Mapping Sub-Second Structure in Mouse Behavior
Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effe...
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Veröffentlicht in: | Neuron (Cambridge, Mass.) Mass.), 2015-12, Vol.88 (6), p.1121-1135 |
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creator | Wiltschko, Alexander B. Johnson, Matthew J. Iurilli, Giuliano Peterson, Ralph E. Katon, Jesse M. Pashkovski, Stan L. Abraira, Victoria E. Adams, Ryan P. Datta, Sandeep Robert |
description | Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
[Display omitted]
•Computational modeling reveals structure in mouse behavior without observer bias•Mouse behavior appears to be composed of stereotyped, sub-second modules•From this perspective, new behaviors result from altering both modules and transitions•Unsupervised analysis reveals how genes and neural activity impact behavior
Mouse behavior appears inherently divided into brief modules of 3D motion. This sub-second structure reveals the influence of the environment, genes and neural activity on action. |
doi_str_mv | 10.1016/j.neuron.2015.11.031 |
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[Display omitted]
•Computational modeling reveals structure in mouse behavior without observer bias•Mouse behavior appears to be composed of stereotyped, sub-second modules•From this perspective, new behaviors result from altering both modules and transitions•Unsupervised analysis reveals how genes and neural activity impact behavior
Mouse behavior appears inherently divided into brief modules of 3D motion. This sub-second structure reveals the influence of the environment, genes and neural activity on action.</description><identifier>ISSN: 0896-6273</identifier><identifier>EISSN: 1097-4199</identifier><identifier>DOI: 10.1016/j.neuron.2015.11.031</identifier><identifier>PMID: 26687221</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Algorithms ; Animals ; Behavior ; Behavior, Animal ; Brain research ; Computer Simulation ; Grammar ; Imaging, Three-Dimensional - instrumentation ; Imaging, Three-Dimensional - methods ; Kinesics ; Machine Learning ; Male ; Methods ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Optogenetics - instrumentation ; Optogenetics - methods ; Scholarships & fellowships ; Three dimensional imaging</subject><ispartof>Neuron (Cambridge, Mass.), 2015-12, Vol.88 (6), p.1121-1135</ispartof><rights>2015 Elsevier Inc.</rights><rights>Copyright © 2015 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Dec 16, 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c491t-1cc5f7a740dc4ab4e228d569cc23ee4dd0d730cfdce27808e5c1afd52e98c0e23</citedby><cites>FETCH-LOGICAL-c491t-1cc5f7a740dc4ab4e228d569cc23ee4dd0d730cfdce27808e5c1afd52e98c0e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0896627315010375$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26687221$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wiltschko, Alexander B.</creatorcontrib><creatorcontrib>Johnson, Matthew J.</creatorcontrib><creatorcontrib>Iurilli, Giuliano</creatorcontrib><creatorcontrib>Peterson, Ralph E.</creatorcontrib><creatorcontrib>Katon, Jesse M.</creatorcontrib><creatorcontrib>Pashkovski, Stan L.</creatorcontrib><creatorcontrib>Abraira, Victoria E.</creatorcontrib><creatorcontrib>Adams, Ryan P.</creatorcontrib><creatorcontrib>Datta, Sandeep Robert</creatorcontrib><title>Mapping Sub-Second Structure in Mouse Behavior</title><title>Neuron (Cambridge, Mass.)</title><addtitle>Neuron</addtitle><description>Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
[Display omitted]
•Computational modeling reveals structure in mouse behavior without observer bias•Mouse behavior appears to be composed of stereotyped, sub-second modules•From this perspective, new behaviors result from altering both modules and transitions•Unsupervised analysis reveals how genes and neural activity impact behavior
Mouse behavior appears inherently divided into brief modules of 3D motion. This sub-second structure reveals the influence of the environment, genes and neural activity on action.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Behavior</subject><subject>Behavior, Animal</subject><subject>Brain research</subject><subject>Computer Simulation</subject><subject>Grammar</subject><subject>Imaging, Three-Dimensional - instrumentation</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Kinesics</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Methods</subject><subject>Mice</subject><subject>Mice, Inbred C57BL</subject><subject>Mice, Transgenic</subject><subject>Optogenetics - instrumentation</subject><subject>Optogenetics - methods</subject><subject>Scholarships & fellowships</subject><subject>Three dimensional imaging</subject><issn>0896-6273</issn><issn>1097-4199</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1uGyEURlHUqHGSvkFUjdRNNzPlMjAMm0pt1CSVEmXhZI0w3EmwbHBhxlLfPljOT9tFVyw494PvHkLOgDZAofuybAJOKYaGURANQENbOCAzoErWHJR6R2a0V13dMdkekeOcl5QCFwrekyPWdb1kDGakuTGbjQ8P1Xxa1HO0MbhqPqbJjlPCyofqJk4Zq-_4aLY-plNyOJhVxg_P5wm5v_hxd35VX99e_jz_dl1brmCswVoxSCM5dZabBUfGeic6ZS1rEblz1MmW2sFZZLKnPQoLZnCCoeotRdaekK_73M20WGPBwpjMSm-SX5v0W0fj9d83wT_qh7jVXNKSJ0vA5-eAFH9NmEe99tniamUClkYapABQXSt26Kd_0GWcUij1dlQrOsopFIrvKZtizgmH188A1Tsheqn3QvROiAbQRUgZ-_hnkdehFwNvTbGsc-sx6Ww9BovOJ7SjdtH__4UnwJueeA</recordid><startdate>20151216</startdate><enddate>20151216</enddate><creator>Wiltschko, Alexander B.</creator><creator>Johnson, Matthew J.</creator><creator>Iurilli, Giuliano</creator><creator>Peterson, Ralph E.</creator><creator>Katon, Jesse M.</creator><creator>Pashkovski, Stan L.</creator><creator>Abraira, Victoria E.</creator><creator>Adams, Ryan P.</creator><creator>Datta, Sandeep Robert</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7QR</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20151216</creationdate><title>Mapping Sub-Second Structure in Mouse Behavior</title><author>Wiltschko, Alexander B. ; Johnson, Matthew J. ; Iurilli, Giuliano ; Peterson, Ralph E. ; Katon, Jesse M. ; Pashkovski, Stan L. ; Abraira, Victoria E. ; Adams, Ryan P. ; Datta, Sandeep Robert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c491t-1cc5f7a740dc4ab4e228d569cc23ee4dd0d730cfdce27808e5c1afd52e98c0e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Behavior</topic><topic>Behavior, Animal</topic><topic>Brain research</topic><topic>Computer Simulation</topic><topic>Grammar</topic><topic>Imaging, Three-Dimensional - instrumentation</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Kinesics</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Methods</topic><topic>Mice</topic><topic>Mice, Inbred C57BL</topic><topic>Mice, Transgenic</topic><topic>Optogenetics - instrumentation</topic><topic>Optogenetics - methods</topic><topic>Scholarships & fellowships</topic><topic>Three dimensional imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wiltschko, Alexander B.</creatorcontrib><creatorcontrib>Johnson, Matthew J.</creatorcontrib><creatorcontrib>Iurilli, Giuliano</creatorcontrib><creatorcontrib>Peterson, Ralph E.</creatorcontrib><creatorcontrib>Katon, Jesse M.</creatorcontrib><creatorcontrib>Pashkovski, Stan L.</creatorcontrib><creatorcontrib>Abraira, Victoria E.</creatorcontrib><creatorcontrib>Adams, Ryan P.</creatorcontrib><creatorcontrib>Datta, Sandeep Robert</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Neuron (Cambridge, Mass.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wiltschko, Alexander B.</au><au>Johnson, Matthew J.</au><au>Iurilli, Giuliano</au><au>Peterson, Ralph E.</au><au>Katon, Jesse M.</au><au>Pashkovski, Stan L.</au><au>Abraira, Victoria E.</au><au>Adams, Ryan P.</au><au>Datta, Sandeep Robert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping Sub-Second Structure in Mouse Behavior</atitle><jtitle>Neuron (Cambridge, Mass.)</jtitle><addtitle>Neuron</addtitle><date>2015-12-16</date><risdate>2015</risdate><volume>88</volume><issue>6</issue><spage>1121</spage><epage>1135</epage><pages>1121-1135</pages><issn>0896-6273</issn><eissn>1097-4199</eissn><abstract>Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
[Display omitted]
•Computational modeling reveals structure in mouse behavior without observer bias•Mouse behavior appears to be composed of stereotyped, sub-second modules•From this perspective, new behaviors result from altering both modules and transitions•Unsupervised analysis reveals how genes and neural activity impact behavior
Mouse behavior appears inherently divided into brief modules of 3D motion. This sub-second structure reveals the influence of the environment, genes and neural activity on action.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>26687221</pmid><doi>10.1016/j.neuron.2015.11.031</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Animals Behavior Behavior, Animal Brain research Computer Simulation Grammar Imaging, Three-Dimensional - instrumentation Imaging, Three-Dimensional - methods Kinesics Machine Learning Male Methods Mice Mice, Inbred C57BL Mice, Transgenic Optogenetics - instrumentation Optogenetics - methods Scholarships & fellowships Three dimensional imaging |
title | Mapping Sub-Second Structure in Mouse Behavior |
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