# Leveraging Latent Factors and Machine Learning to Forecast Internalizing Psychopathology in Emerging Adulthood

> **NIH NIH R01** · UNIVERSITY OF COLORADO · 2022 · $767,282

## Abstract

Project Summary
Mood and anxiety disorders are common and highly comorbid conditions with peak incidence in emerging
adulthood (~ages 18-23). Developmental psychopathology models suggest that vulnerability to internalizing
disorders in emerging adults is driven by interactions between still maturing self-regulatory abilities (as
executive function [EF] continues to mature into young adulthood), and individual differences in reward and
threat sensitivity. Together, this highlights the importance of complex neurocognitive profiles consisting of
abnormalities across these three RDoC constructs for internalizing disorders. However, prior research has
largely investigated these constructs individually, in relation to individual disorders or symptom dimensions.
Given the high co-occurrence and complex multi-causality of internalizing psychopathology, the critical next step
is to build a framework for understanding how these neurocognitive dimensions interact to predict
transdiagnostic person-specific symptom trajectories. The proposed study aims to advance this precision
medicine goal, by evaluating how the neurocognitive dimensions of EF, reward and threat sensitivity interact to
produce risk phenotypes; and by using machine learning techniques to identify the most parsimonious set of risk
markers (across units of analysis) that forecast psychopathology. This longitudinal study will recruit a final
sample of 480 emerging adults during the transition to college, when stress and psychopathology risk increase,
to test risk pathways for transdiagnostic (common across internalizing symptoms) and specific (anhedonia,
anxious arousal, mania) internalizing dimensions, using a methodologically rigorous latent variable approach.
Our first aim is to test interactions among the neurocognitive dimensions of executive function, threat sensitivity
and reward sensitivity as risk mechanisms for transdiagnostic and specific internalizing symptom profiles and
trajectories. We hypothesize that poor EF is a transdiagnostic risk factor, with specific symptom profile
depending on threat (contributing to anxious arousal) and reward (contributing to anhedonia or mania)
sensitivity, and different maladaptive behaviors (e.g., social withdrawal vs. risky behavior). Our second aim is to
perform automated risk profiling, using machine learning to determine most parsimonious set of units that
predict outcome– a key objective for future clinical translation for screening for internalizing psychopathology
risk. Strengths of this approach include model-driven dimensional constructs of cognitive control, negative and
positive valence, spanning units of analysis (physiology, behavior, self-report) at a critical developmental risk
period. The robust sample size enables a rigorous statistical modeling approach and testing of moderating
influences (e.g., sex). By elucidating interactions between neurocognitive risks and the specific biobehavioral
mechanisms involved, we can make a novel im...

## Key facts

- **NIH application ID:** 10366892
- **Project number:** 1R01MH124846-01A1
- **Recipient organization:** UNIVERSITY OF COLORADO
- **Principal Investigator:** ROSELINDE H KAISER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $767,282
- **Award type:** 1
- **Project period:** 2022-06-10 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10366892

## Citation

> US National Institutes of Health, RePORTER application 10366892, Leveraging Latent Factors and Machine Learning to Forecast Internalizing Psychopathology in Emerging Adulthood (1R01MH124846-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10366892. Licensed CC0.

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