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
Adapted from Berridge et al., 2004
 Used to accurately describe alterations in rodent
grooming syntax (Kalueff and Tuohimaa, 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 hypergrooming
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 sideview 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, Provost’s Scholarly
Enrichment, Georges Lurcy, LA Board of Regents PFund granst and the NARSAD YI award