siology 65 (2007) 238–251www.elsevier.com/locate/ijpsycho

International Journal of Psychophy

Interindividual variability in EEG correlates of attention andlimits 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, Brazilb Division of Neurosurgery, University of São Paulo Medical School, Brazilc Department of Neurology, University of São Paulo Medical School, Brazild Department of Psychiatry, Federal University of Rio de Janeiro, Brazil

Received 19 December 2006; received in revised form 2 April 2007; accepted 3 May 2007Available 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. Thistask has been previously analyzed by conventional event-related potential (ERP) computation, and Slow Potentials (SPs) were seen to be highlyvariable 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]. Weobtained 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-lockedactivity. We intended to compare a possibly found spectral correlate of attention, in topographic variability, with stimulus-related activity. Themain results were (1) the detection of two bands of attention-induced beta range oscillations (around 25 and 21 Hz), whose scalp topography andcurrent density cortical distribution were complex multi-focal, and highly variable across subjects (topographic dispersion significantly higher thansensory-related visual theta induced band-power), including prefrontal and posterior cortical areas. Most interesting, however, was the observationthat (2) the generators of task-induced oscillations are largely the same individual-specific sets of cortical areas active during the pre-stimulusbaseline. We concluded that attention-related electrical cortical activity is highly individual-specific, and possibly, to a great extent alreadyestablished during mere resting wakefulness. We discuss the critical implications of those results, in combination with results from other methodsthat 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 ofSlow Potentials (SPs), direct correlates of attention, guided bythe neuroanatomy of cortico–cortical connections. We expectedthat (selective) attention to different visual domains wouldcorrespond to SP generation in given prefrontal cortical areas.

⁎ Corresponding author. Division of Neurosurgery, University of São PauloMedical School, Av. Dr. Ovidio Pires de Campos 785, Cerqueira Cesar, SãoPaulo, SP, 05403-010, Brazil. Tel.: +55 11 30697284, 55 11 32846821; fax: +5511 2894815.

E-mail address: lbasile@usp.br (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 simplefunctional-anatomical equivalence, with functional circuitsparalleling the relatively specific connections between visualareas (Macko and Mishkin, 1985) and other neocortical areas,particularly prefrontal (Pandya et al., 1988; Pandya andYeterian, 1990; Barbas, 1992).We searched for task-specificelectrophysiological indexes of prefrontal cortex activity,starting from MEG studies and one or two dipole models(Basile et al., 1994, 1996, 1997), until the development ofcurrent 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 par-ticularly 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 evokedpotentials and long latency potentials of the P300 class, whichare fairly similar in topography across subjects.

Inter-individual variability is commonly reported in func-tional studies, but with respect to intensity and time pattern ofphysiological changes in given anatomical loci, but much lesscommonly to the loci themselves. Our first studies used rela-tively complex tasks , including stimulus comparison andmemory, in addition to selective attention to visual domains(Basile et al., 2002, 2003). Thus, we recently used a simplevisual attention task to reduce the likelihood of variable hypo-thetical strategies in individual performance. Although as far asreports of a common experience of task performance couldsuggest common strategy, patterns of SP scalp distribution andmodeled current distribution remained equally complex andvariable across subjects (Basile et al., 2006). The present workis a complementary analysis on the same task. Since the highvariability could be a peculiarity of SPs, we decided to searchfor other correlate(s) of attention that could be more universalacross 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-timepattern of spectral changes, to verify which frequency bands aremore closely related to sensory stimulation and detection, asopposed to activity more related to attention, present within theISI (S2 anticipation period of the S1–S2 design). We expecteddelta, theta and alpha range oscillations to fit in the sensory-related category, since they are the main components of evokedpotentials (Basar et al., 2001; Gruber et al., 2005; Hanslmayret al., 2007; Valencia et al., 2006; Fell et al., 2004). As a possiblenew attention correlate, we expected pre-S2 activity in the thetaband, although beta range activity has an old but vagueassociation with arousal and attention, which justifies its stillcontroversial use in biofeedback (Ramirez et al., 2001). Thetabursts have been occasionally observed in the ongoing EEG ofawake, healthy individuals during tasks such as calculation withpen and paper (Mizuki et al., 1980), and in one instance beenreported in cue-target (S1–S2) paradigm (Nakashima and Sato,1993).

Our second objective was to compare the topography of aputative newly found pre-S2 spectral increase, with activitymore closely related to sensory stimulation. In our previouswork on the event-related potentials obtained during this task(Basile et al., 2006), the high topographic variability of SPs wasonly visually compared with the least variable topographyacross subjects, of the N200. We now planned to quantify thedispersion of individual topography from the power-normalizedgroup mean, comparing the most common (across subjects)stimulus-related spectral band with any possibly found inducedpower correlate of attention.

Our third goal was to compute corrected-latency averagingof narrow band filtered epochs, restricted to bands occurring inthe pre-S2 window, and then perform CDR to localize itsgenerators. Finally, and secondarily, we planned to compare thetopographies of evoked and induced activity, and to compute anoverall measure of phase synchrony across all electrodes in a

fixed sub-set of the montage. This phase analysis would afford afirst step to understand the relations between induced power andphase 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 inage between 20 and 45 years, with no history of drug or alcoholabuse, and no current drug treatment. Eleven subjects werecurrent or former medical students. All subjects signed consentforms approved by the Ethics Committee of the University ofSão Paulo Hospital.

2.2. Stimuli and Task

A commercial computer program (Stim, Neurosoft Inc.)controlled all aspects of the task. Visual stimuli composing thecue-target pairs (S1–S2) consisted in small rectangles (eccen-tricity ±0.8°, S1: 100 ms duration, S2: 17ms; white background).In half of the trials, the S2 rectangle contained a grey circle – thetask target – with ±0.3° of eccentricity. A pilot study determinedstimulus luminance and task difficulty: we used the ninth amongfourteen levels of grey, starting from white; the masking stimulushad the same grey level (a ‘checkerboard’ grey and white squarecomposed by one-by-one pixel size squares), and was continu-ously present, along with the fixation point, except during S1 andS2 presentation; a secondary adding task, with visual or auditoryone digit numbers presented inter- and intra-trials, served toconfirm the need of temporal attention for the primary taskexecution, for it greatly impaired its performance. S1 wasfollowed by S2, with onsets separated in time by 1.6 s. The ITIwas variable, ranging from 2.3 to 5 s. We instructed the subjectsthat a rectangle would be presented to indicate that 1.6 s later itwould flash again but quickly, containing or not the target circle.The subject decided whether there was a target inside the S2rectangle, and indicated presence of the target by pressing theright button with the right thumb or absence of the target bypressing the left button with the left thumb. We explicitly de-emphasized reaction time in the instructions and measured per-formance exclusively by the percent correct trials, from the totalof 96 trials comprising the experiment. We did not presentreaction times for two reasons. First, the nature of the in-structions, which also included the possibility of occasionallyholding the response and have a brief pause if some discomfortwas felt, e.g., in the neck or mandible. Second, the program has atechnical limitation, making impossible the recording of fastresponses, when masking and fast presentation are combined. Aneye fixation dot was continually present on the center of thescreen, as well as a stimulus-masking background, to preventafter-images. To confirm the expecting attention index role ofsome possibly found EEG rhythm, we also used a passivestimulation control condition (same number of trials, but S2snever contained targets; the order between conditions was ran-dom 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 systemconsisting of an extended 10–20 system, in a 124-channelmontage (Quik-Cap, Neuromedical Supplies®), and an imped-ance-reducing gel which eliminated the need for skin abrasion(Quick-Gel, Neuromedical Supplies®). Impedances usuallyremained below 3 kΩ, and channels that did not reach thoselevels were eliminated from the analysis. To know the actualscalp sampling or distribution of electrodes in each individualwith respect to the nervous system, we used a digitizer(Polhemus®) to record actual electrode positions with respectto each subject's fiduciary points: nasion and preauricularpoints. After co-registration with individual MRIs, the recordedcoordinates were used for realistic 3D mapping onto MRIsegmented skin models, and later used to set up the sourcereconstruction equations (distances between each electrode andeach dipole supporting point). Two bipolar channels, out of the124-channels in the montage were used for recording bothhorizontal (HEOG) and vertical electrooculograms (VEOG).Left mastoid served as reference only for data collection (com-mon average reference was used for source modeling) and Afzwas the ground. We used four 32-channel DC amplifiers(Synamps, Neuroscan Inc.) for data collection and the Scan 4.3software package (Neurosoft Inc.) for initial data processing(before computation of averages). The filter settings foracquisition were from DC to 30 Hz, and the digitization ratewas 250 Hz. The EEG was collected continuously, and epochsfor averaging spanned the interval from 700 ms before S1 to400 ms after S2 presentation. Baseline was defined as the400 ms preceding S1. Epoch elimination was performed vi-sually for eye movements and muscle artifacts, and then auto-matic: visual inspection served to eliminate occasional transientelectronic or head movement noise present in channels otherthan EOG; epochs containing signals in either HEOG or VEOGchannels above +50 or below −50 μV were eliminated. In ourmontage, the VEOG detected, typically, blinks as deflectionsabove 130 μV in the positive direction.

MRIswere obtained by a 1.5 TeslaGEmachine,modelHorizonLX. Image sets consisted in 124 T1-weighed saggital images of256 by 256 pixels, spaced by 1.5 mm. Acquisition parameterswere: 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 powerscalp topography

After artifact rejection, the signal from each channel wasspectrally analyzed by means of a Short Time Fourier Trans-form (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 powerspectrum (Tallon-Baudry et al., 1996), the time-frequencydecomposition was made for each electrode and each trial, fromDC to 30 Hz, and the resulting charts were then averaged, bothfor each electrode and across electrodes. The evoked powerspectrum was obtained applying the spectral decomposition tothe averaged signal. Recently, it has been demonstrated that thismethod is mathematically equivalent to others like the Hilberttransform, or wavelet decomposition, and that each of themyields equivalent results in practical applications to neuronalsignals (Bruns and Eckhorn, 2004). The decomposition wascomputed on the EEG tapered by a sliding Hamming window,256 points in size for frequencies over 5 Hz, and 512 pointsbetween 2 and 5 Hz, with a temporal resolution of N/10 (Nbeing the number of temporal points of the raw signal), and afrequency resolution of 4 bins per Hertz. Then, we normalizedthe average power for each electrode to obtain z-scores ofincrements or decrements in each frequency bin with respectto the power in the same frequency during the baseline (bPjN=(Pj−μ j)/σj; given Pj = spectral power at each time point inelectrode j, μj and σj are the mean and standard deviation,respectively, of the average power during the baseline for theelectrode). The computation of power changes by z-scorevalues is sufficient for our first purpose of describing task-related oscillations. At this stage, a non-parametric statisticalcomparison (Wilcoxon and Sign tests) was limited to verify ifpower changes, in the pre-S2 time region of interest, differbetween the task and the passive stimulation condition.

We computed realistic three-dimensional topographic mapsof the scalp distribution of normalized power, at each frequencyband that demonstrated task-induced changes, for each subject,over the reconstructed scalp anatomy. To this purpose, we useda commercial sotfware (Curry V 4.6, Neurosoft Inc.), that co-registered individual MRI sets (skin model, see below) with theactual position of each electrode with respect to commonlandmarks, and linearly interpolated the instantaneous values ofpower to obtain continuous maps. We intended to quantitativelycompare the topographic variance across subjects, betweenactivity present in the pre-S2 window, with sensory-related, thatis, activity restricted to post-stimulus windows. For that pur-pose, we used a scalar measure of topographic deviation fromgroup mean, that previously proved to be useful in demonstrat-ing the higher complexity and variability of SP distribution inschizophrenia, 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 dif-ference between individual and group average), computed afterpower normalization, to emphasize topography and reduce theinfluence of absolute voltage differences. The deviation indexesobtained for each type of activity, the most stimulus-bound andpre-S2, were statistically compared, by the non-parametric testof Wilcoxon.

2.5. Computation of corrected latency burst averages

According to the observed induced frequency bandsobserved for each individual, the original artifact-free EEG

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epochs (ranging from 700 ms before S1 through 500 ms afterS2) from each subject were filtered around the bands of interest(Butterworth, 96dB rolloff, 1–3 Hz for delta, 3–7 for theta, 7–9for alpha1, 9–12 for alpha2, 23–26 Hz for beta2, 18–22 forbeta1, and in some subjects, who showed an additional low betaband, between 13 and 15 Hz).Epochs for correct or incorrectresponses were pooled together, since our main interest wascentered on activity preceding S2. The resulting filtered epochswere then processed by an algorithm developed by us forsearching the peaks of bursts within the task-time windows ofinterest (schematic representation of method in Fig. 1). Filtered

Fig. 1. Schematic representation of the steps of the method: (1) Computation of tasspecific narrow frequency bands, and positive voltage peaks in each channel (in thisoriginal epochs. (4) Single-channel guided multi-channel averages thus computed (reaveraged (5). Grand averages were decomposed by Independent Component AnalyDensity Reconstruction (CDR) algorithm.

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

k-induced band-power. (2) EEG Epochs were band-pass filtered in individual-example, Fz) served to create one multi-channel average by (3) realignment ofsulting in ‘wavelet-shaped’ potentials as exemplified for Cz) were finally grandsis (ICA), and their main and second space-time components fed the Current

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also computed partial averages using groups of guidingchannels ranked for latency of occurrence of peaks. That is,using only the first one fourth of channels (those with overallshorter peak latencies), and second, third and last fourth ofchannels. We also performed independent analyses to studypossible time relations between groups of electrodes (nextsection). Finally, in all cases we also computed pre-S1 burstaverages (representing the baseline topography for each fre-quency band), where the program searched peaks from −400 to0 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 presentdata would consist in a separate and major work, we decidedhave only a first approach: we computed only the overall patternof phase relations, in the form of averages across all pair ofchannels. This was used only to verify whether there were overalltask-related phase changes that correlated in time, in astatistically significant way (non-parametric, Spearman's rho),with induced power, for each individual and frequency band. Thepractice of separately computing phase is becoming common inevent-related power studies (Fell et al., 2004; Hanslmayr et al.,2007). Due to volume-conduction effects, we selected a group of25 regularly interspaced electrodes from the original array tocompute this index. A similar procedure used to obtain the powerspectrum of the signal was used to compute the phase-lockingvalue between electrodes (Lachaux et al., 1999). That is, a STFTof the signal from each electrode and trial was computed toobtain the instantaneous angular phase for each frequency, duringa time window centered at time t. Then, after subtracting theconstant angular phase, a complex vector of unitary value wasconstructed for each channel, trial, frequency and time. With thisvalue, a matrix of differences of phase values between electrodes,in each trial, was computed for each frequency-time point, andthen averaged over all the trials. Using the modulus of thiscomplex value, we obtained for each pair of electrodes, in eachfrequency 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, inthe same frequency, time and trial, the phase-locking value wascomputed as

Uij f ; tð Þ ¼ 1=N jRk ¼ 1NUi � Ujj:The phase-locking values obtained for the time interval

posterior to stimulus presentation was then z-normalized by thevalues obtained during the baseline interval in the same way astime-frequency spectral matrices. Finally, we computed correla-tions between task-related power and phase, and verifiedwhether they were statistically significant.

2.7. Intracranial source reconstruction

The computed averaged bursts, MRI sets and electrodeposition digitization files were the raw data for all further source

analysis (Curry V 4.6, Neurosoft Inc.). A detailed description ofthe reconstruction procedure, and a discussion on the criteria formethod 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 thevariance 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 retainedpolarity) were decomposed by Independent Component Anal-ysis (ICA), which searches for the highest possible statisticalindependence or redundancy reduction between components (inthis case, space-time averaged data patterns), a robust method ofblind signal decomposition/deconvolution (for a review, see e.g.Hyvarinen and Oja, 2000). ICAwas applied to each individual'swhole space-time data set, i.e., to the m×n data matrix (m usedchannels times 201 time samples corresponding to the 800 mscomposing the averaged bursts). Finally, we fed the reconstruc-tion algorithm with the main ICA component(s) as data to befitted. Thus, the ‘noise component’ of the model was defined asthe sum of remaining components (with loadings belowSNR=1), all of which added together lead invariably tonegligible scalp potentials when compared to the main com-ponents. In practice, in all cases, only two space-time ICAcomponents were then modeled. MRI sets were linearlyinterpolated to create 3-dimensional images, and semi-auto-matic algorithms based on pixel intensity bands served to re-construct the various tissues of interest. A Boundary ElementModel (BEM) of the head compartments was implemented, bytriangulation of collections of points supported by the skin,skull and cerebrospinal fluid (internal skull) surfaces. Meantriangle edge lengths for the BEM surfaces were, respectively,10, 9 and 7 mm. Fixed conductivities were attributed to theregions enclosed by those surfaces, respectively, 0.33, 0.0042and 0.33 S/m. Finally, a reconstructed brain surface, with meantriangle side of 3 mm, served as the model for dipole positions,corresponding to a minimum of 20 thousand points. The elec-trode positions were projected onto the skin's surface followingthe normal lines to the skin. The detailed description of theassumptions and methods used by the “Curry 4.6” software forMRI processing and source reconstruction may be foundelsewhere (e.g., Buchner et al., 1997; Fuchs et al., 1998; Fuchset al., 1999). The analysis program then calculated the lead fieldmatrix that represents the coefficients of the set of equationswhich translate the data space (SNR values in the set of channelsper time point) into the model space (above 20 thousand dipolesupporting points). The source reconstruction method itself wasLp norm minimization, with p=1.2 both for data and modelterms. The regularization factor, or λ values to be used, typicallyconverged after repeating the fitting process three to four times (λgives the balance between goodness of fit and model size).Resulting foci of current density were inspected with respect tothe individual anatomy directly, in terms of which estimatedcytoarchitectonic areas contained them (areas may then be scoredfor relative intensity; Basile et al., 2003). The estimatedBrodmann areas containing current foci were tabulated afterverification by comparison with classical illustrations and theconventional 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 spectralchanges, followed by their (2) spatial, scalp topographicproperties, in particular contrasting beta with theta bands, thendescribing the (3) amplitude and topographic characteristics ofthe corrected-latency oscillatory averages. After a briefpresentation of relevant (4) phase analysis results, we presentthe (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 criticaltime of S2 presentation. The overall average performance was88.5 % correct responses (standard deviation 8.3 %).

3.2. Induced power with respect to task-time

The overall pattern of task-induced power, in comparisonwith task-evoked power, can be seen on Fig. 2, averaged acrosssubjects and electrodes. Against one of our expectations, task-induced theta power was not present in the ISI interval, showinga clear post-stimulus increase pattern in all subjects, practicallyreturning to baseline level during the ISI. The two peakscorresponded to around 180 ms post S1 or S2, thus coincidingwith the latency of the N200 evoked potential component. Thesame purely stimulus-related behavior was observed forinduced delta power, but in this case the peaks occurred later,around 350 ms, and in almost all subjects with much higheramplitude after S2. Fig. 2(left) shows the overall task-timebehavior of the induced power, with data collapsed acrosselectrodes and subjects. The time pattern of induced power wassimilar to the evoked pattern for theta and delta bands (Fig. 2,right).

Three major frequency bands presented increases during theISI, the pre-S2 region of interest: sub-delta (from DC to 1 Hz,virtually exclusive component of the SPs, whose analysis ispresented in Basile et al., 2006), the alpha bands, and as one

Fig. 2. (left) Task-induced power. In both figures, data are collapsed across channelsbaseline, y-axis: frequency in Hz; x-axis: time in ms). One may appreciate the overalsubjects (see text for few frequency bands where exceptions occur, i.e., low beta and apotentials). A direct quantitative comparison between evoked and induced power is

new observation, two beta bands. Induced alpha behavior inrelation to task time was multiphasic, with a relative (seen as aninflection) reduction/de-synchronization around 200 ms afterthe stimuli, a relative peak (almost unnoticeable when comparedto the maximum induced alpha) overlapping with the deltapeak, and maximum power at the center of the ISI, overallaround 700 ms. Half of the subjects presented a distinct alpha-1band – around 8 Hz – in addition to the alpha-2 band centeredaround 11 Hz, and the remaining half, either a broad or narrowalpha-2 centered band.

One of the two new and main findings in this work regardedthe beta range. In all subjects, a narrow induced beta bandaround 25 Hz was observed, throughout most of the ISI,peaking during the pre-S2 time range, following a time patternthat we originally expected to fit a putative attention-relatedinduced theta band. In addition, all subjects presented a broaderbeta band roughly around 21 Hz, and some subjects anothernarrow band close to 15 Hz, but more variable in frequency andtime pattern. Given beta activity, mainly the 25 Hz band, had thetime distribution between S1–S2 peaking in the pre-S2 window,our main critetion for a correlate of attention, we compared itwith activity recorded during the passive stimulation controlcondition. There was a statistically significant increase in betapower during the ISI, when the task was compared to thepassive stimulation control condition: beta mean global fieldpower from 500 through 1600 ms, differed significantly be-tween conditions both in parametric paired samples t-test (forbeta1, 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; signtest, p=0,006 for beta1 and p=0,039 for beta2). Fig. 3 showsbeta mean global field power collapsed across individuals, andz-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, bothbands had a posterior distribution of task-induced powermaxima. In ten subjects, the topography of theta power waspractically indistiguishable from that of the N200 peak voltagedistribution (typically double peaks at the occipital region); inthe two remaining subjects, delta topography was closer to

and subjects (numbers on color scale indicate z-score – power change relative tol time course of task-induced power changes, which were fairly common acrosslpha-1). (right) Task-evoked power (Fourier transform of averaged event-relatedprecluded by the fact that z-scores are relative to differently defined baselines.

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

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N200. Fig. 4 shows the induced power and correspondingtopography of all bands (at peaks of task-induced power in-creases) in one example subject.

The scalp topography of induced alpha showed the expectedposterior distribution of highest power changes. However, theactual isocontour map shapes were different from the simplepattern resembling the N200 evoked potential component thatwe observed for theta and delta ranges. Each individual pre-sented a complex and specific map shape. For alpha, evokedactivity occurred exactly where the least of total induced powerwas observed, in the more strict post-stimulus time window: inthis case also, the topographic rendering of the data resulted, inall subjects, in undistinguishable evoked and induced maps.Another interesting finding, that was always been observed inour S1–S2 paradigms, was the presence of evoked alphathroughout the ISI (peaking in the ISI with an overall 71% of themaximum post-stimulus, evoked alpha). Given the long ISI of1.6 s, with respect to alpha wavelength, it is curious that somany alpha cycles could be synchronized with the task events.

When computing the scalp distribution of beta power, wefound 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 proceededto quantify the dispersion of individuals from the power-normalized topography for beta-2 (more similar across subjectsin time-pattern and frequency than beta-1) and theta (the mostcommon sensory-related topography across subjects on visualinspection). Results clearly showed the larger dispersion ofindividuals from the (least representative) beta group average,as compared to the topographic dispersion from the theta groupaverage (Fig. 5, where deviation (power) values are z-scoretransformed). A statistical comparison resulted in a highlysignificant difference between beta and theta deviation indexes(Wilcoxon: p=0.002; sign test: pb0.001).

3.4. Amplitude and topography of oscillatory burst averages

The computation of oscillatory burst or corrected latencyaverages allowed the possibility of explicitly analyzing therelative task-related changes with respect to absolute measuresof the baseline activity, both for average peak amplitudes, andmore interesting, for their topographies, in each frequency band.Theta oscillations presented an overall increase in peak am-plitude with respect to baseline of 13.8% (±11.5%), but rangingfrom no change in two subjects (increases around 6% in four

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 correspondingtopographic distribution of the main points of change, that were common to all subjects. Color scale: extreme of power changes (yellow and magenta) correspond to z-score 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 event-related potential global field power (root mean square; bar=5 μV).

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

Fig. 5. On top, examples of individuals presenting similar topography of task-induced theta activity, for which beta distribution seen from the same angle isclearly more variable across subjects. Below, topographic deviation of eachindividual from normalized mean, between beta 2 and theta bands. Deviationwas defined as the quadratic norm of the electrode-by-electrode differencebetween individual and group averaged data (across the montage, see text fordetails), as means to relatively quantify the higher beta variability depicted byvisual inspection.

in one subject, to 89%. Alpha oscillations were increased inall subjects, ranging from 2 to 57% from baseline (overall30.4%±16.3%). Finally, beta oscillations were enhanced inall cases (by 33.5% in average peak amplitude; std=17.1%;range=6 to 67%).

The second new and important finding of this work cameexactly from the topographic comparison between the two typesof corrected latency burst averages: the topography of theaverages computed for the pre-S1 baseline was visually un-distinguishable from that for the task period proper, in all cases(subjects and frequency bands). This was confirmed by the ICAdecomposition of the data, which also in all cases, showed thatthe main spatial component of task-induced oscillations wasidentical to the baseline activity, whereas the task-inducedactivity proper (or task-exclusive) corresponded to the secondICA component: for instance, in the theta range, this secondcomponent had the familiar topography of the N200 event-related potential component. For beta, which became the mainfocus of subsequent source analysis, we compared theamplitudes of task-exclusive with the baseline pattern. Thetask-exclusive activity or second ICA component, correspondedonly to an overall 10.7% of the main component in electricalpower (std=12.2%, ranging from less than 1% in one subject to36% in two subjects, but within 3 to 13% in the remainingsubjects). It became then clear, based on the mere visual in-spection of topographies, that the same sources already activebefore stimuli, presumably task-independent, were the mainsources active during task execution.

Another interesting aspect of the baseline activity was thevery similar topography of average baseline oscillations acrossfrequency bands, within subjects. In the case of our main in-terest, for instance, the beta-1 and beta-2 topographies wereindistinguishable (in the beta band, even the scalp topographyof the second component of beta-1 burst averages, in most of thesubjects, demonstrated a partial overlap with the second

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component of the 25 Hz band). The only exception was thealpha band in half of the subjects, which had a peculiar to-pography, different and prevailing over the pattern similaracross all remaining frequency bands. But in those cases also,the prevailing alpha spatial pattern remained the main com-ponent during the task time window as well. Since those resultswere absolutely unexpected, we performed a comparison, usingfour subjects, between the averages computed for the pre-S1baseline and similar averages computed for a resting conditionof a few minutes which preceded the start of the experiment(filtered continuous EEG was marked in local voltage peaksusing a refractory period of 800 ms, to avoid overlap betweenepochs, since no task events were present). The sametopography of baseline activity was thus observed, indicatingthat the pre-S1 baseline indeed reflects task-unrelated activity.Otherwise, it would be conceivable that baseline activity could

Fig. 6. Current density reconstruction results for all subjects. Current density indicatedcomponent, identical to baseline activity. (B) Second, task-exclusive component. Arrfraction of 10.7% with respect to baseline (std=12.2%, range: 0.3–36%).

still reflect task engagement, due to the cyclical nature of thetask, i.e., if trial expectation were physiologically identical torelevant S2 expectation. In one subject, we also replicated theexperiment after one month, and the same topography ofbaseline activity was observed. On the other hand, and criticalfor future studies, was that in three of the subjects, whoparticipated in previous experiments four and six years before,we observed very different topographies of the resting conditionoscillations.

3.5. Inter-electrode phase analysis

In all frequency bands but beta, the task-related phasecoherence 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

by small red arrows, (arrow size proportional to local current density). (A) Mainow sizes rescaled with respect to (A); actual intensities correspond to an overall

Fig. 6 (continued ).

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

with power. We thus proceeded to analyze intra-individualcorrelations between the two variables, and the results sup-ported the visual impression: beta 1 had significant non-parametric correlations in 11 subjects, and beta 2, highly sig-nificant correlations in 10 subjects.

We may mention here that the beta partial averages, com-puted from subsets of electrodes ranked by peak latency (seeMethods section ), were visually undistinguishable from eachother, suggesting the absence of systematic sequential activationbetween beta generating areas. This corroborates the inter-electrode phase analysis results, in the sense that both suggest atight phase synchrony between all such areas.

3.6. Current density reconstruction of beta burst averages

Corresponding to the complex scalp distribution of betainduced power, the source reconstruction results indicated thesame complexity: multifocal cortical current distribution, highly

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variable across subjects, including frontal and posterior corticalsources in all subjects. Fig. 6(A) shows the current distributionin each subject, accounting for the main ICA space-time datacomponent, indistinguishable from the baseline activity com-ponent. Fig. 6(B) shows the second component (task-exclusiveor task-induced ‘proper’), but in all cases of sufficient SNR forsource reconstruction (average SNR=1.8; std=0.7; range=1.05to 3.3). We may notice the individual-specific pattern of relativecurrent distribution, especially conspicuous for the second,exclusively task-related component. Only parietal area 7 showssome level of activity in all subjects corresponding to thebaseline component (although highly variable in intensity rela-tive to maximum current). The reconstruction results for thebeta-1 (around 21 Hz band) resulted in current distributions ofmain components indistinguishable from beta-2 in all subjects.According to the topographic inspection, reconstruction resultsshowed almost undistinguishable patterns between beta-1 andbeta-2 source models for the task-exclusive component in mostcases, typically with the beta-1 set of current foci representing asubset of those seen for beta-2. In some subjects, however, fewadditional (i.e., complementary to beta-2) weak sources werealso observed.

4. Discussion

Given the number of results and their interrelations, andimplications to be considered, we organized the discussion inthe following manner: first consider the two main findings ofthe present analysis, a) the attention-related beta oscillations incontrast with stimulus-related activity, and b) the relationbetween task-induced and baseline topography. Then, wediscuss c) the (cortical) topographic aspect of those combinedfindings (along with beta phase synchrony), in the context ofinter-individual variability in sets of task-related corticalactivity. Finally, after considering d) a few points regardingthe alpha range, in particular the relations between induced andevoked activity, we e) present a summary hypothesis on theevent-related behavior of all frequency bands, and considerfuture perspectives of the field.

4.1. Attention-induced beta oscillations

The first of the two main findings of this work was thepresence of beta band power increases throughout the ISI,peaking before the S2 stimulus, in all subjects, for which weproceeded to analyze topographically and model the generators.The finding was no absolute surprise, sincethe beta range istraditionally associated with wakefulness and behavioralarousal (e.g., Niedermeyer, 2003), an association that impelledthe widespread but still controversial beta-enhancement bybiofeedback (Ramirez et al., 2001). In the electrophysiologicalperspective, we consider stimulus expectancy or covert ori-enting to be attention in the strict sense, and the direct correlateof SPs and induced beta. Other longer lasting hypotheticalprocesses as vigilance, arousal or sustained attention, probablycontribute to the background beta, but not to its enhancement inthe S1–S2 interval, not seen in the passive condition, that

required some arousal level. On the other end, we prefer toconsider shorter lasting processes, present during and afterstimulus detection, not as attention itself but its consequences,with modulations of event-related potentials being theircorrelates. We agree with Bushnell (1998) that definitions ofall hypothetical processes “under the rubric of attention” arerelatively arbitrary. We also agree that those and psychophys-iological constructs in general, should be iteratively refined bythe interplay between the constructs that guide task design, andthe suggestions given by the actual behavioral and physiolog-ical experimental results themselves. Thus, even if arousal andattention consist in a continuum, where attention would be aphasic enhancement of arousal (but maybe restricted to givensensory domains — selective), beta would nicely fit as a cor-relate of such whole continuum. But this possibility must beconfirmed by future investigation, using tasks specificallydesigned to disentangle the hypothetical processes. Similar tothe present case, where beta power was significantly increasedwith respect to the control condition, beta increases have beenreported with respect to spatial attention, reaching long latenciesin paradigms that adopt the post-stimulus perspective (VazquezMarrufo et al., 2001). Relatively increased beta could also beattributed to movement, since it was absent in the passivecondition, but in such case it would be focal, restricted toelectrodes above sensory-motor areas (Neuper et al., 2006).Delta and theta induced power, on their turn, presented a clearand exclusive, early post-stimulus time distribution. In allsubjects, theta induced power peaked in coincidence with thepeaks of the N200. Thus, theta did not fulfill the expected roleof an attention correlate. The occasional observation of ongoingtheta in some healthy individuals during tasks such as overtcalculation with pen and paper (Mizuki et al., 1980), although ina small proportion of subjects (Takahashi et al., 1997), may bealternatively attributed to sustained mental effort, uncontrolledstimulus presentation or overt movement.

4.2. Task-induced versus pre-stimulus baseline topography

The second most important finding of this work was thebaseline or pre-S1 topographic distribution in all frequencybands. In all cases and subjects, almost all of the task-inducedpower distribution was the same as that present during thebaseline, and verified during rest in three subjects. By explicitlycomputing burst averages for the baseline, we found the scalptopography and cortical current distribution of each band to bealmost the same between resting wakefulness and task-inducedactivity. This means that the main task-related changes, inamplitude or synchronization with task events, take place in thesame areas already active during resting. Also, those baselinetopographic patterns were very similar between delta, theta andbeta ranges, only with alpha-2 presenting a distinct pattern inmost subjects. Only by subtracting the baseline component fromthe task-induced oscillations do we observe the lower powertask-exclusive, familiar sensory evoked potential patterns in thecase of theta and delta, or the complex, multifocal and highlyidiosyncratic patterns in the case of beta. Alpha occupies anintermediate position in complexity and individual variability of

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topography. Hanslmayr and colleagues (2007) have observed anidentity between baseline and task-induced activity, as we alsodid, in the case of the alpha range.

4.3. Induced beta and other evidence for the idiosyncrasy ofcortical functional circuits

Regarding the topography and current density distribution ofinduced and baseline beta, we noticed the qualitative similaritywith the other correlate of attention. In the previous analysis ofSPs for this task (Basile et al., 2006), we observed a commontask-time distribution, multifocal, complex scalp topography,highly variable across subjects, and a significant enhancementduring the task as compared to the passive stimulation controlcondition. Then, by computing beta burst averages centered inpeaks occurring within the 700 to 1600 ms task time window,and modeling their generators by CDR, we obtained analogousresults. Baseline beta burst generators showed an equallycomplex pattern, comprising prefrontal and posterior corticalareas (as do SPs), highly variable across subjects, with onlyparietal area 7 demonstrating some, but variable, degree ofcurrent density in all subjects. Area 7 was also the only commonSP generator region across subjects (Basile et al., 2006). Thisfact may be attributed to the mere wakeful state, that in otherprimates has been considered an index of interested attention tothe environment (Lynch et al., 1977; Yin and Mountcastle,1978). In our previous SP analysis we simply stated theirobvious higher inter-individual variability as compared to theprototypical and least variable sensory evoked potential, theN200. Here, we used a measure of dispersion of individual datafrom the group average (quadratic norm, after power normal-ization, to emphasize topography minimize the influence ofabsolute power differences), to quantitatively compare beta withtheta. The topographic deviation measure of beta was largerthan that of theta from their mean, and this difference washighly significant in a statistical comparison. On the other hand,the comparison between beta and SP generators by visualinspection (SP figure in Basile et al., 2006) revealed a largelycomplementary set of active areas between the two indexes.Beta and SP current foci were typically different from eachother, forming mostly adjacent sets within subjects. Wheneverthere was an overlap of SP and beta generating areas, they didnot 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 thecortical surface, and given the many cases of adjacent generatorpositions, it is conceivable that SPs could represent a fringeeffect stemming from the beta generating areas. SPs are for along time known to be microscopically generated by a majorcontribution from the potassium buffering function of glia(Skinner and Molnar, 1983; Roitbak, 1993; Mitzdorf, 1993), insituations of increased overall neural firing, as seems to occur inareas active in the beta range. Finally, our phase analysis resultspresent some weak evidence that cortical areas active in the betarange also oscillate in phase synchrony with each other, at leastroughly accompanying the power changes. But the stability ofpartial averages (with respect to groups of electrodes ranked by

peak latency), as well as independent studies using single-cell,extra-cellular recordings and model simulations offer strongersupport to this view (Bibbig et al., 2002). Thus, it appears thatbeta generating areas become co-recruited, either reciprocally,or by some common subcortical projection(s).

Our findings have a potentially critical implication to psy-chophysiology: In contrast to cortical activity linked to sensorystimulation (evoked potentials, delta, theta and partly alpharhythms), which is relatively simple in distribution and moresimilar across subjects, electrical activity related to expectingattention (SPs and beta) is multifocal, complex in distribution,and highly variable across subjects. Moreover, from the baselineresults, it appears that when one engages in the task, it is largelythe same individual-specific set of cortical areas, continuouslyactive during simple resting wakefulness, that either enters inphase or suffer changes in power or both, with a few other areassecondarily emerging, even more individual-specific andrepresenting lower power. Our data are qualitatively equivalentto the results from the fMRI and PET studies that presentindividual data on event-related metabolic changes (Cohenet al., 1996; Herholz et al., 1996; Fink et al., 1997; Davis et al.,1998; Hudson, 2000; Brannen et al., 2001; Tzourio-Mazoyeret al., 2002). The data from both sets of studies are compatiblewith the theoretical view of a “degenerate” mapping betweenpresumed functions and their implementing cortical areas(Noppeney et al., 2004). The hypothesis that has guided oursearch for a universal functional mapping regards thepreferential patterns of cortico–cortical connections in mam-mals (Pandya et al., 1988), first detailed between visual cortices(Macko and Mishkin, 1985) but known to apply throughout theneocortex, including prefrontal areas (Pandya and Yeterian,1990; Barbas, 1992). However, it is possible that the merecomplexity and number of possible excitatory cortico–corticalfunctional pathways are sufficient to allow the formation ofvariable sets of interconnected cortical areas across individuals,before and during execution of any given task. In studies thatpresent individual data, some subjects do not present at allchanges in areas that would appear in spatial group averaging,whereas weakly but consistently active areas may be deempha-sized by grand averaging. We propose, with some other authors,that functional claims regarding cortical areas never be madebased on group averaged data (Steinmetz and Seitz, 1991; Daviset al., 1998; Noppeney et al., 2004). The radical idea that fixedand universal functions cannot be attributed to given non-sensory-motor areas across individuals is also compatible withmicro-electrophysiological data from other primates. In thosedata (apart from sensory-motor areas), cells considered to betypical of given areas (e.g., ‘delay’ cells in prefrontal cortex) areknown to be distributed along the cortical mantle, and theirimplemented functions such as memory, can be considered to bedelocalized, (Fuster, 2003). It is the prevalence of cells clas-sified by response type to tasks, i.e., their distribution in dif-ferent cortical association areas, that is highly variable acrossindividuals (Fuster, personal communication). Finally, the ideaof relatively delocalized functions in individualized patterns, isradically different from a ‘mass-action’ concept of delocaliza-tion, and much like the traditional problem of variability in

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Neuropsychology (Noppeney et al., 2004). That is, similar focallesions in cortical association areas lead to critical impairmentsin some individuals, but their effects may only be noticed inothers after incidental or thorough laboratory examinations.

4.4. Alpha range characteristics and the topography ofinduced versus evoked oscillations

After a consideration of the topographic relations betweeninduced and evoked oscillations, we may conclude with asummary hypothesis of the behavior of generator areas of eachband with respect to stimulation and attention. Since ourcomputation of total induced power includes the evoked part,and delta and theta presented the same time distribution in bothcases, it appears that for such bands and in the present task, thetwo computations do not differ. In the case of alpha bands, wherethe induced and evoked power peaks are well separated in task-time, it was important to see no difference between maps fromboth time windows in all subjects. One recent study using thestandard 10–20 montage directly agrees with this topographicidentity between induced and evoked alpha (Hanslmayr et al.,2007). Thus, it appears that a single set of areas generate thealpha rhythms, with part of same distributed cell populationentering into synchrony with the whole task cycle (‘evoked’throughout the S1-post S2 window), but most of the powerstemming from a population that remains out of phase withevents. The presence of evoked alpha throughout the ISI, aphenomenon that we had observed previously even in experi-ments using ISIs of 2.5 s, made us speculate that this part of thealpha generators could serve as a kind of task-time estimator.This mixed alpha behavior is equivalent to the simultaneousobservation of alpha de-synchronization and synchronization,during the immediate post-stimulus time, known to depend onthe method of power analysis (Klimesch et al., 2000). We believethat the alpha rhythm can indeed be considered to reflect ‘corticalidling’ (Pfurtscheller, 2001), and that this concept can bereconciled with that of ‘preparation for detection’ of forthcomingstimuli (Knyazev et al., 2006), if one considers the stimulus-locked part occurring in the ‘reference interval’ of cyclical taskssuch as the present.

4.5. Summary of event-related oscillations

A summary hypothesis on the behavior of cortical areasgenerating all frequency bands, with respect to stimulation andattention, may now be attempted: (1) During waking rest, indi-viduals would have a highly overlapping, multi-focal, main set ofcortical areas, including parietal area 7, but otherwise idiosyn-cratic, 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 mainlyphase-reset delta and theta baseline activity, and part of alphagenerator cell population, besides (2b) adding sensory-specificgenerators of theta (mainly compounding P1/N2 ERP compo-nents), delta and alpha (major components of “P1”/P2/P3 ERPcomponents), of lower power and more similar across subjects;

(3a) attention would be concurrent with SP (sub-delta) generationand beta amplitude increase in baseline activity generating areas,and (3b) an engagement of additional highly idiosyncratic betagenerating areas. Main issues to be clarified by future researchinclude: (1) confirmation of variability in larger sample (a map-ping into, e.g., personality, genetics or gender may be found), andsearch for event-related changes specific to other psychologicalconditions (emotion, memory, comprehension, problem solving)and frequency bands (baseline and task-induced gamma, andbaseline sub-delta); (2) testing of stability in time andmalleabilityof the topography of baseline activity (spontaneous versustraining); (3) systematic analysis of the (causal) time relationsbetween baseline and secondary components; (4) systematicallyanalyze the relations (coherence) between frequency bands.

Acknowledgments

This research was supported by the grants 03/02297-9 and02/13633-7 from Fapesp, São Paulo, Brazil. We wish to thankDr Cláudia Leite, Dr Edson Amaro Jr. and the staff from theDepartment of Radiology of the University of São PauloMedical School, for kindly acquiring and preparing the MRIsets, to Márcio A. Costa for his valuable technical support, andto Prof. Walter Thomas Bourbon for his advice.

References

Barbas, H., 1992. Architecture and cortical connections of the prefrontal cortexin 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 andnew strategies. Int. J. Psychophysiol. 39 (2-3), 91–127.

Basile, L.F.H., Rogers, R.L., Bourbon, W.T., Papanicolaou, A.C., 1994. Slowmagnetic 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. BrainTopogr. 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 recognitiontasks. Int. J. Psychophysiol. 27, 29–41.

Basile, L.F.H., Ballester, G., Castro, C.C., Gattaz, W.F., 2002. Multifocal slowpotential generators revealed by high-resolution EEG and current densityreconstruction. Int. J. Psychophysiol. 45 (3), 227–240.

Basile, L.F.H., Baldo, M.V., Castro, C.C., Gattaz, W.F., 2003. The generators ofslow 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 slowpotential generators in a simplified attention paradigm. Int. J. Psychophysiol.61 (2), 149–157.

Bibbig, A., Traub, R.D., Whittington, M.A., 2002. Long-range synchronizationof gamma and beta oscillations and the plasticity of excitatory and inhibitorysynapses: 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 formapping Broca's area. AJNR Am. J. Neuroradiol. 22 (9), 1711–1718.

Bruns, A., Eckhorn, R., 2004. Task-related coupling from high- to low-frequency signals among visual cortical areas in human subdural recordings.Int. J. Psychophysiol. 51 (2), 97–116 Jan.

251L.F.H. Basile et al. / International Journal of Psychophysiology 65 (2007) 238–251

Buchner, H., Knoll, G., Fuchs, M., Rienacker, A., Beckman, R., Wagner, M.,Silny, J., Pesch, J., 1997. Inverse localization of electric dipole currentsources in finite element models of the human head. Electroencephalogr.Clin. Neurophysiol. 102 (4), 267–278.

Bushnell, P.J., 1998. Behavioral approaches to the assessment of attention inanimals. Psychopharmacology 138, 231–259.

Cohen, M.S., Kosslyn, S.M., Breiter, H.C., Di Girolamo, G.J., Thompson, W.L.,Anderson, A.K., Brookheimer, S.Y., Rosen, B.R., Belliveau, J.W., 1996.Changes in cortical activity during mental rotation. A mapping study usingfunctional MRI. Brain 119 (Pt 1), 89–100.

Davis, K.D., Kwan, C.L., Crawley, A.P., Mikulis, D.J., 1998. Functional MRIstudy of thalamic and cortical activations evoked by cutaneous heat, cold,and tactile stimuli. J. Neurophysiol. 80 (3), 1533–1546 Sep.

Fell, J., Dietl, T., Grunwald, T., Kurthen, M., Klaver, P., Trautner, P., Schaller,C., Elger, C.E., Fernandez, G., 2004. Neural bases of cognitive ERPs: morethan phase reset. J. Cogn. Neurosci. 16 (9), 1595–1604 Nov.

Fink, G.R., Frackowiak, R.S., Pietrzyk, U., Passingham, R.E., 1997. Multiplenonprimary motor areas in the human cortex. J. Neurophysiol. 77 (4),2164–2174.

Fuchs, M., Wagner, M., Wischmann, H.A., Kohler, T., Theissen, A.,Drenckhahn, R., Buchner, H., 1998. Improving source reconstructions bycombining bioelectric and biomagnetic data. Electroencephalogr. Clin.Neurophysiol. 107 (2), 93–111.

Fuchs, M., Wagner, M., Kohler, T., Wischmann, H.A., 1999. Linear and nonlinearcurrent density reconstructions. J. Clin. Neurophysiol 16 (3), 267–295.

Fuster, J.M., 2003. Cortex and Mind: Unifying Cognition. Oxford UniversityPress, New York.

Gruber, W.R., Klimesch, W., Sauseng, P., Doppelmayr, M., 2005. Alpha phasesynchronization predicts P1 and N1 latency and amplitude size. Cereb.Cortex 15 (4), 371–377 Apr.

Hanslmayr, S., Klimesch, W., Sauseng, P., Gruber, W., Doppelmayr, M.,Freunberger, R., Pecherstorfer, T., Birbaumer, N., 2007. Alpha Phase ResetContributes to the Generation of ERPs. Cereb Cortex 17 (1), 1–8.

Herholz, K., Thiel, A., Wienhard, K., Pietrzyk, U., von Stockhausen, H.M.,Karbe, H., Kessler, J., Bruckbauer, T., Halber, M., Heiss, W.D., 1996.Individual functional anatomy of verb generation. NeuroImage 3 (3 Pt 1),185–194 Jun.

Hudson, A.J., 2000. Pain perception and response: central nervous systemmechanisms. Can. J. Neurol. Sci. 27 (1), 2–16 Feb.

Hyvarinen, A., Oja, E., 2000. Independent component analysis: algorithms andapplications. Neural Netw. 13 (4-5), 411–430.

Klimesch, W., Doppelmayr, M., Rohm, D., Pollhuber, D., Stadler, W., 2000.Simultaneous desynchronization and synchronization of different alpharesponses in the human electroencephalograph: a neglected paradox?Neurosci. Lett. 284 (1-2), 97–100.

Knyazev, G.G., Savostyanov, A.N., Levin, E.A., 2006. Alpha synchronizationand anxiety: implications for inhibition vs. alertness hypotheses. Int. J.Psychophysiol. 59 (2), 151–158.

Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.J., 1999. Measuring phasesynchrony in brain signals. Hum. Brain Mapp. 8 (4), 194–208.

Lynch, J.C., Mountcastle, V.B., Talbot, W.H., Yin, T.C., 1977. Parietal lobemechanisms for directed visual attention. J. Neurophysiol. 40 (2), 362–389.

Macko, K.A., Mishkin, M., 1985. Metabolic mapping of higher-order visualareas in the monkey. Res. Publ.-Assoc. Res. Nerv. Ment. Dis. 63, 73–86.

Mitzdorf, U., 1993. Contributions of extracellular potassium increases totransient field potentials (review of data). In: Haschke, W., Roitbak, A.I.,Speckmann, E.J. (Eds.), Slow Potential Changes in the Brain. Chap 16.Birkhäuser, Boston.

Mizuki, Y., Tanaka, M., Isozaki, H., Nishijima, H., Inanaga, K., 1980. Periodicappearance of theta rhythm in the frontal midline area during performance of amental task. Electroencephalogr. Clin. Neurophysiol. 49 (3-4), 345–351 Aug.

Nakashima, K., Sato, H., 1993. Relationship between frontal midline thetaactivity in EEG and concentration. J. Hum. Ergol. (Tokyo) 22 (1), 63–67.

Neuper, C., Wortz, M., Pfurtscheller, G., 2006. ERD/ERS patterns reflectingsensorimotor activation and deactivation. Prog. Brain Res. 159, 211–222.

Niedermeyer, E., 2003. Electrophysiology of the frontal lobe. Clin. Electro-encephalogr. 34 (1), 5–12 Jan.

Noppeney, U., Friston, K.J., Cathy, J., 2004. Price degenerate neuronal systemssustaining cognitive functions. J. Anat. 205, 6.

Pandya, D.N., Yeterian, E.H., 1990. Prefrontal cortex in relation to othercortical areas in rhesus monkey: arquitecture and connections. In: Uylings,H.B.M., Van Eden, C.G., De Bruin, J.P.C., Corner, M.A., Feenstra, M.G.P.(Eds.), Progress in Brain Research, vol. 85. Elsevier Science PublishersBV, pp. 63–94.

Pandya, D.N., Seltzer, B., Barbas, H., 1988. Input-output organization of theprimate cerebral cortex. Comp. Primate Biol. 4, 39–80.

Pfurtscheller, G., 2001. Functional brain imaging based on ERD/ERS. VisionRes. 41 (10-11), 1257–1260.

Ramirez, P.M., Desantis, D., Opler, L.A., 2001. EEG biofeedback treatment ofADD. A viable alternative to traditional medical intervention? Ann. N. Y.Acad. Sci. 931, 342–358 Jun.

Roitbak, A.I., 1993. Cortical slow potentials, depolarization of glial cells, andextracellular potassium concentration. In: Haschke, W., Roitbak, A.I.,Speckmann, E.J. (Eds.), Slow Potential Changes in the Brain. Chap 14.Birkhäuser, Boston.

Skinner, J.E., Molnar, M., 1983. Event-related extracellular potassium ionactivity changes in frontal cortex of the conscious cat. J. Neurophysiol. 49,204–215.

Steinmetz, H., Seitz, R.J., 1991. Functional anatomy of language processing:neuroimaging and the problem of individual variability. Neuropsychologia29 (12), 1149–11461.

Takahashi, N., Shinomiya, S., Mori, D., Tachibana, S., 1997. Frontal midline thetarhythm in young healthy adults. Clin. Electroencephalogr. 28 (1), 49–54.

Talairach, J., Tournoux, P., 1993. Referentially oriented cerebral MRI anatomy.Atlas of stereotaxic anatomical correlations for gray and white matter.Thieme Medical Publishers, Inc., New York.

Talairach, J., Tournoux, P., 1997. Co-planar stereotaxic atlas of the human brain.Thieme Medical Publishers, Inc., New York.

Tallon-Baudry, C., Bertrand, O., Delpuech, C., Pernier, J., 1996. Stimulusspecificity of phase-locked and non-phase-locked 40 Hz visual responses inhuman. J. Neurosci. 16 (13), 4240-4209 Jul 1.

Tzourio-Mazoyer, N., Josse, G., Crivello, F., Mazoyer, B., 2002. Interindividualvariability in the hemispheric organization for speech. NeuroImage 16 (3),765–780.

Valencia, M., Alegre, M., Iriarte, J., Artieda, J., 2006. High frequencyoscillations in the somatosensory evoked potentials (SSEP's) are mainlydue to phase-resetting phenomena. J. Neurosci. Methods 154 (1–2),142–148.

Vazquez Marrufo, M., Vaquero, E., Cardoso, M.J., Gomez, C.M., 2001.Temporal evolution of alpha and beta bands during visual spatial attention.Brain Res. Cogn. Brain Res. 12 (2), 315–320.

Yin, T.C., Mountcastle, V.B., 1978. Mechanisms of neural integration in theparietal lobe for visual attention. Fed. Proc. 37 (9), 2251–2257.