Minor and unsystematic cortical topographic changes of attention correlates between modalities.

Abstract

In this study we analyzed the topography of induced cortical oscillations in 20 healthy individuals performing simple attention tasks. We were interested in qualitatively replicating our recent findings on the localization of attention-induced beta bands during a visual task [1], and verifying whether significant topographic changes would follow the change of attention to the auditory modality. We computed corrected latency averaging of each induced frequency bands, and modeled their generators by current density reconstruction with Lp-norm minimization. We quantified topographic similarity between conditions by an analysis of correlations, whereas the inter-modality significant differences in attention correlates were illustrated in each individual case. We replicated the qualitative result of highly idiosyncratic topography of attention-related activity to individuals, manifested both in the beta bands, and previously studied slow potential distributions [2]. Visual inspection of both scalp potentials and distribution of cortical currents showed minor changes in attention-related bands with respect to modality, as compared to the theta and delta bands, known to be major contributors to the sensory-related potentials. Quantitative results agreed with visual inspection, supporting to the conclusion that attention-related activity does not change much between modalities, and whatever individual changes do occur, they are not systematic in cortical localization across subjects. We discuss our results, combined with results from other studies that present individual data, with respect to the function of cortical association areas.

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Minor and Unsystematic Cortical Topographic Changes
of Attention Correlates between Modalities
Luis F. H. Basile1,2*, Mirna D. Lozano1, Milkes Y. Alvarenga2,6, Jose ´ F. Pereira Jr.2, Se ´rgio Machado3,
Bruna Velasques3, Pedro Ribeiro3, Roberto Piedade3, Renato Anghinah5, Gennady Knyazev7, Renato T.
Ramos1,4
1Laboratory of Psychophysiology, Faculdade da Sau ´de, Universidade Metodista de Sa ˜o Paulo, Sa ˜o Paulo, Brazil, 2Division of Neurosurgery, University of Sa ˜o Paulo
Medical School, Sa ˜o Paulo, Brazil, 3Department of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, 4Department of Psychiatry, University of Sa ˜o
Paulo Medical School, Sa ˜o Paulo, Brazil, 5Department of Neurology, University of Sa ˜o Paulo Medical School, Sa ˜o Paulo, Brazil, 6Universidade Sa ˜o Judas Tadeu, Sa ˜o Paulo,
Brazil, 7Institute of Physiology, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russian Federation
Abstract
In this study we analyzed the topography of induced cortical oscillations in 20 healthy individuals performing simple
attention tasks. We were interested in qualitatively replicating our recent findings on the localization of attention-induced
beta bands during a visual task [1], and verifying whether significant topographic changes would follow the change of
attention to the auditory modality. We computed corrected latency averaging of each induced frequency bands, and
modeled their generators by current density reconstruction with Lp-norm minimization. We quantified topographic
similarity between conditions by an analysis of correlations, whereas the inter-modality significant differences in attention
correlates were illustrated in each individual case. We replicated the qualitative result of highly idiosyncratic topography of
attention-related activity to individuals, manifested both in the beta bands, and previously studied slow potential
distributions [2]. Visual inspection of both scalp potentials and distribution of cortical currents showed minor changes in
attention-related bands with respect to modality, as compared to the theta and delta bands, known to be major
contributors to the sensory-related potentials. Quantitative results agreed with visual inspection, supporting to the
conclusion that attention-related activity does not change much between modalities, and whatever individual changes do
occur, they are not systematic in cortical localization across subjects. We discuss our results, combined with results from
other studies that present individual data, with respect to the function of cortical association areas.
Citation: Basile LFH, Lozano MD, Alvarenga MY, Pereira JF Jr, Machado S, et al. (2010) Minor and Unsystematic Cortical Topographic Changes of Attention
Correlates between Modalities. PLoS ONE 5(12): e15022. doi:10.1371/journal.pone.0015022
Editor: Bradley Steven Launikonis, University of Queensland, Australia
Received August 10, 2010; Accepted October 7, 2010; Published December 17, 2010
Copyright: ? 2010 Basile et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grant 07/50390-9 from FAPESP (Fundac ¸a ˜o de Amparo a ` Pesquisa do Estado de Sa ˜o Paulo). The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: lbasile@usp.br
Introduction
Over the last decades, a major scientific effort has been made in
the attempt to map functions of cortical association areas. We have
used as a basis for such possible mapping, the neuroanatomy of
cortico-cortical connections, as opposed to the tradition of using
purely psychological or behavioral distinctions. The relatively
specific connections between visual areas [3], and other neocortical
areas, particularly prefrontal [4–6] guided our task design, in the
searchfor (prefrontal)cortical specializationsof function,particularly
of selective attention to different visual domains [e.g., 7–9].
However, in the last years we have been providing evidence for
high individual variability, in the sets of cortical association areas active
during non sensory-motor tasks: that is, the very concept that
particular non sensory-motor functions are associated to the same
anatomic areas in all individuals must be revised. This individual
variability was firstly observed with respect to scalp and generator
topography of slow potentials, classical correlates of attention, in
relativelycomplextasks[9,10].ButonlybyanalyzingSlowPotentials
(SPs) from a simpler visual attention task we began to consider that
this variability may be inherent to cortical physiology [2]. The
variability was not observed for stimulation-related activity, in an
explicit comparison between SPs and the visual N200s from the
same data set. In a more recent study, we analyzed the topography
of induced beta oscillations during the same simple task, as new
correlates of attention, by corrected latency averaging of band-pass
filtered epochs: this method allows the visualization of actual average
voltage distributions that are not time-locked to stimuli [1]. In this
study of beta oscillations, we observed that the topography of both
baseline (pre-stimulus) oscillations, which increase in amplitude
during the inter-stimulus interval (S1–S2 paradigm), as well as
secondary, task-exclusive components, were also highly variable to
individualswith respect to scalptopographyand their generating sets
of cortical areas. In this same study, the topography of frequency
bands more closely related to sensory stimulation such as theta,
proved to be much closer in topography across subjects (as expected,
given the closeness in topography with evoked potentials), in a
quantitative comparison with attention-related beta activity. We
havestartedtoreinterpretourfirststudiesonslowpotentials,ontasks
involving explicit memorization, stimulus comparisons, categoriza-
tion and feedback anticipation, where the same multifocal, complex,
highly variable sources of slow potentials were always observed.
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In the present study we further explore this hypothesis of
individual-specific distributions of non-sensory-motor cortical
activity, using the same visual task as in our last studies (where
we analyzed SPs and first analyzed induced beta bands, [1,2,11]),
for a dual purpose: 1) to qualitatively replicate those first beta
localization findings on a slightly larger sample, and mainly 2) to
allow an individually-based comparison with an auditory attention
task. The main current interest was to observe how different would
be the topography of attention correlates after displacement of
attention from the visual to the auditory modality. We used as a
reference for comparison the more purely stimulation-related
frequency bands (theta and delta; along with alpha, [12–16]). In
order to support the comparisons made by visual inspection, we
computed the correlations, within individuals, between the cortical
current distributions obtained for the two modalities, for baseline and
main task-related activity, in all frequency bands. Those correla-
tions were then transformed into Fisher’s Z scores for inter-individual
analysis, which we restricted to attention and stimulation represen-
tative bands (beta1 and theta), or to a combination of bands. Since
we were also interested in the explicit computation of the signifi-
cant part of the topographic difference between modalities, we
complemented the analysis of correlations, by illustrating the
significant part of each individual’s difference of attention
correlates between modalities.
Methods
Subjects
Twenty healthy individuals with normal vision and hearing, 12
male and 8 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. All subjects signed consent forms
approved by the Ethics Committee of the University (Comite ˆ de
E´tica em Pesquisa da Universidade Metodista de Sa ˜o Paulo).
Stimuli and Task
A commercial computer program (Stim, Neurosoft Inc.)
controlled all aspects of the tasks. Visual stimuli composing the
cue-target pairs (S1–S2) consisted in small rectangles (eccentricity
60.8u, 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 60.3u of eccentricity. A masking stimulus had the
same grey level as the target (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. S1 was followed by S2, with onsets
separated in time by 1.6 seconds. The ITI was variable, ranging from
2.3 to 5 seconds. We instructed the subjects that a rectangle would
be presented to indicate that 1.6 seconds 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 by the percent correct
trials (and their derivate false positives and hits), from the total of
96 trials comprising each task. An eye fixation dot was continually
present on the center of the screen, as well as a stimulus-masking
background, to prevent after-images. The parameters of the
second task were identical to the above (even with maintenance of
the visual stimuli but without targets), except for the addition of
auditory stimuli, analogous to the visual, and instructions to detect
auditory targets and ignore the visual stimuli: We placed a pure
tone as auditory S1 stimulus (1000 Hz, 60dB, 100 ms duration) as
close as possible to the visual stimulus in time (given the
stimulation program limitations, there was a 100 ms delay
between visual and auditory stimulus onset, but all subjects
reported simultaneity when asked), and an identical auditory S2,
except that 50% of them ‘contained’ the targets (defined by a
slight, transient intensity reduction 210% for about 10 ms –– of
the pure tone waveform).
EEG Recording and acquisition of MRIs
We used a fast Ag/AgCl electrode positioning system consisting
of an extended 10–20 system, in a 128-channel montage (Quik-
Cap, Neuromedical SuppliesH), and an impedance-reducing gel
which eliminated the need for skin abrasion (Quick-Gel,
Neuromedical SuppliesH). Impedances usually remained below 5
kOhms, 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 (PolhemusH) 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 128-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 900 ms before S1 to 400 ms after S2
presentation. Baseline was defined as the 300 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 250 mV
were eliminated. In our montage, the VEOG detected, typically,
blinks as deflections above 130 mV 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 degrees,
F.O.V =26626 cm. Total acquisition time was around 8 minutes.
Frequency-Time analysis
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 spectrum
of the interval from 700 ms previous to S1, to 400 ms after S2. To
obtain the induced power spectrum [17], 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 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
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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 (,Pj. = (Pj- mj)/sj; given Pj=
spectral power at each time point in electrode j, mjand sjare the
mean and standard deviation, respectively, of the average power
during the baseline for the electrode).
Computation of corrected latency burst averages
We used corrected latency averaging instead of conventional
event-related potential (ERP) averaging or event-related (de-
)synchronization (ERS/ERD), because only in this case we would
be able to analyze task-related potential distributions that are not
time-locked to task-events. Conventional ERP averaging results in
exclusively time-locked activity (e.g., induced beta cannot be
observed), whereas ERS/ERD analysis allows visualization of
‘electrical power’ distribution but not of actual voltages (which
would not allow our further current density reconstruction analysis).
According to the observed induced frequency bands for each
individual (and to results from our previous study, [1]), we chose the
bands for band-pass filtering of the original artifact-free EEG
epochs: Butterworth, 96dB rolloff, 0–1 Hz for SPs, 1–3 Hz for
delta, 3–7 for theta, 7–9 for alpha1, 9–12 for alpha2, 18–23 for
beta1, and 23–29 Hz for beta2). The resulting filtered epochs were
then processed by an algorithm developed by ourselves for
searching the peaks of bursts within the task-time windows of
interest (a detailed schematic representation of the method is found
in Figure 1 of [1]). Filtered 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 (we included the
typically few error trials, since our main interest was in the ISI, pre-
S2 window). Then, a grand average was computed using the
averages obtained by guidance from each channel. In all cases, we
also computed pre-S1 burst averages (representing the baseline
topography for each frequency band), where the program searched
peaks from 2400 to 0 ms before S1, for comparison with the task-
induced bursts. We computed the total power of corrected latency
average peaks for all bands, and the overall results were tabulated,
after conversion of into SNR values. We also computed Spearman
correlations between SNR in all bands and task performance.
For a systematic visual inspection, we computed realistic three-
dimensional topographic maps of the scalp distribution of averages
for each frequency band and its main ICA components, over the
reconstructed scalp anatomy. To this purpose, we used a
commercial sotfware (Curry V 4.6, Neurosoft Inc.), that co-
registered individual MRI sets (skin model, see below) with the
actual position of each electrode with respect to common
landmarks, and linearly interpolated the instantaneous voltage
values to obtain continuous maps.
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 [2,10].
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. [18]). ICA was applied to
each individual’s whole space-time data set, i.e., to the m6n data
matrix (m used channels X 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, with SNR.1. In practice, in all cases, only two or
three 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 range
from around 9 to 23 thousand points, depending on the head size.
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., [19–21]). 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 (the thousands of
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 l values to be used, typically
converged after repeating the fitting process two to three times (l
gives the balance between goodness of fit and model size).
Statistics of reconstruction results
In order to quantitatively compare inter-individual or group
results between the visual and auditory conditions, we used inter-
modality correlations as the measure of similarity between the identically
ordered sets of dipoles obtained for (representative) chosen
frequency bands or their combinations: 1) based on an analogous
comparison from previous studies between stimulus versus
attention correlates (N200 versus SPs, [2]; theta versus beta, [1]),
we here chose the inter-modality correlations (their Fisher’s Z
transforms) of the second ICA components of theta versus beta1
bands: we chose theta as the main ‘stimulus-related’ band based on
previous findings and current induced power analysis, since in
most subjects its distribution is virtually identical to the visual
N200 or auditory N100 topographies, whereas beta 1, represent-
ing ‘attention-related’ activity, was observed to be stronger than
beta 2, more uniform in peak frequency across modalities); and
then 2) we also used the average of the Fisher’s Z transforms of
correlations in theta combined with delta versus the transforms of
correlations in beta1 combined with beta2 and with SPs (pooled
sets of bands mainly related to stimulation or attention).
Complementarily, to evaluate the intra-individual differences in
cortical topography of attention correlates between modalities,
we computed a point-by-point dipole strength difference vector.
First, each data pair to be compared was adjusted or ‘normalized’
to became of equal total strength or mean (the weaker set of
dipoles was multiplied by a scalar number so that its average
would match the stronger set; this method is analogous to the
scaling of voltages to a common global mean field power, to
emphasize topographic differences and deemphasize mere differ-
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Figure 1. Task-related EEG Power. Time course of power (converted to z-score with respect to baseline) in one example subject. At left, visual
task, right, auditory. From top to bottom, beta2, beta1, alpha2, alpha1, theta and delta.
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ences in amplitude, which proved useful in a inter-individual
comparison in a clinical application; [22]). Second, we trans-
formed the difference vector distribution into z-score values.
Finally, we considered the points of major contribution to
condition differences as absolute individual Z values, correspond-
ing to global Z =2.57, which depending on the individual’s
number of point-to-point comparisons (i.e., the number of dipole
supporting points, in proportion to head size), ranged from
Z =3.48 to Z =3.68. The cortical distribution of such points of
Z-score beyond the individual’s threshold was thus known a
posteriori, by using the corresponding current density cutoff value as
the minimum current to be plotted. Finally, we also checked
current distributions for a very low cutoff value of Z =1.96.
Results
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 in the visual
task was 83.2% correct responses (standard deviation 15.1%) and
89.6% in the auditory task (standard deviation 11.7%). Neither
this difference was statistically significant (t-test, p=0.08), nor for
false positives (p=0.73) or correct hits (p=0.21).
Topography by visual inspection and Correlations of
source reconstruction results
Table 1 shows the baseline and task-related root mean global
field power changes, converted to SNR values, for both tasks,
averaged across subjects. Significant task-related power changes
(always increases) are indicated in the bands where it occurs.
Notice the lack of increases in the theta band (and alpha1 in
auditory task), which as in previous studies, mainly concentrates
power (phase-locking to stimuli) in the immediate post-stimulus
windows, peaking close to the N200. Only the alpha2 baseline
power during the auditory task correlated significantly with
performance in the same task (negatively; 20,563, p=0.01).
Since corrected latency averages partially collapse task-time course
information, leading do independent components mainly separat-
ed by phase, we exemplify task-related electrical power changes in
two subjects in Figures 1 and 2, from delta to beta 2 bands, and the
similar contributions from separate groups of electrodes to three
bands, by scalp region, in one subject (figure 3). Visual inspection
of topographic isopotential maps and source reconstruction results
supported a qualitative replication of our main recent findings: 1-
Scalp topographic characteristics and the corresponding current
density distributions were variable across subjects, for first ICA
components, and especially so for second or task-related
components, in both visual and auditory conditions; 2- the
baseline activity voltage distribution was again identical to the first
ICA component during the ISI, and similar across all frequency
bands for all subjects (but typically a little more complex – less
smooth isopotential lines - in the beta bands), with three
exceptions, all of which also previously observed: a) the task-
related pattern in the delta band was stronger (ranked as first ICA
component instead of second) than the baseline pattern in 10
subjects; b) a different, peculiar alpha2 pattern was stronger than
the one common across frequencies in three subjects (the
remaining subjects also had a resting or baseline alpha proper
pattern, but secondary to the baseline pattern common to other
frequencies); and c) slow potentials are purely task-related, having
no topographic similarity with all other frequencies.
Regarding the results of our main present interest, similarity or
difference between secondary components in auditory versus
visual conditions in chosen frequency bands, visual inspection was
compatible with our expected results: 1- first ICA component
topographic patterns were virtually indistinguishable between
conditions, in all including the beta bands. We count the baseline
beta activity among the attention-related components, since it
systematically increased in power from pre-S1 activity both in the
previous and present study (by 39% in the present study), as
opposed to theta and delta baseline components, that even
decrease in amplitude in some subjects; 2- secondary component
patterns of stimulus-related bands, theta and delta, were clearly
different between conditions. But the most interesting result was
the 3- inter-modal similarity between task-related or secondary
beta components.
Figure 4 shows the topographic patterns obtained in three
example individuals, selected for representing the lowest, median
and highest audio-visual topographic correlations in the attention-
related bands. It may be noticed that in all cases the first
components are similar across bands, with increased topographic
complexity in the beta bands. One may also notice the overall
similarity between secondary beta components, in spite of the
difficulty in simultaneously controlling the number of isopotential
lines to illustrate component maps, due to the much stronger first
components. Major differences can be seen particularly between
auditory and visual theta band secondary components. Figure 5
shows the source reconstruction results (along with main steps of
analysis) in the example subject of median audio-visual correla-
tions in the attention-related bands. The thin rectangles depict the
results for secondary or ‘task-exclusive’ components in the main
bands for which inter-modal correlations were compared, theta
and beta1.
Given the confirmed normal distribution of our data, we
computed parametric correlations between audio and visual
cortical current distributions, and they quantitatively supported
the observations by visual inspection (figure 6 shows the average
audio-visual topographic correlations across subjects in various
bands). First, by considering an arbitrary cutoff value of Pearson’s
r =0.5, stimulus versus attention-related bands could be separated
in the following way: at least one of the attention-related bands
had their second ICA components with high significant audio-
visual correlations in all but one subject, whereas in no case was
there a high significant correlation between theta or delta task-
related components. Second, the actual correlation values,
according to visual inspection, were highest in all frequency bands
Table 1. Grand average of root mean global field power
(computed at peak of corrected latency averages) converted
into SNR, by frequency band (V=visual task; A=auditory
task).
SNRbaselinetask
VAVA
Beta 25.605.67 7.21**6.83**
Beta 16.546.66 7.72** 7.50**
Alpha 25.185.15 6.19** 5.80**
Alpha 14.935.57 5.94**6.04
Theta5.765.505.365.65
Delta3.323.15 8.89** 6.43**
Significant increases in power with respect to baseline are indicated by asterisks
(p,0.01, paired t-tests).
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Figure 2. Task-related EEG Power. Same as figure 1, but in example subject presenting a single alpha band.
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for first ICA components, intermediary in value for Slow
Potentials and secondary beta bands, and minimal for delta and
theta bands.
Inter-individual comparison between attention and
stimulation-related audio-visual topographic correlations
After conversion of correlations between auditory and visual-
related current density distributions into Fisher’s Z transforms, we
performed paired t-tests that resulted in highly significant
differences, both in the comparison between the transformed
correlations in theta and beta1 bands (t=22.99, p,0.01), and
between the Fisher’s Z scores averaged across theta and delta
bands, and the scores averaged across beta1, beta2 and slow
potentials (t=25.885, p,0.0001). Figure 7 illustrates the pooled
Fisher’s Z scores representing the stimulus and attention-related
bands.
Cortical distribution of significant contribution to within
individual differences between visual and auditory
attention correlates
The Z-score transformation of the point-by-point audio-visual
differences in dipole strength distributions were computed for the
Figure 3. Task-related EEG Power. Time course in three bands (top to bottom: beta2, beta1 and theta) separated by electrode groups,
representing scalp regions: black=all electrodes; green=temporal; red=parietal; magenta=occipital; blue=frontal.
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Figure 4. Audio-visual topographic comparisons. Examples of topographic maps (three-map sets represents first or baseline (I), second (‘task-
exclusive’ used for comparisons- II) and third (III) ICA components in each frequency band (indicated at left of each auditory=A visual=V pair; nasion
on top of maps). Each of the three line sets is labeled according to each representative subject (‘‘high’’, subject that presented the highest inter-
modality correlations of attention-related results; ‘‘medium’’, subject of median correlations; ‘‘low’’, subject of lowest correlations in the group). Hot
versus cold colors indicate opposite polarity of purely spatial patterns (temporal patterns were complex, always with phase differences between
components).
doi:10.1371/journal.pone.0015022.g004
Figure 5. Audio-visual cortical generators comparisons. Example of source reconstruction results, for which further analyses were performed
(correlations for similarity and significant differences for distinction between auditory and visual activity), in the subject with median inter-modality
correlations. At extreme of both sides, z-score transformed frequency-time plots of task-related power changes (bars below represent stimuli).
Processing steps indicated for the auditory beta1 (peak averaging of band-pass filtered epochs, ICA decomposition of resulting averages, and Current
Density Reconstruction – CDR - of single ICA filtered components – only first and second: e.g., b1_1=main or resting beta1 component;
b1_2=second or task-related beta1 component). CDR results for the second ICA components were used both to compute significant audio-visual
differences (example for b1 in this subject on top of the figure) and correlations between those tasks. Bottom half of CDR results exemplify results for
the theta band.
doi:10.1371/journal.pone.0015022.g005
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task-exclusive beta1 and beta2 components, and for the SPs. In
none of the three cases did any single cortical cytoarchitectonic
area contain foci of (differential) current density common across
subjects. Figure 8 shows the localization of the significant portion
of differences in current distribution between tasks, in the beta1
band, in all subjects. The task differences for SPs were not
localized in the same areas as beta1, but the two beta bands were
typically very similar, as previously observed [1]. The inspection of
current distributions after a very permissible cutoff value, of course
increased the extent of current density foci, but in all cases (three
bands) resulted in the same overall conclusion: not a single
cytoarchitectonic area containing current foci was common to all
subjects.
Discussion
The present work reproduced previous findings of high inter-
individual variability of cortical activity patterns during the visual
attention task [1,2]: varying sets of cortical areas and current
density distribution explain the direct electrophysiological atten-
tion correlates, both induced beta bands and slow potentials across
individuals (we left the alpha band out of the analysis on purpose,
due to the complexity of relations with task events, variable set of
sub-bands across subjects, and controversial functional interpre-
tations, previously discussed in an analysis of the visual task,
[1,11]). The literature that presents individual data, mainly using
functional magnetic resonance imaging (fMRI), corroborates our
findings on high individual variability, whenever non sensory-
motor tasks are studied, and consistently adverts against ‘brain
averaging’ (e.g., [23–29]). Occasionally, this variability has been
observed even in the case of passive (cutaneous) stimulation (e.g.,
[26,27]). Those reports are in accordance with our own
unpublished fMRI data from 60 individuals performing auditory
and visual memory recognition tasks, where a high individual
variability was seen, as opposed to their simpler, predictable and
common patterns of activity following passive audiovisual
stimulation. Taken together, all those observations challenge the
validity of our search for predetermined functions of cortical
association areas, of course depending on the definition of
functions. We distinguish (expecting) attention proper from
consequences of attention on stimulation and detection, whose
correlates are the well known modulation of evoked and
endogenous potentials, for instance. In our anatomy-based search
of regional cortical specialization of function, we used the concept
of function as the mentally dealing (e.g. comparing, associating)
with different sensory materials within a modality, such as spatial
Figure 6. Audio-visual correlations. The similarity in current density distributions between visual and auditory tasks was measured by the
correlations between the identically ordered sets of dipole strengths, in each frequency band and ICA component (Inter-subject averages are shown
only for illustration; Comparisons were performed between Fisher’s Z transformed correlations; first components: ‘‘band’’_1; secondary components:
w/o extension; bars: 95% confidence interval of the mean). Thick rectangles depict attention-related bands, and thin rectangle, stimulus-related
bands. However, first beta components were not used in comparison due to their trivial high correlations, common to most bands (see text).
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versus non-spatial ‘mental operations’. Excepting the sensory-
motor components, individuals seem to implement such common
‘functions’ using varying cortico-cortical circuits. On the other
hand, putative functions abstracted exclusively from behavioral
measures, may or may not correspond to distinct physiological
correlates or implementation, at least in the present, macro-
anatomical sense: different timing or accuracy of response may not
necessarily correspond to implementation by differing (cortical)
circuits.
A second qualitative replication was the observation of the
topographic similarity of baseline activity between frequency
bands in each individual, with the same exceptions of slow
potentials, and a main alpha component in some subjects.
The new contribution was the explicit, within subjects compar-
ison between the generators of correlates of the visual task, and
those obtained when attention was displaced to additional
auditory stimuli. Measures of similarity between correlates of
both modalities, the point-by-point correlations between dipole
strength distributions, had an overall correspondence with visual
inspection. They were clearly higher for attention correlates
(induced beta oscillations and SPs) than for more closely stimulus-
related activity (theta and delta bands). Thus, even the secondary
or ‘task-exclusive’ induced beta oscillations were significantly
closer to each other in topography between modalities than theta
and delta topographic patterns. The baseline beta1 and beta2
topography, corresponding to the main induced attention
correlate due to its strength and increase during the ISI, is
virtually indistinguishable between conditions, and present the
highest inter-modality correlations. Those highest correlations
correspond to practically indistinguishable patterns at visual
inspection. However, they may be considered trivial, since they
happen to the baseline components in all frequency bands (we
have data showing pre-S1 baseline topography also to be
indistinguishable from quiet resting), only in lesser degree in the
beta band.
Our findings indicate that attention, at least in the broad sense
that we use, is related to activity in distributed sets of cortical areas
peculiar to each individual. Even in the case of the present major,
modality change in attention, the known electrophysiological
correlates do not appear to significantly change, suggesting an
individual-specific, but modality unspecific cortico-cortical net-
work. Given the high number of compared dipole strengths, on the
order of 104, we complemented our analysis of similarity by an
explicit treatment of the intra-individual condition differences as
well. We illustrated the loci of the portion of current distribution
by a rigorous cutoff value of the z-transform of the difference
vector between conditions (but also inspected results after a non-
rigorous threshold), for the attention-related frequencies. We were
interested in knowing whether any inter-individual systematic
trend in anatomical localization occurred, however small the
Figure 7. Audio-visual correlations. Pooled Fisher’s Z averages, separating stimulus from attention-related activity.
doi:10.1371/journal.pone.0015022.g007
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Figure 8. Auditory-visual attention differences. Significant part of difference in dipole strength distributions between auditory and visual tasks,
in all subjects. Illustrated are the differential currents of absolute z-score value beyond a global z=2.57, adjusted to number of dipole supporting
points in each individual (range: z=3.48 to z=3.62). Relative local current density (to individual maximum) is represented by red arrows and
proportional to arrow size (due to wide differences between maximum and minimum individual values, actual arrows can be noticed entering –or
arrow heads exiting- the brain only in a few cases of high local current density, e.g., first and second subjects on top of right column).
doi:10.1371/journal.pone.0015022.g008
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overall changes following the displacement of attention could be
considered after the correlation analysis.
Once more, no common area across individuals showed to be
specifically related to the shift of attention to the auditory
modality, even when the low significance threshold was used.
This occurred in the case of the bands that we consider more
directly related to attention: the baseline components of beta1 and
beta2 bands, that are enhanced and thus along with SPs form the
major attention correlates, and the second ICA beta1 and beta2
components (‘‘task-exclusive’’ since their topography is not present
during baseline activity). This latter method may be considered
analogous to ‘task-subtraction’, and more exclusively sensitive to
the variable of interest, the displacement of attention, minimizing
the effects of other factors that could interfere with inter-individual
variability. For instance, although based on a still insufficient
number of replication cases, we do suspect of a significant change
even in the baseline topography in the course of years, as opposed
to a few months. In any case, if confirmed, this instability would be
one more ‘uncontrollable’ source of individual variability. Thus,
idiosyncratic cortical activity seems to be a robust phenomenon,
even when those other factors are considered, such as: task
complexity and corresponding individual strategies of execution
(whose minimization of variability was the main intent of the
present task design, a simplification of Posner’s task, [30,31];
discussed in [2]); the unavoidable inclusion of some degree of
memorization in any task; some extent of stimulus evaluation in
any voluntary action; even the extreme possibility of a lack of
relation between our measures and behavior [2,9]; or a
‘‘methodological threshold’’ limitation [1]. Regarding the thresh-
old problem, we may place our studies in one extreme, of a case-
by-case description of occurrence of activity foci in estimated
cytoarchitectonic areas, where the other extreme is represented by
spatial grand averaging studies, which may occasionally present
results that are not shared by all individuals. However, exactly due
to the inconsistency in active areas between individuals, some
groups of authors may be placed in an intermediary position
between those extremes. They are developing interesting methods
to account for such variability, with accompanying theoretical
interpretations. One example is the application of the concept of
‘biological degeneracy’ to functional studies and Neuropsychology
(‘many-to-one’ mapping of areas-funtion) and the ‘multisubject
network’ method (e.g., [32,33]), and ‘fuzzy clustering’ [34].
Another example is the data-driven delimitation of distributed
‘‘partially segregated networks of brain areas’’ to be re-approached
to individual, fMRI and EEG data [35,36]. Both example efforts
represent a compromise between the pure presentation of
individual data and grand spatial averaging. But we believe they
cannot still replace the first extreme, since some methodological
steps somewhat remove their final spatial results from actual
physiological changes, such as the occasional stress in ‘regions of
interest’ (all subjects included in an area, even when ‘extreme’ or
‘outliers’ in original measures), or statistical clustering of ICA
extracted individual patterns and use of very low correlations
between fMRI and EEG results.
But we must still contrast our conclusions with those from other
psychophysiological studies presenting individual data, mainly
those using fMRI. Most reports are concerned with intersubject
variability, but in the extent or intensity of activation in pre-chosen
regions of interest (e.g, [37,38]). Studies explicitly showing the
variability in the actual sets and distribution of active areas are less common
[23–29]. Our findings, along with the last mentioned studies and
our own unpublished fMRI results on visual and auditory
recognition tasks versus mere stimulation, suggest that non
sensory-motor cortical cytoarchitectonic areas cannot be expected
to implement any pre-established physiological function, at least
when function is conceived as classical, general psychological
processes such as expecting attention, perception or target
detection, conception, effort of memorization or evocation. Of
course, among the infinitude of non-physiologically or descrip-
tively-driven constructs of contemporary Psychology, if considered
‘functions’, may prove to be rather localized in cortical domains (as
believed to be the case for ‘error detection’ or auditory ‘change-
detection’, when considered as outside of the sensory domain). The
commonplace observation from clinical practice in Neuropsychol-
ogy, of variable symptomatology and degrees of impairment
following lesions on common areas across subjects, corroborates
this view. Most cases of functional claims regarding particular
cortical areas, but based on group averages, neglect the fact that a
percentage of subjects do not show any activation at all in those
areas. Finally, it is worth mentioning once more (as discussed in
[9]), that whatever the concept of function may be, an unrestrained
literature search will lead to an enormous number of functional
studies involving most cortical areas, and reciprocally, searches
merely using any given cortical association area will result in
functional hypotheses difficult to reconcile with each other, and
especially so with a general biological theoretical framework.
A limitation of our studies is the restriction of observations to
cortical activity. Circuits implementing whatever psychological
process to be considered in the future as in fact basic or
elementary, may essentially involve the interaction between
individually variable functional cortico-striato-pallido-thalamo-
cortical circuits (CSP ‘loops’) and invariable subcortical structures.
Among the latter, a critical role must be ascribed to the structures
mediating the interaction between the telencephalon and effector
systems (somatomotor, vegetarive and endocrine), and between the
telencephalon and the (aminergic) nuclei and cell fields of
relatively diffuse ascending projections. Those structures are the
extended amigdala, the lateral septum, and the habenulo-
interpeduncular and mamillo-tegmental axes [39]. Thus, it may
be the case that an absolutely universal part of circuits across
subjects may only be found on the highly convergent interactions
between CSPs and such mediating structures, which in principle
are inaccessible to EEG/MEG and probably so to fMRI, given
their minute proportions.
In sum, the patterns of cortical electrical activity observed even
during relatively simple mental tasks appear to reflect the high
inter-individual variation that we intuitively perceive in human
associative thinking. Ever present and very general psychological
facts in voluntary action such as ‘association’ or ‘problem-solving’,
may one day prove to be the actual correlates of the
topographically variable electrophysiological measures reported
here. Whatever proves to be the case, individual-case mapping of
task-related neurophysiological
preconceived expectations, seems essential to both clinical and
physiological understanding of human mental functioning.
measures, independentfrom
Acknowledgments
We wish to thank Dr. Cla ´udia Leite, Dr. Edson Amaro, Jr., and the staff
from the Department of Radiology of the University of Sa ˜o Paulo Medical
School, for kindly acquiring and preparing the MRI sets, and to Ma ´rcio
Costa for his valuable technical support. We also wish to thank Mauro de
Salles Aguiar for his special support.
Author Contributions
Conceived and designed the experiments: LFHB. Performed the
experiments: LFHB JFP MDL MYA. Analyzed the data: LFHB MYA
JFP GK. Contributed reagents/materials/analysis tools: PR RP SM BV
LFHB MYA. Wrote the paper: LFHB SM PR GK RA RTR.
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References
1. Basile LF, Anghinah R, Ribeiro P, Ramos RT, Piedade R, et al. (2007)
Interindividual variability in EEG correlates of attention and limits of functional
mapping. Int J Psychophysiol 65(3): 238–251.
2. Basile LF, Brunetti EP, Pereira JF, Jr., Ballester G, Amaro E, Jr., et al. (2006)
Complex slow potential generators in a simplified attention paradigm.
Int J Psychophysiol 61(2): 149–157.
3. Macko KA, Mishkin M (1985) Metabolic mapping of higher-order visual areas
in the monkey. Res Publ Assoc Res Nerv Ment Dis 63: 73–86.
4. Pandya DN, Seltzer B, Barbas H (1988) Input-output organization of the
primate cerebral cortex. Comparative Primate Biology 4: 39–80.
5. Pandya DN, Yeterian EH (1990) Prefrontal cortex in relation to other cortical
areas in rhesus monkey: Arquitecture and connections. In: Uylings HBM, Van
Eden CG, De Bruin JPC, Corner MA, Feenstra MGP, eds. Progress in Brain
Research Elsevier Science Publishers BV 85: 63–94.
6. Barbas H (1992) Architecture and cortical connections of the prefrontal cortex in
the rhesus monkey. Advances in Neurology 57: 91–115.
7. Basile LFH, Rogers RL, Bourbon WT, Papanicolaou AC (1994) Slow magnetic
flux from human frontal cortex. Electroenceph. Clinical Neurophysiol 90:
157–165.
8. Basile LFH, Brunder DG, Tarkka IM, Papanicolaou AC (1997) Magnetic fields
from human prefrontal cortex differ during two recognition tasks.
Int J Psychophysiol 27: 29–41.
9. Basile LFH, Baldo MV, Castro CC, Gattaz WF (2003) The generators of slow
potentials obtained during verbal, pictorial and spatial tasks. Int J Psychophysiol
48: 55–65.
10. Basile LFH, Ballester G, Castro CC, Gattaz WF (2002) Multifocal slow potential
generators revealed by high-resolution EEG and current density reconstruction.
Int J Psychophysiol 45(3): 227–240.
11. Basile LF (2008) Complex, multifocal, individual-specific attention-related
cortical functional circuits. Biol Res 40(4): 451–70.
12. 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.
13. Gruber WR, Klimesch W, Sauseng P, Doppelmayr M (2005) Alpha phase
synchronization predicts P1 and N1 latency and amplitude size. Cereb Cortex
Apr 15(4): 371–377.
14. Hanslmayr S, Klimesch W, Sauseng P, Gruber W, Doppelmayr M, et al. (2006)
Alpha Phase Reset Contributes to the Generation of ERPs. Cereb Cortex 17(1):
1–8.
15. Valencia M, Alegre M, Iriarte J, Artieda J (2006) High frequency oscillations in
the somatosensory evoked potentials (SSEP’s) are mainly due to phase-resetting
phenomena. J Neurosci Methods 154(1-2): 142–8.
16. Fell J, Dietl T, Grunwald T, Kurthen M, Klaver P, et al. (2004) Neural bases of
cognitive ERPs: more than phase reset. J Cogn Neurosci 16(9): 1595–1604.
17. Tallon-Baudry C, Bertrand O, Delpuech C, Pernier J (1996) Stimulus specificity
of phase-locked and non-phase-locked 40 Hz visual responses in human.
J Neurosci 16(13): 4240–4249.
18. Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and
applications. Neural Netw 13(4-5): 411–430.
19. Buchner H, Knoll G, Fuchs M, Rienacker A, Beckman R, et al. (1997) Inverse
localization of electric dipole current sources in finite element models of the
human head. Electroenceph Clin Neurophysiol 102(4): 267–278.
20. Fuchs M, Wagner M, Wischmann HA, Kohler T, Theissen A, et al. (1998)
Improving source reconstructions by combining bioelectric and biomagnetic
data. Electroencephalogr Clin Neurophysiol 107(2): 93–111.
21. Fuchs M, Wagner M, Kohler T, Wischmann HA (1999) Linear and nonlinear
current density reconstructions. J Clin Neurophysiol 16(3): 267–295.
22. Basile LFH, Yacubian J, Ferreira BLC, Valim AC, Gattaz WF (2004)
Topographic abnormality of slow cortical potentials in schizophrenia. Brazilian
Journal of Medical and Biological Research 37(1): 97–109.
23. Cohen MS, Kosslyn SM, Breiter HC, Di Girolamo GJ, Thompson WL, et al.
(1996) Changes in cortical activity during mental rotation. A mapping study
using functional MRI. Brain 119(Pt 1): 89–100.
24. Herholz K, Thiel A, Wienhard K, Pietrzyk U, von Stockhausen HM, et al.
(1996) Individual functional anatomy of verb generation. Neuroimage 3(3 Pt 1):
185–194.
25. Fink GR, Frackowiak RS, Pietrzyk U, Passingham RE (1997) Multiple
nonprimary motor areas in the human cortex. J Neurophysiol 77(4): 2164–2174.
26. Davis KD, Kwan CL, Crawley AP, Mikulis DJ (1998) Functional MRI study of
thalamic and cortical activations evoked by cutaneous heat, cold, and tactile
stimuli. J Neurophysiol 80(3): 1533–1546.
27. Hudson AJ (2000) Pain perception and response: central nervous system
mechanisms. Can J Neurol Sci 27(1): 2–16.
28. Brannen JH, Badie B, Moritz CH, Quigley M, Meyerand ME, et al. (2001)
Reliability of functional MR imaging with word-generation tasks for mapping
Broca’s area. AJNR Am J Neuroradiol 22(9): 1711–1718.
29. Tzourio-Mazoyer N, Josse G, Crivello F, Mazoyer B (2002) Interindividual
variability in the hemispheric organization for speech. Neuroimage 16(3):
765–780.
30. Posner MI (1980) Orienting of attention. Q J Exp Psychol 32(1): 3–25.
31. Posner MI, Snyder CR, Davidson BJ (1980) Attention and the detection of
signals. J Exp Psychol 109(2): 160–174R.
32. Mechelli A, Penny WD, Price CJ, Gitelman DR, Friston KJ (2002) Effective
connectivity and intersubject variability: using a multisubject network to test
differences and commonalities. Neuroimage 17(3): 1459–1469.
33. Noppeney U, Friston KJ, Price CJ (2004) Degenerate neuronal systems
sustaining cognitive functions. Journal of Anatomy 205(6): 433–442.
34. Seghier ML, Friston KJ, Price CJ (2007) Detecting subject-specific activations
using fuzzy clustering. Neuroimage 36(3): 594–605.
35. Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007)
Electrophysiological signatures of resting state networks in the human brain.
Proc Natl Acad Sci U S A 104(32): 13170–13175.
36. Mantini D, Corbetta M, Perrucci MG, Romani GL, Del Gratta C (2009) Large-
scale brain networks account for sustained and transient activity during target
detection. Neuroimage 44(1): 265–274.
37. Wei X, Yoo SS, Dickey CC, Zou KH, Guttmann CR, et al. (2004) Functional
MRI of auditory verbal working memory: long-term reproducibility analysis.
Neuroimage 21(3): 1000–1008.
38. Otzenberger H, Gounot D, Marrer C, Namer IJ, Metz-Lutz MN (2005)
Reliability of individual functional MRI brain mapping of language. Neuropsy-
chology 19(4): 484–493.
39. Basile LFH, e Leite MC (1998) ‘‘Circuitos encefa ´licos e o abuso e depende ˆncia
da cocaı ´na.’’ In: Guerra de Andrade A, e Leite MC, eds. ‘‘Cocaı ´na e Crack-
Abuso e Depende ˆncia: dos fundamentos ao tratamento’’ Artes Me ´dicas, Porto
Alegre, Brazil [chapter in portuguese].
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