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Cited by 98Human Pose and Action RecognitionCell Image Analysis TechniquesUndergraduate Neuroscience Education and Research

Open-source tools for behavioral video analysis: Setup, methods, and best practices

Open access
Luxem, Kevin, Sun, Jennifer J, Bradley, Sean P, Krishnan, Keerthi, Yttri, Eric, Zimmermann, Jan, Pereira, Talmo D, Laubach, Mark·2023·eLife
DOI 10.7554/elife.79305PMC PMC10036114
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Abstract

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional ‘center of mass’ tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

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  • MoSeq and keypoint-MoSeq use AR-HMM, whereas VAME uses deep recurrent neural networks and variational autoencoders, providing two well-established complementary approaches.

    “MoSeq6 and keypoint-MoSeq35 use a statistical framework known as autoregressive hidden Markov model (AR-HMM),6,50,51 whereas VAME1,7 uses deep recurrent neural networks and variational autoencoders, providing two well-established complementary approaches.7,52,53”

    From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [1]

    In the Behavior quantification section (Table 3; page unavailable), the paper says VAME uses a variational autoencoder whose encoder and decoder are recurrent neural networks. The passages found for “MoSeq” and “autoregressive hidden Markov” do not establish that MoSeq or keypoint-MoSeq use AR-HMMs, or characterize the two as complementary approaches. (LLM, conf=0.85)

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Sorted by citations

Authors
Luxem, K., Sun, J. J., Bradley, S. P., Krishnan, K., Yttri, E., Zimmermann, J., Pereira, T. D., & Laubach, M.
Affiliations
  • Leibniz Institute for Neurobiology (DE)
  • California Institute of Technology (US)
  • National Institutes of Health (US)
  • University of Tennessee at Knoxville (US)
  • Carnegie Mellon University (US)
  • University of Minnesota (US)
  • Salk Institute for Biological Studies (US)
  • American University (US)
Corresponding author
Laubach, Mark
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2023-03-23
Volume
12
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.79305
PMC
PMC10036114
OpenAlex
W4353074833
Cited by
98
References
11
Citations by year
2026: 24 · 2025: 33 · 2024: 19 · 2023: 20 · 2022: 2
FWCI
2.54
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
Funders
  • National Science Foundation (1948181, 2024581)
  • National Institutes of Health (DA046375, MH002952, MH124042, MH128177)
  • Natural Sciences and Engineering Research Council of Canada (PGSD3-532647-2019)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
Keywords
open source software, pose estimation, behavior classification, animal tracking, behavioral quantification, reproducibility, ethology, data sharing, code sharing, method comparison, documentation, community standards
MeSH terms
Algorithms, Animals, Behavior, Animal, Ethology, Software, Video Recording

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