The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Revealing the structure of pharmacobehavioral space through motion sequencing
Open accessAbstract
Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically inspired behavioral analysis method that identifies modular components of three-dimensional mouse body language called ‘syllables’. Here, we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuroactive and psychoactive drugs administered to a cohort of nearly 700 mice. MoSeq identifies syllables that are characteristic of individual drugs, a finding we leverage to reveal specific on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior and suggest that behavioral syllables represent a new class of druggable target. By analyzing hundreds of mice treated with a library of neuro- and psychoactive drugs, Wiltschko et al. show that Motion Sequencing can effectively discriminate and categorize drug effects and link molecular targets to behavioral syllables.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Rowan, W., & Tinbergen, N. (1952) · Bird-Banding · doi:10.2307/4510368 · Cited by 2,696
How were new medicines discovered?
Swinney, D. C., & Anthony, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3480 · Cited by 1,812
Genetics of Mouse Behavior: Interactions with Laboratory Environment
Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999) · Science · doi:10.1126/science.284.5420.1670 · Cited by 1,666
Roth, B. L., Sheffler, D. J., & Kroeze, W. K. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1346 · Cited by 1,155
Peñagarikano, O., Abrahams, B. S., Herman, E. I., Winden, K. D., Gdalyahu, A., Dong, H., Sonnenblick, L. I., Gruver, R., Almajano, J., Bragin, A., et al. (2011) · Cell · doi:10.1016/j.cell.2011.08.040 · Cited by 1,075
Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene
Alarcón, M., Abrahams, B. S., Stone, J. L., Duvall, J. A., Perederiy, J. V., Bomar, J. M., Sebat, J., Wigler, M., Martin, C. L., Ledbetter, D. H., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.09.005 · Cited by 818 · cited 1×
“To explore this possibility, we used MoSeq to phenotype mice harboring a mutation in the CNTNAP2 gene, which is associated with human ASD 20,29,30 .”
MethodMapping Sub-Second Structure in Mouse Behavior
Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., Abraira, V. E., Adams, R. P., & Datta, S. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.031 · Cited by 760
Self-citationFast animal pose estimation using deep neural networks
Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S.-H., Murthy, M., & Shaevitz, J. W. (2019) · Nature Methods · doi:10.1038/s41592-018-0234-5 · Cited by 687
Mapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565
Cited by
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
Self-citationMeasuring 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
Neural circuits regulating prosocial behaviors
Walsh, J. J., Christoffel, D. J., & Malenka, R. C. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-022-01348-8 · Cited by 72
Behavioral 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
- Wiltschko, A. B., Tsukahara, T., Zeine, A., Anyoha, R., Gillis, W. F., Markowitz, J. E., Peterson, R. E., Katon, J., Johnson, M. J., & Datta, S. R.
- Venue
- Nature Neuroscience
- Published
- 2020-09-21
- Volume
- 23
- Issue
- 11
- Pages
- 1433-1443
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W3087056597
- Cited by
- 298
- References
- 37
- FWCI
- 12.96
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Revealing the structure of pharmacobehavioral space through motion sequencing. https://app.cochat.ai/papers/10.1038/s41593-020-00706-3
- The paper
- Wiltschko, A. B., Tsukahara, T., Zeine, A., Anyoha, R., Gillis, W. F., Markowitz, J. E., Peterson, R. E., Katon, J., Johnson, M. J., & Datta, S. R. (2020). Revealing the structure of pharmacobehavioral space through motion sequencing. Nature Neuroscience. https://doi.org/10.1038/s41593-020-00706-3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
