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The Journal of Neuroscience, August 15, 2001, 21(16):6329-6337
Long-Range Synchrony in the  Band: Role in Music Perception
Joydeep
Bhattacharya1,
Hellmuth
Petsche2, and
Ernesto
Pereda3, 4
1 Commission for Scientific Visualization Austrian
Academy of Sciences, A-1010 Vienna, Austria, 2 Brain
Research Institute, University of Vienna, A-1090 Vienna, Austria,
3 Department of Systems Engineering, Institute of
Technology and Renewable Energies, Poligono Industrial de Granadilla,
38611 Tenerife, Spain, and 4 Laboratory of Biophysics,
University of la Laguna, 38320 Tenerife, Spain
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ABSTRACT |
Synchronization seems to be a central mechanism for neuronal
information processing within and between multiple brain areas. Furthermore, synchronization in the  band has been shown to
play an important role in higher cognitive functions, especially by binding the necessary spatial and temporal information in different cortical areas to build a coherent perception. Specific task-induced (evoked) oscillations have often been taken as an indication of
synchrony, but the presence of long-range synchrony cannot be inferred
from spectral power in the range. We studied the usefulness of a
relatively new measure, called similarity index to detect
asymmetric interdependency between two brain regions. Spontaneous EEG
from two groups musicians and non-musicianswere recorded during
several states: listening to music, listening to text, and at rest
(eyes closed and eyes open). While listening to music, degrees of the
band synchrony over distributed cortical areas were found to be
significantly higher in musicians than non-musicians. Yet no
differences between these two groups were found at resting conditions
and while listening to a neutral text. In contrast to the degree of
long-range synchrony, spectral power in the band was higher in
non-musicians. The degree of spatial synchrony, a measure of signal
complexity based on eigen-decomposition method, was also significantly
increased in musicians while listening to music. As compared with
non-musicians, the finding of increased long-range synchrony in
musicians independent of spectral power is interpreted as a
manifestation of a more advanced musical memory of musicians in
binding together several features of the intrinsic complexity of
music in a dynamical way.
Key words:
EEG; synchronization; music; band; cognitive task; binding; similarity index
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INTRODUCTION |
Widespread oscillatory activity,
particularly in the range (>30 Hz) has attracted the attention of
researchers studying different cognitive phenomena in human and
mammalian species (for review, see Basar-Eroglu et al., 1996 ;
Tallon-Baudry and Bertrand, 1999). Synchronous 40 Hz oscillations,
found in the olfactory system of the rabbit (Freeman, 1978), were
supposed to play a key role in the detection of different odors
(Freeman and Skarda, 1985). Furthermore, spatially distributed cells in
the visual cortex of both the anesthetized (Gray et al., 1989) and the
alert cat (Gray and DiPrisco, 1997) produced oscillations in the band in response to visual stimuli. Evidence of precise phase locking across different cortical areas with zero phase lag in this frequency range was also reported in alert cats during visual discrimination task
(Roelfsema et al., 1997). In humans, transient phase locking at ~40
Hz generated in the contralateral and parietal cortical areas was found
during selective attention (Desmedt and Tomberg, 1994). It has been
postulated (Bressler et al., 1993; Tallon-Baudry et al., 1998) that the
band serves as a mechanism for the visual representation of objects
and as a means of "binding" various intricate aspects of object
perception into a unitary whole. These interpretations are supported by
observations that have shown enhanced 40 Hz activities during the
perception of multistable figures (Keil et al., 1999) and animated
unstable human postures (Solbounov et al., 2000). Recently, it has been
demonstrated that binding-related oscillations are also produced in
the brain of 8-month-old infants during the perception of illusory
figures (Csibra et al., 2000).
So far, most of the researches investigating the role of the band
were concentrated on visual tasks, whereby only few attempts were made
with other cognitive tasks (Jokeit and Makeig, 1994; Joliot et al.,
1994). Furthermore, more attention was paid to the detection of the oscillations, whereby the functional relationships between distant
multiple cortical areas were not considered. It has been demonstrated
that during a face recognition task neuronal assemblies not only
synchronize locally in the range but are also locked in phase
across distances without any time lag (Rodriguez et al., 1999).
Coherence in the range was also enhanced between two regions of the
brain receiving two classes of stimuli involved in an
associative-learning procedure in humans (Miltner et al., 1999).
Recently, Haig et al. (2000) reported the occurrence of a significant
late poststimulus -synchrony response for task-relevant stimuli,
whereas for task-irrelevant stimuli no such response was found. All
these results indicate that synchrony in the band provides
important insight into the understanding of the cognitive functioning
of our brain.
The main goal of this paper was to examine, under the presumption of a
binding function of the band, (1) whether differences in the
-band synchrony with respect to the EEG at rest may also be found in
listening tasks (listening to text or to music) in groups of musicians
and non-musicians, and (2) whether differences can be found in the
degree of the -band synchrony between these two groups. Our
hypothesis was that if the band serves as a binder also in the
auditory category, the degree of long-range synchrony should be
significantly higher in musicians than non-musicians while listening to
music, whereas no large differences should exist between these two
groups for other conditions such as listening to text or at rest.
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MATERIALS AND METHODS |
Experimental methods. Twenty male subjects belonging
to two broad groups: musicians (10 subjects with mean age of 25.7 years and at least 5 years of musical education on any instrument) and non-musicians (10 subjects with mean age of 25.4 years without any
musical education) were chosen for this study. All subjects were
right-handed and gave consent before the recording. The subjects were
instructed to listen for 5 min attentively to a piece of music composed
by J. S. Bach (French Suite No. 5 for Harpsichord, Gigue: the
piece was not familiar to the subjects), and to a text of neutral
content (a short story, "Versuendigung gegen die Nachwelt" by H. Weigel, read by C. Hoerbiger, 2 min). Periods of EEG at rest with eyes
closed and with eyes open were recorded before, between, and after each
task. Eyes were closed during both listening tasks.
Nineteen gold-cup electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz,
C4, T4, T5, P3, Pz, P4, T6, O1, and O2 numbered as 1, ... , 19),
equally distributed over the skull according to the so-called 10-20
system (Jasper, 1958), were placed (Fig.
1). EEG signals were recorded with
respect to the averaged signals from both earlobes. Sampling frequency
was 128 Hz, and analog-to-digital precision was 12 bit. For each
task as well as for resting conditions, 90 sec of EEG data, visually
free from eye-movement artifacts, were considered. Every signal
originating from an electrode was first normalized to zero mean, and a
second order polynomial was fitted and subtracted from the original
signal to remove any baseline drift. Because we were interested in the
band, the mean and polynomial subtracted signal was bandpass
filtered using a sixth-order Butterworth filter with cutoff frequencies
of 30 and 50 Hz. Here, the band is regarded as the signal lying
strictly between 30 and 50 Hz (Galambos, 1992). In this notion, the
oscillations in the band represent a brain state, which can be
operationally distinct and separable, although the brain might be
simultaneously active in other modes.
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Figure 1.
Spatial positions of the 19 electrodes and their
designations according to the International 10-20 electrode placement
system (Jasper, 1958).
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Classical analysis of synchrony. Traditionally, correlation
and magnitude squared coherence are the two most commonly used measures
to detect synchrony in neurophysiological signal analysis. Let
{x(k)} and {y(k)}
be two EEG signals from two electrodes. The cross-correlation or
covariance between these two signals is:
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(1)
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where N is the length of each signal, and
xm and
ym are the mean values of
{x(k)} and {y(k)},
respectively. A non-vanishing value of c indicates a linear
dependency between two signals, but the opposite conclusion is not
always true. The counterpart of covariance in frequency domain is
coherence, which measures the linear association in specific frequency
range. To obtain the coherence  x,y, one has
to estimate the following:
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(2)
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where
Pxx(f) and
Pyy(f) are the
power spectra of the two signals, and
Pxy(f) is the
cross-spectrum between two signals. To increase the confidence level of
coherence estimation, several epoch lengths must be averaged. Generally
speaking, coherence measures the phase consistency of the two signals
as a function of frequency: at frequency f,
x,y = 1 indicates that the two signals maintain the same phase difference in every epoch, whereas x,y = 0 indicates that the phase difference
varies from epoch to epoch. However, it has been shown that coherence
is not a reliable indicator of phase coupling, and the value of
coherence can be low even when the phases of two systems are
synchronized (Lachaux et al., 1999; Rosenblum et al., 2000).
Furthermore, both of these measures (covariance and coherence) are
symmetric and can detect only linear dependencies. To determine the
asymmetric nature of interaction between different cortical areas, a
relatively new measure, called similarity index (SI), based on
dynamical system theory (the foundation of nonlinear time series
analysis) has been used (Arnhold et al., 1999). The method is briefly
sketched later. For comparison, the results based on coherence have
also been included in this study.
SI. The construction of state space trajectory is an
important step in nonlinear time series analysis (Takens, 1981).
Because one has only indirect access to the original dynamical system itself (i.e., the brain) through the time series (i.e., EEG), one has
to reconstruct a suitable state space for the original system from the
time series to characterize the underlying dynamics. By using the
method of delays based on the embedding theorem (Takens, 1981; Sauer et
al., 1991), it is possible to obtain a set of reconstructed state space
vectors x(k) = [x(k),
x(k   ), ... , x(k (m 1))], where m and are embedding dimension
and time delay, respectively. For a smooth one-to-one mapping between
the reconstructed and the original state space, the embedding theorem
requires m  (2df + 1), where df is the box counting or
fractal dimension of the original state space. The values of
m and can be estimated from the time series by different
methods (Kantz and Schreiber, 1997). Once the state space vectors are
constructed for the two signals {x(k)} and
{y(k)}, for each state space vector pair
x(k) and y(k), their
R nearest neighbors are chosen from their individual state
spaces. Nearest or true neighbors means that the Euclidean distances
between x(k) and these vectors are smaller than
the distance between x(k) and any other state
space vector. Let the time indices of such nearest neighbors in two
state spaces be denoted as
rk(i) and
sk(i), i = 1,... , R, respectively. The squared mean distance from
these neighbors is given by:
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(3)
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Accordingly, the set of mutual neighbors of
x(k) are those vectors
x(sk(i)) that
bear the time indices of the R nearest neighbors of
y(k). The conditional distance can be defined as:
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(4)
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Should the two signals be identical, both sets (true and mutual)
of neighbors are equal; increasing differences between the two sets of
neighbors imply weaker relationship between the two signals. For
independent systems,
D (x y) >>
D (x),
whereas for strongly dependent systems
D (x y)  D (x).
Therefore, the assessment of the interdependence consists in
quantifying the difference between both sets of neighbors. This is
usually performed by means of an index that measures the similarity or dissimilarity between all vectors and their corresponding true and
mutual neighbors. The similarity between the local geometry of the
state spaces can be computed as:
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(5)
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We call this measure SI. In an analogous way,
SR (y|x) is
also computed. The higher the values of SR
(x|y) and SR
(y|x), the stronger is the degree of
synchrony or interdependency, i.e., the connectivity between the two
systems is strongly bidirectional. If SR
(x|y) < SR
(y|x), the system associated with signal
{x(k)} has more influence on the system
associated with signal {y(k)} than vice
versa. By this way, the nature of asymmetric coupling can be
represented. If one system is found to exert more influence on the
other, the former possesses higher complexity or higher degrees of
freedom. It is to be noted that the similarity index is sensitive only to those degrees of freedom, which operate in the amplitudes of the
order of mean distances (Eqs. 3, 4) in the state spaces. This index has
been found to be useful for simulated models (Schmitz, 2000) and also
for real life signals (Pereda et al., 2001).
For each task as well as for resting conditions, the record of 90 sec
EEG was divided into 15 nonoverlapping windows; for each window,
S (Eq. 5) was computed for all possible combinations of
electrodes; their values were stored in a 19 × 19 matrix, which may be asymmetric. These 15 matrices were further compared
statistically as described later. In this study, we have chosen the
following embedding parameters: embedding dimension (m) = 10 (time samples), time delay () = 2 (time samples), and the
number of true (and mutual) neighbors (R) = 10.
The method of obtaining similarity index is graphically represented in
Figure 2. For the sake of clarity, only
one reference vector (x(k) or y(k)) is chosen
from two individual state spaces. It is clear that the true neighbors
always resemble the reference vector in terms of similarity in pattern,
whereas the patterns of mutual neighbors may or may not be similar
depending on the degree of interdependency.
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Figure 2.
Top, Pictorial descriptions
of true and mutual neighbors. Two simultaneous EEG epochs filtered in
the band (1.8 sec each) of two channels (O1 and O2 represented by
system x and y, respectively) for a
musician while listening to music. For a reference vector (o), the
nearest or true neighbors (filled diamonds) and
mutual neighbors (open triangles) are shown. For the
sake of clarity only two vectors (r = 2 in Eq. 3
and 4) are used. For the reference vector x(k),
its nearest neighbors are x(rk(1)) and
x(rk(2)), whereas for the simultaneous
reference vector y(k), its two true neighbors
(filled diamonds) are
y(sk(1)) and
y(sk(2)). The mutual neighbors of
x(k) are those vectors
x(sk(1)) and
x(sk(2)) (open triangles)
simultaneous to the nearest neighbors of y(k),
which consequently bear the same time indices. In an analogous way, the
mutual neighbors of y(k) are y(rk(1)) and
y(rk(2)). All these vectors are shown with
the reconstruction parameters m = 10 and
= 2. Bottom, The reconstructed state
spaces of X and Y, in which both the actual (solid
lines) and conditional (dashed lines) distances
are displayed for comparison. Similarity index S (Eq. 5)
for x(k) is simply the ratio between both
distances. In the top half, note that the pattern for
x(k) is similar to that of its nearest neighbors
but does not match that of its mutual neighbors; this fact is more
prominent in distance measures. For this chosen example,
D2k(x|y) > D2k(y|x) which leads to
S(Y|X) > S(X|Y), i.e., X has
more influence on Y than vice versa. We would like to emphasize that
this index has to be calculated for all possible reference vectors and
finally averaged to get the final value of similarity index.
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Figure 3 describes the scalp maps for the
SI of electrode F4 in a musician (Vp. 632) while listening to
music. For this subject, the influences between this electrode region
and the other electrode regions are mainly symmetric, the active and
passive regions being quite similar. The strong interdependencies
between F4, C4, and Fz are also evident.
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Figure 3.
Scalp maps of the similarity indices for one
electrode F4 (marked by the asterisks) for a subject
(musician) while listening to music. Left, The electrode
region was considered as active or influencing other electrode regions
(S(j|F4), j = 1, ... , 19). Right, The electrode region was considered
as passive or being influenced by the other electrode regions
(S(j|F4)).
S(F4|F4) is set to the mean of the maximum and minimum
of S values in each plot for better visual
clarity.
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Spatial synchrony. To measure the global spatial
synchronization, a linear complexity measure was used (Wackermann,
1999). Nineteen EEG channels
({x1(k)},
{x2(k)}, ... . , {x19(k)}), filtered in
the band, are stacked column-wise to form a multichannel EEG matrix
of size N × 19, where N is the number of
time samples. Each row (uk) of this
matrix corresponds to the vector representing the spatial distribution
of the -band signal over the entire scalp at the kth
instant of time. The covariant matrix (of size 19 × 19) of the
multichannel EEG matrix is formed as:
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(6)
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The eigenvalues { 1,
2, ... , 19}
(Golub and Van Loan, 1996) of the symmetric positive semidefinite
matrix C are computed and subsequently normalized as:
The complexity measure  is defined as follows:
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(7)
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approximately quantifies the amount of spatial
synchronization over the entire scalp and is closely related to the
intrinsic dimensionality (Fukunaga, 1990). varies from 0 for a
one-dimensional distribution (all channels are completely correlated
yielding perfect synchrony) to 19 for identically distributed white
noise (no correlation between channels corresponding to the absence of
synchrony). Thus, the degree of spatial synchronization decreases monotonically with increase of . Here, was computed for data window of 6 sec with overlapping segment of 3 sec.
Statistical analyses. To compare the values of similarity
indices between the two groups, we have performed a normalization procedure, where the indices for non-musicians are considered as
reference. For example,
{Snm(j|i)} is the set of
values of SI between electrode pairs i and j
considering the influence of the ith electrode on the
jth electrode for the group of non-musicians (in this study,
each set associated with every electrode pair contains 150 values (10 subjects × 15 windows)); let
µnm(j|i) and
unm(j|i) be the mean and
variance of the set
{Snm(j|i)}. Say, the SI
for an identical electrode pair for a musician is Sm(j|i); the relative
synchrony of interdependency value is computed as:
 (j|i) = (Sm(j|i) µnm(j|i))/ unm(j|i). If (j|i) > 2.33, it can be
inferred that the degree of interdependency between electrode pair
i and j, assuming the ith electrode
influencing the jth electrode, is higher in musicians than
non-musicians with >99% statistical significance (Theiler et al.,
1992); for (j|i) < 2.33, the
interdependency is significantly higher in non-musicians. In a similar
way, (i|j) can also be obtained
considering the influence of the jth electrode on the
ith electrode. If any electrode is found to exert influence
on many other electrodes, that electrode is considered to be acting as
a diverging (differentiating) node, whereas if it is influenced by many
other electrodes, it is considered as a converging (integrating) node.
In this way, a comprehensive insight into the underlying
differentiation and integration properties of cortical areas associated
with each electrode location can be obtained.
EEG at rest with eyes closed (no-task or baseline condition) was also
considered in this study. To find the change in interdependency pattern
while listening to music or text as compared with rest, paired Wilcoxon
test is applied to find statistical differences between the values of
SI of task conditions (listening to music or listening to text) from
no-task condition. The level of significance is set to 99%
(p < 0.01). Thus, by measuring the degree of
differences between groups and/or tasks and not highlighting the
absolute values of S, interdependency between near and
distant electrode regions can be treated in an equivalent way.
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RESULTS |
Power analysis
Power in the band is obtained by estimating power-spectral
density of each signal by using a fast Fourier transform
algorithm (Matlab; MathWorks Inc., Natick, MA) and averaging
over overlapping windows of 3 sec with overlapping segment of 1 sec.
When spectral power is compared (paired Wilcoxon test, significance
level; p < 0.05) between task and baseline (eyes
closed) conditions within each group, posterior electrodes are mainly
found to be significant for both tasks in both groups. Figure
4 shows the difference (musicians vs
non-musicians) in scalp maps of power spectra in the band between
the two groups in different states. An overall tendency toward higher
spectral power for non-musicians as compared with musicians has been
found. C4 showed higher power in non-musicians while resting with eyes
open, whereas several cortical regions, mostly in the left hemisphere
(F7, F3, T3, C3, T5, P3), showed significantly higher -band power at
rest with eyes closed. While listening to music, enhancement of power
in non-musicians was obtained for electrodes F4, F8, Cz, P3, and T3.
The most significant increases of power in non-musicians as compared
with musicians occurred while listening to text: except temporal
electrodes (T3 and T4), all other electrodes showed large and
significant increase of power in the band in non-musicians.
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Figure 4.
Scalp topographies of the band spectral power
differences (expressed in decibels) between musicians and
non-musicians during no-task (eyes open and eyes closed), and task
(listening to music and to text) conditions. Results were averaged over
overlapping windows and subjects within each group. Nonsignificant
differences (set to zero) appeared in light gray. At
rest with eyes open, spectral power was higher in non-musicians only in
central electrode; when eyes were closed at resting conditions, band power was higher for multiple electrode regions (except frontal
and occipital electrodes) in non-musicians. For task conditions,
spectral power was higher (more black) for various
electrode locations in non-musicians as compared with musicians; this
effect was even more pronounced while listening to text.
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Differences in interdependencies between the two groups
Coherence values (Eq. 2) in the band are computed between all
possible combinations of electrodes. The segmentation scheme is the
same as for power analysis. Because no consistent peaks of coherence
are observed, the mean coherence for the entire band is used for
statistical analysis. Figure 5 shows the
relative band coherence expressed in for musicians
as compared with non-musicians. Although the musicians show weak
tendencies to higher coherence than non-musicians only while listening
to music, no significant differences are found between the two groups
in the degree of coherence values in any state (task or no-task).
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Figure 5.
Degree of the relative band coherence,
expressed in for musicians relative to non-musicians at different
states: eyes open (dotted line), eyes closed
(dash-dot line), listening to music (solid
line), and listening to text (dashed line),
respectively. The two horizontal lines denote the level
of significance (p < 0.01). No significant
differences are evident between the two groups. Coherence is slightly
but not significantly increased in musicians while listening to
music.
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Figure 6 shows the relative
interdependencies (measured by SI) in units of for musicians as
compared with non-musicians in both resting conditions, eyes closed and
eyes open, respectively. As described in the previous section,
S (Eq. 5) is asymmetric, thus, each electrode has two
characteristics: the ability of influencing others and the ability of
being influenced by others. The two profiles depicted in each figure
show the averages of these two features for each electrode region.
Degrees of asymmetry (although not significant) are higher during
opened than closed eyes. Thus, no significant differences in the values
of S are found between the two groups. This fact may be
expected because musical training, by common sense, should not produce
higher (or lower) degrees of interdependency in the brain when no task
was apparently involved.
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Figure 6.
Profiles showing the degrees of relative
interdependencies (in units of ) in the band for musicians
relative to non-musicians at resting conditions: with eyes open
(top) and eyes closed (bottom). Results
were averaged over windows, subjects within each group, and for all
possible electrode combinations for each electrode. For profile
connected by an asterisk, significance level was
computed while assessing the degree of influence of each electrode
region on other electrode regions ("diverging node"). The other
profile connected by an open circle exhibits the average
property of each electrode considering it being influenced by others
("converging node"). The two horizontal lines ( = ±2.33)
represent the line of significance (p < 0.01) i.e., any entry above the higher line indicates that the degree
of interdependency in musicians is significantly higher than
non-musicians and any entry below the lower line indicates higher
interdependency in non-musicians. No significant differences were found
between the two groups at resting conditions.
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Analogous profiles are shown in Figure
7 while both groups were listening to
music and to text. Interestingly, several cortical regions associated
with multiple electrode locations (F3, Fz, F4, C3, Cz, C4, T5, P3, Pz,
T6) are found to be highly significant for musicians while listening to
music. In particular, right midfrontal (F4) and frontobasal cortical
regions (F8) exhibit more influences on other cortical regions in
musicians ("diverging nodes"). Midline posterior parietal regions
(Pz) act more as passive zones ("converging nodes"), which are
likely to be influenced by various cortical regions. On the other hand,
the values of are found to be nearly zero for all electrodes while
listening to text. Thus, no significant differences in the degree of
interaction in the band between musicians and non-musicians are
found during listening to text.
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Figure 7.
Profiles showing the degrees of relative
interdependencies (in units of ) in the band for musicians
relative to non-musicians at task conditions columns: listening to
music (top), and to a text of neutral content
(bottom). Symbols are the same as in Figure 6. Large and
significant increases in long-range synchrony in the band over
multiple electrode regions are found in musicians while listening to
music, particularly in and near midline regions. These profiles are
also presented as scalp maps exhibiting the two different attributes
(convergence and divergence) of individual electrode regions. is
coded from black to white as the value
increases. Text processing does not produce any difference between the
two groups.
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Differences in interdependencies between task and
no-task conditions
Earlier figures have clearly shown the enhanced degrees of
interdependencies between various cortical regions in musicians while
listening to music. As compared with resting conditions, attentively
listening to music (or to a text) is a higher cognitive task that calls
for enhanced coordination between several brain regions even for a
naive listener. To obtain more insight into the possible transfer of
information between brain regions, the values of S (Eq. 5)
are statistically compared between task and resting condition, and the
total number of electrode pairs with significantly higher S,
which are involved at task condition, are plotted in Figure
8 as scalp maps. These maps or
topographies show the average activity of each electrode region where
averaging is done over all possible combinations of pairs of electrodes and over subjects within each group (here, the statistical comparison between task and no-task condition was initially performed for each
subject and finally averaged). In spite of these grand-averaging, several noteworthy features are found: (1) for both groups, listening to music produced higher degrees of interdependency reflected in a
larger number of enhanced electrode pairs than listening to text, (2)
the degree of asymmetry of the profiles is higher in non-musicians than
in musicians: in non-musicians, frontal regions are found to be more
passive, whereas posterior regions are found to be more active for both
tasks. The fact that both groups have comparable numbers of enhanced
connections for listening tasks might seem contradictory at first sight
because the degree of S has earlier been shown to be
significantly higher for musicians. But in comparing the connectivity
pattern of task condition with respect to resting condition, various
levels of significance (i.e., p < 0.01 or
p < 00.001) were treated in a similar fashion. Thus, both cognitive tasks have produced significant effects in the cooperation among cortical areas in both groups, yet the level of
significance is much higher for musicians while listening to music but
not while listening to text.
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Figure 8.
Scalp maps for both groups showing the numbers of
connections (electrode pairs) associated with each electrode with
significantly higher values of S while listening to
music and to text as compared with the values of S at
rest with eyes closed. Here also, the scalp maps of the two different
attributes (divergence and convergence) of individual electrode regions
are shown. For both groups, listening to music produced higher
synchronization than listening to text. However, the degree of
asymmetries of several cortical regions, especially in frontal and
occipital regions while listening to music, is higher for non-musicians
than musicians.
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Spatial synchrony
The degree of spatial synchrony ( as described by Eq. 7) or the
overall linear independency is plotted in Figure
9 for both groups for all time windows.
While listening to music, a clear separation is evident between the two
groups, where the degrees of spatial synchrony, reflected by the low
values of , are much higher in musicians. Also, the resting state
with eyes open is associated with higher than with eyes closed.
Interestingly, musicians show an overall tendency toward higher spatial
synchronization (lower values of ) except during eyes open
condition.
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Figure 9.
Scatter plots of the measure of spatial synchrony
for two groups for resting conditions with eyes closed
(asterisks) and eyes open (open
circles) (a), and task conditions
while listening to music (open diamonds) and to text
(asterisks) (b). was computed
with overlapping window length of 6 sec, which overlaps over 3 sec; the
abscissa and ordinate of each entry represent the value of of each
time window for non-musicians and musicians, respectively. The
diagonal indicates the line of equality between two
groups. Note the clear separation in the degree of spatial
synchronization between the two groups while listening to music:
musicians have significantly lower , implying higher spatial
synchrony.
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DISCUSSION |
In this study, the degree of interdependency between distributed
cortical regions has been assessed primarily by a measure (SI) based on
nonlinear dynamical system theory. This measure has the inherent
capability of detecting asymmetric coupling, whereas the commonly used
measures to detect hidden synchrony are usually symmetric. The degree
of the band long-range synchrony has been measured in subjects
while listening to music, to text, as well as at rest with no-task involved.
Power analysis
Apart from questions of mutual dependencies, the power spectrum of
musicians versus non-musicians was examined as an indicator of the
degree of the local involvement of brain regions. The major finding in
this respect was the higher power in non-musicians as compared with
musicians over several electrode regions. Generally, an increase in
spectral power at an electrode location can be attributable to an
enhancement of local increased in-phase activity of neuronal assemblies
or to the recruitment of additional synchronous neuronal populations
(Srinivasan et al., 1999). Across local synchronization, interdependency measures such as SI can detect long-range
synchronization between distant brain regions. In non-musicians, local
increase in spectral power was accompanied by a decrease in
interdependency; thus, neuronal assemblies were involved more locally,
whereas, at the same time, they were functionally less connected with
distant brain regions. Therefore, the spectral power is not an
appropriate indicator of the degree of long-range synchronization,
which seems to be an essential requirement for the performance of
various cognitive tasks (Bressler et al., 1993; Miltner et al., 1999).
Interdependency analysis and the role of the band synchrony
In interdependency analysis, the following main observations were
made: (1) the degrees of interdependencies were increased under task
with respect to no-task condition; (2) the degrees of interdependencies
or the strength of coupling between distributed cortical areas were
significantly higher in musicians while listening to music. These
increases were found over multiple cortical areas; and (3) no
differences in the degree of interdependency were found between the two
groups while listening to text or at resting conditions.
A growing body of literature (Singer and Gray, 1995; Tallon-Baudry and
Bertrand, 1999 and references therein) suggests that fast (>30 Hz)
oscillatory synchronization of brain areas may serve to establish
dynamic relations between neuronal assemblies; therefore, this
mechanism might pave a way to the solution of the binding problem
inherent to cognitive functions such as scene segmentation and object
representation. In a similar way, because visual objects have many
different attributes (i.e., shape, size, depth, color, etc.), that have
to be integrated for realizing objects as such, any complex music has
various acoustic attributes (i.e., pitch, timbre, rhythm, harmony,
melody, contour, etc.). For the realization of music as a specific
entity, all these acoustic attributes have to be bound in a dynamical
way; every attentive listener even including a naive one performs this
binding task, but subjects with professional musical training do the
same in a more efficient way because of their higher ability to
discriminate between different musical attributes and to mentally
follow and reconstruct acoustic architectonic structures. One possible
reason why the long-range -band synchrony was found to be higher in
musicians may be attributable to their higher ability of binding
musical attributes because of their professional training. Moreover,
attentively listening to music is associated with anticipation: we
anticipate what we already know; thus, anticipation is based on past
musical memories. Therefore, a trained musician as compared with a
non-musician implicitly retrieves a larger repertoire of musical
patterns from his memory while listening to music, although a given
piece of music is unknown to him. Listening to text, on the other hand, obviously involves similar memory contents in both groups. This view is
supported by the lack of differences in the -band synchrony between
the two groups while listening to text and under resting conditions.
Spatial synchrony
Using a linear measure of signal complexity () to characterize
the spatial synchrony taking 19 channels together, it was found that
musicians showed significantly higher spatial (or global) synchronization than non-musicians while listening to music. The degree
of interdependency (measured by SI) was already shown to be higher in
musicians for distributed cortical areas, or in another words, the
cortical areas acted more independently in non-musicians. Thus, in
non-musicians, brain regions were functionally more localized while
listening to music i.e., they were more isolated from other brain
regions; this fact was reflected in the high values of revealing
spatial desynchronization.
Remarks
Several critical points have to be discussed. It is well known
that the band has much smaller amplitudes than signals in  or
 band, and there is interference with muscle activities. For the
following reasons it seems unlikely that our results are caused by
muscles: (1) most emphasis is put on synchrony measure rather than the
raw amplitude of the signal, (2) temporal electrodes T3 and T4 are
usually the most contaminated by muscles, yet the values of for
these two electrodes are insignificant (Fig. 7), whereas significant
differences were found over distributed cortical areas; thus, the
enhanced band synchrony for musicians is even present when temporal
electrodes are excluded from the study. Second, one might ask whether
similar enhancement of the band synchrony could be found for
other pieces of music. In the same group of subjects, we found that
musicians have significantly higher phase synchronization than
non-musicians while listening to different pieces of music, including a
non-rhythmic synthesized computer music (Bhattacharya and Petsche,
2001b). So, attentively listening to any kind of music most
likely results in higher interdependency in musicians than
non-musicians. The correlation between selective attention and the
synchronization in the band, mostly measured by spectral power, is
well known (Sheer, 1989; Desmedt and Tomberg, 1994; Fries et al.,
2001). However, it has to be noted that both groups were paying
attention to both listening tasks, although differences were found only
while listening to music. Third, the EEG recorded at any region of the
scalp approximately quantifies the summed electrical potentials
generated by postsynaptic dendritic currents from pyramidal neurons in
a certain volume of tissue under the electrode. Thus, EEG does not
measure dynamic processes but represents the summation of dynamical
processes; on the other hand, the similarity index, theoretically, has
been proposed to find the coupling between two distinct dynamical
processes. Generally speaking, the possible substrates of synchrony
between neuronal assemblies are the existence of groups of neurons
interconnected by mutual excitatory and inhibitory synaptic connections
and/or the presence of in-phase neuronal firing in different
assemblies. Thus, if S between two signals from two
electrodes is high, the functional integration manifested by synaptic
connections or the enhanced in-phase firing between neuronal assemblies
in the associated cortical regions underlying the two electrodes will
also be high. In the present paper, this working scheme of
interpretation has been adopted, although we think that it will be
important to apply this measure on the data from unit recording to get
clearer insight. Finally, the cutoff of was based on the assumption
of a Gaussian distribution, which might not be fulfilled always, and,
further, the results (Figs. 6-8) provide an average representation
across electrodes. After applying a nonparametric statistical test
(paired Wilcoxon) between the two groups while listening to music, 309 electrode pairs of a possible 342 electrode pairs showed significantly higher interdependency in musicians, and only nine electrode pairs showed higher interdependency in non-musicians. In this paper, the
significance level was computed for all possible electrode pairs
separately, and finally the results were averaged for the compaction of
information; after grand-averaging, any value of associated with
any electrode region above the significance cutoff should necessarily
imply that this electrode region was strongly connected with many other
electrode regions, albeit distant. Because near and distant electrode
pairs were treated in an identical way, the synchronization reported
here is indeed of long range. Thus, the adopted scheme of quantifying
the statistical difference between the two groups is found to be
reasonable, and it is most likely that the reported enhanced
interdependency in musicians while listening to music is associated
with functional cooperation among multiple cortical areas.
Possibilities of other significant frequency bands
Another pertinent question is whether similar results can be
obtained for other frequency bands. In a parallel study, we have found
that musicians showed significantly higher phase synchronization only
in the band while listening to music (Bhattacharya and Petsche,
2001a). According to an earlier report, binding of the visual
features of a complex object can be accomplished within ~100 msec
(König et al., 1995). Oscillations in the or lower frequency
ranges would be too slow to establish synchrony within the required
time. On the other hand, binding cannot either be accomplished by very
high frequency oscillations for the following reason: simulation
studies have indicated that reciprocally coupled oscillators can be
entrained when the conduction delays in the network are less than
one-third of the average period time (König and Schillen, 1994),
otherwise synchrony without phase lag would not be possible.
Interestingly, long-range synchrony in the cortex occurs consistently
with almost zero (<3 msec) phase lag (Engel et al., 1991;
Roelfsema et al., 1997), whereas much greater phase lag would be
expected from the slow speed of axonal conduction across the corpus
callosum (Innocenti, 1980). Considering all these constraints and the
coupling delays, it has been postulated that if oscillation indeed is
an essential requirement for the establishment of long-range synchrony,
the band would be the most suitable candidate for integration
(König et al., 1995).
It should be noted that the mere occurrence of oscillations in the band, as commonly believed, should not be taken as the hallmark of
underlying synchrony, rather the synchrony measures directly reflect
the underlying binding process (Rodriguez et al., 1999). The mechanisms
by which such coherent high-frequency oscillations are generated and
subsequently synchronized are not clearly known, and no claim in this
regard can be made by the data obtained by macroscopic EEG electrodes.
Furthermore, the role of other frequencies in establishing this
enhanced synchrony in the band cannot be ruled out either.
Recently, in animal studies, strong phase coupling was found between
the band and , bands, which supports the hypothesis that band represents bottom-up processing, and lower frequencies represent
top-down processing (von Stein et al., 2000). This is an objective for future researches to investigate whether similar coupling between different frequencies can also be found for music perception, which
inherently calls for the integration between bottom-up and top-down processing.
Conclusion
By using a recent index to detect functional coupling, this study
demonstrates that musicians' brains show significantly higher interdependency in the band between distributed cortical areas as
compared with non-musicians while listening to music. No significant differences were found between the two groups for other conditions. The
similarity index was found to be more useful in the detection of hidden
functional interdependencies than classical indices like coherence. It
has also been shown that long-range synchrony in the band contains
more information than the band spectral power in understanding
higher cognitive functioning, and furthermore, that this long-range
synchrony in the band is not an exclusive property of
visual-feature binding, as commonly believed. Instead, this synchrony
could provide a platform for large-scale general cognitive integration.
Finally, if our hypothesis about the potential role of long-range
synchrony in high-frequency band stands correct, the enhancement in the
cooperation between multiple cortical areas should be present during
the performance of any higher cognitive task in which several complex
attributes must be bound together to complete the task; furthermore,
the degree of this enhancement should be correlated with training in
similar tasks.
| |
FOOTNOTES |
Received Dec. 20, 2000; revised May 14, 2001; accepted May 15, 2001.
This research was partially supported by Herbert von Karajan Centrum
(Vienna, Austria). We are thankful to the anonymous referees for their
most helpful suggestions. Technical assistance of B. Rescher is thankfully acknowledged. Scalp maps are produced by software that is available freely at the website
http://www.cnl.salk.edu/~scott/ica.html.
Correspondence should be addressed to Dr. Joydeep Bhattacharya,
Commission for Scientific Visualization, Austrian Academy of Sciences,
Sonnenfelsgasse 19/2, A-1010 Vienna, Austria. E-mail: joydeep{at}oeaw.ac.at.
| |
REFERENCES |
-
Arnhold J,
Grassberger P,
Lehnertz K,
Elger CE
(1999)
A robust method for detecting interdependencies: application to intracranially recorded EEG.
Physica D
134:419-430.
-
Basar-Eroglu C,
Struber D,
Schurmann M,
Stadler M,
Basar E
(1996)
-band responses in the brain: a short review of psychophyiological correlated and functional significance.
Int J Psychophysiol
24:101-112[Web of Science][Medline].
-
Bhattacharya J,
Petsche H
(2001a)
Musicians and the banda secret affair?
NeuroReport
12:371-374[Medline].
-
Bhattacharya J,
Petsche H
(2001b)
Enhanced phase synchrony in the electroencephalograph band for musicians while listening to music.
Phys Rev E
64:012902.
-
Bressler SL,
Coppola R,
Nakamura R
(1993)
Episodic multiregional cortical coherence at multiple frequencies during visual task performance.
Nature
366:153-156[Medline].
-
Csibra G,
Davis G,
Spratling MW,
Johnson MH
(2000)
oscillations and object processing in the infant brain.
Science
290:1582-1585[Abstract/Free Full Text].
-
Desmedt JE,
Tomberg C
(1994)
Transient phase-locking of 40 Hz electrical oscillations in prefrontal and parietal human cortex reflects the process of conscious somatic perception.
Neurosci Lett
168:126-129[Web of Science][Medline].
-
Engel AK,
König P,
Kreiter AK,
Singer W
(1991)
Interhemispheric synchronization of oscillatory neuronal responses in cat visual coretx.
Science
252:1177-1179[Free Full Text].
-
Freeman WJ
(1978)
Spatial properties of an EEG even in the olfactory bulb and cortex.
Electroencephalogr Clin Neurophysiol
44:585-605.
-
Freeman WJ,
Skarda CA
(1985)
Spatial EEG patterns, nonlinear dynamics and perception. The neo-Sherrington view.
Brain Res Rev
10:147-175.
-
Fries P,
Reynolds JH,
Rorie AE,
Desimone R
(2001)
Modulation of oscillatory neuronal synchronization by selective visual attention.
Science
291:1560-1564[Abstract/Free Full Text].
-
Fukunaga K
(1990)
In: Introduction to statistical pattern recognition. San Diego: Academic.
-
Galambos R
(1992)
A comparison of certain band (40-Hz) brain rhythm in cat and man.
In: Induced rhythms in the brain (Basar E,
Bullock TH,
eds). Boston: Birkhauser.
-
Golub GH,
Van Loan CF
(1996)
In: Matrix computations. Baltimore: The John Hopkins UP.
-
Gray CM,
DiPrisco GV
(1997)
Stimulus-dependent neuronal oscillations and local synchronization in striate cortex of the alert cat.
J Neurosci
17:3239-3253[Abstract/Free Full Text].
-
Gray CM,
König P,
Engel AK,
Singer W
(1989)
Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.
Nature
338:334-337[Medline].
-
Haig AR,
Gordon E,
Wright JJ,
Meares RA,
Bahramli H
(2000)
Synchronous cortical -band activity in task relevant cognition.
NeuroReport
11:4669-4675.
-
Innocenti GM
(1980)
The primary visual pathway through the corpus-callosum: morphological and functional aspects in the cat.
Arch Ital Biol
118:124-188[Web of Science][Medline].
-
Jasper HH
(1958)
Report of the committee on methods of clinical examination in electroencephalography.
Electroencephalogr Clin Neurophysiol
10:371-375.
-
Jokeit H,
Makeig S
(1994)
Different even-related patterns of -band power in brain waves of fast- and slow-reacting subjects.
Proc Natl Acad Sci USA
91:6339-6343[Abstract/Free Full Text].
-
Joliot M,
Ribary U,
Llinás R
(1994)
Human oscillatory brain activity near 40 Hz coexists with cognitive temporal binding.
Proc Natl Acad Sci USA
91:11748-11751[Abstract/Free Full Text].
-
Kantz H,
Schreiber T
(1997)
In: Nonlinear time series analysis. Cambridge: Cambrdige UP.
-
König P,
Schillen TB
(1994)
Binding by temporal structure in multiple feature domains of an oscillatory network.
Biol Cybern
70:397-405[Web of Science][Medline].
-
König P,
Engel AK,
Singer W
(1995)
Relation between oscillatory activity and long-range synchronization in cat visual cortex.
Proc Natl Acad Sci USA
92:290-294[Abstract/Free Full Text].
-
Keil A,
Muller MM,
Ray WJ,
Gruber T,
Elbert T
(1999)
Human band activity and perception of a gestalt.
J Neurosci
19:7152-7161[Abstract/Free Full Text].
-
Lachaux JP,
Rodriguez E,
Martinerie J,
Varela FJ
(1999)
Measuring phase synchrony in brain signals.
Hum Brain Mapp
8:194-208[Web of Science][Medline].
-
Miltner WHR,
Braun C,
Arnold M,
Witte H,
Taub E
(1999)
Coherence of -band EEG activity as a basis for associative learning.
Nature
397:434-436[Medline].
-
Pereda E,
Rial R,
Gamundi A,
Gonzalez J
(2001)
Assessment of changing interdependencies between human electroencephalograms using nonlinear methods.
Physica D
148:147-158.
-
Rodriguez E,
George N,
Lachaux J-P,
Martinerie J,
Renault B,
Varela FJ
(1999)
Perception's shadow: long-distance synchronization of human brain activity.
Nature
397:430-433[Medline].
-
Roelfsema PR,
Engel AK,
König P,
Singer W
(1997)
Visuomotor integration is associated with zero time-lag synchronization among cortical areas.
Nature
385:157-161[Medline].
-
Rosenblum M,
Pikovsky A,
Schäfer C,
Tass P,
Kurths J
(2000)
Phase synchronization: from theory to data analysis.
In: Handbook of biological physics (Gielen S,
Moss F,
eds). Amsterdam: Elsevier.
-
Sauer T,
York JA,
Casdagli M
(1991)
Embedology.
J Stat Phys
65:579-616.
-
Schmitz A
(2000)
Measuring statistical dependence and coupling of subsystems.
Phys Rev E
62:7508-7511.
-
Sheer DE
(1989)
Sensory and cognitive 40 Hz event-related potentials: behavioral correlates, brain function and clinical applications.
In: Brain Dynamics (Basar E,
Bullock TH,
eds). Berlin: Springer.
-
Singer W,
Gray CM
(1995)
Visual feature integration and the temporal correlation hypothesis.
Annu Rev Neurosci
18:555-586[Web of Science][Medline].
-
Solbounov S,
Tutwiler R,
Slobounova R,
Rearick M,
Ray W
(2000)
Human oscillatory brain activity within band (30-50 Hz) induced by visual recognition of non-stable postures.
Cogn Brain Res
9:177-192[Medline].
-
Srinivasan R,
Russell DP,
Edelman GM,
Tononi G
(1999)
Increased synchronization of neuromagnetic responses during conscious perception.
J Neurosci
19:5435-5448[Abstract/Free Full Text].
-
Takens F
(1981)
Detecting strange attractors in fluid turbulence.
In: Dynamical systems in turbulence (Rand D,
Young LS,
eds), pp 366-381. New York: Springer.
-
Tallon-Baudry C,
Bertrand O
(1999)
Oscillatory activity in humans and its role in object representation.
Trends Neurosci
3:151-162.
-
Tallon-Baudry C,
Bertrand O,
Peronnet F,
Pernier J
(1998)
Induced-band activity during the delay of a visual short-term memory task in humans.
J Neurosci
18:4244-4254[Abstract/Free Full Text].
-
Theiler J,
Eubank S,
Longtin A,
Galdrikian B,
Farmer JD
(1992)
Testing nonlinearity in time series: the method of surrogate data.
Physica D
58:77-94[Web of Science].
-
von Stein A,
Chiang C,
König P
(2000)
Top-down processing mediated by interareal synchronization.
Proc Natl Acad Sci USA
97:14748-14753[Abstract/Free Full Text].
-
Wackermann J
(1999)
Towards a quantitative characterization of functional states of the brain: from the nonlinear methodology to the global linear description.
Int J Psychophysiol
34:365-380.
Copyright © 2001 Society for Neuroscience 0270-6474/01/21166329-09$05.00/0
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TOP
ABSTRACT
INTRODUCTION
