Presentation on theme: "Evan Kyzar, Siddharth Gaikwad, Mimi Pham, Jeremy Green, Andrew Roth, Yiqing Liang, Vikrant Kobla, Allan V. Kalueff."— Presentation transcript:
Evan Kyzar, Siddharth Gaikwad, Mimi Pham, Jeremy Green, Andrew Roth, Yiqing Liang, Vikrant Kobla, Allan V. Kalueff
Introduction Grooming is an important, evolutionarily conserved behavior observed in multiple taxa Complex, highly organized behavior regulated by the basal ganglia and hypothalamus
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
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
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
Grooming analysis algorithm Used to accurately describe alterations in rodent grooming syntax ( Kalueff and Tuohimaa, 2004 ) Adapted from Berridge et al., 2004
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.
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 )
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
So the question arises... How do we apply novel behavior recognition techniques to complex biological and behavioral phenomena such as self-grooming syntax?
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
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.)
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
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)
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
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
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)
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
Future directions SERT and BDNF mutants Social grooming Other species (rats, primates, etc.) Pharmacological manipulations Basal ganglia research, autism, OCD, and AD/HD
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
Acknowledgments Special thanks to CleverSys, Inc. for personalized support and expert service Sid Gaikwad and Mimi Pham for helping to run experiment and analyze videos This study was supported by Tulane University Intramural and Pilot funds, Provosts Scholarly Enrichment, Georges Lurcy, LA Board of Regents P- Fund granst and the NARSAD YI award
Your consent to our cookies if you continue to use this website.