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

本项目是对PA-18-437“尖端基础研究奖(CEBRA)”的响应。在过去两年中 几十年来，滥用处方和非法阿片类药物的情况大幅增加；这一增加恰好与 与阿片类药物相关的死亡人数增加。一个关键的挑战是接受治疗的患者复发的发生， 特别是考虑到复发发作存在服药过量的风险。有必要提高我们对 阿片类药物复发的大脑机制，这有望导致基于靶向回路的识别 治疗。 我们建议开发一个高通量计算系统，名为Clear Volume Analyst With Machine 学习(CVA-ML)。我们将把CVA-ML与大鼠优化版本的全脑免疫染色结合起来 和清除方法iDISCO+和一种新的阿片类药物自愿戒断后复发的大鼠模型来鉴定大脑- 阿片类药物复发的广泛神经元群。我们最近将iDISCO+方法应用于完整的大鼠大脑和 开发了Fos免疫染色、脑透明和光片荧光的实验方法 显微镜成像。然而，将iDISCO+方法应用于大规模的大鼠研究目前是有限的 通过(1)缺乏可用于高分辨率配准的可与ABA-CCF相媲美的高分辨率3D鼠脑图谱 3D空间中的活动信号，以及(2)缺乏自动化数据分析管道。 在目标1中，我们建议开发一种数据分析管道，它将采用光片荧光显微镜- 生成大鼠脑图像并自动将其注册到定制的3D大鼠脑图谱中 一份由帕西诺斯和沃森大鼠组成的脑图谱。作为目标1的一部分，我们还建议发展机器学习 方法在三维空间对全脑Fos信号进行识别和分析。在目标2中，我们建议使用 我们在目标1中开发的方法来识别全脑范围的神经元活动模式(神经集合)， 对造成不良后果的自愿戒断后的阿片类药物复发进行编码(电障) 这会导致阿片类药物(羟考酮)自我给药的长期停止。 我们的建议符合PA-18-437的目标：“开发和/或采用革命性的技术或方法 用于成瘾研究。“我们建议的预期结果是一个开源软件包，用于 自动分析大鼠大脑的IDISCO+数据，并评估阿片类药物复发的大鼠全脑活动图 使用一种新的老鼠模型。公开可用的软件将很容易修改，并可供调查人员使用 确定大鼠药物复吸和其他动机行为背后的全脑神经元群。

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.