Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease
As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate linear/nonline...
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| Date: | 2015 |
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Інститут фізіології ім. О.О. Богомольця НАН України
2015
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| Cite this: | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease / O. Vyšata, M. Vališ, A. Procházka, R. Rusina, L. Pazdera // Нейрофизиология. — 2015. — Т. 47, № 1. — С. 55-61. — Бібліогр.: 18 назв. — англ. |
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| author | Vyšata, O. Vališ, M. Procházka, A. Rusina, R. Pazdera, L. |
| author_facet | Vyšata, O. Vališ, M. Procházka, A. Rusina, R. Pazdera, L. |
| citation_txt | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease / O. Vyšata, M. Vališ, A. Procházka, R. Rusina, L. Pazdera // Нейрофизиология. — 2015. — Т. 47, № 1. — С. 55-61. — Бібліогр.: 18 назв. — англ. |
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| description | As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant
neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of
electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate
linear/nonlinear synchronization between EEG samples recorded from different leads. Mutual
information was applied to the complex wavelet coefficients in wavelet scales to estimate
nonlinear synchronization. Synchronization rates for a group of 110 patients with moderate
AD (MMSE score 10 to 19) and a group of 110 healthy control subjects were compared. The
most significant decrease in mutual information in AD patients was observed on the third
scale in the fronto-temporal area and for wavelet coherence within the same areas as for
mutual information; these areas are preferentially affected by atrophy in AD. The new method
used utilizes mutual information in wavelet scales and demonstrates larger discriminatory
values in AD compared to wavelet coherence.
Як відомо, хвороба Альцгеймера (ХА) пов’язана з прогресуючим когнітивним дефіцитом у результаті істотної загибелі
нейронів. Зменшення міжнейронних зв’язків може проявлятись як зміни ступеню синхронізації електричної активності взаємодіючих мозкових структур. Ми використовували
методику оцінки вейвлет-когерентності для оцінки лінійної
або нелінійної синхронізації зразків ЕЕГ, відведених від різних локусів кори. Визначення індексів взаємної інформації
використовувалося для оцінки нелінійної синхронізації згідно з комплексними вейвлет-коефіцієнтами за вейвлет-шкалами. Було порівняно ступені синхронізації ЕЕГ-активності в групі пацієнтів, що страждали на ХА помірної тяжкості
(оцінки за MMSE від 10 до 19 балів), та в групі із 110 контрольних здорових суб’єктів. Найістотніші зменшення індексів взаємної інформації у пацієнтів із ХА спостерігалися
по третій шкалі для фронто-темпоральної зони; зменшення
вейвлет-когерентності відзначались у тих самих зонах, що й
зміни взаємної інформації. Саме ці зони зазнають переважної атрофії при ХА. Використаний новий метод базується на
оцінках взаємної інформації за вейвлет-шкалами та демонструє більшу дискримінаційну здатність в умовах ХА, аніж
визначення вейвлет-когерентності.
|
| first_indexed | 2025-12-07T16:26:42Z |
| format | Article |
| fulltext |
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 1 55
UDC 616.83/003.99:616.072
O. VYŠATA,1,2 M. VALIŠ,2 A. PROCHÁZKA,1 R. RUSINA,3,4 and L. PAZDERA5
LINEAR AND NONLINEAR EEG SYNCHRONIZATION IN ALZHEIMER’S DISEASE
Received October 30, 2013
As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant
neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of
electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate
linear/nonlinear synchronization between EEG samples recorded from different leads. Mutual
information was applied to the complex wavelet coefficients in wavelet scales to estimate
nonlinear synchronization. Synchronization rates for a group of 110 patients with moderate
AD (MMSE score 10 to 19) and a group of 110 healthy control subjects were compared. The
most significant decrease in mutual information in AD patients was observed on the third
scale in the fronto-temporal area and for wavelet coherence within the same areas as for
mutual information; these areas are preferentially affected by atrophy in AD. The new method
used utilizes mutual information in wavelet scales and demonstrates larger discriminatory
values in AD compared to wavelet coherence.
Keywords: Alzheimer’s disease (AD), electroencephalography, EEG synchronization,
mutual information, wavelet coherence.
1 Department of Computing and Control Engineering, Institute of Chemical
Technology, Prague, Czech Republic.
2 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles
University, Hradec Králové, Czech Republic.
4 Department of Neurology, Thomayer Hospital and Institute for Postgraduate
Education in Medicine, Prague, Czech Republic.
5 Department of Neurology and Centre of Clinical Neuroscience, First Faculty
of Medicine, Charles University in Prague, and General University Hospital
in Prague, Czech Republic.
6 Neurocentre Caregroup Ltd., Rychnov nad Kneznou, Czech Republic.
Correspondence should be addressed to O. Vyšata
(e-mail vysatao@gmail.com)
INTRODUCTION
The level of synchronization of neural activity is an
important parameter that demonstrates the intensity
of coordination of activities of different parts of the
brain [1].
The theory of complex systems describes several
types of synchronization. Identical oscillators must be
sufficiently interlinked for complete synchronization
to occur. In the electroencephalogram, this type of
synchronization resembles the situation observed in
epileptic seizures. Generalized synchronization [2]
reflects specific functional relationships between the
states of two systems. Phase synchronization, first
described using coupled chaotic oscillators [3], might
present noncorrelated amplitudes of the respective
oscillations.
Alzheimer’s disease (AD) is associated with the
loss of synchronization between EEGs recorded
from different sites (channels), which, in addition to
slowing down of background activity and decrease
in its complexity, provides a promising target for
analyses [4]. Currently, there are efforts to identify
the most sensitive method for estimation of the
synchronization loss in the diagnosis of early stages of
AD. Different techniques have been used to estimate
the extent of synchronization between two or more
EEG processes. Linear relationships between signals
might be estimated using cross-correlation functions.
Moreover, the frequency range correlation might be
estimated using the spectral coherence function, and
correlations in wavelet scales might be estimated
using wavelet coherence. Unlike other techniques,
wavelet coherence exhibits an advantage related
to the greater time and frequency resolution and is
considered an effective tool for identification of the
changes in brain activity during aging and in the early
AD stages [1]. The usefulness of wavelet coherence in
EEG analysis was first proposed by Lachaux in 2002
[5]. In 2006, Klein showed that wavelet coherence is
a more sensitive indicator of the EEG changes during
sensory stimulation compared to traditional coherence
[6]. In 2006, Sakkalis applied this method to study the
disconnection syndrome in schizophrenia [7]. The first
attempt to study changes in AD using EEG wavelet
coherence was described by Sankari in 2012. Mutual
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 156
O. VYŠATA, M. VALIŠ, A. PROCHÁZKA et al.
information derived from the theory of information
was used to estimate the degree of nonlinear
correlation between EEGs of different channels [8, 9].
This technique has been repeatedly used to estimate
nonlinear EEG associations in patients with AD.
However, this technique has never been used to assess
EEGs in multiple frequency bands. A certain reduction
in mutual information has been previously reported in
the frontal and right anterior temporal areas and in the
inter-hemispheric pathways in the respective cases
[10]. Transfer entropy [11], Granger causality [12],
and nonlinear interdependence [13] are other nonlinear
measures of synchronization.
Because different brain subsystems produce
oscillations of different frequencies, it is expedient
to study interrelations between such oscillations for
different frequency ranges. The spectral correlation
function and wavelet coherence are linear measures
of the similarity that provide a multiband perspective.
Because EEG signals are, in principle, nonstationary,
wavelet transformation-based coherence rather than
Fourier transformation-based coherence is suitable
for modeling the relationships between EEG channels.
Thus, wavelet coherence was used to evaluate the
degree of linear relationship in our study. Transfer
entropy and Granger causality are asymmetric
measures that determine the direction of information
propagation and are difficult to be compared with
wavelet coherence results.
The nonlinear interdependence method [13] relies
on the state of space reconstruction and is more
suitable for the description of chaotic oscillators.
As the presence of a deterministic chaos in EEG is
a controversial topic, we used mutual information in
wavelet scales to estimate nonlinear relationships.
Among the avai lable wavelet t ransformation
techniques, we used complex wavelet transformation.
It is expected that brain dynamics are strongly
affected by neuroanatomical connectivity. Alzheimer’s
disease is a progressive neurodegenerative pathology
clinically characterized by significantly impaired
memory and other cognitive dysfunctions. Previous
studies have shown that this disorder is associated
not only with regional brain abnormalities but also
with changes in the neuronal connectivity between
anatomically distinct brain regions. Cortical areas
of patients with AD show suboptimal topological
organization [14]. A global connectivity deficit was
found in AD [15].
Memory and cognitive impairments are associated
with changes in the coordination of activities of
functional neural networks. Using neurophysiological
and imaging techniques, as well as computational
approaches based on graph theory, Stem et al. [16]
showed that AD patients demonstrate impaired
neuronal integrity in the major structural and functional
systems of the brain such as the associative cortex,
hippocampus, prefrontal cortex, and cerebellum. In
this case, it is expedient to observe changes in the
functional organization of the brain in patients with
AD under resting conditions (as the background
pattern) [17].
The aim of our study was to compare the selected
linear and nonlinear association estimates of EEG
samples between patients suffering from AD and
control subjects.
METHODS
Subjects. In our prospective study, the EEG data were
obtained during examinations of 110 AD patients with
moderate dementia (MMSE score 10 to 19). All these
subjects underwent brain CT, as well as neurological
and neuropsychological examinations. The control
group consisted of 120 age-matched healthy subjects
with no memory or other cognitive impairments.
All of these patients had normal neuropsychological
examinations and did not undergo brain CT. The mean
MMSE of the AD group was 15.8 ± 1.7 (M ± s.d.). The
mean age of the AD patients and control subjects was
71.5 ± 6.8 and 69.1 ± 5.7 years, respectively. There
were 52 men and 68 women in the AD group and
54 men and 66 women in the control group.
EEG Recording and Preprocessing. All recordings
were performed under similar standard conditions.
The subjects were placed in a comfortable position,
on a bed, with their eyes closed. The electrodes
were positioned according to the 10-20 electrode
placement system; the recording was conducted using
a 21-channel digital EEG setup (TruScan 32, Alien
Technik Ltd., Czech Republic) with a 22-bit AD
conversion and a sampling frequency of 128 sec–1.
The filter settings were 0.5 and 60 Hz. The linked ear
contacts were used as references.
Stored digitized data were zero-phase digitally
filtered using a bandpass FIR filter (100 coefficients,
Hamming window) of 0.5–60 Hz and a bandstop
filter of 49–51 Hz. The analysis began after manual
removal of the artefacts. Five to six 60- to 80-sec-long
segments were manually selected from each 20-min-
long record. All of the curves in these segments were
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 1 57
LINEAR AND NONLINEAR EEG SYNCHRONIZATION IN ALZHEIMER’S DISEASE
normalized using the median.
Mutual Information and Wavelet Coherence
Estimation. Both measures of similarity were
estimated for all EEG channel pairs (171) in five
wavelet scales. The mutual information was calculated
for absolute values of the wavelet transformation
coefficients in each scale. Continual wavelet
coherence values were averaged in each segment for
each electrode pair, and calculations were performed
using MATLAB.
Statistics. We estimated the presence of significant
differences between the values for the AD and
control groups using a two-sample t-test. The data
met the criteria for the Shapiro–Wilk test for the
normal distribution. In addition, records from
171 electrode pairs were compared in five wavelet
scales in both groups, and P values were adjusted using
the Bonferroni approach for multiple comparisons
(n = 855).
RESULTS
The number of electrode pairs with a statistically
significant increase in mutual information decreased
from the first to the fifth wavelet scale, i.e., decreased
with decreasing frequency (Table 1). An inverse trend
was evident for a reduction in mutual information.
There was no statistically significant reduction for
any pair of electrodes on the first or second scale.
In AD patients, the centroparietal area showed the
most significant increase in mutual information in
all wavelet scales, which was mainly believed to be
related to short connections between neighboring
sites. In contrast, the frontolateral and temporal areas
showed maximum reductions of this value in the
third to fifth wavelet scales (Fig. 1). Furthermore,
the wavelet coherence decreased in the same area,
predominantly for the right hemisphere, in all of the
Fp1
A
C
B
D
Fp1
Fp1Fp1
F7 F7
F7F7
F3 F3
F3F3
F4
F4
F4
F4
F8 F8
F8F8
F8
F8
F8F8
С3 С3
С3С3
С4 С4
С4С4
T4 T4
T4T4
T5 T5
T5T5
T6 T6
T6T6
P5 P5
P5P5
P4 P4
P4P4
Pz Pz
PzPz
O1 O1
O1O1
O2 O2
O2O2
Сz Сz
СzСz
Fz Fz
Fz
Fz
Fp2 Fp2
Fp2Fp2
F i g. 1. Location of the most statistically significant electrode pairs for wavelet coherence and mutual information in the second and third
scales. Reduction is depicted in gray, and increase is shown in black. A and B) Mutual information scales; C and D) wavelet coherence
scales.
Р и с. 1. Локалізація найбільш статистично вірогідних локусів відведення ЕЕГ при визначенні індексів вейвлет-когерентності та
взаємної інформації за другою та третьою шкалами.
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 158
O. VYŠATA, M. VALIŠ, A. PROCHÁZKA et al.
scales, but with the most significant values in the
second and first wavelet scales (Table 2, Fig. 1).
Distribution of the significant values for the
increase in wavelet coherence differed from the
mutual information and was the most significant for
long frontoparietal and frontooccipital pathways.
TABLE 2: Four pairs of channels with the most significant differences in wavelet coherence and mutual information in the wavelet
scale values for patients with Alzheimer’s disease (AD) and control-group (CG) subjects. All differences were significant at P < 0.01
(after Bonferroni correction, 1.2∙10–5)
Т а б л и ц я 2. Чотири пари локусів, для яких спостерігалися найістотніші відмінності значень вейвлет-когерентності та
взаємної інформації у пацієнтів із хворобою Альцгеймера та осіб групи контролю
Wavelet scale
Wavelet coherence
Reduction Increase
Localization CG AD Localization CG AD
1
F7-Fz 0.71±0.07 0.61±0.09 Fz-O1 0.54±0.05 0.61±0.08
F4-F8 0.82±0.07 0.72±0.10 F3-O1 0.54±0.05 0.59±0.08
Fz-F8 0.71±0.08 0.60±0.09 Fp2-O1 0.54±0.05 0.59±0.08
Fp2-F8 0.80±0.08 0.68±0.12 F4-O1 0.55±0.06 0.60±0.08
2
Fz-F8 0.82±0.04 0.76±0.05 Fp2-Pz 0.72±0.04 0.76±0.06
F7-Fz 0.82±0.04 0.75±0.05 F7-P4 0.73±0.03 0.76±0.04
F4-F8 0.89±0.04 0.82±0.05 F7-Pz 0.72±0.03 0.76±0.05
F4-T4 0.81±0.05 0.74±0.04 Fz-O1 0.72±0.03 0.76±0.04
3
F8-T4 0.89±0.04 0.85±0.03 F7-P4 0.79±0.02 0.84±0.03
T4-T6 0.87±0.03 0.83±0.03 F7-Pz 0.79±0.02 0.84±0.04
F4-C4 0.89±0.03 0.85±0.05 Fz-O1 0.79±0.02 0.84±0.03
F4-F8 0.91±0.03 0.87±0.04 Fp2-Pz 0.79±0.03 0.84±0.04
4
F8-T4 0.91±0.02 0.89±0.03 F7-P4 0.85±0.02 0.89±0.03
Fp2-F8 0.94±0.03 0.90±0.04 F7-Pz 0.85±0.02 0.89±0.03
F4-C4 0.92±0.02 0.90±0.04 T3-Pz 0.85±0.01 0.88±0.02
T4-T6 0.90±0.02 0.88±0.03 F8-P3 0.84±0.02 0.89±0.03
5
F8- T4 0.94±0.02 0.91±0.03 Pz-P4 0.90±0.04 0.94±0.03
Fp2- F8 0.96±0.02 0.93±0.03 P3-Pz 0.90±0.03 0.92±0.03
Fp2- T4 0.93±0.02 0.90±0.03 Pz-O2 0.90±0.03 0.93±0.03
T4- T6 0.93±0.02 0.91±0.03 F7-Pz 0.90±0.02 0.92±0.03
TABLE 1: Number of channel pairs with significantly reduced and increased wavelet coherence and mutual information scales in the
wavelet scale at P < 0.01 (after Bonferroni correction; 1.2∙10–5)
Т а б л и ц я 1. Кількість пар локусів відведення ЕЕГ з істотними зменшеннями або збільшеннями індексів вейвлет-
когерентності та взаємної інформації за вейвлет-шкалою з P < 0.01
Wavelet scale
Wavelet coherence Mutual information
Decrease Increase Decrease Increase
1 72 39 0 171
2 79 41 0 166
3 47 67 16 100
4 19 104 21 85
5 72 37 125 17
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 1 59
LINEAR AND NONLINEAR EEG SYNCHRONIZATION IN ALZHEIMER’S DISEASE
A good discriminatory ability of mutual information
was apparent on the representative histograms plotted
from the ROC curves and accuracy values; examples
are presented in Fig. 2. The accuracy of the ROC curve
separation was 92.5% (Fig. 2).
DISCUSSION
Linear and nonlinear estimates of EEG channel
synchronization suggest that two processes are
involved, where one process results in reduced
high-frequency values in patients with AD in the
frontolateral and parietal areas. Another process results
in increases in the frontoparietal and frontooccipital
values in the lowest-frequency wavelet scales. The
increase exhibits an even higher discriminatory
value when compared with the other two groups
than the reduction. A previous comparative study [1]
showed reductions in the wavelet coherence for the
temporolateral, temporoparietal and temporooccipital
areas in the delta range and for the majority of the
electrode pairs in the alpha, theta, and beta bands.
In contrast to our results, Sankari et al. [1] found
no statistically significant predominant increases in
the wavelet coherence in the frontal and frontopolar
electrodes, most likely due to a considerably smaller
patient sampling.
The lack of a significant reduction in the first and
second wavelet scales when estimating nonlinear
relationships between the channels using mutual
information indicated the importance of using this
synchronization measure in multiple frequency bands.
When comparing the wavelet coherence and mutual
information in wavelet scales, the most significant
increase in mutual information was observed at the
lowest frequencies, and the most significant reduction
was observed at the highest frequencies. We did not
observe this frequency-dependent pattern for wavelet
coherence.
The differences between both methods suggested
that the interrelationship between the EEGs recorded
from different channels was nonlinear. This newly
proposed method utilizes mutual information on
absolute values of the complex wavelet coefficient
in the wavelet scales, and it displayed a greater
discriminatory value compared to wavelet coherence
(Table 2).
Most strikingly, AD patients, compared to the
control group, demonstrated increased synchro-
nization with the maximum in the third wavelet
scale in the centroparietal area for both linear and
nonlinear synchronization rates. Similar changes were
observed when the subjects were presented with fear-
inducing stimuli. We assumed that our patients with
AD developed some stress responses and increase in
the anxiety level caused by an unknown environment
of the EEG laboratory. This was potentiated by the
presence of medical instruments and machinery in the
room. However, the decrease in the values for both
methods in the frontolateral area was an expected
consequence of atrophy-dependent disorders of
neuroanatomical connectivity in the frontal and
temporal lobes in patients suffering from AD [4, 18].
Both techniques are based on wavelet transformation,
which is more suitable for multiresolution analysis of
nonlinear and nonstationary signals such as EEGs,
compared to Fourier transformation that requires
linearity. Mutual information that estimates linear
as well as nonlinear relationships has a higher
discriminatory potency for the comparison of the
two groups, which supports the presence of nonlinear
relationships between EEG channels.
Continuous wavelet coherence also enables monitoring
of temporal changes between channels in different
wavelet scales. However, this advantageous feature was
lost in our study because the evaluated sections were
averaged. The differences in the relationship timeline
may provide additional information and enhance the
usefulness of this method. Because the calculated
value for mutual information is dependent on the signal
amplitude, there is a methodological question of whether
50
11.40 10.44 9.48 8.52 7.56 6.60 5.64 4.68 3.72 2.76 1.80
55
60
65
70
75
80
85
90
F i g. 2. Example of accuracy of calculation for an increase in mutual
information in Alzheimer’s disease for the P3-Pz electrode pair.
Р и с. 2. Приклад розрахунку точності щодо збільшення
значень взаємної інформації при хворобі Альцгеймера для пари
відведень P3-Pz.
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 160
O. VYŠATA, M. VALIŠ, A. PROCHÁZKA et al.
it is expedient to normalize the EEG signals and how
to do so. The absolute value of normalization that is
sensitive to high-amplitude artifacts can be applied
for normalization using the median of the absolute
signal values; this was employed in our study. The
median normalization of the absolute values of signal
samples can also be considered a robust method.
Furthermore, normalization of the absolute values of
wavelet coefficients or histograms can also be used. To
obtain optimum results, the discriminatory value for
different parent wavelets should be compared. Higher-
frequency resolution and information regarding the phase
relationships of signals between channels represents an
advantage of continuous wavelet coherence over mutual
information in the wavelet scales. Thus, we did not
realize calculations within conventional EEG frequency
bands, as would be allowed by conventional wavelet
coherence, but made this within wavelet scales. Smaller
values were observed in AD patients compared to healthy
controls in the left temporocentral and temporoparietal
areas, as well as in the right temporocentral and
temporooccipital areas.
Patients with AD exhibited a moderate disability,
and this circumstance somewhat reduces the clinical
use of these parameters. This parameter set could be
of some diagnostic importance during the early stages
of AD and in the case of a minimum cognitive deficit,
if combined with biomarkers and MRI markers, which
would require a further study related to MCI and mild
AD.
Thus, we have applied a new technique of mutual
information between the absolute values of complex
wavelet coefficients as a sensitive technique that
may help to detect disorders in the neuroanatomical
connectivity in patients suffering from AD. The ability
of this technique to monitor nonlinear relationships
between EEG channel records is probably the most
important factor responsible for more statistically
significant results in the detection of moderate
AD compared to using wavelet coherence. The
dependences on the frequency using this approach by
wavelet scales and on the localization of evaluated
electrode pairs suggest that there is a need to evaluate
various frequency bands and various locations.
Acknowledgment. This study was supported by the
research grant of the “Alzheimer’s Foundation,” Prague, Czech
Republic.
The study was carried out in agreement with the
contemporary internationally accepted ethical requirements
related to research involving humans and approved by the
Ethical Committees of the Institutions where the authors
work. Informed written consent was obtained from all subjects
involved in the tests and attending physicians of patients
suffering from Alzheimer’s disease.
The authors, O. Vyšata, M. Vališ, A. Procházka, R. Rusina,
and L. Pazdera, declare that the research and publication of
the results were not associated with any conflicts regarding
commercial or financial relations, relations with organizations
and/or individuals who may have been related to the study, and
interrelations of co-authors of the article.
O. Вишата1,2, M. Валіх2, A. Прохазка1, Р. Русина3,4, Л. Паз-
дера5
ЛІНІЙНА ТА НЕЛІНІЙНА СИНХРОНІЗАЦІЯ ЕЕГ У
ПАЦІЄНТІВ ІЗ ХВОРОБОЮ АЛЬЦГЕЙМЕРА
1 Інститут хімічної технології, Прага (Чеська Республіка).
2 Карлов Університет, Градець Кральове (Чеська
Республіка).
3 Лікарня Томайєр та Інститут медичної післявузівської
освіти, Прага (Чеська Республіка).
4 Перший медичний факультет Карлова Університету в
Празі та Головна університетська лікарня, Прага (Чеська
Республіка).
5 Група клінічного догляду Нейроцентру, Рихнов над Кнез-
ноу (Чеська Республіка).
Р е з ю м е
Як відомо, хвороба Альцгеймера (ХА) пов’язана з прогресу-
ючим когнітивним дефіцитом у результаті істотної загибелі
нейронів. Зменшення міжнейронних зв’язків може проявля-
тись як зміни ступеню синхронізації електричної активнос-
ті взаємодіючих мозкових структур. Ми використовували
методику оцінки вейвлет-когерентності для оцінки лінійної
або нелінійної синхронізації зразків ЕЕГ, відведених від різ-
них локусів кори. Визначення індексів взаємної інформації
використовувалося для оцінки нелінійної синхронізації згід-
но з комплексними вейвлет-коефіцієнтами за вейвлет-шка-
лами. Було порівняно ступені синхронізації ЕЕГ-активнос-
ті в групі пацієнтів, що страждали на ХА помірної тяжкості
(оцінки за MMSE від 10 до 19 балів), та в групі із 110 конт-
рольних здорових суб’єктів. Найістотніші зменшення індек-
сів взаємної інформації у пацієнтів із ХА спостерігалися
по третій шкалі для фронто-темпоральної зони; зменшення
вейвлет-когерентності відзначались у тих самих зонах, що й
зміни взаємної інформації. Саме ці зони зазнають переваж-
ної атрофії при ХА. Використаний новий метод базується на
оцінках взаємної інформації за вейвлет-шкалами та демон-
струє більшу дискримінаційну здатність в умовах ХА, аніж
визначення вейвлет-когерентності.
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 1 61
LINEAR AND NONLINEAR EEG SYNCHRONIZATION IN ALZHEIMER’S DISEASE
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| id | nasplib_isofts_kiev_ua-123456789-148136 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 0028-2561 |
| language | English |
| last_indexed | 2025-12-07T16:26:42Z |
| publishDate | 2015 |
| publisher | Інститут фізіології ім. О.О. Богомольця НАН України |
| record_format | dspace |
| spelling | Vyšata, O. Vališ, M. Procházka, A. Rusina, R. Pazdera, L. 2019-02-17T10:40:07Z 2019-02-17T10:40:07Z 2015 Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease / O. Vyšata, M. Vališ, A. Procházka, R. Rusina, L. Pazdera // Нейрофизиология. — 2015. — Т. 47, № 1. — С. 55-61. — Бібліогр.: 18 назв. — англ. 0028-2561 https://nasplib.isofts.kiev.ua/handle/123456789/148136 616.83/003.99:616.072 As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate linear/nonlinear synchronization between EEG samples recorded from different leads. Mutual information was applied to the complex wavelet coefficients in wavelet scales to estimate nonlinear synchronization. Synchronization rates for a group of 110 patients with moderate AD (MMSE score 10 to 19) and a group of 110 healthy control subjects were compared. The most significant decrease in mutual information in AD patients was observed on the third scale in the fronto-temporal area and for wavelet coherence within the same areas as for mutual information; these areas are preferentially affected by atrophy in AD. The new method used utilizes mutual information in wavelet scales and demonstrates larger discriminatory values in AD compared to wavelet coherence. Як відомо, хвороба Альцгеймера (ХА) пов’язана з прогресуючим когнітивним дефіцитом у результаті істотної загибелі нейронів. Зменшення міжнейронних зв’язків може проявлятись як зміни ступеню синхронізації електричної активності взаємодіючих мозкових структур. Ми використовували методику оцінки вейвлет-когерентності для оцінки лінійної або нелінійної синхронізації зразків ЕЕГ, відведених від різних локусів кори. Визначення індексів взаємної інформації використовувалося для оцінки нелінійної синхронізації згідно з комплексними вейвлет-коефіцієнтами за вейвлет-шкалами. Було порівняно ступені синхронізації ЕЕГ-активності в групі пацієнтів, що страждали на ХА помірної тяжкості (оцінки за MMSE від 10 до 19 балів), та в групі із 110 контрольних здорових суб’єктів. Найістотніші зменшення індексів взаємної інформації у пацієнтів із ХА спостерігалися по третій шкалі для фронто-темпоральної зони; зменшення вейвлет-когерентності відзначались у тих самих зонах, що й зміни взаємної інформації. Саме ці зони зазнають переважної атрофії при ХА. Використаний новий метод базується на оцінках взаємної інформації за вейвлет-шкалами та демонструє більшу дискримінаційну здатність в умовах ХА, аніж визначення вейвлет-когерентності. This study was supported by the research grant of the “Alzheimer’s Foundation,” Prague, Czech Republic. en Інститут фізіології ім. О.О. Богомольця НАН України Нейрофизиология Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease Лінійна та нелінійна синхронізація ЕЕГ у пацієнтів із хворобою Альцгеймера Article published earlier |
| spellingShingle | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease Vyšata, O. Vališ, M. Procházka, A. Rusina, R. Pazdera, L. |
| title | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease |
| title_alt | Лінійна та нелінійна синхронізація ЕЕГ у пацієнтів із хворобою Альцгеймера |
| title_full | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease |
| title_fullStr | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease |
| title_full_unstemmed | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease |
| title_short | Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease |
| title_sort | linear and nonlinear eeg synchronization in alzheimer’s disease |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/148136 |
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