# Interpretable Deep Forecasting of Hazardous Substance Use during High School

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $459,066

## Abstract

ABSTRACT
Substance use disorder (SUD) affects over 20 million Americans, causing personal strife and cost. A major
SUD risk factor is hazardous use (HU) of substances during high school (HUSH), when the brain continues to
develop, rendering it especially sensitive to environmental factors. Identifying the effects of and risk for HUSH
generally focuses on selected interactions between fixed (i.e., trauma, demographics) and modifiable (i.e.,
mental health, emotion, environment, behavior, sleep) factors, and occasionally brain development features
differentiating substance using and non-using cohorts. Results have yielded limited improvements to risk
assessment. Thus, we propose a paradigm shift in the study of HUSH, replacing measurement selection and
population splitting with mapping individuals to comprehensive multi-dimensional measures. The objective of
our novel data-driven process is to identify constellations of fixed and modifiable factors forecasting HUSH in
individuals. As our analysis is based on public data sets that include brain imaging, we will document
interactions of those constellations with neural circuits to determine neuromechanistic underpinnings of HUSH.
Myriad factors influence hazardous substance use, such as the fixed contributors of sex and family history of
SUD; the modifiable factors of unhealthy sleep habits, peer pressure, and risk-taking propensity; and brain
development characterized by an atypical imbalance between emotion and control network. We will model
this heterogeneity via machine (deep) learning technology identifying constellations of measurements in line
with our hypotheses regarding prevention, i.e., modifiable behaviors interacting with anomalies in
neuroadaptation forecasting HUSH initiation. Aim 1 will forecast initiation of HUSH in the last years of high
school based on the closest visit before turning age 16 years for no/low substance users in National
Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N =350) and confirm findings on
the larger Adolescent Brain Cognitive Developmental cohort (ABCD, N>11K). HUSH will be defined by
substance use criteria recorded through annual self-reports and refined by weekly surveys administered via
cell phones. Aim 2 will create a self-supervised learning model explicitly tracking over time interactions across
modifiable behaviors, fixed factors, and brain circuits important for hypothesis testing. We will cross-validate
the model by identifying HUSH for each high school year and forecast based on data collected prior to high
school. We will explore dynamically updating the model as data are acquired to predict resilience, i.e., youth
who abstain from hazardous substance use during high school, despite having risk factors such as traumatic
and untoward COVID pandemic experiences. Each aim is linked to hypothesis testing concerning factors that
can be altered to mitigate the risk of HUSH.
This project will be the first to provide patterns accuratel...

## Key facts

- **NIH application ID:** 10906941
- **Project number:** 5R01DA057567-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kilian Maria Pohl
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $459,066
- **Award type:** 5
- **Project period:** 2022-09-30 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10906941

## Citation

> US National Institutes of Health, RePORTER application 10906941, Interpretable Deep Forecasting of Hazardous Substance Use during High School (5R01DA057567-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10906941. Licensed CC0.

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