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Automated home-cage behavioural phenotyping of mice

Author

Listed:
  • Hueihan Jhuang

    (McGovern Institute, Massachusetts Institute of Technology)

  • Estibaliz Garrote

    (McGovern Institute, Massachusetts Institute of Technology)

  • Xinlin Yu

    (Broad Fellows in Brain Circuitry Program, California Institute of Technology)

  • Vinita Khilnani

    (Broad Fellows in Brain Circuitry Program, California Institute of Technology)

  • Tomaso Poggio

    (McGovern Institute, Massachusetts Institute of Technology)

  • Andrew D. Steele

    (Broad Fellows in Brain Circuitry Program, California Institute of Technology)

  • Thomas Serre

    (McGovern Institute, Massachusetts Institute of Technology
    †Present address: Department of Cognitive, Linguistic and Psychological Sciences, Brown Institute for Brain Sciences, Brown University, Providence, RI 02912, USA.)

Abstract

Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behaviour.

Suggested Citation

  • Hueihan Jhuang & Estibaliz Garrote & Xinlin Yu & Vinita Khilnani & Tomaso Poggio & Andrew D. Steele & Thomas Serre, 2010. "Automated home-cage behavioural phenotyping of mice," Nature Communications, Nature, vol. 1(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:1:y:2010:i:1:d:10.1038_ncomms1064
    DOI: 10.1038/ncomms1064
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    Cited by:

    1. Hardeep Ryait & Edgar Bermudez-Contreras & Matthew Harvey & Jamshid Faraji & Behroo Mirza Agha & Andrea Gomez-Palacio Schjetnan & Aaron Gruber & Jon Doan & Majid Mohajerani & Gerlinde A S Metz & Ian Q, 2019. "Data-driven analyses of motor impairments in animal models of neurological disorders," PLOS Biology, Public Library of Science, vol. 17(11), pages 1-30, November.
    2. Maarten Loos & Bastijn Koopmans & Emmeke Aarts & Gregoire Maroteaux & Sophie van der Sluis & Neuro-BSIK Mouse Phenomics Consortium & Matthijs Verhage & August B Smit, 2014. "Sheltering Behavior and Locomotor Activity in 11 Genetically Diverse Common Inbred Mouse Strains Using Home-Cage Monitoring," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.

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