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ezTrack: An open-source video analysis pipeline for the investigation of animal behavior

Tracking animal behavior by video is one of the most common tasks in the life sciences. Although commercial software exists for executing this task, they often present enormous cost to the researcher and can entail purchasing hardware that is expensive and lacks adaptability. Additionally, the under...

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Autores principales: Pennington, Zachary T., Dong, Zhe, Feng, Yu, Vetere, Lauren M., Page-Harley, Lucia, Shuman, Tristan, Cai, Denise J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934800/
https://www.ncbi.nlm.nih.gov/pubmed/31882950
http://dx.doi.org/10.1038/s41598-019-56408-9
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author Pennington, Zachary T.
Dong, Zhe
Feng, Yu
Vetere, Lauren M.
Page-Harley, Lucia
Shuman, Tristan
Cai, Denise J.
author_facet Pennington, Zachary T.
Dong, Zhe
Feng, Yu
Vetere, Lauren M.
Page-Harley, Lucia
Shuman, Tristan
Cai, Denise J.
author_sort Pennington, Zachary T.
collection PubMed
description Tracking animal behavior by video is one of the most common tasks in the life sciences. Although commercial software exists for executing this task, they often present enormous cost to the researcher and can entail purchasing hardware that is expensive and lacks adaptability. Additionally, the underlying code is often proprietary. Alternatively, available open-source options frequently require model training and can be challenging for those inexperienced with programming. Here we present an open-source and platform independent set of behavior analysis pipelines using interactive Python that researchers with no prior programming experience can use. Two modules are described. One module can be used for the positional analysis of an individual animal, amenable to a wide range of behavioral tasks. A second module is described for the analysis of freezing behavior. For both modules, a range of interactive plots and visualizations are available to confirm that chosen parameters produce the anticipated results. Moreover, batch processing tools for the fast analysis of multiple videos is provided, and frame-by-frame output makes alignment with biological recording data simple. Lastly, options for cropping video frames to mitigate the influence of fiberoptic/electrophysiology cables, analyzing specified portions of time, and defining regions of interest, are readily implemented.
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spelling pubmed-69348002019-12-31 ezTrack: An open-source video analysis pipeline for the investigation of animal behavior Pennington, Zachary T. Dong, Zhe Feng, Yu Vetere, Lauren M. Page-Harley, Lucia Shuman, Tristan Cai, Denise J. Sci Rep Article Tracking animal behavior by video is one of the most common tasks in the life sciences. Although commercial software exists for executing this task, they often present enormous cost to the researcher and can entail purchasing hardware that is expensive and lacks adaptability. Additionally, the underlying code is often proprietary. Alternatively, available open-source options frequently require model training and can be challenging for those inexperienced with programming. Here we present an open-source and platform independent set of behavior analysis pipelines using interactive Python that researchers with no prior programming experience can use. Two modules are described. One module can be used for the positional analysis of an individual animal, amenable to a wide range of behavioral tasks. A second module is described for the analysis of freezing behavior. For both modules, a range of interactive plots and visualizations are available to confirm that chosen parameters produce the anticipated results. Moreover, batch processing tools for the fast analysis of multiple videos is provided, and frame-by-frame output makes alignment with biological recording data simple. Lastly, options for cropping video frames to mitigate the influence of fiberoptic/electrophysiology cables, analyzing specified portions of time, and defining regions of interest, are readily implemented. Nature Publishing Group UK 2019-12-27 /pmc/articles/PMC6934800/ /pubmed/31882950 http://dx.doi.org/10.1038/s41598-019-56408-9 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Pennington, Zachary T.
Dong, Zhe
Feng, Yu
Vetere, Lauren M.
Page-Harley, Lucia
Shuman, Tristan
Cai, Denise J.
ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title_full ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title_fullStr ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title_full_unstemmed ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title_short ezTrack: An open-source video analysis pipeline for the investigation of animal behavior
title_sort eztrack: an open-source video analysis pipeline for the investigation of animal behavior
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934800/
https://www.ncbi.nlm.nih.gov/pubmed/31882950
http://dx.doi.org/10.1038/s41598-019-56408-9
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