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Automated Analysis of Stroke Mouse Trajectory Data With Traja
Quantitative characterization of mouse activity, locomotion and walking patterns requires the monitoring of position and activity over long periods of time. Manual behavioral phenotyping, however, is time and skill-intensive, vulnerable to researcher bias and often stressful for the animals. We pres...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262161/ https://www.ncbi.nlm.nih.gov/pubmed/32523509 http://dx.doi.org/10.3389/fnins.2020.00518 |
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author | Shenk, Justin Lohkamp, Klara J. Wiesmann, Maximilian Kiliaan, Amanda J. |
author_facet | Shenk, Justin Lohkamp, Klara J. Wiesmann, Maximilian Kiliaan, Amanda J. |
author_sort | Shenk, Justin |
collection | PubMed |
description | Quantitative characterization of mouse activity, locomotion and walking patterns requires the monitoring of position and activity over long periods of time. Manual behavioral phenotyping, however, is time and skill-intensive, vulnerable to researcher bias and often stressful for the animals. We present examples for using a platform-independent open source trajectory analysis software, Traja, for semi-automated analysis of high throughput mouse home-cage data for neurobehavioral research. Our software quantifies numerous parameters of movement including traveled distance, velocity, turnings, and laterality which are demonstrated for application to neurobehavioral analysis. In this study, the open source software for trajectory analysis Traja is applied to movement and walking pattern observations of transient stroke induced female C57BL/6 mice (30 min middle cerebral artery occlusion) on an acute multinutrient diet intervention (Fortasyn). After stroke induction mice were single housed in Digital Ventilated Cages [DVC, GM500, Tecniplast S.p.A., Buguggiate (VA), Italy] and activity was recorded 24/7, every 250 ms using a DVC board. Significant changes in activity, velocity, and distance walked are computed with Traja. Traja identified increased walked distance and velocity in Control and Fortasyn animals over time. No diet effect was found in preference of turning direction (laterality) and distance traveled. As open source software for trajectory analysis, Traja supports independent development and validation of numerical methods and provides a useful tool for computational analysis of 24/7 mouse locomotion in home-cage environment for application in behavioral research or movement disorders. |
format | Online Article Text |
id | pubmed-7262161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72621612020-06-09 Automated Analysis of Stroke Mouse Trajectory Data With Traja Shenk, Justin Lohkamp, Klara J. Wiesmann, Maximilian Kiliaan, Amanda J. Front Neurosci Neuroscience Quantitative characterization of mouse activity, locomotion and walking patterns requires the monitoring of position and activity over long periods of time. Manual behavioral phenotyping, however, is time and skill-intensive, vulnerable to researcher bias and often stressful for the animals. We present examples for using a platform-independent open source trajectory analysis software, Traja, for semi-automated analysis of high throughput mouse home-cage data for neurobehavioral research. Our software quantifies numerous parameters of movement including traveled distance, velocity, turnings, and laterality which are demonstrated for application to neurobehavioral analysis. In this study, the open source software for trajectory analysis Traja is applied to movement and walking pattern observations of transient stroke induced female C57BL/6 mice (30 min middle cerebral artery occlusion) on an acute multinutrient diet intervention (Fortasyn). After stroke induction mice were single housed in Digital Ventilated Cages [DVC, GM500, Tecniplast S.p.A., Buguggiate (VA), Italy] and activity was recorded 24/7, every 250 ms using a DVC board. Significant changes in activity, velocity, and distance walked are computed with Traja. Traja identified increased walked distance and velocity in Control and Fortasyn animals over time. No diet effect was found in preference of turning direction (laterality) and distance traveled. As open source software for trajectory analysis, Traja supports independent development and validation of numerical methods and provides a useful tool for computational analysis of 24/7 mouse locomotion in home-cage environment for application in behavioral research or movement disorders. Frontiers Media S.A. 2020-05-25 /pmc/articles/PMC7262161/ /pubmed/32523509 http://dx.doi.org/10.3389/fnins.2020.00518 Text en Copyright © 2020 Shenk, Lohkamp, Wiesmann and Kiliaan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Shenk, Justin Lohkamp, Klara J. Wiesmann, Maximilian Kiliaan, Amanda J. Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title | Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title_full | Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title_fullStr | Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title_full_unstemmed | Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title_short | Automated Analysis of Stroke Mouse Trajectory Data With Traja |
title_sort | automated analysis of stroke mouse trajectory data with traja |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262161/ https://www.ncbi.nlm.nih.gov/pubmed/32523509 http://dx.doi.org/10.3389/fnins.2020.00518 |
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