# Real-time fMRI neurofeedback of large-scale network dynamics in opioid use disorder

> **NIH NIH R21** · YALE UNIVERSITY · 2020 · $209,375

## Abstract

PROJECT SUMMARY
The misuse of opioids, opioid addiction and overdose are a serious national public health crisis—the opioid
epidemic—that despite increased scientific, clinical and government attention, continues to grow. Methadone is
a generally effective treatment for opioid use disorder, however relapse rates remain high, and risk of overdose
is greatest during relapse. There is a need for improved mechanistic understanding of the factors that
contribute to opioid relapse to improve our understanding of opioid use disorder and its treatment. Using
connectome-based methods (i.e., functional connectivity) in functional magnetic resonance imaging (fMRI), we
recently identified a large-scale brain network that predicted opioid relapse from both resting and task states.
Connectome-based methods enable data-driven characterization of whole brain networks related to behavior
that might be better suited to describe complex clinical phenomena (e.g., opioid relapse). Building on prior
work indicating the utility of real-time fMRI neurofeedback to test brain activation patterns related to specific
functions and individual abilities to regulate these functions, the proposed project will use connectome-based
neurofeedback to target patterns of functional connectivity within our recently identified “opioid abstinence
network”. This information is critical to improve understanding of mechanisms of opioid relapse. Individuals on
methadone will be randomized to receive either active (n=12) or sham (n=12) connectome-based
neurofeedback at 3 weekly scanning sessions including feedback and transfer runs. Additional baseline and
follow-up scans will include resting state and reward and cognitive task runs. Craving, negative affect and
opioid use will be measured weekly and at 1-mo follow-up. Based on our pilot data, connectome-based
feedback will be targeted at the opioid abstinence network and we hypothesize that increased connectivity in
this network will be associated with improved clinical outcomes. Aim 1 will test the hypothesis that active
feedback is associated with reduced opioid use from baseline to follow-up scans (Aim 1a) and at 1-mo follow-
up (Aim 1b). Aim 2 will test the hypothesis that active feedback is associated with increased opioid abstinence
network connectivity in resting state (Aim 2a) and task (reward, cognitive) state (Aim 2b) versus sham
feedback, as in our pilot work. Aim 3 will test the hypothesis that active feedback is associated with greater
improvements in clinical features of opioid use disorder (craving, negative affect) than sham feedback (Aim 3a)
and that increased opioid abstinence network connectivity will correlate with these improvements (Aim 3b).
Overall, this project tests a potentially transformative hypothesis relating large-scale brain network dynamics to
outcomes in opioid use disorder, and tests a highly innovative method for real-time fMRI neurofeedback from
the opioid abstinence network to improve clinical features ...

## Key facts

- **NIH application ID:** 10025590
- **Project number:** 5R21DA049583-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Kathleen A. GARRISON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $209,375
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025590

## Citation

> US National Institutes of Health, RePORTER application 10025590, Real-time fMRI neurofeedback of large-scale network dynamics in opioid use disorder (5R21DA049583-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10025590. Licensed CC0.

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