Introduction

1. Towards high-throughput phenotyping of complex patterned behaviors in rodents: focus on mouse self-grooming Evan Kyzar, SiddharthGaikwad, Mimi Pham, Jeremy Green, Andrew Roth, Yiqing Liang, Vikrant Kobla, Allan V. Kalueff

2. Introduction • Grooming is an important, evolutionarily conserved behavior observed in multiple taxa • Complex, highly organized behavior regulated by the basal ganglia and hypothalamus

3. Translational value • Due to its centrally-organized nature, self-grooming behavior is especially well-suited to research into basal ganglia disorders, autism, OCD, and AD/HD • Grooming behavior is also sensitive to anxiety, with more anxious animals generally exhibiting more robust grooming responses • Can be modulated by various behavioral, genetic, and pharmacological manipulations

4. Grooming research • Animal grooming has been studied extensively, especially in rodent models • Nonetheless, research has focused on ‘quantity’ endpoints such as frequency, duration, and latency • Little inquest has been made into the complex patterning of grooming behavior

5. Rodent grooming patterning • The typical grooming bout begins with paw licking followed by head and face grooming. Rodents then move on to grooming the body/leg area then culminate with tail and genital grooming • While endpoints such as total grooming duration can be both increased and decreased by stress, grooming patterning is more predictably sensitive to anxiety

6. Grooming analysis algorithm • Used to accurately describe alterations in rodent grooming syntax (Kalueff and Tuohimaa, 2004) Adapted from Berridge et al., 2004

7. Grooming analysis endpoints • Global measures – latency to first bout, frequency, duration • Regional distribution – frequency and duration of specific body area grooming (e.g. paws, body, tail, etc.) • Transitions – direction, or syntax, of each bout and the percentage of correct vs. incorrect transitions. A correct transition follows the stereotyped rodent grooming bout of paws to head to body to tail.

8. Abnormal grooming phenotypes • Sapap-3 mutant mice groom their facial regions excessively, similar to OCD and trichotillomania (Welch et al., 2004) • Hoxb8 mutant mice display excessive body grooming, often leading to hair loss (Chen et at. 2010)

9. Automated video-tracking • Recent technology has allowed for automated behavior detection in multiple animal models • Allows for rapid analysis of complex behavioral domains through the use of bioinformatics and efficient data processing • Useful in producing reliable, unbiased, and less variable results

10. So the question arises... How do we apply novel behavior recognition techniques to complex biological and behavioral phenomena such as self-grooming syntax?

11. Methods • 40 adult male C57BL/6J mice • Animals were individually placed in a clear observation cylinder for 5 min to examine grooming behavior • Subjects were manually observed and video-recorded from the front and side

12. Automated analysis • The videos were then analyzed using a custom-upgraded version of the HomeCageScan software (CleverSys, Inc., Reston, VA) • The software generated data on global endpoints (duration, frequency) but also data on the patterning of each grooming episode (paw licks, body/leg washing, etc.)

13. Experiment 1 • Designed to test the degree of agreement between manual and automated data • Mice (n=20) were individually tested in the observation cylinder for 5 min • Manual and HomeCageScan-generated data were compared using the ranked Spearman correlation test and the Mann-Whitney U-test

14. Results – Experiment 1 • Automated data is highly correlated to manual observations, both for total intra-bout transitions and for multiple specific transitions (e.g. head washes to body/leg wash)

15. Experiment 2 • Designed to determine the ability of automated systems to quantify different types of grooming • The experimental group (n=10) was gently misted with water before observation in the cylinder, to elicit a state of hyper-grooming

16. Results – Experiment 2 • Both manual observers and HomeCageScan detected differences in water-induced grooming when compared to novelty-induced grooming • Confirms the utility of automated methods in distinguishing different types of self-grooming activity

17. Results – Camera Comparison • Data from the front-view camera was compared to side-view data to establish the degree of agreement • The side-view camera detected only the small number of bouts “missed” by the front view camera as data generated from both cameras appears to be essentially identical (R = 0.92, p<0.05)

18. Summary • Data from each camera (side view vs. front view) was compared and revealed no significant differences. This suggests that a single camera setup is sufficient for grooming experimentation • This study has validated the use of software-driven techniques to study highly repetitive behaviors in rodents

19. Future directions • SERT and BDNF mutants • Social grooming • Other species (rats, primates, etc.) • Pharmacological manipulations • Basal ganglia research, autism, OCD, and AD/HD

20. Conclusion • This study aimed not to show the utility of a particular software to assess rodent grooming, but to demonstrate as a proof of concept a novel approach to quantify complex grooming phenotypes • Future studies into self-grooming behavior will elucidate many of the neural correlates of highly repetitive, centrally organized behavior

21. Acknowledgments • Special thanks to CleverSys, Inc. for personalized support and expert service • Sid Gaikwadand Mimi Pham for helping to run experiment and analyze videos • This study was supported by Tulane University Intramural and Pilot funds, Provost’s Scholarly Enrichment, Georges Lurcy, LA Board of Regents P-Fund granst and the NARSAD YI award