www.elsevier.com/locate/ynimg

NeuroImage 29 (2006) 593 – 604

Imaging of a synchronous neuronal assembly in the

human visual brain

Maria G. Knyazeva,a,* Eleonora Fornari,a Reto Meuli,a

Giorgio Innocenti,b,c and Philippe Maedera

aDepartment of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, SwitzerlandbDivision of Neuroanatomy and Brain Development, Dept. of Neuroscience, Karolinska Institutet, S-17177 Stockholm, SwedencDepartment of Child Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland

Received 27 April 2005; revised 26 July 2005; accepted 29 July 2005

Available online 22 September 2005

Perception, motion, and cognition involve the formation of cooperative

neuronal assemblies distributed over the cerebral cortex. It remains to

explore what characterizes the assemblies, their location, and the

structural substrate of assembly formation. In this EEG/fMRI study,

we describe the response of the visual areas of the two hemispheres in

subjects who viewed bilateral iso-oriented (IG) or orthogonally-

oriented (OG) moving gratings projected in the two hemifields. The

IG stimulus synchronized activity across the hemispheres, as shown by

an increased EEG coherence. The increase was restricted to the

occipital electrodes and to the beta band. Compared with OG, IG

increased the BOLD signal in a restricted territory corresponding to

area VP/V4. Within this territory, a linear relation was found between

the increased interhemispheric EEG coherence and BOLD. Thus, the

increased BOLD localized a trans-hemispheric, synchronous neuronal

assembly probably achieved by a callosal cortico-cortical connection.

This assembly might reflect an early stage of perceptual grouping since

the IG stimulus conforms to Gestalt psychology principles of

collinearity and common fate.

D 2005 Elsevier Inc. All rights reserved.

Keywords: EEG coherence; fMRI; Perceptual grouping; Corpus callosum;

Human

Introduction

The question of how unified objects emerge in perception from

the activity of spatially distributed neurons, each specialized in the

detection of a specific set of features, highlights one of the most

fundamental problems in the mind–brain relation. It is indeed

likely that answers to this question could be generalized to the

emergence of motor sequences, concepts, plans for action, etc., all

of which involve activity in what Mountcastle called ‘‘distributed

systems’’ (Mountcastle, 1978).

1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2005.07.045

* Corresponding author.

E-mail address: Maria.Knyazeva@chuv.ch (M.G. Knyazeva).

Available online on ScienceDirect (www.sciencedirect.com).

It is usually accepted that, in perceptual, motor, or cognitive

tasks of some complexity, individual neurons associate into short-

lived cooperative assemblies. An important element in the

formation of perception-related assemblies is probably the syn-

chronization of firing (Engel et al., 2001; Singer, 1999a,b).

Stimulus- or task-related synchronizations within visual areas as

well as between visual and/or non-visual areas have been identified

(Bressler et al., 1993; Eckhorn et al., 1988; Engel et al., 1991;

Gray, 1999; Gray et al., 1989; Kiper et al., 1999; Knyazeva et al.,

1999; Liang et al., 2002; Munk et al., 1995; Tallon-Baudry et al.,

1997).

The formation of neuronal assemblies has been largely inferred

from electrophysiological experiments performed on single neu-

rons, field potentials, or EEG signals. These techniques can

identify features characterizing the neuronal assembly in the time

domain, in particular firing rates, synchronicity or sequentiality,

and oscillatory activity; however, they cannot precisely identify the

location and extent of the assemblies.

Recently, fMRI investigations in animals located an increased

BOLD signal, presumably signifying an increased neuronal

activity in a number of visual areas, when figures consisting of

collinear segments embedded in a background of randomly

oriented distractors were presented, compared to the background

alone (Kourtzi et al., 2003). Assuming that the networks involved

in perceptual integration are synchronized, one may suggest a

relation between their activation and synchronization. Such a

correspondence could serve to visualize distant neural networks

coupled under certain conditions.

Ideally, the neuronal assemblies generated by sensory stimuli or

other conditions should be characterized both in the spatial and in

the temporal domains. Furthermore, since the anatomical connec-

tivity engaged in the formation of neuronal assemblies is critical for

understanding their interactions, the connections between the

neurons of the assembly should also be known and accessible to

manipulations. Here, we report the results of stimulating two

hemispheres with coherent visual stimuli (iso-oriented gratings) and

with incoherent visual stimuli (orthogonal gratings). Our previous

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604594

EEG animal and human data (Kiper et al., 1999; Knyazeva et al.,

1999) suggested that these stimuli generate neuronal assemblies in

both hemispheres, whose activity is coordinated by the corpus

callosum (CC). Here, we used a combined EEG/fMRI approach to

characterize and localize these assemblies.

One reason for developing methods for the identification and

characterization of neuronal assemblies in humans is that they

might provide powerful diagnostic as well as heuristic cues in a

number of conditions involving connectional pathology in the

cerebral cortex (e.g., data and discussions in Innocenti et al., 2001,

2003; Knyazeva and Innocenti, 2001).

Methods

Similar EEG and fMRI experiments were performed on the

same subjects with an interval between recording sessions of up to

several weeks. Non-simultaneous collection of EEG and fMRI data

was justified by previously shown within-subject reproducibility of

the EEG coherence measurements under similar conditions of

visual stimulation within a period of between several weeks and

several months (Knyazeva et al., 1999).

All the procedures conformed to the Declaration of Helsinki

(1964) by the World Medical Association concerning human

experimentation and were approved by the local ethical committee

of Lausanne University.

Subjects

Fourteen normal adults (8 women and 6 men; mean age 35,

range of 27–51, SD = 7.4 years) without any known neurological

or psychiatric conditions and with normal or corrected-to-normal

vision participated in the study. Three of the subjects were left-

handed. All subjects gave written informed consent.

Stimuli

During EEG and fMRI recording sessions, subjects viewed

similar visual stimuli generated on a PC with dedicated software.

The stimuli were black-and-white bilateral iso-oriented or orthog-

onally-oriented sine gratings centered on a fixation point. Iso-

oriented gratings (IG) consisted of two identical patches of

collinear, downward-drifting horizontal gratings on both sides of

the fixation point. Orthogonally-oriented gratings (OG) consisted

of a patch of horizontal downward-drifting gratings on one side

and a patch of vertical right-drifting gratings on the other side.

Dephased iso-oriented gratings (DG), used as a control in the

fMRI experiment, were identical to IG except that the right and left

sides of this stimulus were 180- out of phase. All the gratings had aspatial frequency of 0.5 c/degrees, a contrast of 70%; unilateral

patches measured 13.5 (width) by 24- (height). They drifted with a

temporal frequency of 2 Hz. A uniform gray screen of the same

space-averaged luminance as the stimuli (32 cd/m2) with a fixation

point in the center served as a background.

To compensate for retinal naso-temporal overlap, all the stimuli

were separated from the vertical meridian of the visual field by a

narrow stripe of background equal to 1- on each side.

For the EEG experiments, the stimuli were presented on the

computer display with a refresh rate of 75 Hz. They were inter-

spersed with the gray screen background. The vertical and horizontal

gratings of the OG stimulus appeared in the left or right hemifield at

random. Type of stimulus (OG, IG, and Background), stimulus

exposure (2.2–2.6 s), and interstimulus intervals (1.8–2.2 s) were

also randomized.

In the fMRI session, an LCD projector with a refresh rate of 75

Hz displayed the stimuli. It was equipped with a photographic

zoom lens projecting images onto a translucent screen in a custom-

made mirror box positioned inside the magnet. The mirror box was

designed to minimize light reflection. It allowed a subject to view

the stimuli within the space defined by 25- horizontally and by 19-vertically. OG was presented as two stimuli; these were with

horizontal gratings on the right (OG-HR) and on the left side (OG-

HL). Each stimulus was displayed 5 times for 15 s each; it

alternated with the Background in a balanced-randomized order.

The differences between the EEG and fMRI stimulation

protocols were stipulated by the conditions required to obtain a

high signal-to-noise ratio, these conditions being different for the

two methods. Nevertheless, we conducted EEG recordings of 2

subjects under the block-design protocol that we applied in our

fMRI experiments. We found that the interhemispheric coherence

response was comparable to that obtained with the randomized

stimulus presentation, and stable during the entire stimulation time.

Control of eye movements

We monitored five subjects during the fMRI session with an

eye tracking system using pupil position and corneal reflection of

infra-red light (SensoMotoric Instruments GmbH, Teltow, Ger-

many). For the calibration of point-of-gaze, we used a built-in 9-

point routine. Eye movement recording was time-locked to

stimulus presentation by an in-house-made software. Eye positions

were sampled at 50 Hz and stored on a PC for off-line analysis with

Matlab. The readings preceding and following blinks (0.25 s before

and 0.5 s after the start of a blink), when gaze position cannot be

determined, were removed from analysis. We assessed fixation

stability as a percentage of time when the point-of-gaze remained

within a circle (Ø 2-) centered on the fixation point in the center of

the screen. All the subjects maintained stable fixation for 96–99%

of the recording time across all conditions (Fig. 1B).

EEG recording and processing

The EEG data were collected in a semi-dark room with a low

level of environmental noise. Each subject was sitting in a

comfortable chair. S/he was instructed to fixate on the point in

the center of the screen located at a distance of 57 cm. To stabilize

the distance and the head position, we used an adjustable chin-rest

mounted on a table in front of the subject. The experimenter

followed the subjects’ gaze fixation.

The EEGs were recorded with a 128-channel Geodesic Sensor

Net (Tucker, 1993). In Results, the numbers of the sensors are

supplemented, if possible, with designations according to the

International 10–20 system. To ensure an optimal signal-to-noise

ratio, all the electrode impedances were kept under 50 kV as

recommended for the high-input-impedance EGI amplifiers (Ferree

et al., 2001; Picton et al., 2000). The on-going EEG tracings were

constantly monitored during experiments to keep the quality of

recording and the subject’s wakefulness level under steady watch.

The recordings were made with vertex reference using a low-

pass filter set to 100 Hz. The signals were digitized at a rate of 500

samples/s with a 12-bit analog-to-digital converter. They were

further filtered (FIR, band-pass of 3–70 Hz, notch of 50 Hz), re-

Fig. 1. (A) 3D surface reconstruction of the EEG sensor markers located in

the ROI superposed on group BOLD increase for IG vs. Background.

Numbers designate sensor locations according to 128-channel Geodesic

Sensor Net (Tucker, 1993). (B) Eye movement plots for subject AO for a

15-s period under Background (left), IG, OG-HL, OG-HR, and DG

conditions. The red rectangle on the whole Background stimulus defines the

area shown for the other conditions on the right. The width of the gray

stripe along the vertical meridian is 2-. Both the subject and the recording

period were randomly chosen.

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604 595

referenced against common average reference, and segmented into

non-overlapping epochs using NS2/NS3 software (Electrical Geo-

desics, Inc., USA). Artifacts were edited off-line—first, automati-

cally, based on an absolute voltage threshold (100 AV) and on a

transition threshold (50 AV), and then through visual inspection,

which allowed us to identify and to reject channels with moderate

muscle artifacts not reaching threshold values.

It is well established that visual stimulation results in stimulus-

locked and stimulus-induced synchronization, which may play

different roles in perceptual grouping (Eckhorn, 2000). Evoked

synchronization is a short-lived phenomenon characteristic of the

first 200 ms from stimulus onset (Eckhorn, 1994; Tallon-Baudry et

al., 1996; Tallon-Baudry et al., 1999). If saliently presented in our

EEG results, it could disturb their compatibility with the fMRI data

collected with block design. To minimize the impact of stimulus-

onset artifacts and response-onset transients together with stimulus-

locked synchronization, we excluded the first 200–220 ms

(randomized across subjects) after stimulus onset. FFT was applied

to 1-s EEG segments (1 Hz frequency resolution). For each

individual, �45 artifact-free epochs were collapsed for each

stimulation condition to obtain coherence and power spectra. EEGs

with less than 110 good channels were excluded from further

analysis. Spectral analysis was centered on EEG coherence functions

as an index of functional interactions between brain regions.

Estimates of coherence depend on the choice of EEG reference

(Nunez et al., 1999; Nunez et al., 1997). Multiple reference methods

result in partly independent coherence measurements at different

spatial scales, but also introduce different errors mostly due to

various spatial filtering properties and to the reference electrode

itself. In this study, we combined analysis of common average

reference (AR) potentials and surface Laplacian of dense array

EEGs. With adequate sampling of the head surface, both methods

provide a reference-independent estimate of the EEG sources,

though they are sensitive to EEG source activity at different spatial

scales (Bertrand et al., 1985; Srinivasan, 1999, 2003; Srinivasan et

al., 1999). This implies various locations of the sources and their

distinct time series, thus resulting in complementary information.

The surface Laplacian estimates were computed with the three-

dimensional spline algorithm implemented in Matlab routines (Law

et al., 1993; Srinivasan et al., 1996).

EEG spectral analysis and statistics

For spectral analysis, MATLAB routines were used (Srinivasan

et al., 1998). For the analysis of stimulus-induced changes, we used

magnitude-squared-coherence (MSC). At a frequency f, it is

estimated by the formula:

Coh fð Þ ¼ Sxy fð Þ2= Sxx fð ÞTSyy fð Þ� �

;

where Sxx, Syy, and Sxy are auto- and cross-spectrum estimates of

the x and y signals. Scalp surface coherence maps were created by

spherical interpolation and plotted in polar projection (Perrin et al.,

1989). Further analysis was focused on interhemispheric coherence

(ICoh) between EEG signals recorded from symmetrical electro-

des. To stabilize variance, we applied an arc hyperbolic tangent

transformation (Halliday et al., 1995) to the ICoh measured in each

subject, each condition, and electrode pair.

The tanh-1-transformed ICoh values were subjected to a two-

stage statistical analysis. First, we used Student’s t test for paired

samples uncorrected for multiple comparisons as an exploratory

tool to select the EEG sensor locations and frequency range

showing systematic ICoh changes associated with the stimulation

for further analysis. Second, ICoh within the frequency band and

for the sensors identified in the preliminary liberal analysis was

subjected to further statistical examination with Repeated Measures

ANOVA. The P values for multiple comparisons were Bonferroni

corrected, when needed. All the statistical tests were implemented

in SPSS 10.0 for Macintosh (SPSS Inc.).

fMRI protocol and pre-processing

BOLD fMRI acquisitions were performed with a head coil on a

1.5 T Siemens Magnetom Vision system equipped for echoplanar

imaging. The subject’s head was cushioned in the coil with a

vacuum beanbag to prevent motion. Functional MRI images were

acquired with an EPI gradient echo T2*-weighted sequence (FA

90, TE 66, pixel size 3.75 * 3.75 mm, acquisition time 1.7 s, 16

slices of 5 mm with a gap of 1 mm) with a TR = 3 s for a total of 25

acquisitions for each stimulus. fMRI pre-processing steps, con-

ducted with SPM99 (Wellcome Department of Cognitive Neurol-

ogy, London, UK), included realignment of intrasession

acquisitions to correct head movement, normalization to a standard

template (Montreal Neurological Institute (MNI) template) to

minimize intersubject morphological variability, and convolution

with an isotropic Gaussian kernel (FWHM = 9 mm) to increase

signal-to-noise ratio. Single subject analysis was performed

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604596

according to the General Linear Model. The signal drift across

acquisitions was removed with high-pass filter and global signal

changes by proportional scaling. Statistical parametrical maps of

the contrasts of interest were computed for each subject as

input values for the group statistics based on Random Field

Theory.

Voxels were thresholded for peak height at P < 0.001 (T > 3.85)

in the contrasts between stimuli and background and at P < 0.01 (T >

2.65) in the contrasts between different stimuli. In both cases, the

extent threshold k > 30 contiguous voxels, larger than the minimum

number of voxels expected per cluster (Friston et al., 1993), were

applied to SPMs. Corrections for multiple comparisons were used at

a cluster level (P (corrected) < 0.05).

Anatomical identification and the display of results

A sagittal T1-weighted 3D gradient-echo sequence (MPRAGE),

128 slices (with voxel size of 1 * 1 * 1.25 mm), was acquired as the

structural basis for brain segmentation and surface reconstruction.

In addition to the standard SPM display, individual and group

activation maps were denormalized and superimposed on a single

subject brain by inverting them and applying the deformation

matrix previously calculated to fit individual morphology to the

MNI template. Cortex inflation was performed with Brain

Voyager software (Brain Innovation B.V., Maastricht, NL). We

identified the anatomical location of cluster boundaries and

centers via a transformation of MNI coordinates into Talairach

space. Cluster positions were verified according to individual

anatomical landmarks.

To check the EEG electrodes positions against brain morphol-

ogy and fMRI BOLD responses, in five subjects, adhesive

radiographic markers (MM3002, IZI Medical Product Corp,

Baltimore, USA) were attached to the skin in the locations of

occipital and parietal Geodesic Net sensors, as well as in standard

skull landmarks including nasion, inion, pre-auricular notches, and

vertex, and co-registered with fMRI followed by 3D reconstruction

of MRI morphological images of the head.

Interhemispheric coherence—fMRI BOLD correlation analysis

For EEG coherence mapping with fMRI, we targeted voxels for

which BOLD covariates with ICoh across subjects. ROI for

correlation analysis was selected as a volume activated by IG (vs.

Background) stimulus because this stimulus activated the most

extended area, which included all the voxels activated by any other

stimulus. The correlation analysis of ICoh and BOLD responses

was performed for the IG vs. OG contrast on a voxel-by-voxel

basis. This particular contrast resulted in the ICoh increase, which

was not accompanied by EEG power changes (see Stimulus-

specific changes in interhemispheric EEG coherence). The absence

of confounding power effects made the ICoh changes a perfect

target for testing the relation between distant synchronization and

BOLD. As a predictor variable, we used �MSC (see EEG spectral

analysis and statistics), which is analogous to the bivariate

correlation coefficient and measures linear association between

two EEG signals in a frequency domain. In particular, for each

subject, we derived difference scores by subtracting OG-�MSC

values from IG-�MSC values at the peak response frequency. The

individual BOLD changes from IG vs. OG contrast served as an

outcome (dependent) variable. We calculated the distribution of

correlation coefficients between �MSC and BOLD and generated a

correlation map considering only clusters reaching a significance

level of P (corrected) < 0.05.

Results

Stimulus-specific changes in interhemispheric EEG coherence

In our previous experiments, iso-oriented gratings similar to

those used here significantly increased ICoh at parietal and

occipital locations in normal adults (Knyazeva and Innocenti,

2001; Knyazeva et al., 1999, 2002). To estimate the surface spatial

properties of EEG coherence for the present high-density EEG

experiment and to find the strongest and least noisy ICoh responses

for further joint ICoh-BOLD analysis, we defined the region of

interest (ROI) using individual EEG coherences.

Perusal of the individual potential coherence spectra and maps

(Fig. 2A) suggested that ICoh increase with IG stimulus extended

over occipital, parietal, and posterior temporal locations in

agreement with our previous data. Therefore, we defined ROI

as the region covered by the posterior sensors (Fig. 2B). The

electrodes on the outer ring of the sensor net were dropped from

the analysis because of frequent artifacts. We also excluded all the

interhemispheric pairs with interelectrode distance of about 3 cm

as they were dominated by volume-conduction effects. The

remaining 14 pairs of sensors symmetrically located over the

two hemispheres had interelectrode distances of about 6 cm or

more and were analyzed further.

Within the above-defined ROI, we performed a preliminary

analysis aimed at identifying the responsive EEG sensors and

frequency range. It was based on the Student’s t test. Since we

used the Student’s t test as an exploratory tool to choose variables

suitable for further conservative analysis, no correction for

multiple comparisons was applied. We contrasted iso-oriented

gratings, which were expected to result in significant ICoh

increase, with the background. The test was applied to the tanh�1-

transformed potential ICoh values of individual subjects from

each of the 14 sensor pairs at posterior locations and to each EEG

frequency between 4 and 47 Hz. Significance level was set at P <

0.05, one-tailed.

This analysis excluded EEG frequencies lower than 20 and

higher than 30 Hz from further consideration as carrying no

systematic ICoh increases with stimulation. It also highlighted 4

pairs of potentially responsive sensors; within the pairs, the sensors

were spaced 6–9 cm from each other. They are designated with the

128-channel Geodesic Net numbers 70–90, 71–84, 66–85, and

67–78 in Fig. 1A and marked with color in the scheme (Fig. 2B).

In this report, we will use the Extended 10/20 system designations

as a conventional way to designate the location of EEG electrodes.

Specifically, they are referred to as I3–I4 (sensors 70–90, the pair

located approximately at an inion level), O1–O2 (71–84), PO3–

PO4 (66–85), and P1–P2 (67–78).

A more conservative analysis of these 4 occipital and parietal

sensors was performed by Repeated Measures ANOVA with 3

within-subject factors. The Sensor Location factor (4 levels)

contrasted signals from I3–I4, O1–O2, PO3–PO4, and P1–P2

sensor pairs. The Stimulus factor (2 levels) compared the ICoh

responses to IG and OG stimuli. The EEG Frequency factor (11

levels) distinguished each frequency in the 20–30 Hz range. As a

dependent variable, we used the ICoh response defined as the

difference between tanh�1-transformed ICoh values under stim-

Fig. 2. EEG potential coherence under stimulation with orthogonal and collinear gratings. (A) Individual topographic maps of potential coherence for

Background (left), orthogonal gratings, and iso-oriented gratings (right) plotted with respect to sensor 70 for peak response frequency. (B) Schema of 128-

channel Geodesic Sensor Net with sensors screened for ICoh responses to IG stimulus in gray and with statistically confirmed responses in red (on the left).

Group-averaged ICoh responses with standard errors to IG (red line) and OG stimuli for I1– I2 and P1–P2 sensor pairs (on the right).

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604 597

ulation and the background computed for each individual subject,

stimulus condition, and electrode pair.

The main effect of the Stimulus factor was significant at P =

0.045 (F(1,13) = 4.94), suggesting that ICoh increase in response

to IG is greater than that to OG (Fig. 2B). The general effect of

both the Sensor Location and EEG Frequency factors failed to

reach significance levels ((F(3,39) = 2.47), P = 0. 076 and

F(10,130) = 1.35), P = 0.211, respectively). However, their

interaction turned out to be significant (F(30,390) = 1.70), P =

0. 013). As illustrated in Fig. 2, the interaction points to low beta2

frequencies centered on 22 Hz as responsive at occipital locations,

and to high beta2 frequencies peaking at 28 Hz at parietal sensors.

The follow-up pairwise comparisons with a Bonferroni

correction between IG- and OG-induced ICoh responses for each

single EEG frequency revealed a significant difference at the I3–I4

location for a peak frequency of 22 Hz (P = 0.025). For the

adjacent pairs of sensors O1–O2 and PO3–PO4, the response

difference at 22 Hz did not reach a significance level (P = 0.07 and

0.11, respectively); neither did the ICoh responses to IG vs. OG at

other frequencies within the beta2 range, including at the response

peak at 28 Hz.

An analysis performed on Laplacian ICoh revealed a similar

response trend (IG > OG) (see Fig. S1 available as supplemental

data) at a significance level of P < 0.10. This was probably due to

the fact that the sources of the coherent EEG signal were not on the

cortical surface but deep in the sulci as shown below.

The EEG spectral power in the beta band significantly

decreased under both stimulus conditions compared to the Back-

ground, but this parameter did not differentiate the two stimuli for

any sensor within the ROI (Fig. S1 available as supplemental data).

In conclusion, the IG stimulus, compared to the OG stimulus,

caused an ICoh increase specific to a narrow beta frequency band

and focused on a single pair of occipital sensors. This increase in

beta-band distant synchronization was not accompanied by

significant changes in local synchronization (EEG power) over

the whole frequency range (3–47 Hz) or in coherence outside the

beta band. The predominance of the ICoh changes enabled further

analysis of the relation between ICoh and BOLD.

Stimulus-specific BOLD response

Compared to Background, all the stimuli extensively activated

striate and extrastriate areas (Fig. 1). Interhemispheric effects

were traced by contrasting the response of one hemisphere to the

horizontal grating across two conditions, i.e., when the other

hemisphere saw the same iso-oriented grating (IG) or the

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604598

orthogonal grating (OG). The OG condition consisted of two

separate stimuli, namely OG-HR and OG-HL (see Methods).

Higher activation was obtained with the horizontal grating

when the other hemisphere was also stimulated with the same

grating than when it was stimulated with the vertical grating

(Figs. 3A, B). Therefore, the response difference between OG and

IG conditions undoubtedly involved interaction between the

hemispheres.

The response of each hemisphere to the horizontal gratings was

also stronger than to the vertical gratings. This was observed when

a hemisphere received a different visual input across conditions,

i.e., horizontal grating with IG and vertical grating with OG. This

effect was more widespread (Table 1, contrasts IG > OG-HR and

IG > OG-HL) and, probably, was due to both interhemispheric and

intrahemispheric components. To distinguish between the two, we

Fig. 3. Interhemispheric potentiation of BOLD response with IG gratings

(group data). Contrast between IG and OG with horizontal gratings on the

right of the fixation point (projecting to the left hemisphere; A) or to the left of

the fixation point (projecting to the right hemisphere; B), between IG and

mixed OG gratings (C), and between IG and DG (D). The response to

horizontal grating (arrow-marked in brain figurines) is stronger when the

contralateral hemisphere sees horizontal collinear grating than when it sees

vertical or out-of-phase grating (the ‘‘interhemispheric potentiation’’). The

horizontal grating also induces a stronger response than the vertical grating in

either hemisphere (the intrahemispheric effect). Horizontal and coronal

sections numbered according to NMI coordinates. The hot scales represent

T values.

contrasted OG-HR and OG-HL conditions (Table 1). This contrast

eliminated the interhemispheric component and it showed that

indeed the horizontal grating more strongly activated certain areas

in either hemisphere than the vertical grating, thus documenting an

intrahemispheric effect.

The interhemispheric and the intrahemispheric effects were

differently located in the cortex. The interhemispherically

enhanced activation was restricted to the lingual and fusiform

gyri, around the posterior part of the collateral sulcus (areas VP and

V4) in both hemispheres. Instead, the intrahemispheric effect

emerged in the lingual gyrus, middle occipital gyrus, and cuneus—

that is, mostly in V2 and V3 (Fig. S2 available as supplemental

data). These locations are defined in Table 1 according to Talairach

coordinates (McKeefry and Zeki, 1997; McKeefry et al., 1997;

Zeki and Moutoussis, 1997).

To prove that the high response to the IG is indeed specific

for this stimulus, and presumably due to the collinearity of the

grating in the two hemifields (and hemispheres), we analyzed the

responses to dephased gratings (DG), i.e., to bilateral horizontal

gratings, identical to IG except that the right and left sides of this

stimulus were 180- out of phase. The IG vs. DG contrast showed

higher BOLD for IG in a similar location as the IG vs. OG

contrast (Fig. 3D, Table 1). This provided strong support for the

conclusion that the potentiation of the BOLD response located in

the VP/V4 area is indeed specific to the collinear stimulus.

Correlation analysis of the EEG and BOLD responses

The 3D reconstruction of EEG sensor locations and the BOLD

response revealed that EEG sensors which responded to IG vs.

Background with ICoh increase were located over the area of

BOLD activation induced by IG (Fig. 1). The sensor pair 70–90,

from which we obtained the ICoh increase under the IG vs. OG

condition, was the closest to the sites of differential BOLD

activation for the same contrast.

To determine whether these ICoh changes were really

associated with the area defined by BOLD response, we performed

linear correlation analysis across subjects between ICoh and BOLD

responses to IG vs. OG stimulation. The ICoh response was

defined as the difference between ICoh peak values (�MSC at 22

Hz) under the IG vs. OG conditions. The BOLD response was

defined as BOLD contrast value between the same conditions. To

render the fMRI and EEG data directly comparable with each

other, we averaged the BOLD responses to the two OG stimuli

(OG-HR and OG-HL, above). The resulting contrast between IG

and mixed OG conditions replicated the interhemispheric effects

described above with the separate contrasts (Fig. 3C).

Since IG stimulation compared to other stimuli resulted in the

strongest and most extended activation, correlation coefficients

were computed between the amplitude of the ICoh response in

electrodes 70–90 and the BOLD response for each of the 4570

voxels significantly activated by IG vs. Background. Statistically

significant correlation coefficients were obtained for a cluster of

190 voxels (P (corrected) = 0.02) in the right hemisphere and a

cluster of 159 voxels (P (corrected) = 0.04) in the left hemisphere.

These voxels constituted 20% of the whole number of voxels (741)

activated with IG more than with OG. At the same time, out of

4009 voxels, which responded to IG vs. Background but did not to

the IG vs. OG, only 4.3% were correlated with the ICoh response.

Finally, 50% of voxels, in which the BOLD response was coupled

with ICoh response, also showed significant response to IG vs. OG

Table 1

Center

of gravity

[mm]

Height

T value

(threshold

T = 2.65)

Cluster

size and

P value

(corrected)

Left

hemisphere

Right

hemisphere

Cluster

size and

P value

(corrected)

Height

T value

(threshold

T = 2.65)

Center

of gravity

[mm]

IG > OG-HR

�27 �84 0 6.92 413 31%

fusiform

gyrus

VP VP 30% lingual

gyrus

346 5.23 12 �90 9

<0.001 29%

lingual gyrus

(collateral

sulcus)

V4 V2 (v&d) 12%

(middle

occipital

gyrus)

<0.001

V3 50% cuneus

IG > OG-HL

�24 �87 �9 4.51 279 45% lingual

gyrus

VP VP 27% lingual

gyrus

173 4.61 39 �90 3

<0.001 24%

(middle

occipital

gyrus)

V4 V4 19% middle

occipital

gyrus

(inferior part)

<0.001

�21 �99 15 4.80 48 11% cuneus V2

(v&d)

23% lateral

occipital

sulcus

0.04 V3

IG > OG mixed

�24 �87 0 6.01 406 43% lingual

gyrus

VP VP 34% lingual

gyrus

371 4.85 30 �96 �3

<0.001 11% fusiform

gyrus

V4 V4 12% fusiform

gyrus

<0.001

20% (middle

occipital

gyrus)

V3 V2 (v&d) 15% (middle

occipital

gyrus)

V3

OG-HR > OG-HL OG-HL > OG-HR

�15 �96 18 3.98 63 31% lingual

gyrus

V2v VP 26% lingual

gyrus

105 4.32 12 �72 �12

0.02 58% cuneus V3 V2 (v&d) 10% (middle

occipital

gyrus)

0.005

V3 48% cuneus

IG > DG

�27 �87 �9 5.01 119 49% lingual

gyrus

VP VP 30% lingual

gyrus

243 4.86 18 �84 �6

<0.001 8% fusiform

gyrus

V4 V4 9% fusiform

gyrus

<0.001

19% (middle

occipital

gyrus)

V3 V2 (v&d) 27% (middle

occipital

gyrus)

V3

Coordinates are given according to Talairach and Tournoux (1988).

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604 599

at P (corrected) < 0.01. The remaining 50% of the cluster bordered

the same site such that simply decreasing the statistical significance

for the IG > OG contrast to P (corrected) < 0.03 increased the

overlapping portion up to 80%.

Therefore, the area where BOLD response was coupled with

interhemispheric synchronization was mostly located within parts

of the extrastriate areas, which were differentially activated by the

IG and OG stimuli (Fig. 4). In particular, the voxels were

clustered in the collateral sulcus surrounding the area with their

centers located at 17, �81, �3 in the right and at �26, �85, �5

in the left hemisphere (Talairach coordinates). Within this region,

the values of correlation coefficients between ICoh and BOLD

responses varied between 0.27 and 0.99 (mean = 0.71 and SD =

0.12). Consequently, the ICoh peak response amplitude explained

on average 50% of the variance of the BOLD response.

Discussion

In this paper, we show that coherent visual stimuli projected in

the two hemifields increased ICoh within a narrow frequency band

and focused on a single pair of occipital sensors. In the cortical

Fig. 4. ICoh correlates with potentiation of the BOLD signal in the extrastriate areas. (A) Semi-transparent reconstruction shows position of the EEG sensors

relative to the IG vs. OG BOLD activation for the group, denormalized according one subject’s morphology. In the bottom, the time-course of BOLD response

is shown for a single subject. It covers the entire 10-min run consisting of 20 presentations of IG, OG, or DG stimuli alternating with the background. The plot

represents the response in a sphere (9 mm in diameter) centered in the left hemisphere activated cluster. The measured BOLD signal is in red, the fitted one in

yellow. Mean value of the signal has been removed. (B) Statistical correlation map superposed on an individual inflated brain (bottom view) demonstrates

association between the BOLD and ICoh responses within and around collateral sulcus. (C) superposition of the correlation map (as in panel B) on the BOLD

contrast (as in panel A) in three transverse slices (top view). Color bars show T values for BOLD (hot scale) and for correlation (cold scale). The white arrows

point to the EEG sensor markers. (D) Plot of individual BOLD response as a function of ICoh response for all the voxels in clusters showing a significant

correlation coefficient ( P (corrected) < 0.05) in the left (LH) and right (RH) hemisphere. Blue circles define the values for each voxel and black bars show a

single subject’s BOLD mean response with standard deviation.

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604600

territory corresponding to the VP/V4 region, the BOLD signal

increased proportionally to the ICoh. Thus, our experiments

visualized the stimulus-induced formation of a neuronal assembly

characterized by synchronous activity and distributed over the two

hemispheres. This assembly is probably generated by the activity

of callosal axons interconnecting the visual areas and might be

involved in perceptual binding.

Coherent visual stimuli increase EEG synchronization between the

hemispheres

As expected from our previous work (Kiper et al., 1999;

Knyazeva et al., 1999), the bilateral collinear gratings increased

ICoh. It is unlikely that dissimilar eye movements driven by the

IG (rather than the OG) stimulus confounded the ICoh

response. First, we observed similar ICoh changes in paralyzed

animals (Kiper et al., 1999). Second, eye tracking in our

subjects showed stable gaze fixation across the visual stim-

ulation. Known coupling between eye movements and spatial

attention suggests similar attention to various stimuli in our

experiment.

Compared to our previous results, the use of high-density

EEG recordings in the present study significantly improved the

spatial resolution of the signal-detection and localized the ICoh

increase to the occipito-parietal electrodes. While this provided

a first approximation as to the origin of the signal, an attempt

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604 601

to further localize the signal using surface Laplacian failed since

we could not demonstrate significant changes in ICoh with this

approach. The confounding effect of volume conduction in our

EEG data has been minimized by a high-density electrode array

that covered a large portion of the head surface (Hauk et al.,

2002; Junghofer et al., 1999; Luu et al., 2001; Srinivasan et al.,

1998). The frequency-specific nature of the ICoh response to IG

implies that it is not due to a contribution from uncorrelated

sources through volume conduction, because the latter impacts

all frequencies (Nunez, 1981, 1995). Moreover, ICoh increase in

IG vs. OG was not associated with any EEG power changes

between these conditions. Therefore, the discrepancy between

conventional and Laplacian data might point to a relatively deep

location of the EEG sources involved in synchronization. The

surface Laplacian provides a good estimate of the sources

localized on the superficial gyral surface but removes the

contribution of either deep or broadly distributed superficial

sources (Srinivasan, 1999, 2003; Srinivasan et al., 1999).

Assuming that the processing of IG and OG stimuli in the

striate and extrastriate areas is rather similar, it is unreasonable

to expect widespread activity to differentiate these conditions.

Therefore, these could be deep sources. Indeed, the ICoh/BOLD

correlation indicated a deep location of the synchronized

sources, in the region of the collateral sulcus (see below).

In our experiments, interhemispheric synchronization to col-

linear drifting gratings increased at beta frequencies centered on

22 Hz. As justified in Methods, to minimize the impact of

stimulus-locked synchronization, we processed each EEG epoch

starting at 200–220 ms after stimulus onset. The properties of

interhemispheric synchronization we obtained here and in our

earlier experiments on ferrets and humans (Kiper et al., 1999;

Knyazeva et al., 1999) point to a predominance of stimulus-

induced but not stimulus-locked synchronization in response to

drifting gratings. Indeed, stimulus-locked synchronization is a

short-lived phenomenon at stimulus onset, which includes both

low-frequency and gamma-frequency components (Eckhorn,

1994; Tallon-Baudry et al., 1996, 1999). The interhemispheric

synchronization we consider here is limited to the beta frequency

band (20–30 Hz, also included in the gamma range by some

authors). According to our laboratory data, this ICoh response is

lasting within a time-span of about 15–20 s (Knyazeva et al.,

1999). The posterior coherence in the beta range of 20–30 Hz

measured for an epoch of 10 s significantly correlates with the

visual monitoring task performance (Pleydell-Pearce et al., 2002).

And, finally, it is similar to the sustained synchronization

observed in animals stimulated with drifting gratings (Munk

and Neuenschwander, 2000).

Coherent visual stimuli selectively activate V4

According to our evidence, BOLD variation proportional to

EEG coherence increase in response to iso-oriented collinear

gratings vs. orthogonally-oriented gratings, is presumably located

in VP/V4. The comparison between IG and DG ruled out the

possibility that direction of movement or/and eye movement

artifacts critically impacted our data.

Area V4 belongs to the ventral stream of visual areas involved

in object recognition (Mishkin and Ungerleider, 1982; Mishkin et

al., 1983). Receptive field properties in V4 seem to match the

integrative functions of this area (Desimone et al., 1993; Pigarev

et al., 2001; Pollen et al., 2002). Monkeys with lesions or

reversible deactivation of V4 exhibit reduced performance for

texture-defined and for illusory contours, shape discrimination

deficits, and deficits in grouping operations (De Weerd et al.,

1996; Girard et al., 2002; Merigan, 2000).

Thus, the activation of this area during the perception of

collinear stimuli is not surprising. Human observers have been

shown to respond with stronger fMRI activations of V4 and VP

to contours consisting of collinear lines compared to misaligned

elements (Altmann et al., 2003). Several other imaging studies

have also emphasized the role of V4 in various conditions of

perceptual grouping/segregation (Beason-Held et al., 1998;

Hasson et al., 2001; Hirsch et al., 1995; Larsson et al., 2002;

Mendola et al., 1999).

Neurophysiological implications

The present demonstration of a correlation between the

increased EEG-ICoh and BOLD is important in suggesting that

the formation of synchronous, cooperative neuronal assemblies

in the human brain, in particular those associated with sensory

stimuli, is not missed by fMRI. However, the many uncertain-

ties underlying the precise nature of the BOLD signal (Arthurs

and Boniface, 2002; Attwell and Iadecola, 2002; Logothetis,

2002) set limits to the interpretation of the relation between the

increased EEG coherence and the increased BOLD. A com-

monsensical rule of thumb is that, presumably, the correlation

between the results obtained with the two methods implies a

closely coupled neurophysiological substrate for the two signals.

This is probably the case since both the EEG and the fMRI

signals reflect events associated with the depolarization of post-

synaptic membranes (Arthurs and Boniface, 2002; Attwell and

Iadecola, 2002; Creutzfeldt and Houchin, 1974).

Therefore, the most parsimonious neurophysiological interpre-

tation of our results is that the iso-oriented visual stimulus

compared to the orthogonally-oriented one increased the activity

in pools of cortical neurons of the two hemispheres, i.e., their

membrane activity and/or their firing rates. This scenario is in

keeping with the tight correlation between the neuronal synchro-

nization and the increased firing rate postulated in models

(Chawla et al., 1999, 2000). However, while some experimental

results reported that firing rates increase with synchronization,

others did not (Fries et al., 2001, 2002). Since in our case, the

EEG power did not change between IG and OG conditions, we

should assume that the neurons whose activity increased do not

contribute much to the EEG signal possibly because they are

interneurons with local, non-radially-oriented, dendritic arbors.

Finally, other pools of synchronized neurons might have

contributed to the increased ICoh, but without changing the

BOLD signal. In this light, the activated neurons in V4 could

represent the read-out of neuronal synchronizations occurring

either upstream, in areas V1/V2, or downstream along the ventral

pathway. While the neurons in V1 and V2 would have

synchronized without increasing their activity (and the BOLD

signal), those in V4 would have increased both. This interpreta-

tion is compatible with the hypothesis that transmission along

neuronal chains is facilitated by synchronized activity (Abeles,

1991) as well as with recent studies in the visual areas of the cat

(Salazar et al., 2004).

The events we described closely resemble the stimulus-

induced synchronization of the activity of single cortical neurons

in the two hemispheres caused by visual stimuli in the cat

M.G. Knyazeva et al. / NeuroImage 29 (2006) 593–604602

(Engel et al., 1991; Munk et al., 1995; Nowak et al., 1995). The

stimuli used in those experiments both increased the firing rate

of the cortical neurons simultaneously recorded in the two

hemispheres, and synchronized their discharge, although the two

effects were to some extent dissociated. A second analogy is

with the increased firing elicited by the interactions of collinear

stimuli within and outside of the ‘‘classical’’ receptive field of

cortical neurons (Das and Gilbert, 1999; Gilbert, 1998;

Kasamatsu et al., 2001; Polat et al., 1998; Stettler et al.,

2002). In this case, the analogy is less strict though, since

facilitation by stimuli in the other hemisphere was not reported.

Furthermore, the phenomenon was seen in conditions of low

stimulus contrast, which were not those of the present

experiments.

Both the responses quoted above—the stimulus-induced

synchronization of the activity of distributed neurons and the

facilitatory interaction between stimuli within and outside the

receptive field—are likely to be sustained by cortico-cortical

connections, namely callosal connections and local collaterals of

pyramidal neurons, respectively (Chisum et al., 2003; Engel et al.,

1991; Gilbert, 1998; Munk et al., 1995; Stettler et al., 2002; Toth et

al., 1996). Both interpretations suggest a possible mechanism for

binding figural elements into a perceptual whole and apply to the

present results.

The involvement of cortico-cortical axons in the potentiation

of neuronal responses or/and in their synchronization should be

stringently tested against the condition that selective acute

inactivation of the connections promptly impairs the effects. This

condition was met to different degrees in studies of callosal

connections (Engel et al., 1991; Kiper et al., 1999; Munk et al.,

1995). These experiments showed that the stimulus-induced

interhemispheric synchronization of unitary neuronal responses

or of the EEG signals is eliminated by callosal transection in

animals. Similarly, in humans, interhemispheric task-related EEG

synchronization is absent in individuals with the CC agenesis

(Knyazeva et al., 1997). The hypothesis of an involvement of the

CC is also supported by the finding that the region of BOLD

activation corresponds to the anterior border of area VP, a region

strongly connected by callosal axons (Clarke and Miklossy,

1990). These considerations suggest that ICoh/fMRI paradigms

similar to those used here might become applicable in clinical

investigations of callosal function (see also Innocenti et al., 2001;

Knyazeva and Innocenti, 2001). This is a potentially very broad

field of applicability spanning across conditions including

Alzheimer’s disease, injury, dyslexia, and schizophrenia (Inno-

centi et al., 2003; Peru et al., 2003; Teipel et al., 2003; von

Plessen et al., 2002). However, before this might become possible,

hypotheses underlying the formation of the synchronous neuronal

assemblies in the two hemispheres should be tested in subjects with

callosal lesions.

The visual stimuli used in this study were chosen primarily

because we had previously found that they could reliably modify

ICoh in animals and man (Kiper et al., 1999; Knyazeva et al.,

1999). It should be noticed, though, that the two stimuli differ in

ways critical for perception. Stimulus IG consists of two gratings

moving coherently and thus conforms to the Gestalt rules of

colinearity and common fate that robustly result in perceptual

grouping of the objects obeying the rules. Stimulus OG is

experienced as two different and heterogeneous objects, this

perception being exaggerated by the orthogonal movement of the

gratings. Given the nature of the stimuli we used, it seems likely

that both the increased BOLD, presumably signifying increased

neuronal activity and the increased ICoh should be correlates for

perceptual grouping, involving the cooperative activity of the two

hemispheres.

Human cognition involves multiple processes that differ in

hemispheric specialization, making the formation of trans-hemi-

spheric assemblies a necessary step in cortically based operations

(Pulvermuller and Mohr, 1996). It has been suggested that

behavioral effects like the advantage of the bilateral presentation

of words or familiar faces over the best unilateral condition

(Schweinberger et al., 2003) result from the formation of neural

assemblies distributed across hemispheres. Our approach can be

an efficient tool for testing distributed neural assemblies under-

lying higher cognitive functions.

Acknowledgments

Supported by Swiss National Foundation grant #31-63894.00.

We greatly appreciate the participation of scientists and medical

staff of IBCM and the Radiology Dept. as subjects in the

experiments. We are also grateful to Prof. F. Ansermet and to

Prof. S. Clarke for permanent support to our team throughout this

research, and to Ms. D. Polzik for assistance in the preparation of

the manuscript. GMI and PM contributed equally to this work.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in

the online version, at doi:10.1016/j.neuroimage.2005.07.045.

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