Automated Monitoring and Analysis of Social Behavior in Drosophila

By Heiko Dankert, Liming Wang, Eric D. Hoopfer, David J. Anderson, Pietro Perona. Nature Methods, 2009

405 citations recorded by the source corpus.

We introduce a method based on machine vision for automatically measuring aggression and courtship in Drosophila melanogaster. The genetic and neural circuit bases of these innate social behaviors are poorly understood. High-throughput behavioral screening in this genetically tractable model organism is a potentially powerful approach, but it is currently very laborious. Our system monitors interacting pairs of flies and computes their location, orientation and wing posture. These features are used for detecting behaviors exhibited during aggression and courtship. Among these, wing threat, lunging and tussling are specific to aggression; circling, wing extension (courtship 'song') and copulation are specific to courtship; locomotion and chasing are common to both. Ethograms may be constructed automatically from these measurements, saving considerable time and effort. This technology should enable large-scale screens for genes and neural circuits controlling courtship and aggression.

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

View Paper: https://www.semanticscholar.org/paper/6a5347267ee71276cb635dc043507a6fcecc35ca

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Citations in the corpus

High-throughput Ethomics in Large Groups of Drosophila (2009, methodology): “Recently, systems have been developed to automatically detect components of aggression and courtship behavior in flie”
Computational neuroethology: a call to action (2019, methodology): “As with feature extraction, automated labeling often takes advantage of supervised learning approaches, in whichmachine learning algorithms are trained to identify particular behaviors based upon a set of human-curated examples (Branson et al., 2009; Dankert et al., 2009; Kabra et al., 2013; Machado”
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).”
High-Resolution Positional Tracking for Long-Term Analysis of Drosophila Sleep and Locomotion Using the “Tracker” Program (2012, background): “Recently, several fly labs have developed specialized software for video-based data capture [13–17].”
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).”
Fast animal pose estimation using deep neural networks (2018, methodology): “…can be increased through automation 1 including meth-ods to track and analyze animal centroids and shapes over time 3–5 , machine learning techniques for identifying user-defined behaviors such as fighting and courting 6,7 , and software to segment the acoustic signals produced by an animal 8–10 .”
Mapping the stereotyped behaviour of freely moving fruit flies (2013, neutral): “machine-learning techniques [12–16].”
Toward a science of computational ethology. (2014, positive): “At a minimum, such an analysis consists of computing an ethogram (Chen et al., 2002; Dankert et al., 2009), which describes the frequency of each action and the probability that a given action is followed by another.”