Interindividual variability in EEG correlates of attention and limits of functional mapping

International Journal of Psychophysiology 65 (2007) 238 – 251 www.elsevier.com/locate/ijpsycho

Interindividual variability in EEG correlates of attention and limits of functional mapping Luis F.H. Basile a,b,⁎, Renato Anghinah c , Pedro Ribeiro d , Renato T. Ramos a,b , Roberto Piedade d , Gerson Ballester b , Enzo P. Brunetti b a

Laboratory of Psychophysiology, Faculdade de Psicologia e Fonoaudiologia, UMESP, Brazil b Division of Neurosurgery, University of São Paulo Medical School, Brazil c Department of Neurology, University of São Paulo Medical School, Brazil d Department of Psychiatry, Federal University of Rio de Janeiro, Brazil Received 19 December 2006; received in revised form 2 April 2007; accepted 3 May 2007 Available online 10 May 2007

Abstract In this study, we analyzed the EEG oscillatory activity induced during a simple visual task, in search of spectral correlate(s) of attention. This task has been previously analyzed by conventional event-related potential (ERP) computation, and Slow Potentials (SPs) were seen to be highly variable across subjects in topography and generators [Basile LF, Brunetti EP, Pereira JF Jr, Ballester G, Amaro E Jr, Anghinah R, Ribeiro P, Piedade R, Gattaz WF. (2006) Complex slow potential generators in a simplified attention paradigm. Int J Psychophysiol. 61(2):149–57]. We obtained 124-channel EEG recordings from 12 individuals and computed latency-corrected peak averaging in oscillatory bursts. We used current– density reconstruction to model the generators of attention-related activity that would not be seen in ERPs, which are restricted to stimulus-locked activity. We intended to compare a possibly found spectral correlate of attention, in topographic variability, with stimulus-related activity. The main results were (1) the detection of two bands of attention-induced beta range oscillations (around 25 and 21 Hz), whose scalp topography and current density cortical distribution were complex multi-focal, and highly variable across subjects (topographic dispersion significantly higher than sensory-related visual theta induced band-power), including prefrontal and posterior cortical areas. Most interesting, however, was the observation that (2) the generators of task-induced oscillations are largely the same individual-specific sets of cortical areas active during the pre-stimulus baseline. We concluded that attention-related electrical cortical activity is highly individual-specific, and possibly, to a great extent already established during mere resting wakefulness. We discuss the critical implications of those results, in combination with results from other methods that present individual data, to functional mapping of cortical association areas. © 2007 Elsevier B.V. All rights reserved. Keywords: Attention; Cortical electrical activity; High-resolution electroencephalography; Slow potentials; Source localization; Functional mapping

1. Introduction We have been studying the topography and generators of Slow Potentials (SPs), direct correlates of attention, guided by the neuroanatomy of cortico–cortical connections. We expected that (selective) attention to different visual domains would correspond to SP generation in given prefrontal cortical areas. ⁎ Corresponding author. Division of Neurosurgery, University of São Paulo Medical School, Av. Dr. Ovidio Pires de Campos 785, Cerqueira Cesar, São Paulo, SP, 05403-010, Brazil. Tel.: +55 11 30697284, 55 11 32846821; fax: +55 11 2894815. E-mail address: [email protected] (L.F.H. Basile). 0167-8760/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2007.05.001

This possibility was based on the assumption of a simple functional-anatomical equivalence, with functional circuits paralleling the relatively specific connections between visual areas (Macko and Mishkin, 1985) and other neocortical areas, particularly prefrontal (Pandya et al., 1988; Pandya and Yeterian, 1990; Barbas, 1992).We searched for task-specific electrophysiological indexes of prefrontal cortex activity, starting from MEG studies and one or two dipole models (Basile et al., 1994, 1996, 1997), until the development of current density reconstruction (CDR) methods (Basile et al., 2002, 2003). By using CDR, we always obtained a complex, multifocal cortical topography of SPs, and have been particularly intrigued by the high variability in the individual sets

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of cortical generator areas (Basile et al., 2002, 2003, 2006). Those characteristics place SPs in contrast with sensory evoked potentials and long latency potentials of the P300 class, which are fairly similar in topography across subjects. Inter-individual variability is commonly reported in functional studies, but with respect to intensity and time pattern of physiological changes in given anatomical loci, but much less commonly to the loci themselves. Our first studies used relatively complex tasks , including stimulus comparison and memory, in addition to selective attention to visual domains (Basile et al., 2002, 2003). Thus, we recently used a simple visual attention task to reduce the likelihood of variable hypothetical strategies in individual performance. Although as far as reports of a common experience of task performance could suggest common strategy, patterns of SP scalp distribution and modeled current distribution remained equally complex and variable across subjects (Basile et al., 2006). The present work is a complementary analysis on the same task. Since the high variability could be a peculiarity of SPs, we decided to search for other correlate(s) of attention that could be more universal across subjects, but possibly be out of synchrony with stimuli, and thus detected only by induced power analysis. The first objective of this study was to describe the task-time pattern of spectral changes, to verify which frequency bands are more closely related to sensory stimulation and detection, as opposed to activity more related to attention, present within the ISI (S2 anticipation period of the S1–S2 design). We expected delta, theta and alpha range oscillations to fit in the sensoryrelated category, since they are the main components of evoked potentials (Basar et al., 2001; Gruber et al., 2005; Hanslmayr et al., 2007; Valencia et al., 2006; Fell et al., 2004). As a possible new attention correlate, we expected pre-S2 activity in the theta band, although beta range activity has an old but vague association with arousal and attention, which justifies its still controversial use in biofeedback (Ramirez et al., 2001). Theta bursts have been occasionally observed in the ongoing EEG of awake, healthy individuals during tasks such as calculation with pen and paper (Mizuki et al., 1980), and in one instance been reported in cue-target (S1–S2) paradigm (Nakashima and Sato, 1993). Our second objective was to compare the topography of a putative newly found pre-S2 spectral increase, with activity more closely related to sensory stimulation. In our previous work on the event-related potentials obtained during this task (Basile et al., 2006), the high topographic variability of SPs was only visually compared with the least variable topography across subjects, of the N200. We now planned to quantify the dispersion of individual topography from the power-normalized group mean, comparing the most common (across subjects) stimulus-related spectral band with any possibly found induced power correlate of attention. Our third goal was to compute corrected-latency averaging of narrow band filtered epochs, restricted to bands occurring in the pre-S2 window, and then perform CDR to localize its generators. Finally, and secondarily, we planned to compare the topographies of evoked and induced activity, and to compute an overall measure of phase synchrony across all electrodes in a

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fixed sub-set of the montage. This phase analysis would afford a first step to understand the relations between induced power and phase changes, in this experimental task. 2. Methods 2.1. Subjects Twelve healthy individuals with normal vision and hearing, 9 male and 3 female, participated in the study. They ranged in age between 20 and 45 years, with no history of drug or alcohol abuse, and no current drug treatment. Eleven subjects were current or former medical students. All subjects signed consent forms approved by the Ethics Committee of the University of São Paulo Hospital. 2.2. Stimuli and Task A commercial computer program (Stim, Neurosoft Inc.) controlled all aspects of the task. Visual stimuli composing the cue-target pairs (S1–S2) consisted in small rectangles (eccentricity ±0.8°, S1: 100 ms duration, S2: 17 ms; white background). In half of the trials, the S2 rectangle contained a grey circle – the task target – with ±0.3° of eccentricity. A pilot study determined stimulus luminance and task difficulty: we used the ninth among fourteen levels of grey, starting from white; the masking stimulus had the same grey level (a ‘checkerboard’ grey and white square composed by one-by-one pixel size squares), and was continuously present, along with the fixation point, except during S1 and S2 presentation; a secondary adding task, with visual or auditory one digit numbers presented inter- and intra-trials, served to confirm the need of temporal attention for the primary task execution, for it greatly impaired its performance. S1 was followed by S2, with onsets separated in time by 1.6 s. The ITI was variable, ranging from 2.3 to 5 s. We instructed the subjects that a rectangle would be presented to indicate that 1.6 s later it would flash again but quickly, containing or not the target circle. The subject decided whether there was a target inside the S2 rectangle, and indicated presence of the target by pressing the right button with the right thumb or absence of the target by pressing the left button with the left thumb. We explicitly deemphasized reaction time in the instructions and measured performance exclusively by the percent correct trials, from the total of 96 trials comprising the experiment. We did not present reaction times for two reasons. First, the nature of the instructions, which also included the possibility of occasionally holding the response and have a brief pause if some discomfort was felt, e.g., in the neck or mandible. Second, the program has a technical limitation, making impossible the recording of fast responses, when masking and fast presentation are combined. An eye fixation dot was continually present on the center of the screen, as well as a stimulus-masking background, to prevent after-images. To confirm the expecting attention index role of some possibly found EEG rhythm, we also used a passive stimulation control condition (same number of trials, but S2s never contained targets; the order between conditions was random across subjects), during which subjects were only required

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to fixate and relax, but keep focusing and some level of arousal, since they were explicitly told to avoid blinking during the S1– S2 interval on both conditions. 2.3. EEG recording and acquisition of MRIs We used a fast Ag/AgCl electrode positioning system consisting of an extended 10–20 system, in a 124-channel montage (Quik-Cap, Neuromedical Supplies®), and an impedance-reducing gel which eliminated the need for skin abrasion (Quick-Gel, Neuromedical Supplies®). Impedances usually remained below 3 kΩ, and channels that did not reach those levels were eliminated from the analysis. To know the actual scalp sampling or distribution of electrodes in each individual with respect to the nervous system, we used a digitizer (Polhemus®) to record actual electrode positions with respect to each subject's fiduciary points: nasion and preauricular points. After co-registration with individual MRIs, the recorded coordinates were used for realistic 3D mapping onto MRI segmented skin models, and later used to set up the source reconstruction equations (distances between each electrode and each dipole supporting point). Two bipolar channels, out of the 124-channels in the montage were used for recording both horizontal (HEOG) and vertical electrooculograms (VEOG). Left mastoid served as reference only for data collection (common average reference was used for source modeling) and Afz was the ground. We used four 32-channel DC amplifiers (Synamps, Neuroscan Inc.) for data collection and the Scan 4.3 software package (Neurosoft Inc.) for initial data processing (before computation of averages). The filter settings for acquisition were from DC to 30 Hz, and the digitization rate was 250 Hz. The EEG was collected continuously, and epochs for averaging spanned the interval from 700 ms before S1 to 400 ms after S2 presentation. Baseline was defined as the 400 ms preceding S1. Epoch elimination was performed visually for eye movements and muscle artifacts, and then automatic: visual inspection served to eliminate occasional transient electronic or head movement noise present in channels other than EOG; epochs containing signals in either HEOG or VEOG channels above +50 or below − 50 μV were eliminated. In our montage, the VEOG detected, typically, blinks as deflections above 130 μV in the positive direction. MRIs were obtained by a 1.5 Tesla GE machine, model Horizon LX. Image sets consisted in 124 T1-weighed saggital images of 256 by 256 pixels, spaced by 1.5 mm. Acquisition parameters were: standard echo sequence, 3D, fast SPGE, two excitations, RT = 6.6 ms, ET = 1.6 ms, flip angle of 15°, F.O.V = 26 × 26 cm. Total acquisition time was around 8 min. 2.4. Frequency-time analysis (task-induced power) and power scalp topography After artifact rejection, the signal from each channel was spectrally analyzed by means of a Short Time Fourier Transform (STFT), to obtain frequency-time charts of the induced (stimulus related, but not stimulus-locked) and evoked (stimulus -locked) spectrum of the interval from 700 ms prev-

ious to S1, to 400 ms after S2. To obtain the induced power spectrum (Tallon-Baudry et al., 1996), the time-frequency decomposition was made for each electrode and each trial, from DC to 30 Hz, and the resulting charts were then averaged, both for each electrode and across electrodes. The evoked power spectrum was obtained applying the spectral decomposition to the averaged signal. Recently, it has been demonstrated that this method is mathematically equivalent to others like the Hilbert transform, or wavelet decomposition, and that each of them yields equivalent results in practical applications to neuronal signals (Bruns and Eckhorn, 2004). The decomposition was computed on the EEG tapered by a sliding Hamming window, 256 points in size for frequencies over 5 Hz, and 512 points between 2 and 5 Hz, with a temporal resolution of N/10 (N being the number of temporal points of the raw signal), and a frequency resolution of 4 bins per Hertz. Then, we normalized the average power for each electrode to obtain z-scores of increments or decrements in each frequency bin with respect to the power in the same frequency during the baseline (bPjN = (Pj − μ j)/σj; given Pj = spectral power at each time point in electrode j, μj and σj are the mean and standard deviation, respectively, of the average power during the baseline for the electrode). The computation of power changes by z-score values is sufficient for our first purpose of describing taskrelated oscillations. At this stage, a non-parametric statistical comparison (Wilcoxon and Sign tests) was limited to verify if power changes, in the pre-S2 time region of interest, differ between the task and the passive stimulation condition. We computed realistic three-dimensional topographic maps of the scalp distribution of normalized power, at each frequency band that demonstrated task-induced changes, for each subject, over the reconstructed scalp anatomy. To this purpose, we used a commercial sotfware (Curry V 4.6, Neurosoft Inc.), that coregistered individual MRI sets (skin model, see below) with the actual position of each electrode with respect to common landmarks, and linearly interpolated the instantaneous values of power to obtain continuous maps. We intended to quantitatively compare the topographic variance across subjects, between activity present in the pre-S2 window, with sensory-related, that is, activity restricted to post-stimulus windows. For that purpose, we used a scalar measure of topographic deviation from group mean, that previously proved to be useful in demonstrating the higher complexity and variability of SP distribution in schizophrenia, obvious at visual inspection (Basile et al., 2004). This measure is a simple quadratic norm, or spatial variance (square root of the mean electrode-by-electrode squared difference between individual and group average), computed after power normalization, to emphasize topography and reduce the influence of absolute voltage differences. The deviation indexes obtained for each type of activity, the most stimulus-bound and pre-S2, were statistically compared, by the non-parametric test of Wilcoxon. 2.5. Computation of corrected latency burst averages According to the observed induced frequency bands observed for each individual, the original artifact-free EEG

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epochs (ranging from 700 ms before S1 through 500 ms after S2) from each subject were filtered around the bands of interest (Butterworth, 96dB rolloff, 1–3 Hz for delta, 3–7 for theta, 7–9 for alpha1, 9–12 for alpha2, 23–26 Hz for beta2, 18–22 for beta1, and in some subjects, who showed an additional low beta band, between 13 and 15 Hz).Epochs for correct or incorrect responses were pooled together, since our main interest was centered on activity preceding S2. The resulting filtered epochs were then processed by an algorithm developed by us for searching the peaks of bursts within the task-time windows of interest (schematic representation of method in Fig. 1). Filtered

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epochs were thus cut again starting from positive voltage peaks, resulting in new epochs, ranging from 400 ms before to 400 ms after the peaks. A minimum of 60 epochs was the criterion for averaging, for each individual and frequency band, using each channel in the search for peaks. However, the actual minimum number of epochs used ranged from 62 in to 87. Then, a grand average was computed using the averages obtained by guidance from each channel. Since this method would in principle suffer from the limitation of confounding any possible systematic time (direction) relations between active areas, for instance if groups of areas were active in sequence in a given frequency band, we

Fig. 1. Schematic representation of the steps of the method: (1) Computation of task-induced band-power. (2) EEG Epochs were band-pass filtered in individualspecific narrow frequency bands, and positive voltage peaks in each channel (in this example, Fz) served to create one multi-channel average by (3) realignment of original epochs. (4) Single-channel guided multi-channel averages thus computed (resulting in ‘wavelet-shaped’ potentials as exemplified for Cz) were finally grand averaged (5). Grand averages were decomposed by Independent Component Analysis (ICA), and their main and second space-time components fed the Current Density Reconstruction (CDR) algorithm.

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also computed partial averages using groups of guiding channels ranked for latency of occurrence of peaks. That is, using only the first one fourth of channels (those with overall shorter peak latencies), and second, third and last fourth of channels. We also performed independent analyses to study possible time relations between groups of electrodes (next section). Finally, in all cases we also computed pre-S1 burst averages (representing the baseline topography for each frequency band), where the program searched peaks from − 400 to 0 ms before S1, for comparison with the task-induced bursts. 2.6. Inter-electrode phase-synchrony Since a systematic and complete phase analysis of the present data would consist in a separate and major work, we decided have only a first approach: we computed only the overall pattern of phase relations, in the form of averages across all pair of channels. This was used only to verify whether there were overall task-related phase changes that correlated in time, in a statistically significant way (non-parametric, Spearman's rho), with induced power, for each individual and frequency band. The practice of separately computing phase is becoming common in event-related power studies (Fell et al., 2004; Hanslmayr et al., 2007). Due to volume-conduction effects, we selected a group of 25 regularly interspaced electrodes from the original array to compute this index. A similar procedure used to obtain the power spectrum of the signal was used to compute the phase-locking value between electrodes (Lachaux et al., 1999). That is, a STFT of the signal from each electrode and trial was computed to obtain the instantaneous angular phase for each frequency, during a time window centered at time t. Then, after subtracting the constant angular phase, a complex vector of unitary value was constructed for each channel, trial, frequency and time. With this value, a matrix of differences of phase values between electrodes, in each trial, was computed for each frequency-time point, and then averaged over all the trials. Using the modulus of this complex value, we obtained for each pair of electrodes, in each frequency and time point, a phase-difference value between 0 (random phase relation) and 1 (constant phase relation). That is, Φi ( f, t, k) being the phase value of electrode i, at frequency f, time t, and trial k, and Φj ( f, t, k) the phase value of electrode j, in the same frequency, time and trial, the phase-locking value was computed as Uij ð f ; t Þ ¼ 1=N jRk ¼ 1N Ui  Uj j: The phase-locking values obtained for the time interval posterior to stimulus presentation was then z-normalized by the values obtained during the baseline interval in the same way as time-frequency spectral matrices. Finally, we computed correlations between task-related power and phase, and verified whether they were statistically significant. 2.7. Intracranial source reconstruction The computed averaged bursts, MRI sets and electrode position digitization files were the raw data for all further source

analysis (Curry V 4.6, Neurosoft Inc.). A detailed description of the reconstruction procedure, and a discussion on the criteria for method choice and shortcomings, as well as on critical steps, may be found in the methods sections of previous publications (Basile et al., 2002; 2006). Noise in the data was defined as the variance of the 20% lowest amplitude points in each average. For the inclusion of a ‘noise component’ into the source model, the physical unit-free or ‘standardized’ data (with retained polarity) were decomposed by Independent Component Analysis (ICA), which searches for the highest possible statistical independence or redundancy reduction between components (in this case, space-time averaged data patterns), a robust method of blind signal decomposition/deconvolution (for a review, see e.g. Hyvarinen and Oja, 2000). ICA was applied to each individual's whole space-time data set, i.e., to the m × n data matrix (m used channels times 201 time samples corresponding to the 800 ms composing the averaged bursts). Finally, we fed the reconstruction algorithm with the main ICA component(s) as data to be fitted. Thus, the ‘noise component’ of the model was defined as the sum of remaining components (with loadings below SNR = 1), all of which added together lead invariably to negligible scalp potentials when compared to the main components. In practice, in all cases, only two space-time ICA components were then modeled. MRI sets were linearly interpolated to create 3-dimensional images, and semi-automatic algorithms based on pixel intensity bands served to reconstruct the various tissues of interest. A Boundary Element Model (BEM) of the head compartments was implemented, by triangulation of collections of points supported by the skin, skull and cerebrospinal fluid (internal skull) surfaces. Mean triangle edge lengths for the BEM surfaces were, respectively, 10, 9 and 7 mm. Fixed conductivities were attributed to the regions enclosed by those surfaces, respectively, 0.33, 0.0042 and 0.33 S/m. Finally, a reconstructed brain surface, with mean triangle side of 3 mm, served as the model for dipole positions, corresponding to a minimum of 20 thousand points. The electrode positions were projected onto the skin's surface following the normal lines to the skin. The detailed description of the assumptions and methods used by the “Curry 4.6” software for MRI processing and source reconstruction may be found elsewhere (e.g., Buchner et al., 1997; Fuchs et al., 1998; Fuchs et al., 1999). The analysis program then calculated the lead field matrix that represents the coefficients of the set of equations which translate the data space (SNR values in the set of channels per time point) into the model space (above 20 thousand dipole supporting points). The source reconstruction method itself was Lp norm minimization, with p = 1.2 both for data and model terms. The regularization factor, or λ values to be used, typically converged after repeating the fitting process three to four times (λ gives the balance between goodness of fit and model size). Resulting foci of current density were inspected with respect to the individual anatomy directly, in terms of which estimated cytoarchitectonic areas contained them (areas may then be scored for relative intensity; Basile et al., 2003). The estimated Brodmann areas containing current foci were tabulated after verification by comparison with classical illustrations and the conventional Talairach and Tournoux atlases (1993, 1997).

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3. Results We organized the presentation of results in subsections, starting from (1) the time behavior of the task-induced spectral changes, followed by their (2) spatial, scalp topographic properties, in particular contrasting beta with theta bands, then describing the (3) amplitude and topographic characteristics of the corrected-latency oscillatory averages. After a brief presentation of relevant (4) phase analysis results, we present the (5) current density results, restricted to the band of interest, the beta range. 3.1. Task performance All subjects reported that performance was relatively easy, provided that they were strongly attending during the critical time of S2 presentation. The overall average performance was 88.5 % correct responses (standard deviation 8.3 %). 3.2. Induced power with respect to task-time The overall pattern of task-induced power, in comparison with task-evoked power, can be seen on Fig. 2, averaged across subjects and electrodes. Against one of our expectations, taskinduced theta power was not present in the ISI interval, showing a clear post-stimulus increase pattern in all subjects, practically returning to baseline level during the ISI. The two peaks corresponded to around 180 ms post S1 or S2, thus coinciding with the latency of the N200 evoked potential component. The same purely stimulus-related behavior was observed for induced delta power, but in this case the peaks occurred later, around 350 ms, and in almost all subjects with much higher amplitude after S2. Fig. 2(left) shows the overall task-time behavior of the induced power, with data collapsed across electrodes and subjects. The time pattern of induced power was similar to the evoked pattern for theta and delta bands (Fig. 2, right). Three major frequency bands presented increases during the ISI, the pre-S2 region of interest: sub-delta (from DC to 1 Hz, virtually exclusive component of the SPs, whose analysis is presented in Basile et al., 2006), the alpha bands, and as one

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new observation, two beta bands. Induced alpha behavior in relation to task time was multiphasic, with a relative (seen as an inflection) reduction/de-synchronization around 200 ms after the stimuli, a relative peak (almost unnoticeable when compared to the maximum induced alpha) overlapping with the delta peak, and maximum power at the center of the ISI, overall around 700 ms. Half of the subjects presented a distinct alpha-1 band – around 8 Hz – in addition to the alpha-2 band centered around 11 Hz, and the remaining half, either a broad or narrow alpha-2 centered band. One of the two new and main findings in this work regarded the beta range. In all subjects, a narrow induced beta band around 25 Hz was observed, throughout most of the ISI, peaking during the pre-S2 time range, following a time pattern that we originally expected to fit a putative attention-related induced theta band. In addition, all subjects presented a broader beta band roughly around 21 Hz, and some subjects another narrow band close to 15 Hz, but more variable in frequency and time pattern. Given beta activity, mainly the 25 Hz band, had the time distribution between S1–S2 peaking in the pre-S2 window, our main critetion for a correlate of attention, we compared it with activity recorded during the passive stimulation control condition. There was a statistically significant increase in beta power during the ISI, when the task was compared to the passive stimulation control condition: beta mean global field power from 500 through 1600 ms, differed significantly between conditions both in parametric paired samples t-test (for beta1, p = 0,004; beta 2, p = 0,012), as in non-parametric tests (Wilcoxon test, p = 0,005 for beta1 and p = 0,012 for beta2; sign test, p = 0,006 for beta1 and p = 0,039 for beta2). Fig. 3 shows beta mean global field power collapsed across individuals, and z-score of power scatter-plot in both conditions and bands. 3.3. Topography of induced power (beta versus theta patterns) Regarding the scalp topography of delta and theta, both bands had a posterior distribution of task-induced power maxima. In ten subjects, the topography of theta power was practically indistiguishable from that of the N200 peak voltage distribution (typically double peaks at the occipital region); in the two remaining subjects, delta topography was closer to

Fig. 2. (left) Task-induced power. In both figures, data are collapsed across channels and subjects (numbers on color scale indicate z-score – power change relative to baseline, y-axis: frequency in Hz; x-axis: time in ms). One may appreciate the overall time course of task-induced power changes, which were fairly common across subjects (see text for few frequency bands where exceptions occur, i.e., low beta and alpha-1). (right) Task-evoked power (Fourier transform of averaged event-related potentials). A direct quantitative comparison between evoked and induced power is precluded by the fact that z-scores are relative to differently defined baselines.

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Fig. 3. (Left) overall pattern of task-induced power increases in the beta range, collapsed across electrodes and individuals (upper curves), compared to passive stimulation control condition (lower curves). (Right) individual induced beta power z-score distribution in both conditions.

N200. Fig. 4 shows the induced power and corresponding topography of all bands (at peaks of task-induced power increases) in one example subject. The scalp topography of induced alpha showed the expected posterior distribution of highest power changes. However, the actual isocontour map shapes were different from the simple pattern resembling the N200 evoked potential component that we observed for theta and delta ranges. Each individual presented a complex and specific map shape. For alpha, evoked activity occurred exactly where the least of total induced power was observed, in the more strict post-stimulus time window: in this case also, the topographic rendering of the data resulted, in all subjects, in undistinguishable evoked and induced maps. Another interesting finding, that was always been observed in our S1–S2 paradigms, was the presence of evoked alpha throughout the ISI (peaking in the ISI with an overall 71% of the maximum post-stimulus, evoked alpha). Given the long ISI of 1.6 s, with respect to alpha wavelength, it is curious that so many alpha cycles could be synchronized with the task events. When computing the scalp distribution of beta power, we found a qualitative similarity with our findings regarding SPs: the topography of induced beta power changes was complex, multifocal, including frontal, temporal, and more posterior

peaks, and highly variable across subjects. We thus proceeded to quantify the dispersion of individuals from the powernormalized topography for beta-2 (more similar across subjects in time-pattern and frequency than beta-1) and theta (the most common sensory-related topography across subjects on visual inspection). Results clearly showed the larger dispersion of individuals from the (least representative) beta group average, as compared to the topographic dispersion from the theta group average (Fig. 5, where deviation (power) values are z-score transformed). A statistical comparison resulted in a highly significant difference between beta and theta deviation indexes (Wilcoxon: p = 0.002; sign test: p b 0.001). 3.4. Amplitude and topography of oscillatory burst averages The computation of oscillatory burst or corrected latency averages allowed the possibility of explicitly analyzing the relative task-related changes with respect to absolute measures of the baseline activity, both for average peak amplitudes, and more interesting, for their topographies, in each frequency band. Theta oscillations presented an overall increase in peak amplitude with respect to baseline of 13.8% (±11.5%), but ranging from no change in two subjects (increases around 6% in four

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Fig. 4. Task-induced band-power of one example individual (frequency in Hz at left, and power z-score at right), collapsed across all channels, and corresponding topographic distribution of the main points of change, that were common to all subjects. Color scale: extreme of power changes (yellow and magenta) correspond to zscore equal to 9.8 standard deviations, ‘hot’ colors indicate increase and ‘cold’ decrease relative to baseline. Below, time course of stimuli (in seconds) and mean eventrelated potential global field power (root mean square; bar = 5 μV).

subjects), to 34%. Delta oscillations presented an overall increase of 39.2% (± 42.3%), but ranging from reduction in two subjects (to 62 and 91% from baseline), virtually no change

Fig. 5. On top, examples of individuals presenting similar topography of taskinduced theta activity, for which beta distribution seen from the same angle is clearly more variable across subjects. Below, topographic deviation of each individual from normalized mean, between beta 2 and theta bands. Deviation was defined as the quadratic norm of the electrode-by-electrode difference between individual and group averaged data (across the montage, see text for details), as means to relatively quantify the higher beta variability depicted by visual inspection.

in one subject, to 89%. Alpha oscillations were increased in all subjects, ranging from 2 to 57% from baseline (overall 30.4% ± 16.3%). Finally, beta oscillations were enhanced in all cases (by 33.5% in average peak amplitude; std = 17.1%; range = 6 to 67%). The second new and important finding of this work came exactly from the topographic comparison between the two types of corrected latency burst averages: the topography of the averages computed for the pre-S1 baseline was visually undistinguishable from that for the task period proper, in all cases (subjects and frequency bands). This was confirmed by the ICA decomposition of the data, which also in all cases, showed that the main spatial component of task-induced oscillations was identical to the baseline activity, whereas the task-induced activity proper (or task-exclusive) corresponded to the second ICA component: for instance, in the theta range, this second component had the familiar topography of the N200 eventrelated potential component. For beta, which became the main focus of subsequent source analysis, we compared the amplitudes of task-exclusive with the baseline pattern. The task-exclusive activity or second ICA component, corresponded only to an overall 10.7% of the main component in electrical power (std = 12.2%, ranging from less than 1% in one subject to 36% in two subjects, but within 3 to 13% in the remaining subjects). It became then clear, based on the mere visual inspection of topographies, that the same sources already active before stimuli, presumably task-independent, were the main sources active during task execution. Another interesting aspect of the baseline activity was the very similar topography of average baseline oscillations across frequency bands, within subjects. In the case of our main interest, for instance, the beta-1 and beta-2 topographies were indistinguishable (in the beta band, even the scalp topography of the second component of beta-1 burst averages, in most of the subjects, demonstrated a partial overlap with the second

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component of the 25 Hz band). The only exception was the alpha band in half of the subjects, which had a peculiar topography, different and prevailing over the pattern similar across all remaining frequency bands. But in those cases also, the prevailing alpha spatial pattern remained the main component during the task time window as well. Since those results were absolutely unexpected, we performed a comparison, using four subjects, between the averages computed for the pre-S1 baseline and similar averages computed for a resting condition of a few minutes which preceded the start of the experiment (filtered continuous EEG was marked in local voltage peaks using a refractory period of 800 ms, to avoid overlap between epochs, since no task events were present). The same topography of baseline activity was thus observed, indicating that the pre-S1 baseline indeed reflects task-unrelated activity. Otherwise, it would be conceivable that baseline activity could

still reflect task engagement, due to the cyclical nature of the task, i.e., if trial expectation were physiologically identical to relevant S2 expectation. In one subject, we also replicated the experiment after one month, and the same topography of baseline activity was observed. On the other hand, and critical for future studies, was that in three of the subjects, who participated in previous experiments four and six years before, we observed very different topographies of the resting condition oscillations. 3.5. Inter-electrode phase analysis In all frequency bands but beta, the task-related phase coherence changes were complex and variable across subjects, to various degrees, depending on the sub-band in consideration. In all such variable cases, except for delta, a computation of

Fig. 6. Current density reconstruction results for all subjects. Current density indicated by small red arrows, (arrow size proportional to local current density). (A) Main component, identical to baseline activity. (B) Second, task-exclusive component. Arrow sizes rescaled with respect to (A); actual intensities correspond to an overall fraction of 10.7% with respect to baseline (std = 12.2%, range: 0.3–36%).

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Fig. 6 (continued ).

phase coherence collapsed across subjects lead to virtually flat waveforms, an indication of the lack of common time patterns. For delta, however, at least one common aspect was retained in the group average: a peak of inter-electrode coherence increase at 450 ms (of around z-score = 0.6), and an equally low amplitude decrease during the ISI, peaking at 1200 ms. To our surprise, given the highly regular induced-power pattern in time across subjects, theta inter-electrode coherence was the most variable. Subjects even presented opposite results during the peaks of post-stimulus power, with only three subjects presenting parallel increases in power and phase coherence, and three other presenting increases only during the ISI. Alpha 1 was the most variable in time patterns of coherence changes. For alpha 2, five subjects presented overall coherence reduction during the period of increased power, in the center of the ISI, where three subjects presented increases. The beta range, however, presented a relatively simpler, more common aspect across subjects, in the form of changes in coherence roughly parallel

with power. We thus proceeded to analyze intra-individual correlations between the two variables, and the results supported the visual impression: beta 1 had significant nonparametric correlations in 11 subjects, and beta 2, highly significant correlations in 10 subjects. We may mention here that the beta partial averages, computed from subsets of electrodes ranked by peak latency (see Methods section ), were visually undistinguishable from each other, suggesting the absence of systematic sequential activation between beta generating areas. This corroborates the interelectrode phase analysis results, in the sense that both suggest a tight phase synchrony between all such areas. 3.6. Current density reconstruction of beta burst averages Corresponding to the complex scalp distribution of beta induced power, the source reconstruction results indicated the same complexity: multifocal cortical current distribution, highly

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variable across subjects, including frontal and posterior cortical sources in all subjects. Fig. 6(A) shows the current distribution in each subject, accounting for the main ICA space-time data component, indistinguishable from the baseline activity component. Fig. 6(B) shows the second component (task-exclusive or task-induced ‘proper’), but in all cases of sufficient SNR for source reconstruction (average SNR = 1.8; std = 0.7; range = 1.05 to 3.3). We may notice the individual-specific pattern of relative current distribution, especially conspicuous for the second, exclusively task-related component. Only parietal area 7 shows some level of activity in all subjects corresponding to the baseline component (although highly variable in intensity relative to maximum current). The reconstruction results for the beta-1 (around 21 Hz band) resulted in current distributions of main components indistinguishable from beta-2 in all subjects. According to the topographic inspection, reconstruction results showed almost undistinguishable patterns between beta-1 and beta-2 source models for the task-exclusive component in most cases, typically with the beta-1 set of current foci representing a subset of those seen for beta-2. In some subjects, however, few additional (i.e., complementary to beta-2) weak sources were also observed. 4. Discussion Given the number of results and their interrelations, and implications to be considered, we organized the discussion in the following manner: first consider the two main findings of the present analysis, a) the attention-related beta oscillations in contrast with stimulus-related activity, and b) the relation between task-induced and baseline topography. Then, we discuss c) the (cortical) topographic aspect of those combined findings (along with beta phase synchrony), in the context of inter-individual variability in sets of task-related cortical activity. Finally, after considering d) a few points regarding the alpha range, in particular the relations between induced and evoked activity, we e) present a summary hypothesis on the event-related behavior of all frequency bands, and consider future perspectives of the field. 4.1. Attention-induced beta oscillations The first of the two main findings of this work was the presence of beta band power increases throughout the ISI, peaking before the S2 stimulus, in all subjects, for which we proceeded to analyze topographically and model the generators. The finding was no absolute surprise, sincethe beta range is traditionally associated with wakefulness and behavioral arousal (e.g., Niedermeyer, 2003), an association that impelled the widespread but still controversial beta-enhancement by biofeedback (Ramirez et al., 2001). In the electrophysiological perspective, we consider stimulus expectancy or covert orienting to be attention in the strict sense, and the direct correlate of SPs and induced beta. Other longer lasting hypothetical processes as vigilance, arousal or sustained attention, probably contribute to the background beta, but not to its enhancement in the S1–S2 interval, not seen in the passive condition, that

required some arousal level. On the other end, we prefer to consider shorter lasting processes, present during and after stimulus detection, not as attention itself but its consequences, with modulations of event-related potentials being their correlates. We agree with Bushnell (1998) that definitions of all hypothetical processes “under the rubric of attention” are relatively arbitrary. We also agree that those and psychophysiological constructs in general, should be iteratively refined by the interplay between the constructs that guide task design, and the suggestions given by the actual behavioral and physiological experimental results themselves. Thus, even if arousal and attention consist in a continuum, where attention would be a phasic enhancement of arousal (but maybe restricted to given sensory domains — selective), beta would nicely fit as a correlate of such whole continuum. But this possibility must be confirmed by future investigation, using tasks specifically designed to disentangle the hypothetical processes. Similar to the present case, where beta power was significantly increased with respect to the control condition, beta increases have been reported with respect to spatial attention, reaching long latencies in paradigms that adopt the post-stimulus perspective (Vazquez Marrufo et al., 2001). Relatively increased beta could also be attributed to movement, since it was absent in the passive condition, but in such case it would be focal, restricted to electrodes above sensory-motor areas (Neuper et al., 2006). Delta and theta induced power, on their turn, presented a clear and exclusive, early post-stimulus time distribution. In all subjects, theta induced power peaked in coincidence with the peaks of the N200. Thus, theta did not fulfill the expected role of an attention correlate. The occasional observation of ongoing theta in some healthy individuals during tasks such as overt calculation with pen and paper (Mizuki et al., 1980), although in a small proportion of subjects (Takahashi et al., 1997), may be alternatively attributed to sustained mental effort, uncontrolled stimulus presentation or overt movement. 4.2. Task-induced versus pre-stimulus baseline topography The second most important finding of this work was the baseline or pre-S1 topographic distribution in all frequency bands. In all cases and subjects, almost all of the task-induced power distribution was the same as that present during the baseline, and verified during rest in three subjects. By explicitly computing burst averages for the baseline, we found the scalp topography and cortical current distribution of each band to be almost the same between resting wakefulness and task-induced activity. This means that the main task-related changes, in amplitude or synchronization with task events, take place in the same areas already active during resting. Also, those baseline topographic patterns were very similar between delta, theta and beta ranges, only with alpha-2 presenting a distinct pattern in most subjects. Only by subtracting the baseline component from the task-induced oscillations do we observe the lower power task-exclusive, familiar sensory evoked potential patterns in the case of theta and delta, or the complex, multifocal and highly idiosyncratic patterns in the case of beta. Alpha occupies an intermediate position in complexity and individual variability of

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topography. Hanslmayr and colleagues (2007) have observed an identity between baseline and task-induced activity, as we also did, in the case of the alpha range. 4.3. Induced beta and other evidence for the idiosyncrasy of cortical functional circuits Regarding the topography and current density distribution of induced and baseline beta, we noticed the qualitative similarity with the other correlate of attention. In the previous analysis of SPs for this task (Basile et al., 2006), we observed a common task-time distribution, multifocal, complex scalp topography, highly variable across subjects, and a significant enhancement during the task as compared to the passive stimulation control condition. Then, by computing beta burst averages centered in peaks occurring within the 700 to 1600 ms task time window, and modeling their generators by CDR, we obtained analogous results. Baseline beta burst generators showed an equally complex pattern, comprising prefrontal and posterior cortical areas (as do SPs), highly variable across subjects, with only parietal area 7 demonstrating some, but variable, degree of current density in all subjects. Area 7 was also the only common SP generator region across subjects (Basile et al., 2006). This fact may be attributed to the mere wakeful state, that in other primates has been considered an index of interested attention to the environment (Lynch et al., 1977; Yin and Mountcastle, 1978). In our previous SP analysis we simply stated their obvious higher inter-individual variability as compared to the prototypical and least variable sensory evoked potential, the N200. Here, we used a measure of dispersion of individual data from the group average (quadratic norm, after power normalization, to emphasize topography minimize the influence of absolute power differences), to quantitatively compare beta with theta. The topographic deviation measure of beta was larger than that of theta from their mean, and this difference was highly significant in a statistical comparison. On the other hand, the comparison between beta and SP generators by visual inspection (SP figure in Basile et al., 2006) revealed a largely complementary set of active areas between the two indexes. Beta and SP current foci were typically different from each other, forming mostly adjacent sets within subjects. Whenever there was an overlap of SP and beta generating areas, they did not correspond to the most important generators in each case; that is, strong corresponding to weak in almost all cases. Moreover, SP generators were overall more spread over the cortical surface, and given the many cases of adjacent generator positions, it is conceivable that SPs could represent a fringe effect stemming from the beta generating areas. SPs are for a long time known to be microscopically generated by a major contribution from the potassium buffering function of glia (Skinner and Molnar, 1983; Roitbak, 1993; Mitzdorf, 1993), in situations of increased overall neural firing, as seems to occur in areas active in the beta range. Finally, our phase analysis results present some weak evidence that cortical areas active in the beta range also oscillate in phase synchrony with each other, at least roughly accompanying the power changes. But the stability of partial averages (with respect to groups of electrodes ranked by

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peak latency), as well as independent studies using single-cell, extra-cellular recordings and model simulations offer stronger support to this view (Bibbig et al., 2002). Thus, it appears that beta generating areas become co-recruited, either reciprocally, or by some common subcortical projection(s). Our findings have a potentially critical implication to psychophysiology: In contrast to cortical activity linked to sensory stimulation (evoked potentials, delta, theta and partly alpha rhythms), which is relatively simple in distribution and more similar across subjects, electrical activity related to expecting attention (SPs and beta) is multifocal, complex in distribution, and highly variable across subjects. Moreover, from the baseline results, it appears that when one engages in the task, it is largely the same individual-specific set of cortical areas, continuously active during simple resting wakefulness, that either enters in phase or suffer changes in power or both, with a few other areas secondarily emerging, even more individual-specific and representing lower power. Our data are qualitatively equivalent to the results from the fMRI and PET studies that present individual data on event-related metabolic changes (Cohen et al., 1996; Herholz et al., 1996; Fink et al., 1997; Davis et al., 1998; Hudson, 2000; Brannen et al., 2001; Tzourio-Mazoyer et al., 2002). The data from both sets of studies are compatible with the theoretical view of a “degenerate” mapping between presumed functions and their implementing cortical areas (Noppeney et al., 2004). The hypothesis that has guided our search for a universal functional mapping regards the preferential patterns of cortico–cortical connections in mammals (Pandya et al., 1988), first detailed between visual cortices (Macko and Mishkin, 1985) but known to apply throughout the neocortex, including prefrontal areas (Pandya and Yeterian, 1990; Barbas, 1992). However, it is possible that the mere complexity and number of possible excitatory cortico–cortical functional pathways are sufficient to allow the formation of variable sets of interconnected cortical areas across individuals, before and during execution of any given task. In studies that present individual data, some subjects do not present at all changes in areas that would appear in spatial group averaging, whereas weakly but consistently active areas may be deemphasized by grand averaging. We propose, with some other authors, that functional claims regarding cortical areas never be made based on group averaged data (Steinmetz and Seitz, 1991; Davis et al., 1998; Noppeney et al., 2004). The radical idea that fixed and universal functions cannot be attributed to given nonsensory-motor areas across individuals is also compatible with micro-electrophysiological data from other primates. In those data (apart from sensory-motor areas), cells considered to be typical of given areas (e.g., ‘delay’ cells in prefrontal cortex) are known to be distributed along the cortical mantle, and their implemented functions such as memory, can be considered to be delocalized, (Fuster, 2003). It is the prevalence of cells classified by response type to tasks, i.e., their distribution in different cortical association areas, that is highly variable across individuals (Fuster, personal communication). Finally, the idea of relatively delocalized functions in individualized patterns, is radically different from a ‘mass-action’ concept of delocalization, and much like the traditional problem of variability in

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Neuropsychology (Noppeney et al., 2004). That is, similar focal lesions in cortical association areas lead to critical impairments in some individuals, but their effects may only be noticed in others after incidental or thorough laboratory examinations. 4.4. Alpha range characteristics and the topography of induced versus evoked oscillations After a consideration of the topographic relations between induced and evoked oscillations, we may conclude with a summary hypothesis of the behavior of generator areas of each band with respect to stimulation and attention. Since our computation of total induced power includes the evoked part, and delta and theta presented the same time distribution in both cases, it appears that for such bands and in the present task, the two computations do not differ. In the case of alpha bands, where the induced and evoked power peaks are well separated in tasktime, it was important to see no difference between maps from both time windows in all subjects. One recent study using the standard 10–20 montage directly agrees with this topographic identity between induced and evoked alpha (Hanslmayr et al., 2007). Thus, it appears that a single set of areas generate the alpha rhythms, with part of same distributed cell population entering into synchrony with the whole task cycle (‘evoked’ throughout the S1-post S2 window), but most of the power stemming from a population that remains out of phase with events. The presence of evoked alpha throughout the ISI, a phenomenon that we had observed previously even in experiments using ISIs of 2.5 s, made us speculate that this part of the alpha generators could serve as a kind of task-time estimator. This mixed alpha behavior is equivalent to the simultaneous observation of alpha de-synchronization and synchronization, during the immediate post-stimulus time, known to depend on the method of power analysis (Klimesch et al., 2000). We believe that the alpha rhythm can indeed be considered to reflect ‘cortical idling’ (Pfurtscheller, 2001), and that this concept can be reconciled with that of ‘preparation for detection’ of forthcoming stimuli (Knyazev et al., 2006), if one considers the stimuluslocked part occurring in the ‘reference interval’ of cyclical tasks such as the present. 4.5. Summary of event-related oscillations A summary hypothesis on the behavior of cortical areas generating all frequency bands, with respect to stimulation and attention, may now be attempted: (1) During waking rest, individuals would have a highly overlapping, multi-focal, main set of cortical areas, including parietal area 7, but otherwise idiosyncratic, generating most of delta, theta and beta, and another set (only partly overlapping with the one from the other frequencies, in most subjects), more posterior and common across subjects, generating alpha; (2a) cyclical sensory stimulation would mainly phase-reset delta and theta baseline activity, and part of alpha generator cell population, besides (2b) adding sensory-specific generators of theta (mainly compounding P1/N2 ERP components), delta and alpha (major components of “P1”/P2/P3 ERP components), of lower power and more similar across subjects;

(3a) attention would be concurrent with SP (sub-delta) generation and beta amplitude increase in baseline activity generating areas, and (3b) an engagement of additional highly idiosyncratic beta generating areas. Main issues to be clarified by future research include: (1) confirmation of variability in larger sample (a mapping into, e.g., personality, genetics or gender may be found), and search for event-related changes specific to other psychological conditions (emotion, memory, comprehension, problem solving) and frequency bands (baseline and task-induced gamma, and baseline sub-delta); (2) testing of stability in time and malleability of the topography of baseline activity (spontaneous versus training); (3) systematic analysis of the (causal) time relations between baseline and secondary components; (4) systematically analyze the relations (coherence) between frequency bands. Acknowledgments This research was supported by the grants 03/02297-9 and 02/13633-7 from Fapesp, São Paulo, Brazil. We wish to thank Dr Cláudia Leite, Dr Edson Amaro Jr. and the staff from the Department of Radiology of the University of São Paulo Medical School, for kindly acquiring and preparing the MRI sets, to Márcio A. Costa for his valuable technical support, and to Prof. Walter Thomas Bourbon for his advice. References Barbas, H., 1992. Architecture and cortical connections of the prefrontal cortex in the rhesus monkey. Adv. Neurol. 57, 91–115. Basar, E., Schurmann, M., Demiralp, T., Basar-Eroglu, C., Ademoglu, A., 2001. Event-related oscillations are ‘real brain responses’—wavelet analysis and new strategies. Int. J. Psychophysiol. 39 (2-3), 91–127. Basile, L.F.H., Rogers, R.L., Bourbon, W.T., Papanicolaou, A.C., 1994. Slow magnetic flux from human frontal cortex. Electroencephalogr. Clin. Neurophysiol. 90, 157–165. Basile, L.F.H., Simos, P.G., Brunder, D.G., Tarkka, I.M., Papanicolaou, A.C., 1996. Task-specific magnetic fields from the left human frontal cortex. Brain Topogr. 9 (1), 31–37. Basile, L.F.H., Brunder, D.G., Tarkka, I.M., Papanicolaou, A.C., 1997. Magnetic fields from human prefrontal cortex differ during two recognition tasks. Int. J. Psychophysiol. 27, 29–41. Basile, L.F.H., Ballester, G., Castro, C.C., Gattaz, W.F., 2002. Multifocal slow potential generators revealed by high-resolution EEG and current density reconstruction. Int. J. Psychophysiol. 45 (3), 227–240. Basile, L.F.H., Baldo, M.V., Castro, C.C., Gattaz, W.F., 2003. The generators of slow potentials obtained during verbal, pictorial and spatial tasks. Int. J. Psychophysiol. 48, 55–65. Basile, L.F.H., Yacubian, J., Ferreira, B.L.C., Valim, A.C., Gattaz, W.F., 2004. Topographic abnormality of slow cortical potentials in schizophrenia. Braz. J. Med. Biol. Res. 37 (1), 97–109. Basile, L.F., Brunetti, E.P., Pereira Jr., J.F., Ballester, G., Amaro Jr., E., Anghinah, R., Ribeiro, P., Piedade, R., Gattaz, W.F., 2006. Complex slow potential generators in a simplified attention paradigm. Int. J. Psychophysiol. 61 (2), 149–157. Bibbig, A., Traub, R.D., Whittington, M.A., 2002. Long-range synchronization of gamma and beta oscillations and the plasticity of excitatory and inhibitory synapses: a network model. J. Neurophysiol. 88 (4), 1634–1654 Oct. Brannen, J.H., Badie, B., Moritz, C.H., Quigley, M., Meyer, M.E., Haughton, V.M., 2001. Reliability of functional MR imaging with word-generation tasks for mapping Broca's area. AJNR Am. J. Neuroradiol. 22 (9), 1711–1718. Bruns, A., Eckhorn, R., 2004. Task-related coupling from high- to lowfrequency signals among visual cortical areas in human subdural recordings. Int. J. Psychophysiol. 51 (2), 97–116 Jan.

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