A multi-animal tracker for studying complex behaviors

Abstract

BackgroundAnimals 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.ResultsHere, 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.ConclusionsTogether, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.

Principles for coding associative memories in a compact neural network

“…For this, we imaged trained worms during chemotaxis towards butanone (Fig. 4A) using a multi-animal tracking system that extracts key locomotion parameters from over a hundred worms at a time (Itskovits et al , 2017) . As expected, aversively-trained animals were significantly slower to arrive at the butanone target point when compared to positively-trained or naive animals (Fig.…”

An open source platform for analyzing and sharing worm behavior data

“…Several worm trackers have been previously described [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . Most of the trackers either extract detailed postural information of one worm at a time or track multiple worms but extract only coarser-grained features such as centroid speed or orientation.…”

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

“…Trackers can assist in a wide range of experimental tasks such as monitoring activity, measuring response to stimuli 1;2 , and locating body parts over time 3;4 . Some trackers are designed to track and maintain identities of multiple individuals occupying the same arena [5][6][7][8] while others measure the collective activity of groups without maintaining identities or rely on physical segregation of animals to ensure trajectories never collide [9][10][11][12] . But few of these trackers are designed as platforms for high throughput, hardware control, and flexible experimental reconfiguration.…”

“…Real-time tracking offers the benefits of allowing closed-loop stimulus delivery and a small data footprint due to video data not being retained. In general, real-time tracking methods are less capable of tracking individuals through collisions because they cannot use future information to help resolve ambiguities 11 . For that reason, real-time multiple animal trackers can fall back on spatial segregation of animals to distinguish identities or dispense with identity tracking altogether 12;16 .…”