Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computerized video analysis of social interactions in mice

Abstract

The study of social interactions in mice is used as a model for normal and pathological cognitive and emotional processes. But extracting comprehensive behavioral information from videos of interacting mice is still a challenge. We describe a computerized method and software, MiceProfiler, that uses geometrical primitives to model and track two mice without requiring any specific tagging. The program monitors a comprehensive repertoire of behavioral states and their temporal evolution, allowing the identification of key elements that trigger social contact. Using MiceProfiler we studied the role of neuronal nicotinic receptors in the establishment of social interactions and risk-prone postures. We found that the duration and type of social interactions with a conspecific evolves differently over time in mice lacking neuronal nicotinic receptors (Chrnb2−/−, here called β2−/−), compared to C57BL/6J mice, and identified a new type of coordinated posture, called back-to-back posture, that we rarely observed in β2−/− mice.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Physics model and repertoire of social interaction.
Figure 2: Comparison of manual and automatic tracking using MiceProfiler.
Figure 3: Analysis of contact, relative position and dynamic behaviors.
Figure 4: Analysis of back to back postures.
Figure 5: Transitional behavioral graphs.
Figure 6: Analysis of the mouse visual field of view during specific behaviors.

References

  1. Robbins, T.W. & Arnsten, A.F. The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. Annu. Rev. Neurosci. 32, 267–287 (2009).

    CAS  Article  Google Scholar 

  2. Chandler, R.A., Wakeley, J., Goodwin, G.M. & Rogers, R.D. Altered risk-aversion and risk-seeking behavior in bipolar disorder. Biol. Psychiatry 66, 840–846 (2009).

    Article  Google Scholar 

  3. Baker, M. Inside the minds of mice and men. Nature 475, 123–128 (2011).

    CAS  Article  Google Scholar 

  4. Blanchard, D.C., Griebel, G. & Blanchard, R.J. The mouse defense test battery: pharmacological and behavioral assays for anxiety and panic. Eur. J. Pharmacol. 28, 97–116 (2003).

    Article  Google Scholar 

  5. Crawley, J.N. Mouse behavioral assays relevant to the symptoms of autism. Brain Pathol. 17, 448–459 (2007).

    Article  Google Scholar 

  6. Panksepp, J., Siviy, S. & Normansell, L. The psychobiology of play: theoretical and methodological perspectives. Neurosci. Biobehav. Rev. 8, 465–492 (1984).

    CAS  Article  Google Scholar 

  7. Insel, T.R. Mouse models of autism: report from a meeting. Mamm. Genome 12, 755–757 (2001).

    CAS  Article  Google Scholar 

  8. Metaxas, D. & Terzopoulos, D. Shape and nonrigid motion estimation through physics-based synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 15, 580–591 (1993).

    Article  Google Scholar 

  9. Khan, Z., Balch, T. & Dellaert, F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1805–1819 (2005).

    Article  Google Scholar 

  10. Dankert, H., Wang, L., Hoopfer, E.D., Anderson, D.J. & Perona, P. Automated monitoring and analysis of social behavior in Drosophila. Nat. Methods 6, 297–303 (2009).

    CAS  Article  Google Scholar 

  11. Branson, K., Robie, A.A., Bender, J., Perona, P. & Dickinson, M.H. High-throughput ethomics in large groups of Drosophila. Nat. Methods 6, 451–457 (2009).

    CAS  Article  Google Scholar 

  12. de Chaumont, F. et al. Using physics engines to track objects in images. In Proceedings IEEE Intern. Symp. on Biomedical Imaging (Boston, 2009).

  13. de Chaumont, F., Dallongeville, S., Chenouard, N. & Olivo-Marin, J.-C. Tracking multiple articulated objects using physics engines : improvement using multiscale decomposition and quadtrees. In Proceedings IEEE Intern. Conf. on Image Processing (Hong Kong, 2010).

  14. Chenouard, N., Dufour, A. & Olivo-Marin, J.-C. Tracking algorithms chase down pathogens. Biotechnol. J. 4, 838–845 (2009).

    CAS  Article  Google Scholar 

  15. Granon, S., Faure, P. & Changeux, J.P. Executive and social behaviors under nicotinic receptor regulation. Proc. Natl. Acad. Sci. USA 100, 9596–9601 (2003).

    CAS  Article  Google Scholar 

  16. Arakawa, H., Blanchard, D.C. & Blanchard, R.J. Colony formation of C57BL/6J mice in visible burrow system: identification of eusocial behaviors in a background strain for genetic animal models of autism. Behav. Brain Res. 176, 27–39 (2007).

    Article  Google Scholar 

  17. Silverman, J.L., Yang, M., Lord, C. & Crawley, J.N. Behavioural phenotyping assays for mouse models of autism. Natl. Rev. 11, 490–502 (2010).

    CAS  Article  Google Scholar 

  18. Avale, M.E. et al. Prefrontal nicotinic receptors control novel social interaction between mice. FASEB J. 25, 2145–2155 (2011).

    CAS  Article  Google Scholar 

  19. Khan, Z., Herman, R., Wallen, K. & Balch, T. An outdoor 3D visual tracking system for the study of spatial navigation and memory in rhesus monkeys. Behav. Res. Methods 37, 453–463 (2005).

    Article  Google Scholar 

  20. Serreau, P., Chabout, J., Suarez, S.V., Naudé, J. & Granon, S. Beta2-containing neuronal nicotinic receptors as major actors in the flexible choice between conflicting motivations. Behav. Brain Res. 225, 151–159 (2011).

    CAS  Article  Google Scholar 

  21. Ebensperger, L.A., Hurtado, M.J. & Ramos-Jiliberto, R. Vigilance and collective detection of predators in Degus (Octodon degus). Ethology 112, 879–887 (2006).

    Article  Google Scholar 

  22. Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).

    Article  Google Scholar 

  23. Maubourguet, N., Lesne, A., Changeux, J.P., Maskos, U. & Faure, P. Behavioral sequence analysis reveals a novel role for beta2* nicotinic receptors in exploration. PLoS Comput. Biol. 4, e1000229 (2008).

    Article  Google Scholar 

  24. Jamain, S. et al. Reduced social interaction and ultrasonic communication in a mouse model of monogenic heritable autism. Proc. Natl. Acad. Sci. USA 105, 1710–1715 (2008).

    CAS  Article  Google Scholar 

  25. Rossi, F.M. et al. Requirement of the nicotinic acetylcholine receptor beta 2 subunit for the anatomical and functional development of the visual system. Proc. Natl. Acad. Sci. USA 98, 6453–6458 (2001).

    CAS  Article  Google Scholar 

  26. Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S.I. & Battaglia, F.P. Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nat. Neurosci. 12, 919–926 (2009).

    CAS  Article  Google Scholar 

  27. Schulz, D. et al. Simultaneous assessment of rodent behavior and neurochemistry using a miniature positron emission tomograph. Nat. Methods 8, 347–352 (2011).

    CAS  Article  Google Scholar 

  28. Chabout, J. et al. Adult male mice emit context-specific ultrasonic vocalizations that are modulated by prior isolation or group rearing environment. PLoS ONE (in the press).

  29. Comaniciu, D., Ramesh, V. & Meer, P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25, 564–577 (2003).

    Article  Google Scholar 

  30. Millington, I. Game Physics Engine Development (Elsevier, 2010).

  31. Picciotto, M.R. et al. Abnormal avoidance learning in mice lacking functional high-affinity nicotine receptor in the brain. Nature 374, 65–67 (1995).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Institut Pasteur, by the Centre National de la Recherche Scientifique (URA 2582 and UMR 8195), by the Université Paris Sud 11 (Chaire d'Excellence to S.G., post-doctoral fellowship to R.D.-S.C. and PhD grant (Bourse de la Présidence) to J.C.), and the Université Paris 6 (PhD grant to P.S.). It was also supported in part by a grant from the Agence Nationale de la Recherche (ANR-09-BLAN-0340-02 FLEXNEURIM).

Author information

Authors and Affiliations

Authors

Contributions

F.d.C. created the tracking and analysis methods, and developed the software. R.D.-S.C. and A.C. performed and analyzed experiments. P.S. and J.C. analyzed experiments. S.G. designed the behavioral repertoire, performed and analyzed experiments, and conducted statistical analyses. J.-C.O.-M. supervised the design of the tracking method. F.d.C., A.C., S.G. and J.-C.O.-M. conceived the project and wrote the manuscript.

Corresponding authors

Correspondence to Sylvie Granon or Jean-Christophe Olivo-Marin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Notes 1–4 (PDF 1376 kb)

Supplementary Software

Bundle of Icy and MiceProfiler. (ZIP 90801 kb)

Supplementary Video 1

Video demonstration of MiceProfiler. (MP4 83080 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

de Chaumont, F., Coura, RS., Serreau, P. et al. Computerized video analysis of social interactions in mice. Nat Methods 9, 410–417 (2012). https://doi.org/10.1038/nmeth.1924

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1924

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing