High-throughput Ethomics in Large Groups of Drosophila

By Kristin Branson, Alice Robie, John A. Bender, Pietro Perona, Michael H. Dickinson. 2009

809 citations recorded by the source corpus.

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

Subfields: Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics, Genetics

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Citations in the corpus

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila (2014, methodology): “Videography was performed under reflected IR light using a camera (ROHS 1.3 MP B&W Flea3 USB 3.0 Camera; POINT GREY, Richmond, BC, Canada) with an 800-nm long pass filter (B&W filter; Schneider Optics) at 30 frames per sec,1024 × 1024 pixel resolution and analyzed using Fiji (Schindelin et al., 2012”
Mapping the stereotyped behaviour of freely moving fruit flies (2013, neutral): “machine-learning techniques [12–16].”
Fast animal pose estimation using deep neural networks (2018, methodology): “Lastly, through the addition of a segmentation step for analyzing videos of multiple animals 3,14,46 , LEAP can potentially estimate poses for multiple interacting individuals.”
idTracker: tracking individuals in a group by automatic identification of unmarked animals (2014, methodology): “Some of these systems incorporate image processing techniques to separate the images of the individuals when the overlaps are small 6–8 or use species-specific shape models that can help to resolve more complex crossings 8–12 .”
Human Disease Models in Drosophila melanogaster and the Role of the Fly in Therapeutic Drug Discovery (2011, methodology): “For example, analysis software can simultaneously examine locomotor activity and interactions of up to 96 video channels in real time that include aspects of both aggression and courtship (Noldus, Wageningen, NL) (Branson et al., 2009; Dankert et al., 2009).”
BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion (2015, background): “[4,14,15], with notable exceptions such as [7]), individual tracking depends on information from previous frames, and therefore when an individual is either (a) not tracked or (b) incorrectly tracked in one or a few frames (i.”
Computational neuroethology: a call to action (2019, methodology): “Platforms that use this strategy (including CADABRA and JAABA) have been widely used in a variety of contexts (Branson et al., 2009; Dankert et al., 2009; Kabra et al., 2013).”
A Framework for Studying Emotions Across Phylogeny (2014, positive): “…available for imaging and manipulating neural circuit analysis in such systems (e.g., Venken and Simpson, 2011), as well as quantitative and objective, machine vision-based methods for measuring the behavior of such model organisms (Dankert et al., 2009; Branson et al., 2009; Kanra et al., 2013).”