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High-throughput ethomics in large groups of Drosophila


We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

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Figure 1: Walking arena with sample trajectories.
Figure 2: Tracking algorithm and evaluation.
Figure 3: Ethograms of eight automatically-detected behaviors.
Figure 4: Differences within and among individual flies.
Figure 5: Spatial analysis of social interactions.

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  1. Wolf, F.W. & Heberlein, U. Invertebrate models of drug abuse. J. Neurobiol. 54, 161–178 (2003).

    Article  CAS  Google Scholar 

  2. Guarnieri, D.J. & Heberlein, U. Drosophila melanogaster, a genetic model system for alcohol research. Int. Rev. Neurobiol. 54, 199–228 (2003).

    Article  CAS  Google Scholar 

  3. Chan, Y.B. & Kravitz, E.A. Specific subgroups of FruM neurons control sexually dimorphic patterns of aggression in Drosophila melanogaster. Proc. Natl. Acad. Sci. USA 104, 19577–19582 (2007).

    Article  CAS  Google Scholar 

  4. Hoyer, S.C. et al. Octopamine in male aggression of Drosophila. Curr. Biol. 18, 159–167 (2008).

    Article  CAS  Google Scholar 

  5. Ho, K.S. & Sehgal, A. Drosophila melanogaster: an insect model for fundamental studies of sleep. Methods Enzymol. 393, 772–793 (2005).

    Article  CAS  Google Scholar 

  6. Shaw, P., Ocorr, K., Bodmer, R. & Oldham, S. Drosophila aging 2006/2007. Exp. Gerontol. 43, 5–10 (2008).

    Article  CAS  Google Scholar 

  7. Konsolaki, M., Song, H.J., Dobbs, W. & Garza, D. P2–109 Drosophila models of Alzheimer's-related pathways. Neurobiol. Aging 25, S255–S255 (2004).

    Article  Google Scholar 

  8. Zhang, F. et al. Circuit-breakers: optical technologies for probing neural signals and systems. Nat. Rev. Neurosci. 8, 577–581 (2007).

    Article  CAS  Google Scholar 

  9. Callaway, E.M. A molecular and genetic arsenal for systems neuroscience. Trends Neurosci. 28, 196–201 (2005).

    Article  CAS  Google Scholar 

  10. Luo, L., Callaway, E.M. & Svoboda, K. Genetic dissection of neural circuits. Neuron 57, 634–660 (2008).

    Article  CAS  Google Scholar 

  11. Zhou, C., Rao, Y. & Rao, Y. A subset of octopaminergic neurons are important for Drosophila aggression. Nat. Neurosci. 11, 1059–1067 (2008).

    Article  CAS  Google Scholar 

  12. Anonymous. Geneticist seeks engineer: must like flies and worms. Nat. Methods 4, 463 (2007).

  13. Martin, J.R. A portrait of locomotor behaviour in Drosophila determined by a video-tracking paradigm. Behav. Processes 67, 207–219 (2004).

    Article  Google Scholar 

  14. Ramazani, R.B., Krishnan, H.R., Bergeson, S.E. & Atkinson, N.S. Computer automated movement detection for the analysis of behavior. J. Neurosci. Methods 162, 171–179 (2007).

    Article  Google Scholar 

  15. Grover, D., Tower, J. & Tavaré, S. O fly, where art thou? J. Royal Society Interface 5, 1181–1191 (2008).

    Article  Google Scholar 

  16. Valente, D., Golani, I. & Mitra, P.P. Analysis of the trajectory of Drosophila melanogaster in a circular open field arena. PLoS ONE 2, e1083 (2007).

    Article  Google Scholar 

  17. Crocker, J.C. & Grier, D.G. Methods of digital video microscopy for colloidal studies. J. Colloid Interface Sci. 179, 298–310 (1996).

    Article  CAS  Google Scholar 

  18. Ramot, D., Johnson, B.E., Berry, T.L. Jr., Carnell, L. & Goodman, M.B. The parallel worm tracker: a platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3, e2208 (2008).

    Article  Google Scholar 

  19. Ryu, W.S. & Samuel, A.D.T. Thermotaxis in Caenorhabditis elegans analyzed by measuring responses to defined thermal stimuli. J. Neurosci. 22, 5727–5733 (2002).

    Article  CAS  Google Scholar 

  20. Tsunozaki, M., Chalasani, S.H. & Bargmann, C.I. A behavioral switch: cGMP and PKC signaling in olfactory neurons reverses odor preference in C. elegans. Neuron 59, 959–971 (2008).

    Article  CAS  Google Scholar 

  21. Wolf, F.W., Rodan, A.R., Tsai, L.T.Y. & Heberlein, U. High-resolution analysis of ethanol-induced locomotor stimulation in Drosophila. J. Neurosci. 22, 11035–11044 (2002).

    Article  CAS  Google Scholar 

  22. Soll, D.R. & Voss, E. Two-and three-dimensional computer systems for analyzing how animal cells crawl. In Motion analysis of living cells (David R. Soll & Deborah Wessels, eds.) 25–52 (Wiley-Liss, New York, 1997).

  23. 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 

  24. Veeraraghavan, A., Chellappa, R. & Srinivasan, M. Shape-and-behavior-encoded tracking of bee dances. IEEE Trans. Pattern Anal. Mach. Intell. 30, 463–476 (2008).

    Article  Google Scholar 

  25. 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).

    Article  CAS  Google Scholar 

  26. Hall, J.C. Courtship among males due to a male-sterile mutation in Drosophila melanogaster. Behav. Genet. 8, 125–141 (1978).

    Article  CAS  Google Scholar 

  27. Benzer, S. Behavioral mutants isolated by countercurrent distribution. Proc. Natl. Acad. Sci. USA 58, 1112–1119 (1967).

    Article  CAS  Google Scholar 

  28. Götz, K. Flight control in Drosophila by visual perception of motion. Biol. Cybern. 4, 199–208 (1968).

    Google Scholar 

  29. Bülthoff, H., Götz, K.G. & Herre, M. Recurrent inversion of visual orientation in the walking fly, Drosophila melanogaster. J. Comp. Physiol. A 148, 471–481 (1982).

    Article  Google Scholar 

  30. Siegel, R.W. & Hall, J.C. Conditioned responses in courtship behavior of normal and mutant Drosophila. Proc. Natl. Acad. Sci. USA 76, 3430–3434 (1979).

    Article  CAS  Google Scholar 

  31. Piccardi, M. Background subtraction techniques: a review. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics 4, 3099–3104 (2004).

    Google Scholar 

  32. Gonzalez, R.C. & Woods, R.E. Digital Image Processing (Prentice Hall, Upper Saddle River, New Jersey, USA, 2007).

    Google Scholar 

  33. Papadimitriou, C.H. & Steiglitz, K. Combinatorial Optimization: Algorithms and Complexity (Dover Publications, Mineola, New York, USA, 1998).

    Google Scholar 

  34. Perera, A., Srinivas, C., Hoogs, A. & Brooksby, G. Multi-object tracking through simultaneous long occlusions and split-merge conditions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1, 666–673 (2006).

    Google Scholar 

  35. Cormen, T.H. Introduction to Algorithms (MIT Press, Cambridge, Massachusetts, USA, 2001).

    Google Scholar 

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We thank A. Straw for developing and maintaining the camera interface program, J. Simon for assistance in collecting the data presented in Supplementary Videos 6 and 7, W. Korff for help with high-resolution data acquisition and M. Arbietman (University of Southern California) for the gift of the fruitless fly lines. Funding for this research was provided by US National Institutes of Health grant R01 DA022777 (to M.H.D. and P.P.).

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Correspondence to Pietro Perona or Michael H Dickinson.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1–3, Supplementary Note (PDF 750 kb)

Supplementary Video 1

50 female, 0 male tracking results. We annotate a two-minute video of 50 wild-type female flies interacting in the open arena with the computed individual fly trajectories. In the main window, each triangle indicates the position of the fly in the current frame, while a trailing line indicates a fly's previous center positions in the past 5 s (100 frames). In the small windows on the right, we show zoomed views of randomly selected flies. Each color corresponds to a different fly, and colors are consistent in all windows and frames. All videos are encoded with the XviD codec, available at (AVI 16120 kb)

Supplementary Video 2

0 female, 50 male tracking results. We annotate a two-minute video of 50 wild-type male flies interacting in the open arena with the computed individual fly trajectories, as in Supplementary Video 1. (AVI 20240 kb)

Supplementary Video 3

25 female, 25 male tracking results. We annotate a two-minute video of 25 female and 25 male wild-type flies interacting in the open arena with the computed individual fly trajectories, as in Supplementary Video 1. (AVI 20106 kb)

Supplementary Video 4

20 fruitless male tracking results. We annotate a two-minute video of 20 fruitless male flies interacting in the open arena with the computed individual fly trajectories, as in Supplementary Video 1. (AVI 19688 kb)

Supplementary Video 5

Example labeled and detected behaviors. For each behavior, we show randomly selected manually labeled episodes of each of the eight behaviors, as well as randomly selected automatically detected episodes of each behavior. For each behavior example, we show the original video annotated with the behaving fly's trajectory. The triangle indicates the fly's position in the current frame, and the dots indicate its positions in other frames of the video. We show 0.5 s (10 frames) before and after the start and end of the behavior. Bright red indicates frames during the behavior and dark red indicates frames before or after the behavior. For social behaviors (touch and chase), we plot the pair of flies behaving in blue and red. Bright blue and red indicate frames during the behavior and dark blue and red indicate frames before or after the behavior. All videos are shown at one-quarter real time. (AVI 10525 kb)

Supplementary Video 6

14 wild-type males in alternate arena tracking results. We annotate, as in Supplementary Video 1, a two-minute video of 14 wild-type males interacting in the enclosed arena developed by J. Simon and M.H. Dickinson (unpublished data). (AVI 19959 kb)

Supplementary Video 7

14 fruitless males in alternate arena tracking results. We annotate, as in Supplementary Video 1, a two-minute video of 14 fruitless males interacting in the enclosed arena developed by J. Simon and M.H. Dickinson (unpublished data). Note that these flies have not had their wings clipped. (AVI 20154 kb)

Supplementary Software 1

Ctrax, the Caltech multiple fly tracker. (ZIP 17246 kb)

Supplementary Software 2

Ctrax behavior analysis toolbox: a suite of Matlab routines to visualize, manipulate and analyze the behaviors contained within the trajectories output by Ctrax. (ZIP 478 kb)

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Branson, K., Robie, A., Bender, J. et al. High-throughput ethomics in large groups of Drosophila. Nat Methods 6, 451–457 (2009).

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