Analysis of EEG Structural Synchrony in Adolescents with Schizophrenic Disorders
ABSTRACT A total of 39 healthy adolescents and 45 adolescents with schizophrenic disorders (mean age 12.3 years) were examined to study the EEG structural synchrony as reflecting temporal synchronization of the operational activity of neuronal networks. A significant decrease in the EEG structural synchrony was observed in the adolescents with schizophrenic disorders as compared to the healthy adolescents. The decrease was detected predominantly in the interhemispheric pairs of EEG derivations, as well as in the pairs related to the frontal, temporal (predominantly on the left), and right parietocentral regions. The findings provide evidence in favor of Friston’s hypothesis of disintegration of cortical electrical activity in schizophrenia and extend the hypothesis in that it is the operational synchrony of cortical activity that might suffer first in schizophrenia.
- SourceAvailable from: Alexander & Andrew Fingelkurts[Show abstract] [Hide abstract]
ABSTRACT: In the present study, we explore the operational architectonics of alpha activity in different normal and pathological brain states. Aggregated analysis of a set of diverse previously conducted EEG/MEG experimental studies was performed within the same methodological and conceptual framework. It was shown that the characteristics of short alpha activity periods (segments), as well as the spatial structural synchrony of alpha activity, changed considerably in accordance with the type of brain functional state, stimulation, cognitive task, pharmacological influence, and the type of pathology. The results of this study suggest that particular neurophysiological pattern(s) of cortex alpha activity indicates a resting state network, which is characterized by well-defined structure in both the temporal as well as the spatial domain. The optimal functional state of the brain depends upon a delicate metastable balance between local specialized processes and their global integration. Excess or lack of either component would be a deviation from the optimal condition and can lead to pathology. The fact that all observed results were significantly different from surrogate EEG data reflects a non-occasional nature of spatio-temporal dynamics in the operational architectonics of alpha activity. Better understanding of the specific ways in which disrupted dynamics of different characteristics of alpha-generating neuronal assemblies (and their functional connectivity) may underlie neuro/psychopathology might suggest new targets for therapeutic agents.International journal of psychophysiology: official journal of the International Organization of Psychophysiology 03/2010; 76(2):93-106. · 3.05 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Cortex functional connectivity associated with hypnosis was investigated in a single highly hypnotizable subject in a normal baseline condition and under neutral hypnosis during two sessions separated by a year. After the hypnotic induction, but without further suggestions as compared to the baseline condition, all studied parameters of local and remote functional connectivity were significantly changed. The significant differences between hypnosis and the baseline condition were observable (to different extent) in five studied independent frequency bands (delta, theta, alpha, beta, and gamma). The results were consistent and stable after 1 year. Based on these findings we conclude that alteration in functional connectivity of the brain may be regarded as a neuronal correlate of hypnosis (at least in very highly hypnotizable subjects) in which separate cognitive modules and subsystems may be temporarily incapable of communicating with each other normally.Neuropsychologia 05/2007; 45(7):1452-62. · 3.48 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Children aged 5–7 years with early childhood autism were found to have more marked right-sided predominance of alpha-rhythm spectral power both in baseline conditions and on cognitive loading (counting), along with a decreased level of alpha rhythm power than normal children. The spectral power of the fast rhythms increased from baseline on cognitive loading in healthy children. In early childhood autism, the spectral power of the gamma rhythm in baseline conditions was greater than that in healthy children. On cognitive loading, the spectral power of the fast rhythms changed to a lesser extent than in healthy children. D creased alpha rhythm power in children with autism may be a predictor for the transition from autism to schizophrenia (with both positive and negative symptomatology). The increased spectral power of the fast rhythms in baseline conditions observed here in children with early childhood autism is characteristic of schizophrenia with positive symptomatology, while the decreased reactivity of fast rhythms in response to cognitive loading seen here in patients has been described for schizophrenia with negative symptomatology.Neuroscience and Behavioral Physiology 43(1).
0362-1197/05/3103- © 2005
Human Physiology, Vol. 31, No. 3, 2005, pp. 255–261. Translated from Fiziologiya Cheloveka, Vol. 31, No. 3, 2005, pp. 16–23.
Original Russian Text Copyright © 2005 by Borisov, Kaplan, Gorbachevskaya, Kozlova.
Schizophrenia falls into the small category of dis-
eases that impair the total psychic activity rather than
particular brain systems and functions. It is not surpris-
ing that researchers have long been interested in the
integrative activity of the human brain in schizophre-
nia. They have reported considerable data on histologi-
cal and physiological changes in the human brain, pro-
viding evidence for disturbance of interrelationships
and functional association between different parts of
the brain at different stages of schizophrenia [1–3]. The
most conspicuous data have been obtained for the brain
electrical activity [4–10]. Based on these data, a
hypothesis of disintegration of cortical functions (the
disconnection hypothesis) has been advanced  to
explain the schizophrenic disorders [11–13].
In EEG studies, spectral and correlation analyses are
a common method for investigating the integrative
activity of the human brain, yielding evidence for the
impairment of local and distant synchronies of neu-
ronal networks in schizophrenia [4, 5, 7, 8, 14]. How-
ever, a number of limitations typical of spectral meth-
ods, specifically, of the coherence function [7, 15–17],
have motivated the development of new techniques to
examine the interdependences of EEG paired time
series data in schizophrenia, including nonlinear inter-
dependence , mutual information transmission mea-
sure , and phase locking , which reflect nonlin-
ear and, in the last case, also in-phase components of
the interdependence of cortical electrical processes.
The results of the above studies are also in line with
Friston’s hypothesis of disintegration of neuronal net-
works in schizophrenia .
Yet, cortical bioelectrical processes associated with
ontological nonstationarity of the EEG signal [19–21]
are not covered by the traditional or new methods of
quantitative analysis of EEG spatiotemporal correla-
EEG nonstationarity implies that the EEG signal
consists of quasi-stationary segments that reflect the
changes in metastable states of the brain on different
time scales [20, 21], from microstates, with a duration
of no more than several seconds [15, 22], to mac-
rostates, with a duration of tens or hundreds of minutes
. This concept of EEG nonstationarity provides a
means for obtaining new insights into the cooperation
of cortical structures. For this purpose, it is possible to
estimate the EEG structural synchrony , i.e., the
temporal synchronization of intersegmentary transi-
tions between different EEG channels. Estimation of
the spatiotemporal synchronization of local metastable
states of neuronal networks appears to be a new mea-
sure of the integrative activity of the human brain.
The functional importance of the EEG structural
synchrony and segment characteristics has been
described in a series of our works performed in several
laboratories and with several cognitive and pharmaco-
logical paradigms [24–27]. In our previous work ,
changes in quasi-stationary segments of the EEG
activity were detected in adolescents with schizo-
The objective of the present work was to analyze the
disease-related changes in structural synchrony of the
activity in adolescents with schizophrenic dis-
Analysis of EEG Structural Synchrony in Adolescents
with Schizophrenic Disorders
S. V. Borisov*, A. Ya. Kaplan*, N. L. Gorbachevskaya**, and I. A. Kozlova**
* Moscow State University, Moscow, 119992 Russia
** Mental Health Research Center, Russian Academy of Medical Sciences, Moscow, Russia
Received June 7, 2004
years) were examined to study the EEG structural synchrony as reflecting temporal synchronization of the oper-
ational activity of neuronal networks. A significant decrease in the EEG structural synchrony was observed in
the adolescents with schizophrenic disorders as compared to the healthy adolescents. The decrease was detected
predominantly in the interhemispheric pairs of EEG derivations, as well as in the pairs related to the frontal,
temporal (predominantly on the left), and right parietocentral regions. The findings provide evidence in favor
of Friston’s hypothesis of disintegration of cortical electrical activity in schizophrenia and extend the hypothesis
in that it is the operational synchrony of cortical activity that might suffer first in schizophrenia.
—A total of 39 healthy adolescents and 45 adolescents with schizophrenic disorders (mean age 12.3
The study involved 45 boys with schizophrenic dis-
orders (infant schizophrenia and schizotypical and
schizoaffective disorders (F20, F21, and F25 according
to the ICD-10)) with similar symptoms. The diagnoses
of all patients were confirmed by specialists of the
Mental Health Research Center (MHRC). None of the
enrolled patients received chemotherapy during the
examination at the MHRC. The age of the patients var-
ied from 10 years and 8 months to 14 years. The control
group included 39 healthy schoolboys aged from 11
years to 13 years and 9 months. The mean age in both
groups was 12 years and 3 months.
The EEG was recorded in wakeful relaxed adoles-
cents with the eyes closed from 16 electrodes, which
were placed according to the international 10–20 sys-
and monopolarly referenced to coupled
ear electrodes. The EEG recordings were analyzed with
a sampling rate of 128 samples per second, and only
artifact-free EEG segments were used for analysis.
Analyzing the EEG structural synchrony, we used
the SECTION 0.1 technology [24, 29] to perform EEG
adaptive segmentation in order to identify quasi-sta-
tionary segments of the
activity. Thereafter, the index
of structural synchrony (ISS) of the EEG [15, 29] was
calculated using the JUMPSYN 0.1 technology as
coincidences of segment boundaries between two EEG
derivations provided that the derivations are indepen-
is the observed frequency of coincidences of
segment boundaries between the two EEG derivations,
is the mean error of
As can be seen from the formula, the ISS shows the
extent of synchrony of the boundaries of quasi-station-
ary segments, free from random coincidences, for a
given pair of derivations.
The quasi-stationary segments of the EEG
are supposed to reflect changes in local cortical neu-
ronal ensembles [24, 25]; therefore, the ISS shows the
extent of temporal synchronization of integration or
disintegration events in local neuronal ensembles for
pairs of different EEG derivations.
For the 120 possible combinations of the 16 EEG
derivations, 120 ISS values were calculated. To deter-
mine the ISS confidence interval (the stochastic level)
giving an error probability of no more than 5% for the
conclusion of a nonrandom nature of the structural syn-
chrony for a given pair of derivations, a numerical
experiment according to the Monte Carlo technique
was performed with 500 iterations.
The ISS values calculated for each pair of deriva-
tions were averaged for each group. The total syn-
chrony, i.e., the mean ISS for all derivations, and the
is the theoretically predicted frequency of
group synchrony for certain sets of derivation pairs,
including left hemispheric (
), right hemispheric (
), frontal (
), parietocentral (
), posterior (
), and symmetrical bilateral (pairs
T6, T3–T4, C3–C4, F3–F4, and F7–F8) derivations, were
calculated separately for the control and test groups.
The paired Wilcoxon t-test was used to estimate the
significance of differences in the total and group syn-
chronies between the control and test groups.
Along with the group synchrony analysis, a detailed
analysis of the ISS values was performed with regard to
the ISS stochastic level and the distance between the
electrodes for a given pair of derivations. The Mann–
Whitney U-test was used to compare ISS values
exceeding the stochastic level in at least one of the
groups under study.
), central (
Comparison of the ISS values averaged for the 120
pairs for the control and test groups showed that this
index was significantly lower in the adolescents with
schizophrenic disorders (control group, 2.03 ± 0.19;
test group, 1.67 ± 0.17 (M ± m)).
Analysis of the group synchrony, i.e., ISS values
averaged for various sets of EEG derivations, revealed
a decrease in this parameter for most sets of EEG deri-
vation pairs (left hemispheric, right hemispheric, pari-
etocentral, posterior, and bilateral) in the test group
(Fig. 1). The differences in the ISS were nonsignificant
only for the frontal and central pairs of the EEG deriva-
tions, although they showed a trend common to all
groups of derivations.
We concluded that the level of EEG structural syn-
chrony in the adolescents with schizophrenic disorders
was generally lower than in the healthy subjects.
The question arises as to whether a decrease in the
ISS for the above pairs of derivations in the test group
is indicative of a regular trend equally typical of each
derivation pair or the ISS demonstrates different dis-
ease-related trends for different pairs of derivations. To
answer this question, we analyzed in detail the ISS val-
ues for each pair of EEG derivations.
The topographic patterns of the ISS calculated for
each of the 120 derivation pairs with regard to the sto-
chastic level and for both the control and the test groups
are given in Fig. 2. The ISS considerably exceeded the
stochastic level in many pairs of EEG derivations,
thereby evidencing a nonrandom character of coinci-
dences of intersegment transitions in the corresponding
pairs of EEG derivations.
When analyzing the differences in ISS between the
control and the test groups, we were interested in com-
paring the above pairs, in particular, the topographic
distribution of these pairs in each group.
HUMAN PHYSIOLOGY Vol. 31 No. 3 2005
ANALYSIS OF EEG STRUCTURAL SYNCHRONY IN ADOLESCENTS257
Fig. 1. ISS values averaged for different pairs of derivations in the control (light columns) and test (dark columns) groups. Abscissa,
pairs of derivations. Differences in group synchrony indices were significant at (*) P < 0.05 and (**) P < 0.001.
1115 192327 3135 39434751 5559 6367717579838791 9599
Fig. 2. Topographic patterns of the ISS (for each of the 120 pairs of EEG derivations) with the ISS stochastic level for the control
and test groups. Because the ISS stochastic level was virtually the same in both groups, it is given only for the control group.
Abscissa, ordinal numbers of the derivation pairs; ordinate, ISS. Thick line, ISS in the test group; thin line, ISS in the control group;
dashed line, experimentally determined maximum and minimum stochastic levels of the ISS. Asterisks indicate the pairs of leads
in which the differences in ISS between the control and the test groups were significant (the Mann–Whitney U-test) at (*) P < 0.05,
(**) P < 0.01, and (***) P < 0.001.
The topographic distribution of the derivation pairs
with the ISS exceeding the stochastic level was ana-
lyzed for three ranges of interelectrode distance (0.48–
0.67, 0.79–0.95, and 1.14–1.39) in both the control and
the test groups (Figs. 3a–3c, respectively). The inter-
electrode distances were calculated using three-dimen-
sional coordinates for each of the derivations [30, 31].
As can be seen from Fig. 3, the ISS exceeded the
stochastic level in the same derivation pairs for the con-
trol group as for the test group. However, there were
another ten pairs in which the ISS exceeded the sto-
chastic level in the control but not in the test group.
It was found that, in the control group, the pairs of
EEG derivations with the above-threshold ISS were
mostly observed for a large interelectrode distance
(Fig. 3c) and rarely for the minimum interelectrode dis-
tance (Fig. 3a).
Analysis of the percentage of these pairs in both the
control and the test groups (table) showed that the pairs
of EEG channels with the above-threshold ISS in the
test group, when compared to those in the control
group, demonstrated a trend towards a distance-depen-
dent redistribution so that the percentage of such pairs
increased in the case of minimum interelectrode dis-
tances and decreased in the case of maximum interelec-
trode distances. Compared with the control group, the
test group demonstrated a decrease predominantly in
distant structural synchrony. This was true mainly for
Percentage of the pairs of derivations (for different ranges of
interelectrode distance) with the ISS exceeding the stochastic
level in the control and test groups
Range of interelectrode distance
HUMAN PHYSIOLOGY Vol. 31 No. 3 2005
BORISOV et al.
the left fronto–temporal diagonal and bilateral asym-
metrical connections (Fig. 3).
Along with the topographic analysis of the deriva-
tion pairs with ISS values exceeding the threshold in
both the control and the test groups, we compared the
ISSs of individual pairs between the groups. The ISSs
were compared only for the derivation pairs in which
the value exceeded the threshold in at least one of the
two groups. The pairs of derivations with significant
differences in the ISS between the control and the test
groups are represented in Fig. 4.
The ISS was lower in the test group for almost all
such pairs of derivations (except O2–T6). In addition to
the pairs represented in Fig. 2, this was true for bilateral
symmetrical (O1–O2, P3–P4, C3–C4, and F3–F4), pre-
dominantly right parietocentral (Pz–P4, Pz–C4, P4–C4,
Cz–C4, and C3–P4), predominantly left temporal
(T3−T5, T3–C3, and T4–T6), and left prefrontal (F3–Cz)
pairs of EEG derivations.
The results of spectral and correlation analyses of
the interaction between different parts of the brain in
schizophrenia may lead to conflicting conclusions.
Some researchers have reported an increase in EEG
coherence in schizophrenia at rest [6, 32–34] and dur-
ing solving cognitive tasks . In other studies involv-
ing the same frequency ranges and functional states,
either quite opposite effects were observed [7, 8, 35,
36] or virtually no significant differences in EEG
coherence were found between patients and control
Conceivably, such a variety of estimations of EEG
coherence in schizophrenia might be caused by the lack
of uniform standards for the organization of such stud-
ies with respect to test paradigms, EEG frequencies,
and stages of the schizophrenic process [7, 38].
Application of new techniques for studying cortico-
cortical interactions also contributes to the variety of
conclusions. One such example is , where coher-
ence analysis and analysis of coincidences of peak fre-
quencies in pairs of EEG derivations were performed
with the same samples of healthy subjects and schizo-
phrenics. It was found that the topography and the
changes in correlations between EEG parameters
depend not only on the method applied but also on
whether positive or negative symptoms are observed in
schizophrenics , as well as on the frequency ranges
and particular pairs of EEG derivations used in the test
procedures . Entropic analysis of the EEG (mutual
information analysis), which reports the mutual order-
liness of distributions of temporal series, showed a vari-
Fig. 3. Topographic distribution of the pairs of derivations with interelectrode distances in the ranges (a) 0.48–0.67, (b) 0.79–0.95,
and (c) 1.14–1.39 and the ISS exceeding the stochastic level. Thick line, pairs of derivations with the ISS exceeding the stochastic
level only in the control group; thin line, pairs of derivations with the ISS exceeding the stochastic level in both groups.
Fig. 4. Topographic distribution of the pairs of derivations
in which the differences in the ISS between the control and
the test groups were significant (P < 0.05, P < 0.01, and P <
0.01). Solid line: the ISS is higher in the control group; dot-
ted line: the ISS is higher in the test group.
HUMAN PHYSIOLOGY Vol. 31 No. 3 2005
ANALYSIS OF EEG STRUCTURAL SYNCHRONY IN ADOLESCENTS259
ety of changes in information transmission between
different cortical areas in schizophrenics . Detec-
tion of specific nonlinear interdependences through
mutual prediction  led to the conclusion that the dif-
ference between healthy and schizophrenic subjects
concerns the topography of interactions between differ-
ent cortical areas rather than the direction of changes in
EEG synchronization or their dependence on particular
pairs of derivations .
Summarizing the above data, we may argue that
schizophrenia is associated with a variety of changes in
types of mutual determination in pairs of different EEG
In this work, we were the first to obtain data on the
changes in EEG structural synchrony in schizophrenic
disorders. The specific feature of the method we used
for estimating cortical integration is that it describes the
EEG patterns represented by quasi-stationary segments
rather than particular peaks or waves of the EEG signal
Our results are generally consistent with the data on
considerable changes in corticocortical interrelation-
ships in schizophrenia that were reported in other stud-
The topographic analysis of the derivation pairs
with a decreased structural synchrony in the test group
compared to the control group and of the pairs with the
ISS exceeding the threshold only in the control group
revealed a decrease in structural synchrony between the
hemispheres (the O1–O2, P3–P4, and C3–C4, F3–F4 pairs
of bilateral symmetrical derivations and the P3–C4, C3–
P4, F3–C4, F3–F8, and F7–F4 pairs); in the temporal
regions of both hemispheres, predominantly on the left
(T5–T3, T5–C3, T5–F7, T6–T4, and T6–C4); in the frontal
regions, predominantly on the left (F3–F8, F7–F4, F3–
F4, F3–Cz, F3–C4, and T5–F7); and in the right parieto-
central region (Pz–P4, Pz–C4, Cz–P4, P4–C4, and Cz–C4).
Interestingly, the largest number of derivation pairs
with the ISS lower than the stochastic level in the test
group and higher than this level in the control group
were observed for the pairs with the maximum distance
between the electrodes. This suggests a disruption of
functional interdependence between rather distant
regions in the test group, although the percentage of
such interdependences in adjacent regions was even
higher than in the control group.
Our findings concerning the topographic distribu-
tion of the derivation pairs with structural synchrony
lower in the test than in the control group are consistent
to some extent with data obtained using other tech-
niques for EEG synchrony in different regions of the
brain in schizophrenic patients.
Thus, a decrease in the functional hemispheric inter-
dependence in schizophrenia was revealed by the anal-
ysis of EEG coherence and β-range synchronization 
and EEG cross-correlation analysis . A decrease in
EEG synchronization in the frontal and central regions,
predominantly on the left, was also reported for schizo-
phrenics . A decrease in coherence in the ∆, θ, and
α ranges was detected in the left frontal region .
Analysis of nonlinear interdependences showed that
the EEG difference between schizophrenics and
healthy subjects is most pronounced in the left intra-
hemispheric regions . Auto mutual information anal-
ysis and mutual information transmission measure
(CMI analysis) of the EEG in schizophrenic patients
revealed a functional deficit of the left temporal lobe
and increased interhemispheric information transmis-
sion in the temporal lobe .
Topographically, our findings on a decrease in EEG
structural synchrony in the adolescents with schizo-
phrenic disorders are consistent to some extent with
published data on the changes in functional interdepen-
dences between different regions of the brain in schizo-
phrenia. However, fundamental differences in the
methods used for estimating EEG spatial synchrony
can produce unequal results; therefore, one must be
careful when comparing these results either with each
other or with our findings.
What is the physiological significance of the
detected decrease in EEG structural synchrony in ado-
lescents with schizophrenic disorders? The phasic
structure of the EEG, specifically, of the α range,
reflects the dynamic changes in cortical neuronal
ensembles [24, 25], while the ISS is used to estimate
the temporal synchrony of cortical activity of different
regions of the brain with regard to the dynamic changes
in local neuronal ensembles. A significant simultaneous
integration (or disintegration) of such assemblies in dis-
tant cortical regions may be regarded as spatiotemporal
synchronization of local cortical processes [20, 21, 42].
Thus, the decrease in the ISS detected for many
pairs of derivations in the schizophrenic adolescents
may provide evidence for disintegration of operational
In our previous study , we examined EEG seg-
ments of α activity in the same adolescents. According
to our results, the EEG α activity in adolescents with
schizophrenic disorders significantly differs from that
in healthy adolescents. In our opinion, these differences
point to lesser integration of cortical neurons via local
synchronization of their activity in adolescents with
schizophrenic disorders as compared to control sub-
jects. Even when such synchronization occurs, it is of a
lesser duration and is less stable. On the strength of
these data, we assumed that a disintegration trend is
typical of the whole neuronal substrate at all levels
from local neuronal ensembles to spatially distant neu-
ronal networks, causing serious disturbance of interde-
pendences in schizophrenia.
The results of this study support the above assump-
tion and show that this disease is associated not only
with alteration of local synchronization mechanisms
but also with dramatic impairment of intercortical con-
nections, spatiotemporal disintegration being greater
between distant regions.
HUMAN PHYSIOLOGY Vol. 31 No. 3 2005
BORISOV et al.
The results of this work are generally consistent
with Friston’s hypothesis of disintegration of cortical
electrical activity in schizophrenia , although they
extend the hypothesis in that it is the operational syn-
chrony of cortical functions that might suffer first in
(1) The EEG structural synchrony may be used as an
additional index for detecting schizophrenic disorders
(2) Unlike healthy adolescents, those with schizo-
phrenic disorders show a decrease in the ISS, predomi-
nantly, in interhemispheric, left frontal and temporal,
and right parietocentral pairs of derivations.
(3) The results of analysis of EEG structural syn-
chrony in adolescents with schizophrenic disorders
support the hypothesis of disintegration of functional
interdependences between different parts of the brain in
This work was supported by the program “Universi-
ties of Russia” (project no. UR.11.03.001/03-2) and the
Russian Foundation for Basic Research (project no. 03-
1. Selemon, L.D. and Goldman-Rakis, P.S., The Reduced
Neuropil Hypothesis: A Circuit-Based Model of Schizo-
phrenia, Biol. Psychiatry, 1999, vol. 45, p. 17.
2. Benes, F.M., Emerging Principles of Altered Neural Cir-
cuitry in Schizophrenia, Brain Res. Rev., 2000, vol. 31,
3. Lewis, D.A. and Gonzalez-Burgos, G., Intrinsic Excita-
tory Connections in the Prefrontal Cortex and the Patho-
physiology of Schizophrenia, Brain Res. Bull., 2000,
vol. 52, p. 309.
4. Alfimova, M.V., Uvarova, L.G., and Trubnikov, V.I.,
Electroencephalography and Cognitive Processes in
Schizophrenia, Zh. Nevropatol. Psikhiatr. im. S.S. Kor-
sakova, 1998, vol. 98, no. 11, p. 55.
5. Sponheim, S.R., Clementz, B.A., Iacono, W.G., and
Beiser, M., Clinical and Biological Concomitants of
Resting State EEG Power Abnormalities in Schizophre-
nia, Biol. Psychiatry, 2000, vol. 48, p. 1088.
6. Mann, K., Maier, W., Franke, P., et al., Intra- and Inter-
hemispheric Electroencephalogram Coherence in Sib-
lings Discordant for Schizophrenia and Healthy Volun-
teers, Biol. Psychiatry, 1997, vol. 42, no. 8, p. 655.
7. Tauscher, J., Fischer, P., Neumeister, A., et al., Low
Frontal Electroencephalographic Coherence in Neuro-
leptic-Free Schizophrenic Patients, Biol. Psychiatry,
1998, vol. 44, no. 6, p. 438.
8. Strelets, V.B., Novototskii-Vlasov, V.Yu., and Golik-
ova, Zh.V., Cortical Relations in Schizophrenic Patients
with Positive or Negative Symptoms, Zh. Vyssh. Nervn.
Deyat., 2001, vol. 51, issue 4, p. 452.
9. Breakspear, M., Terry, J.R., Friston, K.J., et al., A Distur-
bance of Nonlinear Interdependence in Scalp EEG of
Subjects with First Episode Schizophrenia, Neuroimage,
2003, vol. 20, no. 1, p. 466.
10. Spencer, K.M., Nestor, P.G., Niznikiewicz, M.A., et al.,
Abnormal Neural Synchrony in Schizophrenia, J. Neu-
rosci., 2003, vol. 23, no. 19, p. 7407.
11. Friston, K.J., Theoretical Neurobiology and Schizophre-
nia, Brain Med. Bull., 1996, vol. 52, no. 3, p. 644.
12. Andreasen, N.S., A Unitary Model of Schizophrenia:
Bleuler’s “Fragmented Phrene” as Schizencephaly,
Arch. Gen. Psychol., 1999, vol. 56, p. 781.
13. Peled, A., Multiple Constraint Organization in the Bain:
A Theory for Schizophrenia, Brain Res. Bull., 1999,
vol. 56, p. 781.
14. Berus, A.V., Voronkov, K.A., Plotnikova, O.P., and
Ivashchenko, O.I., Hemispheric Features of EEG Spec-
tral Characteristics at the Baseline and during Different
Types of Cognitive Activity in Patients with Schizophre-
nia, Fiziol. Chel., 1996, vol. 22, no. 3, p. 22.
15. Kaplan, A.Ya., Darkhovskii, B.S., Fingel’kurts, Al.A.,
and Fingel’kurts, An.A., Topological Mapping of Syn-
chronization of Sharp Transformations in Multichannel
EEG in Humans, Zh. Vyssh. Nervn. Deyat., 1997, vol. 45,
issue 1, p. 32.
16. Lachaux, J.P., Rodriguez, M., Martinerie, J., and Val-
era, F.J., Measuring Phase Synchrony in Brain Signals,
Human Brain Map., 1999, vol. 8, p. 194.
17. Ivanitsky, A.M., Nikolaev, A.R., and Ivanitsky, G.A.,
Cortical Connectivity during Word Association Search,
Int. J. Psychophysiol., 2001, vol. 42, no. 1, p. 35.
18. Na, S.H., Jin, S.H., Kim, S.Y., and Ham, B.J., EEG in
Schizophrenic Patients: Mutual Information Analysis,
Clin. Neurophysiol., 2002, vol. 113, no. 12, p. 1954.
19. Lopes Da Silva, F.H., Analysis of EEG Non-Stationari-
ties, Electroencepalogr. Clin. Neurophysiol. Suppl.,
1978, vol. 34, p. 163.
20. Kaplan, A.Ya., EEG Nonstationarity: Methodological
and Experimental Analysis, Usp. Fiziol. Nauk, 1998,
vol. 29, no. 3, p. 35.
21. Kaplan, A.Ya., Segmentary Description of Human EEG,
Fiziol. Chel., 1999, vol. 25, no. 1, p. 125.
22. Lehmann, D. and Koenig, T., Spatio-Temporal Dynam-
ics of α Brain Electric Fields and Cognitive Modes, Int.
J. Psychophysiol., 1997, vol. 26, nos. 1–3, p. 99.
23. Kaplan, A., Roschke, J., Darkhovsky, B., and Fell, J.,
Macrostructural EEG Characterization Based on Non-
parametric Change Point Segmentation: Application to
Sleep Analysis, J. Neurosci. Methods, 2001, vol. 106,
24. Kaplan, A.Ya., Borisov, S.V., Shishkin, S.L., and Ermo-
laev, V.A., Analysis of the Segmentary Structure of the
Human EEG α Activity, Ross. Fiziol. Zh. im. I.M. Sech-
enova, 2002, vol. 88, no. 4, p. 432.
25. Kaplan, A.Ya. and Borisov, S.V., Dynamics of Segmen-
tary Characteristics of Human EEG α Activity at Rest
and during Cognitive Tasks, Zh. Vyssh. Nervn. Deyat.,
2003, vol. 53, issue 1, p. 22.
HUMAN PHYSIOLOGY Vol. 31 No. 3 2005
ANALYSIS OF EEG STRUCTURAL SYNCHRONY IN ADOLESCENTS261
26. Fingelkurts, An.A., Fingelkurts, Al.A., Krause, S.M., et
al., Structural (Operational) Synchrony of EEG α Activ-
ity during an Auditory Memory Task, Neurolmage,
2003, vol. 20, p. 529.
27. Fingelkurts, A.A., Fingelkurts, A.A., Kivisaari, R., et al.,
Enhancement of GABA-Related Signaling Is Associated
with Increase of Functional Connectivity in Human Cor-
tex, Hum. Brain Map., 2004, vol. 22, no. 1, p. 27.
28. Borisov, S.V., Kaplan, A.Ya., Gorbachevskaya, N.L.,
and Kozlova, I.A., Specific Features of the Segmentary
Structure of EEG α Activity in Adolescents with Schizo-
phrenic Disorders, Zh. Vyssh. Nervn. Deyat. (in press).
29. Borisov, S.V., Study of the Phasic Structure of Human
EEG α Activity, Cand. Sci. (Biol.) Dissertation, Mos-
cow: Moscow State Univ., 2002.
30. Bocker, K.B.E., Van Avermaete, J.A.G., and Van Den
Berg-Lenssen, M.M.C., The International 10–20 System
Revisited: Cartesian and Spherical Coordinates, Brain
Topogr., 1994, vol. 6, no. 3, p. 231.
31. Shishkin, S.L., Study of Sharp Transformation Syn-
chrony of Human EEG α Activity, Cand. Sci. (Biol.) Dis-
sertation, Moscow: Moscow State Univ., 1997.
32. Merrin, E., Floyd, T., and Fein, G., EEG Coherence in
Unmedicated Schizophrenic Patients, Biol. Psychiatry,
1989, vol. 25, p. 60.
33. Wada, Y., Nanbu, Y., Kikuchi, M., et al., Aberrant Func-
tional Organization in Schizophrenia: Analysis of EEG
Coherence during Rest and Photic Stimulation in Drug-
Naive Patients, Neuropsychobiology, 1998, vol. 38,
no. 2, p. 63.
34. Nagase, Y., Okubo, Y., Matsuura, M., et al., EEG Coher-
ence in Unmedicated Schizophrenic Patients: Topo-
graphical Study of Predominantly Never Medicated
Cases, Biol. Psychiatry, 1992, vol. 32, no. 11, p. 1028.
35. Morrison-Stewart, S.L., Williamson, P.C., Corning, W.C.,
et al., Coherence on Electroencephalography and Aber-
rant Functional Organization of the Brain in Schizo-
phrenic Patients during Activation Tasks, Br. J. Psychia-
try, 1991, vol. 159, p. 636.
36. Rappelsberger, P., Lacroix, D., Steinberger, K., and
Thau, K., EEG Amplituden und Kohärenzanalyse bei
medikamentenfreien schizofrenen, Z. EEG EMG, 1994,
vol. 25, p. 144.
37. Wuebben, Y. and Winterer, G., Hypofrontality—a Risk
Marker Related to Schizophrenia?, Schizophr. Res.,
2001, vol. 4, nos. 2–3, p. 207.
38. Coutin-Churchmana, P., Aneza, Y., Uzcateguia, M., et al.,
Quantitative Spectral Analysis of EEG in Psychiatry
Revisited: Drawing Signs out of Numbers in a Clinical
Setting, Clin. Neurophysiol., 2003, vol. 114, p. 2294.
39. Strelets, V.B., Novototskii-Vlasov, Y.Y., Golikova, J.V.,
Cortical Connectivity in High Frequency β Rhythm in
Schizophrenics with Positive and Negative Symptoms,
Int. J. Psychophysiol., 2002, vol. 44, p. 101.
40. Schiff, S., So, P., Chang, T., et al., Detecting Dynamical
Interdependence and Generalized Synchrony through
Mutual Prediction in a Neural Ensemble, Phys. Rev.,
1996, vol. 54, p. 6708.
41. Sviderskaya, N.E., Bardenshtein, L.M., and Kura-
shov, A.S., Clinical and Electrophysiological Character-
istics of Nonprocess Personality Changes in Schizo-
phrenic Adolescents, Zh. Nevropatol. Psikhiatr. im. S.S.
Korsakova, 1980, vol. 80, no. 6, p. 886.
42. Kaplan, A.Ya. and Shishkin, S.L., Application of the
Change-Point Analysis to the Investigation of the Brain’s
Electrical Activity, Non-Parametric Statistical Diagno-
sis: Problems and Methods, Brodsky, B.E. and Dark-
hovsky, B.S., Eds., Dordrecht: Kluwer Academic, 2000,