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PyMouseTracks:基于计算机视觉和 RFID 的多鼠标跟踪和行为评估的灵活系统。

PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment.

机构信息

Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada.

Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia Canada V6T 1Z3.

出版信息

eNeuro. 2023 May 16;10(5). doi: 10.1523/ENEURO.0127-22.2023. Print 2023 May.

Abstract

PyMouseTracks (PMT) is a scalable and customizable computer vision and radio frequency identification (RFID)-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi (RPi)-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to six mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared with manual validation of videos containing one to four mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel noncagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-min period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as DeepLabCut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.

摘要

PyMouseTracks(PMT)是一种基于计算机视觉和射频识别(RFID)的可扩展和可定制的多只老鼠跟踪和行为评估系统,可在数分钟内在任何用户定义的场地内以最低成本设置。PMT 由在线基于 Raspberry Pi(RPi)的视频和 RFID 采集以及随后的离线分析工具组成。该系统能够在从几分钟到几天不等的实验中跟踪多达六只老鼠。与在改装的笼中包含一到四只老鼠的视频进行手动验证相比,PMT 的跟踪检测率保持在 88%以上,整体准确率>85%。正如预期的那样,在笼中进行慢性记录显示出昼夜活动模式。在开阔场中,观察到新的非同笼对在 10 分钟内比同笼对在行进轨迹模式上表现出更多的相似性。因此,动物之间行进轨迹中的共享特征可能是一种以前未报道过的社交性度量。此外,PMT 可以与 DeepLabCut 和 Traja 等开源软件包接口,分别用于姿势估计和行进轨迹分析。与 Traja 结合使用,PMT 解决了中风动物表现出的运动缺陷。总体而言,我们提出了一种经济实惠、开源且可定制/可扩展的老鼠行为记录和分析系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/753d/10198609/521d66b899b5/ENEURO.0127-22.2023_f009.jpg

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