The spatiotemporal dynamics of early attention processes: A high-resolution electroencephalographic study of N2 subcomponent sources

Neuroscience 271 (2014) 9–22

THE SPATIOTEMPORAL DYNAMICS OF EARLY ATTENTION PROCESSES: A HIGH-RESOLUTION ELECTROENCEPHALOGRAPHIC STUDY OF N2 SUBCOMPONENT SOURCES P. BOCQUILLON, a,b,c* J.-L. BOURRIEZ, b,c E. PALMERO-SOLER, d B. MOLAEE-ARDEKANI, a,b P. DERAMBURE a,b,c AND K. DUJARDIN a,b,e a

Universite´ Lille Nord de France, UDSL, F-59000 Lille, France

b

EA1046, F-59000 Lille, France

Key words: swLORETA, cognitive event-related potentials, N2, attention, cognitive control, inhibition.

c

Clinical Neurophysiology Department, Lille University Medical Center, F-59037 Lille Cedex, France

INTRODUCTION

d

Eemagine Medical Imaging Solutions GmbH, Gubener Straße 47, 10243 Berlin, Germany

Attention can be focused by relevant signals derived from task demands (i.e. target stimuli) or captured by salient properties of stimuli that are sometimes irrelevant for the task (i.e. distracter stimuli) (Kastner and Ungerleider, 2000). The attention processes were mostly investigated by late cognitive event-related potentials (ERPs), namely the P3. P3 subcomponents are thought to be generated by large fronto-parietal networks (Bledowski et al., 2004a,b; Bocquillon et al., 2011). Particularly, the dorsolateral prefrontal cortex (DLPF) is known to be involved in the generation of the P3 component, which reflects top–down processes as stimulus categorization and voluntary decision-making. The DLPF reportedly interacts with the anterior cingulate cortex (ACC) to fulfill this role (Devinsky et al., 1995; Barch et al., 2001). Although the ACC’s functional role has been widely debated, the available evidence suggests that this structure is part of an executive attention supervisory system involved in the resolution of cognitive conflicts. The latter system may serve to evaluate the demand for cognitive control by monitoring the occurrence of conflict during information processing (Posner and Petersen, 1990; Braver et al., 2001). As suggested by the conflict-monitoring theory, the ACC’s primary function may thus be to detect response conflict, whereas the DLPF cortex would implement top–down control of behavior (Barch et al., 2001), with the hypothesis that the ACC may play a role in an earlier time window. The ACC has indeed been identified as the main (but not the only one) putative generator of the N2 component of the ERP (see Table 1 for a review). N2 is the second, negative peak seen in the cognitive ERP. It usually occurs between 200 and 350 ms after a stimulus (Folstein and Van Petten, 2008) and was shown to be of value in monitoring processes such as cognitive control and inhibition (Donkers and Van Boxtel, 2004). Several N2 subcomponents can be elicited by tasks requiring focused attention on stimuli, such as go/no-go or flanker tasks: a more anterior component with a frontocentral scalp distribution (also known as N2b) and a more posterior component (also called N2c)

e Neurology and Movement Disorders Department, Lille University Medical Center, F-59037 Lille Cedex, France

Abstract—The N2 subcomponents of event-related potentials are known to reflect early attentional processes. The anterior N2 may reflect conflict monitoring, whereas the posterior N2 may be involved in target detection. The aim of this study was to identify the brain areas involved in the generation of the N2 subcomponents, in order to define the spatiotemporal dynamics of these attentional processes. We recorded 128-channel electroencephalograms in 15 healthy controls performing a three-stimulus visual oddball task and identified standard-, distracter- and target-elicited N2 components. Individual N2 sources were localized using standardized-weighted-low-resolution-electromagnetictomography (swLORETA). Comparative analyses were performed with a non-parametric permutation technique. Common N2 generators were observed in the Brodmann area (BA) 24 of the anterior cingulate cortex (ACC). The posterior cingulate cortex and the central precuneus were more involved in distracter processing, whereas the anterior precuneus and BA 32 of the ACC were target-specific. In accordance with previous demonstration of the frontoparietal cortex’s critical role in attentional processes, these new data shed light on the ACC’s role in conflict monitoring and its interaction with other median and frontoparietal structures in early attentional processes. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

*Correspondence to: P. Bocquillon, Service de Neurophysiologie Clinique, Hoˆpital Roger Salengro, F-59037 Lille Cedex, France. Tel: +33-320446461. E-mail address: [email protected] (P. Bocquillon). Abbreviations: ACC, anterior cingulate cortex; ANOVAs, analyses of variance; AP, anterior precuneus; BA, Brodmann area; CP, central precuneus; DLPF, dorsolateral prefrontal; EEG, electroencephalogram; EOG, electro-oculogram; ERN, error-related negativity; ERP, eventrelated potential; fMRI, functional magnetic resonance imaging; PCC, posterior cingulate cortex; RT, reaction time; swLORETA, standardized, weighted low-resolution electromagnetic tomography. http://dx.doi.org/10.1016/j.neuroscience.2014.04.014 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 9

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P. Bocquillon et al. / Neuroscience 271 (2014) 9–22

Table 1. Identification or estimation of generators of N2 components (‘‘no go’’ N2, error-related negativity, novel-N2 and target N2) according to previous studies. Source localization methods are shown in bold, while other activation methods are given in italic type. BESA: brain electrical source analysis; (s)LORETA: (standard) low-resolution brain electromagnetic tomography; ICA: independent component analysis; SWARM: sLORETAweighted accurate minimum norm method; er-fMRI: event-related functional magnetic resonance imaging; PET: positron emission tomography; EEGfMRI: electroencephalography-functional magnetic resonance imaging. Location

Investigational method

‘‘no-go’’ N200

Error-related negativity

Medial frontal lobe and anterior cingulate cortex

Dipole modeling BESA

Van Veen and Carter (2002), Nieuwenhuis et al. (2003), Bekker et al. (2005), Jonkman et al. (2007), Ladouceur et al. (2007)

Dehaene et al. (1994), Dehaene (1996), Badgaiyan and Posner (1998), Holroyd et al. (1998), Nieuwenhuis et al. (2003), Ladouceur et al. (2007), Van Veen and Carter (2002)

fMRI constrained BESA Moving equivalent dipole Minimum current estimates LORETA sLORETA ICA

SWARM er-fMRI

PET lesion study

Dipole modeling BESA Minimum current estimates sLORETA ICA

SWARM er-fMRI

Orbitofrontal

Prefrontal

Posterior target N200

Crottaz-Herbette and Menon (2006) Miltner et al. (2003)

Helenius et al. (2010)

Bokura et al. (2001) Kropotov and Ponomarev (2009), Kropotov et al. (2011) Huster et al. (2010) Casey et al. (1997), Rubia et al. (1999, 2001), Braver et al. (2001), Menon et al. (2001)

Anderer et al. (2003)

Carter et al. (1998), Botvinick et al. (1999), Casey et al. (2000), Kiehl et al. (2000), Menon et al. (2001), Matthews et al. (2005), Chevrier et al. (2007)

Kiehl et al. (2001), Yamaguchi et al. (2004)

Huster et al. (2010) Braver et al. (2001), Kiehl et al. (2001), Yamaguchi et al. (2004), CrottazHerbette and Menon (2006)

Kawashima et al. (1996) Swick and Turken (2002), Stemmer et al. (2004) Debener et al. (2005)

EEG/fMRI Lateral frontal lobe superior, middle, inferior

Novel N200

Anllo-Vento et al. (1998)

Helenius et al. (2010)

Kropotov and Ponomarev (2009), Kropotov et al. (2011) Huster et al. (2010) Konishi et al. (1999), Rubia et al. (1999, 2001), Menon et al. (2001), Chevrier et al. (2007), Matthews et al. (2005)

PET

Kawashima et al. (1996)

LORETA

Casey et al. (1997), Bokura et al. (2001)

Carter et al. (1998), Kiehl et al. (2000)

er-fMRI

Menon et al. (2001)

Dipole modeling BESA LORETA

Ruchsow et al. (2002)

Lavric et al. (2004)

Kirino et al. (2000), Kiehl et al. (2001), Yamaguchi et al. (2004)

Huster et al. (2010) Garavan et al. (1999), Kirino et al. (2000), Kiehl et al. (2001), Braver et al. (2001), Yamaguchi et al. (2004), Crottaz-Herbette and Menon (2006)

Anderer et al. (2003)

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P. Bocquillon et al. / Neuroscience 271 (2014) 9–22 Table 1 (continued) Location

Temporal lobe and temporooccipital junction

Parietal lobe and parietotemporal junction

Investigational method

‘‘no-go’’ N200

Error-related negativity

er-fMRI

Casey et al. (1997), Garavan et al. (1999), Rubia et al. (1999, 2001), Braver et al. (2001), Menon et al. (2001), Matthews et al. (2005), Chevrier et al. (2007)

Carter et al. (1998), Casey et al. (2000), Menon et al. (2001), Chevrier et al. (2007)

Dipole modeling BESA Equivalent current dipoles Minimum current estimates LORETA sLORETA ICA er-fMRI

Posterior target N200

Anllo-Vento et al. (1998)

Hopf et al. (2000, 2006)

Helenius et al. (2010)

Helenius et al. (2010)

Anderer et al. (2003) Kropotov et al. (2011) Garavan et al. (1999), Braver et al. (2001)

Kiehl et al. (2001), Yamaguchi et al. (2004)

Braver et al. (2001), Hopf et al. (2006), Kiehl et al. (2001), Yamaguchi et al. (2004)

Equivalent current dipoles

Hopf et al. (2000)

Minimum current estimates sLORETA ICA

Helenius et al. (2010)

er-fMRI

PET

Kropotov and Ponomarev (2009) Kawashima et al. (1996), Garavan et al. (1999), Braver et al. (2001), Menon et al. (2001), Rubia et al. (2001) Kawashima et al. (1996)

Occipital lobe

er-fMRI

Menon et al. (2001), Rubia et al. (2001)

PET

Kawashima et al. (1996)

Insula

er-fMRI

Rubia et al. (2001)

Basal ganglia

LORETA er-fMRI

Bokura et al. (2001) Rubia et al. (1999), Menon et al. (2001), Chevrier et al. (2007)

PET

Kawashima et al. (1996)

Posterior cingulate

Novel N200

er-fMRI

Menon et al. (2001)

Menon et al. (2001)

Menon et al. (2001)

(Folstein and Van Petten, 2008). The anterior N2 was the most investigated, namely in conflict monitoring studies. This component is sensitive to novelty, deviation from a

Kiehl et al. (2001), Yamaguchi et al. (2004)

Garavan et al. (1999), Braver et al. (2001), Kiehl et al. (2001), Yamaguchi et al. (2004), CrottazHerbette and Menon (2006)

Kiehl et al. (2001), Yamaguchi et al. (2004)

Braver et al. (2001), Kiehl et al. (2001)

Kiehl et al. (2001)

Kiehl et al. (2001), Yamaguchi et al. (2004)

Kiehl et al. (2001)

Garavan et al. (1999), Kiehl et al. (2001), Yamaguchi et al. (2004), CrottazHerbette and Menon (2006)

Kiehl et al. (2001)

Kiehl et al. (2001)

template stimulus, stimulus complexity and the percentage of non-target stimuli (Folstein and Van Petten, 2008). Its functional significance still remains subject to

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debate. It may reflect either (i) detection of a mismatch between the presented and expected stimuli or (ii) the monitoring of response conflict (Nieuwenhuis et al., 2003; Yeung et al., 2004). In view of these hypotheses, it was suggested that the anterior N2 should be further divided into a deviance-related N2 (or ‘‘novelty N2’’) and a control-related N2 (Folstein and Van Petten, 2008). The latter may reflect inhibition and conflict monitoring processes (Donkers and Van Boxtel, 2004). When a ‘‘no-go’’ stimulus is processed, two closely positioned subcomponents are indeed observed as a function of the type of conflict and the event on which the ERPs are locked: (i) the ‘‘no-go’’ N2, a stimulus-locked component reflecting conflict monitoring before correct responses in high-conflict trials (Van Veen and Carter, 2002), and (ii) the error-related negativity (ERN) or error negativity (Ne), a sharp, response-locked, negative deflection occurring between 50 and 150 ms after an erroneous response (Falkenstein et al., 1991; Van Veen and Carter, 2002). Although both subcomponents may be involved in conflict monitoring – and more precisely could represent an index of conflict adaptation (Eimer, 1993; Nieuwenhuis et al., 2003; Yeung et al., 2004; Larson and Clayson, 2011) – only the ‘‘no-go’’ N2 may reflect inhibition (Kok, 1986; Jodo and Kayama, 1992; Falkenstein et al., 1999; Nieuwenhuis et al., 2003; Donkers and van Boxtel, 2004), the ERN reflecting an error detection mechanism (Falkenstein et al., 1991). Although the ERN and the ‘‘no-go’’ N2 have different patterns, they are nevertheless very similar in terms of amplitudes, latencies and scalp topographies (Folstein and Van Petten, 2008) and may share common cortical mechanisms. Apart from this conflict-monitoring theory, ERN is also studied in the framework of the reinforcement-learning theory, according to which ERN occurs in case of a mismatch between a predicted and an actual response outcomes and involves changes in mesencephalic dopaminergic system activity, resulting in an activation of the ACC (Holroyd and Coles, 2002). Even if clearly identified after target stimuli, the posterior N2 is less known (Naatanen and Picton, 1986; Folstein and Van Petten, 2008). It is sensitive to task difficulty and is only observed in visual tasks (Folstein and Van Petten, 2008). It is thought to reflect classification of stimuli (Folstein and Van Petten, 2008). Nevertheless, its underlying processes, as well as its generators (Table 1), were poorly evidenced. One reason would be that usually, the paradigms used to study N2 focus at investigating the no-go trials. Target processing also entails early processes involving specific brain areas or networks that remain to be identified. The aim of the present study was to further investigate the generators of the anterior and posterior N2 subcomponents and their relationships with attentional processes, since these subcomponents clearly reflect different mechanisms of early attentional cognitive processing. Only a few studies have used distributed source methods to localize N2 generators (Bokura et al., 2001; Anderer et al., 2003; Lavric et al., 2004; Kropotov and Ponomarev, 2009; Huster et al., 2010, see Table 1 for a review) but none of them compared

the different subcomponents’ generators after recording a high-resolution electroencephalogram (EEG). We thought that standardized, weighted low-resolution electromagnetic tomography (swLORETA) (Pascual-Marqui, 2002; Palmero-Soler et al., 2007) was likely to be the most accurate method for localizing and comparing the generators of N2 components in healthy, young adult participants during a three-stimulus ‘‘oddball’’ visual paradigm (Bocquillon et al., 2011). This paradigm enables one to elicit (i) standard N2 (control/no-go), (ii) distracterrelated N2 (mismatch or deviance, both of which are supposedly anterior subcomponents) and (iii) target-related (posterior) N2. Of the various source reconstruction methods, the distributed inverse solution technique is interesting for modeling spatially distinct source activities in the absence of prior knowledge of the generators’ anatomical location. In view of current models of attention, we hypothesized that the different N2 subcomponents would be generated by both overlapping and independent areas of the ACC, in conjunction with functionally related frontal and/or parietal areas.

EXPERIMENTAL PROCEDURES Participants Fifteen right-handed, healthy, adult participants (seven females and eight males) with a mean age of 21.1 (SD: 2.15; range: 18–25) and a mean educational level of 14.6 years (SD: 1.59; range: 12–18) participated in the study. All had given their informed consent to participation in the study, which had been approved by the local independent ethics committee. A neurological examination, a visual acuity check on the Early Treatment Diabetic Retinopathy Study scale (ETDRS research group, 1991), (participants with visual impairments were excluded from the study) and a clinical interview were performed before inclusion. Participants had to confirm that they had never suffered from neurological or psychiatric disorders. None were taking psycho-active drugs. Task and recording procedure Participants were comfortably seated and watched a 17inch monitor set 150 cm away. ERPs were recorded as the subjects performed a three-stimulus visual oddball paradigm similar to that used by Bledowski et al. (2004b). A session included two different task types (a circles task with squares as distracters and a squares task with circles as distracters) of 360 stimuli each. The order of the two tasks was counterbalanced, so that half the subjects saw circles first and half saw squares first. Fig. 1 depicts a schematic representation of the tasks: the stimuli were solid blue shapes displayed in a semirandom order for 75 ms each. The interstimulus interval varied from 1800 to 2200 ms. The stimuli were defined as standard stimuli (40-mm-diameter circles or 35-mmsided squares), distracters (the other shape: 35-mmsided squares or 40-mm-diameter circles) or targets (smaller than standard stimuli: 33-mm-diameter circles

P. Bocquillon et al. / Neuroscience 271 (2014) 9–22

Fig. 1. A schematic representation of the three-stimulus visual oddball paradigm. Each session included two different types of task: a circles task with squares as distracters (on the left) and a squares task with circles as distracters (on the right)), with 360 stimuli each. The order of the two tasks was counterbalanced, so that half the participants saw circles first and half saw squares first. The stimuli were solid blue shapes displayed in a semi-random order for 75 ms each. The interstimulus interval varied from 1800 to 2200 ms. The stimuli were defined as standard stimuli (40-mm-diameter circles or 35-mm-sided squares), distracters (the other shape: 35-mm-sided squares or 40-mm-diameter circles, respectively) or targets (smaller than standard stimuli: 33-mm-diameter circles or 30-mm-sided squares) and were displayed with probabilities of 0.84, 0.08 and 0.08, respectively.

or 30-mm-sided squares) and were displayed with a probability of 0.84 (n = 3600), 0.08 (n = 60), and 0.08 (n = 60), respectively. The participant was told to respond to the target stimuli by pressing a button with his/her right hand. Before each task, all participants had a practice run in the absence of distracter stimuli. The reaction time (RT), the omission rate and the standard and distracter commission rates were recorded. The EEG was recorded from 128 scalp sites, using a DC amplifier (ANT Software BV, Enschede, the Netherlands) and a Quick-capÒ 128 AgCl electrode cap (ANT Software BV) placed according to the 10/05 international system with a linked mastoid reference (Oostenveld and Praamstra, 2001). The impedance was kept below 5 kX. An electro-oculogram (EOG) was recorded to detect artifacts related to eye movements and blinking. The EEG and EOG were digitized with a sampling rate of 512 Hz and recorded with EEProbeÒ software (ANT Software BV). These EEG data were used in a previous study investigating the generators of the P3 component of the ERP (Bocquillon et al., 2011). EEG analysis The EEG was analyzed with EEProbeÒ software. The raw data waveforms were band-pass filtered by convolving them with a finite impulse response filter and a Hamming window. The half-power cut-offs were 0.1 and 30 Hz. Any EEG epochs that contained artifacts were automatically detected, manually classified as either blinks or eye movements and then separately corrected with the EEProbeÒ software’s regression algorithm, which allows a compensation for artifact components in

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the EEG based on a principal component analysis of the EOG signals followed by a determination of the propagation of this signal to the individual EEG channels via linear regression (Nowagk and Pfeifer, 1996). Whenever the participant missed a target stimulus or responded to a distracter stimulus, the event was excluded from the EEG analysis. The waveforms (analyzed from 100 ms pre-stimulus to 900 ms post-stimulus) were averaged separately for the standard, distracter and target conditions. For each epoch, a baseline correction was performed using data from 100 ms prior to the stimulus. In the present report, we refer to standard-, distracter- and target-elicited N2 in view of the imprecise definition of the various different subcomponents (as explained in the introduction). The N2 peak was thus defined as the largest negative deflection in the standard, distracter and target stimulus waveforms within the 160– 400-ms time window. The N2 amplitude was defined as the voltage difference between the baseline and the mean amplitude within a 40-ms time window around this N2 peak. Latency was defined as the time between stimulus onset and the largest negative peak. Amplitude and latency measures were performed for the three midline electrodes (Fz, Cz and Pz). swLORETA N2 source localization In the present study, N2 source localization was performed according to the swLORETA procedure described in our previous P3 study (Bocquillon et al., 2011, Fig. 2). The swLORETA solutions were computed with ASAÒ software (ANT Software BV) for each time point within a 40-ms time window around the N2 peak (the ‘‘peak window’’) in each condition. We then calculated the mean value of the swLORETA analysis for all time points. The same calculation was performed within a 40-ms time window during the baseline period ( 70 to 30 ms, the ‘‘baseline window’’). The swLORETA solutions were computed using a three-dimensional grid of points (or voxels) representing the signal’s possible sources. Furthermore, solutions were restricted to the gray matter by selecting only voxels in which the gray matter probability was not equal to zero (based on the probabilistic brain tissue maps available from the Montreal Neurological Institute (Evans and Collins, 1993; Collins et al., 1994; Mazziotta et al., 1995)). Lastly, the 1056 grid points (with a 5-mm grid spacing) and the recording array (128 electrodes) were registered against the Collins 27 MRI map (Evans and Collins, 1993). The boundary model was used to compute the lead field matrix and thus solve the inverse problem (Geselowitz, 1967). Statistical analysis Amplitude and latency data. Two-factor, repeatedmeasures analyses of variance (ANOVAs) were performed, with the stimulus type (standard, distracter or target) and location (Fz, Cz and Pz) as within-group factors. A Greenhouse–Geisser correction was applied when the assumption of sphericity was not met. When

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P. Bocquillon et al. / Neuroscience 271 (2014) 9–22

Fig. 2. Grand average ERP waveforms from three midline electrodes (Fz, Cz, Pz) for standard stimuli (thin black line), distracter stimuli (gray line) and target stimuli (thick black line), with identification of standard-, distracter- and target-elicited N2 components.

necessary, post hoc analyses with paired-t tests were performed. The threshold for statistical significance was set to p < 0.05. Source localization data. Permutation method. One-tailed t-tests were performed for each subject for each voxel of the source space (i.e. 1056 t-tests in total). Given that (i) the central limit theorem cannot rule out an effect of non-normality and (ii) it is difficult to prove that the modulus of the swLORETA solution follows a normal distribution (especially in experiments where there are relatively few degrees of freedom), it is necessary to use a statistical method that does not rely on an assumption of normality. Moreover, since we are performing 1056 simultaneous t-tests, we need to control for the false positives that may result from performing multiple tests. The non-parametric permutation method (Nichols and Holmes, 2002) provides just such a framework and has been implemented by several authors in functional neuroimaging studies (Arndt et al., 1996; Holmes et al., 1996; Brammer et al., 1997) and swLORETA analyses (Cebolla et al., 2011). In contrast to parametric approaches (in which the statistic must have a known, null distributional form), the permutation approach uses the data itself to generate the probability distribution for testing the null hypothesis (for a highly detailed procedure and rationale, see Cebolla et al., 2011). Localization of N2 generators. To locate standard-, distracter- and target-elicited N2 generators, we created difference images by subtracting the modulus of the mean swLORETA solution in the baseline window from that in the peak window, for each condition. We then used this difference image to compute a T-image (T value per voxel) by performing a one-tailed, paired t-test for each voxel of the source space, with the null hypothesis being that the distribution of the voxel values from participants’ difference images has a zero mean. However, instead of assuming a normal distribution when assessing the statistical significance of the T score at each voxel, we used the permutation method (Nichols and Holmes, 2002). The threshold for statistical significance was set to p < 0.001.

Identification of specific distracter- and target-N2 generators. To identify the specific generators of N2 elicited by distracter and target stimulus, we performed paired ttests with several contrasts; (i) distracter versus standard contrast to assess specific generators of deviance-N2; (ii) target versus standard contrast to highlight specific generators of the target-N2. The significance threshold was set to p < 0.01 for these paired t-tests. The generators’ coordinates and anatomical labels. The coordinates (x, y, z Talairach coordinates) were obtained by placing the corresponding Talairach markers in the Collins brain using ASAÒ software (Lancaster et al., 1997, 2000). The coordinates correspond to the voxels that have a local maximum for t-values. To define a voxel as a local maximum, its t-value was automatically compared with the t-value exhibited by its 16 nearest neighbors. If any of the neighbors had a higher t-value than the voxel under comparison, the latter was not considered as a local maximum.

RESULTS Behavioral results The mean RT was 581 ± 92 ms. The omission rate was 9.3% and the commission rate was 0.7% for the standard stimulus and 0.3% for the distracter stimulus. N2 amplitude Table 2 shows the mean (SD) N2 amplitudes in Fz, Cz and Pz for standard, distracter and target stimuli. Grand averages of ERP waveforms for each stimulus type on Fz, Cz and Pz are shown in Fig. 2. The ANOVA revealed a significant main effect of stimulus (F(2,56) = 15.282, p < 0.001) and a significant ‘‘stimulus x location’’ interaction (F(4,56) = 13.322, p < 0.001). Further analysis revealed a significantly more negative N2 amplitude for target stimuli than for standard (t(14) = 4.590, p < 0.001) and distracter stimuli (t(14) = 6.563, p < 0.001) and a significantly more negative amplitude for standard stimuli than distracter stimuli (t(14) = 4.666, p = 0.001). For target stimuli, the N2 amplitude did not

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P. Bocquillon et al. / Neuroscience 271 (2014) 9–22 Table 2. Standard, distracter and target elicited-N2 amplitudes and latencies (mean ± standard deviation). Standard-elicited N200 Fz Amplitude (lV) Latency (ms)

0.99 ± 1.45 266 ± 51

Cz

Distracter-elicited N200 Pz

Fz

Cz

Target-elicited N200 Pz

0.25 ± 1.41 0.46 ± 1.44 0.17 ± 3 1.13 ± 3.08 1.89 ± 2.82 265 ± 49 262 ± 45 269 ± 53 269 ± 51 268 ± 48

vary from one location to another (F(2,28) = 2.0.77, p = 0.153). For standard stimuli, there was a significant effect of location (F(2,28) = 9.001, p = 0.001). The standard N2 was significantly more negative at Fz and Cz than at Pz (t(14)=4.34, p = 0.001 and t(14)= 2.276, p = 0.039, respectively). The amplitudes at Fz and Cz did not differ significantly (t(14)= 1.966, p = 0.069). For distracter stimuli, there was a significant effect of location (F(2,28) = 4.954, p = 0.017). The distracter N2 amplitude was significantly more negative at Fz than at Pz (t(14) = 2778, p = 0.015). The amplitudes did not differ significantly between Fz and Cz (t(14) = 1.881, p = 0.081) as well as between Cz and Pz (t(14) = 1.503, p = 0.155). N2 Latency Mean (SD) latencies in Fz, Cz and Pz are displayed in Table 2. The ANOVA did not reveal any significant main effect of stimulus (F(2,56) = 0.305, p = 0.666), of location (F(2,56) = 0.685, p = 0.449), nor ‘‘stimulus x location’’ interaction (F(4,56) = 0.410, p = 0.679). Localization of N2 Cortical Generators with the swLORETA method Fig. 3 depicts the swLORETA t-test maps and Tables 3–5 summarize the Talairach coordinates of the cortical areas involved and the corresponding t-scores. Identification of the generators of the standard-elicited N2 component. The standard-stimulus-elicited N2 component was mostly generated by a large, frontoparietal network within the bilateral middle, left superior and inferior frontal gyri, the bilateral superior and left inferior parietal lobes, the anterior precuneus (AP), the middle precuneus and the right precentral and postcentral gyri. As shown in Table 3 and Fig. 3A, generators were also found in the ACC (BA 24), posterior cingulate cortex (PCC, BA 30), bilateral insula, thalamus and the occipital and temporal lobes. Identification of the generators of the distracter-elicited N2 component. Distracter-elicited N2 component generators were also found in a frontoparietal network including the bilateral middle and left superior frontal gyri, the right inferior and left superior parietal lobes, the posterior parietal precuneus and the left precentral and postcentral gyri. Medial areas were also involved, including the left internal frontal lobe, the PCC (BAs 23 and 31), the ACC and midcingulate (BA 24), along with the basal ganglia (mostly the thalami and the caudate). As shown in Table 4 and Fig. 3B, generators were also

Fz

Cz

1.01 ± 2.01 273 ± 51

Pz

1.54 ± 1.99 270 ± 49

1.96 ± 1.51 266 ± 47

found in the occipital and temporal lobes and the right insula. Identification of the generators of the target-elicited N2 component. The target-elicited N2 component was found to have sources in the bilateral middle and the right superior frontal gyri and in the bilateral inferior parietal lobules. Generators were also observed in the right precentral and left postcentral gyri, the left ACC and midcingulate (BA 24), the left AP and cuneus and the left temporal and occipital lobes. As shown in Table 5 and Fig. 3C, generators were also found in the right insula and the basal ganglia (mostly the thalami and caudate). Identification of ‘‘specific’’ distracter and target N2 generators. As can be seen in the superimposed t-test maps for standard and distracter (Fig. 3D), standard and target (Fig. 3E) and distracter and target signals (Fig. 3F), the three subcomponents share some generators but some areas only appear to be generators for distracter- or target-elicited N2. Paired ttests comparing target generators and distracter generators with standard N2 generators enabled us to identify areas that are more specific for distracter and target N2 generation, respectively. Fig. 4 shows the swLORETA t-test maps and Table 6 summarizes the Talairach coordinates of the cortical areas involved and the corresponding t-scores. Specific target-elicited N2 generators were found in the ACC (BA32) the left medial lobes and the bilateral middle and frontal lobes (BAs 6–8–9–10). Specific distracter N2 generators were found in bilateral superior lobes and the left medial frontal lobes (BA 9–10).

DISCUSSION The main objective of the present study was to use swLORETA to identify areas involved in the generation of the different N2 subcomponents during a threestimulus oddball task. Our results showed that although the three N2 subcomponents share a common overall network, there are some differences between the locations of the subcomponents’ respective generators. The amplitude analysis confirmed the frontal topography of the standard and distracter-elicited N2 subcomponents. The topography of the target-elicited N2 was more diffuse but overall centroparietal. There were no latency differences between the subcomponents. Common sources of N2 subcomponents As expected, generators were found for all components in the ACC, but also the midcingulate (BA 24), which

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Fig. 3. Localization of N2 generators: (a) Statistical maps (from non-parametric t-tests) of the gray matter current density for the standard-elicited N2 components (swLORETA method, p < 0.001). (b) Statistical maps (from non-parametric t-tests) of the gray matter current density for the distracter-elicited N2 components (swLORETA method, p < 0.001). (c) Statistical maps (from non-parametric t-tests) of the gray matter current density for the target-elicited N2 components (swLORETA method, p < 0.001). (d) Distracter (in blue) and standard (in green)-elicited N2 components are superimposed and areas of overlap (i.e. cortical regions showing significant generators for both stimuli) are displayed in blue-green. (e) Target (in red) and standard (in green)-elicited N2 components are superimposed and areas of overlap (i.e. cortical regions showing significant generators for both stimuli) are displayed in yellow. (f) Distracter (in blue) and target (in red)-elicited N2 components are superimposed and areas of overlap (i.e. cortical regions showing significant generators for both stimuli) are displayed in purple. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

suggests that these areas are necessary for increasing attentional resources after presentation of a stimulus (of whatever type) and efficiently implementing the subsequent steps of mismatch detection and categorization (when the subject has to compare the current stimulus and its representation in working memory). The involvement of the ACC and the midcingulate (BA 24) in cognitive control when competing stimuli are presented has already been suggested (Badgaiyan and Posner, 1998). Moreover, the thalamus and the temporal lobes (BA 37–39) are also involved soon after stimulus presentation – probably in order to maintain focal attention and regardless of the type of stimulus. The common occipital and temporal involvement is probably related to the visual modality and the subsequent processing of visual stimuli (Goodale and Milner, 1992). Specific generators for anterior and posterior N2 subcomponents The paired t-tests (see Fig. 4) showed that there are more target-elicited N2 generators and more distracter-elicited N2 generators than standard-elicited N2 generators in

the superior and medial frontal lobes (namely BAs 9 and 10). This finding suggests that even in the early stages of the attentional process, the frontal areas of the frontoparietal attentional networks (mostly the dorsal network) are active for infrequent stimuli (for which a higher level of attention is necessary), regardless of whether the stimulus is a target or a distracter. Moreover, there were more target-elicited N2 generators in the BA32 part of the ACC and in the DLPF cortex (middle frontal lobe, from BA 6 to BA 8). This suggests that these areas are not only involved in target processing at the P3 time window, as already reported (Bocquillon et al., 2011), but also at early stages of target evaluation. This also suggests close interaction between both areas (Devinsky et al., 1995; Barch et al., 2001). Even if they did not appear as specific distracter or target generators according to the paired t-tests, the peak-to-baseline comparisons also identified the PPC and various parts of the precuneus as generators of these N2 subcomponents only. First, the PCC (BAs 23– 31) was only involved in distracter detection – perhaps in order to inhibit a response and monitor conflicts in the presence of distracter stimuli. In fact, the PCC does not belong to the cognitive control network that includes the

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Table 3. location of the N2 generators for standard stimuli (p < 0.001). Talairach coordinates (T-x, T-y and T-z) are given according to the Collins brain markers. Quoted values are the local maxima of the T values. Anatomic locations are given as the area of the gyrus and as the Brodmann area (BA). ACC: anterior cingulate cortex. Stimulus

Area (gyrus)

BA

Standard

Left insula Right thalamus Left ACC/midcingulate Left middle frontal Left prefrontal Left superior frontal Left medial frontal Right occipital lingual Left superior frontal Right middle frontal Left middle precuneus Left anterior precuneus Left inferior temporal Right occipital cuneus Right superior parietal Right postcentral Right precentral Left middle temporal Left postcentral Right superior temporal Right middle frontal Right superior occipital Left superior parietal Right middle frontal Right insula Right middle temporal Right parahippocampal Left middle temporal Left inferior parietal

13

T-x (mm)

24 6 6 10 8 30 8 8 31 7 37 18 7 3 6 21 2 22 46 19 7 11 13 21 34 22 40

DLPF cortex, the ACC, the pre-supplementary motor area (pre-SMA), the dorsal premotor cortex, the anterior insular cortex, the inferior frontal junction and the posterior parietal cortex (Cole and Schneider, 2007). Nevertheless, the PCC is functionally connected to some regions of the cognitive control network (namely the inferior parietal and DLPF cortices (Margulies et al., 2009; Leech et al., 2011)). Accordingly, this structure is thought to be indirectly involved in controlling the focus of attention (Leech et al., 2011). Another interpretation could be that the PCC takes part in novelty detection, as also suggested by the results of a functional magnetic resonance imaging (fMRI) study (Kiehl et al., 2001). The latter hypothesis might best be investigated by using a truly novel distracter. Secondly, generators were found in the AP (BAs 7–31) only for standard- and target-elicited N2. The AP corresponds to the ‘‘sensorimotor’’ precuneus, which is known to be connected with the motor and premotor areas (the SMA (BA 6), the lateral primary motor cortex on the precentral gyrus and the postcentral area), the cingulate, the secondary somatosensory cortex and the superior parietal cortex (Margulies et al., 2009). After a standard or a target stimulus, a mental representation in working memory is needed to prepare a motor response. Hence, the AP may belong to a network for detecting targets that induce a motor response – especially since some areas connected to the AP (namely BA 6) are also

t Value

Coordinates

30 21 1 29 40 21 1 23 20 29 7 7 47 4 42 41 49 60 38 52 49 43 27 29 41 61 11 48 58

T-y (mm) 14 27 36 8 2 53 32 67 30 33 70 53 69 98 62 21 14 2 35 57 36 80 63 39 27 45 9 38 33

T-z (mm) 13 8 6 56 30 25 41 5 50 41 22 51 4 2 50 45 28 22 62 14 14 30 41 11 17 3 15 2 35

5.16181 5.0707 5.06941 5.01426 4.94824 4.93162 4.78925 4.76753 4.69669 4.66982 4.66675 4.52988 4.44459 4.37687 4.00659 4.00286 3.98455 3.89401 3.83335 3.79754 3.78225 3.77805 3.75809 3.6692 3.5982 3.58423 3.56252 3.52486 3.51227

specific-target-elicited N2 generators. Third, the central precuneus (CP only appears as a standard- and distracter-elicited N2 generator. The CP corresponds to the cognitive/associative part of the precuneus and is connected to the posterior inferior parietal lobule and the prefrontal cortex (BAs 8, 10 and 46, which belong to the attention frontoparietal network). Interestingly, after standard and distracter stimuli, a motor response has to be inhibited since no response is expected. This inhibition could thus be mediated through the connection between the CP and the parietal cortex. This would suggest an integrative functional role of the CP, as suggested by Margulies et al. (2009). The temporal dynamics of the cognitive processes underlying N2 and P3 generation In summary, our results confirm the involvement of the frontoparietal attention networks in the generation of N2 as well as P3 (Bocquillon et al., 2011) and support the presence of a temporal continuum in the underlying cognitive processes (Crottaz-Herbette and Menon, 2006). Moreover, these lateral prefrontal and parietal areas interact early on in the process with medial structures (i.e. the ACC, different parts of the PCC and the precuneus), depending on the detected stimulus. We hypothesize that the N2 reflects a stimulus detection step prior to

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Table 4. location of the N2 generators for the distracter stimuli (p < 0.001). Talairach coordinates (T-x, T-y and T-z) are given according to the Collins brain markers. Quoted values are the local maxima of the T values. Anatomic locations are given as the area of the gyrus and as the Brodmann area (BA). ACC, anterior cingulate cortex. Stimulus

Area (gyrus)

Distracter

Right dorsomedial thalamus Right posterior cingulate Right posterior cingulate Right caudate (head) Left caudate (head) Right inferior parietal lobule Left middle precuneus Left ACC/midcingulate Left internal frontal Right occipital lingual Left middle frontal Right middle occipital Left occipital lingual Left postcentral Left superior frontal Right middle frontal Right occipital cuneus Left thalamus (pulvinar) Left superior parietal Left precentral Right insula Right temporal fusiformis Right middle temporal Left precentral Left middle temporal Right middle frontal

BA

T-x (mm) 23 31

40 31 24 9 30 6 19 18 3 8 6 18 7 6 13 37 21 4 39 46

t Value

Coordinates

11 2 12 10 10 42 7 9 1 23 30 43 26 28 21 31 24 8 27 40 41 52 51 29 47 49

T-y (mm) 19 30 41 7 6 41 70 1 45 67 0 78 98 34 41 6 98 28 63 2 27 64 46 15 62 36

T-z (mm) 9 26 43 3 3 52 22 47 15 5 48 12 6 53 42 46 2 8 41 30 17 12 6 46 24 14

7.02741 6.16036 6.16009 6.09166 5.88499 5.86164 5.37879 5.36461 5.30279 5.29223 5.20107 5.01378 4.91701 4.90064 4.85573 4.83677 4.79193 4.7092 4.67187 4.62786 4.42047 4.39278 4.30866 4.1948 3.82619 3.50261

Table 5. location of the N2 generators for target stimuli (p < 0.001). Talairach coordinates (T-x, T-y and T-z) are given according to the Collins brain markers. Quoted values are the local maxima of the T values. Anatomic locations are given as the area of the gyrus and as the Brodmann area (BA). ACC, anterior cingulate cortex. Stimulus

Area (gyrus)

BA

T-x(mm) Target

Left caudate (head) Right thalamus Left ACC-midcingulate Left middle frontal Right anterior precuneus Right middle frontal Left postcentral Right superior frontal Right superior frontal Left anterior precuneus Right superior frontal Left superior temporal Left middle temporal Right insula Left inferior temporal Right inferior parietal Left inferior parietal Right precentral Left cuneus Left middle temporal Left middle occipital

24 6 31 6 3 9 8 7 6 22 21 13 20 40 40 4 18 39 18

distinction between frequent and infrequent stimuli. In response to increased attention demand, the ACC and the midcingulate (mostly BA24) are quickly involved in this

T value

Coordinates

10 1 0 40 21 40 39 39 20 8 1 58 59 51 59 32 38 21 6 47 26

T-y (mm) 16 10 24 1 59 3 22 34 22 43 9 41 27 37 17 51 52 23 100 71 90

T-z (mm) 4 1 22 48 20 47 36 32 49 52 66 16 8 16 23 42 33 62 11 14 12

9.23375 7.95417 6.96217 6.58866 6.389 6.11576 6.01464 5.41709 5.28356 5.26765 5.05355 4.99957 4.99298 4.9691 4.90288 4.74107 4.59852 4.19309 4.14925 4.00505 3.77543

detection process. The ACC may have a central role in conflict monitoring and mismatch detection, rather than in inhibition per se (since no specific distracter or standard

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Fig. 4. A statistical map (from non-parametric paired t-tests) of the gray matter current density for (i) the target-elicited N2 compared with the standard-elicited N2 (red) and (ii) the distracter-elicited N2 compared with the standard-elicited N2 (blue), using the swLORETA method, p < 0.01. The colors are superimposed and areas of overlap (i.e. cortical regions showing significant generators for both contrasts) are displayed in purple. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 6. location of the specific N2 generators for target stimuli and distracter stimuli. Talairach coordinates (T-x, T-y and T-z) are given according to the Collins brain markers. Quoted values are the local maxima of the T values. Anatomic locations are given as the area of the gyrus and as the Brodmann area (BA). Significance levels: ACC, anterior cingulate cortex. Area (gyrus)

BA

t-Score

Coordinates T-x (mm)

T-y (mm)

T-z (mm)

Target vs. standard

Left ACC Left medial frontal Left middle frontal Left superior frontal Right superior frontal Left middle frontal Left insula Right middle frontal

32 9 6 10 9 8 13 10

1 1 40 31 19 31 30 39

46 52 10 52 53 31 14 35

6 34 48 25 34 41 13 23

3.62152*** 3.61009*** 3.5538*** 3.52633*** 3.48291*** 3.45562*** 3.09763** 3.00069**

Distractor vs. standard

Right superior frontal Left medial frontal Left superior frontal Right superior frontal

10 10 9 9

8 11 11 19

65 44 52 53

17 15 34 34

2.7515* 2.611* 2.50613* 2.47757*

*

p < 0.01. p < 0.005. *** p < 0.001 **

N2 generators were found in this area). According to this hypothesis, the AP, the ACC (BA32) and premotor areas (corresponding to the ventral frontoparietal network described by Corbetta and Shulman (2002) and Corbetta et al. (2008)) would have a preponderant role in implementing the response decision within the P3 time window. In contrast, the CP and PCC may underlie inhibition processes in oddball tasks – possibly through an inhibition of the connections between the AP and the ventral fronto-parietal network. This could prevent a motor response to the stimulus in the P3 time window, since some of the structures in the ventral fronto-parietal network are specifically target-elicited P3 generators. We confirmed the critical role of the ACC in cognitive control and conflict monitoring, as suggested by former studies (Carter et al., 1999; Cohen et al., 1999; Barch et al., 2001; Braver et al., 2001), in good acquaintance with the conflict-monitoring theory. But we also emphasized the involvement of other median structures that contribute in close interaction with the ACC to either inhibition or response selection.

One of the main strengths of the present study is the high time resolution of its EEG recordings. Such a resolution allows to better evidence the time-domain changes in the events underlying N2 and P3, which cannot be dissociated by neuroimaging methods such as fMRI due to the lack of temporal resolution. Considering our current knowledge of the N2 generators’ location and the involvement of the DLPF cortex in the P3 time window (Bocquillon et al., 2011), we can confirm that the ACC (and not the DLPF cortex) detects conflicts and then modulates the cognitive control by the DLPF cortex (and not the ACC) (Kerns et al., 2004). These findings are in good agreement with the ACC’s suggested role as ‘‘the major generator of brain responses that, around 200 ms poststimulus, engender attentional shift whose effects are secondarily manifest over widely distributed cortical regions’’ (CrottazHerbette and Menon, 2006). Our data also support the existence of a dynamic process that involves both the bottom–up and top–down processes underlying attentional control (Crottaz-Herbette and Menon, 2006).

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Methodological issues and study limitations The present work investigated the brain areas involved in cognitive control of the overall attentional process by analyzing the various anterior and posterior N2 subcomponents. This goal prompted us to adopt a conventional, three-stimulus oddball paradigm that has been frequently used to study cognitive ERPs. We deliberately preferred this oddball paradigm to the go/ no-go paradigm generally used to investigate inhibition only. In fact, go/no-go paradigms have been frequently used to investigate anterior N2 (see Table 1) as an inhibition or error processing index but prevent investigation of target or unexpected distracter detection. Rather than studying N2 in isolation, our objective was to investigate the component in a broader context – that of the processes underlying both N2 and P3 generated by different types of stimulus (i.e. targets and distracters). Nevertheless, our paradigm could be improved. For example, a novel stimulus would have been better than a mere distracter for distinguishing between novelty and inhibition in the distracter condition. Lastly, well-known, recurrent limitations of source localization studies include the lower spatial resolution of these methods (relative to fMRI, for example) and the error risk related to the use of statistical inferences. However, use of high-resolution (128-channel) EEG markedly improved the spatial resolution (by up to 5 mm, in our case) and the swLORETA method reduces the localization error (Pascual-Marqui, 2002; Palmero-Soler et al., 2007). Nevertheless, a multimodal, simultaneous EEG–fMRI study (combining swLORETA and an fMRI analysis) would constitute a more robust approach for identifying the neuro-anatomic substrates of attentional processes (as shown by Strobel et al.’s work (2008)).

CONCLUSION We investigated both common and specific sources of standard-, target- and distracter-elicited N2 components in young adults during a three-stimulus visual oddball task. We confirmed the critical role of the ACC and the midcingulate in cognitive control and conflict monitoring. We also demonstrated that these two structures interact closely with the frontoparietal attention networks and other median structures (namely the precuneus and the PCC). The PCC and the CP may be involved in inhibition, whereas the AP may act with BA 32 of the ACC to enable response selection processing. Acknowledgements—The authors thank Dr David Fraser (Biotech Communication, Damery, France) for helpful comments on the manuscript’s English.

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(Accepted 10 April 2014) (Available online 18 April 2014)