Automated high-throughput neurophenotyping of zebrafish social behavior

Jeremy Green, Christopher Collins, Evan J. Kyzar, Mimi Pham, Andrew Roth, Siddharth Gaikwad, Jonathan Cachat, Adam Michael Stewart, Samuel Landsman, Fabrizio Grieco, Ruud Tegelenbosch, Lucas P.J.J. Noldus, Allan V. Kalueff*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

142 Citations (Scopus)


Zebrafish (Danio rerio) are rapidly becoming an important model organism in neuroscience research, representing an excellent species to study complex social phenotypes. Zebrafish actively form shoals, which can be used to quantify their shoaling behaviors, highly sensitive to various experimental manipulations. Recent advances in video-tracking techniques have enabled simultaneous tracking of multiple subjects, previously assessed by manual scoring of animal behavior. Here we examined the effect of group-size in the shoaling paradigm (ranging from 2 to 8 fish), and evaluated the ability of novel video-tracking tools to accurately track an entire shoal, compared to traditional manual analysis of shoaling phenotypes. To further validate our approach, the effects of the psychotropic drugs lysergic acid diethylamide (LSD) and 3,4-methlenedioxymethamphetamine (MDMA), as well as exposure to alarm pheromone, previously shown to affect zebrafish shoaling, were examined. Overall, a significant difference in group size was shown in the 2-fish vs. the 3-, 4-, 5-, 6-, 7- and 8-fish groups. Moreover, both LSD and MDMA treatments reduced shoaling (assessed by increased inter-fish distance) as well as proximity (time spent together) among fish. In contrast, exposure to alarm pheromone yielded an increase in shoaling and in proximity in a time-dependent manner. Importantly, a highly significant correlation for manual vs. automated analyses was revealed across all experiments. Collectively, this study further supports the utility of zebrafish to study social behavior, also demonstrating the capacity of video-tracking technology to assess zebrafish shoaling in a high-throughput and reliable manner.

Original languageEnglish
Pages (from-to)266-271
Number of pages6
JournalJournal of Neuroscience Methods
Issue number2
Publication statusPublished - 30 Sept 2012
Externally publishedYes


  • Automated quantification
  • Shoaling
  • Social behavior
  • Video tracking
  • Zebrafish


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