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Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and
association with disease severity
Heinrich Garn
a,1
, Markus Waser
a
, Manfred Deistler
b
, Reinhold Schmidt
f
,PeterDal-Bianco
d
,
Gerhard Ransmayr
e
, Josef Zeitlhofer
d
,HelenaSchmidt
f
, Stephan Seiler
f
, Guenter Sanin
c
, Georg Caravias
e
,
Peter Santer
d
, Dieter Grossegger
g
, Wolfgang Fruehwirt
g
, Thomas Benke
c,
⁎
a
AIT Austrian Institute ofTechnology GmbH, A-1220 Vienna, Austria
b
Vienna University of Technology, A-1220 Vienna, Austria
c
Department of Neurology, Innsbruck Medical University, A-6020 Innsbruck, Austria
d
Department of Neurology, Vienna Medical University, A-1090 Vienna, Austria
e
Department of Neurology, Linz General Hospital, A-4020 Linz, Austria
f
Department of Neurology, Graz Medical University, A-8036 Graz, Austria
g
Dr. Grossegger & Drbal GmbH, A-1190 Vienna, Austria
abstractarticle info
Article history:
Received 22 March 2014
Received in revised form 3 June 2014
Accepted 6 June 2014
Available online xxxx
Keywords:
Alzheimer's disease
Quantitative EEG
Cognitive state
Resting state
Background: Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to
differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the association
between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been
studied in detail.
Objective: To evaluate if ‘cognitive’qEEG is a useful diagnostic option, particularly if memory paradigms are used
as cognitive stimulators.
Methods:This study is part ofthe Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia
research project. A cohort of 79 probable AD patients was includedin a cross-sectional analysis. qEEG recordings
performed in resting states were compared with recordings during cognitively active states. Cognition was
evoked witha face–name paradigm and a paired-associate word list task,respectively. Relative band powers, co-
herence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms
and during rest. Analyses were adjusted for theco-variables age, sex,duration of dementia and educational level.
Results: MMSE scores explained 36–51% of the variances of qEEG-markers. Face–name encoding with eyes open
was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas
relative alpha power and auto-mutual information yielded more significant results during resting state with
eyes closed. The face–name task yielded stronger correlations with MMSE scores than the verbal memory task.
Conclusion: qEEG alterations recorded during mnestic activity, particularly face–name encoding showed the
highest association with the MMSE and may serve as a clinically valuable marker for disease severity.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
Alzheimer's disease (AD) is a progressive neurological disorder
which can bediagnosed in advanced disease stages with high diagnostic
accuracy. However, early AD is more difficult to detect and currently
requires the use of costly technical diagnostic facilities, such as MRI,
FDG-PET or laboratory based investigations (Dubois et al., 2007).
Alternatively, several previous studies have employed quantitative
electroencephalogram (qEEG) as an economical, noninvasive tool to
differentiate normal controls (NC) or subjects with preclinical disease
stages from patients with manifest AD (for reviews, see: Drago et al.,
2011; Sakkalis, 2011; Platt and Riedel, 2001; Leiser et al., 2001;
Santos et al., 2010; Dauwels et al., 2010; Giannakopoulos et al.,
2009; Rossini et al., 2007; Uhlhaas and Singer, 2006; Menendez,
International Journal of Psychophysiology xxx (2014) xxx–xxx
⁎Corresponding author at: Klinik für Neu rologie, Anichstr. 35, A-6020 Innsbruck,
Austria. Fax: +43 51250423852.
E-mail addresses: heinrich.garn@ait.ac.at (H. Garn), markus.waser@ait.ac.at
(M. Waser), manfred.deistler@tuwien.ac.at (M. Deistler),
reinhold.schmidt@medunigraz.at (R. Schmidt), peter.dal-bianco@meduniwien.ac.at
(P. Dal-Bianco), gerhard.ransmayr@akh.linz.at (G. Ransmayr),
josef.zeitlhofer@meduniwien.ac.at (J. Zeitlhofer), helena.schmidt@medunigraz.at
(H. Schmidt), stephan.seiler@medunigraz.at (S. Seiler), guenther.sanin@i-med.ac.at
(G. Sanin), georg.caravias@akh.linz.at (G. Caravias), peter.santer@meduniwien.ac.at
(P. Santer), office@alphatrace.at (D. Grossegger), w.fruehwirt@alphatrace.at
(W. Fruehwirt), thomas.benke@i-med.ac.at (T. Benke).
1
Tel.: +43 505504103; fax: +43 505504125.
INTPSY-10807; No of Pages 8
http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
0167-8760/© 2014 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
International Journal of Psychophysiology
journal homepage: www.elsevier.com/locate/ijpsycho
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
2005; Jeong, 2004). It has been shown that the essential qEEG hall-
marks of AD are (i) slowing of the frequency spectrum, (ii) changing
synchrony between sites across the cortex, particularly with disease
progression, and (iii) reduced signal complexity. Thus, AD was found
to be associated with an increase of spectral power at low frequencies
(delta and theta waves) and a decrease of power at higher frequencies
(alpha and beta waves). Furthermore, synchrony between EEG chan-
nels as measured by coherence increases during the first years of AD
and decreases in later disease stages. Signal complexity generally de-
creases with disease progression, as evident from an increase of auto
mutual information.
Although previous research has found AD-specific markers in qEEG,
this technique is presently not among the recommended standard diag-
nostic tools of AD. Since impairments of cognitive functions (e.g. amne-
sia) are typical and early symptoms of AD, qEEG recorded during active
cognitive performance may contain more and better diagnostic markers
than recordings during rest. We investigated a large cohort of patients
with early and moderate AD using a comprehensive analysis of qEEG
and cognitive parameters. In particular, we aimed to evaluate a)wheth-
er recording of qEEG during or immediatelyafter cognitive effort reveals
more significant findings than during the unengaged resting state,
b) how well brain dynamics of qEEG can serve as surrogate markers of
AD, and c) which of the cognitive tasks administered evokes the most
prominent qEEG changes. Based on previous studies our tentative
hypotheses were that early and moderate AD is associated with several
pathological qEEG markers, that cognitive activity related to a memory
task is accompanied by abnormal qEEG, and that active cognition pro-
vides more diagnostic clues in qEEG than resting states (Klimesch,
1999).
2. Materials and methods
2.1. Study and participants
The Prospective Registry on Dementia in Austria (PRODEM Austria)
is a longitudinal multicenter study of AD and other dementias in a rou-
tine clinical setting (Seiler et al., 2012; Benke et al., 2013). Participants
were recruited prospectively in 4 tertiary-referral memory clinics. The
study was approved by the Ethic Committees of the individual research
sites, and patients gave their written informed consent to participate in
the study. AD was diagnosed according to the NINCDS-ADRDA criteria
(McKhann et al., 1984). Additional inclusion criteria were non-
institutionalization, no need for 24-hour care, and availability of a
caregiver who agreed to provide information on the patient. Duration
of disease (in months) was estimated by the caregiver who also gave in-
formation regarding the patient's education, disease stage as assessed
by Clinical Dementia Rating (CDR, Berg, 1988), and basic and instru-
mental activities of daily living (Gelinas et al., 1999). The MMSE
(Folstein et al., 1975) was used as a global measure of cognition and
staging of dementia. In the present study, 79 patients (50 females)
with a diagnosis of probable AD were included.Important demographic
and disease variables of the study population are summarized in
Tables 1 and 2. As evident from their CDR and MMSE scores the cohort
was in an early to moderate disease stage of AD. About 49% of the pa-
tients were treated with cholinergic substitution treatment. Five pa-
tients received antipsychotics.
2.2. Experimental procedure
The rationale for the current study was to record, analyze and com-
pare qEEG during periods of rest and cognitive activity, and to explore
which period and qEEG markers best predict the concurrent MMSE
score. With regard to the earliest and most frequent cognitive impair-
ment of AD, two episodic memory paradigms were chosen as disease-
relevant tasks, one visual–verbal and one strictly verbal. Details of the
test procedure and qEEG recording are shown in Table 3.
2.2.1. Tests and administration
A memory test was designed to compare mnestic activity with the
resting state. Test materials contained verbal and figural information
and were adapted to patients with dementia. Details of the testing pro-
cedure are summarized in Table 3.Theface–name task was started after
a rest interval (360 s, rest 1 with eyes closed and rest 2 with eyes open);
then, 3 face–name pairs were presented (encoding 1). Next, the three
faces were shown alone, and the corresponding names had to be report-
ed (name recall 1), followed by a second presentation of the three face–
name pairs (encoding 2). After a 90 s consolidation interval, during
which participants were asked to close their eyes and keep faces and
names in mind, the three encoded faces had to berecognized and distin-
guished from 6 distracter faces (recognition). Next, the names of the
three target faces had to be retrieved again (name recall 2). The word-
pair task consisted of an encoding period (encoding 3) during which 3
word pairs were visually presented twice, and a recall trial (word recall,
completion of the paired associate as response to a given single word).
Maximum possible scores were 18 for the face–name test, and 3 for
the word-pair test. qEEG was recorded both during resting states and
during cognitive activity when patients learned and recalled new infor-
mation. qEEG markerswere calculated separately for the elevenperiods
of the test. Intervals of rest and cognitive activity were compared as to
their corresponding qEEG activity.
2.3. EEG recordings
Patientssat in upright position on a comfortable chair with neck rest.
A 21 inch computer screen was placed in convenient distance to the
patient's head. The room was normally illuminated with no dazzling
light and disturbances were held off during the recordings. EEG data
were collected from 19 monopolar electrode sites of the international
10/20 system. Data acquisition was performed on an AlphaEEG amplifi-
er with NeuroSpeed software (Alpha Trace Medical systems, Vienna,
Austria). Electrodes for the horizontal electrooculogram (H-EOG) were
placed on the right side of the right eye and on the left side of the left
eye. Electrodes for the vertical electrooculogram (V-EOG) were placed
vertically above and below the left eye. The ground electrode was
placed at FCz. Connected mastoid electrodes were used as reference.
Electrode contacts were arranged to achieve impedances below 10 kΩ.
Table 1
Group characteristics for the whole group of 79 patients.
Median Median absolute
deviation
Range
Age (years) 75 6 52–88
Level of education (scale 1–6) 2 1 1–6
Duration of illness (months) 23 13 2–120
MMSE (max. 30) 22 2 26–15
CDR (max. 3) 0.5 0.5 0–2
DAD (max. 100) 90 10 30–100
Table 2
Group characteristics for 40 medication-free patients.
Median Median absolute
deviation
Range
Age (years) 74 7 54–87
Level of education (scale 1–6) 2 1 1–4
Duration of illness (months) 14 8.5 2–60
MMSE (max. 30) 23 2 26–15
CDR (max. 3) 0.5 0 0–2
DAD (max. 100) 92.5 7.5 45–100
2H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
The EEG amplifiers had aband pass from 0.3 to 70 Hz (3 dB points), with
a50Hznotchfilter. Data were sampled at a rate of 256 s
−1
with 16 bit
resolution. The electrocardiographic (ECG) signal was recorded via
clamp electrodes that were put at both wrists.
3. Theory/calculation
3.1. qEEG markers
EEG signal preprocessing included (1) visual selection of usable
epochs to exclude artifacts, (2) elimination of interferences from eye
movements and blinks by linear regression using the horizontal and
vertical electrooculogram (H-EOG, V-EOG) signals, and (3) detection
and correction of interferences from the heart beat by using a modified
Pan–Tompkins algorithmand linear regression (Waser and Garn, 2013).
A 2 Hertz high pass filter was used to eliminate fluctuations caused by
sweating. Finally, a sliding window was moved automatically over the
artifact-free, interference-corrected sections of the EEG to determine
series of 4-second epochs with an overlap of 2 s. These epochs were
used to calculate the EEG-markers for each phase.
Analyses were performed on 19 electrodes in the 2–20 Hz frequency
range. The electrodes T7, T8, F7, F8, P7, and P8 were limited to 15 Hz to
avoid the inclusion of muscle activity. In addition, the markers were
calculated on the first and second principal components of electrode
clusters. Clusters were defined as follows (Dauwels et al., 2010): central
(Ce): Fz, Cz, Pz, C3, and C4; temporal (Te): F7, T7, and P7 (left side) and
F8, T8, and P8 (right side), respectively; posterior (Po): P3, P4, O1, and
O2; parietal–occipital (Pa-Oc): P7, P3, O1, P8, P4, and O2; left (L): FP1,
F3, F7, C3, T7, P3, P7, and O1. For each electrode or cluster, results for
all selected epochs were averaged within each period of the paradigm.
We calculated relative band powers as measures of frequency
slowing in the delta (2–4Hz),theta(4–8Hz),alpha(8–13 Hz), and
beta1 (13–20 Hz) bands using an indirect spectral estimator to estimate
the spectral density for each of the visually selected epochs as demon-
strated in (Waser et al., 2013). Thereby, Fourier transformation was
not applied on the original time signal, but on its covariance function.
By using a so-called lag-window, sample points with greater time lags
were weighted less than sample points that are close to each other. Hav-
ing estimated the spectral density, the power in a certain frequency
band was calculated from the amplitude sum within borderfrequencies.
Values for each frequency band were expressed as percentage of the
power in the total 2–20 Hz range.
Coherences were calculated as measures of synchrony in the
frequency ranges delta (2–4 Hz), theta (4–8Hz),alpha(8–13 Hz), and
beta1 (13–20 Hz) between the following pairs of electrode clusters:
anterior–central, anterior–posterior, anterior-temporal/left, anterior-
temporal/right, central–posterior, central-temporal/left, central-
temporal/right, posterior-temporal/left, posterior-temporal/right, tem-
poral/left–temporal/right, anterior/temporal–parietal/occipital, left–
right.
Auto-mutual information was calculated as a measure of signal
complexity in the frequency range of 2–15 Hz.
3.2. Statistical analysis
Computations were performed using Matlab R 2011a. All qEEG
markers were calculated for each patient and for each period of the
paradigm.
We used scatterplots to visualize the correlation between qEEG
markers and MMSE scores. Univariate, quadratic regressions were fitted
to these graphs. Fisher's F-tests were used to determine statistical signif-
icance in the regressions. Analyses were adjusted for the co-variables
age, sex, duration of dementia, and educational level. Age and duration
of dementia were introduced viaboth linear and quadratic terms. Treat-
ment with anti-dementia drugs (yes/no), APOE (one or two e4 alleles
versus no e4 allele) as well as cross-terms between the confounding
variables were not significant. Coefficients of determination (R
2
) were
calculated to evaluate to what extent the variations in qEEG markers
could be explained by MMSE scores.
To determine statistical significance of the differences found
between markers in resting states and markers in cognitive states,
we first tested the squared deviations of the data points from the
regressions for normal distribution using the one-sample Kolmogorov–
Smirnov test. To test for equality or inequality of variances, the two-
sample F-test for equal variances (normal distribution) or the Ansari–
Bradley test (no normal distribution) was applied.
4. Results
4.1. Behavioural results
Fig. 1 provides an overview of the sum scores of the face–name test
(Fig. 1a) and the word-pair test (Fig. 1b). The majority of AD patients
scored in the upper range of both tests. As evident from their recall
and recognition accuracy it can be assumed that the cohort understood
the test procedure and actively participated in both tests.
4.2. qEEG
The resulting R
2
were compared between resting states and cogni-
tive states. Fig. 2 shows examples of differential results for two selected
markers: For relative delta band power (left column, 2a, 2c, 2e), signif-
icant results (p b0.01) occurred in resting state with eyes closed, but
not during recall of faces or word pairs with eyes open. For coherence
(right column, 2b, 2d, 2e), significant results (p b0.01) occurred during
recall of faces and word pairs, but not in resting state with eyes closed.
Table 4 presents an overview of selected (best) results for the coeffi-
cients of determination (R
2
) for each qEEG measure in its optimal cog-
nitive state. Table 5 presents results for the medication-free patients
Table 3
Timeline, sequence and materials of the cognitive qEEG.
Phase Duration (s) Test and materials Eyes Sum score
Rest 1 180 None Closed
Rest 2 180 None Open
Face–name test Encoding 1 3 × 10 Sequential presentation of 3 face–name pairs Open
Name recall 1 3 × 10 Faces alone, matching of corresponding names Open 3
Encoding 2 3 × 10 Same as encoding 1 Open
Consolidation 90 Silent rehearsal of face–name pairs Closed
Recognition Unlimited Pseudorandom presentation of 3 target faces and 6 distractors,
yes–no recognition of original faces
Open 12
Name recall 2 3 × 10 Same as name recall 1 Open 3
Paired word learning test Encoding 3 140 Presentation of 3 word pairs, 2 times Open
Word recall 60 First words shown alone, second words to be matched Open 3
Rest 3 150 None Closed
3H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
Fig. 1. Response accuracy during a) the Face–name test and b) the Paired word learning test.
a) Relave delta band power (2 – 4 Hz) b) Alpha coherence (8 – 13 Hz)
parietal-occipital electrode cluster between central and le temporal cluster
resng state (eyes closed) resng state (eyes closed)
p =0.0001; R2=0.27 p=0.44; R2=0.03
c) Relave delta band power (2 – 4 Hz) d) Alpha coherence (8 – 13 Hz)
parietal-occipital electrode cluster between central and le temporal cluster
recall of faces (eyes open) recall of faces (eyes open)
p=0.38; R2=0.03 p=1e-6; R2=0.28
e) Relave delta band power (2 – 4 Hz) f) Alpha coherence (8 – 13 Hz)
parietal-occipital electrode cluster between central and le temporal cluster
recall of word pairs (eyes open) recall of word pairs with eyes open
p=0.12; R2=0.07 p=0.0002; R2=0.25
Fig. 2. Comparison of resultsbetween resting state with eyes closed (a, b), recall of faces (c,d), and recall of word pairs (e, f). qEEG measures are 2nd principal components; vertical bars
indicate standard deviations of qEEG measures determined from averaged epochs for each patient.
4H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
alone. Fig. 3 shows regions with significant and nonsignificant results
for each qEEG marker.
Cognitive states yielded higher coefficients of determination for
theta (Fig. 4) and beta1 relative band power as well as for coherence
(Fig. 6). Face–name encodingwas superior to word recall. MMSE scores
thereby explained up to 51% of the variations in theta relative band
power at the left-side electrode cluster and up to 39% of the variations
in beta1 relative band power at C3. Regarding coherence, word
encoding was slightly better than face–name encoding (40% versus
38% explanation). However, the values of coherence were ambiguous
as shown in Fig. 6.
Resting state (rest 1) yielded best results for alpha (Fig. 5) and delta
relative band power (results for cognitive states were not significant)
and auto-mutual information (Fig. 7), word recall was second-best. At
P7, up to 36% of the variations in alpha relative band power were
explained by MMSE scores. Up to 42% of the variations in auto-mutual
information on the left-side electrode cluster were explained by
MMSE scores.
Relative slopes of theregressions reached their maximum values at a
MMSE score of 26 (relative theta band power: 24.6%; theta coherence
23.9%, auto-mutual information 21.6%; others below 8%). At lower
MMSE scores the slopes were much flatter. The only exception was
theta coherence with −21.8% at an MMSE score of 15. Unfortunately,
the values of coherence were ambiguous as shown in Fig. 6.
Standard deviations averaged over all qEEG markers (excluding
delta relative band power with poor values for both p and R
2
) deter-
mined for each phase of theparadigm ranged below 2.4% for the resting
states and below 1.4% for the cognitive states.
5. Discussion
It is uncertain from previous studies' findings if ‘cognitive’qEEG is a
useful diagnostic option and can widen our scope regarding AD. In the
present study we evaluated whether recording of qEEG during or
immediately after cognitive activity evoked AD-typical signals and ex-
amined the association of pathological EEG findings with a commonly
used clinical measure of AD during two conditions, a cognitively active
vs. an unengaged resting state. To receive substantiated results we
included a large, homogeneous group of 79 patients with probable AD.
Advanced signal pre-processing methods and improved algorithms for
calculating qEEG markers were used, and our analyses were adjusted
for the confounding of age, sex, duration of dementia, and educational
level.
Our results suggest that severity of AD is associated with several
prominent qEEG markers, such as frequency slowing (increase of
theta, decrease of alpha and beta relative band power), altered co-
herence expressing synchrony between neural activity in different
regions of the cortex, and changes in auto mutual information indi-
cating loss of signal complexity. This is well in line with previous
investigations which identified qEEG markers specifically related
to AD (for reviews: Drago et al., 2011; Sakkalis, 2011; Platt and
Riedel, 2001; Leiser et al., 2001; Santos et al., 2010; Dauwels et al.,
2010; Giannakopoulos et al., 2009; Rossini et al., 2007; Uhlhaas
and Singer, 2006; Menendez, 2005; Jeong, 2004). Based on the
characteristics of our cohort, our findings further suggest that
even early and moderately advanced disease stages of AD are clear-
ly associated with prominent qEEG-alterations which can be read-
ily identified using adequate technology and signal preprocessing.
A potential limitation of the study is the lack of a normal control
group which would allow a direct comparison between AD-
related and age related changes in qEEG. Future studies should
therefore consider to include age matched normals to widen the
scope of cognitive qEEG and to further evaluate the diagnostic
value of the qEEG methodology.
Changes of qEEG during or immediately after cognitive tasks have
been reported in previous studies of AD (Seal et al., 1998; Hogan
et al., 2003; Pijnenburg et al., 2004; Hidasi et al., 2007). Although
these studies were able to differentiate dementia from other condi-
tions like MCI or normal aging by use of a cognitive condition, they
did not evaluate how well electrophysiological markers can predict
disease relevant findings. Our study evaluated the association be-
tween AD-specific qEEG-markers and the MMSE score, a clinical
measure of AD which represents disease severity and global cogni-
tion. A comparison showed differential findings for the cognitive
vs. rest conditions. During cognitive activity lower variances and
higher R
2
were found for theta and beta1 band powers, as well as
for coherence. Of note, the coherence measure appeared to be relat-
ed to disease stage, showing an increase of coherence in early AD pa-
tients (MMSE 26–20) and a decrease in later stages of the disease.
This likely represents a compensatory effect in early AD which ceases
in more advanced stages (Dauwels et al., 2010) and supports the hy-
pothesis of progressive functional disconnection among cortical re-
gions in the brains of AD patients. In contrast, recordings during
the resting state with eyes closed evidenced a different pattern.
Here, alterations of alpha waves revealed the best correlation. Simi-
larly, coefficients of determination for auto mutual information
were higher in the resting state than during cognitive activity. In
sum, our results indicate that both, resting and cognitive states are
significantly associated with clinical measures of AD. However, a di-
rect comparison showed that qEEG markers recorded during
Table 4
Comparison of selected, optimal R
2
for qEEG in resting and in cognitive states and their localization for the whole group of 79 patients.
qEEG marker Maximum values of R
2
, given at corresponding
electrode sites or electrode clusters
Significance (p-value) of the difference in R
2
between two cognitive states
Resting state
(rest 1)
Face–name test (encoding 1 +
name recall 1 + encoding 2)
Verbal memory test
(encoding 3, word recall 3)
Resting state vs.
face name
Resting state vs.
verbal memory
Face name vs.
verbal memory
Delta relative band power P7: 0.26 FP2: 0.18 n.s. –––
L: 0.27 n.s. n.s. –––
Theta relative band power T7: 0.41 T7: 0.42 F7: 0.41 0.160 0.390 0.150
Te/L:0.42 L:0.51 L:0.44 0.003** 0.148 0.008*
Alpha relative band power P7: 0.31 n.s. P7: 0.17 –0.001** –
Pa-Oc: 0.24 n.s. Pa-Oc: 0.17 –0.009* –
Beta1 relative band power C3: 0.33 C3: 0.38 C3: 0.27 0.307 0.530 0.100
Te/L: 0.27 L: 0.35 L: 0.27 0.875 0.372 0.462
Coherence Delta n.s. Delta P3–P7: 0.17 Theta n.s. –––
Delta Ce-TL: 0.23 Delta Ce-TL: 0.41 Theta Ce-TL: 0.42 0.001** 0.001** 0.476
Auto-mutual information P7: 0.42 T7: 0.25 P8: 0.25 0.000** 0.000** 0.265
L: 0.42 Pa-Oc: 0.27 Pa-Oc: 0.36 0.003** 0.019 0.043
Bonferroni correction Wherever values for R
2
are given, p b0.0083 = 0.05/6 *Significant (p b0.0167 = 0.05/3)
**Highly significant (p b0.0033 = 0.01/3)
Electrodeclusters (Dauwelset al., 2010): central(Ce): Fz, Cz, Pz, C3,and C4; temporal (Te):F7, T7, and P7 (leftside) and F8, T8, andP8 (right side), respectively;posterior (Po): P3, P4, O1,
and O2; parietal-occipital (Pa-Oc): P7, P3, O1, P8, P4, and O2; left (L): FP1, F3, F7, C3, T7, P3, P7, and O1. Results for electrode clusters are second components of markers.
5H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
cognitively active states had a higher correlation with disease severity
and may therefore be of great diagnostic value. Overall, our findings
demonstrate that the implementation of cognitive effort increases the
diagnostic potential of qEEG in AD. For practical purposes it seems ad-
visable to apply both verbal and figural memory paradigms and evalu-
ate particular qEEG-marker as outlined above.
Table 5
Comparison of selected, optimal R
2
for qEEG in resting and in cognitive states and their localization for 40 medication-free patients.
qEEG marker Maximum values of R
2
, given at corresponding
electrode sites or electrode clusters
Significance (p-value) of the difference in R
2
between two cognitive states
Resting state
(rest 1)
Face–name test (encoding 1 +
name recall 1 + encoding 2)
Verbal memory test
(encoding 3, word recall 3)
Resting state vs.
face name
Resting state vs.
verbal memory
Face name vs.
verbal memory
Delta relative band power P7: 0.33 FP2: 0.29 n.s. –––
L: 0.35 n.s. n.s. –––
Theta relative band power T7: 0.46 F7: 0.48 T7: 0.44 0.240 0.579 0.171
Pa-Oc: 0.52 Pa-Oc: 0.54 Pa-Oc: 0.55
Alpha relative band power P7: 0.31 n.s. n.s. –––
Pa-Oc: 0.43 n.s. Pa-Oc: 0.31 –0.170 –
Beta1 relative band power F3: 0.47 F3: 0.41 F3: 0.51 0.013* 0.091 0.397
Te/L: 0.31 Te/L: 0.36 Po: 0.42 0.603 0.498 0.275
Coherence Delta n.s. Delta P3–P7: 0.27 Theta n.s. –––
Delta Ce-TL: 0.43 Delta Ce-TL: 0.44 Theta Po-TL: 0.43 0.170 0.071 0.034
Auto-mutual information P7: 0.50 T7: 0.42 T7: 0.51 0.001** 0.270 0.017
L: 0.48 Pa-Oc: 0.27 Pa.Oc: 0.44 0.000** 0.014* 0.022
Bonferroni correction Wherever values for R
2
are given, p b0.0083 = 0.05/6 *Significant (p b0.0167 = 0.05/3)
**Highly significant (p b0.0033 = 0.01/3)
Electrodeclusters (Dauwelset al., 2010): central(Ce): Fz, Cz, Pz, C3,and C4; temporal (Te):F7, T7, and P7 (leftside) and F8, T8, andP8 (right side), respectively;posterior (Po): P3, P4, O1,
and O2; parietal-occipital (Pa-Oc): P7, P3, O1, P8, P4, and O2; left (L): FP1, F3, F7, C3, T7, P3, P7, and O1. Results for electrode clusters are second components of markers.
Delta relave band power:
a) RS: P7 (max. R2=0.26) b) FN: FP2 (max. R2=0.18) c) VM: n.s.
Theta relave band power
d) RS: T7 (max. R2=0.41) e) FN: T7 (max. R2=0.42) f) VM: F7 (max. R2=0.41)
Alpha relave band power
g) RS: P7 (max. R2=0.31) h) FN: n.s. i) VM: P7 (max. R2=0.17)
Fig. 3. p-Values (Fisher's F-test; color-code fromp = 0 (dark blue) to p = 1 (dark red)) and coefficients of determination (R
2
numbers; given if the F-test was significant after Bonferroni
post-correction (p b0.0083 = 0.05/6)) for each qEEG marker in resting state (RS), during the face–name test (FN) and during theverbal memory test (VM).
6H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
In the scatterplots, data points for patients with anti-dementia drugs
were located in the same range as data points for patients without
treatment. Moreover, our comparison of p- and R
2
values between the
whole group and the medication-free group yielded verysimilar results.
Somewhat higher coefficients of determination in the medication-free
group should not be overrated as they might occur from the smaller
number of patients. Therefore, we believe that drug treatment shifts
both qEEG markers and MMSE scores towards better values.
Beta1 relave band power on individual electrodes
j) RS: C3 (max. R2=0.33) k) FN: C3 (max. R2=0.38) l) VM: C3 (max. R2=0.27)
Coherence between electrode clusters
m) RS: Ce -TL (R2=0.23) n) FN: Ce -TL (max. R2=0.41) o) VM: Ce-TL (max. R2=0.42)
Auto-mutual informaon on individual electrodes
p) RS: P7 (max. R2=0.42) q) FN: T7 (max. R2=0.25) r) VM: P8 (max. R2=0.25)
Fig. 3 (continued).
Fig. 4. Relative theta band power at the left-side electrode cluster (second component)
during face–name test; p = 6.10
−10
,R
2
= 0.51; patients with antidementive treatment
are marked with crosses.
Fig. 5. Relative alphaband power at electrodeP7 in rest 3; p = 2.10
−6
,R
2
=0.36;patients
with antidementive treatment are marked with crosses.
7H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
Previous cognitive qEEG studies of AD have mainly employed tasks
tapping attention (Seal et al., 1998; Hidasi et al., 2007) and working
memory (Pijnenburg et al., 2004, Karrasch 2006). In the present study
we used two episodic memory tasks during EEG recordings tapping
the ability to learn, retain, and recall new information. Both, the verbal
memory and the face–name task were associated with marked qEEG-
abnormalities, and MMSE scores explained up to 51% of the variance.
We assume that this effect reflects an attempt to utilize defective neural
networks engaged in episodic memory functions which are commonly
impaired in AD. However, the face–name task yielded even stronger
correlations with MMSE scores than the verbal memory task. Several
studies agree that forming conjunctions of stimuli as in face–name
association learning is already impaired in early AD (Werheid and
Clare, 2007; Sperling R, 2007; Della Sala et al., 2012). This task may
therefore represent a sensitive method to detect disease specificalter-
ations of cognitive qEEG recordings in AD. We conclude that some
qEEG markers more than others appear to be associated with disease
severity and cognitive decline and may therefore serve as severity-
related markers.
Acknowledgment
This study was supported by the Austrian Research Promotion
Agency (FFG) (project no. 827462), Dr. Grossegger & Drbal GmbH and
by the Austrian Alzheimer Society, http://www.alzheimer-gesellschaft.
at/.
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Fig. 6. Theta coherence between central and left te mporal electrode cluster (second
component) during encoding 3; p = 9.10
−8
,R
2
= 0.40; patients with antidementive
treatment are marked with crosses.
Fig. 7. Auto-mutual information on the left-side electrode cluster (second component)
during rest 1; p = 5.10
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with crosses.
8H. Garn et al. / International Journal of Psychophysiology xxx (2014) xxx–xxx
Please cite this article as: Garn, H., et al., Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease
severity, Int. J. Psychophysiol. (2014), http://dx.doi.org/10.1016/j.ijpsycho.2014.06.003
