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Automated classification of self-grooming in mice using open-source software.

Bastijn J G van den Boom1, Pavlina Pavlidi2, Casper J H Wolf2

  • 1Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands; Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Journal of Neuroscience Methods
|June 27, 2017
PubMed
Summary

Automated analysis of rodent self-grooming is now possible with the Janelia Automatic Animal Behavior Annotator (JAABA). This tool accurately quantifies grooming behavior, offering a cost-efficient and reliable alternative to manual scoring.

Keywords:
Automated video analysisElevated plus mazeInter-rater variabilityJAABA classifierMachine learningMouse behaviorOpen fieldOpen-source softwareSAPAP3 knockout miceSelf-grooming

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Area of Science:

  • Ethology
  • Neuroscience
  • Behavioral Science

Background:

  • Manual behavioral analysis is time-consuming and prone to human error.
  • Automating the quantification of complex behaviors like rodent self-grooming remains a challenge.

Purpose of the Study:

  • To develop and validate an automated classifier for quantifying rodent self-grooming.
  • To assess the accuracy and reliability of the Janelia Automatic Animal Behavior Annotator (JAABA) for this task.

Main Methods:

  • Trained the JAABA automated classifier using video recordings of SAPAP3 knockout and wild-type mice.
  • Compared JAABA's automated scoring of self-grooming with human expert observations across three different experimental setups.

Main Results:

  • JAABA demonstrated high accuracy in identifying grooming and non-grooming behaviors.
  • Automated classifier results showed strong correlation with human expert scoring.
  • Confirmed excessive grooming in SAPAP3 knockout mice, consistent with prior research.

Conclusions:

  • The JAABA classifier provides a valid, reliable, and efficient method for assessing rodent self-grooming.
  • JAABA offers a cost-effective, high-throughput solution that minimizes inter-rater variability.
  • This tool enables expert-quality behavioral classification with minimal user effort.