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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

View ORCID ProfileMatthew R Whiteway, View ORCID ProfileDan Biderman, Yoni Friedman, View ORCID ProfileMario Dipoppa, View ORCID ProfileE Kelly Buchanan, Anqi Wu, John Zhou, View ORCID ProfileNiccolò Bonacchi, Nathaniel J Miska, View ORCID ProfileJean-Paul Noel, Erica Rodriguez, View ORCID ProfileMichael Schartner, View ORCID ProfileKarolina Socha, View ORCID ProfileAnne E Urai, View ORCID ProfileC Daniel Salzman, The International Brain Laboratory, John P Cunningham, Liam Paninski
doi: https://doi.org/10.1101/2021.02.22.432309
Matthew R Whiteway
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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  • For correspondence: m.whiteway@columbia.edu
Dan Biderman
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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Yoni Friedman
1Center for Theoretical Neuroscience, Columbia University, New York, USA
6Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, USA
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Mario Dipoppa
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
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E Kelly Buchanan
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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  • ORCID record for E Kelly Buchanan
Anqi Wu
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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John Zhou
7Department of Computer Science, Columbia University, New York, USA
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Niccolò Bonacchi
8Champalimaud Centre for the Unknown, Lisbon, Portugal
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Nathaniel J Miska
9Sainsbury-Wellcome Centre for Neural Circuits and Behavior, University College London, London, UK
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Jean-Paul Noel
10Center for Neural Science, New York University, New York, USA
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Erica Rodriguez
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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Michael Schartner
8Champalimaud Centre for the Unknown, Lisbon, Portugal
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Karolina Socha
11Institute of Ophthalmology, University College London, London, UK
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Anne E Urai
12Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
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C Daniel Salzman
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
13Department of Psychiatry, Columbia University, New York, USA
14New York State Psychiatric Institute, New York, USA
15Kavli Institute for Brain Sciences, New York, USA
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John P Cunningham
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
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Liam Paninski
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • New analyses have been added: the application of the proposed method to a freely moving mouse, and the extension of the method to handle multiple videos from the same experimental setup.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 01, 2021.
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E Urai, C Daniel Salzman, The International Brain Laboratory, John P Cunningham, Liam Paninski
bioRxiv 2021.02.22.432309; doi: https://doi.org/10.1101/2021.02.22.432309
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E Urai, C Daniel Salzman, The International Brain Laboratory, John P Cunningham, Liam Paninski
bioRxiv 2021.02.22.432309; doi: https://doi.org/10.1101/2021.02.22.432309

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