the ethovision video tracking system— a tool for ... · a tool for behavioral phenotyping of...

The EthoVision video tracking system—

A tool for behavioral phenotyping of transgenic mice

A.J. Spink*, R.A.J. Tegelenbosch, M.O.S. Buma, L.P.J.J. Noldus

Noldus Information Technology B.V., P.O. Box 268, 6700 AG Wageningen, the Netherlands

Received 1 September 2000; accepted 21 December 2000

Abstract

Video tracking systems enable behavior to be studied in a reliable and consistent way, and over longer time periods than if they are

manually recorded. The system takes an analog video signal, digitizes each frame, and analyses the resultant pixels to determine the location

of the tracked animals (as well as other data). Calculations are performed on a series of frames to derive a set of quantitative descriptors of the

animal’s movement. EthoVision (from Noldus Information Technology) is a specific example of such a system, and its functionality that is

particularly relevant to transgenic mice studies is described. Key practical aspects of using the EthoVision system are also outlined, including

tips about lighting, marking animals, the arena size, and sample rate. Four case studies are presented, illustrating various aspects of the

system: (1) The effects of disabling the Munc 18-1 gene were clearly shown using the straightforward measure of how long the mice took to

enter a zone in an open field. (2) Differences in exploratory behavior between short and long attack latency mice strains were quantified by

measuring the time spent in inner and outer zones of an open field. (3) Mice with hypomorphic CREB alleles were shown to perform less

well in a water maze, but this was only clear when a range of different variables were calculated from their tracks. (4) Mice with the trkB

receptor knocked out in the forebrain also performed poorly in a water maze, and it was immediately apparent from examining plots of the

tracks that this was due to thigmotaxis. Some of the latest technological developments and possible future directions for video tracking

systems are briefly discussed. D 2001 Elsevier Science Inc. All rights reserved.

Keywords: Video tracking; Automated observation; Water maze; Rodent

1. Introduction

1.1. Why automated observation?

The behavior of animals, including transgenic mice, is

commonly recorded in either a manual or semiautomated

way. Traditionally, a researcher observes the animal, and if

he or she considers that a certain behavior pattern is

displayed, the behavior is noted — either by writing it

down, or by entering the data into an event-recording

program such as The Observer [1,2]. Although manual

recording of behavior can be implemented with a relatively

low investment, and for some behaviors it may be the only

way to detect and record their occurrence, automated

observation (such as video tracking) can provide significant

advantages. The behaviors are recorded more reliably

because the computer algorithm always works in the same

way, and the system does not suffer from observer fatigue or

drift. In contrast to manual observation, video tracking

carries out pattern analysis on a video image of the observed

animals to extract quantitative measurements of the animals’

behavior (for more details about how this works, see below).

1.2. The development of automated observation systems

Technology for automated detection and recording of

behaviors has evolved dramatically in the past decade.

Early systems, using hard-wired electronics, were only able

to track a single animal in highly artificial environments.

For example, an open field can be sampled with a grid of

infrared beams either as the sole detectors [3–5] or in

combination with other methods such as placing a series

of strain gauge transducers under the arena to estimate the

animal’s position [6]. The magnitude of an animal’s motion

can also be estimated using various types of touch-sensitive

0031-9384/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.

PII: S0031 -9384 (01 )00530 -3

* Corresponding author. Tel.: +31-317-497-677; fax: +31-317-424-

496.

E-mail address: [email protected] (L.P.J.J. Noldus).

Physiology & Behavior 73 (2001) 731–744

sensors ranging from a crude estimate of movement by

placing the animal on a bass loudspeaker and monitoring

the loudspeaker’s electrical output when its cone was

moved by the rat [7], or measuring the movement of a

mouse’s wheel by attaching the wheel’s axle in place of the

ball of a computer mouse [8], to sensors that can also

measure the position of the animal (and hence locomotion),

for instance by changes in the capacitance of a plate when

the animal is in proximity to it [9], or changes in body

resistance [10]. Other comparable detection methods have

included use of ultrasound [11] and microwave Doppler

radar [12]. The position of a rat in an open field has been

recorded by attaching the rat to a computer joystick via a

series of rods attached by a collar to the rat’s neck [13].

Animals’ behavior can also be measured using a compu-

terized version of a Skinner box [14], in which the subject

has to tap a touch-sensitive monitor [15,16]. The motion of

individual limbs can be monitored automatically using

actigraph sensors, which detect movement by means of a

piezoelectric accelerometer [17].

Early video tracking systems offered clear advantages of

flexibility, precision, and accuracy over the various hard-

ware devices listed above. However, the actual path of the

animal still had to be entered manually by the experimenter,

either by following the track of the animal with the com-

puter mouse [18] or joystick [19], or a digitizing tablet or

similar device [20].

Another early method was to feed the analog video signal

to a dedicated video tracking unit, which detected peaks in

the voltage of the video signal (indicating a region of high

contrast between the tracked animal and background), and

used this to produce the x, y coordinates of the tracked

animals as output which was then fed to the serial port of a

computer [21,22]. These analog systems have the disadvant-

age of being relatively inflexible (dedicated to particular

experimental setups) and can normally only track one ani-

mal, in rather restricted lighting and background conditions.

The first video digitizers were of the column scan type.

These had no internal memory of their own, and were only

able to sample the video signal at rather low rates [23]. In

contrast, a modern frame grabber uses a high-speed analog–

digital converter to enable real time conversion of the entire

video image to a high-resolution grid of pixels [23].

It is also possible to acquire digitized video images by

first converting the video input to a digital video format

such as AVI, and then using the AVI file as the input for

object detection [24]. However, this method has disadvan-

tages that the AVI file quickly gets very large (and so only

trials of a limited duration can be carried out), and the

method does not allow for real-time live data acquisition. It

is also possible to use a digital overlay board to obtain

positional data of tracked animals without the need for a

frame grabber [25,26].

Modern systems, which are based on full-color video

frame grabbers and have flexible software, can track multi-

ple animals simultaneously against a variety of complex

backgrounds. Whereas an infrared detector system typi-

cally operates in an arena of 0.5� 0.5 m with 12–32

beams (though systems do exist with a grid of 24� 24

beams, in an arena of 2 m diameter [4]), the frame grabber

used by EthoVision has a resolution of 768� 576 pixels

and can track a rat in an arena with a variable shape and up

to 20 m diameter.

1.3. The application of video tracking

Video tracking is particularly suitable for measuring three

types of behaviors: behaviors that occur briefly and are then

interspersed with long periods of inaction (rare behaviors

[27]), behaviors that occur over many hours [24,28] (such as

diurnal variation in behavior), and spatial measurements

(e.g., Refs. [29,30]) (distance, speed, turning, etc.) that the

human observer is unable to accurately estimate. If a

behavior can be automatically detected using a video track-

ing system, that can greatly reduce the human effort

required [28]. This not only reduces costs, but enables a

larger and therefore statistically more responsible number of

replicates to be used, whilst at the same time reducing the

total number of animals used in an experiment (because

each mouse can be used much more efficiently). This is

particularly important for transgenic mice because in that

case the experiment examines a Heredity�Environment

interaction and a larger sample size is required to test if

interaction effects are significant [31].

It is usually obvious if an animal is experiencing an

extreme behavior such as an epileptic seizure. However,

certain other behaviors, such as behaving in a stressed

manner, may be more open to interpretation in borderline

cases. A key advantage of automated video tracking is that it

forces researchers to define exactly what they mean by a

given behavior, and so enables standardization of method-

ologies [32] — which is one of the most pressing needs in

mouse behavioral phenotyping [31,33,34].

In this paper we present EthoVision—a general-purpose

video tracking, movement analysis, and behavior-recogni-

tion system, and illustrate its use in behavioral phenotyping

with a number of case studies. Noldus Information Tech-

nology introduced the first version of EthoVision in 1993.

The system has undergone numerous updates over the years,

based on feedback from users around the world, which has

resulted in a very comprehensive package for studies of

movement and behavior [35]. The software has recently

been comprehensively redesigned for 32-bit Windows plat-

forms, with a highly interactive graphical user interface for

display of experimental design, experimental arena, and

tracks of movement.

2. How video tracking works

In a video tracking system such as EthoVision, the basis

of the system is that a CCD video camera records the area in

A.J. Spink et al. / Physiology & Behavior 73 (2001) 731–744732

which the subject animals are (the scene). The camera’s

signal can either be fed directly to a computer, or via a video

cassette recorder (see Fig. 1).

In the case of an analog camera (the most practical

option with current technology) the signal is then digitized

(by a hardware device called a frame grabber) and passed

on to the computer’s memory. The video signal consists of

a series of frames (25 or 30 a second, depending on the

TV standard of the camera), and each digitized frame is

composed of a grid of pixels. Each pixel has either a gray

scale value (for a monochrome signal) or values describ-

ing the color of each pixel (red, green, and blue, or hue,

saturation, and intensity [36]). The software then analyzes

each frame in order to distinguish the tracked objects from

the background. Having detected the objects, the software

extracts the required features from each frame (in the case

of EthoVision 2.0 this includes the position of the

mathematical center of each object, and its surface area).

Calculations are carried out on the features to produce

quantified measurements of the animals’ behavior. For

instance, if the position of an animal is known for each

frame, and the whole series of frames is analyzed, the

average speed of locomotion (velocity) of an animal

during an experiment can be calculated. In addition, if

certain regions are identified as being of interest (the

center and edges of an open field experiment, for exam-

ple), the proportion of time spent by the animals in those

regions can also be calculated. Measurements such as

average velocity and time in the center of an open field

can be used as indicators of anxiety (e.g., Case Study 2)

and so in this way an accurate quantified measure can be

obtained of whether animals receiving a particular treat-

ment were more stressed than other animals, and these

measurements can be directly comparable with data from

other workers with similar questions.

3. Practical aspects of video tracking

When using a video tracking system such as EthoVi-

sion, the data obtained will only be as good as the quality

of the experimental design. Of course, basic considerations

such as sufficient replication and avoiding pseudoreplica-

tion [37] must still be adhered to. Likewise, the better the

practical setup, the better the quality of the data obtained.

If the video signal is of poor quality (for example,

containing reflections), the tracked animals may not be

detected, or other objects (such as the reflections) may be

mistaken for the animals. A number of factors should (if

possible) be optimized:

3.1. Illumination

Lighting should be indirect and even. The level of

illumination (brightness) is not so critical, provided that

it is above the detection level of the camera (down to 0.01

lx for a good monochrome CCD camera, about 1 lx for a

color CCD camera). Monochrome cameras can also detect

light in the near infrared (to about 850 nm). However, if

one part of the arena is brighter than another, or if the

brightness changes during an experiment, this may cause

problems; both technically and because it might affect the

behavior of the mice [38]. Above all, reflections must be

avoided. The easiest way to do this is to ensure that the

light only reaches the scene by first bouncing off a mat

surface (such as the wall of the laboratory). For instance, if

Fig. 1. The basic EthoVision setup. The analog image from the camera is fed into a frame grabber in the computer, which digitizes the image. EthoVision then

analyzes the signal to produce quantitative descriptions of the tracked objects’ behavior.

A.J. Spink et al. / Physiology & Behavior 73 (2001) 731–744 733

an overhead light is used, a mat white wooden board can

be suspended in between the light bulb and the scene.

Alternatively the lights can be placed sufficiently far away

from the camera so that they do not produce a reflection

(see Fig. 2 [39]). If color tracking is used, the incident

light source must contain a sufficient range of wavelengths

to enable separation of the colors being tracked. Color

cameras cannot detect infrared light.

3.2. Marking

EthoVision distinguish between two mice in the same

arena on the basis of size, for example an adult mouse and

a pup. To distinguish two mice that are the same size, the

researcher can color part of one of the mice the same

grayness as the background, thus reducing its apparent

size (Fig. 3). If the mouse is light-colored, dark hair dye

can be used, if it is dark, a patch can be bleached on one.

The procedure is to mix the hair bleach with dilute

hydrogen peroxide, then leave it to soak for 30 min,

and then rinse it thoroughly.

Color tracking can be used to distinguish more than two

mice (up to 16 per arena in the case of EthoVision [40]). If

the mice being tracked are the same color, livestock markers

can be used to paint blobs on the mice, which the system

will be able to track. However, these blobs will only last 1 or

2 days, so can only be used for short-term experiments.

Mice can also be colored with permanent marker pens or

highlight markers for short-term experiments. For longer-

term work the mice can be dyed with a brightly colored

human hair dye. If the mouse has dark fur, it can first be

bleached before the dye is applied, using the method

described above. Colored fluorescent tags (illuminated with

a UV light) can also be used to mark the animals. It is also

possible to track mice in the dark by surgically attaching a

small plastic cap to their sculls, and filling the cap with

fluorescent dye. When tracking a relatively large number of

mice, the marker colors must be as different from each other

as possible, in terms of their hue and saturation. Colors

should also be selected to be of a similar intensity (bright-

ness) to each other, and to be not too low an intensity (that

is, not too dull).

Fig. 2. Possible lighting setup for a Morris water maze (after Ref. [39]). The lights are either placed so far away from the camera that the angle is too great to

cause reflections (as in the diagram) or next to the pool and below the water level.

Fig. 3. Mouse marked with dark coloring, to distinguish it from another

mouse by reducing its apparent size.


If different colored markers are applied to different parts

of the body of an animal (head, back, and tail, for

example), these parts can be tracked individually, and

parameters such as orientation (in absolute terms of the

body axis and in respect to other animals) or head contact

can be calculated [41].

3.3. Arena size

When a video signal is digitized by a frame grabber, the

resulting grid of pixels produced for each video frame will

be of certain dimensions (determined by the hardware that

digitizes the signal). The ratio of the minimum number of

pixels of the mouse’s image (usually 3), to the number of

pixels in the entire image, determines the ratio of the

actual size of the tracked mouse to the maximum size of

the scene. If one cage is filmed, this is also the maximum

size of that cage. If more than one cage is placed within

the camera’s field of view, so that they can be analyzed

simultaneously, the maximum total of all the cages’ widths

is the maximum size of the scene. For instance if a frame

grabber has a image resolution of 576� 768, and you are

tracking mice that are 2 cm wide, the maximum size of the

scene is 576/3� 2 = 384 cm. If the cages are placed in a

4� 2 grid under the camera, the external width of each

cage can be no more than 384/4 = 96 cm. In practice, if a

high-resolution frame grabber is used (such as the one in

the EthoVision system), the size of the scene tends to be

limited by how big an area the camera can ‘see’ when

suspended from the laboratory ceiling, with a lens that is

not too wide-angle (less than 4�), for a given size of the

camera’s image detector (CCD).

3.4. Sampling rate

The video camera produces frames at a rate of 25 or 30

per second (depending on its TV standard: NTSC in

America and Japan, PAL in most of the rest of the world).

Because a mouse usually does not move a significant

distance in 1/25th of a second, this sample rate can usually

be reduced to 5–10 samples/second if a measurement such

as velocity or distance moved is required (5 s� 1 was used in

most of the case studies detailed below).

4. The EthoVision system

The EthoVision for Windows software has a number of

features that make it particularly suitable as a tool in studies

such as those described in the Case Studies section (below).

4.1. Data and experiment management

Since a water maze experiment of different transgenic

mice strains tends to be a large experiment with over 1000

data files as well as their associated profiles (which contain

all the settings), data management within EthoVision is

particularly important. The EthoVision Workspace Explorer

(Fig. 5) enables the user to manage these files through a

simple interface (similar to the Windows Explorer), so that,

for instance, settings defining the acquisition method (the

tracking profile) can be dragged and dropped from one

experiment to another. An entire experiment or workspace

can be backed up to a single file to facilitate both safekeep-

ing of the data and transfer between different computers.

In addition, users are able to find their way around the

parts of the program by means of the Experiment menu.

They can use the program’s functions in a logical order

starting with experiment design and arena and zone defi-

nition, then to data acquisition, followed by track visual-

ization, and finally data analysis.

4.2. Independent variable definition

As well as tracking objects, EthoVision allows a re-

searcher to define a complete experimental protocol, in

terms of the independent variables of an experiment, and

their values. Up to 99 independent variables (such as

treatment, room temperature, genetic strain of the mouse)

can be defined, and in addition EthoVision automatically

records a series of system independent variables such as

the profile (setting file) used, time and date of the trial, trial

duration, etc. These independent variables can be used to

select, sort, and group data, both when plotting tracks and

analyzing the data. For instance, you can select to plot all

the tracks from mice of a particular genotype, or calculate

the mean time taken to reach the target of a water maze by

control mice compared with treated mice.

4.3. Object detection methods

When studying different mouse strains, the object detec-

tion method is very important; each method has its advan-

tages and disadvantages. EthoVision offers three detection

methods. Gray scaling is the fastest. It defines all connecting

pixels that are both brighter than a high gray scale threshold

and darker than a low threshold as the animal, and all other

pixels as the background. The thresholds can be set either

manually by the user, or calculated automatically by the

program. It is a fast method (giving the highest sample rate),

but cannot be used if the same gray scale values are present

both in the mouse’s image and the background. The sub-

traction method first makes a reference image, with no mice

present, then subtracts the gray scale value of each pixel of

the reference image from the equivalent pixel of the live

image. Any pixels which have a subtracted value other than

zero are different between the reference and live images, due

to the presence of the mouse. The user can define whether

the mouse has to be lighter than or darker than the back-

ground, or just different. This method is more tolerant for

differences in light intensity across the scene. The third

detection method uses the hue and saturation of the tracked


mouse (or a marker painted on it) to define its color, and

pixels with the defined colors are tracked. Up to 16 different

colors can be tracked in each arena at once. With all three

methods, objects that are either smaller than the mouse

(such as droppings) or larger (such as reflections) can be

excluded on the basis of their size.

4.4. Arena and zone definitions

Up to 16 enclosures can be placed under one camera, and

each enclosure can be treated as a separate independent

replicate (called an arena; see Fig. 4). The animals in these

arenas can be tracked simultaneously. Multiple arena defi-

nitions and zone definitions can be defined for each experi-

ment. This is particularly handy when, for instance, a water

maze experiment is prepared, with multiple platform posi-

tions. When setting up the experiment, the user can assign

the proper arena definition to each track file before the

experiments are started. The user can define up to 99 regions

of interest (zones; see Fig. 4). These can be used in the

analysis (for example, the time taken for a mouse to reach

the platform of a water maze, see Case study 1) and also for

automatic start and stop conditions. For instance the trial can

be stopped automatically when the mouse has reached the

platform of a water maze. Zones can be added together to

make a cumulative zone (for instance, if a maze has more

than one target). Zones can also be defined as hidden zones,

for use with nest boxes, burrows, etc. The system assumes

that when an animal disappears from view and it was last

seen adjacent to a hidden zone, it is inside the hidden zone.

Fig. 4. Arenas and zones of a Morris water maze. The arena is the circular area bounding the outside of the water maze, and it is divided up into four

zones (‘‘quadrants,’’ see Case Study 3) labeled North, South, East, and West.


All the zones can be defined and altered either before of after

data acquisition, allowing iterative exploratory data analysis

of the effects of changing zone positions and shapes.

The arena can be calibrated so that the parameters such as

velocity are calculated in meaningful units. If more than one

arena is used, they can each be calibrated separately, or one

calibration can be used for all arenas. There are two

calibration methods; in standard calibration the user meas-

ures a series of lines (for example, a meter ruler placed on

the floor of a cage). If the camera image is distorted (by a

fish-eye lens, or a lens that is not directly overhead) the

standard calibration will not be accurate (the standard

deviation of the calibration factors is given to enable the

user to determine this), and the user can opt for advanced

calibration. In that method a grid of points is mapped onto

the arena, and a series of coordinate points are entered,

giving a calibration that is accurate for the entire image,

even when it is distorted.

4.5. Behavior recording

In addition to automatically tracking the movement of the

animal, EthoVision can automatically detect certain behav-

iors, such as rearing. EthoVision also allows the researcher

to manually record (by keystroke or mouse-click) behaviors

that cannot be detected automatically (such as sniffing).

4.6. Image filtering

As in most video tracking systems, EthoVision calculates

the mathematical point at the center of the digitized picture

of the animals’ body, including the tail. This means this

point is further towards the mouse’s posterior than is correct.

Furthermore, a tracking system will still detect movement of

the mouse when only the tail is moving. In order to prevent

this, the tail can be removed from the image, using image

filtering. The bars of a cage can also be filtered using a

similar technique.

4.7. Track visualization

The user can specify data sets at several hierarchical

levels for the analysis and visualization of the data. The

data can be selected, sorted, and grouped by independent

variables. This way a selection of the tracks in an experi-

ment can be plotted on a single screen, by treatment or

genetic strain (see Fig. 5). The track plots can be displayed

with or without the arena and zones or the background

Fig. 5. This figure displays the EthoVision Workspace Explorer on the left panel and in the right panel eight tracks of a water maze experiment are plotted. The

tracks are sorted by animal ID and trial number. As well as plotting the complete tracks, the user can also play the plots back in a customized way.


image of the experimental setup. The plots can be exported

as graphic files in a variety of standard formats.

4.8. Data analysis

After initially selecting the tracks for analysis (using

independent variables), the user can carry out further data

selection by nesting over time-windows, zones, and behav-

iors (either ones automatically detected by the system, or

behaviors entered by the researcher manually). The user can

also group by the values of independent variables (such as

treatment values), and further fine-tune the data using filter

settings like minimal distance moved, to eliminate slight

‘‘apparent’’ movements, and down-sampling steps, to elim-

inate redundant data if a lower sample rate describes the

path better. A wide range of quantitative measures of

behavior is available in EthoVision, and these can be

described with a full range of descriptive statistics:� Distance moved — For example, the total path dis-

tance followed by a mouse in a water maze until it reached

the target.� Velocity — For example, the mean speed of the mouse

whilst swimming in a water maze.� In zone — For example, the time taken for a mouse to

reach the target of a maze. The zones can be completely

redefined at any time before or after data acquisition.� Distance to zone and Distance to zone border — For

example, the average distance that a mouse was from the

walls of an open field, except when it was near a novel object.� Heading — For example, the mean direction of a

mouse’s path in relation to a maze’s target.� Turn angle — A large mean and variance in the

turn angle may indicate stereotypical behavior such as

circling movements.� Angular velocity — High values of absolute angular

velocity can be used to indicate a variety of behaviors

including local searching patterns, stereotypic movements

and abnormal behaviors, reaction to toxins, and measures of

behavioral asymmetries in animals with unilateral brain

lesions [42,43].� Meander — The change in direction of movement of

an object relative to the distance it moves. Therefore it can

be used to compare the amount of turning of objects

travelling at different speeds.� Sinuosity — The amount of random turning in a given

path length. This is one value for the entire track and can be

used, for instance, to quantify the foraging behavior of an

animal [44,45].� Moving — Whether or not the mouse is moving,

compared to user-defined thresholds. For example, the

percentage of the time that the mouse in a water maze

was floating or the number of bouts of movement whilst the

mouse was in the central zone of an open field.� Rearing—A useful parameter for assessing exploratory

behavior, for example the frequency of rearing in an open

field with and without a novel object. EthoVision detects

rearing on the basis of the change in surface area of the

mouse (as seen from above), using user-defined thresholds.� Distance between objects — In EthoVision, the user

can select as many combinations of actors and receivers as

wanted to calculate social interactions, including this param-

eter and the ones listed below (which are based on the

distance between objects). Both other animals and zones can

be selected as receivers of social interactions.� Relative movement — Whether a mouse is moving

towards or away from another mouse. This is a good

example of providing an objective measure of behavior,

which can later be explicitly interpreted as aggression or

avoidance [46].� Proximity — Whether a mouse is close to or far away

from another mouse (in relation to user-defined thresholds).

This parameter has been used to study the effect of indi-

vidual or group housing on social behavior [46] and social

isolation as a symptom of schizophrenia [47,48].� Speed of moving to/from — The distance-weighted

speed at which an object moves towards or away from

another object. These parameters can be used to quantify the

intensity of avoidance or aggression.� Net relative movement — A combined measure for the

above two parameters, this parameter is also frequently used

in studies of social interaction [46,49].

The analysis report is a spreadsheet that can be manip-

ulated for optimal presentation and exported in different file

formats (or copied and pasted) to other spreadsheets or

statistics packages for further analysis. The parameters can

be calculated for every individual sample, and their statis-

tics calculated both per track and across groups of tracks

with the same values of user-defined independent variables

(for example, all tracks of mice receiving the same dosage

of a treatment).

5. Case studies

The following case studies are presented in order to

illustrate the use of EthoVision for detection and quantifi-

cation of various behaviors in mice. Therefore, full details of

neither the experimental setup nor full conclusions drawn

from the data are given; the reader is referred to the

researchers or the original publications for that information

[55,70]. However, the raw data from some of these case

studies have been placed on the web; follow the link from

http://www.noldus.com/products/ethovision/. The data can

be downloaded and opened in EthoVision for visualization

and analysis.

5.1. Effect of genetic composition on open field behavior of

mice. Behavioral phenotyping of the effects of disabling the

Munc 18-1 gene

The following case study is based on unpublished data

kindly provided by R.A. Hensbroek and B.M. Spruijt


(Rudolf Magnus Institute for Neuroscience, Utrecht Uni-

versity). For further details about the experimental proto-

col, please contact them at: [email protected].

Mice that lack the gene for munc18-1 have no secretion

of neurotransmitters and consequently die at birth [50].

Heterozygotes still have one functional copy of the gene

and a 50% reduction in munc18-1 protein levels. Earlier

work had indicated that heterozygotes were more active

than wild type mice, but it was not clear whether this was

due to the munc18-1 deletion, or due to differences in

genetic background. Heterozygotes were therefore repeat-

edly backcrossed with the inbred strain 129/Sv. The

resulting mice were also crossed once with C57Bl/6. The

effects of the munc18-1 deletion could now be tested both

in a purely 129/Sv background and in hybrids of 129/Sv

and C57Bl/6.

The mice were placed in an empty circular open field for

15 min, firstly when the field was empty, and secondly with

a 10� 5 cm metallic cylinder in the middle. Heterozygotes

were more active than wild types, both for the hybrids and

129/Sv mice (Fig. 6). The genetic background had a clear

effect on the behavior of the mice: in 129/Sv, heterozygotes

spent more time in the central area of the open field, while

such a difference was not seen in hybrids.

5.2. Open field exploration strategies in mice selected for

short and long attack latency

The following case study is based on unpublished data

kindly provided by M.C. de Wilde and J.M. Koolhaas

(University of Groningen). For further details about the

experimental protocol, please contact them at: m.c.de.wil-

[email protected] or [email protected].

Male wild house mice selected for short and long attack

latency (SAL and LAL, respectively) differ in their way of

coping with environmental challenges [51,52]. SAL males

show an active behavioral response whereas LAL males

show a reactive coping strategy. For instance, in the defen-

sive burying test SAL males actively cover the shock prod

with bedding material and LAL males freeze in a corner of

the cage. Several studies suggest that the coping styles have

a differential fitness in nature, depending on environmental

conditions such as food availability [53,54]. The current

experiment studied the exploratory behavior of SAL and

LAL mice in an open field.

The LAL mice (white bars in Fig. 7) were more active

than the SAL genotype, moving a greater total distance, and

exploring the outer zone more than the SAL mice, whereas

the distance traveled in the inner zone was the same for the

two groups. The SAL animals showed no difference in

behavior in the second time period, compared with the first

time period, whereas the LAL mice were significantly more

active after the first 5-min period was over, moving a greater

distance in both the inner zone and outer zone.

These data are consistent with other work describing the

two strategies. SAL mice continue to exhibit a behavior that

they have found to be successful, whereas LAL mice will

develop new behaviors with time. In the beginning the SAL

and LAL mice explore the open field in a similar way, and

after 5 min the SAL mice continue to explore the arena in

that way. In contrast, the LAL mice develop a new explor-

atory strategy, both moving more, and spending more time

Fig. 6. The behavior of mice with various genotypes (genetic background

and munc18-1 mutation) in an open field expressed as total distance moved

and time spent in the central area. Thick shading represents heterozygotes

and thin shading wild types. Left-slanting shading represents 129/Sv mice,

right-slanting shading represents hybrids. The bars represent ± 1 standard

error of the mean.

Fig. 7. Distance moved by short attack latency (n= 16, black bars,

± standard error of the mean [S.E.M.]) and long attack latency (n= 12, white

bars, ± S.E.M.) wild male house mice in an open field experiment.


in the inner zone as they adapt to the new situation in the

second period.

5.3. The effects of CREB gene dosage on the performance of

mice in the water maze learning task

The following case study was based on data kindly

provided by Dr. D. Wolfer and his colleagues (Department

of Anatomy, University of Zurich). For details, please see

the publication in Learning & Memory [55].

The Morris water maze task is one of the most frequently

used experimental paradigms designed to assess cognitive

performance [56]. EthoVision is used by several groups to

track and analyze water maze experiments (e.g., Refs. [57–

60]). A tank is filled with opaque water (chalk or milk

powder is added) and the animal swims until it locates a

hidden platform just below the surface of the water. Its

speed of learning can be assessed by how rapidly the time to

locate the platform is reduced when the task is repeated.

Most studies have used rats as subjects, whereas mice have

been less frequently used [61,62]. Mice have been shown to

produce a normal learning curve [62]. The behavior of mice

in tasks of learning and memory is less characterized than in

rats and in view of the development of transgenic mouse

models with predefined deficiencies as new tools to identify

putative cognition enhancers, it is important to conduct

preliminary tests on wild type strains that are used to

produce these transgenic animals, to calibrate the behavioral

paradigm before beginning testing of the transgenic or

knockout mice.

The transcription factor CREB is involved in the

formation and retention of long-term memory [63,64].

Although learning in mice is strongly influenced by the

genetic background of the mice [65,66], early water maze

studies did not take this into account. The current study

aimed to validate previous studies using mice with well-

defined genetic backgrounds (see the original paper [55]

for details), with mice with different dosages of the

CREB gene.

Several variables were calculated from the animals’

tracks (the EthoVision data were processed using the

WinTrack program [67]), see Table 1. It was found that

mice with one hypomorphic CREB allele (aD) and one

null CREB allele (CREBcomp, in which all CREB iso-

forms are disrupted) showed statistically significant

(Scheffe test) defects for most measures of the perform-

ance in the water maze (compared with wild type mice)

whereas mutants with a and D isoforms disrupted and the

b isoform up-regulated showed less disruption. Previous

Table 1

Variables calculated from the animals’ tracks in the water maze experiment

in Case Study 3

Variable WT vs. aD ( P) WT vs. comp ( P)

Swim time n.s. < .05

Swim path length n.s. < .01

% time in goal zone n.s. < .05

Mean distance to goal < .05 < .002

Swimming parallel to wall < .05 < .001

Swimming < 22 mm from wall < .05 < .002

Number of wall contacts n.s. n.s.

Swimming speed n.s. n.s.

Table legend: n.s. = not significant (Scheffe test), WT=wild type,

aD=mice with aD isoform disrupted, comp =mice with all CREB

isoforms disrupted.

Fig. 8. Two typical tracks of wild-type (right) and trkB-mice in the Morris water maze. The control mouse finds the hidden platform (the top-left square), on the

basis of previous training, whereas the mutant mouse stays close to the edges of the tank (the gray circle).


studies [68,69] reported major deficits for mice with these

genetic defects in all paradigms tested. It is likely that the

difference is due to the differing genetic background of the

mice used in different studies.

5.4. Learning behavior of mice in a water maze with

the trkB gene knocked out in the forebrain during

prenatal development

The following case study was based on data kindly

provided by Dr. D. Wolfer (Department of Anatomy, Uni-

versity of Zurich). For details, please see the publication in

Neuron [70].

The TrkB receptor for brain-derived neurotrophic factor

regulates short-term synaptic functions and long-term poten-

tiation of brain synapses [71]. Mice were studied which had

had the trkB gene knocked-out in the forebrain during

postnatal development. The mice developed without gross

morphological defects (the mice do not survive until adult-

hood if the gene is knocked-out for the entire brain [72]),

and the activity of the trkB-mice in an open field were

similar to wild-type mice. However, in a Morris water maze

experiment (similar to that of Case study 1), although the

control mice learnt the route to the hidden platform (left

track in Fig. 8), the mutant mice tended to hug the walls of

the maze (thigmotaxis) and therefore did not find the plat-

form (right track in Fig. 8).

6. Discussion and conclusions

The four case studies illustrate different aspects of using

EthoVision to track mice. The first case study (knock-out of

the Munc 18-1 gene) looked at mice in an open field which

were hypothesized to be hyperactive as a result of the

knocked-out gene. Plots of two simple variables quantifying

the mice’s behavior demonstrated the experiment’s effects.

Simple, straightforward parameters such as distance moved

or time in center can be powerful measures to quantify

differences in behavior between different genotypes. Like-

wise the case study of SAL and LAL mice (Case study 2)

showed clear differences in behavior between the genotypes

on the basis of a variable (total distance moved) calculated

from the EthoVision tracks, even though the actual differ-

ences in behavior were rather complex. The Morris water

maze study on mice with differing CREB gene dosage

illustrated that there are occasions one or two variables are

insufficient to provide a good description of the experimen-

tal effects. It was only when a batch of variables was

calculated that the interaction between the mice’s genetic

background and specific effects of the CREB gene became

clear. An advantage of measuring behavior using a video

tracking system is that once the tracks of the animals have

been derived, a large number of dependent variables can

easily be calculated from those tracks. Finally, the study of

mice with the trkB gene disabled in the forebrain illustrated

the fact that although proper statistical analysis is, of course,

necessary, much valuable information, especially about

exactly what to test, can be gained from viewing the plots

of the animals’ tracks.

The case studies demonstrate that video tracking provides

an accurate and powerful means to quantify mouse behavior.

Because it is repeatable and not subject to observer variance,

it is eminently suitable for comparison of different data sets

between different research groups. The case studies shown

are all examples of relatively straightforward uses of a

system like EthoVision. Even with the current system

(EthoVision 2), more complex experiments and analyses

can be carried out. In the future, EthoVision and other video

tracking systems will be able to carry out more complex

tasks. An indication of what these might be can be seen from

projects that have been carried out in research projects, or

customizations of the software. Whether these functions

ever become part of the standard commercial package will

depend on both how technically feasible the solution is, and

how much interest there is in that application.� Synchronization of video (behavioral) and physiolog-

ical data. It is often the case that an experiment involves not

only quantifying the behavior of an animal, but also

measuring a variety of physiological parameters [73]. In

order to carry out a full statistical analysis of the data

obtained, it is necessary for the various data streams to be

synchronized. At the time of writing (November 2000) there

is no straightforward and uniform solution to this problem.

However, Noldus Information Technology, together with

several partners are carrying out a research project to resolve

this issue [74].� Direct extraction of physiological data from video

data. A customized version of EthoVision has been devel-

oped which uses an infrared thermographic camera both to

record an animal’s behavior, and simultaneously to measure

its body temperature (at several points on the body simulta-

neously) [75]. The method is noninvasive and thus partic-

ularly appropriate for monitoring the effects of stress.� Automatic detection of complex behaviors. EthoVision

is able to detect whether a mouse is moving, or whether it is

rearing, but (in common with other commercial systems) is

not able to automatically detect other behaviors (unless, of

course, it is possible to define them in terms of the existing

parameters). A behavior such as an epileptic seizure (char-

acterized by the animal turning over with the tail in a

characteristic position over the body) can be detected by

using EthoVision in combination with neural networks [76]

and statistical classification [77].� 3-D tracking. If more than one camera is used (or one

camera and a mirror [18]), it is in theory possible to obtain

information about the movement of animals in all three

dimensions. Although tracking in 3-D was already possible

with some simple hardware devices [13] and more prim-

itive video-based systems [19], ironically the more sophis-

ticated the tracking system, the more difficult this

becomes. However, a special version of EthoVision has


been used to successfully track objects moving in three

dimensions [78].� Identification of large and indeterminate numbers of

animals. To track a large and continually varying number of

animals, especially if crowded close together, calls for quite

different techniques than the object identification methods

used by EthoVision. Some progress has been made using

techniques such as active modeling and prediction of the

animals’ shapes [79,80] and dynamic imaging with statis-

tical analysis of detected body features [81].

Acknowledgments

We are grateful of those who provided us with their data

for use in the case studies section of this paper. All members

of the EthoVision team at Noldus Information Technology

have contributed towards the product since it was initially

launched in 1993; their hard work and dedication is

acknowledged here.

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