow-up phase, participants will be contacted by phone every 6 months and
administered measures to assess changes in symptoms. Analyses will test whether behavioral and neural
endophenotype measures prospectively predict onset of depressive symptoms during the follow-up phase.
Importantly, to evaluate incremental predictive validity, we will test whether each endophenotype measure
predicts future depressive symptoms above and beyond relevant clinical, familial/demographic and
developmental variables previously linked with risk of future depression (Aim 2). Third, we will test whether
multivariate machine learning models incorporating behavioral and neural endophenotype markers, as well as
clinical, familial/demographic, and developmental characteristics, can be used to predict subject-specific risk of
future depression onset with sufficiently high sensitivity and specificity to be clinically useful (Aim 3). Critically,
recent advances in machine learning allow for the development of algorithms predicting risk at the individual
level, as well as the integration of numerous predictors rather than relying on single variables that may, in
isolation, have limited clinically-useful predictive value. Collectively, results are expected to...

## Key facts

- **NIH application ID:** 9836889
- **Project number:** 5R01MH116969-02
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** Christian Anthony Webb
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $563,046
- **Award type:** 5
- **Project period:** 2018-12-11 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9836889

## Citation

> US National Institutes of Health, RePORTER application 9836889, Predicting the onset of depression in at-risk adolescents from endophenotype profiles (5R01MH116969-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9836889. Licensed CC0.

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