Automated Algorithm for Synchronized Quantification of LFP

Recordings and Individual Behavioural Parameters

in an Animal Model for OCD

T. Tambuyzer

, H. Wu

, K. Bauweleers

, K. van Kuyck

, B. Nuttin

2,3

and J.-m. Aerts

M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium

Laboratory of Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium

Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium

1 OBJECTIVES

The knowledge on the origin of local field potential

(LFP) signals is making progress (Buzsaki et al.,

2012). To prove the clinical relevance of recording

LFP signals, synchronous comparison with

pathological behavioural parameters in animal

models for diseases is essential. This work describes

an algorithm, which is developed for automated

analyses of LFP recordings and behavioural

parameters in freely moving rats. Several studies

showed the advantages of behaviour monitoring

with video recordings (e.g. Zarringhalam et al.,

2012), but there are limited studies describing

correlations between neural and behavioural

recordings for freely moving rodents (Venkatraman

et al., 2010; Fan et al., 2011). Here, we present the

algorithm applied on a rat model for OCD

(schedule-induced polydipsia, SIP; Falk, 1961;

Moreno and Flores, 2012). The algorithm allows to

extract detailed behavioural parameters based on

images of a top view camera. Changes during the

disease conditioning period can be tracked and

correlated with synchronously measured changes in

the LFP recordings. We believe that such automated

algorithms can highly contribute to a deeper

understanding of recorded LFPs and their link with

pathological behaviour of individual animals.

2 MATERIALS AND METHODS

2.1 Animal Conditioning

Electrodes (twisted bipolar platinum electrode,

single strand diameter: 0.203mm, part number

E363/8-2TW, PlasticsOne) were implanted in the

bed nucleus of the stria terminalis (BNST)

bilaterally in one Wistar rat before SIP conditioning.

After three days of recovery the conditioning

commenced. During SIP conditioning, the rat

received food pellets under a fixed-time schedule

each day, and gradually developed compulsive

drinking behaviour (Moreno and Flores, 2012). A

sampling rate of 10000 samples/s was used for all

LFP recordings during conditioning.

2.2 Image Analysis

A webcam (Logitech HD Webcam Pro C910) was

fixed on top of the conditioning cage to record the

rat behaviour during conditioning (sampling rate:

15 images/s). The processing of the images

consisted of three main steps: (i) Cage detection:

Based on colour properties of the cage floor (dark

brown-black), the shape of the cage and the exact

locations of drinking and feeding tube were first

extracted. (ii) Rat detection: Rats could be

recognised using a specific threshold for

(approximately) white pixel values, which represent

the rat. After fixing the threshold to distinguish

between rat and background, a binary image was

obtained. For each video sample, the threshold was

automatically recalculated based on the expected

size of the rat to deal with changes of recorded light

intensities. (iii) Quantification of behavioural

parameters: Based on the rat detection, four

behavioural parameters were determined: rat

location in cage (in x- and y direction), drinking

behaviour (0 or 1), eating behaviour (0 or 1), and

walking patterns (i.e. correlation of changes in x-

and y- direction). All image processing steps were

executed in Matlab using the Image Processing

Toolbox.

Tambuyzer T., Wu H., Bauweleers K., van Kuyck K., Nuttin B. and Aerts J..

Automated Algorithm for Synchronized Quantiﬁcation of LFP Recordings and Individual Behavioural Parameters in an Animal Model for OCD.

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2.3 LFP Analysis

After band-pass filtering (0.1-300 Hz), detrending

and noise cancellation, the power of specific

bandwidths was calculated using the fast Fourier

transform algorithm of Matlab. To create a direct

link between brain signal and the individual animal

in the graphical interface, these power calculations

were afterwards used for replacing the white colour

of the rat with a colour on a cold-hot scale: for high

powers, the rat was represented in dark red and

for low powers the rat was represented in dark

blue.

3 RESULTS

The developed algorithm required two inputs: a top

view video of the cage in .avi format and a

simultaneously recorded LFP signal. The average

time (+-standard deviation) needed by the algorithm

to analyse the video- and LFP recordings is 0.14s (+-

0.015s), which implies that the developed algorithm

could be used for real-time monitoring. The

accuracy of the rat detection was tested based on a

video of 20 minutes, which was not used for

algorithm training. In all samples of the test video

the rat could be correctly detected. Figure 1 shows

the output of the algorithm. The feeding tube and the

drinking tube are marked. The colour of the rat

corresponds to the power of the LFP in a specific

bandwidth, which can be selected by the user

Figure 1: Output of the automated LFP-video algorithm.

Location of food magazine (green) and water bottle (blue)

are shown by rectangular. The rat is represented in a

colour, which corresponds to the power of a specified

frequency band (e.g. delta band power of the left

hemisphere).

(e.g. the delta band in figure 1).Using this graphical

representation, one can easily visualize correlations

between behaviour (image at time t) and LFP

recordings (signal of a one second time interval [t-1

t]).

4 DISCUSSION

The developed algorithm presents an attractive way

to synchronize, visualize and analyse LFP data with

simultaneously recorded behavioural video data. The

algorithm allows to gain insight in neuronal

recordings, to assess animal model validity (e.g.

Nestler and Hyman, 2010) and to quantify severity

of disease in individual animals (e.g. Hooks et al.,

1994). It is expected that such automated

comparison of synchronised video- and LFP-

recordings could substantially contribute to finding

potential neurological biomarkers of psychiatric

disorders.

REFERENCES

Buzsaki, G., Anastassiou, C. A., Koch, C., 2012. The

origin of extracellular ﬁelds and currents—EEG,

ECoG, LFP and spikes. Nature Reviews

Neuroscience, 13, 407-420.

Falk, J. L., 1961. Production of polydipsia in normal rats

by an intermittent food schedule. Science, 133, 195-

196.

Fan, D., Rich, D., Holtzman, T., Ruther, P., Dalley, J. W.,

et al., 2011. A Wireless Multi-Channel Recording

System for Freely Behaving Mice and Rats. PLoS

ONE 6(7): e22033.

Hooks, M. S., Jones, G. H., Juncos, J. L., Neill, D. B.,

Justice, J.B., 1994. Individual differences in schedule-

induced and conditioned behaviors. Behavioural Brain

Research, 60, 199-209.

Moreno, M., Flores, P., 2012. Schedule-induced

polydipsia as amodel of compulsive behaviour:

neuropharmacological and neuroendocrine bases.

Psychopharmacology (Berl), 219, 647–659.

Nestler, E. J., Hyman, S. E., 2010. Animal models for

neuropsyciatric disorders. Nature Neuroscience,

13, 1161–1169.

Venkatraman, S., Jin, X., Costa, R. M., Carmena, J. M.,

2010. Investigating neural correlates of behaviour in

freely behaving rodents using inertial sensors. Journal

of Neurophysiology, 104, 569-575.

Zarringhalam, K., et al. , 2012. An open system for

automatic home-cage behavioral analysis and its

application to male and female mouse models of

Huntington’s disease. Behav. Brain. Res., 229, 216–

225.