Nonlinear Dynamic Analysis of the EEG in Patients.10

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Nonlinear Dynamic Analysis of the EEG in Patients with

Alzheimers Disease and Vascular Dementia

*Jaeseung Jeong, JeongHo Chae, Soo Yong Kim, and SeolHeui Han

*Department of Diagnostic Radiology, School of Medicine, Yale University, New Haven, Connecticut, U.S.A.; Department of

Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Korea; Department of Physics, Korea Advanced

Institute of Science and Technology, Taejon, Korea; andDepartment of Neurology, Chungbuk National University

Hospital, Korea

Summary: To assess nonlinear EEG activity in patients with Alzheimers disease(AD) and vascular dementia (VaD), the authors estimated the correlation dimension(D2) and the first positive Lyapunov exponent (L1) of the EEGs in both patients andage-matched healthy control subjects. EEGs were recorded in 15 electrodes from 12AD patients, 12 VaD patients, and 14 healthy subjects. The AD patients had signifi-cantly lower D2 values than the normal control subjects, (P H 0.05), except at theF7 and the O1 electrodes, and the VaD patients, except at the C3 and the C4 electrodes.The VaD patients had relatively increased values of D2 and L1 compared with the ADpatients, and rather higher values of D2 than the normal control subjects at the F7, F4,F8, Fp2, O1, and O2 electrodes. The L1 values of the EEGs were also lower for theAD patients than for the normal control subjects, except in the O1 and the O2 channels,and for the VaD patients at all electrodes. The L1 values were higher for the VaDpatients than for the normal control subjects (F3, F4, F8, O1, and O2). In addition, theauthors detected that the VaD patients had an uneven distribution of D2 values over theregions than the AD patients and the normal control subjects, although the statistics do

not confirm this. By contrast, AD patients had uniformly lower D2 values in mostregions, indicating that AD patients have less complex temporal characteristics of theEEG in entire regions. These nonlinear analyses of the EEG may be helpful inunderstanding the nonlinear EEG activity in AD and VaD. Key Words: AlzheimersdiseaseVascular dementiaEEGCorrelation dimensionLyapunov exponent.

Two of the most common kinds of dementia in the

elderly are Alzheimers disease (AD) and vascular de-

mentia (VaD). Although AD is a leading cause of de-

mentia in the Western world, VaD prevails in Asian

countries (Jorm, 1991; Kase, 1991). It is important todiagnose AD and VaD accurately, because this enables

the clinician to provide demented patients and their

families with a more reliable prediction of the diseases

course, and it facilitates planning for necessary social

resources. However, the differential diagnosis is compli-

cated by the fact that a co-occurrence of AD and stroke

is common. There have been substantial efforts to dif-

ferentiate AD from VaD using various diagnostic mo-

dalities, including neuropsychological tests (Bowler et

al., 1997; Sultzer et al., 1993; Swanwick et al., 1996),neuroimaging techniques (Mielke et al., 1994; OBrien et

al., 1997), quantitative EEG (Dunkin et al., 1994; Si-

gnorino et al., 1995; Szelies et al., 1994), and transcranial

Doppler sonography (Ries et al., 1993).

Quantitative EEG analyses have been used in attempts

at differential diagnosis in dementia studies. A higher

incidence of focal abnormalities in VaD has been ob-

served repeatedly (Erkinjuntti et al., 1988; Soininen et

al., 1982). Progressive deterioration of the background

Address correspondence and reprint requests to Dr. Jaeseung Jeong,P.O. Box 208042, 333 Cedar Street, Department of Diagnostic Radiology,School of Medicine, Yale University, New Haven, CT 06520.

Journal of Clinical Neurophysiology18(1):5867, Lippincott Williams & Wilkins, Inc., Philadelphia 2001 American Clinical Neurophysiology Society

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rhythms and sequential changes in serial studies are usually

helpful in confirming the existence of a progressive degen-

erative dementia (Markand, 1984). Spectral analysis of the

EEG has shown an increased power of the lower frequency

bands and a decrease of high frequencies in AD patients

(Brenner et al., 1988; Coben et al., 1985, 1990; Hooijer etal., 1990; Penttil et al. 1985; SchreiterGasser et al., 1993;

Soininen et al., 1991). A positive linear relationship be-

tween slowing of the average EEG frequency and the

degree of cognitive impairment has been reported in AD

patients (Coben et al., 1985). Signorino et al. (1995, 1996a,

b) and others (Pucci et al., 1998) showed that EEG spectral

parameters can offer enough data to discriminate between

the subtypes of AD and VaD. EEG coherence studies

demonstrated that long-distance coherence seems to be

more affected than local coherence in AD patients, whereas

the opposite occurs in VaD patients (Comi et al., 1998;

Leuchter et al., 1992). In AD patients, local changes of

coherence of the band have been reported to affect

selectively the left temporoparietaloccipital areas

(Leuchter et al., 1992; Locatelli et al., 1998) or the anterior

areas (Besthorn et al., 1994).

Recent progress in the theory of nonlinear dynamics

has provided new methods for the study of time-series

data from human brain activities. Babloyantz et al.

(1985) first reported that EEG data from the human brain

had chaotic attractors for sleep stages II and IV. Much

research using nonlinear methods revealed that the EEG

is generated by a deterministic neural process (Babloy-

antz, 1988; Rapp et al., 1985; Rochke and Basar, 1988;

Soong and Stuart, 1989). According to these reports, theEEG has a finite correlation dimension (D2) and a

positive Lyapunov exponent (L1). Furthermore, distinct

states of brain activity produce different chaotic proper-

ties quantified by nonlinear invariant measures such as

D2 and L1 (Babloyantz, 1988; Babloyantz and Destexhe,

1987; Fell et al., 1993; Pijn et al., 1991; Rochke and

Aldenhoff, 1991; Wackermann et al., 1993).

By contrast, there is some evidence that an EEG is not

a chaotic signal of low dimension (Osborne and Proven-

zale, 1989; Palus, 1996; Pritchard et al., 1995; Rapp et

al., 1993; Theiler and Rapp, 1996; Theiler et al., 1992).

Research has shown that the normal resting human EEGis nonlinear but does not represent low-dimensional

chaos, and it may be generated from 1/f-like linear

stochastic systems. Our previous study also failed to

detect any determinism in the EEG with the smoothness

method (Jeong et al., 1999).

Although no compelling evidence for a deterministic

nature of the EEG has been adduced, nonlinear dynamic

analyses of the EEG to estimate D2 and L1 have proved to

be useful in differentiating normal and pathologic brain

states (Rapp, 1993). Many studies using nonlinear methods

presented possible medications (Lehnertz and Elger, 1998)

for nonlinear analysis and the possibility that nonlinear

analysis of the EEG may be a useful tool in differentiating

physiologic brain states (Babloyantz and Destexhe, 1986,

1987; Fell et al., 1995; Frank et al., 1990; Jeong et al.,1998a, b; Kim et al., 2000; Lehnertz and Elger, 1998;

Pritchard et al., 1994).

There are several studies of the EEG in AD patients

using nonlinear methods. D2 and L1 from EEG data

were estimated (Besthorn et al., 1995; Jelles et al., 1999;

Jeong et al., 1998a; Pritchard et al., 1991, 1993, 1994;

Stam et al., 1995, 1996). Their results indicated that AD

patients had significantly lower values in D2 and L1 than

age-matched normal subjects, indicating that the dy-

namic processes underlying the EEG recording are less

complex for AD patients than for normal subjects.

The aim of the current study was to examine nonlinear

EEG activity in AD and VaD, and to compare the

nonlinear properties of the EEGs in AD and VaD. D2

and L1 were estimated from the EEGs with the optimal

embedding dimension. We regarded these nonlinear pa-

rameters as operationally defined measures of complex-

ity. They may not be appropriate measures for differen-

tiating between periodic, chaotic, or stochastic dynamics

in the formal sense. Because this study focuses on

comparing nonlinear EEG activity in AD and VaD, we

do not use the surrogate-data method to detect nonlin-

earity and determinism of the EEG.

METHODS

One-dimensional EEG data were transformed into

multidimensional phase space. The concept of phase

space is central to the analysis of nonlinear dynamics. In

a hypothetical system governed by n variables, the phase

space is n-dimensional. Each state of the system corre-

sponds to a point in phase space with n coordinates that

are the values assumed by the governing variables for

this specific state. If the system is observed for a period

of time, the sequence of points in phase space forms a

trajectory. This trajectory fills a subspace of the phase

space, called the systems attractor.Reconstruction of the attractor in phase space was

carried out through the technique of delay coordinates.

To unfold the projection back to a multivariate state

space that is a representation of the original system, we

use the delay coordinates y(t) [xj(t), xj(t T), . . . , xj(t [d 1]T)] from a single time series xj and perform

an embedding procedure, where y(t) is one point of the

trajectory in the phase space at time t, x(t iT) are the

coordinates in the phase space corresponding to the

59NONLINEAR DYNAMIC ANALYSIS OF EEG IN AD AND VAD

J Clin Neurophysiol, Vol. 18, No. 1, 2001


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time-delayed values of the time series, T is the time delay

between the points of the time series considered, and dis

the embedding dimension (Eckmann and Ruelle, 1985;

Takens, 1981).

The choice of an appropriate time delay T and embed-

ding dimension d are important to the success of recon-

struction with finite data. For the time delay T, we used the

first local minimum of the average mutual information

between the set of measurements v(t) and v(t T) in the

current study. Mutual information measures the general

dependence of two variables (Fraser and Swinney, 1986).We used the minimum (optimal) embedding dimension

in the reconstruction procedure (Kennel et al., 1992). The

algorithm is based on the idea that in the passage from

dimension d to dimension d 1, one can differentiate

between points on the orbit that are true neighbors and those

that are false. A false neighbor is a point in the dataset that

is a neighbor solely because we are viewing the orbit (the

attractor) in too small an embedding space (d dmin).

When we have achieved a large enough embedding space

(d dmin), all neighbors of every orbit point in the multi-

variate phase space will be true neighbors.

We defined the embedding rate as the ratio of trueneighbors to neighbors in the embedding dimension.

Figure 1 shows a typical example of the embedding rate

as a function of the embedding dimension for 16,384

EEG data points in a normal control subject. The proper

minimum embedding dimension was selected as 11 in

this case. The detailed algorithm was reported in our

previous paper (Jeong et al., 1998a). We also showed the

increase in the efficiency and accuracy of our method

relative to the old one.

One of the important mathematical quantities charac-

terizing an attractor is its correlation dimension (D2),

which is a metric property of the attractor that estimates

the degree of freedom. It determines the number of

independent variables that are necessary to describe the

dynamics of the original system. For instance, in the case

of steady-state behavior, D2 of the attractor is zero and

D2 of the periodic attractor is one. In chaotic states, D2

usually takes on noninteger values. The larger the D2

value of the attractor, the more complicated the behavior

of the nonlinear system. D2 is thus a measure of thecomplexity of the process being investigated, and it

characterizes the distribution of points in phase space

(Hornero et al., 1999).

We evaluate the D2 values of the attractors from the

EEG using the GrassbergerProcaccia algorithm (Grass-

berger and Procaccia, 1983). With this algorithm, D2 is

based on determining the relative number of pairs of

points in the phase-space set that are separated by a

distance less than r. It is computed from the following

D2 limr3O

limN3

log C(r, N)

log r

(1)

where the correlation integral C(N, r) is defined by the

C(r)1

N2 i,j1ij

N

(r x3

i x3

j ) (2)

where xi and xj are the points of the trajectory in the

phase space, N is the number of data points in the phase

space, the distance r is a radius around each reference

FIG. 1. The embedding rate as afunction of embedding dimension for16,384 EEG data points at T4 in anormal control subject. The optimalminimum embedding dimension forcalculating D2 and L1 was 11 in thiscase.

60 J. JEONG ET AL.



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dynamics. This more detailed algorithm is also reported

in a previous paper (Jeong et al., 1998a).

A consecutive series of patients with AD and VaD

who attended the Department of Neurology, Chungbuk

National University Hospital, Korea, were screened for

inclusion in this study. All patients had undergone athorough clinical evaluation that included clinical his-

tory, physical and neurologic examinations, routine lab-

oratory tests, electrocardiogram, EEG, and brain MRI.

Twelve AD patients (four men and eight women;

mean age, 68.7 5.1 years) met the criteria for AD as

mandated in the Diagnostic and Statistical Manual of

Mental Disorders, 4th edition (American Psychiatric

Association, 1994), and probable AD established by the

National Institute of Neurologic Disorders and Stroke

Association and Alzheimers Disease and Related Dis-

orders Association (McKhann et al., 1984). The probable

AD patients had a Mini-Mental State ExaminationKo-

rean version (MMSE-K) score (Kwon and Park, 1989) of

less than 12 points (mean MMSE-K score, 9.2 3.5

points), indicating a severe degree of dementia. The

average age at onset of dementia was approximately 64.9

3.1 years, and the average length of illness was

approximately 46.0 7.3 months.

Twelve VaD patients (four men and eight women;

mean age, 67.9 4.9 years) met the National Institute of

Neurologic Disorders and Stroke AssociationInternatio-

nale pour la Recherche et lEnseignement en Neuro-

sciences criteria (Roman et al., 1993). The patients with

VaD also had MMSE-K scores less than 12 points (mean

MMSE-K score, 9.9 4.2 points). Additionally, theyeither had experienced the onset of cognitive impairment

after a clinical stroke or had shown clear focal neurologic

signs on examination. They had a Hachinski ischemic

score of more than 7 points. MRI revealed large-vessel

stroke, multiple subcortical lacunar infarcts, and/or ex-

tensive white matter lesions in each VaD patient. Al-

though it is impossible to be certain that no patients had

coexisting AD and VaD, patients with evidence of both

disorders were excluded.

Fourteen age-matched, healthy, elderly control sub-

jects (five men and nine women; mean age, 66.9 5.0

years) were recruited from a senior community club inTaejon, South Korea. The control subjects had a mean

score of 27.1 0.7 points on the MMSE-K.

All subjects were excluded from each group if there

was a history of psychotic disorder unrelated to demen-

tia, a history of head trauma with loss of consciousness,

a psychoactive substance use disorder, a systemic illness,

or other neurologic illness that could account for the

cognitive impairment. The local ethics committee ap-

proved the study. All subjects and all caregivers of the

demented patients gave informed consent for participa-

tion in the current study.

EEGs were recorded from the 15 scalp loci of the

International 1020 System. The EEG reading from the

T5 channel was not recorded because of a hardware

problem. With the subjects in a relaxed state with theireyes closed, 32,768 seconds of data (16,384 data points)

were recorded with a sampling frequency of 500 Hz,

digitized by a 12-bit analogdigital converter in an IBM

personal computer. Recordings were made under the

eyes-closed condition to obtain as many stationary EEG

data as possible. Potentials from 15 electrodes (F7, T3,

Fp1, F3, C3, P3, O1, F8, T4, T6, Fp2, F4, C4, P4, and

O2) against linked earlobes were amplified on a Nihon

Kohden EEG-4421K using a time constant of 0.1 second.

All data were filtered digitally to remove residual elec-

tromyographic activity at 1 to 35 Hz. Each EEG record

was judged by inspection to be free from electro-oculo-

graphic and movement artifacts, and to contain minimal

electromyographic activity.

All analyses were carried out using the Statistical

Package for the Social Sciences (SPSS version 7.0;

SPSS, Inc., Cary, NC USA) for Windows. One-way of

analyses of variance (SPSS General Linear Models mod-

ule) were used to test group-specific effect. If significant

effects between groups were found, the effects were

analyzed additionally using Duncans post hoc test. The

results of group data are expressed as mean standard

deviation. A two-tailed probability of less than 0.05 was

considered to be significant.

RESULTS

During the reconstruction procedure, time delays of 34

to 50 msec and embedding dimensions of 11 to 18 were

used for the AD patients, and time delays of 26 to 46

msec and embedding dimensions of 13 to 19 were used

for the VaD patients. The normal control subjects had

time delays of 28 to 34 msec and embedding dimensions

of 11 to 19.

The average D2 values and standard deviations for the

AD patients, VaD patients, and normal control subjects

for the 15 electrodes identified earlier are summarized inTable 1, which shows that each group had distinctly

different D2 values in all electrodes. The AD patients

had significantly lower D2 values than the normal sub-

jects except at the F7 and O1 electrodes, and lower than

the VaD patients except at the C3 and C4 electrodes. The

differences between the D2 values in the F4, F8, Fp1,

Fp2, O1, and O2 channels were approximately 1.5 to 2.2

U. The VaD patients had significantly higher D2 values

than the normal control subjects in six channels (F7, F4,

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F8, Fp2, O1, and O2). In addition, the VaD patients had

a less uniform distribution of D2 values over the regions

than the AD patients and the normal control subjects,

although statistics do not confirm this (Fig. 3).

Another nonlinear measure, L1, was calculated for all

subjects at all electrodes. The evolving time was selected

using the 1/e spectral frequency, and was approximately180 to 240 msec. The calculation of L1 depended on the

time over which the trajectory was evaluated. After 200

propagation steps, the values converged in an interval of

0.92% around the final value of L1.

The average L1 values and standard deviations for all

subjects at all electrodes are summarized in Table 2. It

shows that the average L1 values were higher for the

normal control subjects than for the AD patients (F7, T3,

Fp1, P3, F8, T4, T6, Fp2, C4, P4, P 0.01; F3, C3, F4,

P 0.01), just as the D2s were. The L1s of the AD

patients in channels O1 and O2 were not significantly

different from those of the normal control subjects.

The differences between the values of L1 in the F7,

T3, Fp1, P3, F8, T4, T6, Fp2, C4, and P4 channels

were very significantapproximately 1.0 to 1.8 U.

The results for L1 were consistent with those for D2,

except in the F7 and O2 channels. AD patients had

significantly lower L1 values than VaD patients (P

0.001) in all channels. The L1 values for the VaD

patients were higher than for the normal control sub-

jects (F3, F4, F8, O1, O2, P 0.01).

DISCUSSION

Our results indicate that AD patients have signifi-

cantly lower D2 and L1 values than VaD patients and the

age-matched healthy control subjects over all regions

except at a few electrodes. By contrast, VaD patients

have relatively higher D2 and L1 values than AD pa-

tients. VaD patients have an uneven distribution of D2

values over the channels than other groups, whereas AD

patients have uniformly lower values of D2 and L1,

indicating that EEGs in AD patients are less complex

than those in other groups.

Our findings of a decreased dimensional complexity

and flexibility of EEGs in AD patients supports a number

of previous findings (Besthorn et al., 1995; Jelles et al.,

1999; Pritchard et al., 1991, 1993, 1994; Pucci et al.,

1998; Stam et al., 1995; Woyshville and Calabrese,

1994; Woyshville et al., 1987; Yagyu et al., 1997). If

dynamic properties of the EEG reflect differential infor-

mation processing in the brain, the states of information

processing could be estimated by nonlinear measures

(Rochke, 1992). Lutzenberger et al. (1992) demon-

strated that human intellectual ability may be reflected

in a higher complexity of the electrical dynamics of

the brain. Accordingly, we may interpret our results to

mean that a decrease of D2 and L1 in AD may be

associated with deficient information processing in the

brain injured by AD.

TABLE 1. The average estimates of D2 in patients with AD and VaD and normal subjects

Leadposition

AD patients(n 12)

VaD patients(n 12)

Normal control subjects(n 14)

Analysis

F value(df 2, 35) P value

F7 8.90 0.35 10.32 0.38 9.13 0.46 6.21 0.01F3* 8.92 0.58 10.29 0.54 10.09 0.65 5.23 0.05F4* 8.71 0.42 11.03 0.43 9.67 0.37 6.96 0.01F8* 8.27 0.55 10.79 0.48 8.82 0.44 5.29 0.01Fp1* 8.41 0.52 10.21 0.38 9.57 0.46 6.86 0.01Fp2* 8.03 0.43 10.25 0.49 9.25 0.41 6.74 0.01C3* 9.06 0.55 9.67 0.48 9.97 0.36 4.98 0.05C4* 8.62 0.63 9.14 0.55 9.85 0.53 5.02 0.05T3* 8.40 0.38 9.56 0.51 9.75 0.32 5.46 0.01T4* 8.25 0.35 9.21 0.48 9.38 0.45 5.26 0.05T6* 8.09 0.39 9.89 0.49 9.54 0.48 6.12 0.01P3* 9.01 0.52 10.12 0.47 9.94 0.39 6.81 0.01P4* 8.63 0.48 9.54 0.46 9.67 0.28 5.47 0.01O1 8.16 0.33 10.24 0.41 8.25 0.45 5.54 0.01O2* 8.15 0.39 10.89 0.49 8.77 0.46 6.21 0.01

* AD control (P 0.05, analysis of variance [ANOVA] with posthoc Duncan). AD VaD (P 0.05, ANOVA with posthoc Duncan). Control VaD (P 0.05, ANOVA with posthoc Duncan).AD, Alzheimers disease; VaD, vascular dementia.




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FIG. 4. Graphical representation of the mean L1 values in patients with(A) (AD) and (B) VaD, and in (C) normal subjects.

FIG. 3. Graphical representation of the mean D2 values in patients with(A) Alzheimers disease (AD) and (B) vascular dementia (VaD), and(C) in normal subjects.

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The finding that VaD patients have relatively higher

values of D2 and L1 than AD patients and even normal

control subjects, and have an uneven distribution of D2

values, may be in accord with previous findings (Johan-

nesson et al., 1979; Saletu et al., 1991). Saletu et al.

(1991) demonstrated the uneven distribution of EEG

abnormality in multi-infarct dementia by multichannel

EEG mapping. In mild cerebrovascular dementia, the

EEG was often normal or slightly abnormal (Johannes-son et al., 1979).

An uneven distribution of D2 values in VaD compared

with those in AD may be the result of uneven neuronal

pathology in VaD. VaD is a heterogeneous syndrome

with various subtypes, including multi-infarct dementia,

strategic single infarct dementia, small-vessel disease

with dementia, hypoperfusion, and hemorrhagic demen-

tia (Roman et al., 1993). VaD may exhibit cortical and/or

subcortical involvement (Weiner et al., 1991). Several

lines of studies using topographic EEG analysis showed

the VaD group had more asymmetric findings than the

AD group (Ding, 1990). Diffuse abnormality of EEGwas found to increase in AD (Erkinjuntti et al., 1988).

Conversely, clinical studies have linked signs and symp-

toms of VaD to patchy and irregular brain damage.

Radiologic studies showed that VaD patients had central

brain atrophy more often than AD patients or normal

control subjects, indicating multiple small infarcts in the

thalamus and other basal brain structures (Cumming and

Beson, 1992). Fenton (1986) showed that VaD patients

had the lowest coherence between the different cortical

areas, indicating asymmetry of functioning, whereas pa-

tients with non-VaD had the greatest EEG coherence

between the centroparietal and temporal regions within

each hemisphere. The current study suggested that the

distributions of lesions were more scattered in VaD than

in AD. Therefore, an uneven distribution in the nonlinear

measures in VaD patients may, to some extent, result

from underlying etiologic heterogeneity.

Similar disturbances in electrical activity may becaused by a variety of disease processes involving either

damage to nerve cells or potentially reversible distur-

bances in cell metabolism resulting from metabolic in-

sults. Hence, electrical patterns associated with brain

dysfunction cannot be used to predict the exact nature of

the underlying disease process (Fenton, 1986). Our re-

sults from the nonlinear analysis of the EEG suggest that

the estimation of nonlinear measures like D2 and L1 may

be helpful in diagnosing AD and VaD more accurately.

Pritchard et al. (1994) found that the addition of nonlin-

ear EEG measures improved the classification accuracy

of the subjects as either AD patients or control subjects.Several limitations of these findings merit consider-

ation. The sample size was small, and the severity of

cognitive dysfunction was not strictly controlled. The

results obtained seem to depend on the clinical degree of

dementia studied. The stage of dementia must affect the

ability of the EEG to distinguish one type of dementia

from the other or from control subjects. The relationship

of EEG alterations to the severity of dementia raises an

issue concerning the strict psychometric or neuropsycho-

TABLE 2. The average estimates of the L1 in patients with AD and VaD and normal subjects

Leadposition

AD patients(n 12)

VaD patients(n 12)

Normal control subjects(n 14)

Analysis

F value(df 2, 35) P value

F7* 4.92 0.22 5.45 0.28 5.32 0.23 5.58 0.01F3* 4.72 0.34 5.54 0.23 5.17 0.22 6.23 0.05F4* 4.93 0.31 5.78 0.25 5.34 0.24 6.36 0.01F8* 4.75 0.21 5.63 0.27 5.12 0.23 6.42 0.01Fp1* 4.94 0.20 5.51 0.35 5.47 0.27 5.26 0.05Fp2* 5.09 0.21 5.47 0.29 5.56 0.20 6.74 0.01C3* 5.24 0.30 5.57 0.36 5.62 0.23 5.25 0.05C4* 4.93 0.21 5.47 0.31 5.53 0.24 5.01 0.05T3* 4.56 0.15 5.31 0.22 5.69 0.29 5.76 0.01T4* 4.71 0.19 5.39 0.28 5.67 0.29 5.72 0.05T6* 4.46 0.21 5.24 0.36 5.41 0.22 6.17 0.01P3* 5.00 0.21 5.45 0.30 5.50 0.21 5.11 0.05P4* 5.22 0.20 5.78 0.34 5.80 0.27 5.17 0.05O1 4.76 0.22 5.73 0.36 4.93 0.17 5.74 0.01O2 4.90 0.13 5.74 0.29 4.91 0.15 5.10 0.05

* AD control (P 0.05, analysis of variance [ANOVA] with posthoc Duncan). AD VaD (P 0.05, ANOVA with posthoc Duncan). Control VaD (P 0.05, ANOVA with posthoc Duncan).AD, Alzheimers disease; VaD, vascular dementia.




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logical evaluation in the nonlinear analysis of EEG.

Additionally, we cannot explain why the VaD patients

had higher D2 and L1 values in some channels than

normal subjects. Our results, however, encourage further

investigation of the complexity of electrocortical re-

sponses in brains injured by dementia, although thecurrent study is quite preliminary.

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