5
EEG correlates in the spectrum of
cognitive decline
K. van der Hiele, MSc3,6
A.A. Vein, MD, PhD2
R.H.A.M. Reijntjes, BEng2
R.G.J. Westendorp, MD, PhD5
E.L.E.M. Bollen, MD, PhD1
M.A. van Buchem, MD, PhD4
J.G. van Dijk, MD, PhD2
H.A.M. Middelkoop, PhD3,6
1
2
Department
of
Neurology
(sections
of
Clinical
From
the
3
4
Neuropsychology),
Department of Radiology,
Neurophysiology,
5
Department of General Internal Medicine (Section of Gerontology and
Geriatrics), Leiden University Medical Center and the 6Neuropsychology
Unit of the Department of Psychology; Leiden University, Leiden, The
Netherlands.
Clin Neurophysiol 2007;118(9):1931-9
Chapter 5
Abstract
To investigate relations between EEG measures and performance on
tests of global cognition, memory, language and executive functioning.
Twenty-two controls, 18 patients with mild cognitive impairment (MCI)
and 16 with probable Alzheimer’s disease (AD) underwent
neuropsychological and EEG investigations. We used the following EEG
measures: theta relative power during eyes closed, alpha reactivity
during memory activation (i.e. the percentual decrease in alpha power
as compared to eyes closed) and alpha coherence during eyes closed
and memory activation. Theta relative power was increased in AD
patients as compared with controls (p<0.001) and MCI patients
(p<0.01) and related to decreased performance in all cognitive domains.
Alpha reactivity was decreased in AD patients as compared with controls
(p<0.005) and related to decreased performance on tests of global
cognition, memory and executive functioning. Alpha coherence did not
differ between groups and was unrelated to cognition. EEG power
measures were associated with decreased performance on tests of
global cognition, memory, language and executive functioning, while
coherence measures were not. The EEG yielded several power measures
related to cognitive functions. These EEG power measures might prove
useful in prospective studies aimed at predicting longitudinal cognitive
decline and dementia.
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EEG correlates in the spectrum of cognitive decline
Introduction
Alzheimer’s disease (AD) is characterized by a progressive, global loss of
cognitive functions [21]. Its early diagnosis and treatment poses a
major challenge in dementia research. Of special research interest are
those patients who present themselves with amnestic mild cognitive
impairment (MCI) and are presumed to be in a preclinical stage of AD.
MCI is characterized by memory loss without impairment in other
cognitive domains, such as language, perception, practical skills and
executive functions [23].
The electroencephalogram (EEG) has been used for many decades as a
non-invasive, cost-effective tool for exploring functional brain changes in
AD. Characteristic phenomena seen in AD patients are EEG slowing and
decreased coherence [1, 5, 16, 34]. Recently, cross-sectional and
prospective EEG research focused on patients with MCI. It was
discovered that memory activation disclosed EEG power and coherence
changes in MCI patients that were not apparent in the resting EEG [17,
24, 35]. In search of markers of cognitive decline and dementia, a
number of prospective and cross-sectional studies explored relations
between EEG and cognition.
Several prospective studies investigated EEG correlates of global
cognition, mostly using compound tests such as the Mini Mental State
Examination (MMSE) [10], Global Deterioration Scale (GDS) [27] or
Cambridge Cognitive Examination (CAMCOG) [30]. One qualitative EEG
study noted that the pattern of cognitive decline depended on the
presence of EEG abnormality in the early stage of AD. In that study, AD
patients with an abnormal EEG at baseline showed a decline in praxic
functions, confrontation naming and automatic speech functions after
three years, while AD patients with a normal EEG did not [12]. Both
groups showed similar decline in visual functions, understanding of
speech, and memory functions. In a quantitative study in mildly
demented AD patients slowing of the EEG, i.e. higher theta power, less
beta power and lower peak frequency, were associated with subsequent
cognitive decline on the CAMCOG [8]. These results were corroborated
by another longitudinal study that used change in GDS score as an
indicator of cognitive decline in subjects with subjective memory
complaints. In that study, increases in theta power, slowing of mean
frequency and changes in coherence among regions were observed at
baseline in subjects who declined after 7-9 years follow-up [25].
Cross-sectional studies in elderly with different levels of cognitive
impairment have reported correlations between EEG spectral
parameters, i.e. higher theta activity during rest and lower alpha activity
during memory activation, and decreased MMSE or GDS scores [7, 17,
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Chapter 5
22, 26]. One recent study investigated specific cognitive domains; in
subjects with subjective memory complaints higher alpha power was
related to a decline in verbal memory performance, maze performance
and working memory reaction time. Higher alpha power was positively
correlated with performance in reverse digit span [2]. Although relations
have been described between longitudinal cognitive decline and EEG
coherence [25], cross-sectional studies did not discover similar
associations using MMSE scores [1, 17, 19]. EEG coherence might
however be associated with specific cognitive domains.
Aforementioned studies explored limited cognitive domains and were
mostly confined to a particular diagnostic group. The current exploratory
study therefore investigated a wide range of cognitive functions in the
entire spectrum of cognitive decline, ranging from elderly without
cognitive complaints to AD. We tested whether EEG power and
coherence measures were related to performance in specific cognitive
domains, including global cognition, memory, language and executive
functioning. Cognitive domains were selected because of their relevance
in the diagnostic work-up of dementia. We chose EEG measures
sensitive to abnormalities in MCI and AD, such as slowing and decreased
coherence in the resting EEG of AD patients [1, 5, 16, 34] and
decreased alpha reactivity and coherence in MCI patients during
memory activation [17, 35]. It should be noted that we purposely
emphasized studies yielding easily obtainable, and therefore clinically
applicable, EEG parameters. Other researchers provided more refined
descriptions of localized EEG and MEG changes in AD and MCI using
flexible frequency bands encompassing both slow and fast frequencies
[3, 4, 11, 31-33].
Methods
Subjects
Twenty-four subjects without cognitive complaints, 20 patients
diagnosed with MCI [23] and 17 with probable AD [21] agreed to
participate. Patients had been referred to the outpatient memory clinics
of the Leiden University Medical Center, Leiden Diaconessenhuis or The
Hague Leyenburg hospital. Control subjects without cognitive complaints
were recruited through an advertisement in a local newspaper.
All patients and controls underwent general medical, neurological,
neuropsychological and brain MRI investigations as part of the standard
diagnostic work-up of dementia. Patient histories were reviewed and
diagnoses reached in multidisciplinary consensus meetings. Within three
months from standard diagnostic work-up patients and controls
participated in an additional EEG examination. Eligible subjects had to
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EEG correlates in the spectrum of cognitive decline
be free of psychotropic medication, aged 60 yrs or above, and without
previous history of psychiatric and neurological disorders or substance
abuse. Moreover, they had no abnormalities on MRI other than an
incidental small lacunar lesion (d 5 mm diameter) or white matter
hyperintensities conforming with age or of a non-specific nature. The
study was approved by the local Medical Ethical Committee. Written
informed consent was obtained from all subjects, or from close relatives
or caregivers in case of dementia.
EEG recording
EEGs were recorded using a Nihon Kohden 2110 apparatus with 21
Ag/AgCl electrodes placed according to the 10/20 system. ECG,
respiration and horizontal eye movement leads were recorded to
facilitate recognition of artifacts. The EEG was band-pass filtered from
0.16-70 Hz for display and analysis, but recorded unfiltered. The sample
frequency was 200 Hz and the AD precision 12 bits. The average
reference montage was used, with the exclusion of electrodes Fp1, Fp2,
A1 and A2. All EEGs were recorded in the afternoon.
During recording subjects sat slightly reclined in a comfortable chair,
approximately 1.5 m in front of a computer screen. The light in the room
was dimmed. Vigilance was monitored constantly by visual inspection of
the EEG and video registration. An auditory stimulus was given in case
of drowsiness.
Experimental procedure
The EEG of each subject was registered during a rest condition and
memory activation.
The rest condition comprised 10 min of being awake with eyes closed.
Three artifact-free samples, 4-8 s in length, were selected visually for
further analysis. It should be noted that we selected the first artifactfree samples we encountered during these 10 min. This was necessary
in view of the high chance of patient’s drowsiness after several minutes
of EEG registration. The use of samples selected after 2-5 min is
therefore highly discouraged.
During memory activation ten pictures of common objects were shown
on a computer screen using a readily available presentation tool
(Microsoft Office PowerPoint 2003). Each was presented for 2 s and
subjects were asked to name the objects aloud and memorize them.
After the last picture, subjects had to close their eyes and memorize the
objects for 15 s (‘memorization period’). They were then asked to open
their eyes and name as many objects as they could. This task was
performed three times using the same 10 pictures shown in the same
order. From each of the three memorization periods one EEG sample, 48 s in length, was selected for further analysis. We noted the total
number of remembered objects.
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Chapter 5
EEG analysis
The selection of EEG samples free of eye movements, blinks and muscle
activity was performed blinded for diagnosis by the first author and
supervised by an experienced clinical neurophysiologist (AV). Frequency
analysis was performed using a Fast Fourier Transformation. For each
sample, absolute power was calculated in the theta (4-8 Hz) and alpha
(8-13 Hz) bands. Lower and higher frequencies were not used as these
are
easily
contaminated
with
blinks,
eye
movements
and
electromyographic activity, especially in demented patients. Although
artifact reduction techniques are available, we chose a more minimalist
and simpler approach by excluding delta, beta and gamma bands.
Subsequently, we calculated theta relative power (absolute theta power
as a percentage of total power in the 4-13 Hz band). Furthermore, alpha
reactivity was calculated during memory activation, which is defined as
the percentile decrease in absolute alpha power during the
memorization period as compared to the eyes closed condition. EEG
power measures were averaged over all electrode positions.
EEG coherence was calculated by the Fast Fourier Transformation
method. Coherence between two waveforms x and y was calculated
spectrally as Cxy (f)=|Pxy(f)|² / Pxx(f)Pyy(f). where Pxy is the cross-power
spectral density of x and y and Pxx and Pyy are the power spectral
densities of x and y. All signal processing was performed using MATLAB
(The MathWorks, Natick, USA). The coherence between one electrode
and each of the other 16 electrodes was computed for each electrode, in
the alpha frequency band. These values were averaged to result in an
overall coherence measure. Coherence values range from 0 to 1 and
indicate the amount of covariation between two EEG signals in the
frequency spectrum. The average coherence can be seen as an overall
measure of synchronous oscillatory activity in the cortex. EEG
parameters were averaged over the three selected EEG samples
available for the eyes closed and working memory periods. We used the
following EEG parameters for further analysis: (1) theta relative power
during eyes closed (2) alpha reactivity during memory activation (3)
alpha coherence during eyes closed and (4) alpha coherence during
memory activation. We selected these parameters from a clinical
viewpoint, i.e. easily available, simple EEG measures with known
sensitivity to dementia. The choice of global EEG parameters was based
on previous research showing global EEG changes in both MCI and AD
patients [17, 24, 31, 35].
EEG data of two controls and one AD patient were later excluded
because of drowsiness. Data of two MCI patients were excluded because
of extremely low EEG voltage on most leads (<10 μV).
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EEG correlates in the spectrum of cognitive decline
Neuropsychological assessment
A standardized neuropsychological test battery was used to assess
cognitive functioning. The Cambridge Cognitive Examination (CAMCOG)
[30], which incorporates the Mini Mental State Examination [10], was
used to assess global cognitive functioning. CAMCOG subtests provided
subscores for memory and language. Memory function was further
tested with the Wechsler Memory Scale (WMS) [36], the picture learning
task used during EEG registration and word recall from the Alzheimer
Disease Assessment Scale (ADAS) [29]. Language ability was further
assessed using the Boston naming task (BNT) [18]. Tests of executive
functioning included letter (FAS) and category fluency (animals) [6], the
Trail Making Test consisting of a simple (trails A) and a complex version
(trails B) [28] and the digit symbol subtest of the Wechsler Adult
Intelligence Scale-Revised (WAIS-R) [37].
Statistical analysis
SPSS for Windows (release 12.0.1) was used for data analysis. Group
differences in sex, age and years of education were assessed using
parametric and nonparametric tests where appropriate. Univariate
ANOVA with age as covariate was used to compare neuropsychological
test scores and EEG measures between groups. Post-hoc Bonferroni
tests were performed when diagnostic group effects were found.
Subsequently, we used partial correlations, controlling for age, to
investigate relations between EEG power and coherence measures on
the one hand and neuropsychological test scores on the other. In view
of the exploratory nature of the study the level of significance was set at
pd0.01, despite the relatively large number of tests.
Results
Clinical characteristics
Clinical characteristics of the study population are shown in Table 1. Sex
and years of education did not differ between groups. However, AD
patients were significantly older than controls (p<0.005). Age was
therefore used as a covariate in all subsequent analyses. AD patients
scored significantly lower on all neuropsychological tests compared with
controls. Furthermore, AD patients had lower scores than MCI patients
on tests of global cognition, memory (CAMCOG memory and picture
learning), language, and executive functioning (trails A and digit
symbol). MCI patients performed worse than controls on tests of global
cognition, memory and category fluency. With the exception of the letter
fluency task, MCI patients scored in between controls and AD patients,
which confirms the concept of a spectrum of cognitive decline.
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Chapter 5
Table 1. Clinical characteristics of the study sample
male/ femaleª
age (yrs)
education (yrs)
Controls
MCI
AD
7/15
70 (5)
11 (3)
7/11
74 (5)
11 (4)
9/7
78 (8)*
10 (4)
28 (1)
96 (4)
121 (11)
23 (2)
19 (3)
25 (2)
26 (3)
34 (2)
33 (9)
21 (5)
42 (12)
96 (32)
43 (8)
24 (3)**
83 (6)**
94 (8)**
15 (4)**
12 (2)**
16 (3)**
24 (3)
32 (3)
33 (10)
15 (4)*
55 (17)
162 (87)
37 (9)
21 (4)** ^^
68 (9)** ^^
88 (13)**
12 (4)** ^
9 (3)**
10 (4)** ^^
19 (5)** ^
27 (5)** ^^
23 (7)*
12 (5)**
108 (72)** ^^
248 (76)**
17 (12)** ^^
Neuropsychological tests
global
memory
language
executive
functioning
MMSE (/30)
CAMCOG (/106)
WMS (Memory Quotient)
CAMCOG memory (/27)
ADAS (/30)
Picture learning (/30)
Boston (/30)
CAMCOG language (/38)
letter fluencyb
category fluency
trails A (s)c
trails B (s)b
digit symbol
Values in the table are means with SD in parentheses. Age corrected p-values are
noted. * differs from controls (p<0.01); ** differs from controls (p<0.001); ^ differs
from MCI patients (p<0.01); ^^ differs from MCI patients (p<0.001). ªF2-test was used
to assess group differences. bTests were administered in a smaller number of AD
patients (letter fluency: N=11 and trails B: N=7). clog transformed values were used for
data analysis. CAMCOG=Cambridge Cognitive Examination; WMS=Wechsler Memory
Scale; ADAS=Alzheimer Disease Assessment Scale.
EEG
EEG data are summarized in Table 2 and illustrated in Fig 1. We found
that theta relative power during eyes closed differed significantly
between diagnostic groups (F(2,52)=7.6, p<0.001). The covariate age
also showed a significant effect (F(1,52)=5.3, p<0.05). Bonferroni posthoc tests indicated that theta relative power was increased in AD
patients as compared with controls (p<0.001) and MCI patients
(p<0.01). MCI patients scored in between controls and AD patients. As
theta relative power was based on theta and alpha absolute power, we
investigated group differences in absolute power as well. We found that
absolute theta power was increased in AD patients as compared with
controls (F(2,52)=3.5, p<0.05) without differences in absolute alpha
power.
Furthermore, we discovered that the amount of alpha reactivity during
memory activation was significantly different between groups
(F(2,51)=6.4, p<0.005). Post-hoc tests indicated that alpha reactivity
was decreased in AD patients as compared with controls (p<0.005).
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EEG correlates in the spectrum of cognitive decline
Again, MCI patients scored intermediate of controls and AD patients.
The covariate age was not significant (F (1,51)=0.49, p=0.49).
The coherence parameters did not differ significantly between groups
during either eyes closed (F(2,52)=1.2, p=0.30) or memory activation
(F(2,51)=0.1, p=0.90).
Table 2. EEG differences between AD, MCI and controls
eyes closed
memory
theta relative power (%)
Controls
MCI
AD
25 (11)
33 (17)
51 (19) ** ^
alpha coherence
0.72 (0.8)
0.69 (0.7)
0.66 (0.4)
alpha reactivity (%)
38 (29)
16 (33)
0.2 (36)*
alpha coherence
0.67 (0.8)
0.67 (0.7)
0.65 (0.4)
Values in the table are means with SD in parentheses. Age corrected p-values are
noted. * differs from controls (p<0.01); ** differs from controls (p<0.001); ^ differs
from MCI patients (p<0.01).
Correlations between EEG and neuropsychological measures
Correlations are displayed in Table 3 and illustrated in Fig 2. Significant
correlations were observed between theta relative power during eyes
closed and tests of global cognition (CAMCOG), memory (WMS, ADAS
and picture learning), language (CAMCOG language) and executive
functioning (category fluency, trails B and digit symbol). In all cases,
decreased performance was related to higher theta relative power, i.e. a
slowing of the EEG.
Fig 1. Grand average power and coherence spectra
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Chapter 5
Alpha reactivity during memory activation was correlated with tests of
global cognition (CAMCOG), memory (WMS, ADAS and picture learning)
and executive functioning (category fluency, trails A and B and digit
symbol). In all cases, decreased performance indicated decreased alpha
reactivity during memory activation.
Alpha coherence during either eyes closed or memory activation was not
related to neuropsychological test performance.
Table 3. Correlations between EEG and neuropsychological measures
eyes closed
memory activation
theta
relative
power
alpha
coherence
alpha
reactivity
alpha
coherence
MMSE
-0.30
0.19
0.20
0.08
CAMCOG
-0.43***
0.26
0.35*
0.07
Neuropsychological tests
global
memory
language
WMS
-0.41**
0.25
0.40**
0.13
CAMCOG memory
-0.29
0.19
0.29
-0.01
ADAS
-0.45***
0.08
0.44***
0.02
Picture learning
-0.49***
0.27
0.55***
-0.001
Boston
-0.29
0.22
0.33
-0.02
CAMCOG language
-0.49***
0.33
0.24
0.20
executive
letter fluency
-0.29
0.21
0.38
-0.02
functioning
category fluency
-0.37*
0.12
0.43**
-0.06
trails Aª
0.32
-0.09
-0.38**
0.11
trails B
0.13
-0.10
-0.29
-0.01
digit symbol
-0.39*
0.17
0.48***
-0.05
Values in the table are partial correlation coefficients (controlled for age). * p<0.01; **
p<0.005; *** p<0.001. Significant correlations are printed in bold. ªlog transformed
values were used for data analysis.
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EEG correlates in the spectrum of cognitive decline
Discussion
The current study examined EEG power and coherence in elderly with
different levels of cognitive functioning, ranging from controls without
cognitive complaints to AD. EEG was registered during both eyes closed
and memory activation. Furthermore, an extensive neuropsychological
test battery was used to assess cognitive functioning. We observed
significant correlations between EEG power and cognitive functioning in
several domains, including global cognition, memory, language and
psychomotor speed. Overall alpha coherence was however not
correlated with performance on any neuropsychological test.
EEG power
As in earlier studies, we observed a slowing of the rest EEG in AD
patients, reflected in higher absolute and relative theta power. An
increase in theta power is considered one of the earliest changes in AD,
whereas alpha activity decreases later during the course of the disease
[9, 16]. As the AD patients in our study were only mildly impaired, our
findings are compatible with these.
Alpha reactivity during memory activation was significantly decreased in
AD patients only. In previous research decreased lower and upper alpha
reactivity has been linked with worse memory performance and is
thought to reflect attentional abilities and semantic memory processes
[20]. Consequently, in view of their memory impairment, we expected
to find decreased alpha reactivity in MCI patients as well. This was
indeed found in a previous study by our research group, particularly in
the lower alpha band [35]. In the current study we decided not to split
the alpha band for several reasons. Firstly, the use of lower and upper
alpha bands is not consistently used in EEG research in memory clinic
populations, limiting the generalizability of our findings [2, 5, 14, 15,
25, 25]. Secondly, we did not expect any additional results as the lower
and upper alpha bands respond similarly to memory activation. In fact,
when comparing only MCI patients and controls, we did find decreased
alpha reactivity during memory activation in MCI patients. Finally, we
aimed to restrict the number of EEG parameters in the study as much as
possible.
Correlations between EEG power and cognitive performance
Increased theta relative power and decreased alpha reactivity indicated
worse neuropsychological test performance in several cognitive
domains. Our findings confirm earlier reports on relations between
increased theta activity and lower scores on the GDS or MMSE [7, 26].
In addition to relations with global cognition, theta relative power was
associated with memory performance and executive functioning in a
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Chapter 5
negative manner. Furthermore, it was the only EEG measure related to
language performance in a similar negative manner. Theta power during
rest has been found to increase with dementia and decreasing cognitive
performance in general [20]. Apparently, theta power during rest is
correlated with global cognitive functioning and cannot be linked to any
cognitive domain in particular.
Previous studies discovered similar relations between decreased alpha
(re)activity and worse performance on memory tasks [20, 35]. In
addition, the current study revealed pronounced positive relations
between alpha reactivity and performance on executive tests. In a lesser
degree, alpha reactivity was positively associated with global cognition.
Reactivity of the lower alpha band has been linked with general task
demands and attentional abilities [20]. As our time-limited, executive
tasks required high attentional resources, the pronounced relation
between alpha reactivity and tests of executive functioning (i.e. category
fluency, trails and digit symbol) seems plausible.
As both EEG power measures complement each other in their relations
to cognitive functioning, their combination might prove useful in
predicting longitudinal cognitive decline or subsequent dementia in MCI
patients. Longitudinal studies are needed in this respect.
EEG coherence
We did not find any group differences in alpha coherence during eyes
closed or memory activation. These results contradict earlier research.
An overall finding in AD is decreased alpha coherence during either rest
or memory activation, which has been observed in several electrode
pairs [1, 13, 16]. In MCI, alpha coherence was found to be increased in
several inter-hemispheric electrode pairs, but only during memory
activation [17]. However, the methodologies of the above mentioned
studies vary widely and coherence effects were found in varying
locations. The absence of a group effect in the current study might be
due to several factors including the use of a global coherence measure,
the correction for age or differences in disease severity. At present we
cannot distinguish between these possibilities.
Correlations between EEG coherence and cognitive performance
Previous cross-sectional studies did not discover any associations
between intra- and interhemispheric coherences in several frequency
bands and cognitive performance [1, 17, 19]. As most of these studies
used only MMSE scores as measure of cognitive performance, we
attempted to look into relations with a more diverse range of
neuropsychological tests. However, the current study did not reveal any
associations between alpha coherence and cognitive performance either.
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EEG correlates in the spectrum of cognitive decline
As EEG power was correlated with performance in multiple cognitive
domains, we conclude that measures of EEG power are superior over
coherence in their relations to cognition and are possibly more suitable
for studies of longitudinal cognitive decline and the prediction of
dementia.
Advantages of the current study include the use of an extensive
neuropsychological test battery, which enabled us to correlate EEG
measures with performance in several cognitive domains. EEG was
registered during both eyes closed and memory activation. Furthermore,
we attempted to limit the number of parameters in the correlation
analysis by choosing four global EEG parameters. This approach is
justified by previous studies that have observed global EEG changes in
both MCI and AD patients [17, 24, 31, 35]. The emphasis on possible
clinical utility lies at the basis of the choices we made, and explains a
preference for a small set of evidence-based EEG parameters that
cannot be affected overly by recording problems, and are therefore
suitable for clinical practice. This approach has its limitations as we were
unable to look into location effects or examine a wider frequency range.
Previous research using a more refined and detailed approach have
unraveled interesting EEG and MEG abnormalities in MCI and AD [3, 4,
11, 31-33]. However, we have from the outset chosen a different
approach, based on the fear that the detailed approach may certainly be
valuable from a scientific point of view, but runs the risk of losing the
ultimate goal of all medical research from sight, and that is to be
clinically useful.
In conclusion, power measures reflecting slowing and decreased
reactivity of the EEG, but not coherence measures, are associated with
decreased performance in multiple cognitive domains. These EEG power
measures might prove useful in prospective studies aimed at predicting
longitudinal cognitive decline and dementia. Furthermore, they are
potentially valuable for evaluating the efficiency of cognitive
enhancement therapies. EEG during alternative cognitive paradigms
targeting language or executive functioning might also be useful in this
respect.
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Chapter 5
Acknowledgements
We thank E.L. Tiesma, A. van der Welle, P.J. van Someren, J.G. van
Vliet- de Regt, M.J. Stijl-Pek, G.H. van Beukering-Louwes, J.C. Albus-de
Meza, A.J.C. van der Kamp-Huyts and F.I. Kerkhof for their help in data
acquisition. Furthermore, we are grateful to M.W. Pleket, A.W.E.
Weverling- Rijnsburger and J.L. Mulder for their role in patient
recruitment and neuropsychological testing at the Diaconessenhuis in
Leiden and the Leyenburg hospital in The Hague.
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EEG correlates in the spectrum of cognitive decline
d
Fig
g 2. Scatterplo
ots of the strong
gest correlation
ns per cognitive
e domain
A. Global cognitio
on and theta relative
po
ower
B. Memory and a
alpha reactivity
y
C. Language and theta relative power
p
D.
D Executive fun
nctioning and alpha
re
eactivity
- 87 -
Chapter 5
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