# Using Machine Learning Approaches to Examine Emotion-Related Brain Activity and Substance Use Among Adolescents

> **NIH NIH F31** · GEORGE MASON UNIVERSITY · 2020 · $38,803

## Abstract

Project Summary/Abstract
 Substance use and substance use disorder are leading causes of death and disability worldwide.
Importantly, most adults with substance use disorder begin using substances as adolescents, making
adolescence an important period for the development of substance use disorder. Thus, it is critical to identify
factors related to substance use among adolescents, consistent with NIDA objective 1.1. One factor linked to
adolescent substance use is negative emotion processing. As adolescents undergo significant biological and
psychosocial changes across development they may experience altered negative emotion processing that can
lead to substance use if not appropriately regulated. Unfortunately, there is limited understanding in how neural
level differences in negative emotion processing is related to substance use among adolescents. Moreover,
extant neuroimaging research on negative emotion processing and substance use has employed univariate
methods instead of multivariate methods. In contrast to univariate methods, multivariate methods are more
sensitive in detecting patterns of neural activation across voxels and yield more generalizable and replicable
findings overall. In addition, this research has employed standardized negative emotion paradigms, which have
high experimental control, but may be less likely to reflect negative emotion processing as it occurs in the real
world. To address these gaps in the literature, the proposed study will use multivariate machine learning
approaches (i.e., multivoxel pattern analysis) to classify patterns of neural activation in a standardized negative
emotion processing task and in a novel naturalistic negative emotion processing task that differentiate
substance using adolescents from non-using adolescents, as well as predict substance use disorder risk
factors. Additionally, the proposed study will use machine learning approaches to examine sex differences in
emotion-related neural activation to these tasks in relation to substance use. This research will be conducted
on a sample of 326 12-13 year old adolescents from Sponsor’s (Chaplin) competing renewal grant (RO1
DA033431-06A1). Knowledge from the proposed study will be used to identify emotion-related neurobiological
markers of adolescent substance use. Ultimately, these markers can be used to target at-risk adolescents in
need of substance use prevention and intervention efforts. The goals of the proposed study will be
accomplished within a research training plan aimed at developing multidisciplinary expertise in affective
neuroscience, particularly in multivariate machine learning methods, and developmental models of substance
use. The training plan includes completion of relevant coursework, attendance at targeted workshops,
individual mentorship by experts in the field of development and neuroscience, and scientific writing and
presentation experience.

## Key facts

- **NIH application ID:** 9991091
- **Project number:** 1F31DA051154-01
- **Recipient organization:** GEORGE MASON UNIVERSITY
- **Principal Investigator:** Stefanie Fraga Goncalves
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $38,803
- **Award type:** 1
- **Project period:** 2020-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991091

## Citation

> US National Institutes of Health, RePORTER application 9991091, Using Machine Learning Approaches to Examine Emotion-Related Brain Activity and Substance Use Among Adolescents (1F31DA051154-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9991091. Licensed CC0.

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