# Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders

> **NIH NIH K23** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $196,560

## Abstract

PROJECT SUMMARY/ABSTRACT
This K23 proposal seeks to provide an early-career clinical psychologist and neuroscientist (Dr. Alexander
Weigard) with the mentorship, training, and resources necessary to launch a career as an independent patient-
oriented investigator focused on using advanced computational methods to elucidate etiological mechanisms
of substance use disorders (SUDs) and generate meaningful predictions for patients. The candidate will work
towards this long-term goal through the completion of a research project focused on assessing whether two
advanced computational methods can facilitate the selection of features from neuroscientific data that are
relevant for the individualized prediction of SUD risk in youth. Although extant research in developmental
neuroscience has identified multiple early risk factors that are associated with development of SUD at the
group level, there is currently a dearth of large scale, replicable research in which neurocognitive data are used
to make reliable and generalizable predictions of SUD outcomes for individual youth. In the proposed project,
the candidate will combine his existing expertise in computational models of cognition with new training in
predictive informatics methods to assess whether two advanced computational approaches, a) sequential
sampling models (SSMs) of cognition and b) network neuroscience, can be used to extract features from
longitudinal neurocognitive data that enhance the prediction of youths’ SUD outcomes. The candidate will
conduct extensive analyses with two large data sets (Michigan Longitudinal Study, Adolescent Brain Cognitive
Development Study) and collect pilot data with 60 young adults to accomplish the following research aims: 1)
Quantify the added benefit of SSM parameters for improving the performance of multivariate SUD
prediction models, and 2) Identify the multivariate neural signature of v, an SSM parameter with
promising links to substance use, and determine the potential of this signature for predicting a
precursor to SUD (substance use initiation in mid-adolescence) in ABCD and differentiating young
adults with SUDs in the newly-collected pilot sample. Completion of the following training objectives will
ensure that the candidate can both carry out the proposed project and establish himself as an independent
investigator who is well-equipped to conduct future projects following from this work: 1) Mastering principles
of machine learning model development and testing in longitudinal data sets, 2) building expertise in
using multivariate network neuroscience methods for feature selection and prediction, 3) increasing
clinical and epidemiological knowledge of SUD risk factors beyond neurocognition, and 4) improving
professional skills necessary to become an independent patient-oriented investigator. The proposed
K23 aims to take a crucial step towards the development of advanced computational neuroscience methods
that may ultimately inform SUD prevention ef...

## Key facts

- **NIH application ID:** 10828813
- **Project number:** 5K23DA051561-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Alexander Weigard
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $196,560
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828813

## Citation

> US National Institutes of Health, RePORTER application 10828813, Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders (5K23DA051561-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10828813. Licensed CC0.

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