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The Journal of Neuroscience, June 1, 2001, 21(11):3942-3948
Transient Interhemispheric Neuronal Synchrony Correlates with
Object Recognition
Tatsuya
Mima,
Tomi
Oluwatimilehin,
Taizo
Hiraoka, and
Mark
Hallett
Human Motor Control Section, Medical Neurology Branch, National
Institute of Neurological Disorders and Stroke, National Institutes of
Health, Bethesda, Maryland 20892-1428
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ABSTRACT |
Object recognition might be achieved by the recreation of a
meaningful internal image from visual fragments. This recreation might
be achieved by neuronal synchronization that has been proposed as a
solution for the perceptual binding problem. In this study, we
evaluated synchronization between the occipitotemporal regions bilaterally using electroencephalograms during several visual recognition tasks. Conscious recognition of familiar objects spanning the visual midline induced transient interhemispheric
electroencephalographic coherence in the  band, which did not occur
with meaningless objects or with passive viewing. Moreover, there was
no interhemispheric coherence when midline objects were not recognized
as meaningful or when familiar objects were presented in one visual
hemifield. These data suggest a close link between site-specific
interregional synchronization and object recognition.
Key words:
object recognition; neuronal synchronization; EEG; interhemispheric coherence; perceptual binding; visual recognition
task
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INTRODUCTION |
Neuronal synchronization among the
spatially distributed cortical visual areas has been proposed as the
mechanism for integrating (perceptual binding) the parallel pathways
that process the different features of visual objects (Eckhorn et al.,
1988 ; Gray and Singer, 1989; Gray et al., 1989). Previous studies
suggested that the coherence between neuronal activities is the
physiologic correlate of the segmentation of a unitary object from a
visual scene (Engel et al., 1991b; Singer and Gray, 1995). In a
cognitively demanding task, it is conceivable that only successfully
bound features might be further processed as meaningful information.
This selective enhancement of synchronization has been proposed as a
substrate for visual recognition (Crick and Koch, 1990; Singer, 1998).
Previous studies using human electroencephalograms (EEGs) showed the
importance of EEG synchronization in various frequencies depending on
the task (for review, see Basar et al., 1997; Andres and Gerloff, 1999;
Klimesch, 1999; Tallon-Baudry et al., 1999). We
investigated the role of EEG synchronization in an object detection
task in humans by using frequency domain analysis.
In the first midline presentation experiment, subjects viewed either
familiar or meaningless objects randomly presented at the center of the
visual field with variable stimulus duration (see Fig. 1A).
To recognize familiar objects successfully, interhemispheric communication must occur to bind images between two visual hemifields that are processed separately in the two cerebral hemispheres; this may
be achieved by synchronization. To test this hypothesis, we measured
the amount of interhemispheric synchronization by the event-related
analysis of EEG coherence after presentation of familiar and
meaningless objects. Second, to determine whether this interhemispheric
coherence, if any, is associated with perception or recognition, the
effect of active attention was assessed by comparing the object
judgment with the passive-viewing task. Third, to investigate whether
this coherence correlates with the internal image or the physical
attributes of external stimuli, the effect of stimulus duration on this
coherence was systematically compared.
To understand the functional relevance of this coherence, it is
important to know whether the interhemispheric coherence reflects the
task-specific binding of visual information separated in the two
hemispheres or just represents the cognitive and attentional demand of
the object detection task itself. To exclude the latter possibility, we
performed a second experiment using the same set of stimuli but
presented either on the right or left side of the visual field. We
expected that the interhemispheric coherence would not be necessary for
the early stage of object recognition, when stimuli are presented in a
hemifield and when one hemisphere handles all of the visual information.
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MATERIALS AND METHODS |
The experimental protocol was approved by the Institutional
Review Board, and written informed consent was obtained from subjects before the experiment.
Midline presentation experiment. Eight subjects (three
males and five females; age range, 25-58 years) viewed black-and-white drawings of either familiar or meaningless objects randomly presented at the center of the visual field (Fig.
1A). The target stimuli were presented between checkerboard-type masks (from  2 sec before the
stimulus to 1.5 sec after the stimulus) to prevent an afterimage, and
pseudo-randomized stimulus durations were 17, 34, 50, and 100 msec.
Subjects were asked to tell whether or not they recognized a familiar
meaningful object by pressing one of the two buttons with their right
hand after the GO stimulus (judgment task, Fig. 1C).
Subjects were instructed to judge the target as a familiar object only
when they were sure about what they saw. These instructions were given
to minimize the number of false-alarm trials (meaningless objects
recognized as familiar ones), and these trials were not analyzed
further. For a control condition using the same set of stimuli,
subjects were asked to respond to the GO stimulus by pressing the
button, regardless of the preceding target (passive viewing).
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Figure 1.
A, Typical examples of familiar
(left) and meaningless (right) objects.
The latter was produced by randomly swirling the familiar object
30-60° ~10 times to degrade its recognizable shape.
B, Location of the electrodes over the scalp. Four
electrode pairs of interest are shown as gray arrows,
connecting the right and left temporal-occipital electrodes (TP7, TP8,
T5, T6). C, Schema of the experiment design. The
go (GO) signal appears 1.5 sec after the target stimulus
onset. The averaged event-related potentials at the occipital area
(O1, O2) reveal an initial cortical
positive deflection at 115 msec. There was no significant difference
among conditions up to ~500 msec. D, Coherence spectra
at the electrode pairs of interest shown in B during
object recognition for the baseline (512 to 0 msec) and
117-629 msec period (mean ± SEM). Coherence computation was
performed using a 512 msec time window with a 2 Hz frequency
resolution. Except for this preliminary analysis, a 256 msec time
window was used to achieve a better temporal resolution. The frequency
at which the statistically significant difference was observed is
indicated by an *. The coherence difference was largest at 8 and 10 Hz
( band, boxed area) and significant
(p < 0.01). There was no significant
coherence change at the other frequencies.
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Target stimuli were ~9° of visual angle centered at the fixation
point on a computer screen 1-1.2 m in front of the subject. Sixty
familiar objects and corresponding meaningless objects were used.
Meaningless objects were produced by swirling the familiar objects.
Stimuli were presented randomly with four different durations. In the
preliminary analysis, we confirmed that repetition of the same stimuli
did not elicit any systematic difference in either hit rate or reaction
time. The GO signal (duration, 100 msec) was given 1.5 sec after the
target stimulus onset. The interval between trials was 3-5 sec. A
total of 480 trials was divided into four sessions. Subjects performed
eight randomized sessions of judgment and passive-viewing tasks. A
delayed reaction time task was used to separate the sensory processing
from the motor preparation.
Lateral presentation experiment. The second experiment
using the same set of stimuli was performed on seven subjects (three males and four females; age range, 28-56 years), but stimuli were presented either on the right or left side of the visual field. Target
stimuli, which were the same as those in the midline presentation experiment, were presented between the 9 × 18° rectangle masks either at the right or left side in randomized order. The stimulus duration was 50 msec. Other conditions were essentially the same as in
the first experiment. Subjects were asked to fixate the center of the
monitor and to discriminate the target irrespective of the stimulus
location (60 meaningless and familiar objects on each side).
Recording and analysis. An EEG was DC-50 Hz filtered
and recorded at 250 Hz from 29 surface electrodes over the scalp
(Neuroscan, Neuroscan, Inc., Herndon, VA; Fig. 1B).
Digitally linked earlobe electrodes served as the reference. Trials
were analyzed separately for correctly recognized objects (object
recognition), correctly rejected ones (meaningless object), and
missed ones (failed object recognition). For the midline presentation
experiment, one subject who had a very high false-alarm rate was
excluded from the analysis. Object recognition for the 17-msec-duration
trials was not analyzed because the hit rate was not significantly
different from the false-alarm rate (p = 0.31).
Signals were segmented into 1 sec before to 3 sec after the target
stimulus onset. Trials with eye movement or excessive muscle artifacts
were removed by a careful visual inspection. For each condition, 15-20
(mean, 17.8) trials were analyzed.
For quantitative analysis of interhemispheric coherence, four electrode
pairs of interest connecting the right and left occipitotemporal areas
(Fig. 1B, TP7, TP8, T5, and T6 electrodes) were
selected on the basis of an a priori hypothesis because previous
studies showed the importance of the lateral occipital area and the
ventral "what" pathway for object recognition (Tanaka, 1993;
Ungerleider and Haxby, 1994; Malach et al., 1995). EEG signals at the
same four electrodes were analyzed for the quantitative power analysis. EEG segments from 512 to 256 msec and from 256 to 0 msec
preceding the stimulus were pooled and used for the baseline. EEG
segments were processed by a fast Fourier transform algorithm using a
Hanning window at 10% of the edge. To observe the temporal pattern of the EEG power change, power spectra were log-transformed and normalized by setting the prestimulus baseline to zero [event-related power change (ERPow)]. Cross-spectra were calculated in the same way and
normalized by the autospectra to compute coherence, according to the
following equation:
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In this equation, fxx(j),
fyy(j), and
fxy(j) are values of auto- and
cross-spectra at a given frequency j. Coherence is expressed as a real number between 0 and 1; 1 indicates a perfect linear association between two signals, and 0 indicates a complete absence of
linear association. Coherence was normalized with an arc hyperbolic tangent transformation to stabilize the variance (Halliday et al.,
1995). The vent-related coherence change (ERCoh) was computed by
setting the prestimulus baseline to the zero level. Power and coherence
spectra were estimated for each subject for each condition. ANOVA (main effects of object, task, and duration) was used for the statistical comparison. Where appropriate, a t test was
also used.
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RESULTS |
Midline presentation experiment
On average, 79 ± 17% (mean ± SD) of meaningless
objects were correctly rejected as meaningless, irrespective of the
stimulus duration (Fig. 2). In contrast,
the number of correct object recognitions was linearly correlated with
stimulus duration (Pearson correlation r = 0.779;
p < 0.001). For a duration of 17 msec, the correct detection rate for familiar objects were not significantly different from the false-alarm rate for meaningless objects
(p = 0.31). Reaction time was 605 ± 173 msec that did not significantly vary for the different conditions.
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Figure 2.
Proportion of correct detection for familiar
and meaningless objects (mean + SD). For meaningless objects, the
correct rejection rate is insensitive to stimulus duration. For
familiar objects, the correct detection rate is positively correlated
with stimulus duration (r = 0.779;
p < 0.001).
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Preliminary analysis of averaged event-related potentials (ERPs)
revealed that the earliest cortical activity after the stimulus was a
positive potential localized over the occipital cortex at 115 ± 14 msec (Fig. 1C). The ERP difference between object
recognition and meaningless objects was widely observed over the
bilateral central, parietal, and temporal areas. ERPs showed larger
positivity for object recognition (onset of the statistically
significant difference determined by the t-map, 460 msec; the mean peak
difference, 570 msec). Starting from the time of the initial positive
potential, we computed the power and coherence spectra for every 256 msec time window; this enabled a 3.98 Hz frequency resolution. For the
coherence analysis, the prestimulus baseline level was not significantly different among conditions (ANOVA, object,
p = 0.1; task, p = 0.4; duration,
p = 0.3). Compared with that of meaningless objects,
the early phase (117-373 msec) of object recognition showed a
coherence increase within the band (8-12 Hz) predominantly over
the posterior part of the brain, connecting the right and left sides
(Fig. 3). A significant coherence
increase during object recognition was not observed in other
frequencies, although we computed the coherence spectra up to 50 Hz
with a finer frequency resolution at 2 Hz (Fig. 1D).
Therefore, only the results in the band are reported.
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Figure 3.
Scalp topography of the event-related power change
(ERPow) and the link map of the event-related coherence change (ERCoh)
in the band (8-12 Hz) for object recognition and
meaningless objects at 117-373 and 373-629 msec periods. Top
row, The topographic distribution of the power change compared
with the baseline level is shown using a color scale
(right). For the construction of the ERPow map, ERPow
less than 0.3 was set to 0.3 (dark blue). ERPow at
TP7, TP8, T5, and T6 electrodes is significantly smaller than the
baseline zero level in the later stage (373-629 msec;
p < 0.05) but not in the earlier stage (117-373
msec). The magnitude of ERPow in the later stage is significantly
larger for the object recognition compared with the meaningless object
(p < 0.001). Bottom row, The
magnitude of the normalized coherence change compared with the
prestimulus baseline (ERCoh) is shown by the color of
the line connecting a pair of electrodes. Warm
colors indicate the coherence increase, and cool
colors indicate the coherence decrease (color
scale at the right). ERCoh >0.15 or smaller
than 0.15 is set to 0.15 and 0.15, respectively, for the
construction of the link map. Electrode pairs that failed to show the
significant difference from the baseline are not shown. Four electrodes
of interest are shown in red. Electrode pairs of
interest are shown as thick lines. In the early stage
(117-373 msec), an increased interhemispheric coherence appeared over
the posterior part of the brain only during object recognition.
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For the object recognition trials, average interhemispheric
coherence at 117-373 msec increased significantly compared with the
baseline (p < 0.001). Trials with meaningless
objects, failed object recognition, and passive viewing of familiar
objects did not induce similar effects (Fig.
4A). This
interhemispheric coherence restricted to object recognition was
transient and immediately followed the earliest visual cortical evoked
potential. Stimulus duration had no effect on its magnitude
(p > 0.4). Phase lag during the coherence
increase (117-373 msec) was 2 ± 3 msec (mean ± SEM) from
right to left, which is not significantly different from zero and is,
therefore, a "near-zero" phase lag.
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Figure 4.
A, ERCoh and ERPow at the
electrodes and electrode pairs of interest shown in the band as a
function of time for the judgment and passive-viewing tasks (mean ± SEM). A significant increase in the interhemispheric coherence is
observed only in the 117-373 msec period for object recognition
(p < 0.001). A significant power decrease
started in the 373-629 msec period (p < 0.001). The magnitude of the power decrease is significantly smaller
for object recognition compared with the meaningless object
(p < 0.001) or failed object
cognition (p < 0.001). The similar
significant difference between the familiar and meaningless objects is
also observed for passive viewing (p < 0.001). *, Only the ERCoh at 117-373 msec during the object
recognition is significantly larger than the baseline level; **, for
all conditions, ERPow is significantly larger than the prestimulus
baseline; and ***, regarding the magnitude of ERPow, the
main effects of object and task are both significant.
However, the interaction between factors is not significant.
B, Detailed time course of ERCoh and ERPow at 8 Hz
(4-12 Hz) during the object recognition 64-192 msec time window
(64 msec overlap). The significant coherence increase started at 128 msec ( p < 0.05), and the significant power
decrease started at 256 msec (p < 0.05).
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To investigate the possible effect of the linked earlobe reference on
coherence estimate, we also computed the ERCoh by using a current
source density (CSD) function of EEG (dipole derivation utility of the
Neuroscan software), which can achieve a reference-free EEG signal with
minimal volume conduction. Interhemispheric coherence at the electrode
pairs of interest was 0.160 ± 0.013 and 0.215 ± 0.029 for
baseline and early recognition, respectively (significantly different,
p < 0.01).
For the power analysis, the prestimulus baseline EEG power in the band during the judgment task was significantly smaller than that
during the passive-viewing task (ANOVA, object, p = 0.9; task, p < 0.01; duration, p = 0.1). To concentrate on the change of the power induced by the task,
ERPow was computed by setting the prestimulus baseline to zero. The power significantly dropped at 373-629 msec during all conditions
(p < 0.05). Because the EEG rhythm is
regarded as the "cortical idling rhythm," we quantitatively
analyzed the normalized power decrease as an index of cortical
activation (Pfurtscheller and Aranibar, 1979). In both judgment and
passive-viewing tasks, the decrease of power was larger for familiar
objects compared with meaningless objects (main effect, object,
p < 0.001; task, p < 0.001;
interaction, object × task, p > 0.1). In
agreement with the task performance during the judgment task, the
difference between familiar and meaningless objects was significant
when the stimuli were presented for 34, 50, or 100 msec in duration but
not for 17 msec (post hoc test with Bonferroni
correction; p < 0.05, p < 0.005, p < 0.005, and p = 0.5, respectively).
For familiar object recognition trials, the magnitude of the EEG power
decrease at 373-629 msec was linearly correlated with the stimulus
duration (Fig. 5, p < 0.001).
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Figure 5.
For the object recognition trials, the magnitude
of the event-related power change (mean + SEM) at 117-373 msec
linearly correlated with the stimulus duration
(p < 0.001). ERPows at four stimulus
durations were significantly different from the baseline level.
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Lateral presentation experiment
Task performance was not different for the side of stimuli. The
mean proportion of correct object recognition was 80 ± 10%, and
that of correctly rejected meaningless objects was 86 ± 13%. Reaction time was 569 ± 144 msec on average.
Event-related coherence and power analysis were performed as described
in the first experiment using a 512 msec time window. Prestimulus baseline coherence and power levels during the lateral presentation experiment were not significantly different from those
during the judgment task of the first experiment
(p = 0.1, and p = 0.3, respectively). No interhemispheric coherence increase was correlated
with object recognition in this experiment (Fig. 6). In contrast, the occipitotemporal EEG
power started to decrease at 373 msec, and the amount of decrease
(373-885 msec) was larger for the object recognition compared with
meaningless objects (p < 0.001). There was no
significant laterality of the power decrease for either stimulus
side.
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Figure 6.
ERCoh and ERPow at the electrodes and electrode
pairs of interest shown in Figure 1B as a
function of time for the lateral presentation experiment. Because the
results for the right and left targets showed essentially the same time
course, the averaged data were plotted (mean ± SEM).
Interhemispheric coherence is stable and similar for object
recognition and the meaningless object. A significant power
decrease compared with the baseline starts at 373-629 msec
(*p < 0.05). The magnitude of ERPow is
significantly larger for the object recognition compared with the
meaningless object (**p < 0.001).
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DISCUSSION |
We found that the dynamic change of interhemispheric coherence in
the band is associated with the recognition of familiar objects
presented at the center of the visual field. The difference in the
coherence estimate between object recognition and passive viewing of
familiar objects suggests the link between a level of neural synchrony
and a visual recognition. Previous animal studies suggested that the
perception of a unitary object might be associated with increased
coherence (Engel et al., 1991b; Singer and Gray, 1995). In our
experimental paradigm, it may be argued that even the swirled object
can be perceived without being recognized as a familiar object. Thus,
it is possible that perceptual binding could occur within and between
the primary visual areas (V1) even for meaningless objects or during
passive viewing. However, detection of these localized binding signals
by a noninvasive macroscale technique, such as EEG coherence, might be
difficult because of the anatomical location of V1 (medial site of the
occipital cortex). On the other hand, it is likely that object
recognition requires synchronous activation of a larger cortical
network extending to the temporal and lateral occipital areas, which
might correlate with the coherence increase in the present experiment.
During binocular rivalry in monkeys, neural activity at only the higher but not the lower level visual areas correlated with conscious perception (Leopold and Logothetis, 1996; Pigarev et al., 1997; Sheinberg and Logothetis, 1997). Human-neuroimaging studies that measured the cerebral blood flow change have shown that active visual
attention enhances local activation in the striate and extrastriate
cortex (Moran and Desimone, 1985; Shulman et al., 1997; Kastner et al.,
1998). It is conceivable that visual recognition also enhances the
task-specific neuronal coupling or interregional coherence.
Coherence computed from the scalp-recorded referenced EEG has
often been criticized because of the possible contamination of the
apparent coherence change associated with volume conduction and the
common reference effect (Mima and Hallett, 1999; Mima et al., 2000).
Electrodes of interest over the right and left hemispheres were,
however, at least 16 cm apart in the present study (Fig.
1B), which enabled us to eliminate the volume
conduction effect (Nunez et al., 1997). To remove the possible
inflation of coherence caused by the common earlobe reference, we used
a reference-free EEG (CSD computation). This procedure also enabled the
sharp spatial filtering, excluding the possible contamination of a
single widespread source affecting both hemispheres. Because the same
results could be obtained by the CSD, our data cannot be explained by
contamination of the earlobe reference activity or by volume conduction.
Because the conventional potential analysis showed no significant
difference at the time when the coherence increase occurred (117-373
msec), contamination of the evoked potential to the computation of
coherence is not likely. Thus, our study should reflect the induced but
not the evoked activity. For the same reason, the phase resetting
associated with the stimuli (Jansen and Brandt, 1991; Haig and
Gordon, 1998) is not the main generator mechanism of this coherence.
The coherence change was largest at the pairs connecting the right and
left temporal areas. It is unlikely that a subcortical central EEG
source would generate this coherence pattern connecting limited focal
areas. At the time when this coherence was shown, the magnitude of the
power stayed at the same level as baseline, which excludes the
possibility of the emergence of a strong widespread subcortical rhythm
generator or the elimination of incoherent sources projecting to
the right and left hemispheres differently. It is more likely that
neuronal synchronization during object recognition was achieved by
modulating the temporal structure of the oscillations without changing
the power in that particular frequency band (Fries et al., 1997).
In agreement with a previous study (Vanni et al., 1997), the power
decrease probably reflects the subjective meaning of the object as well
as the visual stimulus per se. Stimuli duration significantly affected
this power change but not the coherence. This finding supports the idea
that coherence reflects the endogenous process associated with object
recognition. We also confirmed that the power change for failed object
recognition was similar to that associated with meaningless objects
(Vanni et al., 1997). We found that the meaning of stimuli modulates
the power even during passive viewing, possibly reflecting attention capture.
The topographic pattern of the EEG power decrease in both experiments
was not lateralized, suggesting the activation of a large cortical
network associated with a later phase of recognition that is common for
both experiments. The second experiment provides direct evidence that
the transient increase of interhemispheric coherence exclusively
correlates with the processing of visual stimulus spanning the midline
of the visual field.
Several lines of evidence have suggested the functional relevance of
and  band oscillation in human recognition (Basar et al.,
1997; Klimesch, 1999; Knyazeva et al., 1999; Miltner et al.,
1999; Rodriguez et al., 1999; Tallon-Baudry et al., 1999). Recent studies have shown the interaction between the and band
oscillation (Bringuier et al., 1992; Chatila et al., 1992; Young et
al., 1992; Schanze and Eckhorn, 1997; Brecht et al., 1998). More
recently, it was proposed that nonmoving static objects might be partly
represented by the band oscillatory activity (Sewards and Sewards,
1999). Most of the studies in neuronal synchrony within the band
have used a moving bar as a stimulus object (Eckhorn et al., 1988; Gray
et al., 1989; Gray and Singer, 1989), and the recent human EEG study
using "Mooney" faces excluded the band from the analysis
(Rodriguez et al., 1999). Our study provides direct support for the
hypothesis that band coherence plays an important role in (static)
object recognition. In our experimental design, it is possible that
part of the band activity might be diminished by the low-pass
filter at 50 Hz and that the transient band coherence might be
smeared out by the analysis time window of 256 msec.
Previous human studies have suggested the importance of coherence at
other frequencies such as 4-8 Hz (Sarnthein et al., 1998; Srinivasan
et al., 1999) and 13-20 Hz (Classen et al., 1998; Andres et al., 1999;
von Stein et al., 1999). Because these studies did not use an
event-related analysis, our results, which represent the dynamic
aspects of human recognition, cannot be directly compared with them.
To clarify the temporal profile further, we computed the coherence
change every 64 msec using a 128 msec time window (Fig. 4B). Because temporal and frequency resolutions are
trade-offs, we focused at 4-12 Hz, centering at 8 Hz. Onset latency of
the coherence increase was as early as 64-192 msec,
whereas the cognition-related power change began at 192-320 msec.
Thus, the interhemispheric coherence might reflect the earliest stage
of active attention and recognition. The processing of incoming sensory
information can primarily be divided into two systems: preattentive and
attentive (Neisser, 1967). The preattentive system can be activated by
the rare stimuli in the absence of active attention and is
preperceptual, such as echoic or iconic memory. In spite of its very
short latency, interhemispheric coherence might be the first gate of an
active attention system because such coherence was not observed during passive viewing. It is also important to note that familiar objects caused a larger EEG power change even during passive viewing. It is
possible that the EEG power is partly modulated by the preattentive
system. Increased coherence was not followed by a transient decrease.
Because it has been hypothesized that the role of active
desynchronization is the neuronal correlate of a transition/punctuation
from perception to action (Varela, 1995; Rodriguez et al., 1999), it is
likely that this desynchronization, if any, might be smeared in the
delay period between the target and the GO signal in our experiment.
In regard to the neuronal substrate of interhemispheric coherence, our
observation of a near-zero phase lag and the possible relevance of a
thalamocortical circuit in the generation of EEG rhythm might
suggest the contribution of subcortical structures in producing the
interhemispheric coherence (Da Silva et al., 1980; Steriade and Llinas,
1988; Munk et al., 1996). There is evidence that long-range zero-phase
lag cortical synchrony does not necessarily require a common
subcortical drive (Konig et al., 1995; Traub et al., 1996; Roelfsema et
al., 1997). A recent study showed that long-range synchronization
within the thalamus is controlled by corticothalamic feedback
(Contreras et al., 1996). Lesion studies in split-brain patients and
animal models suggested that the corticocortical connections via the
corpus callosum might be essential for generating interhemispheric
coherence (Engel et al., 1991a; Munk et al., 1995; Nowak et al., 1995;
Brazdil et al., 1997). Therefore, it is likely that neuronal networks including corticocortical connections, thalamocortical projections, and
cortical modulation of thalamic activity are all involved in generating
corticocortical coherence.
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FOOTNOTES |
Received Aug. 3, 2000; revised Feb. 26, 2001; accepted March 7, 2001.
We thank Dr. H. Shibasaki for helpful comments and discussions, S. Thomas-Vorbach for expert technical assistance, and D. G. Schoenberg for skillful editing.
Correspondence should be addressed to Dr. Mark Hallett, National
Institutes of Health, National Institute of Neurological Disorders and Stroke, Building 10, Room 5N226, 10 Center Drive, MSC-1428, Bethesda, MD 20892-1428. E-mail: mark_hallett{at}nih.gov.
T. Mima's present address: Department of Brain Pathophysiology, Human
Brain Research Center, Kyoto University Graduate School of Medicine,
Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
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REFERENCES |
-
Andres FG,
Gerloff C
(1999)
Coherence of sequential movements and motor learning.
J Clin Neurophysiol
16:520-527[Web of Science][Medline].
-
Andres FG,
Mima T,
Schulman AE,
Dichgans J,
Hallett M,
Gerloff C
(1999)
Functional coupling of human cortical sensorimotor areas during bimanual skill acquisition.
Brain
122:855-870[Abstract/Free Full Text].
-
Basar E,
Schürmann M,
Basar-Eroglu C,
Karakas S
(1997)
Alpha oscillations in brain functioning: an integrative theory.
Int J Psychophysiol
26:5-29[Web of Science][Medline].
-
Brazdil M,
Brichta J,
Krajca V,
Kuba R,
Daniel P
(1997)
Interhemispheric coherence after corpus callosotomy.
Eur J Neurol
4:419-425[Web of Science].
-
Brecht M,
Singer W,
Engel AK
(1998)
Correlation analysis of corticotectal interactions in the cat visual system.
J Neurophysiol
79:2394-2407[Abstract/Free Full Text].
-
Bringuier V,
Fregnac Y,
Debanne D,
Shultz D,
Baranyi A
(1992)
Synaptic origin of rhythmic visually evoked activity in kitten area 17 neurones.
NeuroReport
3:1065-1068[Web of Science][Medline].
-
Chatila M,
Milleret C,
Buser P,
Rougeul A
(1992)
A 10 Hz "alpha-like" rhythm in the visual cortex of the waking cat.
Electroencephalogr Clin Neurophysiol
83:217-222[Web of Science][Medline].
-
Classen J,
Gerloff C,
Honda M,
Hallett M
(1998)
Integrative visuomotor behavior is associated with interregionally coherent oscillations in the human brain.
J Neurophysiol
79:1567-1573[Abstract/Free Full Text].
-
Contreras D,
Destexhe A,
Sejnowski TJ,
Steriade M
(1996)
Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback.
Science
274:771-774[Abstract/Free Full Text].
-
Crick F,
Koch C
(1990)
Some reflections on visual awareness.
Cold Spring Harb Symp Quant Biol
55:953-962[Abstract/Free Full Text].
-
Da Silva L,
Vos JE,
Mooibroek J,
Van Rotterdam A
(1980)
Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis.
Electroencephalogr Clin Neurophysiol
50:449-456[Web of Science][Medline].
-
Eckhorn R,
Bauer R,
Jordan W,
Brosch M,
Kruse W,
Munk M,
Reitboeck HJ
(1988)
Coherent oscillations: a mechanism of feature linking in the visual cortex?
Biol Cybern
60:121-130[Web of Science][Medline].
-
Engel AK,
König P,
Kreiter AK,
Singer W
(1991a)
Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex.
Science
252:1177-1179[Free Full Text].
-
Engel AK,
König P,
Singer W
(1991b)
Direct physiological evidence for scene segmentation by temporal coding.
Proc Natl Acad Sci USA
88:9136-9140[Abstract/Free Full Text].
-
Fries P,
Roelfsema PR,
Engel AK,
Konig P,
Singer W
(1997)
Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry.
Proc Natl Acad Sci USA
94:12699-12704[Abstract/Free Full Text].
-
Gray CM,
Singer W
(1989)
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex.
Proc Natl Acad Sci USA
86:1698-1702[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
(1998)
EEG alpha phase at stimulus onset significantly affects the amplitude of the P3 ERP component.
Int J Neurosci
93:101-116[Web of Science][Medline].
-
Halliday DM,
Rosenberg JR,
Amjad AM,
Breeze P,
Conway BA,
Farmer SF
(1995)
A framework for the analysis of mixed time series/point process data. Theory and application to the study of physiological tremor, single unit discharges and electromyogram.
Prog Biophys Mol Biol
64:237-278[Web of Science][Medline].
-
Jansen BH,
Brandt ME
(1991)
The effect of the phase of prestimulus alpha activity on the averaged visual evoked response.
Electroencephalogr Clin Neurophysiol
80:241-250[Web of Science][Medline].
-
Kastner S,
De Weerd P,
Desimone R,
Ungerleider LG
(1998)
Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI.
Science
282:108-111[Abstract/Free Full Text].
-
Klimesch W
(1999)
EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.
Brain Res Rev
29:169-195[Medline].
-
Knyazeva MG,
Kiper DC,
Vildavski VY,
Desperand PA,
Maeder-Ingvar M,
Innocenti GM
(1999)
Visual stimulus-dependent changes in interhemispheric coherence in humans.
J Neurophysiol
82:3095-3107[Abstract/Free Full Text].
-
Konig P,
Engel AK,
Roelfsema PR,
Singer W
(1995)
How precise is neuronal synchronization?
Neural Comput
7:469-485[Web of Science][Medline].
-
Leopold DA,
Logothetis NK
(1996)
Activity changes in early visual cortex reflect monkey's percepts during binocular rivalry.
Nature
379:549-553[Medline].
-
Malach R,
Reppas JB,
Benson RR,
Kwong KK,
Jiang H,
Kennedy WA,
Ledden PJ,
Brady TJ,
Rosen BR,
Tootell BH
(1995)
Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.
Proc Natl Acad Sci USA
92:8135-8139[Abstract/Free Full Text].
-
Miltner WH,
Braun AM,
Witte H,
Taub E
(1999)
Coherence of gamma-based EEG activity as a basis for associative learning.
Nature
397:434-436[Medline].
-
Mima T,
Hallett M
(1999)
Electroencephalographic analysis of cortico-muscular coherence: reference effect, volume conduction and generator mechanism.
Clin Neurophysiol
110:1892-1899[Web of Science][Medline].
-
Mima T,
Matsuoka T,
Hallett M
(2000)
Functional coupling of human right and left cortical motor areas demonstrated with partial coherence analysis.
Neurosci Lett
287:93-96[Web of Science][Medline].
-
Moran J,
Desimone R
(1985)
Selective attention gates visual processing in the extrastriate cortex.
Science
229:782-784[Abstract/Free Full Text].
-
Munk MH,
Nowak LG,
Nelson JI,
Bullier J
(1995)
Structural basis of cortical synchronization. II. Effects of cortical lesions.
J Neurophysiol
74:2401-2414[Abstract/Free Full Text].
-
Munk MHJ,
Roelfsema PR,
König P,
Engel AK,
Singer W
(1996)
Role of reticular activation in the modulation of intracortical synchronization.
Science
272:271-274[Abstract].
-
Neisser U
(1967)
In: Cognitive psychology. Englewood Cliffs, NJ: Prentice-Hall.
-
Nowak LG,
Munk MH,
Nelson JI,
James AC,
Bullier J
(1995)
Structural basis of cortical synchronization. I. Three types of interhemispheric coupling.
J Neurophysiol
74:2379-2400[Abstract/Free Full Text].
-
Nunez PL,
Srinivasan R,
Westdorp AF,
Wijesinghe RS,
Tucker DM,
Silverstein RB,
Cadusch PJ
(1997)
EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple sites.
Electroencephalogr Clin Neurophysiol
103:499-515[Web of Science][Medline].
-
Pfurtscheller G,
Aranibar A
(1979)
Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement.
Electroencephalogr Clin Neurophysiol
46:138-146[Web of Science][Medline].
-
Pigarev IN,
Nothdurft HC,
Kastner S
(1997)
Evidence for asynchronous development of sleep in cortical areas.
NeuroReport
8:2557-2560[Web of Science][Medline].
-
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].
-
Sarnthein J,
Petsche H,
Rappelsberger P,
Shaw GL,
von Stein A
(1998)
Synchronization between prefrontal and posterior association cortex during human working memory.
Proc Natl Acad Sci USA
95:7092-7096[Abstract/Free Full Text].
-
Schanze T,
Eckhorn R
(1997)
Phase correlation among rhythms present at different frequencies: spectral methods, application to microelectrode recordings from visual cortex and functional implications.
Int J Psychophysiol
26:171-189[Web of Science][Medline].
-
Sewards TV,
Sewards MA
(1999)
Alpha-band oscillations in visual cortex: part of the neural correlate of visual awareness.
Int J Psychophysiol
32:35-45[Web of Science][Medline].
-
Sheinberg DL,
Logothetis NK
(1997)
The role of temporal cortical areas in perceptual organization.
Proc Natl Acad Sci USA
94:3408-3413[Abstract/Free Full Text].
-
Shulman GL,
Corbetta M,
Buckner RL,
Raichle ME,
Fiez JA,
Miezin FM,
Petersen SE
(1997)
Top-down modulation of early sensory cortex.
Cereb Cortex
7:193-206[Abstract/Free Full Text].
-
Singer W
(1998)
Consciousness and the structure of neuronal representations.
Philos Trans R Soc Lond [Biol]
353:1829-1840[Abstract/Free Full Text].
-
Singer W,
Gray CM
(1995)
Visual feature integration and the temporal correlation hypothesis.
Annu Rev Neurosci
18:555-586[Web of Science][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].
-
Steriade M,
Llinas RR
(1988)
The functional states of the thalamus and the associated neuronal interplay.
Physiol Rev
68:649-742[Free Full Text].
-
Tallon-Baudry C,
Bertrand O,
Delpuech C,
Pernier J
(1999)
Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans.
J Neurosci
17:722-734[Abstract/Free Full Text].
-
Tanaka K
(1993)
Neuronal mechanisms of object recognition.
Science
262:685-688[Abstract/Free Full Text].
-
Traub RD,
Whittington MA,
Stanford IM,
Jefferys GR
(1996)
A mechanism for generation of long-range synchronous fast oscillations in the cortex.
Nature
383:621-624[Medline].
-
Ungerleider LG,
Haxby JV
(1994)
"What" and "where" in the human brain.
Curr Opin Neurobiol
4:157-165[Medline].
-
Vanni S,
Revonsuo A,
Hari R
(1997)
Modulation of the parieto-occipital alpha rhythm during object detection.
J Neurosci
17:7141-7147[Abstract/Free Full Text].
-
Varela FJ
(1995)
Resonant cell assemblies: a new approach to cognitive functions and neural synchrony.
Biol Res
28:81-95[Medline].
-
von Stein A,
Rappelsberger P,
Sarnthein J,
Petsche H
(1999)
Synchronization between temporal and parietal cortex during multimodal object processing in man.
Cereb Cortex
9:137-150[Abstract/Free Full Text].
-
Young MP,
Tanaka K,
Yamane S
(1992)
On oscillatory neuronal responses in the visual cortex of the monkey.
J Neurophysiol
67:1467-1474.
Copyright © 2001 Society for Neuroscience 0270-6474/01/21113942-07$05.00/0
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ABSTRACT
INTRODUCTION
