# Clear Volume Imaging with Machine Learning: a novel tool to identify brain-wide neuronal ensembles of opioid relapse in rat models

> **NIH NIH UG3** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $229,734

## Abstract

This project is in response to PA-18-437 “Cutting-Edge Basic Research Awards (CEBRA)”. Over the two past
decades, there has been a large increase in the abuse of prescription and illegal opioids; this increase coincides
with increases in opioid-related deaths. A critical challenge is the occurrence of relapse in treated patients,
especially given that relapse episodes carry a risk of overdose. There is a need to improve our understanding of
the brain mechanisms of opioid relapse, which hopefully will result in the identification of targeted circuitry-based
treatments.
We propose to develop a high-throughput computation system termed Clear Volume Analysis with Machine
Learning (CVA-ML). We will combine CVA-ML with a rat-optimized version of the whole brain immunostaining
and clearing method iDISCO+ and a new rat model of opioid relapse after voluntary abstinence to identify brain-
wide neuronal ensembles of opioid relapse. We recently adapted the iDISCO+ method to intact rat brains and
developed experimental methods for Fos immunostaining, brain clearing, and light sheet fluorescence
microscopy imaging. However, incorporation of the iDISCO+ method to large scale rat studies is currently limited
by (1) lack of ABA-CCF-comparable high-resolution 3D rat brain atlas that allows for high-resolution registration
of the activity signal in the 3D space, and (2) lack of an automated data analysis pipeline.
In Aim 1, we propose to develop a data analysis pipeline that will take light sheet fluorescence microscopy-
generated rat brain images and automatically register them into a custom-made 3D rat brain atlas encompassing
a converted Paxinos and Watson rat’s brain atlas. As part of Aim 1, we also propose to develop machine-learning
methods to identify and analyze the whole brain Fos signals in 3D space. In Aim 2, we propose to use the
methods we developed in Aim 1 to identify brain-wide patterns of neuronal activity (‘neural ensembles’) that
encode opioid relapse after voluntary abstinence induced by imposing adverse consequences (electric barrier)
that results in long-term cessation of opioid (oxycodone) self-administration.
Our proposal addresses the goal of PA-18-437: “to develop, and/or adapt, revolutionary techniques or methods
for addiction research.” The anticipated outcomes of our proposal are an open-source software package to
automatically analyze iDISCO+ data of rat brains, and a rat whole brain activity map for opioid relapse, assessed
using a new rat model. The publicly available software will be easy to modify and can be used by investigators
to identify brain-wide neuronal ensembles underlying drug relapse and other motivated behaviors in rats.

## Key facts

- **NIH application ID:** 10241671
- **Project number:** 1UG3DA053802-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** RONG CHEN
- **Activity code:** UG3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $229,734
- **Award type:** 1
- **Project period:** 2021-05-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241671

## Citation

> US National Institutes of Health, RePORTER application 10241671, Clear Volume Imaging with Machine Learning: a novel tool to identify brain-wide neuronal ensembles of opioid relapse in rat models (1UG3DA053802-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10241671. Licensed CC0.

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