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\begin{document}

\title{Distinct patterns of spatial attentional modulation of steady state
visual evoked magnetic fields (SSVEFs) in subdivisions of the human
early visual cortex}



\author[1]{Stephan Moratti}%
\author[2]{Christopher Gundlach}%
\author[2]{Javi Echegaray}%
\author[2]{Matthias Müller}%
\affil[1]{Universidad Complutense de Madrid}%
\affil[2]{Universität Leipzig}%


\vspace{-1em}



  
  \date{June 06, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
In recent years, steady state visual evoked potentials (SSVEPs) became
an increasingly valuable tool to investigate neural dynamics of
competitive attentional interactions and brain computer interfaces. This
is due to their good signal-to-noise ratio, allowing for single trial
analysis and their ongoing oscillating nature that enables to analyze
temporal dynamics of facilitation and suppression. Given the popularity
of SSVEPs, it is surprising that only a few studies looked at the
cortical sources of these responses. This is in particular the case when
searching for studies that assessed the cortical sources of attentional
SSVEP amplitude modulations. To address this issue, we used a typical
spatial attention task and recorded neuromagnetic fields (MEG) while
presenting frequency-tagged stimuli in the left and right visual field,
respectively. Importantly, we controlled for attentional deployment in a
baseline period before the shifting cue. Subjects either attended to a
central fixation cross or to two peripheral stimuli simultaneously.
Results clearly showed that signal sources and attention effects were
restricted to early visual cortex: V1, V2, hMT+, precuneus,
occipital-parietal and inferior-temporal cortex. When subjects attended
to central fixation first, shifting attention to one of the peripheral
stimuli resulted in a significant activation increase for the
to-be-attended stimulus with no activation decrease for the
to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but
significant SSVEF decreases from V1 to occipito-parietal cortex. When
attention was first deployed to both rings, shifting attention away from
one ring basically resulted in a significant activation decrease in all
areas for the then to-be-ignored stimulus.%
\end{abstract}%



\sloppy


\textbf{Distinct patterns of spatial attentional modulation of steady
state visual evoked magnetic fields (SSVEFs) in subdivisions of the
human early visual cortex}

\textsuperscript{\textbf{*}} Stephan Moratti\textsuperscript{1,2},
Christopher Gundlach\textsuperscript{3}, Javier de
Echegaray\textsuperscript{3}, Matthias M. M\selectlanguage{ngerman}üller\textsuperscript{3}

\textsuperscript{1}Department of Experimental Psychology, Complutense
University of Madrid, Spain

\textsuperscript{2}Center for Cognitive and Computational Neuroscience,
Complutense University of Madrid, Spain

\textsuperscript{3}Wilhelm Wundt Institute for Psychology, University of
Leipzig, Germany

\textsuperscript{\textbf{*}}\textbf{Corresponding Author:}

Stephan Moratti, PhD

Department of Experimental Psychology Faculty of Psychology

Complutense University of Madrid, Spain

Email: smoratti@ucm.es

\textbf{Acknowledgements: Research was supported by a grant from the
German Research Foundation (MU 972/24-1). JM was supported by a grant
from the Complutense University of Madrid and Santander bank.}

\textbf{The authors declare to have no conflict of interest. Data is
available upon request to the first author SM}

\textbf{Abstract}

In recent years, steady state visual evoked potentials (SSVEPs) became
an increasingly valuable tool to investigate neural dynamics of
competitive attentional interactions and brain computer interfaces. This
is due to their good signal-to-noise ratio, allowing for single trial
analysis and their ongoing oscillating nature that enables to analyze
temporal dynamics of facilitation and suppression. Given the popularity
of SSVEPs, it is surprising that only a few studies looked at the
cortical sources of these responses. This is in particular the case when
searching for studies that assessed the cortical sources of attentional
SSVEP amplitude modulations. To address this issue, we used a typical
spatial attention task and recorded neuromagnetic fields (MEG) while
presenting frequency-tagged stimuli in the left and right visual field,
respectively. Importantly, we controlled for attentional deployment in a
baseline period before the shifting cue. Subjects either attended to a
central fixation cross or to two peripheral stimuli simultaneously.
Results clearly showed that signal sources and attention effects were
restricted to early visual cortex: V1, V2, hMT+, precuneus,
occipital-parietal and inferior-temporal cortex. When subjects attended
to central fixation first, shifting attention to one of the peripheral
stimuli resulted in a significant activation increase for the
to-be-attended stimulus with no activation decrease for the
to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but
significant SSVEF decreases from V1 to occipito-parietal cortex. When
attention was first deployed to both rings, shifting attention away from
one ring basically resulted in a significant activation decrease in all
areas for the then to-be-ignored stimulus.

\textbf{Impact statement}

Although the cortical generators of steady state visual evoked responses
as recorded by EEG or MEG (SSVEPs or SSVEFs, respectively) have been
described in humans, how these generators are involved in the modulation
of these evoked oscillatory responses by spatial attention is less
known. Here, we describe how distinct sub-divisions of the early human
visual cortex contribute differently to the modulation of SSVEFs by
spatial attention.

\textbf{Introduction}

Steady state visual evoked potentials (SSVEPs) and their magnetic
counterpart (SSVEFs) experienced a remarkable evolution in the cognitive
neurosciences and brain computer interface literature. The number of
publications using or investigating SSVEPs increased significantly
during the last 30 years. For example, a snapshot of the last three
years, i.e., from 2020 to 2022, Web of Science lists 298 publications
with the words ``SSVEP'' or ``steady state visual evoked'' in the title.
Ten years ago, i.e., between 2010 and 2012 the list contains 81
publications, and this went down to 14 between 2000 and 2003 and to 16
publications between 1990 and 1992, respectively.

This evolution is not too surprising, given the advantage that SSVEPs
provide to investigate competitive attentional selection mechanisms in
the visual (see for summaries Müller 2014, Norcia et al. 2015), auditory
(cf. Linden et al. 1987, Woldorff and Hillyard 1991, Makeig et al. 1996,
Picton et al. 2003) and somatosensory domain (Snyder 1992, Tobimatsu et
al. 1999, Giabbiconi et al. 2007, Adler et al. 2009, Goltz et al. 2015,
Pang and Müller 2015) and as a robust signal for brain computer
interfaces (for a review see Zhu et al. 2010). The biggest advantage of
SSVEPs is their high signal-to-noise ratio, in particular for
frequencies in the range of 8-15 Hz, that allows to extract them even in
single trials. Their ongoing oscillatory nature permits to track exact
time courses of competitive interactions or attentional resource
allocation in multi element stimulus designs, in particular in the
visual domain (cf. Müller et al. 1998, Antonov et al. 2020, Gundlach et
al. 2020, Gundlach et al. 2022). The power of frequency-tagged stimuli
as a valuable tool in scientific and applied settings was nicely
reviewed in a 2015 paper by Norcia and colleagues (Norcia et al. 2015),
and we would like to refer interested readers to that paper, rather than
listing all the areas here.

In visual attention research, it was demonstrated that the amplitude of
SSVEPs increased significantly when a flickering stimulus was attended,
compared to when it was ignored. This modulation pattern has been
observed for spatial (cf. Morgan et al. 1996, Müller et al. 1998, Müller
and Hübner 2002, Müller et al. 2003, Walter et al. 2014), feature-based
(cf. Müller et al. 2006, Andersen et al. 2008, Andersen et al. 2013,
Störmer and Alvarez 2014, Andersen et al. 2015) and object-based
attention (cf. Brummerloh et al. 2019, Brummerloh and Müller 2019,
Adamian et al. 2020). Despite the wealth of studies using SSVEPs as a
tool, surprisingly few studies were interested in the underlying
cortical generators. Among the earliest studies was a study by Müller
and colleagues (Müller et al. 1997), using a 37-channels magnetometer
and a single equivalent current dipole model for steady state responses
at 6, 12 and 15 Hz. Results revealed sources in early visual cortex, and
suggested differential activation patterns as a function of stimulation
frequency. One year later, Hillyard and colleagues (Hillyard et al.
1997), in a combined electroencephalographic (EEG) and functional
magnetic resonance imaging (fMRI) study, reported sources in ventral and
lateral extrastriate visual areas in a visual spatial attention task,
that were basically replicated in another spatial attention task study
implementing a pure EEG-based source analysis of SSVEP signals (Müller
et al. 1998). While these early studies found sources of SSVEPs
predominately in extrastriate cortex, studies that were conducted after
the turn of the millennium consistently found generators of the SSVEP in
V1 as well. Pastor and colleagues (Pastor et al. 2003) presented their
subjects a broad range of flickering stimuli from 2-90 Hz while they
recorded EEG and used some of these frequencies during positron emission
tomography (PET) recordings. They reported significant activation in
primary visual cortex (see also Pastor et al. 2007). These results are
in line with an MEG study from Fawcett and colleagues (Fawcett et al.
2004) who also presented their subjects a broad range of frequencies up
to 21 Hz, and by Kaufmann and colleagues (Kaufmann et al. 2001) for the
same frequency range in an fMRI study. The most extensive study,
however, was presented by DiRusso and colleagues (Di Russo et al. 2007).
In their study a 6 Hz Gabor stimulus was presented in a passive viewing
task. They recorded EEG and fMRI, and used retinotopic mapping to
estimate SSVEP sources in visual cortex. They found SSVEP generators in
V1, V3A, V4/V8 and hMT+/V5.

In summary, these studies provided clear evidence, that the generators
of SSVEPs are located in early visual cortex that are linked to
low-level visual processing. This holds for a broad range of flicker
frequencies and stimulus types (for flickering facial stimuli see Wieser
and Keil 2011). However, when it comes to the question of what are the
sources that generate attention effects, \emph{e. g. the
attentional}SSVEP amplitude enhancement, these studies have only limited
value. First, almost all studies used a passive viewing task, and
therefore were agnostic to attentional modulations. Second, in the fMRI
study by Hillyard et al. (1997) conditions in which attention was
deployed to one side of the screen were contrasted with passive viewing.
This did not allow to control for attentional deployment during passive
viewing (\emph{e. g.} did subjects attend to the fixation cross, or to
both flickering stimuli, or attended to both flickers in an arbitrary
alternating manner during 36 seconds of recording in blocks of passive
viewing?). In order to get a better estimate of cortical sources that
contribute to attention effects, it is mandatory to control for
subjects' attentional state/deployment during a reference or baseline
stimulation period.

In the current MEG study, we used a spatial attention design with a
flickering stimulus presented in the left and right visual hemifield,
respectively. In a trial-by-trial fashion, in an interval between 1500
and 2000 ms after flicker onset, subjects were cued to shift attention
to the left or right stimulus and to perform a task at the attended
location/stimulus. In order to control for attentional deployment during
the pre-cue period (i.e. baseline), subjects either attended to the
fixation cross or to both stimuli in the left and right visual
hemifields and performed a detection task associated with the respective
spatial location(s). With this manipulation we were able to (a) estimate
the cortical generators while attention was focused away from the
peripheral stimuli (i.e. attend fixation cross), (b) when they attended
to both stimuli (left and right), and (c) the contribution of cortical
areas of attentional amplitude enhancement in the post-cue period,
relative to the two pre-cue baseline conditions,.

\textbf{Methods}

\emph{Participants}

Twenty healthy participants (13 females, one left-handed, mean age 20.5
years, range: 18 years to 27 years) volunteered in the study. The sample
size was determined based on a previous EEG study of ours (Gundlach et
al. 2020) as we used an almost identical paradigm. All participants had
normal or corrected to normal vision and no history of photic epilepsy.
The study had full ethical approval from the local ethics committee
according to the Declaration of Helsinki and all participants gave
written informed consent. We confirm that all methods were performed in
accordance with the relevant guidelines and regulations following the
Spanish and European law (Ley Orgánica 15/ 1999 de Protección de datos
de Carácter Personal LOPD y Real Decreto 994/ 1999).

\emph{Stimuli}

The same stimuli as in Gundlach et al. (2020) were used. All stimuli
were created using the Psychtoolbox 3 (Kleiner et al. 2007) running on a
PC with a Windows 8 operating system (Microsoft Cooperation). All visual
stimuli were presented in a magnetically shielded room by a video
projector (Panasonic PT-D7700E, 60 Hz refresh rate) via a mirror system.
The distance between the projection screen (45 cm by 33.5 cm) and the
participants was kept constant at 133.5 cm. The stimuli display
consisted of two rings presented at 6.4º to the left and right of
central fixation cross (0.25º by 0.25º). The outer and inner diameter of
the two rings were 4.6º and 1.8º, respectively. The background color of
the screen was set to 30\% of maximum white color (luminance 13 cd/
m\textsuperscript{2}). In order to evoke steady state visual evoked
fields (SSVEFs) both rings flickered at 12 Hz and 15 Hz each, whereby
the assignment of each flicker frequency to the left or right visual
field was counter-balanced across subjects. The rings had a luminance of
70\% of maximum white color (60 cd/ m\textsuperscript{2}) during the
on-frames. For one half of the participants, the fixation cross target
stimuli consisted of small decreases of the either vertical or
horizontal length by 0.05º in visual angle depicted for 60 ms. Increases
of the same size served as distractor events. For the other half of
participants, the target and distractor stimuli were reversed.

For the pre-cue task, target events for the peripheral rings were
luminance decreases of a small segment of both rings (10º) ranging
between 55 \% to 65 \% of maximum white screen color (8 cd/
m\textsuperscript{2} to 23 cd/ m\textsuperscript{2}) lasting for 150 ms.
The position of the luminance reduced segment was chosen randomly
between 0º and 360º around an arc spanning each of the two rings.
Changes of luminance of the segment in only one of the two rings were
defined as distractors. For the post-cue task (see below), luminance
changes of the peripheral ring in the visual hemifield that had to be
attended, defined a target event, whereas changes in the opposite
hemifield were considered as distractors.

\emph{Procedure and task}

After subjects were familiarized with the MEG chamber, a written
informed consent was given and signed. Then, subjects were seated in the
shielded room placing their head under the MEG helmet. The experiment
consisted of a speeded detection paradigm similar to the experiment
published by Gundlach et al. (2020). In general, a spatial cueing design
with a pre-cue baseline task was utilized. Before a spatial cue
indicated which visual hemifield had to be attended, participants
performed a detection task. Thereby, the volunteers had to focus onto
the central fixation cross in order to detect small size changes of the
cross or extend spatial attention to the peripheral rings to detect
segments of luminance changes within the rings at both visual hemifields
simultaneously. The central fixation and peripheral rings conditions
were assigned to two blocks of 240 trials each. Half of the participants
started with the central fixation condition and then were administered
the task that required attending the peripheral rings. The other half of
the volunteers executed the tasks in reverse order. Each block contained
60 target, 60 distractor and 120 trials without any changes (no events).
Targets and distractors for both pre-cue tasks were presented randomly
within a time interval between 300 ms after trial onset and 500 ms
before the presentation of the post-cue task (see below).

The pre-cue task duration varied randomly between 1500 ms to 2000 ms.
Then, the fixation cross changed its color either to red or blue
indicating that the left or right visual hemifield had to be attended,
respectively. The assignment of the color and hemifield was
counter-balanced across participants. During the post-cue time interval,
ranging from 3000 ms to 3500 ms, participants had to detect luminance
decreases of a small segment of one of the peripheral rings (as in the
pre-cue task). However, targets were only luminance changes in the
to-be-attended visual hemifield. Luminance changes in segments of the
ring in the to-be-ignored visual hemifield were considered as
distractors. During the post-cue interval one, two or no events could
occur. The events (targets or distractors) were presented between 300 ms
after the post-cue and 500 ms before the trial end but were separated by
at least 800 ms if two events occurred. The participants had to indicate
the detection of a target in pre- and post-cue trials with a button
press of the right index finger (half of the participants had to use the
left index finger). The combinations of pre-cue (attend fixation cross
vs. attend peripheral rings) and post-cue tasks (attend left vs. right
visual hemifield) were fully balanced and each condition was repeated
120 times resulting in 480 trials in total. The experiment started with
12 practice trials (6 pre-cue fixation and 6 pre-cue peripheral rings
trials).

\emph{Behavioral Analysis}

Responses (button presses) after a target were considered as hits.
Button presses after a distractor, or no event were scored as false
alarms. Responses with latencies greater than 1 s were discarded from
analysis.

\emph{MEG data acquisition and pre-processing}

MEG data was continuously recorded (600 Hz sample rate, 0.1 Hz to 200 Hz
online-filter) using a 306-channel (102 magnetometers, 204 orthogonal
gradiometers pairs) system (Elekta-Neuromag® VectorView, Helsinki,
Finland, 2005). For the depiction of sensor space MEG data only
gradiometers will be shown as their magnetic flux topographies are
easier to interpret. However, all cortical source space analyses (see
below) were done based on all gradio- and magnetometer sensors. For
artifact monitoring the electrooculogram (EOG) was recorded with
electrodes attached above, below, left to and right to the outer
canthus. The electrocardiogram (ECG) was recorded with electrodes placed
at the left mid clavicle and lower right rib bone. Additionally, one
electrode was attached to the left earlobe serving as the ground
electrode. EOG and ECG were recorded simultaneously using standard Au
electrodes (NSC Electromedicina) with the same sample frequency and
on-line filters as the MEG data.

MEG data were spatially filtered using the temporal signal space
separation (tSSS) algorithm implemented in Neuromag® MEG software (Taulu
and Hari 2009). Eye blinks and cardiac artefacts were removed from data
by independent component analysis (ICA-JADE) implemented in the
Brainstorm software (Tadel et al. 2011)
(\emph{https://neuroimage.usc.edu/brainstorm/}). Then for each pre-cue
trial (attend central fixation cross and attend peripheral rings) and
participants epochs from -1 s before the pre-cue onset were extracted
from the continuous MEG data. For the post-cue trials (attend left and
right) epochs of 2.9 s after cue onset were derived from the MEG
recordings for each participant. Movement artefacts and horizontal eye
movements as monitored by the EOG were determined for each epoch by
visual inspection and excluded from analysis. The number of trials
analyzed for the pre-cue task and post-cue combinations (fixation left
post-cue: M = 106.1 ± 2.0 s. e. m., fixation right post-cue: M = 106.8 ±
1.8 s. e. m., rings left post-cue: M = 104.9 ± 2.23 s. e. m., rings
right-post-cue: M = 103.9 ± 2.16) were equal (main effect pre-cue task
F(1, 19) = 2.39, p = 0.138, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.11; main effect
post-cue F(1, 19) = 0.02, p = 0.879, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.001;
interaction pre-cue task by post-cue: F(1, 19) = 0.97, p = 0.336,
\selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.05). For each pre-cue task, post-cue condition
and participant the epochs were averaged separately in order to obtain
stimulus evoked MEG data epochs.

\emph{Cortical source estimation of ssVEF responses}

Before the ssVEF responses were extracted from the averaged MEG data
epochs, these evoked MEG responses were transformed into cortical source
space by a weighted minimum norm estimation (wMNE) approach (Gramfort et
al. 2014) using the brainstorm toolbox
(\emph{https://neuroimage.usc.edu/brainstorm/}). The source
reconstruction of evoked MEG data epochs was based on a forward model
based on an overlapping spheres head model (Huang et al. 1999) using a
canonical cortical mesh (3003 vertices) derived from the ICBM152
template (Mazziotta et al. 2001). Thereby, the individual head and
sensor positions of each subject were co-registered with the template
brain by realigning the individual with the template brain's fiducials
and minimizing the mean distance between the individual head shape
points and the template brain scalp surface (Moratti et al. 2011).
Thereafter, the forward model was calculated by using a head model based
on overlapping spheres (Huang et al. 1999). Finally, a weighted Minimum
Norm Estimation (wMNE) (Gramfort et al. 2014) was used to calculate the
current density of the evoked MEG averages (see above) on the cortical
surface for later analysis. Finally, for illustration purposes and later
statistical analysis, regions of interests (ROIs) were defined by
overlaying a co-registered Brodmann Atlas as implemented in Brainstorm
(Tadel et al., 2011;\emph{https://neuroimage.usc.edu/brainstorm/}). The
atlas contains V1, V2, and hMT+ areas and were extended by adding
inferior-temporal visual cortex and the occipito-parietal brain area
(see Figure 1 below).

In order to extract the ssVEF responses in cortical source space, the
time series of the cortical current densities at each vertex of the
cortical surface were demeaned and the 12 Hz and 15 Hz Fourier
components were determined by a Fast Fourier Transform (FFT) using the
fieldtrip toolbox (Oostenveld et al.
2011);\emph{https://www.fieldtriptoolbox.org}). For the pre-cue baseline
conditions (attend the central fixation cross vs. attend both peripheral
rings), the 1 s epochs were zero padded to 10 s in order to obtain a
frequency resolution of 0.1 Hz for the FFT. For the post-cue 2.9 s
epochs overlapping four 1 s intervals were extracted (0.5 s to 1. 5 s, 1
s to 2 s, 1.5 s to 2.5 s and 2 s to 2.9 s). Each interval was zero
padded to 10 s in order to obtain a 0.1 Hz frequency resolution and
submitted to FFT analysis. Then, the obtained FFTs were averaged.
Finally, for all FFTs (pre-cue baseline and post-cue intervals) the 12
Hz and 15 Hz power of the corresponding Fourier components were
determined at each vertex of the canonical brain surface mesh.

\emph{Determination of cortical sources that significantly oscillated at
the stimulus driven frequencies (12 Hz and 15 Hz)}

We determined which cortical sources showed oscillatory neural activity
at the driving ssVEF frequencies significantly different from background
noise. As power estimates are the sum of two squared measures (real and
imaginary parts), power estimates of the signal (here at the ssVEF
frequency of interest) and noise (here at frequency bins different from
the ssVEF frequencies of interest) follow a chi-square distribution
(Dobie and Wilson 1996). Therefore, the power at the frequency of
interest can be tested against noise by an F-ratio test with degrees of
freedom \emph{df} = 2 for the numerator and \emph{df} = 2\emph{m} for
the denominator, whereby \emph{m} is the number of noise frequency bins
(Dobie and Wilson 1996).

Using this method, we created two masks for the pre-cue central fixation
cross task at 12 Hz and 15 Hz (flicker frequency for each visual
hemifield was counter-balanced across subjects) based on the grand
average power spectrum across all subjects, representing our signal.
Noise was defined by 31 noise frequency bins between 17 Hz and 20 Hz
(0.1 Hz steps), that were chosen as they did not contain any harmonics
of the ssVEF responses. The sources included in the masks were
parametrically determined by an F test with the corresponding degrees of
freedom at a false discovery rate corrected alpha threshold of 0.05
(3003 comparisons, threshold at F(2, 62) = 9.03, p\textsubscript{fdr
corrected} \textless{} 0.001). Then, these two masks were OR logically
combined into one mask for the pre-cue central fixation cross task. The
same was done for the pre-cue peripheral rings task (threshold at F(2,
62) = 10.12, p\textsubscript{fdr corrected} \textless{} 0.001). The two
masks were created in order to compare the spatial extension of
significant ssVEF responses in cortical source space between these two
pre-cue baseline tasks (see Results).

Finally, a third mask was created by logically OR combining the source
masks of the F-ratio tests on the grand means of power spectra of the
fully balanced combinations of pre- and post-cue conditions (\emph{e.
g.} pre-cue baseline attend rings, post-cue lvf not attended, post-cue
lvf attended, pre-cue baseline attend cross, post-cue lvf not attended,
post-cue lvf attended, etc.). This was done in order not to bias the
implicated sources in the mass-univariate statistical tests (see below)
towards one pre- and post-cue task combination. The same parameters as
described above were applied to these F-ratio tests (threshold at F(2,
62) = 8.69, p\textsubscript{fdr corrected} \textless{} 0.001). Figure 1
depicts the resulting overall mask for the overall statistical
comparisons (see below).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1:} The right and left back view of the canonical brain
surface and the Fourier components at 12 Hz and 15 Hz significantly
different from noise are shown in red based on the combined F-ratio
tests on the ssVEF signals of all condition combinations. The white
lines indicate the boundaries between ROIs (V1, V2, hMT+,
inferior-temporal and occipito-parietal cortex).

\emph{Statistical Analysis within the masked cortical source space}

First, the oscillatory ssVEF responses between both pre-cue conditions
(attend the central fixation cross vs. attend the peripheral rings) were
compared using a non-parametric cluster-based permutation approach
(Nichols and Holmes 2002) restricted to the cortical sources that
oscillated at the stimulus driven frequencies significantly different
from noise (see Figure 1). From these sources the relative power of the
12 Hz and 15 Hz driving frequencies were extracted by dividing the power
values of interest by the mean power across the noise power bins (31
frequency bins between 17 Hz and 20 Hz in 0.1 Hz steps). Then, within
the cortical source mask (Figure 1) source clusters were formed when at
least one adjacent source neighbor indicated a significant difference
based on a dependent t-test (cluster alpha threshold of p = 0.025
testing for both directions). Then, the t-values were summed across the
cluster. Thereafter, 1000 random permutations between both pre-cue
conditions for each cortical source under the Null hypothesis of no
differences between attending the central fixation cross or the
peripheral rings were done. At each permutation step the same cluster
rule was applied and the maximum t cluster sum entered a permutation
distribution. Finally, the empirically observed t clusters obtained by
the first step (see above) with a sum exceeding the 97.5 percentile
(test in both directions) of the permutation distribution were
considered as significant. In order to test possible interactions of
cortical hemisphere, mean relative ssVEF power were extracted from the
resulting significant clusters (see Results) and compared with the mean
ssVEF activity in the homologous opposite hemisphere by using a repeated
measures ANOVA with within-subject factors pre-cue condition (fixation
cross and peripheral rings) and hemisphere (left and right).
Greenhouse-Geisser corrections were applied when necessary.

However, the main objective of this study was to characterize how
stimulus driven ssVEF responses are modulated by shifts of spatial
attention when spatial attention had been already directed towards the
peripheral or central visual field (rings vs. cross). Using the same
non-parametric cluster-based mass-univariate approach, the interaction
of relative ssVEF power changes between experimental phases (pre-cue
baseline, post-cue hemifield not attended or attended) and pre-cue
baseline conditions (attend central fixation cross or peripheral rings)
was assessed for the left and right visual hemifield stimulations,
separately. However, clusters were derived from a dependent F-test
(equivalent to a repeated measures ANOVA) comparing the differences in
relative ssVEF power between the pre-cue peripheral rings minus central
fixation condition, post-cue visual hemifield not attended when rings
were attended before minus visual hemifield not attended when central
fixation cross was attended before, and post-cue visual hemifield
attended when rings were attended before minus visual hemifield attended
when central fixation cross was attended before. Note, that the
comparison of these differences is equivalent to test the interaction
experimental phase by pre-cue condition. Source clusters were formed
when at least one adjacent source neighbor indicated a significant
interaction based on a dependent F-test (cluster alpha threshold of p =
0.05 testing for one direction). F-values were summed across the
cluster. Then, the same permutation approach as outlined above was
applied. However, the empirically observed F clusters with a sum
exceeding the 95 percentile (F test in only one direction) of the
permutation distribution were considered as significant. All
non-parametric cluster-based permutation tests were implemented using
the fieldtrip toolbox (Oostenveld et al.,
2011;\emph{https://www.fieldtriptoolbox.org}).

In order to test any interactions with hemisphere, mean relative ssVEF
power values of cortical source clusters of both hemispheres as obtained
by the permutation statistics were entered in a repeated measures ANOVA
with within-subject factors of pre-cue baseline, experimental phase and
hemisphere. Greenhouse-Geisser corrections were applied when necessary.

\emph{ROI analysis}

As the cluster-based permutation statistics indicated an interaction of
pre-cue condition by experimental phase in source clusters covering
various ROIs as defined by the co-registered brain atlas (see above),
mean relative ssVEF power across sources that intersected with
significant source clusters and ROI regions were analyzed with a
repeated measures ANOVA with within-subject factors of pre-cue baseline,
experimental phase and ROI collapsed across hemispheres for homologous
left and right source clusters if both ROIs indicated an interaction in
the previous cluster based permutation statistics. For ROIs that only
indicated an interaction in the right hemisphere, only right hemisphere
relative ssVEF power values were considered. Greenhouse-Geisser
corrections were applied when necessary.

As this analysis revealed a significant pre-cue baseline by experimental
phase by ROI interaction, a repeated measure ANOVA with within-subject
factors of pre-cue baseline and experimental phase was fitted to the
data for each ROI, separately. In order to detangle the observed
interactions at each ROI, modulation differences between conditions were
compared using paired t-tests (testing the difference of condition
differences). Finally, mean relative ssVEF power within each ROI were
contrasted between the two pre-cue baseline conditions (attend central
fixation cross vs. attend both peripheral rings) using paired t-tests.

\textbf{Results}

\emph{Behavioral results}

During the pre-cue baseline conditions participants detected on average
78.50 \% (\selectlanguage{ngerman}± 3.43 s. e. m.) of the luminance changes in both peripheral
rings with a false alarm rate of 5.01 \% (± 0.96 s. e. m.).
Surprisingly, the performance during the central fixation cross task was
worse with a mean detection rate of 57.58 \% (± 4.28 s. e. m.) and a
mean false alarm rate of 15.54 \% (± 2.52 s. e. m.). However, in both
pre-cue conditions performance was better than chance as for both tasks
all individual d' indices were greater than zero (peripheral rings
condition: range d' = 1.37 -- 3.96; central fixation cross condition:
range d' = 0.41 -- 2.75). Therefore, all participants were engaged in
the pre-cue tasks. In general, comparing the d' indices between the
pre-cue conditions, participants were more sensitive detecting the
luminance changes of the peripheral rings than the size changes of the
central fixation cross (t(19) = 7.01, p \textless{} 0.001, Cohen's d =
1.57). However, participants applied the same response criteria for both
tasks (mean c-index peripheral rings condition: 0.42 ± 0.09 s. e. m.;
mean c-index central fixation cross task: 0.48 ± 0.10; t(19) = -0.57, p
= 0.575, Cohen's d = 0.13).

During the post-cue task, participants detected on average 76.25 \% (±
3.59 s. e. m.) of the luminance changes within a sector of the spatially
cued ring with a false alarm rate of 1.66~\% (± 0.49 s. e. m. ). Mean d'
indices of 3.32 (range: 1.44 to 4.79) indicated a high sensibility of
the participants to detect the post-cue targets with an average response
criteria c of 0.85 (± 0.11 s. e. m.).

\emph{Neuromagnetic oscillatory brain responses}

First, we compared the oscillatory ssVEF responses in cortical source
space between the two pre-cue conditions, in which subjects had to
attend the central fixation cross or two peripheral flickering rings.
Thereby, the dipoles on the canonical cortex surface that captured
significant oscillatory ssVEF power different from background noise were
determined by the F-ratio test (see Methods) for each pre-cue condition,
separately. Figure 2 shows the power spectra and ssVEF topographies for
both pre-cue conditions, and the spatial distribution of significantly
oscillating cortical sources for the ``attend to fixation cross'' and
``attend to both rings'' conditions, respectively. Only cortical sources
that exceeded the critical threshold of the F-ratio test are shown
(critical minimum F ratio: F(2, 62) = 9.03, p\textsubscript{fdr
corrected} \textless{} 0.001 for the central fixation condition; F
ratio: F(2, 62) = 10.12, p\textsubscript{fdr corrected} \textless{}
0.001 for the peripheral rings condition). Both pre-cue conditions
resulted in ssVEF responses in V1, and V2 in both hemispheres, whereas
attending to the peripheral flickers activated more extended ssVEF power
modulations in right occipito-parietal, and hMT+ areas including a small
part of the right pre-cuneus.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2: (A)} The spectral decomposition, \textbf{(B)} the
averaged 12 Hz and 15 Hz Fourier component topographies, and\textbf{(C)}
the cortical sources for the 12 Hz and 15 Hz Fourier components
significantly different from noise are shown (red clusters) for the
attend to peripheral rings pre-cue condition. \textbf{(D, E, F)}The same
information is shown for the attend to fixation cross pre-cue condition.

In addition to the descriptive reporting of the spatial distribution of
significantly oscillating sources at the ssVEF driving frequencies of
both pre-cue conditions, ssVEF responses to both pre-cue baselines were
directly compared using cluster-based permutations statistics. A
significant positive cluster indicated increased ssVEF power partly in
right occipito-parietal, hMT+ and V2 areas when participants attended
the peripheral rings instead of the central fixation cross (summed
t-values = 82.23, p = 0.022, maximum parametric t(19) = 3.39, p = 0.003,
Cohen's d = 0.76, see Figure 3A).

However, the absence of a left hemisphere effect does not automatically
imply an interaction between the pre-cue condition (attend rings vs.
attend cross) and hemisphere (left vs. right). Therefore, we extracted
the ssVEF power values for the two pre-cue conditions in the homologous
left hemisphere regions in order to test such an interaction by
comparing the mean relative power across the corresponding cortical
sources for each pre-cue condition in the left and right hemisphere. The
observed interaction (F(1, 19) = 7.78, p = 0.012, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} =
0.29) corroborated the increased implication of the right
occipito-parietal, hMT+ and V2 areas when both peripheral rings were
attended (see Figure 3B).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3: (A)} The left column shows the significant cluster in
cortical source space of the comparison between the ``attend to both
rings'' and ``attend to the central fixation cross'' pre-cue conditions.
The upper panel of the right columns depicts the mean relative power
across the sources pertaining to the significant cluster. The lower
panel shows the corresponding paired observation plot for mean
normalized values. Red lines represent participants with a difference
exceeding the lower bound of the 95\% confidence interval of paired
differences. Grey lines represent differences that did not meet this
criterion. \textbf{(B)} The upper panel shows the mean power across the
significant source cluster of (A) for the right and homologous left
hemisphere regions. The lower panel depicts the corresponding paired
observation plot for mean normalized values, so that individual
differences can be observed at the same scale. Red lines for the right
hemisphere represent participants with a difference exceeding the lower
bound of the 95\% confidence interval of differences. Grey lines
represent data that do not meet this criterion. Red lines for the left
hemisphere represent participants with a difference within the 95\%
confidence interval including zero (indicating the absence of an
effect). Grey lines represent differences outside these boundaries.
Error bars in all subplots indicate the s. e. m. ri = rings, cr = cross.

However, the main objective of this study was to characterize how
stimulus driven ssVEF responses are modulated by shifts of spatial
attention when spatial attention is already directed towards the
peripheral or central visual field (rings vs. cross). Therefore, we
assessed the interaction of relative ssVEF power changes across
experimental phases (pre-cue baseline, left or right visual hemifield
not attended or attended) and pre-cue baseline condition (rings vs.
cross). The nonparametric cluster-based permutation test based on
previously formed F clusters (see methods) for the left visual hemifield
(lvf) stimulation indicated an interaction between the pre-cue baseline
condition (attend rings vs. central fixation cross) and experimental
phase (pre-cue baseline, lvf not attended, lvf attended). The
significant source cluster encompassed V1, V2, hMT+, the
occipito-parietal and inferior-temporal visual cortex of the right
hemisphere (summed F value = 1044, p \textless{} 0.001, maximum
parametric F(2, 38) = 9.71, p \textless{} 0.001, \selectlanguage{greek}ε \selectlanguage{english}= 0.84,
\selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.34; Figure 4A). However, right visual hemifield
stimulation (rvf) resulted in an interaction between pre-cue baseline
condition and experimental phase only at trend level localized in left
V1, V2, and a small part of the occipital-parietal region (summed F =
137, p = 0.084; maximum parametric F (2, 38) = 8.09, p = 0.004, \selectlanguage{greek}ε \selectlanguage{english}=
0.73, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.30, Figure 4A).

Given the height of the left hemispheric cluster (F(2, 38) = 8.09) the
trend level cluster based significance was probably due to the smaller
extension of the source cluster. Therefore, in order not to rule out a
significant interaction for the rvf stimulation in the left hemisphere,
the mean ssVEF power values across the significant source clusters of
the left and right hemispheres were entered in a repeated measurements
ANOVA with the within-subject factors pre-cue baseline, experimental
phase and hemisphere. An absent interaction between the pre-cue baseline
condition and experimental phase in the left hemisphere should result in
a pre-cue baseline by experimental phase by hemisphere interaction.
However, no such interaction was observed (F(2, 38) = 1.66, p = 0.20, \selectlanguage{greek}ε
\selectlanguage{english}= 0.93, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.08). Across hemispheres, the
interaction pre-cue baseline by experimental phase indicated that ssVEF
power was differently modulated depending on the pre-cue baseline
condition (F(2, 38) = 8.57, p \textless{} 0.001, \selectlanguage{greek}ε \selectlanguage{english}= 0.71,
\selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.31). Figure 4B depicts the mean relative ssVEF
power changes across all significant cortical sources pertaining to the
previously identified source clusters in the left and right cortical
hemisphere for all experimental phases (pre-cue baseline, post-cue not
attended, and post-cue attended) and the two different pre-cue baseline
conditions (attend rings vs. fixation cross). Whereas the participants
attended the peripheral rings during the pre-cue baseline task, the
ssVEF responses across both cortical source clusters were enhanced for
the pre-cue baseline and post-cue attend experimental phases compared to
the unattended visual hemifield (pre-cue vs. unattended: t(19) = 3.80, p
= 0.001, Cohen's d = 0.85; attended vs. unattended: t(19) = 4.24, p
\textless{} 0.001, Cohen's d = 0.95). However, when the participants
attended to small variations of fixation cross sizes during the pre-cue
experimental phase, relative ssVEF power was similar to the ssVEF
response elicited by the unattended hemifield stimulation during the
post-cue phase (t(19) = 1.68, p~=~0.110, Cohen's d = 0.37). In contrast,
attending the cued hemifield during the post-cue phase provoked enhanced
ssVEF responses in comparison when the hemifield was not attended (t(19)
= 4.43, p \textless{} 0.001, Cohen's d = 0.99). Furthermore, when we
tested the activation level for the attended side between the conditions
cross vs. rings we found a trend for higher activation when subjects
attended to central fixation first (t(19) = 1.93, p = 0.070, Cohen's d =
0.43).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4: (A)} Both, the significant right hemispheric and trend
level left hemispheric, source clusters are shown. The color bar
indicates F-values. \textbf{(B)} The mean relative ssVEF power changes
across source clusters in (A) are shown for the pre-cue baseline,
post-cue unattended, and post-cue attended experimental phases separated
by the pre-cue ring and fixationcross conditions. The error bars depict
s. e. ms. * p \textless{} 0.05

As the observed effects were spread across different cortical regions
within the visual cortex, mean ssVEF relative power changes for each
region of interest (ROIs) were entered in a repeated measure ANOVA with
within-subject factors pre-cue condition (rings vs. cross), experimental
phase (pre-cue baseline, not attend and attend cued visual hemifield),
and ROIs (V1, V2, precuneus, occipito-parietal cortex, hMT+, and
inferior-temporal cortex). Relative power values for homologous left and
right cortical areas that both covered significant clusters from the
previous analysis were collapsed across hemispheres, whereas for the
remaining ROIs only right hemisphere ssVEF responses entered the
analysis.

A significant pre-cue condition by experimental phase by ROIs
interaction, indicated that the previously reported pre-cue condition by
experimental phase interaction is different depending on the ROI (F(10,
190) = 4.03, p = 0.008, \selectlanguage{greek}ε \selectlanguage{english}= 0.11, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = 0.17).
Therefore, the pre-cue condition by experimental phase interaction was
assessed separately for ROI (see Figure 5A). At each ROI a significant
pre-cue condition by experimental phase interaction was observed (Table
1).\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{ROI} & F(2, 38) & p & \selectlanguage{greek}ε & \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2}\tabularnewline
\midrule
\endhead
V1 & 6.48 & 0.010 & 0.41 & 0.25\tabularnewline
V2 & 6.78 & 0.009 & 0.39 & 0.26\tabularnewline
precun & 6.32 & 0.006 & 0.61 & 0.25\tabularnewline
occip-par & 8.69 & 0.003 & 0.48 & 0.31\tabularnewline
hMT+ & 6.39 & 0.009 & 0.54 & 0.25\tabularnewline
inf-temp & 5.16 & 0.014 & 0.50 & 0.21\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1:} Results of the repeated measures ANOVA pre-cue
condition by experimental phase are shown for each ROI. precun =
precuneus, occip-par = occipito-parietal, inf-temp = inferior-temporal,
hMT+ = human middle temporal.

The left column of Figure 5B shows the mean relative power ssVEF
modulations for the pre-cue baseline, post-cue not attended and attended
conditions separated by the pre-cue condition (attend peripheral rings
vs. attend central fixation cross). The interaction pre-cue condition by
experimental phase in V1, V2, precuneus, and occipito-parietal cortex
was explained by bigger ssVEF modulations by spatial attention to the
cued visual hemifield when the central fixation cross was attended
during the pre-cue baseline compared to when the peripheral rings had
been attended (t-tests between the modulation differences V1: t(19) =
2.69, p = 0.014, Cohen's d = 0.60; V2: t(19) = 2.53, p = 0.020, Cohen's
d = 0.57; precuneus: t(19) = 2.59, p = 0.018, Cohen's d = 0.58;
occipito-parietal cortex: t(19) = 2.39, p = 0.027, Cohen's d = 0.54; see
Figure 5B).

However, this was not the case for the hMT+ and inferior-temporal
cortical areas (hMT+: t(19) = 1.33, p = 0.20, Cohen's d = 0.30;
inferior-temporal cortex: t(19) = 0.77, p = 0.45, Cohen's d = 0.17).
Critically, when the participants attended the central fixation cross
during the pre-cue baseline, the peripheral ring flickers did not evoke
greater ssVEF responses than when spatial attention was directed away to
the cued visual hemifield (not attended post-cue condition) in these
brain regions. However, this modulation difference was greater when
participants attended the peripheral rings during the pre-cue baseline
(t-tests between the modulation differences hMT+: t(19) = 3.10, p =
0.006, Cohens' d = 0.69; inferior-temporal cortex: t(19) = 2.83, p =
0.011, Cohen's d = 0.63; see Figure 5B).

Finally, given the ssVEF modulation patterns in each ROI (see Figure 5),
the two pre-cue baseline conditions (rings vs. cross) were compared
directly for each ROI, separately. In V1, V2, and precuneus no
differences were observed (ps \textgreater{} 0.05), whereas in the
occipito-parietal area attending to both peripheral rings elicited
greater ssVEF responses than when attending to the fixation cross
changes (t(19) = 2.15, p = 0.045, Cohen's d = 0.48). This effect was
pronounced in hMT+ (t(19) = 3.13, p = 0.006, Cohen's d = 0.70) and
inferio-temporal cortex (t(19) = 2.39, p = 0.027, Cohen's d = 0.53).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5: (A)} The left panel shows the cluster in cortical
source space for the interaction experimental phase (pre-cue baseline,
post-cue not attended and attended) by pre-cue baseline condition (rings
vs. cross) in the right hemisphere for the lvf flickers. The trend level
cluster for this interaction is also shown in the left hemisphere for
the rvf flickers. The source map was projected onto a plane with the ROI
labels using the Mollweide method. \textbf{(B)} The left row shows the
mean relative ssVEF power changes across the dipoles within each ROI for
the experimental phase by pre-cue baseline condition for each ROI. The
black brackets with the dashed lines indicate the significant
differences of modulation differences between conditions (see main text
for the t-tests between differences * p \textless{} 0.05). The right row
depicts the corresponding single paired observations plots. The red
lines indicate participants with ssVEF modulation differences greater
that the lower bound of the corresponding 95\% CI interval not
containing zero. The grey lines represent participants that do not
fulfill that criterion. Note, that for better visibility the relative
ssVEF power values were mean normalized. Error bars in all subplots
represent the s. e. m. RFV = right visual hemifield stimulation, LVF =
left visual hemifield stimulation. PC = precuneus, OP =
occipito-parietal, IT = inferior temporal, hMT+ = human middle temporal
area.

\textbf{Discussion}

The aim of the present MEG study was to identify areas in the visual
cortex of the human brain that contribute to an attentional enhancement
of steady state amplitudes with attention. In a typical spatial
attention task, after a baseline period, subjects were given an
instructive cue to shift attention to the left or right visual hemifield
and to perform a task at the to-be-attended side. Different from many
studies, we controlled for the attentional deployment before the cue by
instructing to either attend to central fixation or to both stimuli in
the left and right visual hemifield, respectively. Compliance was
assured by having a task at the respective location during the baseline
as well as in the post-cue period. With this manipulation, we were able
to test SSVEF sensory gain modulations for peripheral stimuli under the
conditions that attentional resources were shifted to this peripheral
stimulus (attend cross) or away from an already attended peripheral
stimulus (attend rings). Behavioral data showed that subjects were
compliant with the task, although the ``attend cross'' condition seemed
to be more difficult compared to the ``attend rings'' baseline
condition.

Comparing the baseline conditions, we found a differential activation in
early visual cortex. In particular, when subjects were instructed to
attend to both rings, activation was not just restricted to V1 and V2,
as for the ``attend cross'' baseline, but also included
occipito-parietal cortex, and hMT+. Given that hMT+ was only activated
in the ``attend rings'' pre-cue baseline period is in line with previous
reports on hMT+ activation by motion and also by flickering stimuli
(Treue and Martinez-Trujillo 1999, Huk and Heeger 2002, Palomares et al.
2012). Surprising to us was that this additional activation in that
baseline condition was more prominent in the right cortical hemisphere
(but numerically in the left hemisphere as well, see Figure 3B), whereas
V1/V2 activation was clearly located in both hemispheres.

The attentional activation pattern after the cue, relative to pre-cue
baseline across all areas, was different for the two pre-cue conditions.
When subjects first attended to both rings in the left and right visual
hemifield, cortical activation slightly increased for the side for which
attention was maintained, but significantly decreased for the side for
which attention was withdrawn. If, however, participants first attended
to central fixation, cortical activation was significantly increased for
the to-be-attended stimulus, and data revealed a trend for this
activation boost to be even bigger compared to when subjects attended to
both rings first. For the to-be-ignored side, activation remained
basically on the pre-cue baseline level.

However, a closer inspection on the individual activation pattern of the
respective cortical areas resembled an interactive activation pattern
that not only depended on the pre-cue condition but also on the visual
area. When subjects attended to both rings first, we observed a
reduction in activity in areas V1, V2, pre-cuneus, occipital parietal
and inferior-temporal cortex for the to-be-ignored stimulus, whereas for
the to-be-attended ring activity basically remained at the pre-cue
level. Thus, all areas followed the pattern we observed in the test
across all areas (see above). Critically, when subjects attended to the
fixation cross first, the SSVEF modulation patterns differed
significantly between the cortical areas within the visual cortex. In
V1, V2, pre-cuneus and occipital parietal cortex SSVEF power followed a
similar pattern as during the pre-cue attend to both rings baseline. The
SSVEF amplitudes elicited by the to-be-ignored hemifield flickers were
suppressed. However, the to-be-attended flickers generated a greater
SSVEF power boost after participants attended the central fixation cross
than after having split their attention between both hemifields during
the ring pre-cue baseline task. On the contrary, hMT+ and
inferior-temporal cortex did not show a further reduction in activity
for the to-be-ignored ring and no enhanced boost of SSVEF amplitudes for
the to-be-attended hemifield after the fixate central cross pre-cue
task.

The present observations nicely match our very simple previous source
reconstructions of EEG data in feature-based attention (Andersen et al.
2008, Andersen and M\selectlanguage{ngerman}üller 2010). When subjects attended to red or blue
spatially superimposed random dot kinematograms (RDKs) we also found the
sources of the attention effect in early visual cortex only, including
V1/V2. While it is quite clear that V1/V2 show prominent responses to
flicker stimuli in the respective stimulation frequency plus its
harmonics (Rager and Singer 1998) it is still unknown how far flicker
responses propagate in the visual processing hierarchy. A number of
physiological restrictions suggest that propagation is restricted to
areas of early visual cortex, due to dendritic low-pass filtering, in
particular for higher flicker frequencies (Fortune and Rose 1997, Vaidya
and Johnston 2013), increase in receptive field size (Hubel and Wiesel
1968), or synaptic input from cells that respond to different flicker
frequencies, resulting in so-called intermodulation frequencies (Zemon
and Ratcliff 1984). The idea that propagation of the respective driving
frequencies is limited to early processing stages was also demonstrated
in fMRI (Di Russo et al. 2007, Palomares et al. 2012).

The activation pattern in early visual cortex when subjects first
attended central fixation nicely resembles the pattern of SSVEP
amplitude time courses in spatial shifting designs (Müller et al. 1998,
Müller 2008). In these studies, SSVEP amplitude for the to-be-ignored
side remained on pre-cue baseline level, whereas SSVEP amplitude for the
to-be-attended side exhibited a significant increase. However, the
pattern of amplitude modulations differed in study by Gundlach and
colleagues (2020) that used the same design as in the present study. In
this study, when subjects first attended to the fixation cross, an
increase in SSVEP amplitudes for both, the to-be-attended and unattended
side was found, of course with a significantly greater increase for the
to-be-attended side. When subjects attended to both rings first, we
found no change in amplitude for the side that needed to be ignored
after the cue, but only a significant increase for the then selected
side. The difference between the more recent and the previous studies
may lie in the fact that in the most recent study we used a very broad
range of posterior electrodes including midline electrodes, whereas in
the two older studies we only used one posterior-temporal electrode
left/right, respectively for SSVEP time course analysis. This broader
cluster might have picked-up activity from many more cortical areas. In
previous studies we have shown that the pattern of SSVEP amplitude
modulation is not identical over a broader range of electrodes as used
in the 2020 study (Andersen et al. 2012, Müller et al. 2018), and
therefore, it might be of interest to reanalyze this data with a more
temporal and much smaller electrode cluster, or even single electrodes.
In addition, as the focus of the work by Gundlach and colleagues (2020)
focused on the relationship between SSVEP and alpha-band modulations in
a spatial cueing paradigm, the SSVEP amplitudes were derived from
single-trial FFT spectra. With such an analysis, induced activity of
non-phase locked ongoing neural oscillatory activity is not averaged out
and may contribute to the SSVEP amplitude estimates in the same
frequency range. In the current study, however, SSVEP amplitude values
were derived from trial-averaged FFT spectra promoting evoked signals
such as SSVEPs while minimizing induced signals such as endogenous
neural activity not phase locked to the stimulation. While the relative
amplitude patterns (attended \textgreater{} unattended) are comparable,
differences in the absolute amplitude may stem from differences in
foundations of the signals revealed by different analysis approaches.

To summarize: The present study replicated previous studies that located
cortical sources of SSVEPs driven by flickering (frequency-tagged)
stimuli in early visual cortex. Our results complement and extend these
studies showing that early visual cortex also drives attentional
modulations of SSVEP amplitudes. While the activation pattern in V1/V2
was consistent, the specific modulatory effects differed across
different regions within early visual cortex with a distinct pattern
further up in the processing hierarchy in areas such as hMT+ and
inferior-temporal cortex in the present case. Nevertheless, results
again demonstrate the power of frequency-tagged stimuli to investigate
attentional competitive interactions in multi-element stimulus displays.
To what extent a certain area that is specialized in processing of a
certain feature, such as V4 for color, contributes significantly more to
the observed SSVEP amplitude modulations in color processing, compared
to the other areas that ``just'' follow the on/off is subject to a
current project and we will report the results in the nearer future.

\textbf{References}

Adamian, N., S. K. Andersen and S. A. Hillyard (2020). ''Parallel
attentional facilitation of features and objects in early visual
cortex.'' \emph{Psychophysiology} \textbf{57} (3).

Adler, J., C. M. Giabbiconi and M. M. Müller (2009). ''Shift of
attention to the body location of distracters is mediated by perceptual
load in sustained somatosensory attention.'' \emph{Biological
Psychology}\textbf{81} : 77-85.

Andersen, S. K., S. A. Hillyard and M. M. Müller (2008). ''Attention
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