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