Automated home-cage behavioural phenotyping of mice

Hueihan Jhuang, Estibaliz Garrote, Xinlin Yu, Vinita Khilnani, Tomaso Poggio, Andrew D. Steele () and Thomas Serre ()
Additional contact information
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

Nature Communications, 2010, vol. 1, issue 1, 1-10

Abstract: 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.

Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/ncomms1064 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:1:y:2010:i:1:d:10.1038_ncomms1064

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms1064

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().