# Multivariate Modeling of the Neural Mechanisms of Treatment Response in Opioid Addiction

> **NIH NIH K01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $179,874

## Abstract

PROJECT SUMMARY/ABSTRACT
 The proposed K01 project will use multimodal magnetic resonance imaging (MRI) and machine learning (ML)
to elucidate the neurocognitive processes underlying treatment failure in young adults with opioid use disorder
(OUD). Young adults are at particularly high risk of OUD and fatal opioid overdose. The monthly injectable
extended-release opioid antagonist naltrexone (XR-NTX) is a highly effective OUD treatment and is particularly
well suited for young adults. However, XR-NTX adherence and relapse show considerable individual variability,
and the behavioral and clinical factors associated with such variability remain inconclusive. Previous research
has demonstrated the potential for multimodal MRI and ML techniques to elucidate the neurocognitive factors
that contribute to treatment response beyond behavioral and clinical measures. This project will take advantage
of the cutting-edge MRI and ML methods to model brain structures and functions that predict XR-NTX treatment
outcomes in young adults with OUD. The study will evaluate 18–34 year-old OUD patients before and during
the first three months of XR-NTX treatment, a period associated with the highest rate of dropout from treatment.
The primary outcome will be opioid relapse confirmed by weekly urine toxicology and self-report. The secondary
outcome will be non-adherence defined as failure to complete the first three injections. The study will focus on
five baseline measures of brain structures and functions that are potentially predictive of treatment response: 1)
grey matter volume; 2) functional connectivity with the ventral striatum; 3) reactivity to opioid cues; 4) inhibitory
control; and 5) self-evaluation. ML techniques will be used to reveal the patterns of brain structures/functions
that are associated with each outcome variable. Based on literature and preliminary findings, we anticipate that
combining MRI with behavioral and clinical assessments will better account for individual variability in XR-NTX
treatment outcomes in young adults with OUD, than using the behavioral and clinical variables alone. The data
will unveil novel brain mechanisms that contribute to the risk of treatment failure in this critical population. The
project will also serve as a training vehicle for Dr. Zhenhao Shi to improve his clinical and computational skills
and facilitate his independent career development. Specifically, it will enable Dr. Shi to achieve five training
goals: 1) to advance his knowledge in the methodology of clinical research; 2) to gain hands-on experience in
leading clinical projects; 3) to master ML and multivariate methodologies; 4) to apply multimodal MRI
techniques to translational and clinical research; and 5) to advance his general independent research skills
including leadership, networking, collaboration, scientific writing and grantsmanship. Through a combination of
didactic and hands-on activities, the project will fulfill Dr. Shi's training needs and ...

## Key facts

- **NIH application ID:** 10214440
- **Project number:** 1K01DA051709-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Zhenhao Shi
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $179,874
- **Award type:** 1
- **Project period:** 2021-04-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10214440

## Citation

> US National Institutes of Health, RePORTER application 10214440, Multivariate Modeling of the Neural Mechanisms of Treatment Response in Opioid Addiction (1K01DA051709-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10214440. Licensed CC0.

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