The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors
Open accessAbstract
Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.
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Cites
UMAP: Uniform Manifold Approximation and Projection
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536
Dimensionality reduction for visualizing single-cell data using UMAP
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The functional anatomy of basal ganglia disorders
Albin, R. L., Young, A. B., & Penney, J. B. (1989) · Trends in Neurosciences · doi:10.1016/0166-2236(89)90074-x · Cited by 5,353
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187
Density-Based Clustering Based on Hierarchical Density Estimates
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Spontaneous behaviors drive multidimensional, brainwide activity
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Neuroscience Needs Behavior: Correcting a Reductionist Bias
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Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics
Weinreb, C., Pearl, J. E., Lin, S., Osman, M. A. M., Zhang, L., Annapragada, S., Conlin, E., Hoffmann, R., Makowska, S., Gillis, W. F., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02318-2 · Cited by 228
Identifying behavioral structure from deep variational embeddings of animal motion
Luxem, K., Mocellin, P., Fuhrmann, F., Kürsch, J., Miller, S. R., Palop, J. J., Remy, S., & Bauer, P. (2022) · Communications Biology · doi:10.1038/s42003-022-04080-7 · Cited by 218
Measuring behavior in the home cage: study design, applications, challenges, and perspectives
Grieco, F., Bernstein, B. J., Biemans, B., Bikovski, L., Burnett, C. J., Cushman, J. D., van Dam, E. A., Fry, S. A., Richmond-Hacham, B., Homberg, J. R., et al. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.735387 · Cited by 109
Open-source tools for behavioral video analysis: Setup, methods, and best practices
Luxem, K., Sun, J. J., Bradley, S. P., Krishnan, K., Yttri, E., Zimmermann, J., Pereira, T. D., & Laubach, M. (2023) · eLife · doi:10.7554/elife.79305 · Cited by 98
Self-citationBehavioral decomposition reveals rich encoding structure employed across neocortex in rats
Mimica, B., Tombaz, T., Battistin, C., Fuglstad, J. G., Dunn, B. A., & Whitlock, J. R. (2023) · Nature Communications · doi:10.1038/s41467-023-39520-3 · Cited by 45
Metadata
- Authors
- Hsu, A. I., & Yttri, E. A.
- Venue
- Nature Communications
- Published
- 2021-08-31
- Volume
- 12
- Issue
- 1
- Pages
- 5188-5188
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3197246522
- Cited by
- 298
- References
- 63
- FWCI
- 16.75
- Type
- journal article
- Language
- en
- Open access
- gold
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