Algorithm for Testing Behavioural Phenotypes in a Zebraﬁsh Model

of Parkinson’s Disease

Angela Pimentel

, Hugo Gamboa

1,2

, S

ergio Reis Cunha

and Ana Dulce Correia

CEFITEC, Physics Department, FCT-UNL, Lisbon, Portugal

PLUX - Wireless Biosignals, Lisbon, Portugal

Faculty of Engineering, Porto University, Porto, Portugal

Instituto de Medicina Molecular, Faculty of Medicine, University of Lisbon, Lisbon, Portugal

Keywords:

Parkinson’s Disease (PD), Zebraﬁsh, Behaviour, Biosensor MOBS, Machine Learning.

Abstract:

Parkinson’s disease (PD) is one of the neurodegenerative diseases with an increased prevalence widely studied

by the scientiﬁc community. Understanding the behaviour related to the disease is an added value for diag-

nosis and treatment. Thus the use of an animal model for PD that develops similar symptoms to the human

being allows to the clinic a larger vision over the health of a patient. Zebraﬁsh can be used to study some

human diseases including PD. This work describes the development of an algorithm for the characterization

of behaviour in this specie. The biosensor called Marine On-line Biomonitor System (MOBS) is connected

electrically to chambers where the specimen of zebraﬁsh moves freely providing a signal that is related with

the ﬁsh activity. Using the developed algorithm based on signal processing, statistic analysis and machine

learning techniques we present classiﬁcation of a ﬁsh as normal or ill and characterize its behaviour.

1 INTRODUCTION

Biosensors are an essential control and safety tool for

our environmental and health quality and commonly

used in medicine. Many of today’s biosensor applica-

tions use living organisms which respond to toxic sub-

stances or other stressors at a much lower level than

us to warn us of their presence. Under this scope,

the MOBS was developed, an automated system for

recording behavioural responses of marine and fresh

water species. This device has been applied success-

fully in the environmental ﬁeld, and the next challeng-

ing step is to bring this technology into other research

areas. In particular, by sensing behavioural changes

in organisms as an indication of stress or disease. A

suitable model candidate is the zebraﬁsh, a freshwater

specie which has been used in medical research dur-

ing the past years, e.g in development studies (Lepage

and Bruce, 2008), drug toxicity assessments (Usenko

et al., 2008) and neurodegenerative diseases (Bretaud

et al., 2004).

1.1 PD and Zebraﬁsh

The PD is characterized by tremor, muscle rigidity,

a slowing of physical movement, and can also cause

cognitive and mood disturbances. It results of the

loss of nerve cells in part of the brain known as the

substancia nigra. These cells are called dopaminer-

gic (DA) neurons as they produce the neurotransmit-

ter, dopamine, which is used to send messages to the

parts of the brain that co-ordinate movement (Fish for

Science, 2012). Most insights into human disease are

a result of experiments that would be unethical or un-

feasible to perform on humans. Instead biomedical

research uses models to look at the functions of the

genes involved in maintaining healthy organisms in

order to obtain vital clues about the causes and pro-

gression of human diseases. Zebraﬁsh are an ideal

model organism to bridge the gap between too simple

(yeast) and too complex (mice or rats). They are ver-

tebrates and have similar body plans (and similar tis-

sues and organs) to humans, and they’re much easier

and with reduced cost to breed than mice and rats. Ze-

braﬁsh mutations phenocopy many human disorders

and the genome sequence of zebraﬁsh is near com-

pletion. The DA nervous system in zebraﬁsh is well

characterized in both embryos and adult zebraﬁsh.

Some toxins known to induce DA cell loss in other

animal models have now also been tested in adult ze-

braﬁsh, as for example, the 6-hydroxydopamine (6-

196

Pimentel A., Gamboa H., Reis Cunha S. and Dulce Correia A..

Algorithm for Testing Behavioural Phenotypes in a Zebraﬁsh Model of Parkinson’s Disease.

DOI: 10.5220/0004238101960202

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 196-202

ISBN: 978-989-8565-36-5

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

OHDA) which is a neurotoxin that induces death of

the DA cells. After injecting the neurotoxin via intra-

muscular, locomotor activity and dopamine levels of

the brain decreases (Kalueff and Cachat, 2010; Mc-

Grath, 2012; Breese et al., 2005; Flinn et al., 2008).

Thus the evaluation of swimming behaviour can be

related with the loss of DA cells, and consequently

with the PD. In the work performed by (Correia, Ana

Dulce and Soares, Rui S. and Sousa, Sara and Out-

eiro, Tiago F. and Afonso, Nuno and Willemsen, Rob

and Herma van der Linde, 2012) a new transgenic line

of zebraﬁsh was developed to study the DA neurons,

which were validated with the use of the neurotoxin

6-OHDA and with the behaviour analysis using the

biosensor MOBS. They veriﬁed behavioural changes

that were related with the death of the DA neurons.

The algorithm to be developed can be a contribution

for this work: an algorithm that is sensible in the be-

haviour characterizations to allow the responses to be

comparable with the loss of the DA neurons.

1.2 Current Approach

The current algorithm used to characterize the be-

haviour of zebraﬁsh consists in the evaluation of a

speciﬁc locomotion behaviour, with a series of bursts

in the domain of MOBS corresponding to the tail-

ﬂip activity of zebraﬁsh. Thus the outcome reﬂects

the number of tail-ﬂips per minute per individual ﬁsh

(Correia et al., 2011). The behaviour detection is

based on the derivative peaks resulted from the strong

bursts in the signal. However, these peaks require a

threshold for the behaviour detection, and this is ac-

complished using the standard deviation multiplied by

a factor so that these two parameter, standard devia-

tion and derivative, may be comparable. It’s essential

to conﬁrm if the current algorithm is in fact detecting

the right behaviour, the tail-ﬂips. The ﬁrst intention

of this research would be to understand and improve

the current algorithm, however it will be proved the

need to create a new one using supervised learning.

1.3 Supervised Learning

By Arthur Samuel (1959), machine learning is the

ﬁeld of study that gives computers the ability to learn

without being explicitly programmed. There are dif-

ferent types of machine learning algorithms, the main

two types are: unsupervised and supervised learning.

With supervised learning, the scheme operates un-

der supervision by being provided with the actual out-

come for each of the training examples. In this type

of machine learning is included regression problems

that predicts continuous valued outputs and classiﬁ-

cation problems which intends to predict discrete val-

ued outputs (Machine Learning, 2012). For classiﬁca-

tion problems, a known method is the Support Vector

Machine (SVM) which looks for the optimal hiper-

plane between two classes by maximizing the mar-

gin. A non-linear separator is possible by projection

the data points to higher-dimension space to become

linearly separable (projection with kernel techniques)

(Machine Learning, 2012). Also the method Na

ıve

Bayes which applies Bayes theorem to estimate the

probability with the ”na

ıve” assumption of indepen-

dence between each feature. For validation, a possi-

ble statistic test is leave one out, which given a dataset

of m instances, only one instance is left out as the val-

idation set (instance) and training uses the m − 1 in-

stances (Witten et al., 2011).

2 METHODS

2.1 MOBS

The main device is controlled via an USB port by ex-

ternal processing software which produces signals in

the digital domain (at 48000 samples/s or 48 kHz).

These are converted by the main device into analogi-

cal electrical signals, power ampliﬁed and transmitted

to the independent testing units at which they are con-

ducted into the water by a pair of non-invasive stain-

less steel electrodes. In response to the behavioural

signatures of the organisms as a change in impedance

of the water, the amplitudes of the electrical signals

are modulated and then received by a second pair

of electrodes. In the main device they are ampliﬁed

and converted back to the digital domain at 48000

samples/s, before ﬁltered, demodulated and down-

sampled at 100 Hz by the external computer soft-

ware. Upon processing, the system provides a signal

in the frequency band of 0.2 Hz to 40 Hz that is corre-

lated with the ﬁsh activity (Cunha et al., 2008). With

MOBS, locomotion can be presented with a series of

bursts in the time domain, and can cover a broad fre-

quency spectrum, at which ventilation is occasionally

present. Typically ventilation generates waves of tri-

angular shape with a higher frequency and smaller

amplitude than the most of the energy located for lo-

comotion. However ventilation will not be studied

with zebraﬁsh given its high level of activity.

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2.2 Experimental Design

2.2.1 Test Animals and 6-OHDA

The zebraﬁsh (D. rerio Hamilton 1822) strain used for

this work was the AB line (Zebraﬁsh Facility, IMM,

Portugal). Animals were maintained under standard

conditions and experiments were approved by the In-

stitutional Animal Care and Use Committee. A mas-

ter stock solution of 6-hydroxydopamine hydrochlo-

ride (6-OHDA, Sigma-Aldrich, USA) was prepared in

0.2% ascorbic acid solution (analytical grade, Sigma)

and stored at -20

◦

2.2.2 Behaviour Assay

Before the experiments, small groups of female ﬁsh

(24 animals, body weight 0.5 ± 1 g) were acclima-

tized to the experimental testing conditions (temper-

ature 22

◦

C ± 1

◦

C, 10 h:12 h light-dark cycle) in

17 litre glass aquaria under static conditions and for

a minimum of one week. Food was not provided 24

h before or during the experiments. The behaviour

analysis was divided in two groups: non-treated (12

ﬁsh) and for that considered as normal ﬁsh in which

no injection was administered, and treated (12 ﬁsh)

also considered as ill or less active where 5µL of 6-

OHDA (33 mg/kg) was injected via intramuscular.

During the injection they were in a medium-to deep-

plane level of anaesthesia (tricaine 50 mg/L) and had

lost their reﬂex responses and muscular control. Af-

terwards they returned to their original test chambers

and allowed 30 min to recover from the anaesthesia.

On the day of experiments, either the treated or non-

treated groups of ﬁsh were placed individually in the

test chambers (22

◦

C ± 1

◦

C) and acclimated for 30

min. Then individual baseline responses were mon-

itored using MOBS and recorded using video (prop-

erty of 25 frames per second) for ﬁve minutes between

10 and 12 a.m. After behavioural recording, treated

ﬁsh were sacriﬁced with tricaine.

2.3 Synchronism

The signal in the time domain is delayed in relation

to the instant of acquisition start. This delay is caused

by the main device, which makes it difﬁcult to com-

pare a video where the ﬁsh movements are present,

with its respective signal from MOBS. The Open Sig-

nals is a platform designed and programmed by PLUX

- Wireless Biosignals. Using this platform, synchro-

nism is possible with a visible stimulus in the signal

and video. This stimulus must be sufﬁcient to not be

confused with the ﬁsh activity in the signal. A touch

in the chamber is a possible stimulus and to not cor-

rupt the signal from the ﬁsh activity for further analy-

sis the stimulus should be produced at the end of the

recording.

2.3.1 Visual Analysis

To verify what the algorithm is detecting a detailed

analysis using Open Signals was necessary after syn-

chronism. This analysis using the video frame by

frame consisted in the detection of the behaviour tail-

ﬂip. The tail-ﬂip is characterized as an abrupt and

fast change of direction implying a strong burst in the

tail. The visual analysis will consist in counting the

number of tail-ﬂips detected and divide it by the total

time in minutes. Since the visual analysis is a long

process, 24 study cases were made, 12 of them were

non-treated and the rest were submitted to the drug

6-OHDA. Each visual analysis consisted in 3 minutes

of the video. Since the visual analysis depends of the

user that is interpreting the data, it’s important to test

other user and compare the results. A visual test us-

ing a different user was made. The test consisted in

a precise analysis frame by frame using a signal with

30 seconds, and for this time both users detected 46

abrupt tail-ﬂips. After the User 1 detect the abrupt

tail-ﬂip it was considered an interval of 0.25 seconds

in which the User 2 had also to detect the same abrupt

tail-ﬂip to be a valid success.

2.4 Current Algorithm Evaluation

In this subsection is intended to compare the visual

analysis with the algorithm result using linear regres-

sion for each group (treated and non-treated) and esti-

mate the relative error with the leave one out method.

This was chosen because the number of points anal-

ysed is small. Also in consideration is the correlation

coefﬁcient which is a numerical value that indicates

the degree and direction of relationship between two

variables (O’Toole, 2006). The relative error obtained

will show the need to improve the algorithm.

The multiplicative factor in the current algorithm

is used so that the derivative can be comparable to

the standard deviation thus allowing to detect the be-

haviour abrupt tail-ﬂips. Given that, to improve the al-

gorithm the multiplicative factor should be analysed.

The value used so far has been 0.1. To understand

which is the best factor value, it was decided to vary

the factor according to the outcome of the algorithm,

and with the visual analysis choose the factor that was

closer to reality. A unique study case isn’t sufﬁcient

to choose the ideal factor, thus with all data analysed

for each group it’s estimated the average relative error

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from the current algorithm result with the visual anal-

ysis. In the end we chose the factor that minimizes

the relative error.

2.5 New Algorithm

2.5.1 Behaviour Characterization

To characterize the behaviour in number of tail-ﬂips

per minutes it was necessary to use the parameter zero

crossing rate. The zero crossing rate it is deﬁned as

the number of time-domain zero crossings within a

deﬁned region of signal, divided by the number of

samples of that region (Gouyon et al., 2000). Each

data was divided by its standard deviation, so that all

data is at the same scale to be comparable and be-

cause the signal is centred at zero, it wasn’t necessary

to subtract the average. Also the signal was smoothed

using a Hanning window of 0.05 seconds. To vali-

date this parameter it was used the statistic analysis

leave one out. This was chosen because the number

of points analysed is small. This study also consid-

ered the correlation coefﬁcient.

2.5.2 Classiﬁcation

The Orange is a software suitable for machine learn-

ing. It is a free software and open source. It allows

to use data mining through visual programming and

Python scripting (Curk et al., 2005). The classiﬁer

was studied with the methods SVM and Na

ıve Bayes.

The validation used the statistic analysis leave one out

to provide the accuracy for each method used (SVM

or Naive Bayes) which is the proportion of correctly

classiﬁed examples (Curk et al., 2005). Thus varying

the number of parameters obtained from the data we

choose the ones that give higher accuracy for the re-

spective method. The parameters extracted from each

data were the zero crossing rate, the standard devia-

tion, the maximum power using the periodogram, the

maximum number of occurrences using the histogram

and the current algorithm output. Also the optimal

values for the SVM, namely the Cost parameter and

the gamma value for the kernel function were chosen

by the Orange software which uses the LIBVSM li-

brary. Since the classiﬁer doesn’t require the visual

analysis as output, which is a long process, instead

of using the data obtained so far (24 study cases), it

was used data from a previous work to provide more

points to the classiﬁer (108 study cases with equal

number for each class). This work developed at the

Instituto de Medicina Molecular provides data with

non-treated and treated ﬁsh (submitted to the drug 6-

OHDA).

3 RESULTS AND DISCUSSION

3.1 Synchronism

3.1.1 Visual Analysis

In 46 detections between both users, 44 were ac-

cepted, leading to an error of 4.35%. The agreement

between the users characterizing the behaviour, leads

that the visual result can be a valid information to be

compared with the current algorithm or with future

works.

3.2 Current Algorithm Evaluation

We can now compare the algorithm output with the

visual analysis. The results are shown in ﬁgure 1. It

Figure 1: Comparison between the visual analysis and the

algorithm result.

is visible that there is no direct relation between the

visual analysis and the algorithm output as it would

be expected both for treated and non-treated ﬁsh. Af-

ter applying linear regression in each group it was es-

timated the relative error with the method leave one

out which resulted in an error of 17.29% for the non-

treated and 25.31% for treated. Also the correlation

coefﬁcient obtained was 0.20 and 0.76 for the non-

treated and treated respectively which can be con-

sidered as a poor relation between the visual anal-

ysis with the algorithm output. These errors imply

an improvement in the algorithm, more speciﬁcally

in the multiplicative factor. To choose the best fac-

tor it was decided to study the error associated with

the visual analyse. Figure 2 indicates the minimum

error accepted as well as the error used with the ac-

tual factor for the treated and non-treated ﬁsh. The

error using the actual factor is 55.26% and 68.79%

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199

Figure 2: Relative error in percentage. Black dotted lines:

Actual multiplicative factor (0.1); Red dotted lines: Best

multiplicative factor for treated; Blue dotted lines: Best

multiplicative factor for non-treated.

for non-treated and treated respectively, and even im-

proving the factor, the minimal error accepted would

be 53.20% for non-treated which leads to a best fac-

tor of 0.11 and 44.53% for treated with a best factor of

0.13. To be able to choose the best factor these errors

obtained should be as close to zero as possible which

indicates that even with these improvements the best

multiplicative factor cannot be certain. Therefore, and

considering that the visual analysis is a valid measure,

it is suggested the development of a new algorithm.

3.3 New Algorithm

With the visual analysis it will be possible to study

new parameters using supervised learning, more pre-

cisely, regression models.

3.3.1 Behaviour Characterization

Figure 3 shows visually that there is a linear ten-

dency between the zero crossing rate results with the

visual analysis both for treated and non-treated ﬁsh.

Considering ﬁrst the normal ﬁsh for validation, it was

used the statistic analysis leave one out. The result

leaded to a error of 2.55%. The relative error of

2.55% compared with the 17.29% from the previous

algorithm can be considered as an excellent improve-

ment. The user test from the previous section showed

an error of 4.35%. Given that, the reason why this

parameter shows a smaller error (2.55%) it’s because

it suits the user that performed this analysis. If User

2 had also performed this analysis, it should be ex-

pected a bigger error. The correlation coefﬁcient ob-

tained in this case was 0.99, indicating that there is a

very good positive relation between the zero crossing

Figure 3: Comparison between the zero crossing rate with

the visual analysis for normal and ill ﬁsh with a window of

180 seconds.

rate and the visual analysis. Finally using all points

for a window of 180 seconds, linear regression can be

applied to deﬁne our hypothesis:

(x) = 15.42 +26.43x (1)

To characterize the behaviour for ill ﬁsh, Figure 3

shows that this group presents an inverse linear ten-

dency between the zero crossing rate and visual analy-

sis, which means that the higher the number of counts

per second the less active the ﬁsh is. Again it was used

the leave one out method to validate this parameter.

The relative error obtained was 5.75% which can be

a good estimative even thought it’s higher than the er-

ror obtained to characterize normal ﬁsh (2.55%). This

error in comparative to the 25.31% from the previ-

ous algorithm can also be considered as an excellent

improvement. The correlation coefﬁcient was −0.99,

meaning there is a very good inverse relation between

the visual analysis and zero crossing rate.

Using all points for a window of 180 seconds lin-

ear regression can be applied to deﬁne our hypothesis:

(x) = 47.45 − 11.65x (2)

The value of 47.45 tail-ﬂips per minute limits the ﬁsh

activity, which means that ill ﬁsh won’t show a higher

value of activity than 47.45 tail-ﬂips per minute. Also

for a ﬁsh that doesn’t present any activity (0 tail-

ﬂips per minute) it should be expected a value of

4.07 counts per second. Since both groups use differ-

ent equations to characterize the behaviour, to know

which equation to use for the development of this al-

gorithm a classiﬁer is needed to distinguish between

normal or ill ﬁsh.

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3.3.2 Classiﬁcation

Now our output is deﬁned by two classes: normal and

ill ﬁsh. The parameters used that leaded to a higher

accuracy for the SVM were the zero crossing rate, the

standard deviation, the maximum power using the pe-

riodogram, the maximum number of occurrences us-

ing the histogram, and the previous algorithm output.

The learning options used were for the kernel func-

tion the Sigmoid function (tanh(8 ∗ x.y)), a Cost of

2.0 (Model Complexity - penalty parameter) and a

numeric precision of 0.001. The accuracy obtained

using leave one out for the SVM method was 100%,

meaning that all cases analysed were correctly clas-

siﬁed. On the other hand, the Naive Bayes method

based on the relative frequency presented a maximum

accuracy of 67.59% using the parameters standard de-

viation, the maximum power using the periodogram

and the previous algorithm output.

As we want to choose the classiﬁer that predicts

the classes with a higher accuracy value we choose

the method SVM to build our ﬁnal classiﬁer. Because

the Orange program is open source, with the access to

the functions that build the classiﬁer SVM we can use

them to construct the ﬁnal algorithm in python.

3.3.3 Final Algorithm

Now it’s possible to built the ﬁnal algorithm. First we

prepare the data with the removal of the initial peak

from the main device, the application of a ﬁlter to ex-

clude possible noise, the normalization of the data and

the smooth of the signal using a Hanning window of

0.05 seconds. Then we use the classiﬁer to predict if

the ﬁsh is normal or ill. Consequently, according to

the classiﬁcation it’s possible to characterize the be-

haviour in number of tail-ﬂips per minute using the

corresponding hypothesis that consists in the use of

the parameter zero crossing rate. The ﬁnal result will

present the classiﬁcation, the probability for that clas-

siﬁcation, and the number of tail-ﬂips per minute.

4 CONCLUSIONS

A new algorithm was developed to classify and char-

acterize the behaviour of zebraﬁsh. To facilitate its

use, the algorithm should be integrated in the plat-

form Open Signals. The fact that this algorithm uses

classiﬁcation can be an advantage as it may bring an

efﬁcient separation between a healthy ﬁsh from one

that has been genetically modiﬁed to have PD. Also,

the algorithm should be applied in a case study as ex-

ecuted by (Correia, Ana Dulce and Soares, Rui S. and

Sousa, Sara and Outeiro, Tiago F. and Afonso, Nuno

and Willemsen, Rob and Herma van der Linde, 2012),

to verify that the responses are in agreement with the

ﬁsh behaviour and literature. This algorithm may be

useful for further studies not only related with PD, but

any other that uses zebraﬁsh behaviour as an end point

to study human diseases.

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