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

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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.