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An open-source video tracking system for mouse locomotor activity analysis.

ABSTRACT: OBJECTIVE:The ability to accurately and efficiently quantify mouse locomotor activity is essential for evaluating therapeutic efficacy and phenotyping genetically modified mice, in particular for the research of neuromuscular diseases. Our objective is to develop a program for video tracking of mice and locomotion analysis. RESULTS:Here we describe a MATLAB program for video tracking of mice and locomotion analysis. The system is composed of a webcam, an open field, and a computer with MATLAB installed. Animal behavior is recorded by the webcam and the video is then analyzed for mouse position on each frame by a customized MATLAB code. The system has been tested for analyzing two or more mice simultaneously placed in individual chambers. The accumulative moving distance, velocity and thigmotaxis (percentage of time spending in the outer peripheral of the arena, which is commonly used as an index of anxiety) within a test period can be readily obtained. This system can be easily implemented in any laboratory as an in vivo locomotion assay to assess the neuromuscular abnormality of genetically modified animals and the impact of therapeutic interventions.

SUBMITTER: Zhang C

PROVIDER: S-EPMC6990588 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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An open-source video tracking system for mouse locomotor activity analysis.

Zhang Chen C Li Haiwen H Han Renzhi R

BMC research notes 20200130 1

<h4>Objective</h4>The ability to accurately and efficiently quantify mouse locomotor activity is essential for evaluating therapeutic efficacy and phenotyping genetically modified mice, in particular for the research of neuromuscular diseases. Our objective is to develop a program for video tracking of mice and locomotion analysis.<h4>Results</h4>Here we describe a MATLAB program for video tracking of mice and locomotion analysis. The system is composed of a webcam, an open field, and a computer ...[more]

PMID: 32000855

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Project description:IntroductionComplex gait disturbances represent one of the prominent manifestations of various neurophysiological conditions, including widespread neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Therefore, instrumental measurement techniques and automatic computerized analysis appears essential for the differential diagnostics, as well as for the assessment of treatment effectiveness from experimental animal models to clinical settings.MethodsHere we present a marker-free instrumental approach to the analysis of gait disturbances in animal models. Our approach is based on the analysis of video recordings obtained with a camera placed underneath an open field arena with transparent floor using the DeeperCut algorithm capable of online tracking of individual animal body parts, such as the snout, the paws and the tail. The extracted trajectories of animal body parts are next analyzed using an original computerized methodology that relies upon a generalized scalable model based on fractional Brownian motion with parameters identified by detrended partial cross-correlation analysis.ResultsWe have shown that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover. More detailed analysis of gait disturbances revealed that the detrended cross-correlations between the movements of the snout, paws and tail relative to the animal body midpoint exhibit statistically significant discrepancies in the Alzheimer's disease mouse model compared to the control group at scales around the location of the crossover.DiscussionWe expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models. We further believe that the suggested mathematical models could be relevant as a complementary tool in clinical diagnostics of various neurophysiological conditions associated with movement disorders.

Project description:BACKGROUND: In many laboratories, researchers store experimental data on their own workstation using spreadsheets. However, this approach poses a number of problems, ranging from sharing issues to inefficient data-mining. Standard spreadsheets are also error-prone, as data do not undergo any validation process. To overcome spreadsheets inherent limitations, a number of proprietary systems have been developed, which laboratories need to pay expensive license fees for. Those costs are usually prohibitive for most laboratories and prevent scientists from benefiting from more sophisticated data management systems. RESULTS: In this paper, we propose the EnzymeTracker, a web-based laboratory information management system for sample tracking, as an open-source and flexible alternative that aims at facilitating entry, mining and sharing of experimental biological data. The EnzymeTracker features online spreadsheets and tools for monitoring numerous experiments conducted by several collaborators to identify and characterize samples. It also provides libraries of shared data such as protocols, and administration tools for data access control using OpenID and user/team management. Our system relies on a database management system for efficient data indexing and management and a user-friendly AJAX interface that can be accessed over the Internet. The EnzymeTracker facilitates data entry by dynamically suggesting entries and providing smart data-mining tools to effectively retrieve data. Our system features a number of tools to visualize and annotate experimental data, and export highly customizable reports. It also supports QR matrix barcoding to facilitate sample tracking. CONCLUSIONS: The EnzymeTracker was designed to be easy to use and offers many benefits over spreadsheets, thus presenting the characteristics required to facilitate acceptance by the scientific community. It has been successfully used for 20 months on a daily basis by over 50 scientists. The EnzymeTracker is freely available online at http://cubique.fungalgenomics.ca/enzymedb/index.html under the GNU GPLv3 license.

Project description:JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.

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An open-source video tracking system for mouse locomotor activity analysis.

Publications

An open-source video tracking system for mouse locomotor activity analysis.

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