Automated Home-Cage Behavioural Phenotyping of Mice
Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long periods of time. Here, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and an extensive manually annotated video database...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2011
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| Online Access: | http://hdl.handle.net/1721.1/64492 https://orcid.org/0000-0002-3944-0455 |
| _version_ | 1826212525970030592 |
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| author | Jhuang, Huei-Han Garrote, Estibaliz Yu, Xinlin Khilnani, Vinita Poggio, Tomaso A. Steele, Andrew D. Serre, Thomas R. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Jhuang, Huei-Han Garrote, Estibaliz Yu, Xinlin Khilnani, Vinita Poggio, Tomaso A. Steele, Andrew D. Serre, Thomas R. |
| author_sort | Jhuang, Huei-Han |
| collection | MIT |
| description | Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long
periods of time. Here, we describe a trainable computer vision system enabling the automated analysis
of complex mouse behaviors. 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 behaviors of two standard inbred and two
non-standard mouse strains. From this 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 behavior. |
| first_indexed | 2024-09-23T15:23:52Z |
| format | Article |
| id | mit-1721.1/64492 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:23:52Z |
| publishDate | 2011 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/644922022-10-02T02:34:56Z Automated Home-Cage Behavioural Phenotyping of Mice Jhuang, Huei-Han Garrote, Estibaliz Yu, Xinlin Khilnani, Vinita Poggio, Tomaso A. Steele, Andrew D. Serre, Thomas R. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Poggio, Tomaso A. Jhuang, Huei-Han Garrote, Estibaliz Poggio, Tomaso A. Serre, Thomas R. Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long periods of time. Here, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. 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 behaviors of two standard inbred and two non-standard mouse strains. From this 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 behavior. McGovern Institute for Brain Research California Institute of Technology. Broad Fellows Program in Brain Circuitry National Science Council (China) (TMS-094-1-A032) 2011-06-20T14:10:50Z 2011-06-20T14:10:50Z 2010-01 Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/64492 Jhuang, Hueihan et al. “Automated Home-cage Behavioural Phenotyping of Mice.” Nat Commun 1 (2010) : 68. https://orcid.org/0000-0002-3944-0455 en_US http://dx.doi.org/10.1038/ncomms1064 Nature Communications Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Nature Publishing Group Prof. Poggio via Lisa Horowitz |
| spellingShingle | Jhuang, Huei-Han Garrote, Estibaliz Yu, Xinlin Khilnani, Vinita Poggio, Tomaso A. Steele, Andrew D. Serre, Thomas R. Automated Home-Cage Behavioural Phenotyping of Mice |
| title | Automated Home-Cage Behavioural Phenotyping of Mice |
| title_full | Automated Home-Cage Behavioural Phenotyping of Mice |
| title_fullStr | Automated Home-Cage Behavioural Phenotyping of Mice |
| title_full_unstemmed | Automated Home-Cage Behavioural Phenotyping of Mice |
| title_short | Automated Home-Cage Behavioural Phenotyping of Mice |
| title_sort | automated home cage behavioural phenotyping of mice |
| url | http://hdl.handle.net/1721.1/64492 https://orcid.org/0000-0002-3944-0455 |
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