Goal-directed behavior requires the continuous monitoring and dynamic adjustment of ongoing actions. Here, we report a direct cou-
we found the single-trial error-related negativity of the EEG to be systematically related to behavior in the subsequent trial, thereby
In a rapidly changing environment, goal-directed behavior re-
quires the monitoring and dynamic adjustment of ongoing ac-
successful adjustments of future behavior (Ridderinkhof et al.,
have been studied intensively in humans by means of electroen-
cephalogram (EEG) and functional magnetic resonance (MR)
imaging (fMRI). One of the EEG signatures is the error-related
ative feedback, response conflict, and decision uncertainty (Rid-
derinkhof et al., 2004). Informed by both ERN and fMRI
by a fundamental performance monitoring system signaling the
optimization (Ridderinkhof et al., 2004; Ullsperger and von
A common notion is that performance monitoring is a dy-
adjustments can, for instance, lead to prolonged reaction times
(RTs) on trials subsequent to errors, thereby reflecting a more
cautious response mode (Rabbitt, 1966; Ridderinkhof et al.,
2004). This view is supported by fMRI work showing that en-
hanced activity in the posterior frontomedian cortex predicts
et al., 2004). For the ERN, this question is difficult to address,
because ERPs usually are derived by averaging across trials. To
account for this problem, independent component (IC) analysis
(ICA) can be applied, a statistical source separation technique
the ERN indeed reflects performance monitoring, ICA-filtered
tematic behavioral changes in the same trial and particularly in
subsequent trials. Based on a previous account (Gehring et al.,
1993), we predicted greater single-trial ERN amplitudes after er-
in subsequent trials.
EEG source localization studies (Dehaene et al., 1994; Ull-
sperger and von Cramon, 2001) have suggested the posterior
frontomedian cortex as neural generator of the ERN. However,
these analyses do not inform on whether the ERN is related to
hemodynamic changes of error monitoring. To date, it remains
poorly understood how hemodynamic and electrophysiological
correlates of cognitive processes relate to each other. Regarding
the relationship between electrophysiological measures and
11730 • TheJournalofNeuroscience,December14,2005 • 25(50):11730–11737
fMRI, local field potentials recorded in the visual cortex of anes-
thetized monkeys have been shown to predict the local fMRI
blood-oxygen-level-dependent (BOLD) signal (Logothetis et al.,
2001). Here, we tested whether the electrophysiological correlate
sponse in the rostral cingulate zone (RCZ) of the posterior fron-
formance monitoring and the relationship between its
hemodynamic and electrical signatures, we performed a single-
trial analysis of simultaneous EEG/fMRI measurements. We
thereby avoided the usual problem that within-subject behavior
Participants. Eighteen healthy right-handed volunteers participated in
recordings of either EEG or fMRI, data from five subjects had to be
discarded. The final sample consisted of eight females and five males
(22–29 years of age; mean age, 25.2 years). Written informed consent
before the start of the experiment was obtained from each participant
according to the declaration of Helsinki.
Behavioral task. Stimuli were presented using Presentation 0.76 (Neu-
robehavioral Systems, San Francisco, CA) and appeared on a back-
projection screen mounted inside the scanner bore behind the partici-
pants head. A speeded modified flanker task was used known to yield
sufficient error rates to study the ERN (see Fig. 1). Participants were
horizontal flanker arrows appeared for 110 ms. The arrows were 0.46° tall
and 1.08° wide and appeared 0.52° and 1.04° above and below the screen
of the 400 trials, the flankers pointed in the same direction as the target
(incompatible trials). Compatible and incompatible trials appeared in ran-
and accuracy to the target arrow with the response hand indicated by the
arrow direction. Whenever participants responded after an individual, dy-
was presented. The trials were interspersed with a total of 32 nonevents,
during which only the fixation cross was presented and no response was
required. Trials occurred at multiple, systematically offset time points
(range, 0–1.5 s) in relation to fMRI data acquisition to improve temporal
Simultaneous EEG/fMRI recording. Imaging was performed at 3 tesla
on a Siemens (Erlangen, Germany) Trio system equipped with the stan-
dard bird cage head coil. Twenty-two functional slices were obtained
parallel to the anterior commissure–posterior commissure line (thick-
ness, 4 mm; interslice gap, 1 mm) using a gradient-echo echo planar
imaging (EPI) sequence with an echo time of 30 ms, a flip angle of 90°, a
repetition time (TR) of 2000 ms, and an acquisition bandwidth of 100
kHz. Acquisition of the slices was arranged such that they all were ac-
quired within 1500 ms and were followed by a 500 ms no-acquisition
period to complete the TR. This was done to visually monitor proper
recording of the EEG signal during MR scanning and to include for each
(Fig. S1, available at www.jneurosci.org as supplemental material). The
fMRI matrix acquired was 64 ? 64 with a field of view of 19.2 cm,
was acquired. Functional data were motion-corrected off-line with the
Siemens motion correction protocol. Before the functional runs, ana-
tomical modified driven equilibrium Fourier transform (MDEFT) and
EPI-T1 slices in the plane with functional images were collected.
the BrainAmps MR plus, a high-input impedance amplifier specifically
Germany). Sintered Ag/AgCl ring electrodes with built-in 5 k? resistors
were used and mounted into an electrode cap according to the 10–20
system (Falk Minow Services, Herrsching, Germany). Two additional
electrodes were placed below the left eye and on the lower back to mon-
itor eyeblinks and electrocardiograms, respectively. Electrode imped-
EEG amplifier was fixed beside the head coil and powered by a recharge-
able power pack placed outside the scanner bore. The subject’s head was
immobilized using vacuum cushions and sponge pads. The amplified
EEG signals were transmitted with a fiber optic cable to a recording
personal computer placed outside the scanner room. All 32 channels
were recorded with FCz as reference. Although this is an unusual refer-
ence site for ERN studies, it allowed us to keep the distance between
recording reference and “active” electrodes small, thereby minimizing
the chance of amplifier saturation. The data were recorded with a pass-
band of 0.016–250 Hz and digitized with 5000 samples per second at 16
bit with 0.5 ?V resolution (dynamic range, 16.38 mV).
EEG data analysis. EEG data were corrected for MR gradient and bal-
listocardiac artifacts by applying modified versions of the algorithms
proposed by Allen and colleagues (Allen et al., 1998, 2000). Gradient
artifacts were removed as implemented in Vision Analyzer 1.04 software
(BrainProducts) by subtracting an artifact template from the 40 Hz low-
pass-filtered data, using a baseline-corrected sliding average of 20 con-
secutive volumes. This resulted in EEGs denoised for MR gradients, as
shown for representative 10 s traces in Figure S1 (available at www.
jneurosci.org as supplemental material). Further processing of the 250
Hz downsampled data was performed using Matlab 6.5 (MathWorks,
Natick, MA) and EEGLAB 4.51 (Delorme and Makeig, 2004), a freely
EEG data analysis, Swartz Center for Computational Neurosciences, La
Jolla, CA; http://www.sccn.ucsd.edu/eeglab). The EEGLAB plug-in
FMRIB 1.0 (Niazy et al., 2005) (FMRIB EEGLAB plug-in for removal of
fMRI-related artifacts, Center for functional MRI of the Brain, Oxford,
UK; http://www.fmrib.ox.ac.uk/?rami/fmribplugin) was used for
0.4–35 Hz filtered data to remove ballistocardiac artifacts. Based on the
identified heartbeat events, an artifact template was defined as the me-
beat event being processed. As a result, EEG data denoised for ballisto-
cardiac artifacts were derived but with common EEG artifacts such as
eyeblinks still being present (Fig. S1, available at www.jneurosci.org as
The MR-denoised EEG data were re-referenced to common average,
and stimulus- and response-locked ERPs were calculated separately for
the experimental conditions of interest. The time-locking event for all
?400 ms to avoid contamination of the baseline period with stimulus-
evoked potentials. Stimulus-locked ERPs clearly indicated the common
ERP morphology (i.e., an N1 at occipital channels, a P300 at parietal
reasonable data quality (Fig. S2a, available at www.jneurosci.org as sup-
plemental material). Grand mean ERP images were computed by color-
coding the single-trial amplitudes aligned to stimulus onset, smoothed
with a moving average across 30 adjacent trials (Delorme and Makeig,
2004). Topographical inspection of both scalp ERPs and reaction-time-
portion of the signal with different artifacts (Fig. S2, available at www.
jneurosci.org as supplemental material). In addition to the typical eye-
temporal sites, as characterized by a reversed polarity between left and
right hemisphere channels. This event-related artifact probably was
caused by button-press-related small body movements and related cur-
rent induction. For this and other reasons, it was inevitable to linearly
decompose the response-related process of interest from these and fur-
ther signal contributions. We performed extended infomax ICA (Bell
the number of channels, which, when matrix-multiplied with the raw
Debeneretal.•TheDynamicsofPerformanceMonitoringJ.Neurosci.,December14,2005 • 25(50):11730–11737 • 11731
data, reveals maximally temporally independent activations. A weight
change of 10?7as stop criterion resulted in stable decompositions after
?800 iterations. Each IC can be characterized by a time course (IC acti-
vation) and a topography (IC map), the latter being given by the inverse
weights. The 30 ICs for each subject were screened for maps resembling
the typical frontocentral radial ERN topography and a contribution to
trials, that is, a larger negative deflection at the response interval for
presumably reflecting the contribution of the neural correlate of perfor-
mance monitoring to the scalp EEG. Figure S3 (available at www.
jneurosci.org as supplemental material) shows the individual IC maps
identified along with the average map, after root mean square normal-
ization of individual maps (see Fig. 2a). For each subject, the selected IC
was then back-projected to the scalp to reveal unique polarity informa-
tion and microvolt scaling. Figure S4 (available at www.jneurosci.org as
as supplemental material), spatiotemporally overlapping contributions
were now absent in the IC ERPs and ERP images.
To model the neural source of the selected ICs, the grand average IC
map was submitted to BESA 2000, version 4.2 (MEGIS, Graefeling, Ger-
many). A standardized finite element model (FEM), as provided by
BESA, was used. It was created from an averaged head of 24 individual
(Fig. 2c). The BESA FEM provides a realistic approximation of three
compartments (brain/CSF, skull, scalp) and was applied with default
by placing an equivalent current dipole into the RCZ. The location [Ta-
lairach coordinates (x, y, z) ? 0, 20, 30] was derived from the second-
level fMRI result from the same subjects in the same recording session,
with the x-axis value set to zero.
Time–frequency analysis of single-trial IC activations was performed
for data collapsed across three frontocentral channels (FC1, FC2, Cz) by
convolving the data with a complex Morlet wavelet w (t,f0) having a
Gaussian shape in the time (?t) and frequency (?f) domain around the
center frequency f0. A constant wavelet is characterized by a constant
ratio Q ? ( f0/?f). We used nonconstant wavelets with Q increasing
Hz), which results in an increase in spectral versus temporal resolution
with increasing frequency. The Q at 5 Hz was characterized by a fre-
ms. For every single trial, the norm of the complex result of the convo-
subtracting for each frequency the mean value of the ?500 to ?200 ms
prestimulus interval from the poststimulus values. The epoch length for
(?500–1500 ms relative to target onset) did not interfere with invalid
edge effects, as indicated by the half length of the wavelet scales.
low ? increase at about the response interval, the back-projected IC
subject was then computed as follows. First, the minimum value in the
subtracted (see Fig. 3a). These latency windows were determined based
on the grand average IC ERP (see Fig. 2b) and also were compatible with
the time–frequency results confirming a prominent theta activity (see
as parametric regressor for fMRI analysis (see below).
MRI data analysis. MR data processing was performed using the soft-
ware package LIPSIA (Lohmann et al., 2001). Functional data were cor-
nal changes and baseline drifts were removed by applying a temporal
high-pass filter with a cutoff frequency of 1/120 Hz. Spatial smoothing
was applied using a Gaussian filter with 5.65 mm full width at half max-
imum (FWHM). To align the functional data slices with a three-
dimensional stereotactic coordinate reference system, a rigid linear reg-
istration with six degrees of freedom (three rotational and three
translational) was performed. The rotational and translational parame-
ters were acquired on the basis of the MDEFT and EPI-T1 slices to
achieve an optimal match between these slices and the individual three-
dimensional reference data set [MDEFT volume data set with 160 slices
ing a previous scanning session. The rotational and translational param-
eters were subsequently transformed by linear scaling to a standard size.
The resulting parameters were then used to transform the functional
slices using trilinear interpolation so that the resulting functional slices
were aligned with the stereotactic coordinate system, generating output
data with a spatial resolution of 3 mm3.
were performed to identify significant power changes in the time–fre-
quency plane relative to baseline activity in the EEG. This analysis was
applied separately to each subject and each condition. In addition, ran-
domization statistics on the time–frequency power differences between
incompatible error and incompatible correct conditions were per-
formed. To summarize these results for the group of 13 subjects, bino-
mial statistical analysis was applied.
Statistical analysis of the association between single-trial EEG ampli-
tudes and reaction times was achieved by determining the linear regres-
Fig. 3c). Separate analyses were performed for the current trial, that is,
associated to reaction times whenever the following trial belonged to the
same stimulus condition. For group analysis, the resulting individual
slopes were tested against zero (see Fig. 3d) by applying one-sided t tests
for conditions in which a prediction could be made on the basis of the
performance monitoring model.
The statistical analysis of fMRI data was based on a least squares esti-
mation using the general linear model for serially autocorrelated obser-
vations (random effects model) (Friston et al., 1995; Worsley and Fris-
were implemented, that is, the hemodynamic response function was
modeled by the experimental conditions for each stimulus (event ?
onset of stimulus presentation). The measured signal was described by a
response function. The design matrix was generated using a synthetic
the observation data, the design matrix, and the error term, was con-
volved with a Gaussian kernel with a dispersion of 4 s FWHM. The
effective degrees of freedom were estimated as described by Worsley and
Friston (1995). In the following, contrast maps, that is, estimates of the
raw-score differences among specified conditions, were generated for
each subject. The individual functional datasets were all aligned to the
same stereotactic reference space, and a group analysis was performed.
one-sample t test on the resulting contrast images across subjects and
sessions (Worsley and Friston, 1995; Holmes and Friston, 1998). Subse-
quently, t values were transformed into z scores. The design matrix con-
sisted of onset vectors for compatible correct, incompatible correct, and
incompatible erroneous trials. Trials involving late response feedbacks
and rotational motion correction parameters provided by the Siemens
motion correction protocol were included as regressors. As in previous
studies (Ullsperger and von Cramon, 2001, 2004), analysis of error-
related brain activity was performed by contrasting incompatible erro-
neous with incompatible correct trials, thus extracting specific signal
increases on errors. Conflict-related activity should cancel out, because
response conflict occurs on both incompatible correct and erroneous
trials. To minimize the probability of false positives (type I error), only
?180 mm3(five voxels) were considered as activated voxels (Braver et
11732 • J.Neurosci.,December14,2005 • 25(50):11730–11737 Debeneretal.•TheDynamicsofPerformanceMonitoring
In a second analysis testing whether the single-trial ERN measure co-
varies with the fMRI signal, a parametric design was used (Bu ¨chel et al.,
1996, 1998). The single-trial amplitudes vector was used as a parameter
referring to the onsets of all responses in the task, regardless of their
vectors for late-response feedbacks and nonevents. The six translational
While participants underwent concurrent EEG and fMRI data
acquisition, they performed a speeded flanker task. They were
required to respond with button-presses according to the direc-
tion indicated by a centrally presented target arrow, which was
surrounded by irrelevant but distracting flanker arrows (Fig. 1).
Participants made errors on 0.58% (SEM, 0.16) of compatible
ence; t(12)? 7.75; p ? 0.0001). The number of compatible errors
was insufficient for meaningful statistical analyses, such that this
stimulus–response type was excluded from further analysis. Hit
reaction times were 380.8 ms (SEM, 7.9) for compatible and
445.0 ms (SEM, 8.1) for incompatible trials (significant differ-
(SEM, 17.7). It was missed in 9.46% (SEM, 1.8) of compatible
and in 27.69% (SEM, 3.50) of incompatible trials. Error reaction
times for incompatible trials were 388.2 ms (SEM, 9.8), thus be-
ing significantly shorter than for incompatible correct trials
(t(12)? 4.41; p ? 0.001). In sum, these behavioral results are
consistent with previous findings for flanker tasks (Eriksen and
Eriksen, 1974; Ullsperger and von Cramon, 2001). Importantly,
allow meaningful data analyses.
After denoising the EEG from MR gradient and ballistocardiac
artifacts, ERPs were computed (Fig. S1, available at www.jneurosci.
org as supplemental material). As expected, a frontocentral ERN
was clearly visible for incompatible error trials but strongly re-
duced, if not absent, for the correct response conditions (Fig. S2,
available at www.jneurosci.org as supplemental material). To
artifacts, we performed ICA on each subject’s MR-denoised raw
EEG data (Bell and Sejnowski, 1995). ICA returns a set of spatial
filters, which yield component activations that are maximally
temporally independent from each other. In each subject, we
was the best candidate to account for the ERN. First, the IC
jects) IC activation ERPs for the vertex electrode (Cz), time-locked to response-onset times,
which explained 90.2% of the variance, is plotted on a canonical magnetic resonance image
bels) between IC incompatible error and incompatible correct trials. Significantly more theta
activity for the error condition is indicated by the black contour line. The white vertical lines
denote the stimulus onset time (0 ms) and mean reaction time for erroneous responses,
The selected ICs are equivalent to the scalp-recorded ERN. a, Identified compo-
Debeneretal.•TheDynamicsofPerformanceMonitoring J.Neurosci.,December14,2005 • 25(50):11730–11737 • 11733
should have a near-radial central topogra-
phy. Second, its backprojected ERP time
tion at the response interval in the incom-
patible error compared with the incom-
topographies of the selected ICs for each
terial (Fig. S3, available at www.jneuro-
ing the parietal error positivity (Pe) or the
N200 were not observed.
We performed several analyses to test
whether the IC selected for each subject
indeed reflects the spatial and temporal characteristics of the
grand average IC map (Fig. 2a,c). A single equivalent current
dipole was seeded into the RCZ, with the exact location taken
from the conventional second-level fMRI analysis contrasting
ordinates (x, y, z) ? 0, 20, 30] (Table 1) (see Fig. 5). This source
dipole accounted for 90.2% of the variance (Fig. 2c). Seeding
fMRI analysis (lateral prefrontal cortex, left anterior inferior in-
ICs represent activity originating in the RCZ.
Second, grand mean ERPs of the back-projected IC activa-
tions revealed a clear ERN in the incompatible error condition
(Fig. 2b). These IC ERPs strongly resembled those from the orig-
inal scalp channel data, but their topography appeared now free
of artifact contributions (Fig. S4, available at www.jneurosci.org
as supplemental material). Typically, the ERPs showed a polarity
at www.jneurosci.org as supplemental material), which, taking
located in the RCZ.
are usually derived by averaging across trials, which prevents the
study of trial-by-trial variations of the EEG signal. To overcome
respective single-trial IC activations sorted by reaction time.
in most trials (Fig. 2d).
Fourth, we performed a time–frequency analysis of the IC
single-trial signals. ERPs and ERP images both suggest that the
ing an oscillation in the theta frequency range (Luu et al., 2004).
Indeed, time–frequency analysis revealed a prominent theta
power increase after stimulus onset lasting for ?600 ms. This
theta power increase was significant in 11 of 13 subjects for the
incompatible correct condition and in 10 of 13 subjects in the
incompatible error condition (randomization test; p ? 0.01). At
the time of the erroneous response, the theta power increase was
correct trials (Fig. 2e) (binomial; p ? 0.00001). In summary,
component map topography, fMRI-informed source modeling,
led us to conclude that the IC identified in each subject is very
likely equivalent to the ERN as usually obtained outside the MR
ing models is that physiological signatures of error monitoring
taking peak-to-peak measures of the 2–10 Hz bandpass-filtered
3c). For incompatible errors, the single-trial amplitude was sig-
one-sided t test). Interestingly, the opposite relationship was
found for incompatible correct trials. Here, short RTs were asso-
ciated with small single-trial amplitudes (b ? ?2.14; p ? 0.046),
consistent with previous findings for the ERN in speeded tasks
using a deadline procedure (Luu et al., 2000). However, these
effects did not remain significant when RT and single-trial am-
plitude outliers (values ? 3 SD) and trials followed by a late
feedback were excluded from the analysis (incompatible errors,
More importantly, we identified a clear relationship between
ERN dynamics and subsequent behavioral adjustments. We
found that higher single-trial amplitudes were associated with
the single-trial EEG measure, significantly predicted posterror
slowing (b ? ?5.38; p ? 0.043). After exclusion of outliers and
trials followed by the late feedback, this effect was even more
pronounced (b ? ?5.90; p ? 0.032). Moreover, this effect could
on subsequent trials, which was absent (second level mean; r ?
0.06; NS). The present analysis thus demonstrates that posterror
slowing (Rabbitt, 1966) was driven by postresponse electrophys-
iological activity in the RCZ.
A key question in the present context is whether hemodynamic
signals related to error monitoring covary with the single-trial
ERN (Nunez and Silberstein, 2000; Logothetis et al., 2001). If
used the EEG single-trial amplitude to predict the fMRI BOLD
the single-trial EEG amplitudes were convolved with the hemo-
dynamic response function at button-press onset times. The fol-
lowing parametric random effects fMRI analysis identified a sig-
nificant correlation of the single-trial amplitude with the BOLD
0 17 42
vectors. Contrast incompatible correct versus compatible correct is shown. Parametric analysis used single-trial amplitude quantification of error-related
11734 • J.Neurosci.,December14,2005 • 25(50):11730–11737Debeneretal.•TheDynamicsofPerformanceMonitoring
lute single-trial ERN amplitudes were associated with stronger
BOLD responses in the RCZ. Although the EEG-informed ap-
proach as well as the conventional fMRI analysis identified the
RCZ, the conventional fMRI contrast re-
vealed additional brain structures, such as
insular and lateral prefrontal cortex (Fig.
5, Table 1). The conventional contrast ex-
tracted error-specific signal increases,
activations present in both correct and in-
correct trials cancelled out to a large de-
the other hand, was more specific to vari-
ations of the monitoring signal itself.
These differences in process specificity
might also account for the observation
that the maxima of the RCZ foci were sep-
rection (Ullsperger and von Cramon,
2001). However, with the given the
between-plane resolution, a functional in-
terpretation of this finding remains
The present study demonstrates an event-
related trial-by-trial coupling of simulta-
neously recorded EEG, fMRI, and behav-
ior in humans. The major advantage of
simultaneous recordings is that these dif-
ferent measures are studied under identi-
cal sensory and motivational conditions,
thereby allowing the investigation of trial-
by-trial fluctuations. We found the single-
trial ERN to be systematically related to
ensuing behavioral adjustments. As pre-
dicted by performance-monitoring mod-
that the action outcome is worse than ex-
pected (Holroyd et al., 2004; Brown and
Braver, 2005). On error trials, this likeli-
hood estimate has been suggested to be
driven primarily by postresponse conflict
between executed and concurrently acti-
vated response tendencies (Yeung et al.,
2004). Moreover, the single-trial ERN amplitude reflects activa-
tion of the RCZ neurons involved in controlling subsequent ad-
ing that performance adjustments are preceded by enhanced
firing rates of RCZ neurons (Shima and Tanji, 1998; Williams et
al., 2004). Our finding that posterror slowing was predicted by
which this relationship was investigated by the study of the aver-
aged ERN and correlations across subjects. At the group level,
however, many other factors may contribute to averaged ERN
amplitude variations (e.g., skull thickness, variations of the cin-
gulate sulcus, and trait factors) (Pailing and Segalowitz, 2004).
posterror slowing effect. In line with our results, however, is one
ror slowing (Gehring et al., 1993). We conclude that the single-
trial measure of the ERN reflects trial-by-trial fluctuations in the
activity of performance monitoring circuits that are responsible
response condition. The color code is as in d. Note the considerable amount of variance within each experimental condition
plitudes, plotted on an individual brain. fMRI signals correlated with single-trial amplitudes
Debeneretal.•TheDynamicsofPerformanceMonitoringJ.Neurosci.,December14,2005 • 25(50):11730–11737 • 11735
for behavioral adjustments. The ability to
measure the dynamics of the monitoring
signal will significantly facilitate address-
term control adjustments (Ridderinkhof
et al., 2004).
The peak-to-peak measure used to
EEG activity takes into account the ERN
also maximal at frontocentral sites. This
frontocentral positive EEG deflection
seems to result from activity in the RCZ as
well, which is consistent with a previous
source localization study (van Veen and
Carter, 2002). Note that this positivity
should not be confused with the parietal
2000), which was not captured in the ICA
decomposition. Furthermore, it has been
hypothesized that ERN and preresponse
N200 on correct trials may both reflect
similar processes, namely response con-
flict monitoring (Yeung et al., 2004). We
did not obtain a reliable N200 conflict ef-
IC reflecting an N200 modulation. Usu-
ally, the N200 preresponse conflict effect is very small (up to
2?V) (van Veen and Carter, 2002), and we therefore may have
missed it as a result of the recording environment or the limited
number of electrodes. The relationship between ERN and the
N200 conflict effect remains an open question that should be
optimally addressed by ICA decomposition of high-density EEG
data acquired outside the MR environment.
For resting conditions, it has been shown previously that si-
multaneous EEG and fMRI BOLD can reveal systematic correla-
trial-by-trial correlation approach applied here presents a new
strategy for integrating EEG and fMRI. Alternative approaches
such as fMRI-informed ERP dipole seeding (Ullsperger and von
Cramon, 2001; Thees et al., 2003) implicitly assume a tight link
between the neural generators of ERPs and fMRI activation foci.
contrasts may well reveal cortical regions that do not comprise
neuronal sources of the ERP and vice versa (Nunez and Silber-
stein, 2000). Because fMRI BOLD and ERP components can dif-
fMRI-informed dipole seeding is not necessarily a valid solution
of the inverse problem in EEG.
High magnetic fields provide adverse conditions for EEG re-
cordings. Application of ICA offers a practical solution to mini-
ity on a trial-by-trial basis (Debener et al., 2005; Makeig et al.,
2002, 2004). The identified IC reflected all features of the ERN,
including scalp topography, ERP morphology, and time–fre-
quency characteristics. We also found that the fMRI-informed
dipole seeding approach confirmed the RCZ as the major source
of the selected ICA correlate of the ERN. This latter finding also
validates the main assumption of ICA as applied to EEG data.
Under favorable circumstances, a clean independent component
tex expressing a dipolar projection. More importantly, we con-
clude that the covariation of the single-trial ERN measure with
the fMRI BOLD response in the RCZ strongly supports the pro-
et al., 2004; Ullsperger and von Cramon, 2004). Thus, the fMRI
signal and the ICA single-trial correlate of the ERN appear to
reflect directly related neuronal and metabolic processes in the
RCZ. Our data fit well with animal research on a coupling be-
tween BOLD and local field potentials (Logothetis et al., 2001;
Logothetis, 2003), the latter being known to be the basis of the
scalp-recorded EEG (Nunez and Silberstein, 2000).
ising because future studies may succeed in identifying multiple
functionally relevant ICs. This would allow to relate several IC
time courses to regional fMRI activation, thereby holding great
potential to address two cardinal questions in the field of cogni-
tive neuroscience. First, concurrent single-trial EEG/fMRI will
facilitate addressing the dynamic interplay between ongoing and
event-related brain activity (Arieli et al., 1996; Makeig et al.,
high temporal precision and thus helps to shape models on how
tical areas (Stephan et al., 2004). By successfully demonstrating a
trial-by-trial coupling of noninvasive event-related EEG and
fMRI, new avenues are opened for future experiments that ad-
dress the dynamics of information processing within both ana-
tomically and functionally defined neural networks.
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