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Deep Behavioral Phenotyping of Mouse Autism Models using Open-Field Behavior

View ORCID ProfileUgne Klibaite, View ORCID ProfileMikhail Kislin, View ORCID ProfileJessica L. Verpeut, Xiaoting Sun, View ORCID ProfileJoshua W. Shaevitz, View ORCID ProfileSamuel S.-H. Wang
doi: https://doi.org/10.1101/2021.02.16.431500
Ugne Klibaite
1Department of Organismic and Evolutionary Biology Harvard University
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  • For correspondence: klibaite@fas.harvard.edu shaevitz@princeton.edu sswang@princeton.edu
Mikhail Kislin
2Princeton Neuroscience Institute princeton University
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Jessica L. Verpeut
2Princeton Neuroscience Institute princeton University
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Xiaoting Sun
2Princeton Neuroscience Institute princeton University
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Joshua W. Shaevitz
3Department of Physics Lewis-sigler Institute for Integrative Genomics princeton University
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  • For correspondence: klibaite@fas.harvard.edu shaevitz@princeton.edu sswang@princeton.edu
Samuel S.-H. Wang
2Princeton Neuroscience Institute princeton University
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  • For correspondence: klibaite@fas.harvard.edu shaevitz@princeton.edu sswang@princeton.edu
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Abstract

Autism is noted for both its genotypic and phenotypic diversity. Repetitive action, resistance to environmental change, and motor disruptions vary from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we use advances in computer vision and deep learning to develop a framework for characterizing mouse behavior on multiple time scales using a single popular behavioral assay, the open field test. We observed male and female C57BL/6J mice to develop a dynamic baseline of adaptive behavior over multiple days. We then examined two rodent models of autism, a cerebellum-specific model, L7-Tsc1, and a whole-brain knockout model, Cntnap2. Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://doi.org/10.34770/bzkz-j672

  • https://github.com/PrincetonUniversity/MouseMotionMapper

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 17, 2021.
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Deep Behavioral Phenotyping of Mouse Autism Models using Open-Field Behavior
Ugne Klibaite, Mikhail Kislin, Jessica L. Verpeut, Xiaoting Sun, Joshua W. Shaevitz, Samuel S.-H. Wang
bioRxiv 2021.02.16.431500; doi: https://doi.org/10.1101/2021.02.16.431500
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Deep Behavioral Phenotyping of Mouse Autism Models using Open-Field Behavior
Ugne Klibaite, Mikhail Kislin, Jessica L. Verpeut, Xiaoting Sun, Joshua W. Shaevitz, Samuel S.-H. Wang
bioRxiv 2021.02.16.431500; doi: https://doi.org/10.1101/2021.02.16.431500

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