Analysis of Spontaneous MEG Activity in Mild Cognitive Impairment

using the Wavelet Turbulence

Jesús Poza

, Carlos Gómez

, María García

, Alberto Fernández

and Roberto Hornero

Biomedical Engineering Group, Dept. of Signal Theory and Communications, University of Valladolid, Valladolid, Spain

Departmento de Psiquiatría y Psicología Médica, Complutense University of Madrid, Madrid, Spain

Keywords: Mild Cognitive Impairment, Magnetoencephalogram, Continuous Wavelet Transform, Wavelet Turbulence,

Irregularity.

Abstract: Mild cognitive impairment (MCI) is usually considered a pre-clinical stage of Alzheimer’s disease (AD).

An appropriate characterization of MCI is crucial to achieve an early diagnosis of AD. Over the last few

years, much effort has been devoted to identifying new diagnostic tests; tough further research is still

required. In this study, we analyzed the spontaneous magnetoencephalographic (MEG) activity from 18

MCI subjects and 27 healthy controls to characterize the irregularity patterns in MCI. For that purpose, the

wavelet turbulence (WT) was calculated from the time-scale representation provided by the continuous

wavelet transform (CWT). Our results revealed that the mean values and the standard deviation of WT for

MCI subjects were significantly higher and lower (p < 0.05) than for controls, respectively. These findings

support the notion that MCI is associated with a significant decrease in irregularity and variability when

compared to normal aging. A Receiver Operating Characteristic (ROC) analysis with a leave-one-out cross-

validation procedure was applied to assess the diagnostic ability of WT. We obtained an accuracy of 66.7%

and an area under ROC curve of 0.704. We conclude that the WT extends the concept of irregularity and

provides potential descriptors of spontaneous MEG activity in MCI.

1 INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative

disorder that represents the leading form of dementia

in western countries (Blennow et al., 2006). In order

to optimize treatments, an intervention in early

stages is required, even before the appearance of the

first clinical symptoms (Reitz et al., 2011). For that

purpose, the understanding of mild cognitive

impairment (MCI) is a key point.

MCI is usually considered as an important risk

factor, since the probability of developing AD

among MCI subjects is about 5 to 10 times higher

than for healthy subjects (Petersen and Morris,

2003). No definite diagnostic test is currently

available to detect MCI (Albert et al., 2011).

Therefore, more research is required to address the

gaps in our understanding of MCI.

Scientific evidence suggests that MCI and AD

affect several regions in the cortex (Reitz et al.,

2011).Therefore, brain activity will be modified to

some extent. Electroencephalographic (EEG) and

magnetoencephalographic (MEG) recordings are

useful to describe abnormal neurooscillatory

activity. There exist some differences between EEG

and MEG oscillations. MEG recordings are

reference free and are less affected by the volume

conduction when compared to EEG rhtyms (Stam,

2010). Even some studies suggest that MEG might

be more sensitive to measure the cortical activity

than scalp EEG (Stam, 2010). As a consequence,

they reflect slightly different features (Rampp and

Stefan, 2007).

Several MEG studies have addressed the

characterization of spontaneous brain oscillations in

AD (Stam, 2010). Nevertheless, only a few studies

have focused on MCI. Spectral and non-linear MEG

studies found subtle differences between MCI and

control subjects (Bruña et al., 2012; Escudero et al.,

2011; Fernández et al., 2006, 2010; Gómez et al.,

2009). Their results showed that MCI subjects

exhibit intermediate abnormalities between AD

patients and elderly controls. However, further

efforts are needed to appropriately characterize

abnormal brain dynamics in MCI.

721

Poza J., Gómez C., García M., Fernández A. and Hornero R..

Analysis of Spontaneous MEG Activity in Mild Cognitive Impairment using the Wavelet Turbulence.

DOI: 10.5220/0004182107210726

In Proceedings of the 4th International Joint Conference on Computational Intelligence (SSCN-2012), pages 721-726

ISBN: 978-989-8565-33-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

In this study, we applied a new method to

characterize MEG rhythms in MCI. The proposed

parameter is based on the continous wavelet

transform (CWT), which provides an alternative

description to conventional spectral and non-linear

measures. Thus, the wavelet turbulence (WT) was

used to explore the irregularity of MEGs in terms of

the degree of similarity among adjacent wavelet

decompositions. The WT provides an alternative

way to characterize the irregularity in comparison to

previously applied spectral and non-linear methods.

Therefore, the aims of this study were: (i) to analyze

the irregularity patterns based on a new parameter,

(ii) to describe the abnormalities of MCI in

comparison to cognitive decline in normal aging,

and (iii) to introduce an alternative framework to

understand brain dynamics.

2 MATERIALS

2.1 Subjects

MEG signals from forty-five subjects were recorded

at the “Centro de Magnetoencefalografía Dr. Pérez-

Modrego” (Complutense University of Madrid,

Spain). Eighteen subjects (8 men and 10 women, age

= 74.9 ± 5.6 years, mean ± standard deviation M ±

SD) were MCI patients derived from the

“Asociación de Familiares de Enfermos de

Alzheimer”. Diagnoses were made following

Petersen’s criteria (Petersen et al., 2001). The

cognitive and functional deficits were assessed using

the Mini-Mental State Examination (MMSE) and the

Functional Assessment Staging (FAST). MCI

subjects obtained mean scores of 25.7 ± 1.8 and 3.0

± 0.0 on the MMSE and FAST, respectively. None

of these MCI patients suffered from any other

significant medical, neurological or psychiatric

disorder.

Twenty-seven healthy subjects (11 men and 16

women, age = 71.5 ± 6.2 years, M ± SD)

participated in the study as a control group. They

were cognitively normal elderly controls with no

history of neurological or psychiatric disorders.

Mean MMSE and FAST scores were 29.0 ± 1.2 and

1.6 ± 0.5, respectively.

None of the subjects was taking drugs that could

affect MEG activity at the recording time. Mean age

and gender were not significantly different for MCI

subjects and controls (p > 0.05, Mann-Whitney U-

test). All healthy volunteers and caregivers of

patients accepted to participate in the study and gave

their written informed consent. The research was

approved by the Research Ethics Committee of the

center.

2.2 Meg Recording

MEG signals were obtained using a 148-channel

whole-head magnetometer (MAGNES 2500 WH,

4D Neuroimaging, San Diego, CA), placed in a

magnetically shielded room in the “Centro de

Magnetoencefalografía Dr. Pérez Modrego”. During

recordings participants were asked to remain awake,

relaxed and with their eyes closed, in order to

minimize the presence of artifacts. Five minutes of

spontaneous MEG activity were acquired for each

subject with a sampling rate of 678.17 Hz. A 0.1-

200 Hz hardware band-pass filter and a 50 Hz notch

filter were applied. Each MEG recording was

downsampled by a factor of four to reduce the data

length. Artifact-free epochs of 10 s (26.6 ± 5.8

artifact-free epochs per channel and subject, M ±

SD) were selected for further analysis. Prior to

calculation of parameters, MEG signals were

processed using a finite impulse response (FIR) filter

designed with a Hamming window and cut-off

frequencies at 1 and 70 Hz.

3 METHODS

3.1 Continuous Wavelet Transform

Electromagnetic brain signals are non-stationary

biomedical recordings (Blanco et al., 1995). In order

to accurately characterize their spectral time-varying

properties, non-stationary signal analysis techniques

are required. There exist different time-frequency

representations, like the widely used short-time

Fourier transform (STFT). However,

electromagnetic brain signals show high frequency

and short time patterns, or low frequency and long

time oscillations (Figliola and Serrano, 1997).

Therefore, it should be appropriate to use time-

frequency representations with a variable time-

frequency resolution. This is the case of the wavelet

transform, which provides a good time resolution at

high frequencies and a good frequency resolution at

low frequencies (Figliola and Serrano, 1997).

In this study, the time-frequency (or time-scale)

maps were computed for each 10 s MEG epoch

(x(t)) using the CWT with the real Morlet wavelet

as,



),(





















 dt

skCWT



(1)

IJCCI2012-InternationalJointConferenceonComputationalIntelligence

722

where ψ(k) is the “mother” wavelet, s is the scaling

factor and * denotes the complex conjugate. The

wavelet analysis was carried out for scales [1:128] to

include the 1-70 Hz frequency range. Scales 1 and

128 were discarded from analysis, since they are out

of the considered bandwidth.

As previously mentioned, the “mother” wavelet

was the real Morlet wavelet. Previous EEG studies

have recommended the use of the complex Morlet

wavelet (Vialatte et al., 2011). Nevertheless, in this

preliminary analysis we selected the simplest Morlet

wavelet as a first step to characterize the spectral

content of MEG recordings. Its definition can be

read as,

 

,5cos

kek







(2)

Figure 1 illustrates the time-scale maps for a 10 s

MEG epoch from a healthy control (Figure 1.a) and

a MCI subject (Figure 1.b).

Usually, the wavelet power is computed instead

of the CWT coefficients, as a useful and intuitive

way to analyze the characteristics of a signal. The

so-called wavelet spectrum (WS) or scalogram is a

function that represents the distribution of wavelet

power in the time-scale map (Percival, 1995).

 

.,,

skCWTskWS 

(3)

3.2 Wavelet Turbulence

The WT is a parameter that quantifies the spectral

(or scale) changes over time (Kelen et al., 1991).

From

the time-scale map of the WS, the adjacent

wavelet power spectra are compared using the

correlation coefficients (Kelen et al., 1991; Barbosa

et al., 2006).







 





,,1,, skWSskWSkWT 





(4)

where



[] denotes the Kendall correlation between

WS(k,s) and WS(k+1,s). The mean (<WT>) and the

standard deviation (SD[WT]) of WT are calculated

from the time series formed by the Kendall

correlation coefficients (Kelen et al., 1991; Barbosa

et al., 2006). The mean summarizes the average

degree of similarity between the spectral content of

adjacent time slices, whereas the standard deviation

describes the lack of homogeneity in correlation

around the average value (Kelen et al., 1991).

3.3 Statistical Analysis

An exploratory analysis was initially performed to

study the data distribution. Variables did not meet

parametric test assumptions. Hence, non-parametric

Mann-Whitney U-tests were carried out to assess

statistical significance for each parameter ( = 0.05).

A Receiver Operating Characteristic (ROC)

analysis with a leave-one-out cross-validation

procedure (ROC-LOO-CV) was employed to study

diagnostic performance of the parameters.

Classification results were summarized in terms of

sensitivity (Sens.), specificity (Spec.), accuracy

(Acc.) and area under ROC curve (AUC).

The statistical and signal processing analyses

were performed using the software package Matlab

(version 7.8.0; Mathworks, Natick, MA).

Figure 1: Absolute wavelet coefficients for an epoch of 10 s obtained from the CWT using the Morlet wavelet (for scales:

[1:128]). (a) Healthy control; (b) MCI subject. CWT coefficients at scales 1 and 128 are zero, since MEG recordings have

been filtered between 1 Hz and 70 Hz.

AnalysisofSpontaneousMEGActivityinMildCognitiveImpairmentusingtheWaveletTurbulence

723

4 RESULTS AND DISCUSSION

We calculated the WS of the 10 s MEG epochs for

the 148 channels. The WT was then computed using

the WS. Finally, mean values (<WT>) and standard

deviations (SD[WT]) of the WT were calculated.

Statistical significance was assessed using Mann-

Whitney U-tests, whereas classification performance

was evaluated by means of a ROC-LOO-CV.

Detailed results for <WT> and SD[WT] values are

shown in Figure 2, where the differences in the

spatial distributions can be observed. Specifically,

the analyses showed a significantly higher <WT>

(right fronto-temporal region, p < 0.05) in MCI

subjects than controls. Likewise, SD[WT] displayed

significant decreases in the right frontal region (p <

0.05), though the differences were more localized

than for <WT>. These results suggest that MEG

background activity in MCI have a significantly

higher degree of similarity and lower variability in

the spectral content than in normal aging. Figure 1

illustrates this issue. Figure 1.a depicts how the

time-scale map from a healthy control is blurred or

distorted (mainly at scales [78:120]) in comparison

to that from a MCI subject (Figure 1.b). Due to the

fact that <WT> is as an indirect measure of the

irregularity in the signal, MCI can be associated with

an irregularity loss in the spontaneous MEG activity.

The decrease in irregularity has also been reported in

previous studies which analyzed spontaneous MEG

activity in MCI using spectral entropies, statistical

disequilibrium (Bruña et al., 2012) and non-linear

parameters (Fernández et al., 2010). Likewise,

similar spatial patterns of significant differences

were observed in a previous MEG study that

reported a lateralization of the reduction in spectral

entropies and statistical disequilibrium (Bruña et al.,

2012). The irregularity decrease can be linked to a

loss in frequency components and, as a consequence,

to a decrease in information content and processing

within the brain cortex (Baraniuk et al., 2003; Poza

et al., 2008a).

Classifications statistics using the ROC-LOO-

CV analysis for <WT> and SD[WT] are summarized

in Table 1. The highest AUC was achieved by <WT>

(0.704, AUC). It is noteworthy that the same

accuracy value (66.7%, Acc.) was obtained both by

<WT> (83.3%, Sens.; 55.6%, Spec.) and SD[WT]

(100.0%, Sens.; 44.4%, Spec.). MCI identification is

a crucial issue to establish an early AD diagnosis

(Morris, 2012). Classification rates about 65% have

usually been reported by previous MEG studies

(Bruña et al., 2012; Escudero et al., 2011; Fernández

et al., 2006, 2010; Gómez et al., 2009). Thus, it is

worth noting that our classification results are

comparable or even higher than those reported in

several MEG studies, though they did not use any

LOO-CV procedure (Fernández et al., 2006, 2010;

Figure 2: Spatial distribution of the parameters extracted from the WT and its significant difference between healthy

controls and MCI subjects. (a) <WT>; (b) SD[WT].

IJCCI2012-InternationalJointConferenceonComputationalIntelligence

724

Gómez et al., 2009). Certainly, the diagnostic

performance of the parameters could have been

evaluated using other classification strategies, such

as the division into independent training and test

sets. However, due to the limited size of the dataset,

a LOO-CV procedure might provide more general

results.

Table 1: Results of the ROC-LOO-CV analysis using the

averaged <WT> and SD[WT] for the significant channels.

Parameter

Sens.

(%)

Spec.

(%)

Acc.

(%)

AUC

<WT> 83.3 % 55.6 66.7 0.704

SD[WT] 100.0 44.4 66.7 0.698

Finally, a number of limitations merit further

consideration. In this study, the computation of the

WT was based on the CWT with a real Morlet

wavelet. However, other time-frequency

representations or other “mother” wavelets could

also be considered. Thus, the STFT has been

previously used to compute the so-called spectral

turbulence (ST), which is analogous to the WT. In

an early study, the ST was applied to characterize

MEG activity in AD (Poza et al., 2008b). In line

with our findings, their results support the notion

that a significant decrease in irregularity can be

observed in brain dynamics due to dementia

progression. Likewise, some researchers applied

complex-valued wavelets to describe EEG brain

dynamics (Vialatte et al., 2011). Further efforts

should be devoted to exploring the differences that

can arise in the characterization of MEG activity

using real- or complex-valued wavelets. It should be

appropriate to increase the cohort of subjects

enrolled in the study. Other neurodegenerative

disorders also exhibit similar abnormalities to those

reported in the present study. Further studies are

required to accurately characterize the specific

patterns associated to each disease. In addition, a

longitudinal analysis would be appropriate to

analyze the different patterns between MCI patients

that subsequently progress to AD and those who do

not develop AD.

5 CONCLUSIONS

In summary, our findings suggest that spontaneous

MEG activity in MCI is characterized by a

significant loss of irregularity, in terms of the degree

of similarity and variability in the spectral content.

The WT extend the concept of irregularity in

comparison to spectral and non-linear entropies.

Therefore, this parameter may lead to a better

understanding of the underlying brain dynamics in

MCI.

Future research will explore other definitions of

WT based on different “mother” wavelets and time-

frequency representations. Likewise, further efforts

will be addressed to analyze the WT patterns in

other dementias.

ACKNOWLEDGEMENTS

The authors would like to thank the “Asociación de

Familiares de Enfermos de Alzheimer” and the

Geriatric Unit of the “Hospital Clínico Universitario

San Carlos” for recruiting the subjects who

participated in this study.

This research was supported in part by: the

Ministerio de Economía y Competitividad and

FEDER under project TEC2011-22987; the

‘Proyecto Cero 2011 on Ageing’ from Fundación

General CSIC, Obra Social La Caixa and CSIC; and

project VA111A11-2 from Consejería de Educación

(Junta de Castilla y León).

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