| Title |
Argus: An open-source and flexible software application for automated quantification of behavior during social interaction in adult zebrafish
|
|---|---|
| Published in |
Behavior Research Methods, August 2018
|
| DOI | 10.3758/s13428-018-1083-y |
| Pubmed ID | |
| Authors |
Soaleha Shams, Shahid Amlani, Matthew Scicluna, Robert Gerlai |
| Abstract |
Zebrafish show great potential for behavioral neuroscience. Promising lines of research, however, require the development and validation of software tools that will allow automated and cost-effective behavioral analysis. Building on our previous work with the RealFishTracker (in-house-developed tracking system), we present Argus, a data extraction and analysis tool built in the open-source R language for behavioral researchers without any expertise in R. Argus includes a new, user-friendly, and efficient graphical user interface, instead of a command-line interface, and offers simplicity and flexibility in measuring complex zebrafish behavior through customizable parameters. In this article, we compare Argus with Noldus EthoVision and Noldus The Observer, to validate this new system. All three software applications were originally designed to quantify the behavior of a single subject. We first also performed an analysis of the movement of individual fish and compared the performance of the three software applications. Next we computed and quantified the behavioral variables that characterize dyadic interactions between zebrafish. We found that Argus and EthoVision extract similar absolute values and patterns of changes in these values for several behavioral measures, including speed, freezing, erratic movement, and interindividual distance. In contrast, the manual coding of behavior in The Observer showed weaker correlations with the two tracking methods (EthoVision and Argus). Thus, Argus is a novel, cost-effective, and customizable method for the analysis of adult zebrafish behavior that may be utilized for the behavioral quantification of both single and dyadic interacting subjects, but further sophistication will be needed for the proper identification of complex motor patterns, measures that a human observers can easily detect. |
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| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
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| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
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| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 17% |
| Student > Ph. D. Student | 5 | 14% |
| Student > Master | 5 | 14% |
| Student > Bachelor | 3 | 9% |
| Other | 3 | 9% |
| Other | 6 | 17% |
| Unknown | 7 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 20% |
| Neuroscience | 5 | 14% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Psychology | 2 | 6% |
| Computer Science | 2 | 6% |
| Other | 8 | 23% |
| Unknown | 9 | 26% |
Attention Score in Context
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Outputs of similar age from Behavior Research Methods
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