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ORIGINAL RESEARCH
published: 14 June 2022
doi: 10.3389/fnins.2022.903977
Edited by:
Kai Yu,
Carnegie Mellon University,
United States
Reviewed by:
Ke Zeng,
University Health Network (UHN),
Canada
Kristin Sellers,
University of California,
San Francisco, United States
*Correspondence:
Jennifer Rodger
jennifer.rodger@uwa.edu.au
†These authors share senior
authorship
Specialty section:
This article was submitted to
Neural Technology,
a section of the journal
Frontiers in Neuroscience
Received: 24 March 2022
Accepted: 17 May 2022
Published: 14 June 2022
Citation:
Moretti J, Marinovic W,
Harvey AR, Rodger J and Visser TAW
(2022) Offline Parietal Intermittent
Theta Burst Stimulation or Alpha
Frequency Transcranial Alternating
Current Stimulation Has No Effect on
Visuospatial or Temporal Attention.
Front. Neurosci. 16:903977.
doi: 10.3389/fnins.2022.903977
Offline Parietal Intermittent Theta
Burst Stimulation or Alpha
Frequency Transcranial Alternating
Current Stimulation Has No Effect on
Visuospatial or Temporal Attention
Jessica Moretti1,2 , Welber Marinovic3, Alan R. Harvey2,4,5 , Jennifer Rodger1,2*†and
Troy A. W. Visser6†
1School of Biological Sciences, The University of Western Australia, Perth, WA, Australia, 2Perron Institute for Neurological
and Translational Science, Perth, WA, Australia, 3School of Population Health, Curtin University, Perth, WA, Australia,
4School of Human Sciences, The University of Western Australia, Perth, WA, Australia, 5Lions Eye Institute, Perth, WA,
Australia, 6School of Psychological Science, The University of Western Australia, Perth, WA, Australia
Non-invasive brain stimulation is a growing field with potentially wide-ranging clinical
and basic science applications due to its ability to transiently and safely change brain
excitability. In this study we include two types of stimulation: repetitive transcranial
magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS).
Single session stimulations with either technique have previously been reported to
induce changes in attention. To better understand and compare the effectiveness of
each technique and the basis of their effects on cognition we assessed changes to both
temporal and visuospatial attention using an attentional blink task and a line bisection
task following offline stimulation with an intermittent theta burst (iTBS) rTMS protocol
or 10 Hz tACS. Additionally, we included a novel rTMS stimulation technique, low-
intensity (LI-)rTMS, also using an iTBS protocol, which uses stimulation intensities an
order of magnitude below conventional rTMS. Animal models show that low-intensity
rTMS modulates cortical excitability despite sub-action potential threshold stimulation.
Stimulation was delivered in healthy participants over the right posterior parietal cortex
(rPPC) using a within-subjects design (n= 24). Analyses showed no evidence for an
effect of any stimulation technique on spatial biases in the line bisection task or on
magnitude of the attentional blink. Our results suggests that rTMS and LI-rTMS using
iTBS protocol and 10 Hz tACS over rPPC do not modulate performance in tasks
assessing visuospatial or temporal attention.
Keywords: rTMS, iTBS, transcranial alternating current stimulation (tACS), attention, line bisection, attentional
blink
INTRODUCTION
Non-invasive brain stimulation is a growing field with wide clinical and basic science applications
due to its ability to transiently and safely change brain excitability and oscillatory activity
(Dayan et al., 2013;Lefaucheur et al., 2020). Research has shown that several brain stimulation
techniques, including repetitive transcranial magnetic stimulation (rTMS) and transcranial
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electrical stimulation (tES), can modulate cognition in patients
or healthy individuals by facilitating or disrupting mental
processes. This has been suggested to arise via multiple potential
mechanisms including induction of action potentials, changes
to membrane potential and entrainment of endogenous brain
oscillations (Miniussi et al., 2013). In particular, longer-term
offline plastic changes are thought to be facilitated with rTMS
through simultaneous depolarisation of pre- and post-synaptic
neurons (Lenz et al., 2015), likely through rTMS induction of
action potentials. However, brain stimulation in rodent models
using a novel low-intensity (LI-) rTMS technique has shown that
stimulation delivered at intensities below the action potential
threshold (1–150 mT) can also induce behavioural and cellular
changes, suggesting that direct induction of action potentials may
not be necessary to induce such changes (Moretti and Rodger,
2022). Transcranial electrical stimulation also uses sub-action
potential threshold stimulation and is able to induce various
neuromodulatory effects on motor and cognitive function (Kuo
and Nitsche, 2012;Flöel, 2014). Therefore LI-rTMS may be an
intermediate approach combining the high focality of rTMS and
the lower intensity stimulation of tES.
Low-intensity stimulation has several potential benefits
including fewer side effects (e.g., headaches), reduced power
requirements and the potential for more compact and portable
design. LI-rTMS allows for these benefits while maintaining
the focality of rTMS making it a desirable tool for translation.
However, unlike conventional rTMS [which we will refer to as
high-intensity (HI-) rTMS] and transcranial alternating current
stimulation (tACS), LI-rTMS has not previously been used in
humans, although there have been studies with sub-threshold
pulsed magnetic fields, which is similar to LI-rTMS, that showed
low intensity stimulation could modulate mood in humans
(Rohan et al., 2004, 2014;Martiny et al., 2010). To explore LI-
rTMS effects in humans for the first time, we included a LI-rTMS
condition and assessed whether it could influence cognition.
We also aimed to compare LI-rTMS alongside HI-rTMS
and tACS to explore the effects of different brain stimulation
techniques in neuromodulation. There are not many studies
in the literature which combine rTMS and tES in the same
experiment to allow for direct comparisons between stimulation
techniques. Single-session stimulation with HI-rTMS and tACS
has previously been reported to induce cognitive change,
including various aspects of attention (for reviews see Luber
and Lisanby, 2014;Santarnecchi et al., 2015;Reteig et al., 2017).
We test LI-rTMS in contrast with HI-rTMS, which has similar
focality and includes a magnetic field, and tACS, which, like
LI-rTMS, is a subthreshold stimulation, but uses widespread
electrical, alternating current stimulation applied directly to the
scalp. tACS was chosen as a comparative tES technique in order
to match the alternating frequency and biphasic waveform of
rTMS, as opposed to direct current stimulation. Therefore in this
study, we assessed the impact of LI-rTMS, HI-rTMS, and tACS
on human visuospatial attention in a within-subject design to
compare relative efficacy.
Another aspect of cognitive modulation is the frequency
protocol used to induce effects. Theta burst stimulation (TBS)
is a complex patterned rTMS frequency often used in studies,
with bursts of 3 pulses at 50 Hz applied at a frequency of
5 Hz. The bursts can be applied continuously for a set time
[continuous (c)TBS], or intermittently in 2 s periods at a rate
of 0.1 Hz [intermittent (i)TBS] to produce effects that are
generally inhibitory or excitatory, respectively. Applying cTBS
or iTBS to induce motor excitability changes is more efficient
compared to simple patterned rTMS protocols (1 Hz, 10 Hz,
etc.). The short application time of TBS protocols (3 min)
also makes it an attractive stimulation technique. Despite the
short stimulation time, cortical excitability changes induced by
iTBS and cTBS have been observed for up to 60 and 50 min,
respectively, after stimulation ends (Wischnewski and Schutter,
2015). Several studies have explored the use of iTBS and cTBS in
cognitive domains to determine whether it is similarly effective
for neuromodulation, with mixed results (e.g., Esterman et al.,
2017;Gan et al., 2019;Mariner et al., 2021;Schintu et al.,
2021;Whybird et al., 2021). We explore whether HI- and LI-
rTMS applied using iTBS protocol are effective in enhancing
visuospatial attention. We chose to assess attention as it is a
higher-order cognitive process (Posner and Petersen, 1990) with
several levels of processing susceptible to modulation by brain
stimulation. Attention collectively refers to processes involved in
the selection of environmental information to support behaviour.
Here we focus on two of these processes–spatial and temporal
attention–which are used to direct cognitive resources to specific
locations in space or specific periods of time. We assessed
participants’ spatial attention using the line bisection (Landmark)
task and temporal attention with an attentional blink (AB) task
across three sessions with different stimulation types.
The stimulation site, over the rPPC, was kept consistent
between groups with the Cz as the reference electrode with tACS.
We hypothesised that excitatory offline HI- and LI- rTMS over
the right posterior parietal cortex would induce a leftward shift in
spatial bias in the line bisection task and reduce the attentional
blink in the AB task in line with previous studies (e.g., line
bisection: Fierro et al., 2000;Hilgetag et al., 2001;Kim et al., 2005;
Thut et al., 2005;Nyffeler et al., 2008; attentional blink: Cooper
et al., 2004). In contrast, alpha frequency (10 Hz) is associated
with inhibition of visual perception and attention, therefore alpha
frequency tACS is thought to inhibit visual attention (Jensen and
Mazaheri, 2010;Foxe and Snyder, 2011;Clayton et al., 2015;
c.f. Clayton et al., 2019). Therefore, we hypothesised that rPPC
tACS would induce a rightward shift in spatial bias and inhibit
temporal attention, possibly increasing the attentional blink.
MATERIALS AND METHODS
This study was approved by the University of Western Australia
Human Research Ethics Committee (RA/4/20/6005) and all
participants gave informed consent. Twenty-four participants
(15 female, 9 male, all self-reported as right-handed, mean
age = 19.5 years, SD = 2.7) with normal or corrected-to-normal
vision participated in the study. The exclusion criteria used for
selection conformed to the guidelines for rTMS (Rossi et al.,
2009) and tES research (Antal et al., 2017). Participants were
undergraduate university students and received partial course
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credit in exchange for their participation. Four participants
withdrew from the experiment: one without an explanation,
one due to an injury between sessions affecting their vision
and two due to adverse side effects following HI-rTMS session.
Adverse side effects included a headache for one participant
and “tightness” in the jaw for the other, possibly in reaction
to the repeated tapping sensations. Available data from these
participants from previous sessions with no adverse effects
were still included.
Participants received three types of stimulation (HI-rTMS, LI-
rTMS, or tACS) in separate sessions (counterbalanced) separated
by at least a week to prevent carry-over effects using a cross-
over, within-subject design. In order to minimise the number
of repeat visits required and increase retention, participants
received both sham and active stimulation in each session. Sham
was delivered first to avoid carry-over effects of stimulation.
Participants were informed that they would receive both sham
and active stimulation each session but were blinded to the
order. Each session followed the same sequence (Figure 1A).
For the HI-rTMS session, there was an additional thresholding
step at the beginning of the session to determine the participant’s
phosphene threshold.
A post-stimulation questionnaire was administered to assess
for possible side-effects and whether participants thought they
received a sham or active stimulation. All experiments were run
on a Windows computer using specialised software programmed
in PsychoPy (Peirce et al., 2019). Stimuli were presented on a 24-
inch monitor running at a refresh rate of 60 Hz with a viewing
distance of 55 cm.
Line Bisection Task
The line bisection task was similar to Kim et al. (2005; see
Figure 1B). Stimuli were white, horizontal lines transected or
bisected by a white 2.2 degree vertical line on a black background.
All lines were 0.1 degree thick. The horizontal line was one of 5
lengths (36–40 degrees) with each length presented equally often.
When the horizontal line was transected the elongated side was
longer by 1 degree, and the vertical transecting line remained in
the centre of the screen.
A single trial consisted of a fixation cross which appeared
for 1000 ms followed by a line stimulus presented for 100 ms.
The line stimulus was then masked for 1000 ms by a noise
mask (50.6 degrees ×20.92 degrees) consisting of randomly
generated white or grey solid circles of various sizes. Before each
block, participants were instructed to report either which side
of the line was longer or which side was shorter. The question
alternated each block, and each task alternated which question
began the first block. Participants were instructed to respond
quickly without sacrificing accuracy by pressing the left and right
arrow keys with their right index and middle finger, respectively.
If participants did not respond within 1000 ms of mask onset, the
trial was considered an error and the next trial was initiated.
Prior to each session, two blocks of 30 practice trials were
completed, each consisting of 15 left-elongated, and 15 right-
elongated lines presented for 200 ms to allow participants to
familiarise themselves with the task. This was followed by the
main task consisting of four blocks of 40 trials (10 lines transected
with left-side elongation, 10 lines transected with right-side
elongation, 20 evenly bisected lines presented in random order).
Attentional Blink Task
The Attentional Blink task was similar to Cooper et al. (2004; see
Figure 1C). Letter stimuli were presented in black, 48 pt Helvetica
font on a grey background. A single trial consisted of a fixation
cross presented for 1000 ms followed by a stream of 17 letters
presented for 20 ms each with an 80 ms blank inter-letter interval.
The first target (T1) was a white letter that could appear randomly
in positions 4, 5, 6, 7, or 8 in the stream. The second target (T2)
was a black letter X that could appear 1, 2, 3, 5, or 7 positions
(lags) after the white letter. The white letter (T1) was chosen
from a subset of letters: N, Z, B, E, L, T, W, and M. Non-target
letters were chosen from the remaining letters of the alphabet
(except X). After the stream was complete, participants were
prompted to report the identity of the white letter by pressing the
matching key with their left hand and to report whether there had
been an X presented by pressing marked arrow keys with their
right hand. Participants were instructed to emphasise accurate
responding. Following participant responses, there was a 500 ms
blank interval before a new trial began.
The experimental tasks consist of 2 blocks of 55 trials. Forty
trials included T2, presented equally often at each lag. The order
of the trials were randomised for each block. Before beginning the
task, participants were told that at least 50% of the trials contained
an X, in order to reduce a bias towards reporting the absence
of T2. Prior to beginning the experimental task, participants
completed two blocks of 125 trials as practice to thoroughly
familiarise themselves with the task requirements.
Stimulation
Determining Phosphene Threshold
For rTMS we used the MagPro R30 Stimulator (Magventure,
Denmark) with a 75 mm Figure-of-8 coil (MC-B65-HO-2). At
the beginning of the session single TMS pulses are delivered
to the back of the head under dim lighting to determine the
phosphene threshold of the participant based on the methods of
Kammer et al. (2001). We conduct a searching procedure for a
phosphene “hot spot” over the right hemisphere, beginning 3 cm
dorsal and 5 cm lateral from the inion. We deliver single TMS
pulses at a high intensity [up to 80% maximum stimulator output
(MSO)] and systematically move the coil until the participant
reliably reports seeing phosphenes following the TMS. Once the
hot spot is located, we adjust the TMS intensity down in steps of
5%, and then 1% MSO, delivering 10 consecutive pulses at each
intensity level. The lowest intensity at which 5 out of 10 pulses
are reported to induce a phosphene in the participant’s vision is
determined to be the phosphene threshold. If a participant failed
to reliably see phosphenes, stimulation at 50% MSO for HI-rTMS
was used, or the next highest intensity that was comfortable for
the participant.
Repetitive Transcranial Magnetic Stimulation
For the rTMS stimulation we delivered 600 pulses (biphasic
sine waves, 3 min) using the iTBS protocol at either 90%
phosphene threshold [34–53% maximum stimulator output
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FIGURE 1 | (A) Overall order and structure of each experimental session. (B) Example of a trial in the line bisection task. (C) Example letter sequence in the
attentional blink task (T2 shown at lag 2 position).
(MSO)] (HI-rTMS) or 7% MSO (LI-rTMS) over the right
posterior parietal cortex (rPPC) (electrode site P4). Seven percent
MSO was equivalent to approximately 50 mT at the estimated
distance of the cortical surface (2.5 cm from the scalp), based
on magnetic field measurements from the coil. This intensity was
chosen to match LI-rTMS parameters that have previously been
delivered in animal models (Heath et al., 2018).
In each session participants received a sham and active
stimulation. For the sham stimulation, the coil was set to 0%
MSO and held above electrode site P4 by the experimenter, with
a speaker playing a recording of the appropriate rTMS protocol
to mimic the auditory sensation.
Transcranial Alternating Current Stimulation
A multichannel neuromodulation system (Soterix Medical,
United States, Model: MXN-5) was used to deliver 20 min (with
30 s ramp up/down) of 10 Hz tACS at 2 mA peak-to-peak
amplitude (biphasic sine waves) to the rPPC. Two 5 ×7 cm
rubber electrodes in saline-soaked sponges were placed above
electrode sites P4 and Cz with electrode gel for added conduction
and secured in place with bandages. There was no overlap
between the two electrodes. The induced e-field produced with
the electrode positioning was modelled using Soterix software
(Figure 2). The Cz was chosen as the reference electrode
based on previous tES studies that examined attention when
stimulating rPPC (Sparing et al., 2009;Loftus and Nicholls, 2012;
Filmer et al., 2015;Hopfinger et al., 2017). Participants received a
sham and active stimulation. For sham stimulation, current was
ramped up over 30 s and immediately ramped down over 30 s at
both the beginning and end of stimulation.
RESULTS
Data Analysis
For the AB task one participant was excluded due to self-reported
inability to see T2 at any point during a session (n= 23).
Remaining data were analysed using generalised logistic
mixed models at the trial level. For the line bisection task, bias
scores were calculated by coding responses to bisected lines
as “0” when the response indicated that the left side appeared
longer, and “1” when the response indicated that the right side
appeared longer.
Line Bisection Task
Task Accuracy
Accuracy on the line bisection task for unevenly transected lines
was analysed using generalised mixed model with fixed effects
set as elongated side, Stimulation Type, and Active vs. Sham
stimulation (see Table 1) and subject as a random effect. There
was a significant main effect for Stimulation Type (χ2= 9.54,
p= 0.008), but no other significant effects or interactions
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FIGURE 2 | Induced e-field modelling of tACS parameters when electrodes are positioned at Cz and P4 and delivering 2 mA peak to peak intensity.
(χ2≤5.44, p>0.066). Follow up analyses indicated that accuracy
during the HI-rTMS sessions was significantly lower compared
to tACS sessions (z=−2.66, p= 0.008) and LI-rTMS sessions
(z=−2.73, p= 0.006). However, accuracy during the tACS and
LI-rTMS sessions did not differ (z=−0.06, p= 0.949).
Spatial Bias
Initial Bias
One-sample t-tests showed bias scores for evenly bisected
lines during following sham stimulation were not significantly
TABLE 1 | Mean accuracy (%) when responding to transected line stimuli in the
line bisection task.
Stimulation type Mean accuracy (SD) (%)
Left elongated Right elongated
HI-rTMS Sham 57.1 (5.0) 61.7 (4.9)
Active 58.2 (4.9) 63.3 (4.8)
LI-rTMS Sham 66.7 (4.7) 64.8 (4.8)
Active 65.2 (4.9) 63.6 (4.8)
tACS Sham 61.1 (4.9) 71.2 (4.5)
Active 65.2 (4.8) 62.5 (4.9)
Numbers in brackets represent standard deviation.
different from 0.5 for HI-rTMS and LI-rTMS sessions, but
there was a slight but significant rightward bias for the tACS
session [HI-rTMS: M= 0.4997, t(1484) = −0.026, p= 0.979;
LI-rTMS: M= 0.4968, t(1716) = −0.265, p= 0.791; tACS:
M= 0.5404, t(1657) = 3.30, p<0.001]. This suggests none of the
participant conditions showed the conventional leftward spatial
bias (pseudoneglect) (Milner et al., 1992;Learmonth et al., 2015)
prior to stimulation.
Effect of Stimulation
Bias scores were analysed with a generalised linear mixed
model with fixed factors of Stimulation Type, Active vs. Sham
Stimulation and Block and subject included as a random effect
(Figure 3). Block was included as a variable in order to assess
for any delayed effects of stimulation (Gamboa et al., 2010;Gan
et al., 2019). There was a significant main effect of Stimulation
Type (χ2= 14.9, p<0.001), but no significant main effects
of factors Active vs. Sham Stimulation (χ2= 0.806, p<0.369)
or Block (χ2= 4.89, p= 0.180). There was also a significant
interaction for Stimulation Type∗Block (χ2= 12.8, p= 0.046)
and Active vs. Sham Stimulation ∗Stimulation Type ∗Block
interaction (χ2= 15.9, p= 0.014). In order to understand the
nature of the interaction, we followed up with simple effect
comparisons, contrasting sham vs. active stimulation between
the same block for each Stimulation Type (i.e., comparing Sham
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FIGURE 3 | Spatial bias scores for sham and active stimulation for each stimulation type across blocks of the line bisection task. No significant effects or interactions
present. Individual points represent mean spatial bias for individual participants. For the bias score: 0 = absolute leftward bias, 1 = absolute rightward bias.
HI-rTMS Block 1 with Active HI-rTMS Block 1; see Table 2).
There were significant effects for HI-rTMS block 3 and 4;
LI-rTMS block 4 and tACS block 4, but none survived adjustment
for multiple comparison using Holm–Sidak corrections. We
also performed simple effect comparisons for the Stimulation
Type∗Block interaction, comparing stimulation types across each
block. The only comparison that survived Holm–Sidak multiple
comparison correction was a difference between HI-rTMS and
tACS in block 1 (z=−2.99, p= 0.035). The simple effects
also suggest that the main effect of Stimulation Type cannot be
interpreted as there was no pairwise comparison between two
stimulation types that was significantly different across all blocks.
Thus, inter-session performance was relatively stable.
Attentional Blink
T1 Accuracy
T1 accuracy (Table 3) was analysed using generalised linear
mixed model with Stimulation Type, Active vs. Sham stimulation
and Lag included as fixed factors, and subject included as a
random effect. As can be seen in the Table, overall accuracy was
TABLE 2 | Simple effect comparisons for the Active vs. Sham Stimulation
*Stimulation type *Block interaction for spatial bias.
Block Stimulation type Contrast z punadjusted padjusted
1 HI-rTMS Active vs. Sham −1.312 0.190 0.815
LI-rTMS Active vs. Sham −0.793 0.428 0.955
tACS Active vs. Sham −1.028 0.304 0.921
2 HI-rTMS Active vs. Sham 0.147 0.883 0.986
LI-rTMS Active vs. Sham −0.106 0.915 0.986
tACS Active vs. Sham −0.602 0.547 0.958
3 HI-rTMS Active vs. Sham −2.028 0.043 0.356
LI-rTMS Active vs. Sham −0.532 0.595 0.958
tACS Active vs. Sham 0.835 0.404 0.955
4 HI-rTMS Active vs. Sham 2.433 0.015 0.166
LI-rTMS Active vs. Sham 1.970 0.049 0.364
tACS Active vs. Sham −2.135 0.033 0.309
Adjusted p-values use Holm–Sidak corrections for multiple comparison.
close to ceiling. Nevertheless, there was a significant main effect
of Stimulation Type (χ2= 9.87, p= 0.007) and a main effect of
Active vs. Sham stimulation (χ2= 3.86, p= 0.049), but no main
effect of Lag (χ2= 5.47, p= 0.361) and no significant interactions
(χ2≤8.15, p>0.258). Follow up comparisons indicated
that accuracy during the HI-rTMS session (92.1% ±1.47) was
significantly higher compared to both LI-rTMS (90.4% ±1.73;
z= 2.97, p= 0.003) and tACS sessions (90.7 ±1.69; z= 2.50,
p= 0.0012). T1 accuracy during LI-rTMS and tACS sessions did
not differ from each other (z=−0.479, p= 0.632). The difference
between Sham and Active stimulation, although significant, was
quite small, and not necessarily meaningful, with mean accuracy
reduced by 1% following active stimulation (Sham T1 Accuracy:
91.6% ±1.53; Active T1 Accuracy: 90.6% ±1.68). The lack of
an interaction effect with Stimulation Type also indicates that the
stimulation effect was not specific to, or more pronounced for a
particular stimulation technique.
T2|T1 Accuracy
In order to assess the group effects of stimulation on temporal
attention, T2 accuracy calculated only on trials when T1 is correct
(T2|T1 Accuracy) was analysed using a generalised linear mixed
model with fixed factors of Stimulation Type, Active vs. Sham
Stimulation and Lag, with subject included as a random effect.
There was a significant effect of Lag (χ2= 986, p<0.001) and a
significant interaction with Stimulation Type ∗Lag (χ2= 19.7,
p<0.032), indicating a robust attentional blink with Lag 1
sparing (Figure 4). There were no other significant main effects
or interactions, χ2≤4.91, p>0.092. The interaction between
Stimulation Type ∗Lag suggest that there was some slight
difference in attentional blink between sessions, but since there
was no interaction with Active vs. Sham Stimulation, it is not
connected with application of active stimulation.
Sensation and Blinding During
Stimulation
For HI-rTMS, 85% of participants correctly guessed when
they received the sham stimulation, and 90% correctly guessed
the active stimulation. For LI-rTMS, 57% correctly guessed
the sham stimulation, but only 38% correctly guessed the
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TABLE 3 | Mean accuracy (%) when responding to T1 in the attentional blink task.
Stimulation type Mean accuracy (SD) (%)
T2 absent Lag 1 Lag 2 Lag 3 Lag 5 Lag 7
HI-rTMS Sham 89.5 (30.7) 90.8 (29.0) 91.1 (28.5) 88.5 (32.0) 88.8 (31.6) 91.1 (28.5)
Active 89.4 (30.8) 91.7 (27.7) 86.5 (34.3) 90.6 (29.2) 87.2 (33.5) 88.9 (31.5)
LI-rTMS Sham 88.5 (31.9) 89.4 (30.9) 89.1 (31.3) 89.1 (31.3) 87.5 (33.1) 87.2 (33.5)
Active 87.1 (33.5) 88.4 (32.1) 86.0 (34.7) 84.2 (36.5) 87.5 (33.1) 84.8 (35.9)
tACS Sham 86.5 (34.2) 87.8 (32.8) 89.3 (31) 90.2 (29.8) 86.3 (34.4) 86.3 (34.4)
Active 90.2 (29.8) 86.3 (34.4) 87.8 (32.8) 87.8 (32.8) 87.2 (33.5) 84.2 (36.5)
Numbers in brackets represent standard deviation.
Accuracy for T1 was significantly lower with stimulation.
FIGURE 4 | Percentage accuracy for reporting T2| T1 across lag positions for each stimulation type. The attentional blink occurred with Lag 1 sparing. Accuracy did
not differ between stimulation parameters for T2| T1. Individual points represent mean accuracy for individual participants.
active stimulation. For tACS, 41% correctly guessed the sham
stimulation, while 36% correctly guessed the active stimulation.
Tapping and tingling sensations were reported following HI-
rTMS and tACS, respectively, in some participants. No physical
sensation was reported following LI-rTMS. The HI-rTMS sham
was not as effective as tACS or LI-rTMS, however, as there were
no stimulation type effects in tasks it does not appear that there
were disproportionate sham or expectancy effects.
DISCUSSION
In this study we assessed whether offline LI-rTMS or HI-
rTMS delivering iTBS and 10 Hz tACS would induce shifts in
visuospatial attention in a line bisection task and alter temporal
attention in an AB task. Overall, offline brain stimulation did
not change performance in either task, with the exception of a
small reduction in T1 accuracy during the attentional blink task
following active stimulation.
The lack of significant differences following stimulation
in task performance related to attention was unexpected as
several studies report changes to cognition following stimulation,
particularly for the line bisection task (e.g., line bisection: Fierro
et al., 2000;Hilgetag et al., 2001;Kim et al., 2005;Thut et al.,
2005;Nyffeler et al., 2008; attentional blink: Cooper et al.,
2004). On the face of it, one might speculate that the absence
of stimulation effects may reflect the absence of pseudoneglect
at a group level, potentially suggesting a lack of sensitivity to
spatial bias. However, we think this explanation is unlikely for
two reasons. First, our task was based on Kim et al.’s (2005)
study, which showed a robust pre-stimulation leftward bias and
thus should be sensitive to stimulation effects on spatial bias
if present in our sample. Second, despite the lack of evidence
for pseudoneglect at a group level, our statistical analyses
accounted for individuals’ biases and their change over time to
maximise statistical sensitivity to modulation. Notably, our other
attentional task–the attentional blink–showed a robust group-
level attention effect but also no changes in temporal attention
following stimulation, making the suggestion that the absence
of stimulation effects depends on the presence of group level
attention affects prior to stimulation less plausible.
It is also possible that our study design, comparing task results
following sham and active stimulation, may overlook effects
elicited due to sham intervention. If such effects occurred it
may have resulted in ceiling effects that active stimulation could
not improve upon. For example, in a study of discrimination
sensitivity, there was an instance of a sham intervention effect
where active transcranial direct current stimulation (tDCS)
stimulation compared against baseline resulted in a significant
effect, but comparison against sham did not (Benwell et al.,
2015). However, this pattern was not found for attentional bias
which was examined in the same study. Moreover, other studies
have included separate baseline vs. sham comparisons in similar
cognitive tasks and not shown a significant sham intervention
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effect (Kim et al., 2005;Giglia et al., 2011;Learmonth et al.,
2017). Therefore, we believe previous studies suggest that the
contribution of a sham-elicited effect is unlikely in our design;
however, this needs further investigation. Below, we discuss our
results in relation to the current brain stimulation literature
and consider possible contributing factors to our non-significant
results in greater detail.
Contributing Factors–Stimulation
Protocols
Theta Burst Stimulation
We differed from several previous experimental designs in that
we applied HI- and LI-rTMS delivering iTBS rather than a
simple patterned rTMS protocol such as 10 or 1 Hz stimulation
(e.g., Hilgetag et al., 2001;Kim et al., 2005). However, previous
studies have shown iTBS and cTBS can induce cognitive effects
(for review see Demeter, 2016). Specifically, after stimulation
over the right parietal cortex, cTBS has been shown to induce
spatial attention deficits in healthy participants (Nyffeler et al.,
2008;Cazzoli et al., 2009;Rizk et al., 2013;Varnava et al.,
2013;Chechlacz et al., 2015;Schintu et al., 2021) and alleviate
deficits in neglect patients (Nyffeler et al., 2009;Cazzoli et al.,
2015;Fu et al., 2015;Yang et al., 2015). cTBS stimulation was
also more effective at alleviating deficits than simple patterned
protocols for neglect patients (Fu et al., 2015). Cerebellar iTBS
was also shown to improve performance in an AB task (Esterman
et al., 2017), while cerebellar cTBS increased the AB (Arasanz
et al., 2012). Unfortunately, iTBS over the PPC has not been
extensively assessed in cognitive studies, although one recent
study assessing TBS over the parietal cortex showed changes
to inhibition, sequence learning and working memory, but not
spatial attention in a simple cue task following both iTBS and
cTBS (Whybird et al., 2021). Another recent study compared
iTBS over the left PPC with sham, high definition-tDCS and
a cTBS protocol in tasks assessing working memory, divided
attention, and generalised attention (Stroop task) (Gan et al.,
2019). All active stimulation conditions improved reaction times
in the generalised attention task, but there was no significant
effect on divided attention or working memory. iTBS also had the
largest effect size, followed by tDCS and cTBS (Gan et al., 2019).
Both Gan et al. (2019) and Whybird et al. (2021) show that iTBS
can induce cognitive changes, however, it is not yet established
which areas of cognition iTBS can reliably modulate. Whybird
et al. (2021) demonstrated modulation of working memory, but
Gan et al. (2019) showed no significant modulation of working
memory. Despite the contrasting working memory results, both
studies show reduced reaction time in inhibition related tasks
following left PPC stimulation (emotional Stroop task: Gan et al.,
2019; NoGo task: Whybird et al., 2021).
For this study we were interested in whether an iTBS protocol
was able to be an effective cognitive enhancement tool. Although
iTBS often has the opposing action to cTBS, it may be that
iTBS over the rPPC does not induce the opposing behavioural
effects evidenced by cTBS in previous spatial attention studies.
Disruption of cognition also tends to be more easily induced than
cognitive enhancement (Luber and Lisanby, 2014), which could
explain the propensity for cTBS but not iTBS effects, especially
if attention is already operating at high efficacy. Although when
iTBS did appear to induce changes, it was more effective than
cTBS (Gan et al., 2019). Compared to simple patterned protocols
(i.e., 10 Hz, 1 Hz), iTBS can induce stronger and longer lasting
effects compared to simple patterned protocols in measurements
of synaptic plasticity (Huang et al., 2005) and has also been more
effective than simple protocols in other cognition studies (Fu
et al., 2015;Wu et al., 2021). However, this may not be the case for
the tasks included in this study. The lack of significant changes to
spatial attention in this study are in line with the lack of changes
to attention cuing, a spatial attention task, seen in Whybird et al.
(2021).
Transcranial Alternating Current Stimulation vs.
Transcranial Direct Current Stimulation
We included offline tACS as a comparison with HI- and LI-
rTMS in order to compare whether biphasic stimulation via
application of alternating current directly onto the scalp would
differ in strength of effect compared to magnetic stimulation.
We theorised that differences could possibly provide information
about differences in mechanisms between rTMS and tES,
particularly between LI-rTMS and tES as both induce sub-action
potential threshold levels of electrical stimulation. We therefore
chose tACS for this study in order to have alternating current
stimulation across all three stimulation types. One potential
limitation of this choice, is that tDCS is more commonly used
to induce cognitive effects. However, tACS has previously been
shown to affect attention and various other forms of cognition
(for review see Klink et al., 2020). For example, Yaple and
Vakhrushev (2018) reported changes to temporal attention in
an attentional blink task following 20 Hz tACS. Schuhmann
et al. (2019) also reported a shift in spatial attention in cued
attention and detection tasks with 10 Hz tACS and Otsuru
et al. (2019) documented changes to spatial bias and temporal
discrimination in a temporal order judgement task with 10 Hz
tACS. Nonetheless, since there is less evidence for spatial and
temporal attention modulation with tACS, it remains possible
that tDCS could have been a more effective stimulation method.
Offline Stimulation
Another difference with many other tACS and tDCS studies is
that we applied tACS offline, rather than online, in order to match
the timing of rTMS stimulation and to facilitate a comparison
between electrical vs. magnetic stimulation. However, a potential
problem with this choice is that one of the main proposed
mechanisms of tACS is its ability to entrain alpha wave brain
oscillation during a cognitive task to induce cognitive effects
(Dayan et al., 2013;Miniussi et al., 2013), and previous positive
results used online stimulation (Yaple and Vakhrushev, 2018;
Otsuru et al., 2019;Schuhmann et al., 2019). In addition,
Veniero et al. (2017) initially showed that tACS during the
line bisection task (online) but not preceding the task (offline)
was able to shift spatial attention, although they could not
replicate the result. That said, offline tACS can induce cognitive
effects in other domains (e.g., memory and perception, see Klink
et al., 2020 for review), and can enhance alpha oscillations,
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apparently via spike-timing dependent plasticity rather than
direct entrainment (Vossen et al., 2015). Nonetheless, for future
studies, if the aim is modulation of attention, it may be better to
use online interventions.
Stimulation Methodology
An additional consideration is advancements in brain stimulation
techniques which can refine stimulation protocols. For example,
although the use of 10–20 EEG positions to target stimulation
sites are quick and easy to administer, it has limited accuracy.
A study comparing methods of determining stimulation sites
found that the EEG coordinate approach using “P4” was
associated with the lowest behavioural effect size in a number
comparison task, while fMRI- and MRI-guided neuronavigation
was most effective (Sack et al., 2009). Therefore using MRI-
guided rTMS would allow for more precise and consistent
stimulation site targeting which could lead to greater likelihood
of significant stimulation findings. However, the need for
MRI scanning and specialised equipment means this option is
highly dependent on the resources available to the researcher.
Furthermore, applying tACS using individual alpha frequency
rather than fixed frequency may be a more successful way to
induce tACS effects. Individual alpha frequency tACS has been
associated with long-lasting after effects due to plastic changes
(e.g., Vossen et al., 2015) and is increasingly the preferred method
for applying tACS. However, comparisons between fixed and
individualised alpha frequency tACS are still needed to compare
the efficacy of the two techniques.
Stimulation Intensity
Another factor of stimulation is the intensity chosen for each
stimulation. This is a source of variation across all brain
stimulation studies, with no uniform approach, particularly
since various brain regions may respond differently to differing
intensities and “the more, the better” is often not the case.
Without a specific dose-response curve, it is difficult to conclude
whether the intensity dosage was optimal for each stimulation,
however, selection of intensities reflected previous studies.
For HI-rTMS, iTBS is usually applied between 70 and 90%
of an individual’s active or resting motor threshold (Turi et al.,
2021). A limitation with regards to intensity comparisons is that
we are in the minority as studies who use phosphene thresholds
as a way to individualise stimulation intensities (Turi et al.,
2021). There is some criticism for whether motor thresholds
or phosphene thresholds are appropriate for guiding amplitude
selection in non-motor or non-visual areas (Stewart et al., 2001;
Boroojerdi et al., 2002;Beynel et al., 2019). Furthermore, a meta-
analysis of online rTMS studies’ effects on cognition found that
use of fixed versus thresholded rTMS intensities did not differ in
terms of rTMS effects (Beynel et al., 2019).
For tACS, stimulation intensity is not usually individualised
and similar to HI-rTMS, the intensity applied varies, usually
between 1 and 2 mA. There are not any robust comparison
studies that assess the “optimal” intensity for attention tasks,
but different intensities could possibly affect outcomes. Perhaps
2 mA was not the optimal intensity for tACS, however, 2 mA
has previously induced significant outcomes in cognitive studies
(e.g., Kasten and Herrmann, 2017;Kasten et al., 2020). As
discussed further in Section “Previous Replication Failures,” there
is evidence for intensity-dependent effects in spatial attention
(Benwell et al., 2013), but it was not replicated in a follow up study
(Learmonth et al., 2015). Studies investigating the biophysics
of various tACS intensities could shed light on the interaction
between intensity and functional effects. For example, a recent
study in non-human primates reported that higher intensities of
tACS (comparing 0.5, 1, and 1.5 mA) were able to entrain more
cells to induce spike-timing dependent changes, and also increase
“burstiness” of neurons (Johnson et al., 2020). Note, this study
only looked at short-term stimulation (2 min) so the relevance to
longer stimulation used in most tACS studies, offline effects, and
functional outcomes are still to be investigated.
Finally, with regards to LI-rTMS, intensity plays a large part in
its consideration as a possible new stimulation approach. Animal
models using a range of intensities have shown biological and
functional effects (Moretti and Rodger, 2022). Our intensity is
on the higher range to match with what has been most effective
behaviourally in animals (Heath et al., 2018), however, there
is still a lot that is unknown about any “optimal” intensity.
Future studies to explore dosage parameters and determine
the minimum effective intensity, both in humans and animals,
would be useful. Since LI-rTMS is subthreshold, we approached
the intensity choice in a similar way to tACS and in line
with previous LI-rTMS animal models–using a fixed intensity.
Part of this reasoning was to remain in line with the animal
models and follow a translational pipeline approach, but this
differs from convention in HI-rTMS. Although individualising
intensity can help normalise stimulation across intra-individual
differences in physiological excitability, there are drawbacks with
regards to relating thresholded intensities to basic and preclinical
research (Turi et al., 2021). Plus, as discussed above, using fixed
vs. thresholded intensity approaches do not necessarily predict
different rTMS effects (Beynel et al., 2019). In addition, since LI-
rTMS remains below the action-potential threshold, individual
excitability on the scale of motor or phosphene thresholds are not
necessarily relevant to mechanisms of action of LI-rTMS. Similar
to tACS, intensity may be important on a cellular level, but using
an aggregate measure of cortical excitability is less relevant to LI-
rTMS. To be able to step away from the practice of individualised
rTMS intensities would make application of LI-rTMS easier
and require less expertise for stimulation delivery, more in line
with tES approaches. With all of this in consideration, the fixed
intensity approach may still be a limitation. Further exploration
of dosage-response curves with LI-rTMS comparing the fixed and
individualised approaches in the future could help elucidate this.
Previous Replication Failures
At a group level, previous studies had found that 10 Hz
rTMS over the rPPC increased visuospatial attention in the
left, contralateral hemispace and increased leftward biases in
healthy participants (Kim et al., 2005). Multiple sessions of
10 Hz rTMS also improved hemispatial neglect in stroke
patients, when assessed with a line bisection task (Kim et al.,
2013). Inhibitory rTMS (1 Hz) also facilitated visuospatial
attention in the unilateral hemisphere (e.g., Hilgetag et al., 2001),
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demonstrating how excitatory and inhibitory stimulation of
different hemispheres can effect visuospatial attention in similar
ways. Shifts in visuospatial attention have been reported with
offline tDCS in a polarity-dependent manner (Sparing et al.,
2009;English et al., 2018), online tDCS (Giglia et al., 2011; e.g.,
Benwell et al., 2015) and online tACS (Schuhmann et al., 2019).
Modulation of temporal attention has also been demonstrated
with improved attentional blink following TMS over the rPPC
(Cooper et al., 2004) and online 20 Hz tACS of frontal and parietal
regions (Yaple and Vakhrushev, 2018). Online 10 Hz tACS
has also shown evidence for improved temporal discrimination
following stimulation on either side of the PPC and leftward shift
in spatial bias following rPPC stimulation in a temporal order
judgement task (Otsuru et al., 2019).
However, there are also several examples of an absence
of cognitive change following brain stimulation. For example,
Learmonth et al. (2017), followed up reports of significant
modulation of spatial attention in a line bisection task following
tDCS seen by Benwell et al. (2015), using a within-subject study
design. They were unable to reproduce the same positive results,
reporting no significant changes to spatial bias following bi-
parietal online tDCS for 15 min. Learmonth et al. (2017) were
also unable to replicate an interaction found by Benwell et al.
(2015) that a rightward shift in visuospatial attention depended
on participant’s baseline task performance and tDCS intensity (1
vs. 2 mA). Similarly, Veniero et al. (2017) ran two experiments
assessing tDCS and 10 Hz tACS on spatial attention bias. In their
first experiment, they were unable to replicate a shift in spatial
attention with cathodal tDCS previously reported by Giglia et al.
(2011) and Benwell et al. (2015), but did show significant change
in bias during online 10 Hz tACS. However, when they attempted
to replicate the 10 Hz tACS experiment in a separate sample using
a within subjects design, they were unable to reproduce the shift
in spatial attention. When they combined the two experimental
samples, the previous 10 Hz tACS result also disappeared with
the increase in sample size (Veniero et al., 2017).
Our protocol differed in several ways compared to various
online tDCS and tACS experiments detailed above, and
therefore is not a direct replication attempt. However, the
unreliable nature of brain stimulation-induced cognitive changes,
particularly with crossover study designs further underlines
the difficulty of interpreting the results of studies that apply
new stimulation parameters. Interpretation requires evaluation
of the reason behind negative results when using exploratory
neurostimulation techniques to better determine whether they
reflect a true lack of neuromodulatory effects, or are instead
the result of other confounding factors which can underlie
unreliable or inconsistent modulatory effects reported in both
brain stimulation literature and broader cognitive research
(Draheim et al., 2021).
Limitations–Additional Measures of
Individual Variation
Another limit to establishing consistent effects of brain
stimulation is the high rate of inter-individual variability. There
has been an increasing push to identify predictors and biomarkers
that can help predict whether an individual will respond
favourably to brain stimulation which could help guide patient
or participant selection. For example, functional and structural
connectivity have been identified as possible determinants
of stimulation effects in individuals. Mariner et al. (2021)
showed that at a group level cTBS did not show the expected
rightward shift in visuospatial attention with a Landmark test.
However, EEG connectivity, specifically connectivity between
the rPPC and left temporal-parietal region, was a significant
predictor and likely determinant of whether cTBS was able to
influence spatial attention on the individual level. Other studies
also link inter-individual variability in visuospatial attention
following cTBS with changes in functional connectivity and
structural connectivity particularly related to the posterior corpus
callosum (Schintu et al., 2021). Schintu et al. (2021) discuss the
possibility that differences in connectivity change stimulation
outcomes due to differential effects on inhibition or excitation of
interhemispheric pathways that modulate visuospatial attention
(Koch et al., 2011). For example, individuals with more robust
callosal pathways may have less effective inhibition of the
interhemispheric PPC pathway following cTBS than individuals
with weaker connections (Schintu et al., 2021). Therefore
an individual’s baseline structural and functional connectivity,
which can be influenced by several factors such as sex, genetic,
and environmental influences [e.g., training in music (de
Manzano and Ullén, 2018) or motor skills (Scholz et al., 2009)]
(for review see Lebel and Deoni, 2018) may determine how
susceptible they are to stimulation effects.
Availability of γ-aminobutyric acid (GABA) and glutamate,
as measured by magnetic resonance spectroscopy, have also
been suggested as biomarkers for tDCS effects (Filmer et al.,
2019). Training in a response selection task was disrupted by
cathodal tDCS over the left the prefrontal cortex. The degree
to which training was disrupted was associated with individuals’
concentration of GABA and glutamate in the prefrontal cortex.
Individual levels of cortical inhibition, suggested by the ratio
between GABA and glutamate concentrations (i.e., more GABA
than glutamate), had larger disruptions in task training. The
disruption in task training and association with neurochemical
availability was only evident with cathodal, not anodal or sham
stimulation (Filmer et al., 2019). Interestingly, although they
did not assess changes on an individual level, Vidal-Piñeiro
et al. (2015) demonstrated that a single stimulation of iTBS,
but not cTBS over the left inferior parietal lobe was able to
increase GABA concentration in the posterior cingulate cortex,
a distal region to the stimulation site. The change in distal
GABA concentration and a non-significant change in combined
glutamate/glutamine concentration was significantly associated
with intrinsic connectivity between inferior parietal lobe and
posterior cingulate cortex before TBS. This further suggests that
individual functional connectivity modulates brain stimulation
effects. Finally, other factors such as genetic variation among
plasticity-related genes are also beginning to be explored as
contributors to inter-individual differences in brain stimulation
responses, e.g., BDNF Val66Met polymorphisms (Cheeran et al.,
2008;Abellaneda-Pérez et al., 2022). In sum, multiple levels
of variation, down to the genetic level likely influence brain
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Moretti et al. Offline Brain Stimulation and Attention
connectivity and consequent responses to brain stimulation, and
future work should assess such individual variations in order to
attempt to bolster consistency across stimulation studies.
Future Directions
It may be that cognitive changes assessed solely through
experimental tasks were not sensitive enough to pick up on any
subtle changes to attention induced by the stimulation. Assessing
changes to excitability using motor evoked potentials or EEG
may be more suitable as a barometer of whether LI-rTMS can
induce changes in humans, and easier to compare quantitatively
against HI-rTMS or tES. As demonstrated by Mariner et al.
(2021), including EEG can also allow connectivity analysis to
be used to further assess determinants behind inter-individual
variability, and would allow the ability to use individualised
alpha-frequency tACS methods rather than fixed frequency tACS
to possibly increase tACS efficacy or after-effects. Comparing
effects of online stimulation may also be more likely to produce
significant changes and allow more suitable comparison between
tES and LI-rTMS, in order to assess the effects of sub-threshold
stimulation on cognition and behaviour. However, HI-rTMS is
difficult to administer online as it can induce muscle twitching
and is accompanied by a loud clicking sound which could distract
participants from the task. Due to the exploratory nature of
this study in relation to LI-rTMS it may also be that attention
was not the most suitable behaviour to assess LI-rTMS effects,
although our choice was guided by animal models which have
suggested some attention-related effects of LI-rTMS (Poh et al.,
2018;Moretti et al., 2021). LI-rTMS may be able to modulate
behaviour in other tasks, although it is not yet clear which tasks
would be most suitable. For example, it may be that sub-threshold
stimulation using LI-rTMS acts under principles of stochastic
resonance which was suggested after LI-rTMS modulation of
visual evoked potentials in mice (Makowiecki et al., 2018) and
is similar to theories proposed for tES (Miniussi et al., 2013).
Therefore, perception tasks and inclusion of online LI-rTMS
may be a good starting point to look at potential behavioural
changes through the lens of optimising signal-to-noise ratio of
neural activity.
CONCLUSION
This study was the first to assess the effects of LI-rTMS on
cognition in humans. LI-rTMS was tolerated extremely well,
however, we did not observe any significant changes to spatial
or temporal attention. We also did not observe changes to
spatial or temporal attention following offline rTMS delivering
iTBS and 10 Hz tACS. Since we were unable to modulate
attention as has been seen in previous studies using rTMS and
tES we cannot yet draw conclusion on how LI-rTMS compares
with conventional stimulation currently used in humans and
the possible mechanisms underlying these techniques. Our
null results following HI-rTMS and tACS provide evidence
supporting ineffective modulation of attention when applying
iTBS and offline tACS.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the University of Western Australia Human
Research Ethics Committee. The patients/participants provided
their written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
JM: conceptualisation, methodology, writing – original draft,
review and editing, visualisation, formal analysis, investigation,
and software. WM: writing – review and editing, resources,
and formal analysis. AH: writing – review and editing and
supervision. JR and TV: writing – review and editing, supervision,
conceptualisation, and methodology. All authors contributed to
the article and approved the submitted version.
FUNDING
JM was supported by an Australian Government Research
Training Program (RTP) scholarship, and Byron Kakulas Prestige
scholarship. JR was supported by a fellowship from Multiple
Sclerosis Western Australia (MSWA) and the Perron Institute for
Neurological and Translational Science.
REFERENCES
Abellaneda-Pérez, K., Martin-Trias, P., Cassé-Perrot, C., Vaqué-Alcázar, L.,
Lanteaume, L., Solana, E., et al. (2022). BDNF Val66Met gene polymorphism
modulates brain activity following rTMS-induced memory impairment. Sci.
Rep. 12:176. doi: 10.1038/s41598-021- 04175-x
Antal, A., Alekseichuk, I., Bikson, M., Brockmöller, J., Brunoni, A. R., Chen, R.,
et al. (2017). Low intensity transcranial electric stimulation: safety, ethical,
legal regulatory and application guidelines. Clin. Neurophysiol. 128, 1774–1809.
doi: 10.1016/j.clinph.2017.06.001
Arasanz, C. P., Staines, W. R., and Schweizer, T. A. (2012). Isolating a cerebellar
contribution to rapid visual attention using transcranial magnetic stimulation.
Front. Behav. Neurosci. 6:55. doi: 10.3389/fnbeh.2012.00055
Benwell, C. S. Y., Learmonth, G., Miniussi, C., Harvey, M., and Thut, G. (2015).
Non-linear effects of transcranial direct current stimulation as a function
of individual baseline performance: evidence from biparietal tDCS influence
on lateralized attention bias. Cortex 69, 152–165. doi: 10.1016/j.cortex.2015.
05.007
Benwell, C. S. Y., Thut, G., Learmonth, G., and Harvey, M. (2013). Spatial attention:
differential shifts in pseudoneglect direction with time-on-task and initial bias
Frontiers in Neuroscience | www.frontiersin.org 11 June 2022 | Volume 16 | Article 903977
fnins-16-903977 June 10, 2022 Time: 13:11 # 12
Moretti et al. Offline Brain Stimulation and Attention
support the idea of observer subtypes. Neuropsychologia 51, 2747–2756. doi:
10.1016/j.neuropsychologia.2013.09.030
Beynel, L., Appelbaum, L., Luber, B., Crowell, C., Hilbig, S., Lim, W., et al.
(2019). Effects of online repetitive transcranial magnetic stimulation (rTMS)
on cognition: a meta-analysis and recommendations for future studies. Brain
Stimul. 12:565. doi: 10.1016/j.brs.2018.12.870
Boroojerdi, B., Meister, I. G., Foltys, H., Sparing, R., Cohen, L. G., and Töpper, R.
(2002). Visual and motor cortex excitability: a transcranial magnetic stimulation
study. Clin. Neurophysiol. 113, 1501–1504. doi: 10.1016/S1388-2457(02)0
0198-0
Cazzoli, D., Rosenthal, C. R., Kennard, C., Zito, G. A., Hopfner, S., Müri, R. M.,
et al. (2015). Theta burst stimulation improves overt visual search in spatial
neglect independently of attentional load. Cortex 73, 317–329. doi: 10.1016/j.
cortex.2015.09.009
Cazzoli, D., Wurtz, P., Müri, R. M., Hess, C. W., and Nyffeler, T. (2009).
Interhemispheric balance of overt attention: a theta burst stimulation
study. Eur. J. Neurosci. 29, 1271–1276. doi: 10.1111/j.1460-9568.2009.06
665.x
Chechlacz, M., Humphreys, G. W., Sotiropoulos, S. N., Kennard, C., and Cazzoli,
D. (2015). Structural organization of the corpus callosum predicts attentional
shifts after continuous theta burst stimulation. J. Neurosci. 35, 15353–15368.
doi: 10.1523/JNEUROSCI.2610-15.2015
Cheeran, B., Talelli, P., Mori, F., Koch, G., Suppa, A., Edwards, M., et al. (2008). A
common polymorphism in the brain-derived neurotrophic factor gene (BDNF)
modulates human cortical plasticity and the response to rTMS. J. Physiol. 586,
5717–5725. doi: 10.1113/jphysiol.2008.159905
Clayton, M. S., Yeung, N., and Cohen Kadosh, R. (2015). The roles of cortical
oscillations in sustained attention. Trends Cogn. Sci. 19, 188–195. doi: 10.1016/
j.tics.2015.02.004
Clayton, M. S., Yeung, N., and Kadosh, R. C. (2019). Electrical stimulation of alpha
oscillations stabilizes performance on visual attention tasks. J. Exp. Psychol. Gen.
148, 203–220. doi: 10.1037/xge0000502
Cooper, A. C. G., Humphreys, G. W., Hulleman, J., Praamstra, P., and Georgeson,
M. (2004). Transcranial magnetic stimulation to right parietal cortex modifies
the attentional blink. Exp. Brain Res. 155, 24–29. doi: 10.1007/s00221-003-
1697-9
Dayan, E., Censor, N., Buch, E. R., Sandrini, M., and Cohen, L. G. (2013).
Noninvasive brain stimulation: from physiology to network dynamics and back.
Nat. Neurosci. 16, 838–844. doi: 10.1038/nn.3422
de Manzano, Ö., and Ullén, F. (2018). Same genes, different brains:
neuroanatomical differences between monozygotic twins discordant for
musical training. Cereb. Cortex 28, 387–394. doi: 10.1093/cercor/bhx299
Demeter, E. (2016). Enhancing cognition with theta burst stimulation. Curr. Behav.
Neurosci. Rep. 3, 87–94. doi: 10.1007/s40473-016- 0072-7
Draheim, C., Tsukahara, J. S., Martin, J. D., Mashburn, C. A., and Engle, R. W.
(2021). A toolbox approach to improving the measurement of attention control.
J. Exp. Psychol. Gen. 150, 242–275. doi: 10.1037/xge0000783
English, M. C. W., Kitching, E. S., Maybery, M. T., and Visser, T. A. W. (2018).
Modulating attentional biases of adults with autistic traits using transcranial
direct current stimulation: a pilot study. Autism Res. 11, 385–390. doi: 10.1002/
aur.1895
Esterman, M., Thai, M., Okabe, H., DeGutis, J., Saad, E., Laganiere, S. E.,
et al. (2017). Network-targeted cerebellar transcranial magnetic stimulation
improves attentional control. Neuroimage 156, 190–198. doi: 10.1016/j.
neuroimage.2017.05.011
Fierro, B., Brighina, F., Oliveri, M., Piazza, A., La Bua, V., Buffa, D., et al. (2000).
Contralateral neglect induced by right posterior parietal rTMS in healthy
subjects. Neuroreport 11, 1519–1521. doi: 10.1097/00001756-200005150-0
0031
Filmer, H. L., Dux, P. E., and Mattingley, J. B. (2015). Dissociable effects of anodal
and cathodal tDCS reveal distinct functional roles for right parietal cortex in
the detection of single and competing stimuli. Neuropsychologia 74, 120–126.
doi: 10.1016/j.neuropsychologia.2015.01.038
Filmer, H. L., Ehrhardt, S. E., Bollmann, S., Mattingley, J. B., and Dux, P. E. (2019).
Accounting for individual differences in the response to tDCS with baseline
levels of neurochemical excitability. Cortex 115, 324–334. doi: 10.1016/j.cortex.
2019.02.012
Flöel, A. (2014). tDCS-enhanced motor and cognitive function in neurological
diseases. Neuroimage 85, 934–947. doi: 10.1016/j.neuroimage.2013.05.098
Foxe, J. J., and Snyder, A. C. (2011). The role of alpha-band brain oscillations as
a sensory suppression mechanism during selective attention. Front. Psychol.
2:154. doi: 10.3389/fpsyg.2011.00154
Fu, W., Song, W., Zhang, Y., Yang, Y., Huo, S., Zhang, R., et al. (2015). Long-term
effects of continuous theta-burst stimulation in visuospatial neglect. J. Int. Med.
Res. 43, 196–203. doi: 10.1177/0300060513498663
Gamboa, O. L., Antal, A., Moliadze, V., and Paulus, W. (2010). Simply longer is not
better: reversal of theta burst after-effect with prolonged stimulation. Exp. Brain
Res. 204, 181–187. doi: 10.1007/s00221-010-2293-4
Gan, T., Nikolin, S., Loo, C. K., and Martin, D. M. (2019). Effects of high-
definition transcranial direct current stimulation and theta burst stimulation for
modulating the posterior parietal cortex. J. Int. Neuropsychol. Soc. 25, 972–984.
doi: 10.1017/S1355617719000766
Giglia, G., Mattaliano, P., Puma, A., Rizzo, S., Fierro, B., and Brighina, F. (2011).
Neglect-like effects induced by tDCS modulation of posterior parietal cortices
in healthy subjects. Brain Stimul. 4, 294–299. doi: 10.1016/j.brs.2011.01.003
Heath, A., Lindberg, D. R., Makowiecki, K., Gray, A., Asp, A. J., Rodger, J.,
et al. (2018). Medium- and high-intensity rTMS reduces psychomotor agitation
with distinct neurobiologic mechanisms. Transl. Psychiatry 8:126. doi: 10.1038/
s41398-018- 0129-3
Hilgetag, C. C., Théoret, H., and Pascual-Leone, A. (2001). Enhanced visual spatial
attention ipsilateral to rTMS-induced “virtual lesions” of human parietal cortex.
Nat. Neurosci. 4, 953–957. doi: 10.1038/nn0901-953
Hopfinger, J. B., Parsons, J., and Fröhlich, F. (2017). Differential effects of 10-Hz
and 40-Hz transcranial alternating current stimulation (tACS) on endogenous
versus exogenous attention. Cogn. Neurosci. 8, 102–111. doi: 10.1080/17588928.
2016.1194261
Huang, Y.-Z., Edwards, M. J., Rounis, E., Bhatia, K. P., and Rothwell, J. C. (2005).
Theta burst stimulation of the human motor cortex. Neuron 45, 201–206. doi:
10.1016/j.neuron.2004.12.033
Jensen, O., and Mazaheri, A. (2010). Shaping functional architecture by oscillatory
alpha activity: gating by inhibition. Front. Hum. Neurosci. 4:186. doi: 10.3389/
fnhum.2010.00186
Johnson, L., Alekseichuk, I., Krieg, J., Doyle, A., Yu, Y., Vitek, J., et al. (2020).
Dose-dependent effects of transcranial alternating current stimulation on spike
timing in awake nonhuman primates. Sci. Adv. 6:eaaz2747. doi: 10.1126/sciadv.
aaz2747
Kammer, T., Beck, S., Erb, M., and Grodd, W. (2001). The influence of
current direction on phosphene thresholds evoked by transcranial magnetic
stimulation. Clin. Neurophysiol. 112, 2015–2021. doi: 10.1016/S1388-2457(01)
00673-3
Kasten, F. H., and Herrmann, C. S. (2017). Transcranial alternating current
stimulation (tACS) enhances mental rotation performance during and after
stimulation. Front. Hum. Neurosci. 11:2. doi: 10.3389/fnhum.2017.00002
Kasten, F. H., Wendeln, T., Stecher, H. I., and Herrmann, C. S. (2020). Hemisphere-
specific, differential effects of lateralized, occipital–parietal α- versus γ-tACS
on endogenous but not exogenous visual-spatial attention. Sci. Rep. 10:12270.
doi: 10.1038/s41598-020- 68992-2
Kim, B. R., Chun, M. H., Kim, D. Y., and Lee, S. J. (2013). Effect of high- and low-
frequency repetitive transcranial magnetic stimulation on visuospatial neglect
in patients with acute stroke: a double-blind, sham-controlled trial. Arch. Phys.
Med. Rehabil. 94, 803–807. doi: 10.1016/j.apmr.2012.12.016
Kim, Y. H., Min, S. J., Ko, M. H., Park, J. W., Sung, H. J., and Lee, P. K. W.
(2005). Facilitating visuospatial attention for the contralateral hemifield by
repetitive TMS on the posterior parietal cortex. Neurosci. Lett. 382, 280–285.
doi: 10.1016/j.neulet.2005.03.043
Klink, K., Paßmann, S., Kasten, F. H., and Peter, J. (2020). The modulation
of cognitive performance with transcranial alternating current stimulation: a
systematic review of frequency-specific effects. Brain Sci. 10, 1–33. doi: 10.3390/
brainsci10120932
Koch, G., Cercignani, M., Bonnì, S., Giacobbe, V., Bucchi, G., Versace, V.,
et al. (2011). Asymmetry of parietal interhemispheric connections in humans.
J. Neurosci. 31, 8967–8975. doi: 10.1523/JNEUROSCI.6567-10.2011
Kuo, M.-F., and Nitsche, M. A. (2012). Effects of transcranial electrical stimulation
on cognition. Clin. EEG Neurosci. 43, 192–199. doi: 10.1177/1550059412444975
Frontiers in Neuroscience | www.frontiersin.org 12 June 2022 | Volume 16 | Article 903977
fnins-16-903977 June 10, 2022 Time: 13:11 # 13
Moretti et al. Offline Brain Stimulation and Attention
Learmonth, G., Felisatti, F., Siriwardena, N., Checketts, M., Benwell, C. S. Y.,
Märker, G., et al. (2017). No interaction between tDCS current strength and
baseline performance: a conceptual replication. Front. Neurosci. 11:664. doi:
10.3389/fnins.2017.00664
Learmonth, G., Gallagher, A., Gibson, J., Thut, G., and Harvey, M. (2015). Intra-
and inter-task reliability of spatial attention measures in pseudoneglect. PLoS
One 10:e0138379. doi: 10.1371/journal.pone.0138379
Lebel, C., and Deoni, S. (2018). The development of brain white matter
microstructure. Neuroimage 182, 207–218. doi: 10.1016/j.neuroimage.2017.
12.097
Lefaucheur, J.-P., Aleman, A., Baeken, C., Benninger, D. H., Brunelin, J., Di
Lazzaro, V., et al. (2020). Evidence-based guidelines on the therapeutic use of
repetitive transcranial magnetic stimulation (rTMS): an update (2014–2018).
Clin. Neurophysiol. 125, 2150–2206. doi: 10.1016/j.clinph.2019.11.002
Lenz, M., Platschek, S., Priesemann, V., Becker, D., Willems, L. M., Ziemann, U.,
et al. (2015). Repetitive magnetic stimulation induces plasticity of excitatory
postsynapses on proximal dendrites of cultured mouse CA1 pyramidal neurons.
Brain Struct. Funct. 220, 3323–3337. doi: 10.1007/s00429-014-0859- 9
Loftus, A. M., and Nicholls, M. E. R. (2012). Testing the activation-orientation
account of spatial attentional asymmetries using transcranial direct current
stimulation. Neuropsychologia 50, 2573–2576. doi: 10.1016/j.neuropsychologia.
2012.07.003
Luber, B., and Lisanby, S. H. (2014). Enhancement of human cognitive
performance using transcranial magnetic stimulation (TMS). Neuroimage 85,
961–970. doi: 10.1016/j.neuroimage.2013.06.007
Makowiecki, K., Garrett, A., Harvey, A. R., and Rodger, J. (2018). Low-intensity
repetitive transcranial magnetic stimulation requires concurrent visual system
activity to modulate visual evoked potentials in adult mice. Sci. Rep. 8:5792.
doi: 10.1038/s41598-018- 23979-y
Mariner, J., Loetscher, T., and Hordacre, B. (2021). Parietal cortex connectivity as a
marker of shift in spatial attention following continuous theta burst stimulation.
Front. Hum. Neurosci. 15:718662. doi: 10.3389/fnhum.2021.718662
Martiny, K., Lunde, M., and Bech, P. (2010). Transcranial low voltage pulsed
electromagnetic fields in patients with treatment-resistant depression. Biol.
Psychiatry 68, 163–169. doi: 10.1016/j.biopsych.2010.02.017
Milner, A. D., Brechmann, M., and Pagliarini, L. (1992). To halve and to halve not:
an analysis of line bisection judgements in normal subjects. Neuropsychologia
30, 515–526. doi: 10.1016/0028-3932(92)90055- Q
Miniussi, C., Harris, J. A., and Ruzzoli, M. (2013). Modelling non-invasive brain
stimulation in cognitive neuroscience. Neurosci. Biobehav. Rev. 37, 1702–1712.
doi: 10.1016/j.neubiorev.2013.06.014
Moretti, J., Poh, E. Z., Bolland, S. J., Harvey, A. R., Albrecht, M. A., and Rodger,
J. (2021). Concurrent LI-rTMS induces changes in c-Fos expression but not
behavior during a progressive ratio task with adult ephrin-A2A5−/−mice.
Behav. Brain Res. 400:113011. doi: 10.1016/j.bbr.2020.113011
Moretti, J., and Rodger, J. (2022). A little goes a long way: neurobiological effects
of low intensity rTMS and implications for mechanisms of rTMS. Curr. Res.
Neurobiol. 3:100033. doi: 10.1016/j.crneur.2022.100033
Nyffeler, T., Cazzoli, D., Hess, C. W., and Müri, R. M. (2009). One
session of repeated parietal theta burst stimulation trains induces long-
lasting improvement of visual neglect. Stroke 40, 2791–2796. doi: 10.1161/
STROKEAHA.109.552323
Nyffeler, T., Cazzoli, D., Wurtz, P., Lüthi, M., Von Wartburg, R., Chaves, S., et al.
(2008). Neglect-like visual exploration behaviour after theta burst transcranial
magnetic stimulation of the right posterior parietal cortex. Eur. J. Neurosci. 27,
1809–1813. doi: 10.1111/j.1460-9568.2008.06154.x
Otsuru, N., Kamijo, K., Otsuki, T., Kojima, S., Miyaguchi, S., Saito, K., et al. (2019).
10 Hz transcranial alternating current stimulation over posterior parietal cortex
facilitates tactile temporal order judgment. Behav. Brain Res. 368:111899. doi:
10.1016/j.bbr.2019.111899
Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., et al.
(2019). PsychoPy2: experiments in behavior made easy. Behav. Res. Methods 51,
195–203. doi: 10.3758/s13428-018- 01193-y
Poh, E. Z., Harvey, A. R., Makowiecki, K., and Rodger, J. (2018). Online LI-
rTMS during a visual learning task : differential impacts on visual circuit and
behavioural plasticity in adult Ephrin-A2A5 Mice. eNeuro 5:ENEURO.0163-
17.2018. doi: 10.1523/ENEURO.0163-17.2018
Posner, M. I., and Petersen, S. E. (1990). The attention system of the human
brain. Annu. Rev. Neurosci. 13, 25–42. doi: 10.1146/annurev.ne.13.030190.00
0325
Reteig, L. C., Talsma, L. J., van Schouwenburg, M. R., and Slagter, H. A. (2017).
Transcranial electrical stimulation as a tool to enhance attention. J. Cogn.
Enhanc. 1, 10–25. doi: 10.1007/s41465-017-0010-y
Rizk, S., Ptak, R., Nyffeler, T., Schnider, A., and Guggisberg, A. G. (2013). Network
mechanisms of responsiveness to continuous theta-burst stimulation. Eur. J.
Neurosci. 38, 3230–3238. doi: 10.1111/ejn.12334
Rohan, M., Parow, A., Stoll, A. L., Demopulos, C., Friedman, S., Ph, D.,
et al. (2004). Low-field magnetic stimulation in bipolar depression using a
MRI-based stimulator. Am. J. Psychiatry 161, 93–98. doi: 10.1176/appi.ajp.1
61.1.93
Rohan, M. L., Yamamoto, R. T., Ravichandran, C. T., Cayetano, K. R., Morales,
O. G., Olson, D. P., et al. (2014). Rapid mood-elevating effects of low field
magnetic stimulation in depression. Biol. Psychiatry 76, 186–193. doi: 10.1016/
j.biopsych.2013.10.024
Rossi, S., Hallett, M., Rossini, P. M., Pascual-Leone, A., Avanzini, G., Bestmann, S.,
et al. (2009). Safety, ethical considerations, and application guidelines for the
use of transcranial magnetic stimulation in clinical practice and research. Clin.
Neurophysiol. 120, 2008–2039. doi: 10.1016/j.clinph.2009.08.016
Sack, A. T., Kadosh, R. C., Schuhmann, T., Moerel, M., Walsh, V., and Goebel,
R. (2009). Optimizing functional accuracy of TMS in cognitive studies: a
comparison of methods. J. Cogn. Neurosci. 21, 207–221. doi: 10.1162/jocn.2009.
21126
Santarnecchi, E., Brem, A. K., Levenbaum, E., Thompson, T., Kadosh, R. C., and
Pascual-Leone, A. (2015). Enhancing cognition using transcranial electrical
stimulation. Curr. Opin. Behav. Sci. 4, 171–178. doi: 10.1016/j.cobeha.2015.
06.003
Schintu, S., Cunningham, C. A., Freedberg, M., Taylor, P., Gotts, S. J., Shomstein,
S., et al. (2021). Callosal anisotropy predicts attentional network changes
after parietal inhibitory stimulation. Neuroimage 226:117559. doi: 10.1016/j.
neuroimage.2020.117559
Scholz, J., Klein, M. C., Behrens, T. E. J., and Johansen-Berg, H. (2009). Training
induces changes in white-matter architecture. Nat. Neurosci. 12, 1370–1371.
doi: 10.1038/nn.2412
Schuhmann, T., Kemmerer, S. K., Duecker, F., de Graaf, T. A., ten Oever, S., De
Weerd, P., et al. (2019). Left parietal tACS at alpha frequency induces a shift
of visuospatial attention. PLoS One 14:e0217729. doi: 10.1371/journal.pone.
0217729
Sparing, R., Thimm, M., Hesse, M. D., Küst, J., Karbe, H., and Fink, G. R. (2009).
Bidirectional alterations of interhemispheric parietal balance by non-invasive
cortical stimulation. Brain 132, 3011–3020. doi: 10.1093/brain/awp154
Stewart, L. M., Walsh, V., and Rothwell, J. C. (2001). Motor and phosphene
thresholds: a transcranial magnetic stimulation correlation study.
Neuropsychologia 39, 415–419. doi: 10.1016/S0028-3932(00)00130- 5
Thut, G., Nietzel, A., and Pascual-Leone, A. (2005). Dorsal posterior parietal rTMS
affects voluntary orienting of visuospatial attention. Cereb. Cortex 15, 628–638.
doi: 10.1093/cercor/bhh164
Turi, Z., Lenz, M., Paulus, W., Mittner, M., and Vlachos, A. (2021). Selecting
stimulation intensity in repetitive transcranial magnetic stimulation studies:
a systematic review between 1991 and 2020. Eur. J. Neurosci. 53, 3404–3415.
doi: 10.1111/ejn.15195
Varnava, A., Dervinis, M., and Chambers, C. D. (2013). The predictive nature of
pseudoneglect for visual neglect: evidence from parietal theta burst stimulation.
PLoS One 8:e65851. doi: 10.1371/journal.pone.0065851
Veniero, D., Benwell, C. S. Y., Ahrens, M. M., and Thut, G. (2017). Inconsistent
effects of parietal α-tACS on Pseudoneglect across two experiments: a failed
internal replication. Front. Psychol. 8:952. doi: 10.3389/fpsyg.2017.00952
Vidal-Piñeiro, D., Martín-Trias, P., Falcón, C., Bargalló, N., Clemente, I. C., Valls-
solé, J., et al. (2015). Neurochemical modulation in posteromedial default-mode
network cortex induced by transcranial magnetic stimulation. Brain Stimul. 8,
937–944. doi: 10.1016/j.brs.2015.04.005
Vossen, A., Gross, J., and Thut, G. (2015). Alpha power increase after transcranial
alternating current stimulation at alpha frequency (a-tACS) reflects plastic
changes rather than entrainment. Brain Stimul. 8, 499–508. doi: 10.1016/j.brs.
2014.12.004
Frontiers in Neuroscience | www.frontiersin.org 13 June 2022 | Volume 16 | Article 903977
fnins-16-903977 June 10, 2022 Time: 13:11 # 14
Moretti et al. Offline Brain Stimulation and Attention
Whybird, M., Coats, R., Vuister, T., Harrison, S., Booth, S., and Burke, M. (2021).
The role of the posterior parietal cortex on cognition: an exploratory study.
Brain Res. 1764, 147452. doi: 10.1016/j.brainres.2021.147452
Wischnewski, M., and Schutter, D. J. L. G. (2015). Efficacy and time course of theta
burst stimulation in healthy humans. Brain Stimul. 8, 685–692. doi: 10.1016/j.
brs.2015.03.004
Wu, X., Wang, L., Geng, Z., Wei, L., Yan, Y., Xie, C., et al. (2021).
Improved cognitive promotion through accelerated magnetic stimulation.
eNeuro 8:ENEURO.0392-20.2020. doi: 10.1523/eneuro.0392-20.2020
Yang, W., Liu, T.-T., Song, X.-B., Zhang, Y., Li, Z.-H., Cui, Z.-H., et al. (2015).
Comparison of different stimulation parameters of repetitive transcranial
magnetic stimulation for unilateral spatial neglect in stroke patients. J. Neurol.
Sci. 359, 219–225. doi: 10.1016/j.jns.2015.08.1541
Yaple, Z., and Vakhrushev, R. (2018). Modulation of the frontal-parietal network
by low intensity anti-phase 20 Hz transcranial electrical stimulation boosts
performance in the attentional blink task. Int. J. Psychophysiol. 127, 11–16.
doi: 10.1016/j.ijpsycho.2018.02.014
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