Gerry Herrera, PhD discusses key aspects of designing behavioral studies using examples from fear conditioning paradigms in rodents where automated "freeze" scoring video technology is used. This webinar deals with the hot topic of automated analysis of behavioral data. Don’t fall victim to the approach of “set it and forget it” that is all too common in this field. It is important to understand the underlying behavior that is being measured. The test system used for automated analysis should provide a way for the user to take control of and validate the settings used for analysis. Only after careful validation can the research results be trusted and relied upon. During this educational webinar Dr. Gerry Herrera, Vice President of Med Associates Inc., reviews hardware and system configuration strategy as it relates to the popular Video Freeze® System for automated fear conditioning studies in laboratory rodents. In addition, he discusses key principles behind fear conditioning paradigms, correct protocol design, and the importance of both methods and equipment validation.

1. Automated Analysis
of Behavioral Data –
Examples From Fear
Conditioning Studies
Sponsored by:

2. InsideScientific is an online educational environment
designed for life science researchers. Our goal is to aid in
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3. Automated Analysis of
Behavioral Data
Gerry Herrera, PhD
Catamount Research &
Development Inc.,
MED Associates Inc.
Copyright 2015 InsideScientific & MED Associates Inc. All Rights Reserved.

4. VideoFreeze® NIR Video Fear
Conditioning Systems for Rodents
Thank you to our event sponsor

5. What will we cover today…
1. Basic concepts in fear conditioning and the components
of a video-based fear conditioning (“freeze”) system
2. The principles behind automated motion analysis and proper
detection of freezing
• Importance of validating settings
3. Applied examples of fear conditioning experimental design

6. Training Context –
Naïve Exploratory Behavior
Fear Conditioning –
Tone-Shock Pairing (CS-US)
Training Context –
Freezing Behavior
Concepts Behind Fear Conditioning

7. Novel Context –
Naïve Exploratory Behavior
Novel Context –
Presentation of the CS -> Freezing Behavior
Concepts Behind Fear Conditioning

8. • Animals exhibit high activity in
a novel environment –
exploratory behavior
• Contextual cues are important
(odor, illumination, geometry,
etc)
• A neutral stimulus
(Conditioned Stimulus, CS) is
paired with an aversive
Unconditioned Stimulus (US)
Concepts Behind Fear Conditioning
Training:

9. • Animals exhibit high activity in
a novel environment –
exploratory behavior
• Contextual cues are important
(odor, illumination, geometry,
etc)
• A neutral stimulus
(Conditioned Stimulus, CS) is
paired with an aversive
Unconditioned Stimulus (US)
Training:
Concepts Behind Fear Conditioning

10. • Some time after training (e.g.
1 Day), the animal is returned
to the training context.
• Animals usually exhibit
freezing behavior right away
when placed in the training
context. This is interpreted as
fear to the contextual cues in
the training environment.
Context Test:
Concepts Behind Fear Conditioning

11. Concepts Behind Fear Conditioning
• Some time after training (e.g.
1 Day), the animal is returned
to the training context.
• Animals usually exhibit
freezing behavior right away
when placed in the training
context. This is interpreted as
fear to the contextual cues in
the training environment.
Context Test:

12. • Some time after training
(e.g. 1 or 2 days), the animal is
placed in a new environmental
context.
• Initially, the animal exhibits
normal exploratory behavior
(high activity, low freezing).
• Subsequent presentation of
the CS elicits the freezing
response.
Tone Test:
Concepts Behind Fear Conditioning

13. Concepts Behind Fear Conditioning
• Some time after training
(e.g. 1 or 2 days), the animal is
placed in a new environmental
context.
• Initially, the animal exhibits
normal exploratory behavior
(high activity, low freezing).
• Subsequent presentation of
the CS elicits the freezing
response.
Tone Test:

14. Components of a Video-based Fear Conditioning (Freeze) System
• Sound attenuating
cubicle
• Low-noise digital video
camera
• Near-infrared
illumination system
• Conditioning chamber
• Aversive stimulation

15. Why Acoustic
Foam Lining?
• Improved sound
attenuation
properties
• Minimizes
vocalizations and
noises from one
chamber interfering
with adjacent
chambers

16. Camera and Illumination System – Minimize Video Noise
• Since an automated fear conditioning
system must measure animal movement,
a low noise camera is essential.
• Visual cues are often used in fear
conditioning paradigms, so the automated
system should not be impacted by these
manipulations. This is achieved by use of
near infrared (NIR) illumination.
Variable Intensity White Light
INTENSITY
White Light NIR Light
POWER

17. Contextual Inserts
• In fear conditioning
studies, it is necessary to
alter the context of the
testing environment.
• An automated system
should be able to score
behavior similarly in
multiple contexts.

18. A-Frame and Floor
Cover Inserts for
Contextual Changes
• Cage geometry can be
altered significantly, even
without impacting
illumination levels.
• This results in high quality
video signal regardless of
the contextual configuration
of the test chamber.

19. How do we
measure freezing?

20. • A video-based system that
measures freezing behavior
must have some way of
detecting when an animal is
moving or not.
• When the animal is not
moving, or when there is no
animal present at all, the
system should score 100%
freezing.
• Subtle movements (grooming,
sniffing, etc) should be
differentiated from freezing.

21. • Animal movement can be
tracked in many different ways
(photobeams, video, load cell)
• Tracking of animal movement
should not be highly influenced
by experimental variables
• The Motion Index score in
VideoFreeze captures animal
movement to distinguish slight
movements from freezing…
Regardless of coat color, animal
size, environmental context and
visual cues

22. PARAMETERS
1. Motion Threshold:
Arbitrary limit above which
the subject is considered
moving
2. Minimum Freeze Duration:
Duration of time that a
subject’s motion must be
below the Motion Threshold
for a Freeze Episode to be
counted
“Linear” Analysis:
every data point is examined
How Can a System Measure Freezing?

23. “Linear” Analysis:
every data point is examined
DEPENDENT MEASURES
1. Percent Freeze:
Time immobile/total
session time
2. Freeze Episodes:
Number of freezing
events
3. Freeze Duration:
Total amount of time
spent immobile
How Can a System Measure Freezing?

24. 1. Validating any automated behavioral analysis
system is a key step in data collection.
2. Validation ensures that the system is analyzing the
behavior in question in a reliable manner that is
consistent with traditional/accepted methods.
Validating Analysis Settings:
Computer-Scoring versus Hand-Scoring

25. Tally observations:
% Freezing = #YES Obs./#Total Obs. x 100%
Alternatively, some people watch video
continuously and start/stop a stopwatch every
time the animals starts/stops freezing.
%Freezing=Time Freezing/Total Time x 100%
Validating Analysis Settings:
Computer-Scoring versus Hand-Scoring
Anagnostaras et al., 2010
Freezing is often defined as suppression
of all movement except that required for
respiration
• Look up at video every few seconds
(e.g. 8 sec)
• Make instantaneous judgment
(Freezing: YES or NO)
Suggested Resources:
Curti, 1935, 1942;
Grossen and Kelley, 1972;
Fanselow and Bolles, 1979;
Fanselow, 1984

26. Validation – Frontiers in Behavioral Neuroscience
• This paper describes the
principles involved with
validating Video Freeze®
software by comparing
software scores with
human observer scores.
• This approach can be used
as a reference for general
validation of automated
behavioral analysis
systems.

27. Anagnostaras et al, Front Behav Neurosci 2010
1. Since freezing is defined as absence of
movement, the system should measure
movement in some way. Then near-zero
movement can be equated with freezing.
2. System should detect small movements, such
as grooming, and not count them as freezing.
If there is no animal present, the system should
score 100 % freezing (reject video noise).
Requirements of an Automated Detection System

28. 3. Signals from small movements, such as
grooming, should be well-above video noise.
4. Detection should be fast.
5. Scores generated by the computer should
correlate “very well” with scores obtained from
multiple trained human observers.
Correlation coefficient should be near 1, fit should
have a near zero y-intercept, and a slope of ~1.
Requirements of an Automated Detection System
Anagnostaras et al, Front Behav Neurosci 2010

29. Compare various computer scores of a data set to the same data that has
been hand-scored by a few observers.
Anagnostaras et al., 2010
Validating Your Analysis Settings

30. Validating Your Analysis Settings
Computer-Scoring VS Hand-Scoring:
Anagnostaras et al., 2010
Correlation:
The linear correlation between VideoFreeze-
scored and human-scored freezing is
compared with number of frames (minimum
freeze duration) for various motion index
thresholds. A larger number of frames
yielded higher correlations.

31. Validating Your Analysis Settings
Computer-Scoring VS Hand-Scoring:
Anagnostaras et al., 2010
Intercept:
The linear fit between VideoFreeze-socred and
human-scored freezing is compared for the y-
intercept. The y-intercept is important because
it reflects how much the system overestimates
or underestimates freezing. A threshold of 18
yielded the lowest non-negative intercept.

32. Validating Your Analysis Settings
Computer-Scoring VS Hand-Scoring:
Anagnostaras et al., 2010
Slope:
The slope term from the linear fit is depicted
compared with frames and motion threshold.
Larger frame numbers yielded a slope closer to
1. A motion threshold of 18 and number of
frames of 30 was chosen for having the best
combination of high correlation, intercept close
to 0, and slope close to 1.
*Au = arbitrary units

33. Possible Scoring Outcomes
1. Automated system over-
estimates freezing at low
levels of movement

34. Possible Scoring Outcomes
Linear Fit Results:
• Slope may or may not be near 1
• Relatively low correlation coefficient
• Y-intercept > 0
1. Automated system over-
estimates freezing at low
levels of movement

35. Possible Scoring Outcomes
Possible Causes:
• Motion Index Threshold too HIGH
• Minimum Freeze Duration too SHORT
1. Automated system over-
estimates freezing at low
levels of movement

36. Possible Scoring Outcomes
2. Automated system under-
estimates freezing at low and
mid levels of movement

37. Possible Scoring Outcomes
2. Automated system under-
estimates freezing at low and
mid levels of movement
Linear Fit Results:
• Slope may or may not be near 1
• Relatively low correlation coefficient
• Y-intercept < 0

38. Possible Scoring Outcomes
2. Automated system under-
estimates freezing at low and
mid levels of movement
Possible Causes:
• Motion Index Threshold too LOW
• Minimum Freeze Duration too LONG

39. Example Scoring Challenge:
Measuring Subtle and Brief
Movements

41. • With appropriate Motion Index
Threshold and Minimum Freeze
Duration, it is possible to distinguish
subtle, brief movements from
freezing.
• This is the most significant
challenge for any automated system
that scores freezing behavior.
Scoring Challenge: Subtle, Brief Movements

43. • With appropriate Motion Index
Threshold and Minimum Freeze
Duration, it is possible to distinguish
subtle, brief movements from
freezing.
• This is the most significant
challenge for any automated system
that scores freezing behavior.
Scoring Challenge: Subtle, Brief Movements
Risk of over-estimating freezing if
Minimum Freeze Duration is too short.

44. Applied Examples:
Context Discrimination and
Shock Reactivity

45. Context A
(Training Context) Context B Context C
Example Experiment: Context Discrimination in Rats

46. Example Experiment: Context Discrimination in Rats
Cued Freezing

47. Example Experiment: Context Discrimination in Mice
Day 1:
Train Context A
5 CS-US pairings
Sound
Shocker
Day 2:
Test Context A
Day 3:
Test Context B

48. Day 1:
Train Context A
5 CS-US pairings
• Very low, basically 0,
freezing prior to fear
conditioning
• Freezing behavior
(learning) is evident with
successive CS-US parings.
n=3 C57Bl6 male mice
Training: Context A

49. Day 2:
Test Context A
• Mice show significant freezing
in response to training
context (non-zero baseline
freezing; compare to baseline
during training).
• Also high freezing in response
to CS presentation.
n=3 C57Bl6 male mice
Tone Test: Context A
Training data

50. Day 3:
Tone Test in
Context B
• Low baseline freezing;
novel context.
• High freezing in response
to CS presentation.
Tone Test: Context B
n=3 C57Bl6 male mice
Training data

51. CS
US
n=2 CD-1 mice per group
Example Experiment : Shock Intensity in Mice
• Expression of fear response depends upon
strength of the unconditioned stimulus
(US).
• Mice trained in response to 0.45 mA foot
shock freeze more than mice trained in
response to 0.27 mA foot shock.

52. Summary
• Care should be taken to setup your behavioral assay
in a way that limits interference from other lab activities
(sound attenuation, enclosed cubicles, dedicated space, etc.).
• When relying on automated systems for scoring complex behaviors,
special attention should be paid to validating the settings used in
data analysis. Make sure that the automated system measures
behavior in a manner analogous to trained human observers.

53. Acknowledgements
• Mary Beth Klinger-Lawrence, PhD
Lab Director
• Mahalia McGill, Research Assistant
• Sierra Bruno, Research Assistant
• Nakia Wighton, Summer Student
Lab team at Catamount R&D and MED Associates

54. Gerry Herrera, PhD
gerry.herrera@med-associates.com
(802) 527-2343
Thank You!
For additional information on fear conditioning
technologies, in particular NIR video response
systems please visit:
www.med-associates.com

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educational environment designed
for life science researchers.
Our goal is to aid in the sharing and
distribution of scientific information
regarding innovative technologies,
protocols, research tools and
laboratory services.