权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

机器学习清晰体积成像：一种识别大鼠模型中阿片类药物复发的全脑神经元群的新工具

基本信息

批准号：
10405028
负责人：
RONG CHEN
金额：
$ 19.35万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-15 至 2025-04-30
项目状态：
未结题

项目摘要

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.

该项目是对PA-18-437“尖端基础研究奖（CEBRA）"的回应。在过去的两年内，几十年来，滥用处方和非法阿片类药物的情况大量增加;这一增加与与阿片类药物相关的死亡增加。一个关键的挑战是治疗患者复发的发生，尤其是考虑到复发会带来过量的风险有必要提高我们对以下问题的认识：阿片类药物复吸的大脑机制，这有望导致识别基于靶向回路的治疗。我们建议开发一个高通量的计算系统，称为清除体积分析与机器学习（CVA-ML）。我们将联合收割机CVA-ML与大鼠优化版本的全脑免疫染色相结合，和清除方法iDISCO+和自愿戒断后阿片类药物复吸的新大鼠模型，以确定脑- 广泛的阿片类药物复发的神经元集合。我们最近将iDISCO+方法应用于完整的大鼠大脑，开发了Fos免疫染色、大脑清除和光片荧光的实验方法显微成像然而，将iDISCO+方法纳入大规模大鼠研究目前受到限制（1）缺乏ABA-CCF可比的高分辨率3D大鼠脑图谱，可进行高分辨率配准活动信号在3D空间中的分布，以及（2）缺乏自动数据分析管道。在目标1中，我们建议开发一个数据分析管道，该管道将采用光片荧光显微镜- 生成大鼠大脑图像，并自动将其注册到定制的3D大鼠大脑图谱中，一个经过转换的帕克西诺斯和沃森大鼠的大脑图谱。作为目标1的一部分，我们还建议开发机器学习方法在三维空间中识别和分析全脑Fos信号。在目标2中，我们建议使用我们在Aim 1中开发的方法用于识别神经元活动的全脑模式（“神经集合”），编码通过施加不良后果（电屏障）诱导的自愿戒断后阿片类药物复吸这导致阿片样物质（羟考酮）自我给药的长期停止。我们的提案涉及PA-18-437的目标：“开发和/或适应革命性的技术或方法进行成瘾研究。”我们的提案的预期成果是一个开源软件包，自动分析大鼠大脑的iDISCO+数据，以及阿片类药物复发的大鼠全脑活动图，评估使用一种新的大鼠模型。公开提供的软件将易于修改，并可供调查人员使用以确定大鼠的药物复吸和其他动机行为背后的全脑神经元集合。