Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful informati...
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| Veröffentlicht in: | PLoS computational biology Jg. 17; H. 9; S. e1009439 |
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| Sprache: | Englisch |
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Public Library of Science
22.09.2021
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| Abstract | Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. |
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| AbstractList | Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. The quantification of animal behavior is a crucial step towards understanding how neural activity produces coordinated movements, and how those movements are affected by genes, drugs, and environmental manipulations. In recent years video cameras have become an inexpensive and ubiquitous way to monitor animal behavior across many species and experimental paradigms. Here we propose a new computer vision algorithm that extracts a succinct summary of an animal’s pose on each frame. This summary contains information about a predetermined set of body parts of interest (such as joints on a limb), as well as information about previously unidentified aspects of the animal’s pose. Experimenters can thus track body parts they think are relevant to their experiment, and allow the algorithm to discover new dimensions of behavior that might also be important for downstream analyses. We demonstrate this algorithm on videos from four different experimental setups, and show how these new dimensions of behavior can aid in downstream behavioral and neural analyses. |
| Audience | Academic |
| Author | Wu, Anqi Schartner, Michael Rodriguez, Erica Noel, Jean-Paul Zhou, John Salzman, C. Daniel Paninski, Liam Friedman, Yoni Miska, Nathaniel J. Cunningham, John P. Socha, Karolina Buchanan, E. Kelly Whiteway, Matthew R. Dipoppa, Mario Biderman, Dan Bonacchi, Niccolò Urai, Anne E. |
| AuthorAffiliation | 14 New York State Psychiatric Institute, New York, New York, United States of America 7 Department of Computer Science, Columbia University, New York, New York, United States of America 15 Kavli Institute for Brain Sciences, New York, New York, United States of America 13 Department of Psychiatry, Columbia University, New York, New York, United States of America 5 Department of Neuroscience, Columbia University, New York, New York, United States of America 12 Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands 2 Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America 6 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America 3 Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America 4 Department of Statistics, Columbia University, New York, New York, United States of America 10 Center for Ne |
| AuthorAffiliation_xml | – name: 11 Institute of Ophthalmology, University College London, London, United Kingdom – name: 5 Department of Neuroscience, Columbia University, New York, New York, United States of America – name: 13 Department of Psychiatry, Columbia University, New York, New York, United States of America – name: 15 Kavli Institute for Brain Sciences, New York, New York, United States of America – name: 3 Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America – name: 10 Center for Neural Science, New York University, New York, New York, United States of America – name: 12 Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands – name: 6 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America – name: 7 Department of Computer Science, Columbia University, New York, New York, United States of America – name: 9 Sainsbury-Wellcome Centre for Neural Circuits and Behavior, University College London, London, United Kingdom – name: 1 Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America – name: 8 Champalimaud Centre for the Unknown, Lisbon, Portugal – name: 2 Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America – name: 14 New York State Psychiatric Institute, New York, New York, United States of America – name: 4 Department of Statistics, Columbia University, New York, New York, United States of America – name: University of California at Berkeley, UNITED STATES |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34550974$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Whiteway et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Whiteway et al 2021 Whiteway et al |
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| Title | Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders |
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