evan kyzar, siddharth gaikwad, mimi pham, jeremy green, andrew roth, yiqing liang, vikrant kobla,...
TRANSCRIPT
Towards high-throughput phenotyping of complex patterned behaviors in rodents:
focus on mouse self-groomingEvan Kyzar, Siddharth Gaikwad, Mimi Pham,
Jeremy Green, Andrew Roth, Yiqing Liang, Vikrant Kobla, Allan V. Kalueff
IntroductionGrooming is an
important, evolutionarily conserved behavior observed in multiple taxa
Complex, highly organized behavior regulated by the basal ganglia and hypothalamus
Translational valueDue 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 researchAnimal grooming has been studied extensively,
especially in rodent modelsNonetheless, research has focused on ‘quantity’
endpoints such as frequency, duration, and latencyLittle inquest has been made into the complex
patterning of grooming behavior
Rodent grooming patterningThe 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 endpointsGlobal 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 phenotypesSapap-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-trackingRecent 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?
Methods40 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 analysisThe 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 1Designed 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 2Designed 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 2Both 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 ComparisonData 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)
SummaryData 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 directionsSERT and BDNF
mutantsSocial groomingOther species (rats,
primates, etc.)Pharmacological
manipulationsBasal ganglia
research, autism, OCD, and AD/HD
ConclusionThis 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
AcknowledgmentsSpecial 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, Provost’s Scholarly Enrichment, Georges Lurcy, LA Board of Regents P-Fund granst and the NARSAD YI award