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A multi-animal tracker for studying complex behaviors

View Article: PubMed Central - PubMed

ABSTRACT

Background: Animals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.

Results: Here, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.

Conclusions: Together, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

Electronic supplementary material: The online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


A novel end-to-end Multi-Animal Tracker software allows recording, tracking, and analyzing multiple animals at a time. a Flowchart describing the sequential usage of the different packages included in the software suite. b A simple, user-friendly Graphical User Interface is provided to analyze tracks of multiple animals throughout a movie recording typically consisting of thousands of frames. The tracking software uses a machine learning algorithm to identify animal instances. The user is asked to ‘teach’ the software what is an animal by clicking on several animal instances. c–f The software is able to track and analyze the movement of different animals. Shown are representative tracks extracted from movies of behaving (c) worms, (d) flies, (e) zebrafish, and (f) mice
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Fig1: A novel end-to-end Multi-Animal Tracker software allows recording, tracking, and analyzing multiple animals at a time. a Flowchart describing the sequential usage of the different packages included in the software suite. b A simple, user-friendly Graphical User Interface is provided to analyze tracks of multiple animals throughout a movie recording typically consisting of thousands of frames. The tracking software uses a machine learning algorithm to identify animal instances. The user is asked to ‘teach’ the software what is an animal by clicking on several animal instances. c–f The software is able to track and analyze the movement of different animals. Shown are representative tracks extracted from movies of behaving (c) worms, (d) flies, (e) zebrafish, and (f) mice

Mentions: Our novel MAT includes three software modules, namely (1) video acquisition, (2) track extraction, and (3) advanced functions for analyzing complex behaviors (Fig. 1a). All packages are based on MATLAB (MathWorks© Inc.) and a detailed guideline for installing and using the different modules is provided in the accompanying Additional file 1.Fig. 1


A multi-animal tracker for studying complex behaviors
A novel end-to-end Multi-Animal Tracker software allows recording, tracking, and analyzing multiple animals at a time. a Flowchart describing the sequential usage of the different packages included in the software suite. b A simple, user-friendly Graphical User Interface is provided to analyze tracks of multiple animals throughout a movie recording typically consisting of thousands of frames. The tracking software uses a machine learning algorithm to identify animal instances. The user is asked to ‘teach’ the software what is an animal by clicking on several animal instances. c–f The software is able to track and analyze the movement of different animals. Shown are representative tracks extracted from movies of behaving (c) worms, (d) flies, (e) zebrafish, and (f) mice
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5383998&req=5

Fig1: A novel end-to-end Multi-Animal Tracker software allows recording, tracking, and analyzing multiple animals at a time. a Flowchart describing the sequential usage of the different packages included in the software suite. b A simple, user-friendly Graphical User Interface is provided to analyze tracks of multiple animals throughout a movie recording typically consisting of thousands of frames. The tracking software uses a machine learning algorithm to identify animal instances. The user is asked to ‘teach’ the software what is an animal by clicking on several animal instances. c–f The software is able to track and analyze the movement of different animals. Shown are representative tracks extracted from movies of behaving (c) worms, (d) flies, (e) zebrafish, and (f) mice
Mentions: Our novel MAT includes three software modules, namely (1) video acquisition, (2) track extraction, and (3) advanced functions for analyzing complex behaviors (Fig. 1a). All packages are based on MATLAB (MathWorks© Inc.) and a detailed guideline for installing and using the different modules is provided in the accompanying Additional file 1.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Animals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.

Results: Here, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.

Conclusions: Together, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

Electronic supplementary material: The online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.

No MeSH data available.