Integration of fMRI and simultaneous EEG: towards a

www.elsevier.com/locate/ynimg

NeuroImage 22 (2004) 83–94

Integration of fMRI and simultaneous EEG: towards a

comprehensive understanding of localization and time-course

of brain activity in target detection

Christoph Mulert,a,* Lorenz Jager,b Robert Schmitt,a Patrick Bussfeld,a Oliver Pogarell,a

Hans-Jurgen Moller,a Georg Juckel,a and Ulrich Hegerla

aDepartment of Psychiatry, LMU, Munich, Germanyb Institute of Clinical Radiology, LMU, Munich, Germany

Received 6 May 2003; revised 29 October 2003; accepted 29 October 2003

fMRI and EEG are complimentary methods for the analysis of brain

activity since each method has its strength where the other one has

limits: The spatial resolution is thus in the range of millimeters with

fMRI and the time resolution is in the range of milliseconds with EEG.

For a comprehensive understanding of brain activity in target

detection, nine healthy subjects (age 24.2 F 2.9) were investigated

with simultaneous EEG (27 electrodes) and fMRI using an auditory

oddball paradigm. As a first step, event-related potentials, measured

inside the scanner, have been compared with the potentials recorded in

a directly preceding session in front of the scanner. Attenuated

amplitudes were found inside the scanner for the earlier N1/P2

component but not for the late P300 component. Second, an

independent analysis of the localizations of the fMRI activations and

the current source density as revealed by low resolution electromag-

netic tomography (LORETA) has been done. Concordant activations

were found in most regions, including the temporoparietal junction

(TPJ), the supplementary motor area (SMA)/anterior cingulate cortex

(ACC), the insula, and the middle frontal gyrus, with a mean Euclidean

distance of 16.0 F 6.6 mm between the BOLD centers of gravity and

the LORETA-maxima. Finally, a time-course analysis based on the

current source density maxima was done. It revealed different time-

course patterns in the left and right hemisphere with earlier activations

in frontal and parietal regions in the right hemisphere. The results

suggest that the combination of EEG and fMRI permits an improved

understanding of the spatiotemporal dynamics of brain activity.

D 2004 Elsevier Inc. All rights reserved.

Keywords: fMRI; EEG; ERP; P300; Auditory-evoked potential; Target

detection; Oddball

Introduction

For years, research in auditory target detection has been focused

on event-related potentials. Using an oddball paradigm, a large

positive component can be recorded after about 300 ms (the P300

1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2003.10.051

* Corresponding author. Department of Psychiatry, LMU, Nußbaum-

straße 7, 80336 Munchen, Germany. Fax: +49-89-5160-5542.

E-mail address: [email protected] (C. Mulert).

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

potential), if a rare, task-relevant target has been presented. This

component can be reliably found with largest amplitudes in parietal

electrodes on the scalp. Major interest to this potential was caused

by the finding that in several neuropsychiatric disorders, like

dementia of the Alzheimer type or schizophrenia, attenuations of

the amplitude and latency have been described (Frodl et al., 2002a;

Hegerl et al., 1995; Polich and Pitzer, 1999; Winterer et al., 2001).

However, the clinical benefit has been limited so far, since the

finding of an attenuated P300 amplitude seems to be rather

unspecific when looking at scalp-data. The topography on the

scalp allows apparently only a rough distinction between a more

frontally pronounced early P3a component and a more parietally

pronounced P3b component. The P3a has been described to be

involved in automatic novelty detection and the P3b is more

concerned with volitional target detection (Soltani and Knight,

2000). Using intracranial measurements, however, it could be

demonstrated that a complex network of brain regions is involved

in the generation of the P300 potential, including temporoparietal

junction, posterior superior parietal regions, cingulate cortex,

dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and

medial temporal regions (Kiss et al., 1989; McCarthy and Wood,

1987). Interestingly, these regions also show different time-course

patterns (Baudena et al., 1995; Halgren et al., 1995a,b).

With regard to the meaning of a disturbed P300 in brain

diseases, it became clear that the important next step would be to

get similar information with high spatial and time resolution

noninvasively, since intracranial measurements are usually not

appropriate in a clinical setting. With the development of nonin-

vasive hemodynamic-based brain imaging methods like fMRI,

with its high spatial resolution in the range of millimeters, several

studies have been undertaken to figure out brain regions involved

in auditory target detection (Downar et al., 2000; Kiehl et al., 2001;

Linden et al., 1999). Interestingly, beside the medial temporal

region, all other brain regions that have been known from the

intracranial measurements could be detected again using fMRI,

suggesting that there is a high degree of concordance between the

electrical signal and hemodynamic response. Concerning the

disturbed P300 potential in diseases like schizophrenia, it was

evident that a noninvasive investigation of disturbed brain function

C. Mulert et al. / NeuroImage 22 (2004) 83–9484

in target detection became possible now with high spatial resolu-

tion (Kiehl and Liddle, 2001).

However, the intracranial measurements by Halgren et al. had

revealed clear differences in the time-course patterns of the

involved brain regions with latency peak differences in the range

of 100 ms and less. Therefore, it was also clear that hemodynam-

ic-based methods would not have a sufficient high temporal

resolution for a distinct analysis of the timing of brain activity

in target detection. Thus, a combination of electrical activity- and

hemodynamic-based methods is suggestive since these methods

are complementary because of the good temporal resolution of

EEG/ERP-based approaches and the good spatial resolution of

fMRI. For such a combination approach, it would be critical that

the signals generated by each method correspond to the same set

of underlying generators (Horwitz and Poeppel, 2002). The good

concordance of the intracranial measurements and the fMRI

studies did already encourage this approach. Concerning basic

research, important support came from recent physiological stud-

ies in monkeys (Logothetis et al., 2001), investigating simulta-

neously intracortical recordings of electrical neural signals and the

blood-oxygen-level-dependent (BOLD) fMRI-responses in the

visual cortex. Clear correlations were found between the local

field potentials, which are mostly reflecting a weighted average of

synchronized dendro-somatic components of the input signals of a

neural population, and the BOLD responses. No such clear

relationship could be found between multiunit spiking activity

and the BOLD signal. Since the local field potentials are the

physiological basis of the scalp EEG, this result is in favor to the

hypothesis that the signals generated by EEG and fMRI might

correspond to the same set of underlying generators.

In principle, there are three possibilities: Cerebral activity might

be detectable only with EEG, only with fMRI, or with both

methods. The first might be true if, for example, short-lasting

neuronal activity, leading to a short ERP component, does not

evoke a measurable (and significant) hemodynamic response. The

second could occur if long-lasting neuronal activity would result in

a large hemodynamic response but maybe only in a slow shift of the

electrical baseline that is not detected in the classical peak picking

approach. Finally, brain activity should be detectable with both

methods in the case of a synchronous activity of a large number of

neurons for some hundred milliseconds. Then, probably both a large

ERP component and a significant BOLD signal would be expected.

This could be true for the P300 component: An important study by

Horovitz et al. (2002) could already show a close relationship

between the scalp-P300 and the BOLD signal in an oddball

paradigm. Manipulation of the task (probability of the occurrence

of a target) was found to influence both the amplitude of the P300-

component and the BOLD-signal changes in most of the involved

brain regions in the same direction. So, an experimental condition

that did result in an increased P300 amplitude at Pz did also result in

an increased BOLD-signal change (e.g., in the supramarginal gyrus)

and vice versa (Horovitz et al., 2002). EEG and fMRI measurement

was done in separate sessions in this experiment.

Due to the technical challenge of a simultaneous measurement

of EEG and fMRI, a combination of EEG and fMRI so far has

mostly been done measuring with each method in separate sessions.

Using such an approach, many additional variables are convoluted,

such as attention, vigilance, familiarity with a task, which might not

be the same in two separate data acquisition sessions, or experi-

mental environment (lying inside a noisy and narrow scanner with

dim lightning, or sitting comfortable in a quiet EEG lab with natural

light). That in fact the arousal level of a subject is crucial for the

activation of brain regions in cognitive tasks has recently be

demonstrated by Matsuda et al. using a simultaneous EEG/fMRI

study. Here, the authors could find significant activations in a

smooth-pursuit eye movement task in brain areas like the parietal

eye field and the supplementary eye field only during the high

arousal level (Matsuda et al., 2002). Beside the possible implica-

tions of this finding in studies dealing with the comparison between

healthy volunteers and patients with potentially disturbed vigilance/

arousal dynamics, it becomes clear that vigilance and arousal is a

variable that could influence neural activity and therefore should be

controlled if the interpretation of the results shall not be extremely

limited. The only way to make results comparable in a way that

differences between the results obtained by EEG or fMRI are based

on the physiology and on fundamental properties of the methods

and not on confounding variables would be to measure simulta-

neously with both methods. Thus, to get usable and interpretable

results, it is worthwhile to make more effort concerning methodol-

ogy. Simultaneous measurement of EEG and fMRI requires a

special EEG hardware that can be used in the MRI scanner without

making artifacts and with no safety risk. These efforts have been

primarily undertaken in the analysis of epileptic activity (Jager et

al., 2002). Obviously, epileptic activity is in fact a single event that

cannot simply be repeated on demand and therefore a simultaneous

measurement with both EEG and fMRI is especially important.

However, the abovementioned influence of vigilance or arousal on

activations in cognitive fMRI studies make a simultaneous data

acquisition also necessary in target detection. We have therefore

established an experimental set-up that allows a simultaneous

measurement of EEG and fMRI data.

With this study, it was intended to answer three questions: (1) Is

the EEG-signal (P300 potential), as measured inside the MR

scanner, comparable to the signal outside the scanner, measured

in a directly preceding session? (2) Are the same localizations for

activated brain regions found with fMRI and EEG signals? (3) Do

the involved brain regions differ with respect to the time-course of

the neuroelectric activity?

Methods

Subjects

Ten healthy volunteers with no history of neurological or

psychiatric disturbance or reduced hearing were recruited from

an academic environment. One data set was later excluded due to

technical problems. Finally, the data of nine subjects (six men,

three women: range, 20–30 years old; mean age, 24.2 F 2.9) were

analyzed. The study was approved by the local ethics committee of

the Ludwig-Maximilians-University of Munich and written in-

formed consent was obtained from each subject.

Paradigm

Auditory stimuli were generated on a Personal Computer using

the BrainStim software package (Brain Products, Munich, Ger-

many) and conducted through a pair of plastic tubes into a set of

headphones placed over the subjects ears. The auditory stimuli

were a 800-Hz sinus-tone (nontarget, 80%) and a 1300-Hz sine-

wave tone (target, 20%) at a sound pressure level of 95 dB. The

tones were presented with an interstimulus of 3 or 6 s (mean: 4 s).

Fig. 1. Comparison between the auditory-evoked potentials recorded inside and outside the scanner. (a) Clearly attenuated N1/P2 potential inside the scanner (at

Cz). (b) Only slightly reduced P300 potential inside the scanner (at Pz). Outside the scanner, a pronounced P3a component is evoked.

Fig. 2. Comparison between target and nontarget. (a) Inside the scanner: neither the target nor the nontarget stimulus evokes a pronounced P3a component. (b)

Outside the scanner: both the target and nontarget stimulus evoke a P3a component.

C. Mulert et al. / NeuroImage 22 (2004) 83–94 85


Targets appeared in pseudo-random order. The volunteers had to

press a button with the right index finger.

Acoustic environment, sound system, and headphones

For the measurement of the sound pressure level inside and

outside the scanner, we have used the following equipment: A 1/2-

in. microphone (B&K 4313, Bruel and KJAER, Denmark) attached

with a preamplifier (B&K 2639) with extension cable (B&K A027)

connected to a measuring amplifier (B&K) served as sound level

recording unit. To enable offline recording, the AC output of the

measuring amplifier was connected to the line input of a digital

audio tape (DAT) recorder (SONY 300 ES, Japan). The measuring

amplifier and the DAT recorder were placed outside the shielded

MR room and the microphone cable was routed via an opening

shielded by copper tubes to suppress radio frequency (RF) induc-

tion. Before the recordings, a calibration procedure was performed

using an acoustic calibrator (B&K 4230). The signals were

recorded at the position of the human head in the isocenter of

the MR system and the second one, at 1.2 m in front of the opening

of the MR. Due to the impulse characteristic of the MR noise, a

large difference between averaging measurements applying ‘‘fast’’

time constant and peak detection occurred. The echo planar

imaging (EPI) sequence used in our study produced a sound

measured in sound pressure level (SPL) of 90.5 dB (dB = decibel)

(fast) and of 106.5 dB (peak) inside the MR system. The basic

Fig. 3. Global field power of the target-specific activity (target minus nontarget)

highly corresponding. The target-specific brain activity is even more pronounced

noise (due to the vacuum pump) inside the MR system was 76 dB.

At 1.2 m in front of the opening of the MR, a basic noise of 62 dB

was measured. Binaural sound transmission was performed using

an air tubing sound delivery system (Resonance Technology, Inc.

Van Mays, USA). The personal computer was placed outside the

shielded MR room. The sound delivery system was evaluated

using standardized equipment (microphone 4144, preamplifier

2619, calibrator 4230, and artificial ear with 2-cm coupler 4152;

Bruel and KJAER, Denmark). Because we combined an invert

earphone with circum-aural ear muffs, a high attenuation against

the noise of the MR (20 dB at low and 50 dB in the high

frequencies) occurred. Due to the tube-based sound transportation

and the sound driver characteristics, a rippled band pass frequency

response occurred. Therefore, the desired level of 95 dB SPL was

achieved by a digital attenuation to the correct intensity.

fMRI setup

Imaging was performed in a 1.5 T Siemens Magnetom Vision

scanner with echo planar capability. For functional BOLD imag-

ing, 10 slices with a T2-weighted EPI sequence (TR: 3s; TE: 64

ms; FOV: 210/280 mm; matrix: 128 � 64; interleaved slice

acquisition; slice thickness: 8 mm; interslice gap: 0.8 mm; result-

ing pixel size: 3.16 � 2.11 mm) were acquired in the same position

as the anatomical images. Each functional time series consisted of

120 volumes each. Five functional time series were acquired. The

inside the scanner and outside the scanner. The shapes of both curves are

inside the scanner.

Fig. 4. Average (n = 9) three-dimensional maps of BOLD-signal increase

for targets versus nontargets ( P < 0.05, corrected for multiple testing) on

the inflated hemispheres of a single subject. Lateral views.

Fig. 5. Average (n = 9) three-dimensional maps of BOLD-signal increase

for targets versus nontargets ( P < 0.05, corrected for multiple testing) on

the inflated hemispheres of a single subject. Medial views.


first two functional image frames of each time series were

discarded to allow for signal equilibration, giving a total of 590

frames used in analysis. For each subject, a three-dimensional

MPRAGE data set (T1-weighted) was acquired. The subject’s head

was immobilized during the whole experiment to minimize invol-

untary head movements.

Post-processing

The complete post-processing of the data including motion

correction, statistics, and transfer of the data into Talairach space

was done with the Brainvoyager software package, version 4.9

(Rainer Goebel, Maastricht, Netherlands). The first two scans of

each run have been excluded from further analysis because of T1

saturation effects. Motion correction was done using Trilinear

Interpolation on a reduced (12.5%) data set. Data smoothing was

performed as temporal data smoothing and linear trend removal.

No spatial data smoothing has been performed.

Functional data were transformed into Talairach space, aligned

with the three-dimensional anatomical volumes from the same

session, and interpolated to a resolution of 1 mm3. For further

statistical analysis, multiple regression analysis was performed on

the three-dimensional functional volume time courses (5 for every

subject with 118 time points each) using the General Linear Model

(GLM). The design matrix contained the idealized response func-

tions that corresponded to the signal time-courses (target, nontar-

get). For statistical analysis, any combination of predictors can be

used. Statistical maps were computed for analysis of individuals

(contrast: target vs. nontarget) and thresholded at P < 0.05 (cor-

rected for multiple testing). Additionally, to obtain information

about activation that are consistent for the entire group, a statistical

group analysis was performed, thresholded at P < 0.05 or P < 0.001

(corrected for multiple testing), respectively (see figures and tables).

EEG

We used an EEG amplifier that cannot be saturated by any MR

activity (EMR; Schwarzer, Munich, Germany) and which has been

specifically developed for operation in the MR system. The EEG

recording was performed with 27 electrodes (sintered silver and

silver-chloride) referred to Cz and placed on the scalp according to

the international 10–20 system with the additional electrodes FC1,

FC2, FC5, FC6, T1, T2, CP5, CP6. Electrode skin impedance was

usually less than 5 kV. Data were collected with a sampling rate of

500 Hz and an analogous band-pass filter (0.16–30 Hz). Two

hundred milliseconds of prestimulus baseline and 575 ms post-

stimulus periods were evaluated for every sweep. For artifact

suppression, all sweeps were automatically excluded from averag-

ing when the voltage exceeded 100F AV in any of the 27 channels

at any point during the averaging period. For each subject, the

remaining sweeps were averaged after baseline correction. Only

wave-shapes, based on at least 40 averages, were accepted. The

Fig. 6. fMRI activations ( P < 0.05, corrected for multiple testing) and

EEG–LORETA current source density activations at corresponding slices

(Z = 8, 15, 22). Good correspondence in the temporoparietal region, but not

in subcortical regions (not covered by LORETA).

Fig. 7. fMRI activations ( P < 0.05, corrected for multiple testing) and

EEG–LORETA current source density activations at corresponding slices

(Z = 29, 36, 44). Good correspondence in the midline and the right middle

frontal gyrus.

C. Mulert et al. / NeuroIma88

EEG recording was acquired and displayed using the BrainLab

software (OSG, Rumst, Belgium). All data processing (artifact

rejection and averaging) has been done using the BrainVision

Analyzer software (Brain Products).

Peak detection, statistics

Peak detection has been done semiautomatically using the

Analyzer software. The N1 peak has been defined as the most

negative value between 80 and 150 ms, the P3 peak as the most

positive value between 250 and 500 ms poststimulus. Differences

between N1 and P3 peaks inside and outside the scanner have been

compared with paired t tests using the SPSS-software (SPSS 11.5).

Parametrization

According to the global field power time course (difference

wave corresponding to the infrequent tone minus frequent tone),

the timeframe 250–540 ms poststimulus has been chosen for

further analysis (Fig. 3).

LORETA

Low resolution electromagnetic tomography (LORETA)

assumes that the smoothest of all activity distributions is most

plausible (‘‘smoothness assumption’’) and therefore, a particular

current density distribution is found (Pascual-Marqui et al., 1994).

This fundamental assumption of LORETA directly relies on the

neurophysiological observation of coherent firing of neighboring

cortical neurons during stimulus processing (Gray et al., 1989;

Llinas, 1988; Silva et al., 1991) and therefore can be seen as a

physiologically based constraint. However, this coherent firing has

been described on the level of cortical columns, which have a

much smaller diameter than the voxels used in the LORETA

software; the empirical basis for coherent firing in the millimeter

range is not strong enough to fully accept this constraint as a

physiological one, even if it might help to produce useful results.

The characteristic feature of the resulting solution is its relatively

low spatial resolution, which is a direct consequence of the

smoothness constraint. Specifically, the solution produces a

‘‘blurred-localized’’ image of a point source, conserving the

ge 22 (2004) 83–94

Fig. 8. LORETA current source density maxima (green squares) and fMRI statistical maps ( P < 0.05, corrected for multiple testing) superimposed on an

anatomical MRI image. Good correspondence in insula, temporoparietal junction (TPJ), and bilateral middle frontal gyrus. The middle and inferior temporal

gyri have not been covered by the fMRI slices.


location of maximal activity, but with a certain degree of disper-

sion. It should be emphasized that this solution will typically

produce a ‘‘blurred-localized’’ image of arbitrary distributions

due to the principle of superposition. However, some distributions

of point sources may superpose in such a way that they actually

cancel out on the scalp and therefore cannot be correctly localized

by any method. The version of LORETA used in the present study

used the digitized Talairach atlas (Talairach and Tournoux, 1988)

available as digitized MRI from the Brain Imaging Center, Mon-

treal Neurologic Institute, estimating the current source density

(mA/mm2) distribution for either single time-points or epochs of

brain electric activity on a dense grid of 2394 voxels at 7-mm

spatial resolution (Pascual-Marqui et al., 1999). The solution space

Table 1

Corresponding activations (target minus nontarget) in fMRI and EEG

Region fMRI Number of E

X, Y, Z (Talairach)activated

voxelsX

SMA/gyrus cinguli 0, �10, 43 7268 (**) �TPJ left �49, �27, 32 2301 (**) �TPJ right 57, �24, 19 3214 (**) 6

Insula left �38, �1, 7 7973 (**) �Insula right 42, 4, 6 6777 (**) 5

STG left �42, �24, 12 2390 (**) �Lobulus parietalis

inferior left

�39, �43, 46 1286 (**) �

Middle frontal

gyrus right

37, 36, 23 104 (**) 5

Precuneus �5, �70, 46 165 (*) �Middle frontal

gyrus left

�30, 39, 25 48 (*) �

SMA = supplementary motor cortex; TPJ = temporoparietal junction; STG = supe

(corrected for multiple testing); (*) number of voxels significant activated on P va

(the three-dimensional space where the inverse EEG problem is

solved) was restricted to the gray matter and hippocampus in the

Talairach atlas (anatomically based constraint). Localization

concerning spherical and realistic head geometry was done using

EEG electrode coordinates reported by Towle et al. (1993). A voxel

was labeled as gray matter if it met the following three conditions:

its probability of being gray matter was higher than that of being

white matter, its probability of being gray matter was higher than

that of being cerebrospinal fluid, and its probability of being gray

matter was higher than 33% (Pascual-Marqui et al., 1999). LOR-

ETA has been widely used in the last years to localize electrical

generators of scalp EEG data (Gallinat et al., 2002; Mulert et al.,

2001, 2002, 2003; Park et al., 2002; Pizzagalli et al., 2001).

EG (LORETA) Current source density Euclidean

, Y, Z (Talairach)[AV/mm2 (�10�3)] distance (mm)

3, �11, 64 5.21 21

59, �25, 22 3.90 14

0, �18, 15 3.17 7

38, 17, 1 2.69 17

3, 10, 8 3.82 12

59, �32, �6 3.57 26

45, �46, 50 4.19 8

3, 17, 22 3.47 24

3, �53, 57 4.96 20

31, 38, 36 4.08 11

rior temporal gyrus; (**) number of voxels significant on a P value < 0.001

lue < 0.05 (corrected for multiple testing).

Table 2

No correspondence between activations (target minus nontarget) in fMRI

and EEG

Region fMRI Number EEG (LORETA) Current

X, Y, Z

(Talairach)

of

activated

voxels

X, Y, Z

(Talairach)

source

density

[AV/mm2

(�10�3)]

Motor Cortex �35, �27, 50 9435

Striatum left �25, �6, 6 4196

Striatum right 26, �5, 6 2642

Thalamus left �8, �16, 8 2881

Thalamus right 10, �12. 9 1782

Posteriorer

gyrus cinguli

0, �33, 44 142

Precuneus 9, �70, 35 171

Gyrus temporalis

inferior

left (y)

�52, �4, �27 3, 53

Gyrus temporalis

medialis

right (y)

3.53�13 4.19

Middle frontal

gyrus left

�45, 10, 29 3.59

(y) Region not covered by the fMRI slices.


In the present study, LORETA-time-course peak detection

(Table 3) has been done by visual inspection.

Experimental procedure

Session in the scanner

Five runs with 120 scans each have been performed. During

each run, 15 target tones and 63 nontarget tones have been

presented, resulting in 75 target tones and 315 nontarget tones

for each subject. Between the end of the EPI sequence and the

stimulus presentation, there has been a quiet period of 1 s. The

Fig. 9. Current source density time courses. Left hemisp

EEG was recorded continuously during the entire experiment.

After stimulus onset, additional 580 ms poststimulus EEG could

be acquired until the next EPI sequence started.

Session in front of the scanner

Before the simultaneous EEG/fMRI measurement has been

performed, the same experiment has been done in front of the

scanner, using the same paradigm, the same number of stimuli, and

the same EEG equipment. Between the two runs, there was a break

of about 10 min.

In one subject, due to a technical problem of trigger marker

coding, no averaging was possible. All together, nine subjects

showed clear fMRI activations including the primary motor cortex

on a P value of 0.05, corrected for multiple testing, and were used

for further analyses.

Comparison between fMRI and EEG localizations

This comparison has been done in calculating the Euclidean

distances between the Talairach coordinates of the LORETA

current density maxima and the Talairach coordinates of the BOLD

centers of gravity in the same anatomical structures.

Results

Comparison between the EEG inside and in front of the scanner

There has been a clear difference in the amplitudes of the N1

component with higher amplitudes outside the scanner (mean:

10.15 F 5.70 AV vs. 5.06 F 2.58 AV, T = �3.37, P = 0.010).

However, no significant difference could be found between the

P300 potential with a mean amplitude at Pz of 8.00 F 4.25 AVoutside the scanner versus 6.60 F 3.98 AV inside, T = 1.39, P =

0.202 (Fig. 1). The N1 latency was longer outside the scanner

(mean: 118 F 10 vs. 109 F 9 ms, T = �3.130, P = 0.014). There

here (Talairach coordinates are given in Table 3).

Table 3

Time-course of the activations in the left and right hemisphere

Region P300 peak EEG (LORETA)

(ms)X, Y, Z (Talairach)

Left hemisphere

Gyrus temporalis inferior 294 � 52, � 4, � 27

STG 371 � 59, � 32, � 6

TPJ 385 � 59, � 25, 22

Lobulus parietalis inferior 387 � 45, � 46, 50

Insula 388 � 38, 17, 1

Middle frontal gyrus I 389 � 45, 10, 29

Middle frontal gyrus II 390 � 31, 38, 36

SMA/gyrus cinguli 396 � 3, � 11, 64

Precuneus 396 � 3, � 53, 57

Right hemisphere

Gyrus temporalis medialis 345 53, 3, � 13

Insula 356 53, 10, 8

TPJ 360 60, � 18, 15

Middle frontal gyrus 361 53, 17, 22

SMA/gyrus cinguli 396 4, � 11, 64

Precuneus 396 4, � 53, 57

Earlier activations in the right and the left insula, TPJ, and middle frontal

gyrus. SMA = supplementary motor cortex, TPJ = temporoparietal

Junction, STG = superior temporal gyrus.


was no significant difference between the P3 latencies outside and

inside the scanner (mean: 343 F 73 vs. 326 F 23 ms inside, T =

�0.63, P = 0.55). Both the frequent and the infrequent tone evoked

a P3a outside the scanner, but the P3a component of both the

frequent and infrequent tone inside the scanner was less distinct

(Fig. 2). In order to analyze the brain activity specifically related to

target detection, we have calculated difference waves (based on the

potentials related to the infrequent tone minus the potentials related

to frequent tone). Here, the specific target-related activity was

similar inside and outside the scanner (mean difference wave

amplitude at Pz inside the scanner 5.95 AV F 3.99 vs. 5.45 F3.49 outside the scanner, T = �0.35, P = 0.73). In addition, no

significant difference in the difference wave latencies was detected

(Fig. 3).


fMRI activations were found in several brain regions including

the supplementary motor cortex (SMA), the anterior cingulate

cortex (ACC), the bilateral temporoparietal junction (TPJ), the

bilateral insula, and the frontal regions such as the middle frontal

gyrus (Figs. 4 and 5).

In the LORETA analysis, current density maxima have been

found in similar regions (Figs. 6, 7, and 8). Additionally, LOR-

ETA-maxima were found in the left inferior temporal gyrus (Z =

�27) and the right middle temporal gyrus (Z = �13). These

regions have not been covered by the fMRI slices. In the compar-

ison between the BOLD cluster centers of gravity and the LOR-

ETA-maxima, consistent spatial activation patterns were found

with Euclidean distances between 7 and 26 mm (mean of 16.0 F6.6 mm, Table 1).

Table 2 shows the regions with no correspondence between

fMRI and current source density including the motor cortex, the

subcortical regions (thalamus, striatum). Subcortical regions are

not included in the LORETA analysis, which is restricted on the

cortical gray matter and the hippocampus. Two fMRI activa-

tions, one in the posterior cingulate and one in the precuneus,

could not exactly be matched to a current source density

maximum. One current source density maximum in the left

Fig. 10. Current source density time courses. Right hemis

middle frontal gyrus did also have no direct equivalence in the

fMRI analysis.

Time-course analysis of the current source density maxima

Figs. 9 and 10 show the time course of brain activity in target

detection in the left and right hemispheres. In Table 3, the exact

peak latencies are given. There is early activity in the temporal

lobes, with the temporoparietal junction being active afterwards

and finally activation of frontal and supplementary motor regions.

Peak activity in frontal regions has been earlier on the right than on

the left, and also, activity in the insula was peaking earlier on the

right.

phere (Talairach coordinates are given in Table 3).


Discussion

Comparison between the EEG inside the scanner and in front of

the scanner

Clear differences between the amplitudes of the N1 component

of the auditory-evoked potential inside the scanner and outside the

scanner could be detected, but no significant difference for the

P300 component. The N1 is a relatively early component and has

strong generators in the auditory cortex (Naatanen and Picton,

1987; Picton et al., 1999). The noisy environment inside the

scanner with the noise by the gradients has certainly an effect on

the activation of the auditory cortex: The gradient noise can thus be

seen as an additional auditory stimulus. In our study design,

between the quiet interval between the end of the scanner gradient

noise and the tone presentation was about 1 s. Therefore, the

auditory cortex has been in a partial refractory period and therefore

producing attenuated N1 amplitudes (Budd et al., 1998).

Outside the scanner, the same interstimulus interval of 3 or 6 s

has been used, but not interrupted by scanner (gradient) noise.

Therefore, both the infrequent and the frequent tone have evoked a

strong P3a component, representing an orientation reaction (Fried-

man et al., 2001). No such clear P3a component has been evoked

inside the scanner. With regard to the specific brain activity in

target detection, which has been analyzed in comparing the

difference waves of the potentials evoked by the infrequent minus

the potentials evoked by the frequent tones, the related activity was

quite similar inside and outside the scanner (Fig. 3). An effect of

the recording order of the two conditions is less likely since there

has been a break between the two runs and there is no learning

effect in an oddball paradigm.


Beside the activity in the motor cortex, which is not stimulus-

locked and therefore not represented in the P300 potential, but

represented largely in the fMRI signal, all major cortical regions

have been well detected by the EEG-based LORETA analysis and

the fMRI analysis independently, including the temporoparietal

junction, the SMA/cingulate cortex, the insula, and the middle

frontal gyrus with a right > left asymmetry (Downar et al., 2000;

Halgren et al., 1998; Kiehl et al., 2001; Linden et al., 1999). Here,

an additional LORETA-maximum has been detected in the left

middle frontal gyrus with no correspondence in the fMRI signal.

Two additional LORETA-maxima in the left inferior temporal and

right middle temporal gyrus (Z = �13 and �27) have not been

covered by our fMRI slices (covering about Z = 55 to �10).

Finally, subcortical regions such as the thalamus and the striatum

have been strongly activated in the fMRI analysis. However,

thalamus and striatum are not included in the LORETA solution

space.

The mean Euclidean distance between EEG- and fMRI-based

localizations has been 16 mm, that is, in the range of the spatial

resolution of LORETA, which is between 1 and 2 cm. Our finding

is corresponding to a recent result of Vitacco et al. (2002)

describing a mean distance of 14.5 mm between fMRI and ERP

local maxima.

The results suggest that a close relationship exists between the

electrical P300-signal on the scalp and the corresponding BOLD

signal. Earlier studies, using from 19 to 124 electrodes (present

study: 27) and the LORETA-method or similar approaches, have

already described current source activity in parietal and frontal

regions, suggesting that the topographical information on the scalp

is sufficient for a plausible localization (Anderer et al., 2003;

Moores et al., 2003; Wang et al., 2003). The present study

demonstrates in addition a close relationship between EEG- and

fMRI-based localizations. This finding is in accordance with the

results of Horovitz et al. (2002) demonstrating a close correlation

between changes in the P300 amplitude and BOLD-signal changes

after manipulation of the experimental condition. The comparison

of the localizations in this study has been performed based on

current density images (ERP) and statistical images (fMRI). Beside

the good correspondence of the localizations, it may be best to use

similar methodology (e.g., the same kind of statistics) for the

different data types in the future, which has not been possible with

the applied software packages yet.

Time-course analysis of the current source density maxima

In both hemispheres, the activation sequence (within the P300

time range 250-540 ms) started in the temporal lobe with later

activations of the temporoparietal junction (TPJ) and latest activity

in frontal and supplementary cortex. Interestingly, the activity in

several brain regions (insula, TPJ, and SMA) has been peaking

earlier on the right than on the left side.

In comparison to the time-course patterns of the intracranial

recordings (Halgren et al., 1998), several differences are obvious:

In the present investigation, the time courses of the specific target-

related brain activity was analyzed, that is, the difference wave

between the potentials evoked by the infrequent versus frequent

tone. Furthermore, our paradigm did not evoke a strong early

(P3a)-component inside the scanner. Therefore, the time-course

patterns in our analysis do not show different time courses

contributing either to the P3a or P3b, but mainly different time-

course patterns within the P3b. Here, further studies are necessary,

using a paradigm that is evoking P3a and P3b inside the scanner

separately (Comerchero and Polich, 1999).

Applications in neuropsychiatric research

Concerning the possible clinical impact of this approach, we

suppose that a simultaneous measurement of EEG and fMRI,

enabling us to analyze data with high resolution spatial and

temporal information, should allow a distinct analysis of disturbed

brain function in diseases like schizophrenia, dementia, or depres-

sion. In these conditions, with very different pathologies, attenu-

ated P300 potentials can be recorded on the scalp (Polich and

Herbst, 2000).

Our finding of asymmetric time course patterns with earlier

activations in the TPJ, the insula, and the middle frontal gyrus on

the right in healthy subjects might be worth to be further inves-

tigated. Several authors are describing asymmetric P300 ampli-

tudes on the scalp in healthy subjects (Alexander et al., 1996;

Bruder et al., 1998). With a simultaneous EEG/fMRI approach,

this asymmetry could be further clarified, specifying the brain

regions involved and their respective time courses. Furthermore,

the relationship between P300 attenuations and psychopathology

could be further elucidated: Several authors have described re-

duced P300 amplitudes in patients with distinct thought disorder

(Frodl et al., 2002b; Higashima et al., 1998; Iwanami et al., 2000;

Juckel et al., 1996). Here, the combination of EEG and fMRI

might help to identify reduced activity in related brain regions and/


or a disturbed timing and synchrony of brain activity as a cerebral

correlate.

Based on the fact that high quality EEG recordings are possible

now in the scanner and that elimination of artifacts (originating,

e.g., from MR gradients) from the EEG-signal has recently much

improved (Allen et al., 1998; Goldman et al., 2000; Hoffmann et

al., 2000; Sijbers et al., 1999), further applications of a simulta-

neous EEG/fMRI approach are appropriate. For example, it is

obviously worthwhile to further investigate the relationship be-

tween electrical oscillations and the fMRI signal, since close

relationships have been described between the energy consuming

high frequency oscillations in the gamma (40 Hz)-range, and the

BOLD signal (Logothetis et al., 2001) and gamma oscillations are

also known to play a critical role in higher cognitive functions

(Engel et al., 2001). Furthermore, the investigation of EEG

coherence might improve our understanding of connectivity within

the brain, expanding our methodology here beyond the analysis of

hemodynamic-based connectivity approaches (Bokde et al., 2001;

Buchel and Friston, 2001).

In summary, this study shows diminished N1 amplitudes and

shorter N1 latencies inside the scanner but comparable P3 peaks

and latencies inside and outside the scanner as well as a high

degree of concordance between fMRI- and EEG-based localiza-

tions and distinct time-course patterns in the involved temporal,

parietal, and frontal regions.

Acknowledgment

Parts of this work were prepared in the context of Robert

Schmitt’s dissertation at the Faculty of Medicine, Ludwig-

Maximilians-University, Munich.

References

Alexander, J.E., Bauer, L.O., Kuperman, S., Morzorati, S., O’Connor, S.J.,

Rohrbaugh, J., Porjesz, B., Begleiter, H., Polich, J., 1996. Hemispheric

differences for P300 amplitude from an auditory oddball task. Int.

J. Psychophysiol. 21, 189–196.

Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R., Lemieux, L., 1998. Iden-

tification of EEG events in the MR scanner: the problem of pulse

artifact and a method for its subtraction. Neuroimage 8, 229–239.

Anderer, P., Saletu, B., Semlitsch, H.V., Pascual-Marqui, R.D., 2003. Non-

invasive localization of P300 sources in normal aging and age-asso-

ciated memory impairment. Neurobiol. Aging 24, 463–479.

Baudena, P., Halgren, E., Heit, G., Clarke, J.M., 1995. Intracerebral poten-

tials to rare target and distractor auditory and visual stimuli: III. Frontal

cortex. Electroencephalogr. Clin. Neurophysiol. 94, 251–264.

Bokde, A.L., Tagamets, M.A., Friedman, R.B., Horwitz, B., 2001. Func-

tional interactions of the inferior frontal cortex during the processing of

words and word-like stimuli. Neuron 30, 609–617.

Bruder, G.E., Tenke, C.E., Towey, J.P., Leite, P., Fong, R., Stewart, J.E.,

McGrath, P.J., Quitkin, F.M., 1998. Brain ERPs of depressed patients to

complex tones in an oddball task: relation of reduced P3 asymmetry to

physical anhedonia. Psychophysiology 35, 54–63.

Buchel, C., Friston, K., 2001. Interactions among neuronal systems assessed

with functional neuroimaging. Rev. Neurol. (Paris) 157, 807–815.

Budd, T.W., Barry, R.J., Gordon, E., Rennie, C., Michie, P.T., 1998. Decre-

ment of the N1 auditory event-related potential with stimulus repetition:

habituation vs. refractoriness. Int. J. Psychophysiol. 31, 51–68.

Comerchero, M.D., Polich, J., 1999. P3a and P3b from typical auditory and

visual stimuli. Clin. Neurophysiol. 110, 24–30.

Downar, J., Crawley, A.P., Mikulis, D.J., Davis, K.D., 2000. A multimodal

cortical network for the detection of changes in the sensory environment

(In Process Citation). Nat. Neurosci. 3, 277–283.

Engel, A.K., Fries, P., Singer, W., 2001. Dynamic predictions: oscilla-

tions and synchrony in top-down processing. Nat. Rev., Neurosci. 2,

704–716.

Friedman, D., Cycowicz, Y.M., Gaeta, H., 2001. The novelty P3: an event-

related brain potential (ERP) sign of the brain’s evaluation of novelty.

Neurosci. Biobehav. Rev. 25, 355–373.

Frodl, T., Hampel, H., Juckel, G., Burger, K., Padberg, F., Engel, R.R.,

Moller, H.J., Hegerl, U., 2002a. Value of event-related P300 subcom-

ponents in the clinical diagnosis of mild cognitive impairment and

Alzheimer’s Disease. Psychophysiology 39, 175–181.

Frodl, T., Meisenzahl, E.M., Muller, D., Holder, J., Juckel, G., Moller, H.J.,

Hegerl, U., 2002b. P300 subcomponents and clinical symptoms in

schizophrenia. Int. J. Psychophysiol. 43, 237–246.

Gallinat, J.,Mulert, C., Bajobouji,M., Rentzsch, J., Senkowski, D., Schunter,

J., Mientus, S., Muchtieva, R., Kronfeldt, D., Nissle, S., et al., 2002.

Frontal and temporal dysfunction of auditory stimulus processing in

schizophrenia. Neuroimage 17, 110–127.

Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., 2000. Acquiring

simultaneous EEG and functional MRI. Clin. Neurophysiol. 111,

1974–1980.

Gray, C.M., Konig, P., Engel, A.K., Singer, W., 1989. Oscillatory responses

in cat visual cortex exhibit inter-columnar synchronization which

reflects global stimulus properties. Nature 338, 334–337.

Halgren, E., Baudena, P., Clarke, J.M., Heit, G., Liegeois, C., Chauvel, P.,

Musolino, A., 1995a. Intracerebral potentials to rare target and distrac-

tor auditory and visual stimuli. I. Superior temporal plane and parietal

lobe. Electroencephalogr. Clin. Neurophysiol. 94, 191–220.

Halgren, E., Baudena, P., Clarke, J.M., Heit, G., Marinkovic, K., Devaux,

B., Vignal, J.P., Biraben, A., 1995b. Intracerebral potentials to rare

target and distractor auditory and visual stimuli: II. Medial, lateral

and posterior temporal lobe. Electroencephalogr. Clin. Neurophysiol.

94, 229–250.

Halgren, E., Marinkovic, K., Chauvel, P., 1998. Generators of the late

cognitive potentials in auditory and visual oddball tasks. Electroence-

phalogr. Clin. Neurophysiol. 106, 156–164.

Hegerl, U., Juckel, G., Muller-Schubert, A., Pietzcker, A., Gaebel, W.,

1995. Schizophrenics with small P300: a subgroup with a neurodeve-

lopmental disturbance and a high risk for tardive dyskinesia? Acta

Psychiatr. Scand. 91, 120–125.

Higashima, M., Urata, K., Kawasaki, Y., Maeda, Y., Sakai, N., Mizukoshi,

C., Nagasawa, T., Kamiya, T., Yamaguchi, N., Koshino, Y., 1998. P300

and the thought disorder factor extracted by factor-analytic procedures

in schizophrenia. Biol. Psychiatry 44, 115–120.

Hoffmann, A., Jager, L., Werhahn, K.J., Jaschke, M., Noachtar, S., Reiser,

M., 2000. Electroencephalography during functional echo-planar imag-

ing: detection of epileptic spikes using post-processing methods. Magn.

Reson. Med. 44, 791–798.

Horovitz, S.G., Skudlarski, P., Gore, J.C., 2002. Correlations and disso-

ciations between BOLD signal and P300 amplitude in an auditory

oddball task: a parametric approach to combining fMRI and ERP.

Magn. Reson. Imaging 20, 319–325.

Horwitz, B., Poeppel, D., 2002. How can EEG/MEG and fMRI/PET data

be combined? Hum. Brain Mapp. 17, 1–3.

Iwanami, A., Okajima, Y., Kuwakado, D., Isono, H., Kasai, K., Hata, A.,

Nakagome, K., Fukuda, M., Kamijima, K., 2000. Event-related

potentials and thought disorder in schizophrenia. Schizophr. Res. 42,

187–191.

Jager, L., Werhahn, K.J., Hoffmann, A., Berthold, S., Scholz, V., Weber, J.,

Noachtar, S., Reiser, M., 2002. Focal epileptiform activity in the brain:

detection with spike-related functional MR imaging-preliminary results.

Radiology 223, 860–869.

Juckel, G., Muller-Schubert, A., Gaebel, W., Hegerl, U., 1996. Residual

symptoms and P300 in schizophrenic outpatients. Psychiatry Res. 65,

23–32.


Kiehl, K.A., Liddle, P.F., 2001. An event-related functional magnetic reso-

nance imaging study of an auditory oddball task in schizophrenia. Schiz-

ophr. Res. 48, 159–171.

Kiehl, K.A., Laurens, K.R., Duty, T.L., Forster, B.B., Liddle, P.F., 2001.

Neural sources involved in auditory target detection and novelty pro-

cessing: an event-related fMRI study. Psychophysiology 38, 133–142.

Kiss, I., Dashieff, R.M., Lordeon, P., 1989. A parieto-occipital generator

for P300: evidence from human intracranial recordings. Int. J. Neurosci.

49, 133–139.

Linden, D.E., Prvulovic, D., Formisano, E., Vollinger, M., Zanella, F.E.,

Goebel, R., Dierks, T., 1999. The functional neuroanatomy of target

detection: an fMRI study of visual and auditory oddball tasks. Cereb.

Cortex 9, 815–823.

Llinas, R.R., 1988. The intrinsic electrophysiological properties of mam-

malian neurons: insights into central nervous system function. Science

242, 1654–1664.

Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001.

Neurophysiological investigation of the basis of the fMRI signal. Na-

ture 412, 150–157.

Matsuda, T., Matsuura, M., Ohkubo, T., Ohkubo, H., Atsumi, Y., Tamaki,

M., Takahashi, K., Matsushima, E., Kojima, T., 2002. Influence of

arousal level for functional magnetic resonance imaging (fMRI) study:

simultaneous recording of fMRI and electroencephalogram. Psychiatry

Clin. Neurosci. 56, 289–290.

McCarthy, G., Wood, C.C., 1987. Intracranial recordings of endogenous

ERPs in humans. Electroencephalogr. Clin. Neurophysiol., Suppl. 39,

331–337.

Moores, K.A., Clark, C.R., Hadfield, J.L., Brown, G.C., Taylor, D.J.,

Fitzgibbon, S.P., Lewis, A.C., Weber, D.L., Greenblatt, R., 2003.

Investigating the generators of the scalp recorded visuo-verbal P300

using cortically constrained source localization. Hum. Brain Mapp. 18,

53–77.

Mulert, C., Gallinat, J., Pascual-Marqui, R., Dorn, H., Frick, K., Schlatt-

mann, P., Mientus, S., Herrmann, W.M., Winterer, G., 2001. Reduced

event-related current density in the anterior cingulate cortex in schizo-

phrenia. Neuroimage 13, 589–600.

Mulert, C., Juckel, G., Augustin, H., Hegerl, U., 2002. Comparison be-

tween the analysis of the loudness dependency of the auditory N1/P2

component with LORETA and dipole source analysis in the prediction

of treatment response to the selective serotonin reuptake inhibitor cit-

alopram in major depression. Clin. Neurophysiol. 113, 1566–1572.

Mulert, C., Gallinat, J., Dorn, H., Herrmann, W.M., Winterer, G., 2003. The

relationship between reaction time, error rate and anterior cingulate

cortex activity. Int. J. Psychophysiol. 47, 175–183.

Naatanen, R., Picton, T., 1987. The N1 wave of the human electric and

magnetic response to sound: a review and an analysis of the component

structure. Psychophysiology 24, 375–425.

Park, H.J., Kwon, J.S., Youn, T., Pae, J.S., Kim, J.J., Kim, M.S., Ha, K.S.,

2002. Statistical parametric mapping of LORETA using high density

EEG and individual MRI: application to mismatch negativities in

schizophrenia. Hum. Brain Mapp. 17, 168–178.

Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Low resolution

electromagnetic tomography: a new method for localizing electrical

activity in the brain. Int. J. Psychophysiol. 18, 49–65.

Pascual-Marqui, R.D., Lehmann, D., Koenig, T., Kochi, K., Merlo,

M.C., Hell, D., Koukkou, M., 1999. Low resolution brain electro-

magnetic tomography (LORETA) functional imaging in acute, neu-

roleptic-naive, first-episode, productive schizophrenia. Psychiatry Res.

90, 169–179.

Picton, T.W., Alain, C., Woods, D.L., John, M.S., Scherg, M., Valdes-Sosa,

P., Bosch-Bayard, J., Trujillo, N.J., 1999. Intracerebral sources of hu-

man auditory-evoked potentials. Audiol. Neuro-Otol. 4, 64–79.

Pizzagalli, D., Pascual-Marqui, R.D., Nitschke, J.B., Oakes, T.R., Lar-

son, C.L., Abercrombie, H.C., Schaefer, S.M., Koger, J.V., Benca,

R.M., Davidson, R.J., 2001. Anterior cingulate activity as a predictor

of degree of treatment response in major depression: evidence from brain

electrical tomography analysis. Am. J. Psychiatry 158, 405–415.

Polich, J., Herbst, K.L., 2000. P300 as a clinical assay: rationale, evalua-

tion, and findings. Int. J. Psychophysiol. 38, 3–19.

Polich, J., Pitzer, A., 1999. P300 and Alzheimer’s disease: oddball task

difficulty and modality effects. Electroencephalogr. Clin. Neurophy-

siol., Suppl. 50, 281–287.

Sijbers, J., Michiels, I., Verhoye, M., Van Audekerke, J., Van der Lin-

den, A., Van Dyck, D., 1999. Restoration of MR-induced artifacts in

simultaneously recorded MR/EEG data. Magn. Reson. Imaging 17,

1383–1391.

Silva, L.R., Amitai, Y., Connors, B.W., 1991. Intrinsic oscillations of neo-

cortex generated by layer 5 pyramidal neurons. Science 251, 432–435.

Soltani, M., Knight, R.T., 2000. Neural origins of the P300. Crit. Rev.

Neurobiol. 14, 199–224.

Talairach, J., Tournoux, P., 1988. Co-Planar Stereotaxic Atlas of the Human

Brain. Thieme, Stuttgart.

Towle, V.L., Bolanos, J., Suarez, D., Tan, K., Grzeszczuk, R., Levin, D.N.,

Cakmur, R., Frank, S.A., Spire, J.P., 1993. The spatial location of EEG

electrodes: locating the best-fitting sphere relative to cortical anatomy.

Electroencephalogr. Clin. Neurophysiol. 86, 1–6.

Vitacco, D., Brandeis, D., Pascual-Marqui, R., Martin, E., 2002. Corres-

pondence of event-related potential tomography and functional magnet-

ic resonance imaging during language processing. Hum. Brain Mapp.

17, 4–12.

Wang, J., Hiramatsu, K.I., Hokama, H., Miyazato, H., Ogura, C., 2003.

Abnormalities of auditory P300 cortical current density in patients with

schizophrenia using high density recording. Int. J. Psychophysiol. 47,

243–253.

Winterer, G., Mulert, C., Mientus, S., Gallinat, J., Schlattmann, P.,

Dorn, H., Herrmann, W.M., 2001. P300 and LORETA: comparison

of normal subjects and schizophrenic patients. Brain Topogr. 13,

299–313.

Documents

Integration of fMRI and simultaneous EEG: towards a