Quantification and Analysis of Complex Behaviors in Zebrafish Using Argus

1. SONA: B8 Quantification and Analysis of Complex Behaviors in Zebrafish Using Argus Matthew Sciclunaa, SoalehaShamsb, & Robert Gerlaib,c a Department of Mathematical and Computational Sciences, bDepartment Cell & Systems Biology, cDepartment of Psychology, University of Toronto Mississauga, Mississauga, ON, Canada • Conclusion • Most of the tests returned results consistent with the hypothesis that the residuals between our model and other competing models were merely white noise (p<0.05) • The experimental treatment did not seem to have any effect on the fitting of the data. • The programs agreed more often (had similar p-values) with the total distance travelled behavioral variable than with the distance to stimuli variable. This indicates computer programs may have more variability in measuring this particular variable. • INTRODUCTION • ZEBRAFISH • Zebrafish (DanioRanio) are a model organism for vertebrate behavior due to the ease of their maintenance and their (relative) evolutionary closeness to humans. • This species is reproductively prolific, and is an ideal choice to be used for experiments involving high throughput screening of behavioral patterns. • They exhibit a range of quantifiable behaviors whose expression under various environmental manipulations can help us understand the functional relevance of vertebrate behaviour • ARGUS • A computer program that recognizes complex behavioral patterns and quantifies them meaningfully. • Argus utilizes a previously created program called the RealFishTracker, which tracks movement of each fish and outputs x and y coordinates indicating their location in 2-dimentions. • Argus uses these coordinates to calculate behavioral variables such as speed, distance traveled, and distance to any specified stimulus. • RATIONALE • Argus was made to work with large data sets consistent with high throughput sequencing, so it can handle large data sets with ease, producing result summaries for multiple trials simultaneously. • Argus is a free, specialized alternative to more expensive and limited commercially available software packages. • RESEARCH QUESTION • Does Argus produce similar output to other commercially available software packages like Ethovision by Noldus? • RESULT • The p-values were plotted and graphed on a heatmap, so the influence of the experimental treatment groups and the different behavioral variable could be checked as well as the relationship between any of the three separate measurements. The residuals and the ACF were also plotted to further analyze any outliers among the residuals. • Next Steps • We are going to continue testing Argus and modifying it until it can replicate the results of the current standards at least as well as any other software packages commercially available. • Since the program was made in R there is no limit to the changes we can do to it. • We will continue to add more exotic and specialized commands to it to give Argus a more robust functionality. • We are beginning to explore the possibility of using neural networks with a hidden Markov model to do more complex behavioral screening. Currently there are no programs available that do this well. • We are exploring the possibility of implementing a more user-friendly interface. • REFERENCES • Ljung, G & Box, G (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. • Kokel, D, et al. (2012) Behavioral barcoding in the cloud: Embracing data-intensive digital phenotyping in neuropharmacology. Trends Biotechnol. 30(8): 421–425. • Mirat, O, et al. (2013). ZebraZoom: an automated program for high-throughput behavioral analysis and categorization. Frontiers in Neural Circuits. 107(7): 1-12. • Gerlai, R, and Blaser, R. (2006) Behavioral phenotyping in zebrafish: Comparison of three behavioral quantification methods behavior research methods. 38(3): 456-469 • METHOD • We compared the output of 10 trials from 2 experimental treatments using Argus Ethovision, and the built-in output from RealFishTrackerto calculate the total distance travelled and the average distance from a stimulus. • We used a portmanteau test called the Ljung-Box test to determine whether the residuals were white noise. • If the residuals are white noise then they ~ ! So we can compare our test statistic to this! • Better models will have higher p-values, we want the variation between the models to only be white noise (and not some trend we would otherwise miss). • I ran a loop over the behavioral trials from 2 different measured behaviors and compared the difference in output for each program in a pairwise fashion. ACKNOWLEDGEMENTS I would like to thank Dr. Robert Gerlai for opening up his lab for me,Soaleha Shams for her time and (indefinite) patience, James McCrae for developing theRealFishTracker, Dr. Alison Weir for her advice, NiveenFulcher for graciously donating her data, as well as to my other colleagues from the Gerlai Lab for their continued help and support. Funding was provided by NSERC and NIH/NIAAA Grant 1R01AA015325-01A2.