RoMon: An open-source web-based solution for rodent behavioural training and monitoring

Surjeet Singh1*; Edgar Bermudez Contreras1; Robert J. Sutherland1; Majid H. Mohajerani11Canadian Center for Behavioural Neuroscience, University of Lethbridge, Lethbridge, Canada T1K 3M4

*surjeet.singh@uleth.ca

1. Jhuang H, Garrote E, Mutch J, Yu X, Khilnani V, Poggio T, Steele AD, Serre T. Automated home-cage behavioral phenotyping of mice. Nat Commun 1:68 (2010) 2. Poddar, R., Kawai, R. & Olveczky, B. P. A fully automated high-throughput training system for rodents. PLoS ONE 8, e83171 (2013).3. https://www.raspberrypi.org/products/4. Curzon P, Rustay NR, Browman KE. Cued and Contextual Fear Conditioning for Rodents. In: Buccafusco JJ, editor. Methods of Behavior Analysis in Neuroscience. 2nd edition. Boca Raton (FL): CRC Press/Taylor & Francis; 2009. Chapter 2. Available from: https://www.ncbi.nlm.nih.gov/books/NBK5223/

References

Measuring behavioural phenotypes in animals in their home-

cage environment is important for assessing the effects of

experimental manipulations [1].

Existing solutions do not accommodate either complete

automation of multi-stage training processes involving large

numbers of animals [2].

Thus the significant human involvement currently required for

experiments on complex behaviours still represents a

considerable impediment to large-scale rodent studies.

OBJECTIVE:

The objective of this work is to provide an open source low-

cost web-based solution for automated animal training and

monitoring.

Introduction

The core of this system is a Raspberry Pi which is a low cost

(35$), credit-card sized computer [3].

A web interface has been developed in php that allows the

user to control the infrared camera module v2 (Pi NoIR) and

the general-purpose input/output (GPIOs).

Python and OpenCV are used for controlling the experiment

variables and perform data analysis.

To test this system we used a cued fear conditioning paradigm

[4] to study animal behaviour before, during and after training

in a mouse model of AD (APP NL-G-F) and C57BL/6 mice as

control (n=2, 10 months old).

The automated and systematic implementation of training

protocols with simultaneous monitoring of animal behaviour

within the animal’s home-cage dramatically reduces the effort

involved in animal training and data acquisition while also

removing human errors and biases from the process. Further,

our system is available to the research community as open-

source, which will facilitate the creation of new research

avenues in behavioural neuroscience using a low-cost open-

source platform.

Conclusion

Methods and Materials

Figure 5. Occupancy heat maps and trajectory plots of animal activity in their home cages.

Chart 2. Activity during tone test. (n=2)

Chart 1. Average day and night time activity of 10 months old control and AD mice before and after cued fear conditioning. (n=2)

Poster Number: 2-G -232

Figure 1. System Architecture.

Automated data collection and analysis using the proposed

web based solution.

Results

Table 1. List of components

ComponentsRaspberry pi 3

Infrared Camera Module v2 (Pi NoIR)

AUKEY 180° Fisheye Lens

Speakers

CROUZET Miniature Side Rotary (High Sensitivity) Switch

LEDs (Red, Yellow, Green, IR)

Bio-Chem Valve™ 2-Way NC Pinch Valve

Optimice® Animal Home Cage

0

20

40

60

80

100

1

% a

ctiv

ity

Ctrl (C57BL/6)

AD (APP NL-G-F )

Chart 3. Freezing time during tone test.(n=2)

Figure 3. System setup and online animal activity monitoring using frame differencing algorithm in home cage.

Figure 4. Online animal tracking using intensity based thresholding.

Figure 2. (A) Web interface for live preview, (B) view stored data, and (C) manual control of GPIOs with camera settings

NOTE: Using this system we have been able to record videos @ 120 fps (4X4 binning)

php filesindex.phpview_files.phpselect_exp.phpmanual.phpcam_ctrl_sc.php

python scriptsmotion_back.pytrack_thresh.pyfc_test.pylp_test.py…

Apache HTTP Server

mod_wsgi

Peripheral ControlsExternal Triggers IN

andFrame Clock OUT

0

20

40

60

80

100

Day Night Day Night

Before Cued Fear Conditioning After Cued Fear Conditioning

% a

ctiv

ity

Ctrl (C57BL/6)

AD (APP NL-G-F )

Nigh

t activity mo

nito

ring

Day activity m

on

itorin

g

• Canadian Institutes of Health Research

• IBRO-CAN Travel Award

Acknowledgement

USFoot

Shock

CS Tone

120 sec 20 sec 120 sec

2 sec

24 hrs. activity monitoring for AD and Control mice before behaviour.

Cued fear conditioning.

(5 tone-shock pairings)

24 hrs. activity monitoring for AD and Control mice after behaviour.

Tone test

(3 times tone presentation)

CS Tone

120 sec 20 sec 120 sec

Figure 6. Experiment timeline

Animal 1 Animal 2 Animal 3

Trac

k P

lot

Occ

up

ancy

he

at m

aps

A B

C

0

100

200

300

400

500

1

fre

ezin

g ti

me

(s)

Ctrl (C57BL/6)

AD (APP NL-G-F )