Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire
In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using...
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Published in | Neuron (Cambridge, Mass.) Vol. 109; no. 3; pp. 420 - 437.e8 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
03.02.2021
Elsevier Limited |
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Abstract | In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat’s head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE’s ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.
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•CAPTURE can track a rat’s whole-body movements in 3D across days•It is far superior in resolution and reliability to existing tracking approaches•It allows comprehensive profiling of behavioral kinematics, usage, and hierarchy•We demonstrate detailed phenotypic analysis after drugs and in Fmr1-KO rats
Marshall et al. present CAPTURE, a new method for long-term continuous 3D motion tracking in freely behaving rats. Combining motion capture, body piercings, and deep learning, CAPTURE improves tracking precision many-fold over existing techniques. Comprehensive profiling of behavioral kinematics, usage, and hierarchical structure in normal and diseased animals is demonstrated. |
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AbstractList | In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat’s head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE’s ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.
[Display omitted]
•CAPTURE can track a rat’s whole-body movements in 3D across days•It is far superior in resolution and reliability to existing tracking approaches•It allows comprehensive profiling of behavioral kinematics, usage, and hierarchy•We demonstrate detailed phenotypic analysis after drugs and in Fmr1-KO rats
Marshall et al. present CAPTURE, a new method for long-term continuous 3D motion tracking in freely behaving rats. Combining motion capture, body piercings, and deep learning, CAPTURE improves tracking precision many-fold over existing techniques. Comprehensive profiling of behavioral kinematics, usage, and hierarchical structure in normal and diseased animals is demonstrated. In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE, a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat’s head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10–100 fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE’s ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of Fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis. SummaryIn mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat’s head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE’s ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis. In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE's ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis. |
Author | Ölveczky, Bence P. Marshall, Jesse D. Wang, William L. Aldarondo, Diego E. Berman, Gordon J. Dunn, Timothy W. |
AuthorAffiliation | 5 Lead Contact 4 Department of Biology, Emory University, Atlanta GA 30322, USA 2 Program in Neuroscience, Harvard University, Cambridge MA 02138, USA 1 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge MA 02138, USA 3 Department of Statistical Science, Duke University, Durham NC 27710, USA |
AuthorAffiliation_xml | – name: 2 Program in Neuroscience, Harvard University, Cambridge MA 02138, USA – name: 1 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge MA 02138, USA – name: 3 Department of Statistical Science, Duke University, Durham NC 27710, USA – name: 4 Department of Biology, Emory University, Atlanta GA 30322, USA – name: 5 Lead Contact |
Author_xml | – sequence: 1 givenname: Jesse D. surname: Marshall fullname: Marshall, Jesse D. email: jesse_d_marshall@fas.harvard.edu organization: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA – sequence: 2 givenname: Diego E. surname: Aldarondo fullname: Aldarondo, Diego E. organization: Program in Neuroscience, Harvard University, Cambridge, MA 02138, USA – sequence: 3 givenname: Timothy W. surname: Dunn fullname: Dunn, Timothy W. organization: Department of Statistical Science, Duke University, Durham, NC 27710, USA – sequence: 4 givenname: William L. surname: Wang fullname: Wang, William L. organization: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA – sequence: 5 givenname: Gordon J. orcidid: 0000-0003-3588-7820 surname: Berman fullname: Berman, Gordon J. organization: Department of Biology, Emory University, Atlanta, GA 30322, USA – sequence: 6 givenname: Bence P. surname: Ölveczky fullname: Ölveczky, Bence P. email: olveczky@fas.harvard.edu organization: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33340448$$D View this record in MEDLINE/PubMed |
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Keywords | animal tracking computational ethology grooming individuality behavior motion capture phenotyping autism |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The project was conceived by B.P.Ö and J.D.M. J.D.M. developed the technique with consultation from B.P.Ö. J.D.M. performed all experiments with assistance from W.L.W. D.E.A. wrote the imputation package. J.D.M. performed all data analyses, with the aid of additional analysis design, software, and support provided from G.J.B., and T.W.D. J.D.M wrote the manuscript in close consultation with the co-authors. LeadContact Author Contributions |
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Snippet | In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe... SummaryIn mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to... |
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SubjectTerms | Animal models animal tracking Animals autism Behavior Behavior, Animal - physiology Biomechanical Phenomena - physiology Body piercing Cameras computational ethology Deep learning Embedding Fragile X syndrome Grooming Grooming - physiology individuality Intellectual disabilities Kinematics Laboratory animals Machine learning Mammals Motion capture Movement - physiology phenotyping Rats Recording sessions |
Title | Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire |
URI | https://dx.doi.org/10.1016/j.neuron.2020.11.016 https://www.ncbi.nlm.nih.gov/pubmed/33340448 https://www.proquest.com/docview/2486124558 https://search.proquest.com/docview/2471462601 https://pubmed.ncbi.nlm.nih.gov/PMC7864892 |
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