DANCE provides an open-source and low-cost approach to quantify aggression and courtship in
Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, India
Department of Biochemistry, Kasturba Medical College, Manipal Academy of Higher Education, India
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DANCE provides an open-source and low-cost approach to quantify aggression and courtship in
open-source and cost-effective method for automating the quantification of male aggression and courtship in
evidence that the use of the behavioral setup that the authors designed - using readily available laboratory equipment and standardised high-performing classifiers they developed using existing software packages - accurately and reliably characterises social behavior in Drosophila. The work will be of interest to Drosophila neurobiologists and particularly to those working on male social behaviors.
https://doi.org/10.7554/eLife.105465.3.sa0
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Quantifying animal behavior is pivotal for identifying the neuronal and genetic mechanisms involved. Computational approaches have enabled automated analysis of complex behaviors such as aggression and courtship in
. However, existing approaches rely on rule-based algorithms and expensive hardware, limiting sensitivity to behavioral variations and accessibility. Here, we present the
valuator (DANCE), a low-cost, open-source platform that combines machine learning-based classifiers and inexpensive hardware to quantify aggression and courtship. DANCE consists of six novel behavioral classifiers trained using a supervised machine learning algorithm. DANCE classifiers address key limitations of rule-based algorithms, capturing dynamic behavioral variations more effectively. DANCE hardware is constructed using medicine blister packs and acrylic sheets, with recordings acquired using smartphones, making it affordable and accessible. Benchmarking demonstrated that DANCE hardware performs comparably to high-cost setups. We validated DANCE in diverse contexts, including social isolation vs. enrichment, which modulates aggression and courtship; RNAi-mediated downregulation of the neuropeptide Dsk; and optogenetic silencing of dopaminergic neurons, which promotes aggression. DANCE provides a cost-effective and portable solution for studying behaviors in resource-limited settings or near natural habitats. Its accessibility and robust performance democratize behavioral neuroscience, enabling rapid screening of genes and neuronal circuits underlying complex social behaviors.
Detailed and accurate annotation and analysis of complex behaviors are necessary for understanding the underlying neural and molecular mechanisms. The fruit fly
is one of the most accessible and well-studied model organisms for identifying the neuronal and molecular underpinnings of behavior. Multiple large-scale screens have been conducted in
to study complex social behaviors such as aggression and courtship (
) to identify the underlying neural circuitry (
). These behaviors exhibit distinct, stereotyped patterns. For example, aggression involves chasing, fencing (
). Similarly, courtship consists of multiple stereotyped behaviors exhibited by the male fly, such as orienting, circling, and following the female (
). To stimulate the female to be more receptive, the male produces a species-specific song by vibrating and extending its wing (
). The male then attempts copulation by curling its abdomen and finally mounts the female for copulation (
Manual analysis by trained observers is considered the gold standard in behavioral analysis, but it is time-consuming and unsuitable for large-scale screens (
) helps address this challenge by automating behavioral annotation by leveraging advances in computer vision and machine learning (
). This enables high-throughput behavioral screening to identify responsible genes and circuits.
A typical computational ethology workflow involves recording animal behaviors and tracking their positions along with body movements. This is followed by the analysis and classification of the observed behaviors from hundreds to thousands of video frames capturing behavioral instances. Several software programs, such as Ctrax, Caltech FlyTracker, and Deep Lab Cut (
), are widely used for tracking behaviors in
. Each comes with strengths and weaknesses. Ctrax (
) can accurately track fly position and movement, but identity switches remain a challenge, especially when tracking groups of flies. While both Ctrax and FlyTracker (
) may produce identity switches, when groups of flies were tracked simultaneously, Ctrax led to inaccuracies that required manual correction using specialized algorithms such as FixTrax (
The effectiveness of various machine learning pipelines is eventually measured by comparing their output to human annotation, called ‘ground-truthing’. A rule-based algorithm such as CADABRA (
) is used to quantify aggression, but it can lead to mis-scoring and identity switches, as revealed by ground-truthing (
), which needs to be corrected in a semiautomated manner (
) is another rule-based algorithm used to quantify courtship; however, similar to CADABRA, it tends to miss true-positive events, leading to significant mis-scoring of behaviors under certain experimental conditions.
The Janelia Automatic Animal Behavior Annotator (JAABA) (
) addresses the challenges of rigid rule-based approaches by employing a supervised learning approach. In the JAABA pipeline, user-labeled data are utilized for training to encompass the dynamic variations in behaviors, allowing it to predict behaviors on the basis of learning from input data.
Several studies have developed JAABA-based behavioral classifiers for measuring aggression (
). However, many of these studies did not make these classifiers publicly available (
). In other cases, the reported approaches relied on specialized hardware, such as custom 3D-printed parts (
), limiting their accessibility and wider adoption.
valuator), an open-source, user-friendly analysis and hardware pipeline to simplify and automate the process of robustly quantifying aggression and courtship behaviors. DANCE has two components: (1) A set of robust, machine vision-based behavioral classifiers developed using JAABA to quantify aggression and courtship. (2) An inexpensive hardware setup built from off-the-shelf materials and consumer smartphones for behavioral recording. Compared with previous methods (
), the DANCE classifiers improved accuracy and reliability, while its low-cost hardware eliminates the need for specialized arenas and cameras. All classifiers and analysis codes are publicly available, enabling broad adoption, especially in resource-limited settings. Together, DANCE provides a powerful, accessible platform for behavioral screening and the discovery of mechanisms underlying complex social behaviors and neurological disorders.
To overcome the challenge of time-consuming manual behavioral annotation or resource-intensive, complex hardware, we developed an automated, high-throughput quantification pipeline—DANCE and trained new behavioral classifiers using an existing machine learning algorithm, JAABA (
)—to robustly quantify aggression and courtship in
). We also designed a simple, low-cost recording setup constructed from repurposed transparent medicine blister packs, acrylic sheets, and paper tape, enabling easy behavioral recordings. To