Abstract
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.
Highlights
We present a Python package, Traja, for automated analysis of activity and position extracted via the 12 capacitive homecage sensors in the DVC [Tecniplast S.p.A., Buguggiate (VA), Italy] (Iannello, 2019; Pernold et al, 2019) to quantify several behavioral modalities in a stroke mouse model
We demonstrate the capability of metrics derived from home-cage activity and position tracking to study differences in the neurobehavioral phenotypes of mice over extensive lengths of time, within a method that is accessible to researchers possessing moderate programming background
We have demonstrated several capabilities of Traja relevant to behavioral analysis of a stroke mouse model
Summary
Rodent locomotion has been studied in the context of various disease models such as spinal cord injury (Barrière et al, 2008; Tester et al, 2011, 2012), neurodegenerative diseases such as Parkinson’s (Morris et al, 1996, 2001; Amende et al, 2005) and Down syndrome (Hampton et al, 2004; Herault et al, 2017), assessment of pharmacological agents (Masocha and Parvathy, 2009), genetic mutations (Bothe et al, 2004; Crone et al, 2009), and stroke (Encarnacion et al, 2011; Hetze et al, 2012). Locomotion monitoring has been used both as a proxy for measuring illness and fatigue as well as overall development and recovery. Automated quantitative analysis of mouse phenotype allows researchers to objectively assess cognitive and motor abilities and disturbances brought about by genetics, disease processes, and interventions. The ability of these effects to be observed, measured and communicated, is constrained by the availability of assays which reflect physiological and cognitive changes occurring over indeterminate time intervals. The time resolution of Automated Stroke Mouse Trajectory Analysis observations and analysis are limited by the availability of behavioral data and analytical methods. Sharing data and analytical methods allows for increasing the validity of experimental modalities
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