The cortical generators of P3a and P3b: A LORETA study

Brain Research Bulletin 73 (2007) 220–230

Research report

The cortical generators of P3a and P3b: A LORETA study U. Volpe ∗ , A. Mucci, P. Bucci, E. Merlotti, S. Galderisi, M. Maj Department of Psychiatry, University of Naples SUN, Largo Madonna delle Grazie, 80138 Naples, Italy Received 2 October 2006; received in revised form 1 March 2007; accepted 1 March 2007 Available online 3 April 2007

Abstract The P3 is probably the most well known component of the brain event-related potentials (ERPs). Using a three-tone oddball paradigm two different components can be identified: the P3b elicited by rare target stimuli and the P3a elicited by the presentation of rare non-target stimuli. Although the two components may partially overlap in time and space, they have a different scalp topography suggesting different neural generators. The present study is aimed at defining the scalp topography of the two P3 components by means of reference-independent methods and identifying their electrical cortical generators by using the low-resolution electromagnetic tomography (LORETA). ERPs were recorded during a three-tone oddball task in 32 healthy, right-handed university students. The scalp topography of the P3 components was assessed by means of the brain electrical microstates technique and their cortical sources were evaluated by LORETA. P3a and P3b showed different scalp topography and cortical sources. The P3a electrical field had a more anterior distribution as compared to the P3b and its generators were localized in cingulate, frontal and right parietal areas. P3b sources included bilateral frontal, parietal, limbic, cingulate and temporo-occipital regions. Differences in scalp topography and cortical sources suggest that the two components reflect different neural processes. Our findings on cortical generators are in line with the hypothesis that P3a reflects the automatic allocation of attention, while P3b is related to the effortful processing of task-relevant events. © 2007 Elsevier Inc. All rights reserved. Keywords: Event-related potentials; P3a; P3b; P300; LORETA; Neural generators; Orienting response; Attention

1. Introduction The so-called “P300” (or “P3”) is probably the most well known component of the brain event-related potentials (ERPs). Recently it has gained interest as an endophenotype for research into genetic predisposition to psychosis [6] and several studies have reported an association between P300 amplitude and the COMT val/met polymorphism [27,93,40,7,19]. According to several experimental findings, P300 is independent of the stimulus physical properties and reflects attention/memory processes related to changes in the neural representation of the environment induced by new sensory inputs (“context-updating” theory) [38,17,77,65,30,39]. The “oddball” paradigm has frequently been used to record the P3: a subject is asked to discriminate between two different stimuli by responding (overtly or covertly) to the “target” stimulus, which

∗

Corresponding author. Tel.: +39 0815666512; fax: +39 0815666523. E-mail address: [email protected] (U. Volpe).

0361-9230/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.brainresbull.2007.03.003

usually occurs less frequently, while ignoring the standard (nontarget) stimulus, which occurs more frequently. The stimuli are presented in a random series and vary on some dimensions (e.g., physical properties). The P3 potential is generally recorded across the scalp and has its maxima over the midline central and parietal leads, at about 300 ms after the onset of the rare target stimulus. A modified version of the paradigm includes an additional stimulus, a rare non-target, inserted into the sequence of target and frequent standard stimuli. The P3 obtained using this paradigm includes two different components [88,14,82]: the so-called P3b, elicited by target stimuli, with a maximum over the parietal regions, and the P3a, elicited by rare non-target stimuli, with a more anterior distribution than the P3b. The rare non-targets used to elicit the P3a include both “novel”, unrecognizable, and common, easily discriminable stimuli [12,39,11,82,87,28]. Several studies have reported that the P3 components elicited by the two types of infrequent non-target stimuli have similar topographies, show an amplitude reduction with stimulus repetition (habituation) and are similarly influenced by the stimulus context [82,87,28]. They are believed

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

to be the same component, generated by the same neural network [23,28]. The scalp topography of the P3b is consistently determined in both the standard and three-tone oddball tasks, the topography of the P3a is more variable, depending on the number of standards preceding the rare non-target stimuli and their discriminability from the targets, as well as the number of repetitions of the rare non-target stimuli (novel stimuli are never-repeated patterns while other rare non-target stimuli used in several seminal studies have the same or lower repetition rates with respect to the targets) [13,76,29,39,82,28]. All the studies, but one [76], have reported a more anterior distribution of the P3a with respect to the P3b [13,29,39,82,28]. The P3a is thought to represent the automatic attentional switch to deviant stimuli or distractors with respect to the ongoing task, while the P3b reflects the match between the incoming stimulus and the voluntarily maintained attentional trace of the task relevant stimulus [22,39,28]. The different scalp topography and influence of different experimental conditions suggest that the two components have different neural generators. However, traditional peak analyses cannot reliably separate components with a partial overlap in time and space and topographic inferences are severely affected by the reference electrodes used in ERP recording [16,52,87,64]. Alternative, reference-independent methods of identification of ERP components might better determine the time frames of the underlying brain processes [48,64]. Depth recordings have shown that the P3a generators are located in the anterior cingulate and fronto-parietal cortex and the P3b generators in superior temporal, posterior parietal, hippocampal, cingulate and frontal structures [2,30,32,33]. However, intracranial investigations are not methodologically flawless and, mainly due to the extreme invasiveness of the technique itself, they are not suitable for investigations involving healthy volunteers or psychiatric patients. In the last decades, new brain imaging techniques became available, which allow direct investigation of brain activity, but do not require invasive procedures. The functional magnetic resonance imaging (fMRI), achieving an acceptable compromise between spatial and temporal resolution [57,25], has been used in several studies to investigate the neural basis of P300 [61,62,10,18,44,89,42,36,43,4,66,56,90,5]. Most of them have confirmed the involvement of the frontal, parietal, temporal and cingulate areas in the genesis of this ERP component. However, the contribution of the medial temporal structures and hippocampus cannot be adequately assessed by echo-planar imaging (EPI) sequences currently used in most fMRI experiments [86] and, due to the partial temporal overlap of the two processes [85,88], the identification of brain generators of P3a and P3b components might be hindered the relatively poor temporal resolution of fMRI techniques [3]. Electrophysiological techniques have a higher temporal resolution. However, due to the so-called “inverse problem”, precise inference on the brain generators of scalp recorded activities cannot be made. In recent years, different algorhythms have been proposed to solve the inverse problem and new methods for reconstructing the current source for a given scalp electrical distribution have been devel-

221

oped [64,96]. These algorhythms can be divided in equivalent current dipole models [80,81] and current distributed source models [34,72,94]. According to recent comparative studies, the dipole models are suitable only when a single source is expected [96]. Among the distributed source methods, the lowresolution brain electromagnetic tomography [(LORETA) [73] has been proved to present the smallest localization error, particularly when multiple sources and noise are present [74,96]. This algorithm selects the smoothest spatial source distribution by minimizing the Laplacian of the weighted sources [72], under the main assumption that the activity of neighbor neurons is highly correlated. LORETA software limits the solution space to cortical gray matter and hippocampus, excluding subcortical sources, for which the spatial resolution of the method could be extremely poor. Several studies reported consistency between LORETA and neuroimaging studies: Strik et al. [92] used LORETA to study the electrical generators of the P300 produced during a cued continuous performance test and found frontal activation, in line with previous positron emission tomography and near infrared spectroscopy studies. Worrell et al. [95] used LORETA to identify the electrical sources of ictal EEG discharges and the results were consistent with well-defined symptomatic MRI lesions. Consistency between LORETA findings and MRI results in subjects with schizophrenia was also reported [71,70]. Mulert et al. [66] used both LORETA and fMRI to investigate the time-course of the activations corresponding to the P300 component and found that LORETA findings were consistent with those provided by fMRI and were in line with previous results obtained by intracranial recordings. According to these data, LORETA might significantly improve knowledge on neural processes underlying the P3a and P3b components. In this study, we used a modified (three tones) auditory oddball paradigm to record P3a and P3b components of ERPs in a group of healthy subjects. Advanced reference-independent topographical analysis was used to identify the two P300 components. LORETA was used to characterize the cortical distribution of P3a and P3b electrical generators. 2. Materials and methods 2.1. Subjects Subjects were selected among university students by flyers. Before entering the study, they participated in a 1-h clinical interview to verify their conformity to the following selection criteria: (1) no axis I and II DSM-IV diagnoses; (2) no personal or family history of psychiatric disorders; (3) no history of pre- and perinatal problems, headache, head injury with loss of consciousness, epilepsy or drug abuse; (4) no use of psychotropic drugs; (5) right-handedness, as assessed by the Edinburgh Inventory [68]. Forty-three university students (18 males, 25 females) gave their written consent to participate in the study procedures. Nine subjects (4 males and 6 females) had an insufficient number of artifact-free epochs in their ERPs recordings. The analyzed sample included 32 subjects (13 males, 19 females) with a mean age of 22.9 years (S.D., 2.7; range, 18–28), and a mean education of 15.4 years (S.D., 2.0; range, 13–19). Males and females did not differ for age and education. The study was conducted under a research protocol approved by the local Ethical Committee.

222

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

2.2. ERPs recording procedure ERPs were recorded during a three-tone auditory oddball paradigm. Three stimuli were binaurally presented through headphones, with different tone frequencies: 1000-Hz, 3000-Hz and 6000-Hz tones (60 dB SL, 10 ms rising/falling time, 200 ms plateau). The 1000-Hz tone was designated as the target stimulus, which required the subject to press a button at its occurrence; the 3000-Hz and 6000-Hz tones were designated as frequent standard and rare non-target tones, respectively. Two hundred stimuli were used (52 target, 104 standard, 44 rare non-target tones). The slightly lower frequency of the rare non-target compared to target stimuli was meant to prevent habituation of the P3a, which might influence the reliable identification of the component [23,15]. The interstimulus interval varied randomly between 1500 and 2000 ms; tones were presented in a randomized order, within the constraints that there would be no more than five successive and no less than two standard and rare non-target tones before a target stimulus. Subjects were asked to press a button when listening to rare target tones only. Scalp electrical activity was recorded, using the Digital EEG System EASYS2 (M&I, Prague; www.brainscope.cz), from 25 unipolar leads (FpZ, FZ, CZ, PZ, OZ, F3, F4, C3, C4, P3, P4, O1, O2, Fp1, Fp2, F7, F8, T3, T4, T5, T6, AF3, AF4, PO7, PO8), following the 10–10 system [1], referred to the linked earlobes (a resistor of 10 k was interposed between the earlobe leads). The forehead was used for grounding. Two electrooculogram leads (Fp1-Pg1 for vertical and Pg1-Pg2 for horizontal eye movements) were added for artifact monitoring. The impedance of the electrodes was maintained below 5 k. Light gauze pads were placed over the closed eyelids to reduce blink artifacts. Subjects were instructed to relax and to avoid movements throughout the recording session. A time constant of 0.3 and a low-pass filter of 70 Hz were used. The amplification of the signal was 10.000 times and the sampling rate was 256 Hz. A calibration was performed for all channels, using a 50 ␮V sine wave, before each recording session. The computer software identified the stimuli off-line and selected epochs of electrical activity starting 120 ms before stimulus presentation and ending 1200 ms after it. The 120 ms activity preceding each stimulus was averaged, for each channel, across all time points and epochs, to calculate a single baseline value that was subtracted from the post-stimulus average. ERPs were obtained by averaging 800 ms activity following stimulus presentation, after the exclusion of epochs contaminated by artifacts, for each stimulus type and channel. Epochs were also excluded from the average when omission or commission errors occurred.

Table 1 Mean (±S.D.) and range of on- and offset latency for P3a and P3b, as measured at Pz electrode; all values are expressed in milliseconds Mean ± S.D. P3a onset P3a offset P3b onset P3b offset

266.63 395.25 322.39 425.49

± ± ± ±

36.50 36.82 65.05 60.75

Min.

Max.

185.76 332.78 184.28 313.48

334.27 510.99 439.71 554.06

represent the topographic descriptors of each map. Segmentation of data into microstates was performed on the grand average of data across stimulus types. The identified microstates were used to define the on- and offset latencies of the P300 time periods, which have been used for further analyses. When the segmentation was completed, the microstate topographic descriptors were calculated, for each stimulus type, as the mean value of the spatial coordinates of all maps included in the microstate (for further details on the microstate identification procedure, see [53,26,54]).

2.4. Control analyses In order to verify the presence of the microstate of interest at the individual level, after the identification of the microstates, a control analysis was performed. We used the spatial fitting procedure previously described by Khateb et al. [41], in which the spatial correlation coefficients between individual maps and the grand mean ERP maps in the time frames of interest. In all cases, the mean spatial correlation was constantly above 0.80, ensuring that the microstate topography was present at the individual level. To allow comparisons of our results with literature data, a further control analysis was carried out: P3a and P3b were identified as the largest positive peak between 200 and 500 ms at Pz and on and offset latency were manually measured (Table 1). The mean absolute error (MAE) among the on- and offset P300 latencies measured with the peak analysis approach with those identified by the BEM N technique was calculated. The MAE (MAE = 100 i=1 |Ei − Oi |/(N − 1)) provides the average deviation of the latencies measured with the first approach (Oi ) from the values measured with the BEM technique (Ei ). The lowest the MAE the more comparable are the measures. For both rare target and non-target stimuli, the MAE was <0.10 (rare target stimuli: 0.039 for onset and 0.012 for offset; rare non-target stimuli: 0.095 for onset and 0.042 for offset), demonstrating a substantial agreement between the two approaches.

2.3. Identification and processing of ERPs components The “brain electrical microstates” (BEM) technique [48,50,51,91] was used to obtain a reference-independent estimate of the P300 scalp topography. The BEM technique was used to obtain a reference-independent estimate of the P300 scalp topography. The use of this reference-independent method to identify ERP components is highly recommended before the application of source analyses [64]. Furthermore, the technique provides an objective data driven approach, avoiding operators’ subjective judgments on the occurrence of peaks in selected leads. However, for comparisons with literature data, control analyses were also carried out to verify the correspondence of the P300 components identified by means of the BEM technique with those defined on the basis of classical peak analyses. Brain electrical microstates were identified using the procedure developed by Koenig and Lehmann [48]. Briefly, it involves the adaptive segmentation of the ERP data to identify topographically stable periods of the electrical field configuration at the scalp, the so-called “brain microstates”, corresponding to each ERP component. ERP data were digitally filtered with a 0.5–15 Hz bandpass FIR filter and corrected for the spatial offset, subtracting the average reference, i.e., the average of potentials at all scalp leads. For each data point, map topography is uniquely determined by the locations of the positive and negative centroids, i.e. the points of gravity of the positive and negative areas of the map. The spatial coordinates of the centroids, along the horizontal (from left to right) and vertical (from anterior to posterior) axes, are expressed in arbitrary units (interelectrode distance) and

2.5. LORETA The three-dimensional distribution of the P3a and P3b cortical generators was analyzed for each subject and stimulus type using the LORETA software [72], in the version providing current density values of 2394 voxels in the cortical areas, modeled in the digitized Talairach atlas. This method uses a Laplacian Weighted Minimum Norm algorithm with no a priori assumption about a predefined number of activated brain regions, and thus it achieves a more open solution to the EEG inverse problem, which is closer to other brain imaging approaches [64].

2.6. Statistical analysis Multivariate repeated measure ANOVA, with stimulus type as the within factor, was carried out on the topographic descriptors of the P3 microstate. If a significant main effect of the stimulus type was found, planned comparisons were carried out by means of univariate ANOVAs (target versus rare non-target; target versus standard; rare non-target versus standard). In order to gather information on cortical sources specifically involved in the P3b and P3a generation, LORETA images for target and rare non-target tones were compared with those for standard tones using paired t-test statistics, after logarithmic transformation of the data. Holmes’ non-parametric correction for multiple comparisons was used [35].

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

223

Fig. 1. ERPs grand average time course, displayed at all active leads. Vertical scale: amplitude, expressed in microvolts; horizontal scale: time, expressed in milliseconds (range: 0–800 ms). Bottom right: global field power (GFP) of grand average of all stimuli, with the timeframes of the four identified brain electrical microstates, is shown as a black solid line (Y axis: arbitrary units; X axis: time).

224

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

To control for the role of the overt behavioral response on cortical sources of P3b, Pearson correlation coefficients between current source density values for P3b and reaction time were calculated. Significance level for all statistical tests was set at p ≤ 0.05 (two-tailed). In order to further characterize the time-course of the identified P3a and P3b generators, for the regions showing significant activations for rare target and rare non-target, with respect to standard stimuli, the current source density values were plotted against time, within the P300 microstate.

3. Results Subjects performed the test with no errors and very few omissions: mean accuracy rate was 99.36%. The mean reaction time (±S.D.) was 395.95 ± 119.13 ms. Grand averages of auditory P3s for each stimulus type are shown in Fig. 1. Between 0 and 600 ms after the stimulus onset, the ERP segmentation procedure identified four brain electrical microstates (MS): MS1, with a time window from 59 to 145 ms (duration: 86 ms); MS2, from 148 to 222 ms (duration: 75 ms); MS3, from 227 to 383 ms (duration: 156 ms) and MS4, from 387 to 598 ms (duration: 211 ms). The timeframe 227–383 ms, corresponding to the P300 component, was selected for further analyses. GFP time course of the grand average of all stimuli is depicted at the bottom of Fig. 1. ANOVA on the GFP values yielded a significant main effect of the stimulus type (F2,84 = 94.28; p < 0.0005). Follow-up ANOVAs demonstrated that GFP values were higher for target than both standard (F1,42 = 119.98; p < 0.00005) and rare nontarget tones (F1,42 = 97.85; p < 0.0005) and for rare non-target than standard tones (F1,42 = 7.53; p < 0.009). ANOVA on the centroids values showed a significant main effect of the stimulus type for both positive and negative centroids (F2,84 = 14.04; p < 0.00005; F2,84 = 8.88; p < 0.003, respectively) on the anterior–posterior coordinates only. Followup ANOVAs revealed that the topography of the P3a elicited by rare non-target tones was significantly different from that of the P3b elicited by the target stimuli (F1,42 = 22.97; p < 0.00005 for the positive centroid; F1,42 = 16.47; p < 0.0003, for the negative centroid). These results indicate that the positive centroid was shifted towards the anterior regions and the negative centroid towards the posterior regions for the P3a, with respect to the P3b (Fig. 2). LORETA t-test maps, for comparisons among stimulus types, are depicted in Fig. 3. For each comparison, a detailed summary of the t-scores and of the Talairach coordinates for the activated cortical areas is provided in Tables 2–4. For target versus frequent standard tones, a greater source activity was observed in a distributed network including cingulate, insula, and frontal, parietal, parahippocampal, and temporo-occipital areas (p < 0.01) (Fig. 3A). For rare non-target tones versus frequent standard tones a greater source activity was found in anterior cingulate, medial frontal gyrus, superior frontal gyrus and in the right parietal lobe (p < 0.05) (Fig. 3B). Finally, for target tones, with respect to rare non-target tones, a greater source activity was found in frontal, left parietal and temporal as well as in limbic structures bilaterally (p < 0.05) (Fig. 3C).

Table 2 Stereotaxic coordinates and significance level (p < 0.01) of regions showing increased activation for target vs. standard stimuli Brain region (Brodmann area)

Anterior cingulate gyrus (BA25) Anterior cingulate gyrus (BA32) Cingulate gyrus (BA31) Cingulate gyrus (BA23) Cingulate gyrus (BA31) Cingulate gyrus (BA24) Cingulate gyrus (BA31) Fusiform gyrus (BA37) Fusiform gyrus (BA37) Inferior frontal gyrus (BA47) Inferior frontal gyrus (BA46) Inferior parietal lobule (BA40) Inferior parietal lobule (BA40) Inferior temporal gyrus (BA20) Inferior temporal gyrus (BA20) Insula (BA13) Insula (BA13) Lingual gyrus (BA19) Medial frontal gyrus (BA8) Medial frontal gyrus (BA10) Middle occipital gyrus (BA18) Middle occipital gyrus (BA18) Paracentral lobule (BA6) Parahippocampal gyrus (BA30) Precentral gyrus (BA4) Superior frontal gyrus (BA11)

Talairach coordinates x

y

z

−3 11 4 4 4 4 11 −31 46 46 −45 60 −52 60 −59 39 −38 11 4 11 32 −31 4 −17 −38 18

10 38 −25 −25 −46 10 −39 −46 −46 38 31 −39 −32 −53 −25 17 −18 −53 38 38 −81 −81 −32 −32 −18 45

−6 15 36 29 36 36 43 −6 −20 1 15 36 50 −20 −27 8 15 1 43 −6 −13 −13 57 −6 43 −20

T-scores

8.48 6.37 12.22 12.22 11.21 11.22 11.98 9.68 8.24 8.53 8.05 7.18 9.20 7.19 6.76 5.31 7.86 7.81 8.24 7.33 4.98 6.61 12.17 8.67 8.15 7.57

Correlation analyses did not reveal any association between the reaction time and P3b current source density measured in the regions showing a difference from P3a (Table 4). Time courses of activation for the different brain areas, for both P3a and P3b, are shown in Fig. 4. For both components, an early peak and a subsequent decrement of activation was observed in frontal and cingulated areas, while a constant growth of activation was seen in inferior parietal lobule. Only P3b was associated with the sustained activity of a larger network, encompassing temporal and limbic structures. Table 3 Stereotaxic coordinates and significance level (p < 0.05) of regions showing increased activation for rare non-target vs. standard stimuli Brain region (Brodmann area)

Anterior cingulate gyrus (BA32) Anterior cingulate (BA32) Inferior parietal lobule (BA40) Medial frontal gyrus (BA6) Medial frontal gyrus (BA6) Postcentral gyrus (BA2) Postcentral gyrus (BA2) Precentral gyrus (BA4) Superior frontal gyrus (BA11) Superior frontal gyrus (BA6) Superior frontal gyrus (BA6) Superior frontal gyrus (BA6)

Talairach coordinates x

y

z

11 −3 53 4 −3 60 46 53 18 18 −10 11

45 45 −46 −11 −18 −32 −25 −11 52 −11 −4 −4

1 8 22 71 71 43 50 50 −20 71 71 71

T-scores

4.09 3.27 3.91 4.35 2.31 4.05 3.96 4.35 4.53 4.37 3.43 3.32

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

225

Fig. 2. P3a and P3b topographic features. Upper panel (A): topographic maps of scalp activity in the brain electrical microstate corresponding to P3 for rare target (left) and rare non-target (right) stimuli; head seen from above, left ear left; the color scale represents positive (red) and negative (blue) electrical activity values vs. average reference. (B) On the left, scheme of the spatial coordinates, expressed in arbitrary units (interelectrode distance) along the horizontal (from left to right) and vertical (from anterior to posterior) axes; on the right, the positive and negative centroid locations for P3a and P3b are shown. *Anterior shift of the positive centroid for P3a vs. P3b, p = 0.00005. ◦ Posterior shift of the negative centroid for P3a vs. P3b, p = 0.0003. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

4. Discussion In our study, a three-tone oddball paradigm elicited two different P300 components: the P3b for target tones and P3a for rare non-target stimuli. The two components, in spite of the temporal overlap, differed both in terms of topographic characteristics and location of electrical generators. Topographical analysis showed significant differences between the electrical fields of the two P3 components: the P3a positive centroid was more anteriorly located than the P3b one. LORETA source analyses demonstrated that the rare nontarget stimuli produce a P3 component involving different brain sources, with respect to the P3 elicited by target tones. The P3a had electrical generators in cingulate, frontal and right parietal areas, whereas the P3b had generators localized in a more distributed network including bilateral frontal, parietal, hippocampal, cingulate and temporo-occipital areas. The LORETA source imaging method employed in this study restricts the localization of electrical generators to cortical gray

matter/hippocampus and has a “blurred” spatial resolution [72]. In spite of these limitations, our findings are substantially in line with previous studies, which have been carried out with different experimental techniques and paradigms. More in detail, previous fMRI studies, dealing with P3b component arising from an oddball auditory stimulation, reported that its generators were located in frontal, parietal, cingulate and temporal areas [56,10,36,9,20,90,8], along with the contribution of insular [55,36], subcortical [8], and cerebellar activations [10,43]; recent fMRI studies carried out with visual oddball paradigms also reported the involvement of frontal [89,44], parietal [89,4], cingulate [90,42] and temporal areas [42,4,5] in the genesis of P3b. Our results about the source activity underlying a target detection task are consistent with those reported in an recent study by Mulert et al. [67] who also used a LORETA analysis. Our results also showed an activation of the visual Broadman area 18, which has not been reported by other intracranial and brain imaging studies for the auditory P3b. The finding is probably due to the blurred source solution provided by LORETA for

226

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

Fig. 3. Three dimensional maps of voxel-by-voxel t-statistics using LORETA: comparisons among stimuli, in the time frame ranging from 227 to 383 ms and corresponding to the ERP P3 component. Source activity is superimposed on an inflated cortex (anatomy is shown in gray scale, white-to-black; subcortical structures are removed to show medial brain cortex). The scales on the right show negative (blue) and positive (red) t-values for which the alpha is significant after Holmes’ correction for multiple comparisons. All the comparisons are statistically significant. (A) Positive values represent increase of activity for target vs. frequent standard tones (p < 0.01); (B) positive values represent increase of activity for rare non-target vs. frequent standard tones (p < 0.05); (C) positive values represent increase of activity for target vs. rare non-target tones (p < 0.05).

Fig. 4. Current source density time course of the active regions, within the P3 time frame (227–383 ms), for rare target and rare non-target stimuli. Vertical scale: current source density (CSD), expressed in ␮V/mm3 ; horizontal scale: time, expressed in milliseconds. L: left; R: right; AC: anterior cingulate; CG: cingulate gyrus; Fusi: fusiform gyrus; IFG: inferior frontal gyrus; IPL: inferior parietal lobule; ITG: inferior temporal gyrus; INS: insula; Ling: lingual gyrus; MdFG: medial frontal gyrus; MOG: middle occipital gyrus; PCL: paracentral lobule; PHG: parahippocampal gyrus; PrCG: precentral gyrus; PCG: postcentral gyrus; SFG: superior frontal gyrus.

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230 Table 4 Stereotaxic coordinates and significance level (p < 0.05) of regions showing increased activation for target vs. rare non-target stimuli Brain region (Brodmann area: BA)

Anterior cingulate (BA32) Anterior cingulate (BA25) Anterior cingulate (BA25) Anterior cingulate (BA25) Fusiform gyrus (BA37) Fusiform gyrus (BA37) Fusiform gyrus (BA20) Hippocampus (subgyral) Inferior parietal lobule (BA40) Inferior parietal lobule (BA40) Inferior temporal gyrus (BA37) Lingual gyrus (BA19) Medial frontal gyrus (BA11) Middle frontal gyrus (BA10) Middle frontal gyrus (BA10) Middle frontal gyrus (BA10) Orbital gyrus (BA11) Paracentral lobule (BA5) Parahippocampal gyrus (BA30) Parahippocampal gyrus (BA35) Precuneus (BA7) Subcallosal gyrus (BA25) Superior frontal gyrus (BA11)

Talairach coordinates x

y

z

4 −3 4 −3 −31 39 −45 25 −45 −52 −45 −17 4 −38 −45 −38 4 −10 11 18 11 −3 4

31 10 17 17 −53 −46 −25 −39 −32 −32 −46 −46 52 45 45 38 52 −39 −39 −32 −46 10 59

−6 −6 −6 −6 −6 −20 −27 1 43 43 −20 −6 −13 15 15 15 −20 50 1 −13 50 −13 −20

T-scores

6.23 5.95 5.95 5.95 6.65 5.32 4.69 4.69 5.04 5.04 4.76 7.00 5.46 5.18 5.18 5.18 5.46 8.89 4.76 4.69 8.40 5.95 5.46

spatially close associative cortices, such as fusiform and other temporo-occipital cortices, involved in P3 generation for both auditory and visual modalities [30,31]. As for P3a, in several previous fMRI studies [10,42,43,4], it has been reported that a wide fronto-parietal network could represent the neurophysiological substrate of P3a, as suggested also by our results. Previous studies [88,12,46] have reported that attended to (target) and ignored infrequent (rare non-target) stimuli elicit different P3 components, which reflect different attentive and integrative processes. In our experimental paradigm, an overt response was required only for the rare target stimuli, and the frequency of rare target and rare non-target stimuli was slightly different, raising the possibility that topographic and source differences between the two P3 components be related to these confounds. However, it should be noted that our findings concerning the topography and the cortical generators of P3b and P3a are in line with those reported by several independent studies, including those using the same frequency of rare target and non-target stimuli and requiring no overt motor response following target detection [29,30,31,11,39,37,28]. In our study, no correlation was observed between the current source density values observed in the regions activated for the P3b and the reaction time, arguing against an involvement of these regions in the response selection processes. As already noted [49,59,60], the P3b is largely independent from response selection and mainly reflects stimulus categorization activity. Although the functional role of the two P300 components is still debated, some data indicate that P3a reflects a stimulus-

227

driven attentional shift, while P3b reflects the process of effortful attentional allocation and stimulus evaluation for task relevance [28,39,15,78,28,79,16,75]. Our findings that the P3a is generated in fronto-parietal and cingulate regions, the cerebral network for the orienting of attention (i.e., a shift of attention towards new and/or unexpected stimuli [63,58,83,84,24], are in line with the hypothesis that P3a reflect automatic allocation of attention. Both frontal lesion studies [46,47] and dipole studies [21,24] have clearly shown that the orienting response requires a frontal lobe engagement, confirming the relationship between frontal activity and the P3a component. In a previous attempt to characterize the brain response to novel events, Opitz et al. [69] investigated the electrophysiological and hemodynamic activity associated to the response to different sets of novel stimuli (identifiable and non identifiable sounds) and found an activation of fronto-parietal areas, but also of a temporal network. The use of unique complex environmental stimuli, with more episodic “novelty” characteristics, might explain the involvement of brain regions underlying conceptual and semantic processing, which may account for the discrepancy with our results. As to P3b, our findings, in line with those of several brain imaging [69,10,44,42,95] and intracranial recording [30,31,33] studies, indicate the involvement of a more posterior and distributed neural network with respect to P3a, including associative cortices implicated in attentional, perceptual and memory processes, and support the view that P3b represents effortful processing of task-relevant events [4]. Finally, our analyses of time course information revealed that frontal and cingulated areas showed an early peak and a subsequent decrement of activation, while inferior parietal lobule activity tended to grow constantly within the whole time frame, for both P3a and P3b; only for the latter component a sustained activity of a larger network, encompassing temporal and limbic structures was observed. The findings concerning P3b are in line with those of a previous study in which the same high temporal resolution approach was used [67]. Although subcortical involvement in the genesis of the P3 has been demonstrated by neuroimaging and intracerebral recording studies [31,10,42,8,45], the possible involvement of subcortical structures in the genesis of the investigated ERP components could not be assessed, due to the exclusion of these structures from the LORETA solution space. This remains a limitation of the present study. In the future, the integration of EEG-based neuroimaging techniques with fMRI would probably represent an optimal approach to the study of the neurocognitive processes underlying the P3 components. Conflict of interest None of the authors, of any member of his/her family, or of any associated entity, had significant financial interests or anything of monetary value, including but not limited to, salary or other payments for services (e.g., consulting fees or honoraria), equity interests (e.g., stocks, stock options or other ownership interests), and intellectual property rights (e.g., patents, copy-

228

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

rights and royalties from such rights), that would reasonably affect the results and/or the conclusions of the present paper. Acknowledgments Authors wish to thank Dietrich Lehmann and Roberto D. Pascual-Marqui, from the Key Institute for Brain and Mind Research of the University Hospital of Psychiatry (Zurich, Switzerland), for their comments and constant support. References [1] American Electroencephalographic Society, in: Guideline thirteen: Guidelines for standard electrode position nomenclature, J. Clin. Neurophysiol. 11 (1994) 111–113. [2] P. Baudena, E. Halgren, G. Heit, G.M. Clark, Intracerebral potentials to rare target and distractor auditory and visual stimuli. III. Frontal cortex, Electroencephalogr. Clin. Neurophysiol. 94 (1995) 251–264. [3] R.M. Birn, R.W. Cox, P.A. Bandettini, Detection versus estimation in eventrelated fMRI: choosing the optimal stimulus timing, Neuroimage 15 (2002) 252–264. [4] C. Bledowski, D. Prvulovic, R. Goebel, F.E. Zanella, D.E. Linden, Attentional systems in target and distractor processing: a combined ERP and fMRI study, Neuroimage 22 (2004) 530–540. [5] C. Bledowski, K. Cohen Kadosh, M. Wibral, B. Rahm, R.A. Bittner, K. Hoechstetter, M. Scherg, K. Maurer, R. Goebel, D.E. Linden, Mental chronometry of working memory retrieval: a combined functional magnetic resonance imaging and event-related potentials approach, J. Neurosci. 26 (2006) 821–829. [6] E. Bramon, C. McDonald, R.J. Croft, S. Landau, F. Filbey, J.H. Gruzelier, P.C. Sham, S. Frangou, R.M. Murray, Is the P300 wave an endophenotype for schizophrenia? A meta-analysis and a family study, Neuroimage 27 (2005) 960–968. [7] E. Bramon, E. Dempster, S. Frangou, C. McDonald, P. Schoenberg, J.H. MacCabe, M. Walshe, P. Sham, D. Collier, R.M. Murray, Is there an association between the COMT gene and P300 endophenotypes? Eur. Psychiatry 21 (2006) 70–73. [8] M. Brazdil, M. Dobsik, M. Mikl, P. Hlustik, P. Daniel, M. Pazourkova, P. Krupa, I. Rektor, Combined event-related fMRI and intracerebral ERP study of an auditory oddball task, Neuroimage 26 (2005) 285–293. [9] V.D. Cahloun, T. Adali, G.D. Pearlson, K.A. Kiehl, Neural chronometry of target detection: fusion of hemodynamic and event-related potential data, Neuroimage 30 (2006) 544–553. [10] V.P. Clark, S. Fannon, S. Lai, R. Benson, L. Bauer, Responses to rare visual target and distractor stimuli using event-related fMRI, J. Neurophysiol. 83 (2000) 3133–3139. [11] M.D. Comerchero, J. Polich, P3a and P3b from typical auditory and visual stimuli, Clin. Neurophysiol. 110 (1999) 24–30. [12] E. Courchesne, S.A. Hillyard, R. Galambos, Stimulus novelty, task relevance, and the visual evoked potential in man, Electroencephalogr. Clin. Neurophysiol. 39 (1975) 131–143. [13] E. Courchesne, Changes in P3 waves with event repetition: long-term effects on scalp distribution and amplitude, Electroencephalogr. Clin. Neurophysiol. 45 (1978) 754–766. [14] E. Courchesne, B.A. Kilman, R. Galambos, A. Lincoln, Autism: Processing of novel auditory information assessed by event-related brain potentials, Electroencephalogr. Clin. Neurophysiol. 59 (1984) 238– 248. [15] S. Debener, C. Kranczioch, C.S. Herrmann, A.K. Engel, Auditory novelty oddball allows reliable distinction of top-down and bottom-up processes of attention, Int. J. Psychophysiol. 46 (2002) 77–84. [16] J. Dien, K.M. Spencer, E. Donchin, Parsing the late positive complex: mental chronometry and the ERP components that inhibit the neighborhood of the P300, Psychophysiology 41 (2004) 665–678. [17] E. Donchin, M.G.H. Coles, Is the P300 component a manifestation of context updating? Brain Behav. Sci. 11 (1988) 357–374.

[18] J. Downar, A.P. Crawley, D.J. Mikulis, K.D. Davis, A multimodal cortical network for the detection of changes in the sensory environment, Nat. Neurosci. 3 (2000) 277–283. [19] A.C. Ehlis, A. Reif, M.J. Herrmann, K.P. Lesch, A.J. Fallgatter, Impact of catechol-O-methyltransferase on prefrontal brain functioning in schizophrenia spectrum disorders, Neuropsychopharmacology 32 (2007) 162–170. [20] T. Eichele, K. Specht, M. Moosmann, M.L. Jongsma, R.Q. Quiroga, H. Nordby, K. Hugdahl, Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI, Proc. Natl. Acad. Sci. U.S.A. 102 (2005) 17798–17803. [21] M. Fabiani, D. Friedman, Changes in brain activity patterns in aging: the novelty oddball, Psychophysiology 32 (1995) 579–594. [22] M. Falkenstein, J. Hohnsbein, J. Hoormann, Late visual and auditory ERP components and choice reaction time, Biol. Psychol. 35 (1993) 201–224. [23] D. Friedman, V.A. Kazmerski, Y.M. Cycowicz, Effects of aging on the novelty P3 during attend and ignore oddball tasks, Psychophysiology 35 (1998) 508–520. [24] D. Friedman, Y.M. Cycowicz, H. Gatea, The novelty P3: an event-related brain potential (ERP) sign of brain’s evaluation of novelty, Neurosci. Biobehav. Rev. 25 (2001) 355–373. [25] K.J. Friston, Models of brain function in neuroimaging, Annu. Rev. Psychol. 56 (2005) 57–87. [26] S. Galderisi, P. Bucci, A. Mucci, A. Bernardo, T. Koenig, M. Maj, Brain electrical microstates in subjects with panic disorder, Brain Res. Bull. 54 (2001) 427–435. [27] J. Gallinat, M. Bajbouj, T. Sander, P. Schlattmann, K. Xu, E.F. Ferro, D. Goldman, G. Winterer, Association of the G1947A COMT (Val(108/158)Met) gene polymorphism with prefrontal P300 during information processing, Biol. Psychiatry 54 (2003) 40–48. [28] A. Goldstein, K.M. Spencer, E. Donchin, The influence of stimulus deviance and novelty on the P300 and novelty P3, Psychophysiology 39 (2002) 781–790. [29] C. Grillon, E. Courchesne, R. Ameli, R. Elmasian, D. Braff, Effects of rare non-target stimuli on brain electrophysiological activity and performance, Int. J. Psychophysiol. 9 (1990) 257–267. [30] E. Halgren, P. Baudena, J.M. Clarke, G. Heit, C. Li`egeois, P. Chauvel, A. Musolino, Intracerebral potentials to rare target and distractor auditory and visual stimuli. I. Superior temporal plane and parietal lobe, Electroencephalogr. Clin. Neurophysiol. 95 (1995) 191–220. [31] E. Halgren, P. Baudena, J.M. Clarke, G. Heit, K. Marinkovic, B. Devaux, J.P. Vignal, A. Viraben, Intracerebral potentials to rare target and distractor auditory and visual stimuli. II. Medial, lateral and posterior temporal lobe, Electroencephalogr. Clin. Neurophysiol. 95 (1995) 229–250. [32] E. Halgren, K. Marinkovic, Neurophysiological networks integrating human emotions, in: M.S. Gazzaniga (Ed.), The Cognitive Neurosciences, MIT Press, Cambridge, 1996, pp. 217–244. [33] E. Halgren, K. Marinkovic, P. Chauvel, Generators of the late cognitive potentials in auditory and visual oddball tasks, Electroencephalogr. Clin. Neurophysiol. 106 (1998) 156–164. [34] M.S. Hamalainen, R.J. Ilmoniemi, Interpreting magnetic fields of the brain: minimum norm estimates, Med. Biol. Eng. Comput. 32 (1994) 35–42. [35] A.P. Holmes, R.C. Blair, J.D. Watson, I. Ford, Non-parametric analysis of statistic images from functional mapping experiments, J. Cereb. Blood Flow Metab. 16 (1996) 7–22. [36] H. Horn, N. Syed, H. Lanfermann, K. Maurer, T. Dierks, Cerebral networks linked to the event-related potential P300, Eur. Arch. Psychiatry Clin. Neurosci. 253 (2003) 154–159. [37] Y.W. Jeon, J. Polich, P300 asymmetry in schizophrenia: a meta-analysis, Psychiatry Res. 104 (2001) 61–74. [38] R. Johnson, The amplitude of the P300 component of the event related potentials: review and synthesis, in: P.K. Ackles, J.R. Jennings, M.G.H. Coles (Eds.), Advances in Psychophysiology, vol. 2, JAI Press, Greenwich, CT, 1988, pp. 69–137. [39] J. Katayama, J. Polich, Stimulus context determines P3a and P3b, Psychophysiology 35 (1998) 22–23. [40] S. Keri, Z. Janka, Critical evaluation of cognitive dysfunctions as endophenotypes of schizophrenia, Acta Psychiatr. Scand. 110 (2004) 83–91.

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230 [41] A. Khateb, J.M. Annoni, T. Landis, A.J. Pegna, M.C. Custodi, E. Fonteneau, S.M. Morand, C.M. Michel, Spatio-temporal analysis of electric brain activity during semantic and phonological word processing, Int. J. Psychophysiol. 32 (1999) 215–231. [42] K.A. Kiehl, K.R. Laurens, T.L. Duty, B.B. Forster, P.F. Liddle, Neural sources involved in auditory target detection and novelty processing: an event-related fMRI study, J. Psychophysiol. 38 (2001) 133–142. [43] K.A. Kiehl, P.F. Liddle, Reproducibility of the hemodynamic response to auditory oddball stimuli: a six-week test-retest study, Hum. Brain Mapp. 18 (2003) 42–52. [44] E. Kirino, A. Belger, P. Goldman-Rakic, G. McCarthy, Prefrontal activation evoked by infrequent target and novel stimuli in a visual target detection task: an event-related functional magnetic resonance imaging study, J. Neurosci. 20 (2000) 6612–6618. [45] F. Klostermann, M. Wahl, F. Marzinzik, G.H. Schneider, A. Kupsch, G. Curio, Mental chronometry of target detection: human thalamus leads cortex, Brain 129 (2006) 923–931. [46] R.T. Knight, Decreased response to novel stimuli after prefrontal lesions in man, Electroencephalogr. Clin. Neurophysiol. 59 (1984) 9–20. [47] R.T. Knight, M.F. Grabowecky, D. Scabini, Role of human prefrontal cortex in attention control, in: H.H. Jasper, S. Goldman-Rakic, S. Riggio (Eds.), Epilepsy and the Functional Anatomy of the Frontal Lobe, Raven Press, New York, 1995, pp. 21–36. [48] T. Koenig, D. Lehmann, Microstate in language-related brain potential maps show noun-verb differences, Brain Lang. 53 (1996) 169–182. [49] M. Kutas, G. McCarthy, E. Donchin, Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time, Science 197 (1977) 792–795. [50] D. Lehmann, W. Skrandies, Reference-free identification of components of checkerboard-evoked multichannel potential fields, Electroencephalogr. Clin. Neurophysiol. 48 (1980) 609–621. [51] D. Lehmann, W. Skrandies, Spatial analysis of evoked potentials in man. A review, Prog. Neurobiol. 23 (1984) 227–250. [52] D. Lehmann, H. Ozaki, I. Pal, EEG alpha map series: brain micro-states by space-oriented adaptive segmentation, Electroencephalogr. Clin. Neurophysiol. 67 (1987) 271–288. [53] D. Lehmann, W.K. Strik, B. Henggeler, T. Koenig, M. Koukkou, Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking. I. Visual imagery and abstract thoughts, Int. J. Psychophysiol. 29 (1998) 1–11. [54] D. Lehmann, P.L. Faber, S. Galderisi, W.M. Herrmann, T. Kinoshita, M. Koukkou, A. Mucci, R.D. Pascual-Marqui, N. Saito, J. Wackermann, G. Winterer, T. Koenig, EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study, Psychiatry Res. 138 (2) (2005) 141–156. [55] D.E. Linden, D. Prvulovic, E. Formisano, M. Vollinger, F.E. Zanella, R. Goebel, T. Dierks, The functional neuroanatomy of target detection: an fMRI study of visual and auditory oddball tasks, Cereb. Cortex 9 (1999) 815–823. [56] D.E. Linden, The p300: where in the brain is it produced and what does it tell us? Neuroscientist 11 (2005) 563–576. [57] N.K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann, Neurophysiological investigation on the basis of the fMRI signal, Nature 412 (2001) 150–157. [58] A.R. Luria, The Working Brain, Basic Books, New York, 1973. [59] A. Magliero, T.R. Bashore, M.G. Coles, E. Donchin, On the dependence of P300 latency on stimulus evaluation processes, Psychophysiology 21 (1984) 171–186. [60] G. McCarthy, E. Donchin, A metric for thought: a comparison of P300 latency and reaction time, Science 211 (1981) 77–80. [61] G. McCarthy, M. Luby, J. Gore, P. Goldman-Rakic, Infrequent events transiently activate human prefrontal and parietal cortex as measured by functional MRI, J. Neurophysiol. 77 (1997) 1630–1634. [62] V. Menon, J.M. Ford, K.O. Lim, G.H. Glover, A. Pfefferbaum, Combined event-related fMRI and EEG evidence for temporal–parietal cortex activation during target detection, Neuroreport 8 (1997) 3029– 3037.

229

[63] M.M. Mesulam, Large-scale neurocognitive networks and distributed processing for attention, language, and memory, Ann. Neurol. 28 (1990) 597–613. [64] C.M. Michel, M.M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, R. Grave de Peralta, EEG source imaging, Clin. Neurophysiol. 115 (2004) 2195–2222. [65] M. Moln´ar, On the origin of the P300 component of the event-related potential component: review and synthesis, Int. J. Psychophysiol. 17 (1994) 129–144. [66] C. Mulert, L. Jager, R. Schmitt, P. Bussfeld, O. Pogarell, H.J. Moller, G. Juckel, U. Hegerl, Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection, Neuroimage 22 (2004) 83–94. [67] C. Mulert, O. Pogarell, G. Juckel, D. Rujescu, I. Giegling, D. Rupp, P. Mavrogiorgou, P. Bussfeld, J. Gallinat, H.J. Moller, U. Heger, The neural basis of the P300 potential. Focus on the time-course of the underlying cortical generators, Eur. Arch. Psychiatry Clin. Neurosci. 254 (2004) 190–198. [68] R.C. Oldfield, The assessment and analysis of handedness: the Edinburgh inventory, Neuropsychologia 9 (1971) 97–113. [69] B. Opitz, A. Mecklinger, A.D. Frederici, D.Y. von Cramon, The functional neuroanatomy of novelty processing: integrating ERP and fMRi results, Cereb. Cortex 9 (1999) 279–391. [70] J.S. Pae, J.S. Kwon, T. Youn, H.J. Park, M.S. Kim, B. Lee, K.S. Park, LORETA imaging of P300 in schizophrenia with individual MRI and 128channel EEG, Neuroimage 20 (2003) 1552–1560. [71] H.J. Park, J.S. Kwon, T. Youn, J.S. Pae, J.J. Kim, M.S. Kim, K.S. Ha, Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia, Hum. Brain Mapp. 17 (2002) 168–178. [72] R.D. Pascual- Marqui, C.M. Michel, D. Lehmann, Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain, Int. J. Psychophysiol. 18 (1994) 49–65. [73] R.D. Pascual-Marqui, D. Lehmann, T. Koenig, K. Kochi, M.C.G. Merlo, D. Hell, M. Koukkou, Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia, Psychiatr. Res. Neuroimaging 90 (1999) 169–179. [74] R.D. Pascual-Marqui, M. Esslen, K. Kochi, D. Lehmann, Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review, Methods Find Exp. Clin. Pharmacol. 24 (Suppl C) (2002) 91–95. [75] S.H. Patel, P.N. Azzam, Characterization of N200 and P300: selected studies of the event-related potential, Int. J. Med. Sci. 2 (2005) 147– 154. [76] A. Pfefferbaum, J.M. Ford, B.G. Wenegrat, W.T. Roth, B.S. Kopell, Clinical application of the P3 component of event-related potentials. I. Normal aging, Electroencephalogr. Clin. Neurophysiol. 59 (1984) 85– 103. [77] T.W. Picton, The P300 wave of the human event-related potential, J. Clin. Neurophysiol. 59 (1992) 456–479. [78] J. Polich, Habituation of P300 from auditory stimuli, Psychobiology 17 (1989) 19–28. [79] J. Polich, Overview of P3a and P3b, in: J. Polich (Ed.), Detection of Change: Event-related Potential and fMRI Findings, Kluwer, Boston, 2003, pp. 83–98. [80] M. Scherg, P. Berg, Use of prior knowledge in brain electromagnetic source analysis, Brain Topogr. 4 (1991) 143–150. [81] M. Scherg, H. Buchner, Somatosensory evoked potentials and magnetic fields: separation of multiple source activities, Physiol. Meas. 14 (Suppl. 4A) (1993) 35–39. [82] R.F. Simons, F.K. Graham, M.A. Miles, X. Chen, On the relationship of P3a and the Novelty-P3, Biol. Psychol. 56 (2001) 207–218. [83] E.N. Sokolov, The modeling properties of the nervous system, in: M. Cole, F. Maltzman (Eds.), A Handbook of Contemporary Soviet Psychology, Basic Books, New York, 1969, pp. 671–704. [84] E.N. Sokolov, The orienting response, and future directions of its development, Pavlov. J. Biol. Sci. 25 (1990) 142–150. [85] M. Soltani, R.T. Knight, Neural origins of the P300, Crit. Rev. Neurobiol. 14 (2000) 199–224.

230

U. Volpe et al. / Brain Research Bulletin 73 (2007) 220–230

[86] A.W. Song, Single-shot EPI with signal recovery from the susceptibilityinduced losses, Magn. Reson. Med. 46 (2001) 407–411. [87] K.M. Spencer, J. Dien, E. Donchin, Spatiotemporal analysis of the late ERP responses to deviant stimuli, Psychophysiology 38 (2001) 343–358. [88] N.K. Squires, K.C. Squires, S.A. Hillyard, Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man, Electroencephalogr. Clin. Neurophysiol. 38 (1975) 387–401. [89] A.A. Stevens, P. Skudlarski, J.C. Gatenby, J.C. Gore, Event-related fMRI of auditory and visual oddball tasks, Magn. Reson. Imaging 18 (2000) 495–502. [90] M.C. Stevens, K.R. Laurens, P.F. Liddle, K.A. Kiehl, The hemodynamics of oddball processing during single-tone and two-tone target detection tasks, Int. J. Psychophysiol. 60 (2006) 292–303. [91] W.K. Strik, T. Dierks, E. Franzek, G. St¨ober, K. Maurer, P300 asymmetries in schizophrenia revisited with reference-independent methods, Psychiatry Res. 55 (1994) 153–166. [92] W.K. Strik, A.J. Fallgatter, D. Brandeis, R.D. Pascual-Marqui, Threedimensional tomography of event-related potentials during response

[93]

[94]

[95]

[96]

inhibition: evidence for phasic frontal lobe activation, Electroencephalogr. Clin. Neurophysiol. 108 (4) (1998) 406–413. S.J. Tsai, Y.W. Yu, T.J. Chen, J.Y. Chen, Y.J. Liou, M.C. Chen, C.J. Hong, Association study of a functional catechol-O-methyltransferase-gene polymorphism and cognitive function in healthy females, Neurosci. Lett. 338 (2003) 123–126. M. Wagner, T. Kohler, M. Fuchs, J. Kastner, An extended source model for current density reconstruction, in: J. Nenonen, J.R. Ilmoniemi, K. Katila (Eds.), Proceedings of the XII International Conference on Biomagnetism, BIOMAG 2000, Espoo, Finland, Helsinki University of Technology, 2000, pp. 749–752. G.A. Worrell, T.D. Lagerlund, F.W. Sharbrough, B.H. Brinkmann, N.E. Busacker, K.M. Cicora, T.J. O’Brien, Localization of the epileptic focus by low-resolution electromagnetic tomography in patients with a lesion demonstrated by MRI, Brain Topogr. 12 (2000) 273–282. J. Yao, J.P. Dewald, Evaluation of different cortical source localization methods using simulated and experimental EEG data, Neuroimage 25 (2005) 369–382.