Argus: An open-source and flexible software application for automated quantification of behavior during social interaction in adult zebrafish
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 trac...
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Published in | Behavior research methods Vol. 51; no. 2; pp. 727 - 746 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
15.04.2019
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1554-3528 1554-3528 |
DOI | 10.3758/s13428-018-1083-y |
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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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AbstractList | 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.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. 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. |
Author | Shams, Soaleha Scicluna, Matthew Gerlai, Robert Amlani, Shahid |
Author_xml | – sequence: 1 givenname: Soaleha surname: Shams fullname: Shams, Soaleha email: soaleha.shams@utoronto.ca organization: Department of Cell & Systems Biology, University of Toronto Mississauga – sequence: 2 givenname: Shahid surname: Amlani fullname: Amlani, Shahid organization: Department of Psychology, University of Toronto Mississauga – sequence: 3 givenname: Matthew surname: Scicluna fullname: Scicluna, Matthew organization: Department of Computer Science, University of Montreal – sequence: 4 givenname: Robert surname: Gerlai fullname: Gerlai, Robert email: robert_gerlai@yahoo.com organization: Department of Cell & Systems Biology, University of Toronto Mississauga, Department of Psychology, University of Toronto Mississauga |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30105442$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1139_cjfas_2023_0004 crossref_primary_10_1016_j_neuroscience_2022_12_016 crossref_primary_10_1111_gbb_12803 crossref_primary_10_1155_2023_3071147 |
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SubjectTerms | Automation Behavior Behavioral Science and Psychology Cognitive Psychology Computer programs Danio rerio Data processing Freezing Nervous system Neural coding Psychology Social behavior Social interaction Software Zebrafish |
Title | Argus: An open-source and flexible software application for automated quantification of behavior during social interaction in adult zebrafish |
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