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

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)...

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Autores principales: Fong, Tony, Hu, Hao, Gupta, Pankaj, Jury, Braeden, Murphy, Timothy H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198609/
https://www.ncbi.nlm.nih.gov/pubmed/37185293
http://dx.doi.org/10.1523/ENEURO.0127-22.2023
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author Fong, Tony
Hu, Hao
Gupta, Pankaj
Jury, Braeden
Murphy, Timothy H.
author_facet Fong, Tony
Hu, Hao
Gupta, Pankaj
Jury, Braeden
Murphy, Timothy H.
author_sort Fong, Tony
collection PubMed
description 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.
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spelling pubmed-101986092023-05-20 PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment Fong, Tony Hu, Hao Gupta, Pankaj Jury, Braeden Murphy, Timothy H. eNeuro Research Article: Methods/New Tools 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. Society for Neuroscience 2023-05-12 /pmc/articles/PMC10198609/ /pubmed/37185293 http://dx.doi.org/10.1523/ENEURO.0127-22.2023 Text en Copyright © 2023 Fong et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Research Article: Methods/New Tools
Fong, Tony
Hu, Hao
Gupta, Pankaj
Jury, Braeden
Murphy, Timothy H.
PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title_full PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title_fullStr PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title_full_unstemmed PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title_short PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment
title_sort pymousetracks: flexible computer vision and rfid-based system for multiple mouse tracking and behavioral assessment
topic Research Article: Methods/New Tools
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198609/
https://www.ncbi.nlm.nih.gov/pubmed/37185293
http://dx.doi.org/10.1523/ENEURO.0127-22.2023
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