NeuroImage 54 (2011) 622–634

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Spatio-temporal brain dynamics in a combined stimulus–stimulus andstimulus–response conflict task

Sascha Frühholz a,b,⁎, Ben Godde b,c, Mareike Finke a, Manfred Herrmann a,b,d

a Department of Neuropsychology and Behavioral Neurobiology, Bremen University, Bremen, Germanyb Center for Cognitive Sciences (ZKW), Bremen University, Bremen, Germanyc Jacobs Center on Lifelong Learning and Institutional Development, Jacobs University Bremen, Bremen, Germanyd Center for Advanced Imaging (CAI), Bremen University, Bremen, Germany

⁎ Corresponding author. Department of NeuropsychologCenter for Cognitive Sciences (ZKW), University of Breme28359 Bremen, Germany. Fax: +49 421 218 68759.

E-mail address: fruehholz@uni-bremen.de (S. Frühh

1053-8119/$ – see front matter © 2010 Elsevier Inc. Adoi:10.1016/j.neuroimage.2010.07.071

a b s t r a c t

a r t i c l e i n f o

Article history:Received 2 March 2010Revised 9 July 2010Accepted 30 July 2010Available online 5 August 2010

Keywords:S–S conflictS–R conflictFlankerSimonDouble conflict

It is yet not well known whether different types of conflicts share common or rely on distinct brainmechanisms of conflict processing. We used a combined Flanker (stimulus–stimulus; S–S) and Simon(stimulus–response; S–R) conflict paradigm both in an fMRI and an EEG study. S–S conflicts induced strongerbehavioral interference effects compared to S–R conflicts and the latter decayed with increasing responselatencies. Besides some similar medial frontal activity across all conflict trials, which was, however, notstatically consistent across trials, we especially found distinct activations depending on the type of conflict.S–S conflicts activated the anterior cingulate cortex and modulated the N2 and early P3 component withunderlying source activity in inferior frontal cortex. S–R conflicts produced distinct activations in theposterior cingulate cortex and modulated the late P3b component with underlying source activity in superiorparietal cortex. Double conflict trials containing both S–S and S–R conflicts revealed, first, distinct anteriorfrontal activity representing a meta-processing unit and, second, a sequential modulation of the N2 and theP3b component. The N2 modulation during double conflict trials was accompanied by increased sourceactivity in the medial frontal gyrus (MeFG). In summary, S–S and S–R conflict processing mostly rely ondistinct mechanisms of conflict processing and these conflicts differentially modulate the temporal stages ofstimulus processing.

y and Behavioral Neurobiology,n, Cognium, Hochschulring 18,

olz).

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© 2010 Elsevier Inc. All rights reserved.

Introduction

The cognitive ability to selectively attend task-relevant and toignore task-irrelevant information is central to many daily lifeactivities because the cognitive processing system is able to processonly a limited amount of information at a time. Task-irrelevantinformation can act as a distractor and can elicit different types ofconflicts during the processing of task-relevant information. Stimu-lus–stimulus conflicts (S–S conflicts; for a taxonomy of conflicts (seeKornblum et al., 1990) occur, for example, when task-irrelevantstimulus information is mapped to a different response as the task-relevant target feature, such as in the Flanker task (Eriksen andEriksen, 1974). The latter typically requires the identification of acentral stimulus surrounded by additional task-irrelevant stimulusfeatures. Stimulus–response conflicts (S–R conflicts), on the otherhand, can be inducedwhen task-irrelevant spatial information such asthe location of the stimulus automatically primes a spatially

corresponding response that is opposite to the response required bythe task-relevant information, like in the Simon task (Simon, 1969).

Though most researchers basically agree about how S–S and S–Rconflicts are elicited, there is an ongoing discussion on the underlyingcognitive and brain mechanisms during the processing of thesedifferent types of conflicts. Particularly, two questions recentlyemerged about the underlying spatio-temporal brain dynamics in S–S and S–R conflict processing. The first question relates to thecognitive and temporal level of processing at which both types ofconflicts modulate recorded brain signals (Galashan et al., 2008;Melara et al., 2008; Valle-Inclan, 1996; Van't Ent, 2002), and howthese different types of conflicts might temporally interact whenelicited simultaneously (see De Jong et al., 1994; Hommel, 1997;Kornblum et al., 1999). The second question refers to the problemwhether both types of conflicts share common or rely on distinct brainnetworks of conflict processing (Egner, 2008; Mansouri et al., 2009;Nee et al., 2007; Peterson et al., 2002; Wager et al., 2005).

To add evidence to these questions and to additionally combinethe underlying research approaches, we used both recordings of theelectroencephalogram (EEG) and functional magnetic resonanceimaging (fMRI) in the present experiment. EEG provides theadvantage of high temporal resolution to investigate the temporal

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level where S–S and S–R conflicts differentially modulate the EEGsignal. FMRI recordings, on the other hand, provide high spatialresolution and allow for the assessment of common as well as distinctbrain regions underlying the processing of S–S and S–R conflicts.Though conflict processing and conflict resolution generally involvedifferent subcomponents (Egner, 2008;Wittfoth et al., 2009), we heretried to analyze the more general difference between the temporaland spatial effects of S–S and S–R conflict processing across thesedifferent subcomponents. The term “conflict processing”, thereby,refers to these general cognitive and brain responses when subjectsare exposed to the different types of conflicts and does not target at adetailed analysis of conflict resolution. Specifically, we tried tocombine fMRI and EEG data in order to get temporo-spatial evidenceaboutwhere andwhen these different types of conflicts are reflected inbehavioral and brain physiological processes.

With respect to the first question about the temporal level ofconflict processing, recent EEG studies demonstrated that both typesof conflicts can affect early, mid-latency and late ERP components.Both types of conflicts, for example, have been shown to modulate theearly fronto-central N2 component. This N2 component is supposed toreflect early response inhibition (van Boxtel et al., 2001) or responseconflict (Bartholow et al., 2005; Melara et al., 2008; Praamstra et al.,1998; Van't Ent, 2002). Furthermore, S–S and S–R conflicts also resultin reduced amplitudes and delayed peak latencies of the centro-parietal P3 component (Galashan et al., 2008; Melara et al., 2008;Valle-Inclan, 1996). The P3 is supposed to represent the essentialnexus between stimulus identification and response selection(Verleger et al., 2005) and might indicate the updating of task-specific response representations specifically after conflict inducedtemporary uncertainties (Donchin and Coles, 1988; Melara andAlgom, 2003; Melara et al., 2008). Finally, the lateralized readinesspotential (LRP) is a rather late ERP component reflecting responsepreparation and was also found to be modulated by both types ofconflicts (Gratton et al., 1992; Melara et al., 2008; Praamstra andOostenveld, 2003; Smid et al., 1990; Sturmer and Leuthold, 2003;Valle-Inclan, 1996).

These ERP data provide evidence that (1) S–S and S–R conflictsmodulate a variety of ERP components, and (2) both types of conflictsshow modulatory effects during similar rather than different levels ofstimulus processing suggesting similar underlying conflict mechan-isms. However, there are no studies which directly compared ERPsignals during the combined processing of S–S and S–R conflicts. Thisprocedure allows for the direct investigation of common and distincttemporal characteristics as well as the temporal interplay betweenthe processing of both types of conflicts. Indirect evidence, however,comes from behavioral studies which either support the notion thatS–S conflicts become evident prior to S–R conflicts (Kornblum et al.,1999), that both S–S and S–R conflicts modulate later stages ofinformation processing beyond the level of stimulus encoding (Simonand Berbaum, 1990) or at the response preparation stage (Treccaniet al., 2009). According to Hommel's (1997) temporal overlap modelthe processing of both types of conflicts is separated but with someoverlap in time. The temporal overlap model also proposes that thetask-irrelevant spatial location code for the S–R conflict and itsinfluence on task performance decays with increasing responselatencies.

Thus, there is divergent evidence about the level of S–S and S–Rconflict processing ranging from similar to distinct processing levels.Additionally, though S–S and S–R conflicts might be processed at asimilar level of processing when elicited separately, the simultaneouspresentation of S–S and S–R conflicts might result in a temporal andspatial separation of the processing of both types of conflict. Somestudies indicate that S–R conflicts mainly affect the response selectionstage while S–S conflicts might be processed at earlier levels whenboth conflicts were elicited simultaneously (De Jong et al., 1994;Hommel, 1997; Kornblum et al., 1999). This separation of temporal

processing of different types of conflicts might reduce conflict load ateach level of stimulus processing. Specifically, S–S compared to S–Rconflict processing might be shifted to earlier processing levels inorder to decrease processing load at later stages (see Zurron et al.,2009). Therefore, S–S and S–R conflicts could appear at differentstages of processing (Hommel, 1997; Kornblum et al., 1999) andmight indicate different conflict processing mechanisms for thesedifferent types of conflicts.

The latter relates to the second question in the current discussionabout S–S and S–R conflicts and pertains to the problem whetherconflict processing of both types of conflicts is based on common ordistinct brain mechanisms. Some imaging studies reported commonbrain regions during the processing of different types of conflicts, suchas the anterior cingulate cortex (ACC), the dorso-lateral prefrontalcortex (dlPFC) or regions in the parietal cortex (Bush et al., 2003; Liuet al., 2004; Mansouri et al., 2009; Nee et al., 2007; Peterson et al.,2002; Wager et al., 2005; Wittfoth et al., 2006; Wittfoth et al., 2009).The ACC is usually assumed to detect conflicts, whereas the dlPFC issupposed to be responsible for conflict resolution (Kerns et al., 2004;MacDonald et al., 2000), but there is also evidence for a reverse role ofboth brain regions (Mansouri et al., 2009; Markela-Lerenc et al.,2004). However, there also exist different brain regions whichbecome uniquely active during these different types of conflicts. S–Sconflict tasks, for example, showed distinct activation in right-hemispheric dlPFC, inferior parietal cortex, insula and basal ganglia,whereas S–R conflict tasks revealed distinct activations in superiortemporal and superior parietal cortex, in pre-motor areas and inferiorfrontal cortex (Egner et al., 2007; Nee et al., 2007; Peterson et al.,2002;Wager et al., 2005;Wittfoth et al., 2006). Furthermore, previousdata from our group (Wittfoth et al., 2009) showed an additionalrecruitment of the left dlPFC during a double conflict condition withtwo versions of a S–R conflict task. This data, additionally, giveevidence that different types of conflicts rely on distinct brainmechanisms and these different brain mechanisms have to beintegrated by the recruitment of additional frontal brain resources.Summarizing recent empirical evidence concerning the conflictadaption effect, that is, conflict processing on the current trialdepending on the preceding trial, Egner (2008) concluded thatprocessing of these different kinds of conflicts rely on conflict specificrather than on general mechanisms.

Taken together, there seems partly divergent evidence about thelevel of S–S and S–R conflict processing and about whether thesedifferent types of conflicts share common or rely on distinct brainmechanisms for the processing of these different types of conflicts. Inthe present experiment we tried to analyze the temporal sequence aswell as the brain networks involved in S–S and S–R conflict processingby the use of a combined Flanker and Simon conflict paradigm,respectively. In contrast to recent studies which compared S–S and S–R conflicts within the same experiment but separated on a trial-by-trial basis (Egner et al., 2007; Liu et al., 2004; Peterson et al., 2002;Wager et al., 2005), we used a experimental designwhere S–S and S–Rconflicts could occur separately in different trials but also simulta-neously during the same trial. We expected to temporarily andspatially differentiate brain networks involved in S–S and S–R conflictprocessing as well as in the combination of both conflict types.

The analysis of the brain physiological data was divided into threesteps. First, we analyzed the fMRI data for each type of conflict trials toinvestigate brain regions involved in the processing of these differenttypes of conflict. This relates to the question of distinct or overlappingbrain networks across different types of conflicts. Specifically, sincesingle S–S and S–R conflicts refer to different conflict sources weexpected to identify distinct conflict dependent brain regions. Indouble conflict trials involving both S–S and S–R conflict dimensionswe expected a cross-talk between both types of conflicts that dependson the temporal dynamics of conflict processing for each type ofconflict. In a second step, we analyzed how S–S and S–R conflicts

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differentially affect ERP components to find the temporal level ofprocessing where these different types of conflicts reveal modulationsof ERP components. We hypothesized that S–S conflicts will show ERPmodulations earlier than those elicited by S–R conflicts. Third, weused the combined information of fMRI and EEG data for an fMRIconstrained EEG source analysis procedure. This analysis should allowfor the analysis of where and when different brain regions becomeactive during the processing of different types of conflict.

Experimental procedures

Participants

Twenty-four healthy students recruited from the Bremen Universityand the Jacobs University campuses took part in the fMRI experiment(three male; mean age 23.91 years, SD=5.31, age range 18–43 years).Twenty of these subjects also took part in the EEG experiment(three male; mean age 24.73 years, SD=5.37, age range 19–43 years) and the order of fMRI and EEG testing was counter-balanced across subjects. All subjects were right-handed accordingto the Edinburgh Handedness Inventory Scale (Oldfield, 1971), hadnormal or corrected to normal vision and normal color vision(Ishihara color-tables; (Ishihara, 1974)). No subject presented aneurologic or psychiatric history. All subjects gave informed andwritten consent for their participation in accordance with ethic anddata security guidelines of the University of Bremen. The study wasapproved by the local ethics committee.

Fig. 1. (a) Stimuli consisted of a central color dot surrounded by four Flanker dots. The centdimension), namely red or blue. Stimuli were presented to the left or right of a central fixatignoring the color of the surrounding dots. The color bars beneath the stimulus indicate theindex finger. This response button assignment was not presented during the task and is onlpercent error rates for both fMRI (dark bars) and EEG testing (light grey bars); error bars derank ordered and subdivided into quintiles resulting in five time bins which contain 20% of trseparately in each of the five time bins (quintiles 1–5 on the abscissa; “1” is the time bin withan index of the amount of conflict for each type of conflict trials separately. These conflict indof conflict trials (RT conflict) and double congruent trials (RT congruent). While Flanker cconflict (FcSi; dark grey lines) decreases with increasing RTs indicating a slow decay of thelines) seem to elicit the same general amount of conflict as single Flanker conflict trials (FiSc)Bold lines are the behavioral results from the fMRI session and dashed lines depict the resu

Stimulus material and trial sequence

The stimulus design was adapted from that used by Li et al. (2004)and by Voelcker-Rehage et al. (2010). Stimuli consisted of five red(CIELab color space, Lab 54, 81, 70) or blue (Lab 54,−3,−50) coloreddots (0.8°×0.8°) with one central dot surrounded by four Flanker dots(total stimuli size 4°×4°; see Fig. 1a). Stimuli were presented eitherleft or right to the center of the screen (4.5° eccentricity of the centraldot from fixation), and subjects were told to maintain fixation at thecenter of the screen. While subjects had to ignore the Flanker dotsthey had to indicate the color of the central dot by pressing one of tworesponse buttons with their right or left index finger (response sidefor “red” and “blue” was counterbalanced across subjects). Subjectswere asked to respond fast, but without sacrificing accuracy. Incongruent Flanker trials the Flanker dots were of the same color as thecentral dot, whereas in incongruent Flanker trials the color of thecentral dot did not match the color of the Flanker dots therebyeliciting S–S conflicts. In congruent Simon trials the location of thecentral dot of the lateral stimulus array matched the side of theresponse to the color of this dot. In incongruent Simon trials theresponse was opposite to the side of stimulus presentation.

By combining congruent and incongruent trials both for theFlanker and Simon dimensions we introduced 60 trials where theFlanker and Simon dimensions were congruent (FcSc), where eitherFlanker (FiSc) or Simon (FcSi) was incongruent or where both theFlanker and Simon were incongruent (double conflict trials, FiSi).Stimuli were presented for 300 ms followed by a blank screen for

ral and the surrounding dots could be either of the same or of a different color (Flankerion cross (Simon dimension). The task was to determine the color of the central dot byresponse assignment, in this example red for the left index finger and blue for the righty included here for illustrative purposes. (b) Shows reactions times (RT) and (c) showsnote the standard error of the mean (SEM). (d) Reaction time data of each subject wereials each. RTs during FcSc trials were subtracted from RTs during FiSc, FcSi and FiSi trialsthe fastest responses, “5” is the time bin with the slowest responses, respectively) to getices are plotted on the ordinate for each quintile as the RT differences between each typeonflict (FiSc; black lines) seems to enhance with increasing RTs, especially the SimonSimon conflict with increasing response latencies. Double conflict trials (FiSi, light grey, but show a similar decay of the amount of conflict as single Simon conflict trials (FcSi).lts from the EEG session.

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2000 ms. The next trial was preceded by a fixation cross (1°×1°)presented for 500±100 ms. Stimuli were presented in randomizedorder in blocks of 60 trials separated by short breaks. Each blockconsisted of an equal amount of the different trial types with therestriction of maximum three times the same stimulus or the sameresponse in succession. Since trials show differential activationsdepending on the preceding trial, we balanced the trial sequences foreach block in order to avoid trial succession effects, such as theGratton effect of post-conflict adjustment (Gratton et al., 1992), whichmeans that each trial type had the same probability to be preceded byany trial type.

Using Presentation®-Software (Neurobehavioral Systems;https://nbs.neuro-bs.com) stimuli were projected via a JVC videoprojector onto a projection screen positioned at the rear end of thefMRI scanner with a viewing distance of about 38 cm. For EEG testing,stimuli were presented on a computer screen (refresh rate 60 Hz).Subjects were seated in a moderately darkened room with a viewingdistance of approximately 60 cm to the screen. Prior to eachexperimental session, subjects accomplished a training session withtwenty trials to accommodate to the task conditions.

Behavioral data analysis

Behavioral data were analyzed separately for the EEG and the fMRIexperiment, using repeated measures 2×2 ANOVAs with the factorsFlanker (congruent, incongruent) and Simon (congruent, incongru-ent). For the analysis of reaction time (RT) data we only included trialswith a correct response. Several studies demonstrated that the Simonconflict effect is smaller when combined with a Flanker conflict(Hommel, 1997; Wendt et al., 2006). However, the strength of theSimon conflict effect has also shown to depend on the speed ofresponse. The Simon conflict strength is usually increased for fastcompared to slow responses (Hommel, 1997; Treccani et al., 2009).The latter fact may partly mask the Simon effect if behavioral data areaveraged over fast and slow responses. Since S–R conflict strength,thus, varies depending on the speed of responses we additionallyperformed a time depended analysis of reaction time data. We,therefore, subdivided RTs of each subject and of each trial type inquintiles according to rank ordered reaction times resulting in fivetime bins where each of these time bins contained 20% of trials. Foreach quintile we analyzed the amount of conflict by subtracting meanRTs for FcSc trials frommean RTs of each the FiSc, FcSi and FiSi trials toget an index of conflict level. These conflict indices were subjected to a5×3 repeated measure ANOVA with the within subjects factorsquintile (first to fifth quintile) and conflict (FiSc, FcSi, FiSi).

To reveal functional correlates of the Simon conflict effectdepending on fast and slow responses we additionally performedBOLD signal analysis depending on the speed of responses in regionswhich became generally active during the fMRI experiment (seebelow).

Image acquisition

Imaging data were obtained on a 3-T SIEMENSMagnetom Allegra®

System (Siemens, Erlangen, Germany) using a T2*-weighted gradientecho-planar imaging (EPI) sequence (28 contiguous axial slicesaligned to the AC–PC plane, slice thickness 4 mm, no gap, TR=1.5 s,TE=30 ms, FA=73°, in-plane resolution 3×3 mm), and using amanufacturer supplied circularly polarized head coil to measurechanges in blood oxygenation level-dependent (BOLD) signals. A highresolution magnetization prepared rapid acquisition gradient echo(MPRAGE) T1-weighted sequence (176 contiguous slices, TR=2.3 s,TE=4.38 ms, TI=900 ms, FA=8°, FOV 296×296 mm, in-planeresolution 1×1 mm, slice thickness 1 mm) was obtained in sagittalorientation to obtain structural images from each subject.

Image analysis

We used the statistical parametric mapping software SPM(Version 5; Welcome Department of Cognitive Neurology, London,UK) for preprocessing and statistical analysis of functional images.Functional images were first corrected for latency differences in sliceacquisition to the middle slice in each image, and after motionestimation realigned to the tenth image for each data set. Theanatomical images were coregistered to the functional images toreveal warping parameters to normalize the functional images to theMontreal Neurological Institute (MNI) stereotactic template brain.During normalization functional images were resampled to2×2×2.66 mmvoxel size. Normalized images were spatially smoothedusing a non-isotropic Gaussian kernel of FWHM 8×8×10.66 mm todecrease differences in individual structural brain anatomy and toincrease the signal-to-noise ratio. Images were high-pass filtered(128 s) to remove low-frequency signal drifts. A first-order autoregres-sivemodel (AR-1)was used for estimating temporal autocorrelationsbyusing restrictedmaximumlikelihoodestimates of variance components.

We used delta functions defined by the onset of a stimulus on atrial-by-trial basis, convolved with a canonical hemodynamic re-sponse functions (HRF) and its first temporal derivative. Experimentalconditions were subsequently entered into a general linear model.Four regressors were defined for FcSc, FcSi, FiSc and FiSi trials withcorrect responses and with an additional regressor for all erroneoustrails. Additionally, six motion correction parameters were includedas regressors of no interest to minimize false positive activations dueto task correlated motion (see Johnstone et al., 2006).

Double congruent trials where both the Flanker and the Simoncondition were congruent served as baseline condition for thecomparison of the different types of conflict trials. First level contrastswere built by comparing activation during specific conflict trials withactivation in double congruent trials ([FiScNFcSc], [FcSiNFcSc] and[FiSiNFcSc]). First level contrasts were subjected to a second levelANOVA analysis and were thresholded at a combined voxel andcluster-size threshold of pb0.001 (uncorrected) and a cluster extentof k=7. The cluster level threshold was set to the estimatedsmoothness of the functional data. To find regions which arecommonly active during processing of any conflict type we performeda conjunction analysis over conflict specific contrasts. We used theconjunction null hypothesis (Nichols et al., 2005) to identify brainregions which became active during each of the conflict trials(pb0.001, uncorrected, k=7). Regions of distinct activation forspecific conflict trials were analyzed by an interaction analysiswhich compared activations during these specific conflict trialswith activations during the remaining trials (pb0.001, uncorrected,k=7).

According to the rationale for the quintile analysis which weapplied for the behavioral data analysis, we additionally introduced asimilar analysis for the BOLD signal in regions of interest (ROIs) whichbecame significantly active for the contrasts described above. Wecomputed contrast estimates separately for trials with fast responsesand slow responses according to the time depended analysisdescribed in the behavioral analysis section. For fast responses wemerged the two fastest quintiles of RTs and for slow responses wemerged the two slowest quintiles of RTs to examine BOLD signaldifferences between the two conditions (Hommel, 1997; Treccaniet al., 2009).

We did not apply a classical factorial analysis for the fMRI data forseveral reasons. First, we tried to introduce a similar analysisprocedure for the fMRI and the ERP data since the final purpose ofthe present study was to integrate both types of data in a sourceanalysis for ERP effects. Averaged ERP time courses are usuallycomputed for each experimental condition or as a linear combinationof conditions. Here, we determined the ERP time courses for eachexperimental condition and computed differences waves for each of

Table 1Coordinates (MNI) of brain regions taken from the common and distinct fMRIactivations during the different types of conflict trials. Thirteen regional sources wereused as starting solution with the constraint that sources should have a minimumEuclidian distance of 2 cm. This starting solution was consequently reduced to a fivedipole solution. The brain regions for the final solution are printed in bold.

Source MNI x, y, z

R medial frontal gyrus 1, 12, 41L superior frontal gyrus −27, −12, 56R superior frontal gyrus 29, −5, 64R middle frontal gyrus 40, 54, 19L inferior frontal gyrus −42, 14, −1R inferior frontal gyrus 38, 20, 0L precentral gyrus −52, 2, 16R precentral gyrus 52, 2, 16L superior parietal lobule −10, −54, 46R superior parietal lobule 26, −60, 59R posterior cingulate gyrus 4, −46, 29L fusiform gyrus −34, −70, −14R fusiform gyrus 34, −70, −14

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the different conflict trials compared to the double congruent trials(see below). A similar procedurewas applied to the fMRI data. Second,a factorial analysis for interactive effects during the double conflicttrials reveals super-additive effects which go beyond the simpleadditive effects of the Simon and Flanker conflict. This procedure,however, does not allow common brain areas during double conflicttrials which are shared with any of the simple conflict trials. Thepurpose of the present study was, however, to find both common aswell as distinct brain areas.

EEG recording and offline processing

A sixty-four channel electroencephalogram (EEG; see supplemen-tary material S1 for a list of electrodes) was recorded using Ag–AgClelectrodes mounted on an elastic cap according to the 10–10-systemwith a reference electrode on the nose tip (Nz). EEG signal wasamplified by a REFA® multi-channel system (TMS International;www.tmsi.com) and digitized with a sampling rate of 512 Hz.Impedances were kept below 10 kΩ. Horizontal and vertical electro-oculogramm (EOG) was recorded at the outer canthi of the eyes andboth supra- and suborbital to the right eye to monitor eye blinks andmovements. Individual electrode positions for each subject weredetermined by the use of a 3DMotion Analysis System (zebris MedicalGmbH).

During offline processing of EEG signal from channels (equal orless than 3 channels per dataset) containing high-frequency and high-amplitude noise was reconstructed by spherical spline interpolation.EEG signal was offline bandpass filtered (0.1–30 Hz) and re-referenced to an average reference. ERP data were averaged for eachexperimental condition within a stimulus-locked time epoch of100 ms pre-stimulus to 800 ms post-stimulus. ERP data were baselinecorrected to the mean amplitude of the pre-stimulus interval. Onlytrials where subjects responded correctly and where amplitude wasbelow 100 μV at every channel were included into data analysis. EOGartifacts were corrected using an algorithm introduced by Ille et al.(2002)). Careful visual inspection of the resulting epochs ensured thatonly artifact-free epochs were entered in the following ERP analysis.

Statistical analysis

Visual inspection of the ERP data revealed two relevant timewindows. The first ERP modulation appeared about 320 ms post-stimulus especially at fronto-central electrodes resembling propertiesof the N2 component, but also with some slight parietal extensionaffecting an early branch of the P3 (eP3) process and, therefore, waslabeled the early component as “N2–eP3” complex. The secondmodulation appeared around 460 ms as a modulation of the late partof the P3, most likely reflecting the P3b component. These N2–eP3 andP3b components were analyzed at midline electrodes Fz, Cz and Pz astheir most prominent sites. Peak amplitudes and latencies wereanalyzed in a 280–380 ms time window for the N2–eP3 as the mostnegative peak and in a 410–510 ms time window for the P3b as themost positive peak. We computed a 2×2×3 repeated measureANOVA with Flanker (congruent, incongruent), Simon (congruent,incongruent) and electrode (Fz, Cz, Pz) as within-subject factors forpeak latencies and peak amplitudes. To control for violations ofsphericity Greenhouse–Geisser (GG) epsilon correction was appliedin order to adjust degrees of freedom and significance levels ifnecessary. Post hoc test were performed as least significant differencet-test with a significance level of pb0.05.

We also applied the same quintile analysis to the ERP data as forthe fMRI data. Specifically, we scored peak amplitudes for the N2–eP3and the P3b component separately for trials with fast (the twoquintiles with the fastest responses) and slow responses (the twoquintiles with the slowest responses). Peak amplitudes weresubjected to 3×2×2×2 repeated measure ANOVA with electrode

(Fz, Cz, Pz), Flanker (congruent, incongruent), Simon (congruent,incongruent) and responses (fast, slow) as within-subject factors forpeak latencies and peak amplitudes.

Source localization

We used the Brain Electrical Source Analysis software (BESA®,Version 5.1.8.10, MEGIS Software GmbH, Gräfelfing, Germany) todetermine brain areas most likely involved in the generation of scalprecorded effects on the N2–eP3 and P3b component. Main fMRI peakactivations from the analysis of general and specific activations foreach type of conflict were taken as generator locations for the sourceanalysis. With the restriction that sources should have a minimumEuclidian distance of 2 cm, thirteen regional sources were finallydefined as starting solutionwhich resembled themain fMRI activation(see Table 3). The second constraint for the starting solution wassymmetrical sources in the fusiform and precentral gyri. Symmetricalsources in fusiform gyrus were chosen because of left and right visualfield stimulation and its relation to visual selective attention (Hillyardand Anllo-Vento, 1998). Symmetrical sources in precentral gyruswere used because subjects had to give bi-manual response andpossible conflicts at the level of response preparation and execution(Melara et al., 2008; Praamstra and Oostenveld, 2003; Valle-Inclan,1996). This starting solution was subsequently applied to the grandaverage waves irrespective of the task condition [FcSc+FiSc+FcSi+FiSi] in time epochs of 280–380 ms (N2–eP3 component) and 410–510 ms (P3b component). A four-shell spherical head model wasused. For each time epoch, we eliminated any source which explainedless than 2% of total variance and the remaining source model wasexpected to explain more than 90% of scalp potential variance. Theremaining regional sources were subsequently converted to singledipoles and the orientation of the dipoles was fitted to the timeepochs of the N2–eP3 and the P3b (see Table 1 and Fig. 5a).

A five source solution of dipoles (Fig. 5a) consisting of sources inmedial frontal gyrus (MeFG), right inferior frontal cortex (IFG),superior parietal lobule (SPL), and bilaterally in the fusiform gyri (FG)was finally found to most appropriately fit the above mentionedcriteria and was applied to individual ERP waves for FcSc, FiSc, FcSiand FiSi trials in order to extract individual source waveform activityfor each source in each subject.We subsequently computed differencesource waves by subtracting the source wave for double congruenttrials from each of the incongruent trials separately ([FiSc – FcSc],[FcSi – FcSc], and [FiSi – FcSc]). These difference source waves wereaveraged across subjects and significant source activity was deter-mined by a bootstrap bias-corrected and adjusted (BCa) method(Efron and Tibshirani, 1993) to determine a 95%-confidence interval

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for each averaged source waveform. Significant activation of thesources were determined in time windows were the confidenceinterval did not include the zero line. Note that since ERP datarevealed no reaction time dependent effect on peak amplitudes of theN2–eP3 and the P3b components (see below) we only computed ageneral source analysis without separating fast and slow responses.

Results

Behavioral data

Behavioral data (see Table 2) were analyzed separately for the EEGand the fMRI experiment, using repeated measures 2×2 ANOVAs withthe factors Flanker and Simon. For RTs during fMRI testing we found asignificant main effect for the factor Flanker (F(1,23)=233.933,pb0.001) but not for the factor Simon (F(1,23)b1). RT in trials withincongruent (M=621 ms, SEM=18) compared to congruent Flankertrials (M=563ms, SEM=17)were significantly increased (see Fig. 1b).The Flanker × Simon interaction became significant (F(1,23)=4.557,pb0.05). All Flanker incongruent trials, namely FiSc and FiSi trials,resulted in significantly increased RTs compared to the correspondingFlanker congruent trials, namely FcSc and FcSi trials (all t(23)N7.178, allpb0.001) as already indicated by the main effect for the factor Flanker.But this RT increase for Flanker incongruent trials was particularlypronounced in trials where the Simon dimension was congruent,namely FiSc trials (ΔRT=67ms; t(23)=10.359,pb0.001) as comparedto the incongruent Simon dimension (ΔRT=49ms; t(23)=10.461,pb0.001). The Simon conflict effect was not modulated by the Flankerdimension (all t(23)b1.502, all pN0.147).

An ANOVA for percent error rates during the fMRI session revealedno significant main effect for the factor Simon (F(1,23)b1) nor for theFlanker × Simon interaction (F(1,23)b1), but a significant main effectfor the factor Flanker (F(1,23)=5.523, pb0.05) due to increased errorrates for incongruent (M=4.86%, SEM=0.87) compared to congruentFlanker trials (M=2.81%, SEM=0.51) (see Fig. 1c).

During EEG testing, an ANOVA revealed similar results for RTs(see Fig. 1b). We found a significant main effect for the factor Flanker(F(1,19)=150.130, pb0.001) but not for the factor Simon (F(1,19)b1).RTs in incongruent (M=587 ms, SEM=21) compared to congruentFlanker trials (M=533 ms, SEM=21) were significantly increased. TheFlanker × Simon interaction became significant (F(1,19)=18.297,pb0.001) because, again, Flanker incongruent trials resulted insignificantly increased RTs when compared to the correspondingFlanker congruent trials (all t(19)=6.214, all pb0.001), but again theFlanker effect was more pronounced during congruent trials(ΔRT=70 ms; t(19)=11.262, pb0.001) as compared to incongruentSimon trials (ΔRT=38 ms; t(19)=7.030, pb0.001). Additionally,during Flanker incongruent trials Simon congruent trials (FiSc)revealed increased RTs compared to Simon congruent trials (FiSi;t(19)=3.072, pb0.01). This effect was not present during Flankercongruent trials (t(19)=1.502, p=0.127) which might indicate a

Table 2Reaction times (RT; in ms) and percent error rates (Error; in %) for the (a) fMRI and the(b) EEG experiment for the different experimental trials, namely FcSc, FiSc, FcSi and FiSitrials. Numbers in brackets denote the standard error of the mean (SEM).

Experimental trials

FcSc FiSc FcSi FiSi

(a) fMRI experimentRT 561 (19) 627 (19) 572 (18) 621 (17)Error 2.83 (0.57) 4.58 (1.03) 3.08 (0.79) 4.75 (0.91)

(b) EEG experimentRT 527 (20) 597 (20) 538 (18) 576 (19)Error 2.92 (0.61) 4.91 (0.97) 3.33 (0.72) 2.75 (0.62)

facilitated conflict processing during Simon conflicts in the contextof Flanker incongruence.

The ANOVA for percent errors during the EEG session revealed nosignificant main effect for the factor Simon (F(1,19)b1), but the maineffect for the factor Flanker became significant (F(1,19)=150.130,pb0.001) due to increased error rates for trials with Flankerincongruence (M=3.83%, SEM=0.83) compared to congruentFlanker trials (M=3.13%, SEM=0.59). We found a significantFlanker × Simon interaction (F(1,19)=18.297, pb0.001). TheFlanker conflict was specifically pronounced during Simon congru-ent trials, that is, FiSc trials revealed significantly increased errorrates compared to FcSc (t(19)=2.397, pb0.05). There was noFlanker conflict during Simon incongruent trials (t(19)b1). Withrespect to the Simon conflict, we found increased error rates duringSimon congruent compared to incongruent trials (t(19)=2.316,pb0.05), but not during Flanker congruent trials (t(19)=1.100,p=0.255; see Fig. 1c).

An ANOVA for the time course of each type of conflict (see Fig. 1d)revealed no significant main effect for the factor quintile (F(4,92)=2.321, p=0.063) but a significant main effect for the factor conflict(F(2,46)=35.881, pb0.001) and for the quintile × conflict interaction(F(8,184)=2.867, pb0.01) for the RTs during fMRI investigation(Fig. 1d). This interactionwas explained by the fact that FiSc (all t(19)N3.607, allpb0.001)andFiSi trials (all t(19)N2.987, allpb0.007) revealedsignificant interference effects in all five time bins from fastest toslowest quintiles,whereas significant conflicts effects for FcSi trialswereonly found for thefirst three quintiles (all t(23)N2.717, all pb0.012) butnot for the fourth and fifth quintile (all t(23)b1.073, all pN0.294). Asimilar pattern was obtained in behavioral data during EEG investiga-tion. The main effect for the factor quintile (F(4,84)b1) was notsignificant, but we found a significant main effect for the factorconflict (F(2,42)=47.766, pb0.001) and for the quintile × conflictinteraction (F(8,168)=2.685, pb0.01). Again, significant interfer-ence effects for FiSc (all t(19)N6.991, all pb0.001) and FiSi trials (allt(19)N4.132, all pb0.001) were found in all five quintiles, significantconflict effects for FcSi trials, however, were only found for the firstquintile (t(19)=3.468, pb0.01) but not for the remaining quintiles(all t(19)b1.112, all pN0.279).

Taken together, the behavioral data demonstrate a significantFlanker effect, whereas the Simon conflict did not produce generalsignificant interference effects and with same slight improvements oftask performance during Simon conflict trials on the context of aFlanker conflict. However, a speed-of-response dependent analysisrevealed significant Simon conflict effects during fast but not duringslow responses indicating a decay of the Simon effect over time ofprocessing. Double conflict trials revealed an additional effect of taskinduced incongruence when compared to trials with both congruentFlanker and Simon conditions.

Imaging data

We analyzed brain activation for S–S conflict trials by thecomparison of Flanker incongruent (FiSc) against double congruenttrials (FcSc), activation for S–R conflict trials by the comparison ofSimon conflict (FcSi) against FcSc trials and activation for doubleconflict trials by the comparison of FiSi against FcSc trials. We alsoinvestigated brain activation during fast and slow responses since thebehavioral data indicated that especially the S–R conflict decays overtimewith increasing response latencies.We computed brain signals inselected ROIs for the two fastest as well as for the two slowestquintiles according to rank ordered RTs.

The analysis of the Flanker conflict trials (FiSc) compared to FcSctrials revealed stronger activations inmedial cingulate (BA 24, 32) andmedial frontal gyrus (BA 6) and in lateral superior (BA 6) and inferiorfrontal cortex (BA 45, 47), the latter extending into anterior insula inthe right hemisphere (BA13). Bilateral activations were found in

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superior (BA 7) and right inferior parietal cortex (BA 40) and rightsuperior temporal regions (BA 21, 22). Bilateral ventral occipitalactivation was located in the fusiform gyrus (BA 19) (see Table 3a andFig. 2a).

Simon conflict trials (FcSi) compared to FcSc trials revealed onlysparse activation in medial frontal gyrus (BA 9) (see Table 3b andFig. 2b). However, when reversing this contrast and comparing FcScagainst FcSi trials we found activation in left middle frontal gyrus (BA46) (see Table 3c and Fig. 2b). This was the only contrast for which wefound significant activation during double congruence of the Flanker

Table 3Peak activations for (a) FiSc, (b) FcSi and (d) FiSi trials compared to FcSc trials. (c)Shows the results for FcSc compared to FcSi trials. Functional activations arethresholded at pb0.001 and k=7.

Region BA MNI coordinates t Clustersize

(a) FiScNFcScFrontal lobe

L cingulate gyrus 24 −8, 22, 27 5.02 86L cingulate gyrus/medialfrontal gyrus

32 −8, 12, 43 6.64 1343

L superior frontal gyrus 6 −26, −14, 59 4.85 545R superior frontal gyrus 6 28, −6, 64 5.9 151L inferior frontal gyrus 47 −38, 20, 0 3.61 27R inferior frontal gyrus 45 60, 20, 8 3.51 11R inferior frontal gyrus/insula 47, 13 28, 22, 5 4.22 296R precentral gyrus 6 60, 0, 32 3.99 76

Parietal lobeL superior parietal lobule 7 −20, −58, 48 3.94 64

7 −16, −80, 48 3.79 48R superior parietal lobule 7 26, −60, 59 3.57 8R inferior parietal lobule 40 60, −18, 37 3.99 113L postcentral gyrus 1 −46, −34, 56 3.6 21

2 −62, −28, 35 3.6 16Temporal lobe

R superior temporal gyrus 22 48, −2, 3 3.52 1922 60, −18, 11 3.91 58

R superior/middletemporal gyrus

21, 22 50, −38, 0 3.67 24

Occipital lobeL fusiform gyrus 19 −32, −82, −13 4.42 30

19 −30, −68, −16 3.46 8R fusiform gyrus 19 38, −70, −19 4.55 65

SubcorticalL tectum −2, −28, −8 3.56 10R red nucleus 6, −16, −5 3.4 9

(b) FcSiNFcScR medial frontal gyrus 9 10, 44, 35 3.88 13

(c) FcScNFcSiL middle frontal gyrus 46 −44, 28, 24 3.62 27

(d) FiSiNFcScFrontal lobe

R cingulate gyrus/medialfrontal gyrus

32, 6 10, 12, 40 4.84 542

L superior frontal gyrus 6 −28, −12, 56 4.66 307R superior/middle frontal gyrus 6 30, −4, 64 5.01 95R middle frontal gyrus 10 42, 54, 13 4.15 41R inferior frontal gyrus 44 60, 6, 35 4.97 284

44 50, 8, 3 3.8 80R inferior frontal gyrus/insula 47, 13 36, 20, 0 3.58 28

Parietal lobeL inferior parietal lobule 40 −64, −32, 27 3.87 60L postcentral gyrus 1 −54,−20, 48 3.76 12

Temporal lobeL superior temporal gyrus 22 −64, −26, 11 3.47 7

Occipital lobeL inferior occipital gyrus 18 −26, −96, −3 3.62 13L fusiform gyrus 19 −32, −80, −13 3.67 10

19 −36, −68, −16 3.91 18R fusiform gyrus 18 36, −76, −16 4.04 32

SubcorticalR tectum 2, −28, −8 4.19 46

and the Simon dimension compared to the various incongruence trialswhich might point to a facilitation effect during congruent ascompared to incongruent Simon trials.

Double conflict trials (FiSi) compared to FcSc trials revealed asimilar pattern of activations as Flanker conflict trials (FiSc) in medialcingulate (BA 32), medial and superior frontal cortex (BA 6) as well asinmiddle and inferior frontal cortex (BA 10, 44, 47), with an extensionof inferior frontal activation into the right insula (BA 13). Rightinferior parietal cortex was active (BA 40) together with superiortemporal (BA 22) and inferior (BA 18) and ventral occipital regions(BA 19) (see Table 3d and Fig. 2c).

A conjunction analysis revealed no significant activation patternacross the different types of conflict trials. Distinct conflict-specificactivation was determined by an interaction analysis which resultedin activations in medial (BA 6, 8, 24, 32), superior (BA 6), middle (BA10, 46) and inferior frontal and insular cortex (BA 13, 45, 47) as well asin superior parietal cortex (BA 7) and in fusiform gyrus (BA 37) forFlanker conflict trials (FiSc). Simon conflict trials (FcSi) revealeddistinct activations in posterior cingulate (BA 31) and superior frontalcortex (BA 8), whereas double conflict trials (FiSi) revealed distinctactivations in anterior frontal (BA 10) and superior parietal cortex(BA 7) (see Table 4 and Fig. 3).

In addition to the whole brain analysis we also performed ROIanalyses in regions of significant activation and separated signals inthese regions depending on fast and slow responses (Fig. 2d). FiSc andFiSi trials showed a significant activation in SFG with a prominentactivation for fastest responses, whereas the right IFG was generallymore active during slow responses. Moreover, FcSi trials resulted inactivation of the IFG during slow responses and also showed a delayedpeak amplitude of the BOLD signal. For regions of distinct activationwe also found a differential activity for fast and slow responses.

Taken together, similar to the strong interference effects in Flankerconflict trials in the behavioral data, the Flanker (S–S) conflict wasable to induce an activation in a fronto-parietal network of brainregions, whereby the interaction analysis revealed distinct activationin ACC for this type of conflict trials. For the Simon (S–R) conflict trialswe found only sparse medial frontal activation, whereby theinteraction analysis revealed distinct activation in the posteriorcingulate cortex (PCC). Double conflict trials, finally, revealedwidespread brain activations similar to Flanker conflict trials, butwith additional specific activation in the anterior fronto-polar cortex.

ERP data

For the N2–eP3 amplitude the main factor electrode becamesignificant (F(2,38)=16.363, pb0.001) with a significantly morenegative amplitude at Fz (M=−1.14 μV, SEM=0.32) compared toCz (M=2.12 μV, SEM=0.47;pb0.001) andPz (M=1.80 μV, SEM=0.60;pb0.01). Furthermore, we found a significant main effect for the factorFlanker (F(1,19)=9.966, pb0.01) with an increased amplitude forincongruent (M=0.57 μV, SEM=0.29) compared to congruentFlanker trials (M=1.28 μV, SEM=0.37). There was no significantmain effect for the factor Simon (F(1,19)b1) and no significantinteraction between the main factors (all pN0.207). The peak latencyfor the N2–eP3 showed a significant main effect for the factorelectrode (F(2,38)=7.696, pb0.01) with a delayed latency atFz (M=340 ms, SEM=6) compared to Cz (M=321 ms, SEM=7;pb0.05) and Pz (M=305 ms, SEM=6; pb0.05). The factor Flanker(F(1,19)=1.401, p=0.251) and the factor Simon (F(1,19)b1) aswell as any interaction (all pN0.346) between these main factors didnot reach significance (see Fig. 4).

For the P3b amplitude we found a main effect for the factorelectrode (F(2,38)=21.134, pb0.001) due to an increased amplitudeat Cz (M=5.56 μV, SEM=0.67; pb0.001) and Pz (M=4.91 μV,SEM=.067, p=0.001) compared to Fz (M=0.80 μV, SEM=0.38).Themain effect for the factor Simon (F(1,19)=5.739, pb0.05) became

Fig. 2. Functional activations for (a) Flanker conflict (FiSc), (b) Simon conflict (FcSi) trials and (c) double conflict (FiSi) trials compared to FcSc trials revealed similar regions ofactivation such as in medial frontal gyrus (MeFG). For FiSc and FiSi trials we also found activation in bilateral superior frontal gyrus (SFG) and FiSc trials showed additional activationin superior parietal lobule (SPL), inferior frontal gyrus (IFG) and in ventral extrastriate cortical regions such as the fusiform gyrus (FG). The comparison of FcSc compared to FcSi trials(b, lower panel; d, top right) revealed activation in left middle frontal gyrus (MFG) indicating a facilitation effect for Simon congruent compared to Simon incongruent trials.(d) BOLD signal time courses and contrast estimates for regions showing functional activation during conflict trials. Contrast estimates are plotted separately for trials with fastresponses (dark color bars; the two fastest quintiles of RTs) and slow responses (light color bars; the two slowest quintiles of RTs); error bars denote the standard error of the mean(SEM). Signal in the SFG, for example, showed prominent activation for fastest responses in FiSc and FiSi trials, whereas the right IFG was mainly active during slow responses.Especially, FcSi trials revealed activation in IFG during slow responses and also showed a delayed peak amplitude of the BOLD signal. Functional contrasts are rendered on the humanColin atlas implemented in the CARET software (Van Essen et al., 2001). Coordinates refer to MNI space and error bars indicate the standard error of the mean (SEM).

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significant with a significantly higher amplitude in incongruent(M=3.98 μV, SEM=0.46) compared to congruent Simon trials(M=3.54 μV, SEM=0.42). Furthermore, the electrode × Simoninteraction became significant (F(2,38)=3.255, pb0.05) becauseincongruent compared to congruent Simon trials showed increasedamplitude especially at Cz (ΔM=0.64 μV; t19=2.525, pb0.05) andPz (ΔM=0.67 μV; t19=2.288, pb0.05). No other interaction betweenmain factors reached significance (all pN0.574). For theP3b peak latencywe found a significant main effect for the factor electrode (F(1.5,28.3)=6.140, pb0.05) with a significantly delayed latency at Pz (M=475 ms,SEM=5) compared to Cz (M=452 ms, SEM=5; pb0.05). The maineffect for the factor Flanker did not reach significance (F(1,19)=3.923,p=0.062), but the main effect for the factor Simon (F(1,19)=18.849,pb0.001) was significant because incongruent Simon trials(M=465 ms, SEM=3) revealed a delayed latency compared tocongruent Simon trials (M=454ms, SEM=4). No interaction betweenmain factors reached significance level (all pN0.410) (see Fig. 4).

For both the N2–eP3 and the P3b component we additionallyanalyzed peak amplitudes separately for fast and slow responses(see Fig. 4b). For the N2–eP3 component we found a significant mainaffect for the factor electrode (F(2, 38)=30.265, pb0.001) and Flanker(F(1,19)=4.669, pb0.05), but not for the factors Simon (F(1,19)b1)and response (F(1,19)b1). The missing main effect for responseindicates that fast and slow responses did not differentially affect theN2–eP3 component. This finding was also corroborated by the fact that

the factor responsedidnot interactwith the Flanker conflict (F(1,19)b1)and Simon conflict (F(1,19)b1). Similar results were found for theP3b peak amplitude. Only the factor electrode became significant(F(2, 38)=22.658, pb0.001), but all other main effects (all F(1,19)b1)and interactions (all F(1, 19)b2.511,pN0.095)didnot reachsignificance.

We additionally analyzed ERP time courses at electrode Oz since thefixed stimulus offset at 300 ms might have affected ERP components inthe P3b timewindow (Busch et al., 2004). Stimulus offset effects shouldappear at Oz located over visual cortex and might spread to adjacentscalp regions due to volume conductance effects. The analysis of the ERPtime courses at Oz, however, provided no evidence that the P3bcomponent at dorsal brain regions was affected by stimulus offset(see supplementary material S2 and supplementary Fig. S1).

Taken together, ERP data demonstrate a sequential modulation fordifferent types of conflict trials. Whereas the N2–eP3 was specificallymodulated by Flanker conflict, the P3b was particularly sensitive to theSimon conflict. Double conflict trials including Flanker as well as Simonincongruence modulated both components in a sequential order.

Source localization

We applied a source model including sources in the MeFG, rightIFG, SPL, and bilateral FG to the ERP data to find significant sourceactivity within the time windows of the early time epoch of the N2–eP3 and the later time epoch of the P3b. Simon conflict (FcSi) trials

Table 4Specific peak activations for (a) FiSc, (b) FcSi and (c) FiSi trials as revealed by aninteraction analysis (pb0.001, k=7).

Region BA MNI coordinates t Clustersize

(a) Flanker conflict: FiScL cingulate gyrus 24 −10, 24, 27 5.27 62L medial frontal gyrus 6, 8 −8, 12, 43 5.40 585L medial frontal gyrus,cingulate gyrus

6, 24 −12, −22, 43 4.14 36

R cingulate gyrus 32 12, 16, 32 4.64 122R medial frontal gyrus 6 18, −16, 56 4.37 48L superior frontal gyrus 6 −22, −4, 53 3.44 11R superior frontal gyrus 6 18, 0, 59 3.46 7L middle frontal gyrus 10 −26, 44, 27 3.46 8R middle frontal gyrus 46 28, 40, 11 3.96 16L inferior frontal gyrus 45 −44, 28, 21 3.93 110R inferior frontal gyrus 45 60, 20, 3 4.01 10R inferior frontal gyrus, insula 47, 13 28, 20, 5 3.62 38L insula 13 −38, 18, 0 3.55 8

13 −20, −14, 13 3.47 14L precentral gyrus 6 −52, 2, −16 3.78 32

4 −32,−8, 37 3.96 33L superior parietal lobule 7 −22, −60, 37 3.88 25R superior parietal lobule 7 18, −82, 37 4.32 38R fusiform gyrus 37 46, −40, −16 3.65 12

(b) Simon conflict: FcSiR cingulate gyrus 31 4, −46, 29 3.38 29R superior frontal gyrus 8 18, 36, 51 3.29 11

(c) Flanker+Simon conflict: FiSiR middle frontal gyrus 10 40, 54, 19 3.83 16L superior parietal lobule 7 −22, −53, 64 3.48 7

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only revealed late activity in SPL and left FG during the P3 epoch. ForFlanker conflict (FiSc) trials we found early activity in right IFG andSPL, late activity in left FG and sustained early and late activity in rightFG. Therefore, early source activity in IFG was specific for Flankerconflict trials, whereas late SPL activity was specific for Simon conflict

Fig. 3. An interaction analysis for each type of conflict trials revealed distinct activation (a) fosuperior parietal lobule (SPL; see Fig. 2 for response profiles), but a unique activation wassuperior frontal gyrus (SFG) and especially in posterior cingulate cortex (PCC). (c) FiSi trialdistinct activation in PCC for fast responses in FcSi trials revealed a similar level of activityslowest responses.

trials. Similarly to FiSc trials, double conflict (FiSi) trials elicitedcomparable early source activity bilateral in FG, in right IFG and SPLand similar to FcSi trials showed late SPL source activity. Moreinterestingly, FiSi trials were the only ones wherewe found significantsource activity in MeFG which became active during the period of theN2–eP3 (see Fig. 5b and Table 5).

Discussion

In thepresentexperimentwe tried to investigate the temporo-spatialdynamics of brain regions which are involved in S–S and S–R conflictprocessing, both in single S–S or S–R conflict trials and in task conditionswhere S–S and S–R conflicts were elicited simultaneously. We used acombinationof amodifiedEriksen–Flanker task to elicit S–S conflicts andaSimon task to evokespatial S–Rconflicts.Wewere interested ingeneralprocesses underlying these different types of successful conflictprocessing and, therefore, we only analyzed correct trials. Thus thepresent experimental design does not allow for a detailed analysis ofdifferent temporal stages of conflict resolution.We found strong Flankerinduced S–S conflict effects on a behavioral level, but the Simon-likespatial S–R conflict only elicited significant interference effects duringfast responses and decayed over timewith increasing response latencies(De Jong et al., 1994; Hommel, 1997) as indicated by a RT quintileanalysis. Thus, our data do not support the most recent report ofincreasing S–R conflict effects with increasing response latencies(Treccani et al., 2009) but are in line with the temporal overlap modelas suggested by Hommel (1997). The temporal overlapmodel proposesa temporal decay of the task-irrelevant stimulus location code and,therefore, a decay of its modulatory effect on task performance.

The behavioral data demonstrate qualitative differences betweenS–S and S–R conflict processing as indicated by the different temporalconflict characteristics. Similarly, though the different types of conflicttrials separately activated similar regions such as in the dorsal medialfrontal cortex, we mainly identified distinct brain regions which onlyresponded to the characteristics of a certain conflict type. With

r FiSc trials in several regions which were already active for themain contrasts such as infound in anterior cingulate cortex (ACC). (b) FcSi trials revealed specific activation ins specifically activated the SPL and anterior MFG (all pb0.001, k=7). Interestingly, thecompared to fast responses in FiSc trials but showed a significant deactivation during

Fig. 4. Grand average ERP data. The peak amplitude of the N2–eP3 was determined between 280 and 380 ms and the P3b peak amplitude was determined between 410 and 510 msas indicted by the grey vertical bars. (a) We found a differential modulation of the N2–eP3 and the P3b depending on Flanker or Simon conflicts. The conflict specific ERP waves (leftpanel) and the ERP differences waves (right panel; ERP wave for the double congruent condition was subtracted from ERP waves for conflict trials) show a significantly increasednegativity of the N2–eP3 for trials including a Flanker conflict (FiSc, FiSi), whereas the fronto-central P3b amplitude was significantly increased for trials including a Simon conflict(FcSi, FiSi). Double conflict trials (FiSi), therefore, showed both a modulation of the N2–eP3 and the P3b, but without any interaction effects on these components. (b) Similar to thebehavioral and fMRI data we analyzed N2–eP3 (left panel) and P3b (right panel) peak amplitudes separately for fast (dark color bars) and slow (light color bars) responses atelectrodes Fz (upper panel), Cz (middle panel) and Pz (lower panel). Though there were trends for different peak amplitudes for fast and slow responses for different types ofconflicts, none of these differences reached significance indicating no differential modulation of ERP components by fast and slow responses. Error bars indicate the standard error ofthe mean (SEM). (c) A broad fronto-parietal scalp potential distribution was found for the N2–eP3 during Flanker conflict trials (FiSc, FiSi) compared to trials without any conflict(FcSc) (upper row). A more centro-parietal scalp distribution was found for P3b during trials containing Simon conflicts (FcSi, FiSi) compared to double congruent trials (FcSc)(lower row).

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respect to common activation across conflict types, overlappingactivations in medial frontal cortex for common brain regions havealso been described during multiple conflict trials in previous studies(Bush et al., 2003; Wittfoth et al., 2009) and during different conflicttasks (Liu et al., 2004; Mansouri et al., 2009; Nee et al., 2007; Petersonet al., 2002; Wager et al., 2005). The medial wall of the frontal cortexhas been found to be involved in action monitoring (Ridderinkhof etal., 2004) and in conflict processing (Wittfoth et al., 2006). However,we have to note that the conjunction analysis revealed no commonactivations across all type of conflict trials. Though all conflict trialsrevealed similar activation inmedial frontal cortex, this activationwasapparently less consistent across conflicts trials to reveal significantcommon activation in a conjunction analysis. Therefore, the presentdata provide little evidence about common brain regions which areinvolved all types of conflict processing.

Besides theseputative commonactivations, S–S and S–Rconflict trialsrevealed distinct patterns of functional activations indicating differentconflict processing mechanisms. For S–S conflict trials we found a signalincrease in the right inferior frontal cortex which was supposed to beinvolved in response inhibition (Aronet al., 2004) and in superior parietalcortex (Egner et al., 2007; Liu et al., 2004) which is thought to regulatevisual information flow by biasing the processing of the task-relevantstimulus feature (Egner and Hirsch, 2005a). More specifically, besidesother regions we found a very unique distinct activity in dorsal ACCfor S–S conflict trials as revealed by an interaction analysis is supposedto be responsible for both general aspects of conflict control andresponse selection (Egner and Hirsch, 2005b; Liu et al., 2004; Mansouriet al., 2009;VanVeenandCarter, 2002; vanVeenet al., 2001) but also forstimulus-identity based S–S conflicts (Markela-Lerenc et al., 2004;Melcher and Gruber, 2009; Nee et al., 2007; van Veen and Carter, 2005).

Fig. 5. (a) The final source model consisted of five dipole sources located in MeFG, SPL, right IFG and in left and right FG (see Table 1, for MNI and Talairach coordinates). (b) Grandaverage source wave activity (nAm) for sources was computed as the difference source activity for FiSc, FcSi and FiSi trials subtracted by source activity for FcSc trials. The light greyvertical bars depict the time windows where we fitted the orientation of source dipoles for the N2–eP3 (280–380 ms) and the P3b component (410–510 ms). The dark grey squaresindicate the timewindows for which we found a significant source activity in the time range of the N2–eP3 and the P3b (see Table 5). Simon conflict (FcSi) trials revealed late activityin SPL and left FG during the P3 epoch. Flanker conflict (FiSc) trials revealed early activity in right IFG and SPL, late activity in left FG and sustained early and late activity in right FG.More specifically, early source activity in IFGwas specific for FiSc trials (see arrow, left panel), whereas late SPL activity was specific for FcSi trials (see arrow, middle panel). Similarlyto FiSc trials, double conflict (FiSi) trials elicited comparable early source activity bilaterally in FG, in right IFG and SPL and similar to FcSi trials showed late SPL source activity. For FiSitrials we uniquely found significant source activity in MeFG which became active during the period of the N2–eP3 (see arrow, right panel).

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Compared to S–S conflict trials, S–R conflict trials revealed onlysparse functional activations. Functional activation during S–R conflicttrials were found in anterior medial frontal cortex as well as distinctactivations in superior frontal cortex, which have been found duringS–R conflict trials in former studies (Wager et al., 2005;Wittfoth et al.,2009), and especially in the posterior part of the cingulate cortex.Besides other functions, the posterior cingulate cortex (PCC) isinvolved in spatial orienting (Vogt et al., 1992) or spatial targetdetection (Mesulam et al., 2001). Both processes were crucial for theprocessing of spatial S–R conflicts in the present study. Two furtherfindings were of interest in relation to functional activations during S–R conflict trials. First, distinct decreased activation in PCC during slowresponses on S–S conflict trials might indicate a reduced allocation ofattentional resources to the spatial information during these trials.Second, in comparison to S–R conflict trials we found activation in leftmedial frontal cortex during S–R non-conflict trials. A congruencybetween stimulus location and response side might directly facilitatethe transformation of a spatial stimulus location code into theresponse code which is supposed to be held active in middle frontalcortex (Melara and Algom, 2003; Melara et al., 2008).

The sparse functional activations which we found for S–R conflicttrials were comparable to the small interference effectswhichwe foundon the behavioral level. Furthermore, on the behavioral level S–R

Table 5Time windows [in ms] of significant activity for five dipole sources where we foundsignificantly increased source activity during conflict trials (FiSc, FcSi, FiSi) compared toFcSc trials.

FiSc FcSi FiSi

MeFG 294–347riIFG 329–349 305–368SPL 300–421 441–474 301–336

549–558leFG 350–400 441–507 327–401

480–598 460–560riFG 305–476 321–390

503–529 417–443

conflicts only occurred during fast response. Since in our analysis weaveraged functional activations across fast and slow responses, thisprocedure might have lead to an underestimation of functionalactivations for S–R conflict trials. However, an additional analysis ofthe functional data with reaction times as covariate did not support thishypothesis. There was a decreased level and extent of functionalactivations for S–R conflict trials (see Supplement S2) suggesting thatour main analysis provides a reliable estimation of S–R conflictactivation.

Functional activation during double conflict trials resulted inwidespread activations similar to single S–S and S–R conflict trials butalso with distinct and additional activations. Besides activations inmedial and superior frontal cortex double conflict trials revealed adistinct activation in fronto-polar and superior parietal cortex. Thesuperior parietal cortex might have a more general role in regulatingvisual information flow (Egner and Hirsch, 2005a), which might beespecially necessary during double conflict processing. Regions in theanterior fronto-polar cortex have been assumed to become activeduring tasks where multiple processing streams have to be regulatedat the same time by branching cognitive processing into subdivisions(Koechlin et al., 2003; Petrides, 2005). In particular, the regulation ofdifferent and competing information streams will become necessaryduring the simultaneous processing of S–S and S–R conflicts. Our dataindicate that different kinds of conflict processing streams areregulated by a meta-unit in the fronto-polar cortex. The existence ofseparate conflict processingmechanisms for each type of conflict doesnot provide evidence for a common brain mechanism involved inevery type of conflict. These different streams during the processing ofS–S and S–R conflicts are also corroborated by distinct activationpatterns for S–S conflict trials in ACC and for S–R-conflict trials in PCCas mentioned above, and further support the notion of differentconflict processing strategies for these different types of conflicts(Egner et al., 2007; Mansouri et al., 2009; Nee et al., 2007; Petersonet al., 2002; Wager et al., 2005).

Additionally to these different brain regions involved in processingof S–S and S–R conflicts, we found a RT depend dissociation in regionswhich were active during different kinds of conflict trials. Superior

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frontal cortex was more active for fast responses during S–S anddouble conflict trials, whereas inferior frontal cortex was moreengaged in conflict processing during slow responses across alltypes of conflict trials. Superior frontal activity has been reportedduring S–S (Nee et al., 2007; Peterson et al., 2002) or double conflicttrials (Bush et al., 2003;Wittfoth et al., 2009), andmight be associatedwith generalmonitoring andmaintenance processes especially duringfast responses (Mansouri et al., 2009). Inferior frontal cortex is ofteninvolved in response inhibition (Aron et al., 2004). Increased effort forinhibiting a false response should then be indicated both by anincreased activity in the inferior frontal cortex as well as by slowedresponses. This association is corroborated by the present data.

Altogether, functional imaging data revealed rather distinct thancommon brain activations during the processing of S–S and S–Rconflicts. However, we have to note that our analysis of the presentfMRI data only allows general inferences on the brain mechanismsunderlying S–S and S–R conflict processing but do not separate differentstages of conflict resolution. Cognitive conflict processing is assumed toconsist of different subcomponents (Egner, 2008). At least, conflictprocessing subsumes two major components labeled as conflictdetection and conflict resolution and differentmedial and lateral frontalareas seem to be differentially involved in these control processes. Theinvestigation of trial succession effects (Egner et al., 2007; Etkin et al.,2006; Wittfoth et al., 2009) has allowed to separate both componentssuch that conflict trial following non-conflict trials involve a higherdegree of conflict detection whereas conflict trials following conflicttrials involve ahigher degree of conflict resolution since in the latter casethe cognitive system is already tuned to conflict control by theprecedingtrial.Wedidnot analyze trial succession effects in the present study and,therefore, we cannot disentangle conflict detection from conflictresolution processes with the present data.

However, our functional imaging data generally revealed ratherdistinct than commonbrain activationsduring theprocessingof S–S andS–R conflicts. Similarly, the analysis of EEG data revealed a differentialmodulation of ERP components by the different types of conflicts.Whilewe found increased amplitudes of the early N2–eP3 component duringS–S conflict trials, S–R conflicts revealed increased amplitudes and adelayed peak latency of the later P3b component located over centro-parietal scalp regions. Accordingly, the simultaneous occurrence of S–Sand S–R conflicts during double conflict trials resulted in a consecutivemodulation of both the early N2–eP3 and the late P3b component. Sincesingle S–S conflicts modulated the N2–eP3 and single S–R conflictsmodulated the P3bwehypothesize that during double conflict trials S–Sand S–R conflicts are processed sequentially. This is in line with thetemporal overlap model which proposes a partial temporal separationbetween the processing of different types of conflicts (Hommel, 1997).But we also have to note that though S–S and S–R trials showedsequential effects at the level of the measured ERP components, thismight not necessarily indicate a sequential order of the underlyingconflict processes. These conflict processes might have occurred atsimilar processing levels but were only reflected in modulations ofdifferent scalp recorded components. However, the combined modu-lation of both the early N2–eP3 and the late P3b component duringdouble conflict trials resembles some additive effect of those modula-tions for single conflict trials, namely theN2–eP3modulationduring S–Sconflict trials and the P3b modulation during S–R conflict trials. If theunderlying processes for both types of conflicts would have occurredduring similar levels of processing this should have resulted in strongerinteractive effects at the level of the measured ERP components.

Therefore, S–S and S–R conflict processing might be reflectedduring different time windows of stimulus processing with differentunderlying conflict processing mechanisms. The N2–eP3 componentwas mainly affected by the S–S conflict and might reflect responseinhibition (van Boxtel et al., 2001) on the level of responserepresentations prior to response selection (Bartholow et al., 2005;Van Veen and Carter, 2002) as characteristic for the N2 component.

The P3b component was mainly affected by the S–R conflict and hasbeen related to short-term memory updating (Donchin and Coles,1988) but might also figure as a central nexus between stimulusidentification and response selection (Verleger et al., 2005). Thespatial S–R conflict was provoked by a mismatch of stimulus locationand response site and was associated with a modulation of the P3bcomponent. This mismatch induced conflict might more stronglyrequire a continuous updating of spatial stimulus–response mappingswhich might be held active in short-term memory for a longer periodof time (Melara and Algom, 2003; Melara et al., 2008).

Both fMRI and ERP data, therefore, demonstrate that the proces-sing of S–S and S–R conflicts partly shares common but more stronglyrelies on distinct functional brain networks operating in temporallyseparated levels of information processing. This data was alsoconfirmed by the source localization approach for the N2–eP3 andthe P3b component. Common source activity for S–S and S–R conflictprocessing was found in inferior temporal cortex in the fusiform gyruswhichmight indicate deeper stimulus processing during both types ofconflicts. Apart from this common source activity, S–S conflictsrevealed distinct source activity in inferior frontal cortex for the timeperiod of the N2–eP3 which is indicative of response inhibitoryprocesses with respect to both the inferior frontal cortex (Aron et al.,2004; Nee et al., 2007) and the N2 component (van Boxtel et al.,2001). Superior parietal source activity as revealed in S–R conflictsespecially in the time period of the P3b component might be involvedin spatially based stimulus–response transformations during S–Rconflict trials (Melara and Algom, 2003; Melara et al., 2008) whichinvolves stimulus location representations (Mansouri et al., 2009) aswell as the generation of motor response plans according to thislocation representations (Andersen and Buneo, 2002). Source activityduring double conflict trials partly resembled those for single conflicttrials. Both the early inferior frontal source during S–S conflicts andthe late superior parietal source during S–R conflicts were similaractive during double conflict trials indicating activity in brain regionsresponsible for the processing of both types of conflicts. But apartfrom this shared source activity for single and double conflict trials weadditionally found medial frontal cortex source activity uniquelyactive in double conflict trials. This additional source activity in MeFGprobably provides evidence for an increased action monitoringdemand during trials involving two different types of conflicts(Maclin et al., 2001; Ridderinkhof et al., 2004). This finding alsoshares functional similarities to distinct activation in anterior frontalcortex for double conflict trials in the fMRI data which we suppose toreflect the integration of two different processing mechanisms.

In conclusion, the present data revealed three main findings. First,on a behavioral level S–R conflicts seem to decay with and S–Sconflicts seem to slightly increase with increasing response latencies.Double conflicts trials share both the decay of conflict effects with S–Rconflict trials and the generally increased conflict effect with S–Sconflict trials. Second, though S–S and S–R conflicts partly share thesame brain mechanisms of conflict processing (MeFG) they morestrongly rely on distinct brain mechanisms. S–S conflicts resulted inincreased activity in ACC indicating response conflict detection at thelevel of response representations (Melara and Algom, 2003; Melaraet al., 2008). S–R conflicts resulted in an activation of PCCwhichmightrepresent an enhanced attentional processing in spatial stimulus–response mapping. Third, S–S and S–R conflicts might be processedduring different levels of information processing since S–S conflictsaffected the N2–eP3 component and S–R conflicts modulated the P3bcomponent. This was especially evident for double conflict trials forwhich we found both a modulation of the N2–eP3 and the P3bcomponent whereby the former is most likely modulated by the S–Sand the latter by the S–R conflict. Double conflict trials also sharedsource activity in inferior frontal cortex and superior parietal cortexas we found for S–S and S–R conflicts, respectively. However,double conflict trials also revealed distinct activity in anterior frontal

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cortex involved in hyper-monitoring of multiple processing streams(Petrides, 2005) and source activity inmedial frontal cortex indicatingincreased processing load (Ridderinkhof et al., 2004). The latterresults, again, indicate that the processing of S–S and S–R conflictspartly relies on different brain resources which had to be integratedand regulated during the temporal course of conflict processing.

Supplementarymaterials related to this article can be found onlineat doi:10.1016/j.neuroimage.2010.07.071.

Acknowledgment

This study was supported by the BMBF Neuroimaging programme(01GO0506) from the Center for Advanced Imaging (CAI) — Magde-burg/Bremen (MH).

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