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...

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Main Authors: Justin Shenk, Klara J. Lohkamp, Maximilian Wiesmann, Amanda J. Kiliaan
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-05-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00518/full
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spelling doaj-61b1f1a06db24fb1ba075a979d0998732020-11-25T03:49:17ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-05-011410.3389/fnins.2020.00518530556Automated Analysis of Stroke Mouse Trajectory Data With TrajaJustin ShenkKlara J. LohkampMaximilian WiesmannAmanda J. KiliaanQuantitative 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.https://www.frontiersin.org/article/10.3389/fnins.2020.00518/fullanimal trackingneuropsychiatric disordersmachine learninghome-cagemousestroke
collection DOAJ
language English
format Article
sources DOAJ
author Justin Shenk
Klara J. Lohkamp
Maximilian Wiesmann
Amanda J. Kiliaan
spellingShingle Justin Shenk
Klara J. Lohkamp
Maximilian Wiesmann
Amanda J. Kiliaan
Automated Analysis of Stroke Mouse Trajectory Data With Traja
Frontiers in Neuroscience
animal tracking
neuropsychiatric disorders
machine learning
home-cage
mouse
stroke
author_facet Justin Shenk
Klara J. Lohkamp
Maximilian Wiesmann
Amanda J. Kiliaan
author_sort Justin Shenk
title Automated Analysis of Stroke Mouse Trajectory Data With Traja
title_short 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_sort automated analysis of stroke mouse trajectory data with traja
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2020-05-01
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.
topic animal tracking
neuropsychiatric disorders
machine learning
home-cage
mouse
stroke
url https://www.frontiersin.org/article/10.3389/fnins.2020.00518/full
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