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ORIGINAL RESEARCH
published: 27 February 2019
doi: 10.3389/fnins.2019.00161
Edited by:
Mikhail Lebedev ,
Duke University , United States
Reviewed by:
Makii Muthalib,
Université de Montpellier , France
Raffaella Ricci,
University of T urin, Italy
T akashi Hanakawa,
National Center of Neurology
and Psychiatry , Japan
*Correspondence:
Andreas Jooss
[email protected]
† These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Neural T echnology ,
a section of the journal
Frontiers in Neuroscience
Received: 30 October 2018
Accepted: 12 February 2019
Published: 27 February 2019
Citation:
Jooss A, Haberbosch L, Köhn A,
Rönnefarth M, Bathe-Peters R,
Kozarzewski L, Fleischmann R,
Scholz M, Schmidt S and Brandt SA
(2019) Motor T ask-Dependent
Dissociated Effects of T ranscranial
Random Noise Stimulation in a
Finger -T apping T ask V ersus
a Go/No-Go T ask on Corticospinal
Excitability and T ask Performance.
Front. Neurosci. 13:161.
doi: 10.3389/fnins.2019.00161
Motor T ask-Dependent Dissociated
Ef fects of T ranscranial Random
Noise Stimulation in a Finger -T apping
T ask V ersus a Go/No-Go T ask on
Corticospinal Excitability and T ask
Performance
Andreas Jooss 1 * , Linus Haberbosch 1 , Ar vid Köhn 1 , Maria Rönnefarth 1 ,
Rouven Bathe-Peters 1 , Leonard Kozarzewski 1 , Robert Fleischmann 1,2 , Michael Scholz 3 ,
Sein Schmidt 1 † and Stephan A. Brandt 1 †
1 Department of Neurology , Charité – Universitätsmedizin Berlin, Berlin, Germany, 2 Department of Neurology ,
Universitätsmedizin Greifswald, Greifswald, Germany, 3 Neural Information Processing Group, T echnische Universität Berlin,
Berlin, Germany
Backgr ound and Objective: T ranscranial random noise stimulation (tRNS) is an
emerging non-invasive brain stimulation technique to modulate brain function, with
pr evious studies highlighting its considerable benefits in therapeutic stimulation of the
motor system. However , high variability of r esults and bidir ectional task-dependent
ef fects limit mor e widespr ead clinical application. T ask dependency largely results fr om
a lack of understanding of the interaction between exter nally applied tRNS and the
endogenous state of neural activity during stimulation. Hence, the aim of this study
was to investigate the task dependency of tRNS-induced neur omodulation in the motor
system using a finger -tapping task (FT) versus a go/no-go task (GNG). We hypothesized
that the tasks would modulate tRNS’ effects on corticospinal excitability (CSE) and task
performance in opposite dir ections.
Methods: Thirty healthy subjects r eceived 10 min of tRNS of the dominant primary
motor cortex in a double-blind, sham-controlled study design. tRNS was applied during
two well-established tasks tied to diverging brain states. Accordingly , participants were
randomly assigned to two equally-sized gr oups: the first gr oup performed a simple
motor training task (FT task), known primarily to incr ease CSE, while the second gr oup
performed an inhibitory contr ol task (go/no-go task) associated with inhibition of CSE.
T o establish task-dependent effects of tRNS, CSE was evaluated prior to- and after
stimulation with navigated transcranial magnetic stimulation.
Results: In an ‘activating’ motor task, tRNS during FT significantly facilitated CSE.
FT task performance impr ovements, shown by training-r elated r eductions in intertap
intervals and incr eased number of finger taps, were similar for both tRNS and sham
stimulation. In an ‘inhibitory’ motor task, tRNS during GNG left CSE unchanged while
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Jooss et al. T ask-Dependent Dissociated Effects of tRNS
inhibitory contr ol was enhanced as shown by slowed reaction times and enhanced task
accuracy during and after stimulation.
Conclusion: W e pr ovide evidence that tRNS-induced neur omodulatory ef fects ar e
task-dependent and that r esulting enhancements ar e specific to the underlying
task-dependent brain state. While mechanisms underlying this effect r equire further
investigation, these findings highlight the potential of tRNS in enhancing task-dependent
brain states to modulate human behavior .
Keywords: random noise stimulation, transcranial electrical stimulation, task dependency , finger -tapping task,
go/no-go task, corticospinal excitability , neuroplasticity
INTRODUCTION
Transcranial electrical stimulation applied to t he primary motor
cortex is a non-invasive, portable , and low-cost method shown
to enhance motor function in healthy subjects and maximize
recovery after stroke ( T alelli and Rothwell, 2006 ; Hummel et al.,
2008 ). In addition to tD CS, tRNS is emer ging as a promising
neuromodulatory tool ( Terney et al., 2008 ; Schmidt et al., 2013b ;
Prichard et al., 2014 ). In contrast to the constant direct current
of tD CS, tRNS uses a biphasic alternating current with a random
amplitude and frequency, drawn from a frequency range between
0.1–640 Hz (full spectrum) or 100–640 Hz (high-frequency).
While tD CS modulates resting membrane potential, tRNS is
understood to facilitate transmission of existing subthreshold
neural activity to increase neuron excitability ( Terney et al., 2008 ;
Schmidt et al., 2013b ).
Transcranial random noise stimulation is reported to provide
considerable benefits over tDCS including polarity independence
of stimulation effects ( Terney et al., 2008 ), more pronounced
effect sizes ( Fertonani et al., 2011 ) and possibly improved
reliability ( Antal et al., 2010 ). Interestingly, tRNS has been
suggested to be a vital component in a patterned, individualized
stimulation algorithm aiming to maximize recovery after stroke
( Schmidt et al., 2013b ). Together , these findings suggest that tRNS
might be more reliable, safer and better suited for therapeutic
stimulation of the motor system.
However , a major and largely unresolved c hallenge across all
transcranial electrical stimulation met hods is the high variability
of results, limiting more widespread clinical application.
Important factors influencing interindividual variability in
transcranial electrical stimulation studies are the b aseline
neuronal level of motor and cognitive function, psychological
factors, cir cadian rhythm, genetics, anatomy, age, and varia bility
in assessment methods (e.g., TMS) ( Li et al., 2015 ). Additionally,
since the state of neuron populations during stimulation is
likely to play a pivotal role for the final behavioral effect, a
significant part of variability is understood to be related to
the brain’ s task dependent activity state during stimulation
( Silvanto et al., 2008 ; Li et al., 2015 ). The term brain state is
A bbreviations: CSE, corticospinal excitability; FT , finger-tapping; GNG, go/no-
go; ITI, intertap inter val; MEP , motor evoked potential; n TMS, na vigated
transcranial magnetic stimulation; R T , reaction time; tD CS, transcranial direct
current stimulation; TMS, transcranial magnetic stimulation; tRNS, transcranial
random noise stimulation.
used to describe characteristic changes in global brain activity
dynamically adjusted to task demands ( Gilbert and Sigman,
2007 ; Lee and Dan, 2012 ). T ask dependency is a well-established
phenomenon in non-invasive brain stimulation studies ( Antal
et al., 2007 ; Silvanto et al., 2008 ; Terney et al., 2008 ). It implies that
the neuromodulatory effects of non-invasive brain stimulation
might vary strongly dependent on the endogenous brain state
both prior to as well as during stimulation.
In the motor system, CSE, acquired by TMS, is an
electrophysiological parameter providing a direct, temporally and
spatially precise readout to monitor task-dependent activation
and inhibition via MEP s. CSE quantifies state changes of the
stimulated motor cortex by probing post-synaptic corticospinal
projections ( Bestmann and Krakauer, 2015 ).
Studies aiming to modulate CSE and induce beha vioral
changes with tRNS highlight the controversial role of
task-dependent brain states. tRNS was shown to have
bidirectional task-dependent effects on CSE, which is associated
with motor learning and recovery. tRNS applied offline ,
i.e., in idle subjects, was shown to increase CSE ( Terney
et al., 2008 ). Motor and cognitive tasks carried out online ,
i.e., during stimulation were shown to reduce CSE ( Terney
et al., 2008 ). Nevertheless, motor skill learning enhancements
were found to be driven primarily by online effects during
stimulation ( Prichard et al., 2014 ). Saiote and colleagues
investigated functional magnetic resonance imaging c hanges
following a visuomotor task with online tRNS and found
stimulation related blood-oxygen-level dependent changes only
in regions related to the task, implying direct interaction
of online tRNS with task related activity ( Saiote et al.,
2013 ). Results from these and other studies conducted in
the visual- and cognitive domains ( Fertonani et al., 2011 ;
Pirulli et al., 2013 ; Snowball et al., 2013 ) suggest that the
neuromodulatory effects of tRNS are dependent on whether
a task and wha t type of task is performed online during
stimulation, with enhancements specific to the engaged neural
population or brain state.
The aim of this study was to investigate the task
dependency of tRNS-induced neuromodulation in the motor
system. The hypothesis of this study was that tRNS would
modulate task effects in opposite directions, depending
on the underlying brain state. Hence, for tRNS during a
simple motor training task (FT task), known primarily
to increase CSE, we hypothesize an increase in CSE and
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beha vioral per formance ( Koeneke et al., 2006 ). For tRNS
during an inhibitory control task (GNG task), associated
with inhibition of CSE, we hypothesize a decrease in CSE
and enhanced beha vioral performance refle cting greater
inhibition ( Bestmann and Duque, 2016 ).
For this purpose, we closely monitored online as well as
offline changes of beh avioral and electrophysiological parameters
that are established indicators of task-dependent brain states
( Schmidt et al., 2013b ). As the primary electrophysiological
parameter , CSE was acquired via MEP s by n TMS. Compared
to conventional, non-na vigated TMS, n TMS uses an optical
tracking system to control the physical variance related
to the 3D parameters of the TMS coil in space. Since
small divergences in TMS coil location and orientation
can lead to significant variance in CSE estimates, n TMS
is an often neglected, but essential prerequisite to reliably
quantify changes of task-dependent brain states ( Schmidt et al.,
2009 ). Understanding the interaction between tRNS and task-
dependent brain activity is imperative for increasing reliability,
repeatability, and ultimately, therapeutic usefulness of this
emerging neuromodulatory technique.
MA TERIALS AND METHODS
Participants
Thirty healthy, right-handed individuals (18 females, mean age
22.8 ± 2.8 years) received tRNS as well as sham stimulation
to the dominant (left) primary motor cortex. All participants
were right handed as assessed with the Edinburgh handednes s
inventory. General exclusion criteria for non-invasive brain
stimulation were applied ( Brunoni et al., 2011 ). Specifically,
none of the subjects had a history of neurological disease,
including movement disorders or epilepsy ( Brunoni et al.,
2011 ). All participants gave written informed consent. The
study was approved by the local ethics committee and
adheres to the principles of good clinical practice of the
Charité – Universitätsmedizin Berlin (“Grundsätze der Charité
zur Sicherung guter wissenscha ftlicher Praxis ”), as well as
“The Code of Ethics of the World Medical Association”
(Declaration of Helsinki).
Experimental Paradigm
A double-blind sham-controlled design was used in this study.
The participants were randomly divided into two groups
according to the task they were to perform during tRNS or
sham stimulation: one group (15 participants) performed an
‘ activating ’ t ask (FT task) during stimulation, known primarily
to increase CSE. The other 15 participants performed an
‘inhibitory’ task (GNG t ask), as sociated with inhibition of CSE.
Beha vioral and ele ctrophysiological measurements were acquired
offline in a baseline condition prior to stimulation, and a
post-stimulation condition following 10 min of stimulation.
Offline measurements were complemented by online beha vioral
assessments during stimulation as described below and in
Figure 1 . In this context, it is important to note that tasks
ser ved two functions during stimulation: they are indicators
of task performance changes in response to stimulation and
utilized to induce a well-established task-dependent brain
state ( Figure 1 ).
Finger -T apping T ask (FT T ask)
The experimental timeline for the FT task is depicted in
Figure 1A . For the FT task, subjects were instructed to use the
index finger of either hand to repeatedly exert a vertical force
on a standard telegraph key as quickly and regularly as possible
while receiving visual feedback on a screen. V isual feedback
was provided with a live graphical display of ITIs on the x-axis
and the corresponding number of taps on the y-axis. For t he
first block, the starting hand was randomly allocated and the
tapping duration for one hand was 30 s before switching to
the other hand for 30 s ( S chulze et al., 2002 ). Two blocks for
each hand (i.e., 4 × 30 s = 2 min) were followed by a 120 s
pause (60 s pause during stimulation) to avoid e xcessive build-
up of fatigue ( Rönnefarth et al., 2018 ). As another precaution, the
vertical force required to complete a tapping motion was ad justed
to the lowest possible setting. Preventing excessive fatigue with
regular pauses ser ved to minimize its confounding influence on
CSE ( Terney et al., 2008 ). Prior to the experiment, participants
were instructed and practiced the task for two blocks for each
hand, resulting in a total of 1 min practice for each hand.
The baseline condition consisted of two blocks for each hand,
the stimulation condition (10 min) consisted of six blocks for
each hand and the post-stimulation condition consisted of four
blocks for each hand.
Go/No-Go T ask (GNG T ask)
The experimental timeline for the GNG task is depicted in
Figure 1B . One GNG trial with a total duration of 2.5–3 s
followed the following time course: first, a fixation cross was
presented on a screen, which lasted 1 s and was followed
by a 250 ms warning cue (yellow square) ( Joundi et al.,
2012 ). Subsequently, a 250 ms target cue was presented with a
varied latency of 250–750 ms based on an underlying, linearly
increasing hazard rate, in line with ( Schoffelen et al., 2005 ).
Subjects exerted a maximal horizontal force on the lever only
when a “go” cue (green circle) appeared (91%), while 9% of
target cues were “no-go” cues (red cir cle) ( Schoffelen et al.,
2005 ). The hazard rate and the low probability of “no-go”
trials were utilized to ensure optimal inhibition-related activity
( Schoffelen et al., 2005 ; Wessel, 2018 ). The response period was
limited to 750 ms.
During the GNG task, when no response was required,
subjects maintained a horizontal isometric force of 4% of
maximum voluntary contraction, with the index finger
of the dominant hand on a lever , in line with ( Kristeva
et al., 2007 ). A low force output was used since it was
shown to effectively enable corticospinal interaction and
recruit most neurons in M1 ( Evarts et al., 1983 ; Kristeva
et al., 2007 ). The predetermined force was monitored
throughout task exe cution and verbal feedback was given
in case of deviations.
Prior to the experiment, participants were instructed and
practiced 10 GNG trials. One block consisted of 37 GNG trials
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FIGURE 1 | Experimental timelines. Behavioral measurements of the FT task (A) and the GNG task (B) wer e conducted along with nTMS to evaluate CSE.
Behavioral and electrophysiological measur ements were acquired in a baseline condition and a post-stimulation condition. Of fline measurements were
complemented by online behavioral assessments during 10 min stimulation with tRNS or sham stimulation. (A) Experimental timeline of the FT task. 15 participants
performed the FT task. During one block of 60 s, one hand was tapping for 30 s before switching to the other hand for 30 s. Double slashes (“//”) denote a 60 s
pause between blocks (“// //” = 120 s), to avoid excessive fatigue. (B) Experimental timeline of the GNG task. 15 participants performed the GNG task. One block
consisted of 37 GNG trials and ended with a 15 s pause, resulting in 2 min per block.
and a 15 s pause, resulting in 2 min per block. The baseline
condition consisted of one block, the stimulation condition
(10 min) consisted of five consecutive blocks (i.e., a total of
5 × 37 trials = 185 trials) and the post-stimulation conditions
consisted of two blocks.
T ranscranial Random Noise Stimulation
(tRNS)
R andom noise stimulation was applied by a multi-c hannel
low-voltage stimulation and EEG device certified for clinical
use (NextWa ve, EBS Technologies GmbH, Kleinmac hnow,
Germany), which delivered weak random noise stimulation
through conductive-rubber electrodes (NeuroConn GmbH,
Ilmenau, Germany), placed in two saline-soaked sponges.
One electrode (circular , 12.5 cm 2 ) was situated over the
dominant motor cortex at the C3 EEG electrode position
(since all subjects were right-handed), the other electrode
(rectangular ele ctrode , 30 cm 2 ) was placed over the contralateral
frontopolar cortex ( Moliadze et al., 2012 ). For tRNS, a
peak-to-peak stimulation intensity of 1.51 mA (0.8 mA
effective current intensity) was applied for 10 min with no
D C offset. The random signal was drawn from a uniform
probability density with a sample rate of 1280 Hz and
digitally filtered to ensure a frequency distribution of 100–
640 Hz, based on Terney et al. (2008) . For sham stimulation,
a 15 s ramp-up and 15 s ramp-down current was used in
line with recommendations for tD CS ( Nitsche et al., 2008 ;
Schmidt et al., 2013a ). Respective sessions of tRNS and
sham stimulation were at least 7 days apart to a void c arry-
over effects.
Navigated T ranscranial Magnetic
Stimulation (nTMS)
Single pulse n TMS (eXimia VR T MS, Nexstim, Helsinki, Finland)
with optical tracking and subject-specific magnetic resonance
images was used in combination with a biphasic figure-
of-eight coil (70-mm wing diameter) to evaluate CSE with
optimal control of physical parameters ( Schmidt et al., 2015 ).
Compared to conventional, non-navig ated TMS, n TMS was
shown to reduce MEP amplitude variance by 27% ( Schmidt
et al., 2009 ). Electromyography activity in response to n TMS
was recorded from the dominant first dorsal interosseus muscle
with Neuroline 700 surface electrodes (Ambu VR , Ballerup,
Denmark) arranged in belly-tendon montage. MEP amplitude
was defined by peak-to-peak measurement. The stimulation
target was the “center of gravity” of the dominant first
dorsal interosseus ( Wassermann et al., 1992 ). Resting motor
threshold was defined as the stimulation intensity required
to elicit a 500 µ V MEP appearing with 50% prob a bility
using the maximum-likelihood threshold detection method
and a 95% confidence inter val, ensuring an individually
calibrated intensity prior to data acquisition in each session
( Awiszus, 2003 ). CSE was then assessed with 20 MEP s
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prior to and after electrical stimulation at the timepoints
specified in Figure 1 .
Analysis and Statistics
Two subjects withdrew consent to participate in the study before
completion. The remaining 28 subjects (13 in the FT group, 15 in
the GNG group) were included in the analysis and statistics.
CSE data was manually reviewed and outliers, defined
as values above or below 2.2x the interquartile range, were
identified in each session and removed ( Hoaglin and Iglewicz,
1987 ). CSE was estimated by using an in-house algorithm that
accounted for physiological and physical confounders, such
that MEP s associated with confounding prestimulus muscle
contraction (preinner vation) above 20 µ V and 100 ms prior to
stimulation were excluded and further physical and physiological
covariance was partitioned out of CSE estimation with step-
wise regression ( Schmidt et al., 2015 ). Mean CSE data was t hen
baseline normalized by subtracting baseline values from post-
stimulation values. Normality of data was graphically confirmed
with histograms and by using the Shapiro–W ilk test. Levene ’ s
test confirmed homogeneity of variances. Statistical analysis was
conducted using a mixed model ANOV A to compare t he main
and interaction effects on CSE, with T ASK (i.e., GNG, FT) as
between-subjects factor and STIMULA TION (i.e., tRNS, sham)
as within-subjects factor.
Go/no-go task R T s, GNG t ask accuracy, FT ITI and FT
taps were manually reviewed, which lead to exclusion of three
subjects in the GNG group due to technical artifacts in the
data. R T s and ITIs were outlier corrected, baseline normalized
and z-transformed on a per subject basis over each session,
in line with recommendations for within-subject designs and
psychophysiological data ( Bush et al., 1993 ). GNG accuracy data
and FT taps were outlier corrected and baseline normalized for
statistical analysis. Outlier correction involved trimming dat a by
5% of highest and lowest scores ( Bush et al., 1993 ; Whelan, 2017 ).
For GNG R T s specifically, trials wit hout response and R T s below
100 ms after target cue presentation were rejected ( Joundi et al.,
2012 ). Baseline normalization required the mean of the b aseline
condition to be subtracted from the data. Z-transformation
was used to increase power in comparison to raw means by
accounting for intraindividual variability acros s subjects ( Bush
et al., 1993 ). A normal distribution could be confirmed both
graphically as well as mathematically by the Shapiro–W ilk test.
A linear mixed model for repeated measures was used to analyze
the effect of tRNS on behavioral performance in the FT task
and GNG task. It was used in favor of a repeated measures
ANOV A due to its extended flexibility with regard to unbalanced
data and precision in giving less biased estimates of fixed
effects in repeated, correlated measurements ( Cnaan et al., 1997 ;
Krueger and Tian, 2004 ). A s fixed effects, STIMULA TION (i.e.,
tRNS/sham) and TIME (i.e., block) was entered into the model.
S UBJECTS was entered as random effects. For a significant
interaction of STIMULA TION × TIME, post hoc tests for
individual blocks were controlled for multiple comparisons using
Bonferroni correction.
All digital signal processing was carried out with custom-
made scripts within the MA TL AB programming environment
(MA TLAB R2014a, The MathWorks, Inc., N atick, MA,
United States). All statistical analysis was performed using
SPSS Statistics with statistic al significance level set at α = 0.05
(IBM SPSS Statistics for W indows, Version 21.0. Armonk, NY ,
United States: IBM, Corp.). Results are presented as mean values
and standard errors of the mean unless stated otherwise.
RESUL TS
Corticospinal Excitability (CSE)
Effects of tRNS on CSE are depicted in Figure 2 . Mean
uncorrected baseline CSE for the FT group was similar for
the tRNS (691 ± 89 µ V) and the sham condition (FT , sham:
686 ± 125 µ V) [ t (12) = 9.032, p = 0.975]. Baseline CSE for
the GNG group was also similar for the tRNS (530 ± 71 µ V)
and the sham condition (500 ± 105 µ V) [ t (14) = 0.250,
p = 0.806]. In the mixed model ANOV A, there was no
significant main effect of T ASK [ F (1,26) = 1.961, p = 0.173,
η 2
p = 0.07] and STIMULA TION [ F (1,26) = 1.814, p = 0.19,
η 2
p = 0.05] on CSE. However , t here was a significance for
the interaction STIMULA TION × T ASK [ F (1,26) = 5.474,
p = 0.027, η 2
p = 0.17], indicating that excitability change s
were dependent on the specific stimulation applied during
task execution. Pairwise comparisons revealed that in the FT
group, baseline corrected MEP responses were significantly
facilitated following tRNS (381 ± 146 µ V) compared to sham
stimulation (14 ± 133 µ V) ( p = 0.018, η 2
p = 0.2). In the GNG
group, tRNS ( − 36 ± 97 µ V) did not influence MEP responses
compared to sham stimulation ( − 63 ± 93 µ V) ( p = 0.473,
η 2
p = 0.02). This shows that tRNS specifically increased CSE
FIGURE 2 | Effects of tRNS on corticospinal excitability . Mean CSE change
( µ V) was calculated by subtracting baseline CSE measurements fr om
post-stimulation measurements. CSE change is depicted for r espective task
type (GNG or FT) performed during 10 min of stimulation with either tRNS or
sham stimulation. Error bars depict the standar d error of the mean. In the FT
group, MEP r esponses were significantly facilitated ( ∗ ) after tRNS compared to
sham stimulation and tRNS in the GNG group.
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FIGURE 3 | Effects of tRNS on FT ITI. Mean FT ITIs ar e baseline corrected and z-transformed. Blocks 3–8 (30 s per block) depict ITIs during electrical stimulation,
while blocks 9–12 present data post-stimulation. Double slashes (“//”) denote a 60 s pause between blocks (“// //” = 120 s), to avoid excessive fatigue. Mean ITI is
displayed with standard err or of the mean. Significant changes from baseline are marked with “+.” ITIs of the right hand (A) and the left hand (B) wer e not
significantly differ ent between the tRNS condition compared to the sham condition. For both hands, singular significant reductions in ITIs in block 3 of one condition
likely repr esent a rebound effect after a prior pause. Reductions in ITIs post-stimulation for both the tRNS and sham conditions imply motor learning.
after the FT task but not after the GNG task ( p = 0.022,
η 2
p = 0.19) ( Figure 2 ).
FT : Intertap Interval (ITI)
Effects of tRNS on FT ITIs are depicted in Figure 3A (right
hand) and Figure 3B (left hand). Uncorrected b aseline ITIs
were shorter for the right hand (tRNS, 148 ± 6 ms; sham,
149 ± 5 ms) compared to the left hand (tRNS, 170 ± 6 ms; sham,
170 ± 6 ms).
For the right hand, a linear mixed model did not show
a significant main effect of STIMULA TION on FT ITIs
[ F (2) = 2.35, p = 0.6]. However , a significant interaction of
STIMULA TION × TIME could be obser ved [ F (20) = 3.03,
p < 0.001]. Post hoc tests revealed significant reductions in
ITIs after both tRNS (block 9, − 0.246 ± 0.104, p = 0.02;
block 11, − 0.361 ± 0.101, p < 0.001) and sham stimulation
(block 9, − 0.345 ± 0.105, p = 0.001; block 11, − 0.323 ± 0.101,
p = 0.001). ITIs at the beginning of stimulation in block 3 were
significantly faster only in the tRNS condition ( − 0.256 ± 0.101,
p = 0.012). Bonferroni corrected pairwise comparisons between
individual blocks and stimulation did not reach significant
results ( Figure 3A ).
For the left hand, a linear mixed model did not show
a significant main effect of STIMULA TION on FT ITIs
[ F (2) = 2.86, p = 0.58]. However , a significant interaction of
STIMULA TION × TIME could be obser ved [ F (20) = 3.29,
p < 0.001]. Post hoc tests revealed significant reductions in
ITIs after both tRNS (block 11, − 0.423 ± 0.117, p < 0.001)
and sham stimulation (block 3, − 0.371 ± 0.112, p = 0.001;
block 11, − 0.434 ± 0.112, p < 0.001). There was a significant
increase in ITIs the sham condition in block 6 (0.262 ± 0.112,
p = 0.02) during stimulation. Bonferroni corrected pairwise
comparisons between individual blocks and stimulation did not
reach significant results ( Figure 3B ).
FT : Finger T aps
Effects of tRNS on FT taps are depicted in Figure 4A (right hand)
and Figure 4B (left hand). Mean uncorre cted baseline finger
taps were higher for the right hand (tRNS, 171.62 ± 7.18; sham
173.65 ± 7.32) compared to the left hand (tRNS 150.81 ± 6.17;
sham 154.23 ± 6.11).
For the right hand, a linear mixed model with b aseline
corrected data did not show a significant main effect of
STIMULA TION on FT taps [ F (2) = 1.98, p = 0.14]. However ,
a significant interaction of STIMULA TION × TIME could be
obser ved [ F (20) = 3.39, p < 0.001]. Post hoc tests revealed
significant increases in the number of finger taps versus baseline
for tRNS (block 3, 5.69 ± 2.34, p = 0.016; block 9, 5.54 ± 2.34,
p = 0.019; block 11, 8.38 ± 2.34, p < 0.001) and sham stimulation
(block 9, 7 ± 2.44, p = 0.004; block 11, 8.88 ± 2.33, p < 0.001).
Additionally, toward the end of tRNS, the number of finger taps
was significantly reduced versus baseline (block 7, − 5.54 ± 2.34,
p = 0.019; block 8, − 4.77 ± 2.34, p = 0.043). Bonferroni corrected
pairwise comparisons between individual blocks and stimulation
did not reach significant results ( Figure 4A ).
For the left hand, a linear mixed model with baseline corrected
data showed a significant main effect of STIMULA TION on
FT finger taps [ F (2) = 3.45, p = 0.03] with a significant
increase in FT finger tap estimates of fixed effects for tRNS
(2.06 ± 0.79) [ t (255) = − 2.62, p = 0.09] but not for sham
(0.16 ± 0.8) [ t (255) = 0.2, p = 0.84]. However , post hoc tests
between tRNS and sham did not reveal a significant difference
between stimulation conditions [ t (255) = − 1.7, p = 0.09].
A significant interaction of STIMULA TION × TIME could be
obser ved [ F (20) = 2.63, p < 0.001]. Post hoc tests revealed
significant increases in the number of finger taps versus baseline
after both tRNS (block 9, 4.73 ± 2.37, p = 0.047; block 11,
8.58 ± 2.37, p < 0.001; block 12, 5.69 ± 2.34, p = 0.029)
and sham stimulation (block 3, 5.69 ± 2.37, p = 0.017;
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FIGURE 4 | Effects of tRNS on FT taps. Mean FT number of taps ar e shown which illustrate an overall higher tapping performance of the right hand (A) compared to
the left hand (B) and complement changes in FT ITIs observed in Figure 3 . Blocks 3–8 (30 s per block) depict finger taps during electrical stimulation, while blocks
9–12 present data post-stimulation. Double slashes (“//”) denote a 60 s pause between blocks (“// //” = 120 s), to avoid excessive fatigue. Mean finger taps ar e
displayed with standard err or of the mean. Significant changes from baseline are marked with “+.” (A,B) Number of finger taps for both hands wer e not significantly
differ ent between the tRNS condition compared to the sham condition. For both hands, singular significant increases in the number of finger taps in block 3 of one
condition likely repr esent a rebound effect after a prior pause. Significant r eductions during stimulation represent fatigue. Incr eased number of finger taps
post-stimulation for both tRNS and sham conditions imply motor learning.
block 11, 6.92 ± 2.37, p = 0.004). Additionally, toward the
end of sham stimulation, the number of finger taps was
significantly reduced versus baseline (block 6, − 6.08 ± 2.37,
p = 0.011). Bonferroni corrected pairwise comparisons between
individual blocks and stimulation did not reach significant
results ( Figure 4B ).
GNG: Reaction Time (R T)
Effects of tRNS on GNG R T are depicted in Figure 5A . Mean
uncorrected baseline R T for the tRNS condition was 303 ± 5 ms,
and 313 ± 7 ms for the sham condition. A linear mixed model
showed a significant main effect of STIMULA TION on GNG
R T s [ F (2) = 11.69, p < 0.001] with a signific ant increase in
estimates of fixed effects for tRNS (0.21 ± 0.045) [ t (160) = 4.65,
p < 0.001] but not for sham (0.06 ± 0.045) [ t (160) = 1.33,
p = 0.19]. Importantly, post hoc tests between tRNS and sham
revealed a significant difference between stimulation conditions
[ t (160) = − 2.35, p = 0.019]. Breaking down the main effect of
STIMULA TION into a stimulation period (blocks 2–6) and a
post-stimulation period (blocks 7–8), the linear mixed model for
R T s post-stimulation was significant [ F (2) = 5.48, p = 0.007], with
a significant difference in estimates of fixed effects: GNG R T s were
attenuated after tRNS (0.24 ± 0.085, p = 0.06) compared to sham
( − 0.14 ± 0.085, p = 0.107) [ t (44) = − 3.19, p = 0.002]. There
was no significant difference between tRNS and sham during
the stimulation period [ t (114) = − 0.79, p = 0.43). A significant
interaction of STIMULA TION × TIME could also be obser ved
[ F (14) = 2.57, p = 0.002]. Post hoc tests showed attenuated R T s
for tRNS in block 5 (0.284 ± 0.122, p = 0.021) and block 7
(0.32 ± 0.122, p = 0.01) and at the start of sham stimulation
(block 2; 0.256, ± 0.117, p = 0.031). Bonferroni corrected
pairwise comparisons between individual blocks and stimulation
did not reach significant results. Together , these results show
that tRNS specifically attenuated R T s in t he GNG task in the
post-stimulation period ( Figure 5A ).
GNG: T ask Accuracy
Effects of tRNS on GNG task accuracy are depicted in Figure 5B .
Mean uncorrected baseline GNG t ask accuracy for the tRNS
condition was 96.88 ± 0.91 and 98.34 ± 0.58% for the sham
condition. A linear mixed model showed a significant main
effect of STIMULA TION on baseline corrected GNG accuracy
[ F (2) = 18.01, p < 0.001] with a significant increase in estimates of
fixed effects for tRNS (1.89 ± 0.32) [ t (173) = 5.94, p < 0.001] but
not for sham (0.29 ± 0.33) [ t (173) = 0.86, p = 0.39]. Importantly,
post hoc tests between tRNS and sham revealed a significant
difference between stimulation conditions [ t (173) = − 3.49,
p < 0.001]. Breaking down the main effect of STIMULA TION
into a stimulation period (blocks 2–6) and a post-stimulation
period (blocks 7–8), the linear mixed model for GNG accuracy
during stimulation was significant [ F (2) = 12, p < 0.001], with a
significant difference in estimates of fixed effects: GNG accuracy
was increased during tRNS (1.74 ± 0.36, p < 0.001) compared to
sham (0.37 ± 0.38, p = 0.973) [ t (123) = − 2.62, p = 0.009]. GNG
accuracy was also significantly increased in the post-stimulation
period [ F (2) = 5.97, p = 0.005] with significant differences in
estimates of fixed effects after tRNS (2.27 ± 0.66, p = 0.001)
compared to sham (0.083 ± 0.68, p = 0.904) [ t (48) = − 2.30,
p = 0.023). A significant interaction of STIMULA TION × TIME
could also be obser ved [ F (14) = 2.78, p = 0.001]. Post hoc
tests showed increased task accuracy during tRNS in block 3
(1.85 ± 0.86, p = 0.033), block 5 (2.69 ± 0.86, p = 0.002) and after
tRNS in block 7 (2.69 ± 0.86, p = 0.002) and block 8 (1.85 ± 0.86,
p = 0.033). The sham condition did not reach significant results.
Bonferroni corrected pairwise comparisons between individual
blocks and stimulation did not reach significant results. Together ,
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Jooss et al. T ask-Dependent Dissociated Effects of tRNS
FIGURE 5 | Effects of tRNS on GNG RT and task accuracy . (A,B) Mean GNG R T and task accuracy ar e baseline corrected. RT s ar e z-transformed. Blocks 2–6
(2 min per block) depict RT s and task accuracy change during electrical stimulation, while blocks 7 and 8 present data post-stimulation. Means are displayed with
standard err or of the mean. Significant changes from baseline are marked with “+.” Significant changes compar ed to sham are marked with “ ∗ .” (A) RT s wer e
significantly longer in the tRNS condition compared to sham. (B) T ask accuracy was significantly improved during and after tRNS compar ed to sham. T ogether , these
results suggest that tRNS specifically str engthened motor inhibition and inhibitory control in the GNG task.
these results show that tRNS specifically increased task accuracy
in the GNG task during stimulation and in the post-stimulation
period ( Figure 5B ).
DISCUSSION
The purpose of this study was to investigate the task dependency
of tRNS-induced neuromodulation in the motor system. The
main results of this study show task-dependent dissociated
effects on CSE and beha vioral performance following tRNS
during a FT task versus a GNG task. After motor training (FT
task), characterized by repetitive motor activation, tRNS led to
significant facilitation of CSE compared to sham stimulation,
while beha vioral performance was not signific antly different to
sham stimulation. Conversely, in the inhibitory control task
(GNG task), tRNS-enhanced inhibition led to an attenuation of
R T s without effects on CSE. Toget her , these findings support
the notion that tRNS enhances the predominant task-dependent
brain state. Our results highlight t he interaction between tRNS
and task-dependent brain activity and provide further e vidence
for tRNS ’ proposed mechanisms of action.
Motor Activation
In the simple motor training task (FT), online tRNS significantly
facilitated CSE as compared to sham stimulation. To our
knowledge, we are the first to show CSE enhancements
after task execution during tRNS. CSE enhancements after
tRNS ha ve been previously shown only in idling subjects.
In idling subjects, reliable CSE increases lasting 60 min are
possible ( Terney et al., 2008 ). Additionally, with regards to
tRNS parameters, high frequency tRNS (100–640 Hz) ( Terney
et al., 2008 ) at high current intensities (1 mA) ( Moliadze
et al., 2012 ) with a duration of at least 5 min ( Chaieb
et al., 2011 ) was also shown to reliably increase CSE. In
contrast, online tRNS was previously reported to impede
CSE enhancements: CSE was found to be slightly attenuated
for a cognitive task and strongly attenuated for a motor
task ( Terney et al., 2008 ). Attenuation after the motor task
was suggested to be associated with task-induced fatigue
( Terney et al., 2008 ).
Results from this study suggest that CSE facilitation after
the FT task with online tRNS refle cts an enhancement of task-
dependent activation, i.e., additional motor activation in primed
neural populations. Simple tapping tasks are well-established as
prototype tasks to study motor training-induced neuroplasticity
in the primary motor cortex (for a review see, Ljubisa vlje vic,
2006 ; Bezzola et al., 2012 ). Maximal sequential movements of
the FDI ensure a maximum task-related activation of its cortical
representation in M1, minimizing a confounding influence from
other brain areas ( Bezzola et al., 2012 ). Motor activation is
independent of the physical tapping speed of subjects, since the
amount of neural effort determines maximal neurophysiological
activation ( Lutz et al., 2005 ). Motor training leads to larger
muscle representations, specific to t he muscles involved in the
task, and increased CSE ( Pascual-Leone et al., 1994 ; Muellbacher
et al., 2001 ; Koeneke et al., 2006 ).
The obser vation of further enhancement of task-dependent
activation with tRNS fits well in line with the current
understanding of tRNS ’ proposed mechanism of action: increase
of CSE via transmission of subthreshold neural signals – a
phenomenon known as stochastic resonance ( Terney et al.,
2008 ). Stochastic resonance, i.e., the mechanism by whic h an
optimal noise condition improves signal detection in non-linear
systems, has been known in the physics community since at least
the early 1980s and has been universally obser ved in various
neural systems including the human brain ( Moss et al., 2004 ;
Schmidt et al., 2013b ).
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Unchanged CSE levels after sham stimulation suggest
that the tapping training duration was not sufficiently
long to increase functional recruitment in the absence
of tRNS. Motor training studies typically last 30–60 min
( Classen et al., 1998 ; Muellbacher et al., 2001 ; Koeneke
et al., 2006 ). These studies highlight the crucial addition
of online tRNS in our study to dramatically reduce the
required time for motor training-induced neuroplasticity in the
primary motor cortex.
Motor Fatigue and Motor Lear ning
The FT task is a simple motor training task involving motor
fatigue and motor learning indexed by a change in ITIs. It has
been utilized as a clinical tool to characterize motor deficits in
P arkinson’ s disease, cerebellar dysfunction, stroke and as a result
of aging ( Shimoyama et al., 1990 ; Arias et al., 2012 ).
In the present study, a linear increase in me an ITIs and
finger taps during ele ctrical- and sham stimulation represents
task-induced motor fatigue ( Rönnefarth et al., 2018 ). Fatigue
inevitably occurs within seconds of task initiation ( Shimoyama
et al., 1990 ; Aoki et al., 2003 ; Rönnefarth et al., 2018 ). It involves
not only peripheral, but also central mechanisms (central motor
fatigue) as evidenced by reduced CSE after a fatiguing task
( Kluger et al., 2012 ). Therefore, fatigue is a potential confounder
in brain stimulation studies aiming to enhance CSE levels and
likely explains CSE disruptions previously obser ved after online
tRNS in the motor system ( Antal et al., 2007 ; Terney et al., 2008 ).
Several measures were taken in our study to tune the FT task to
reduce the influence of fatigue (see section “Finger-T apping T ask
(FT T ask)). These measures were effective in preventing fatigue
outlasting the stimulation condition, since post-stimulation ITIs
and finger taps were equal to or lower than baseline le vels and
CSE inhibition, typically seen after ex cessive fatigue, was a bsent.
Reduced ITIs and increased number of finger taps compared to
baseline in block 3 (right hand), at the beginning of tRNS were
not significant compared to sham stimulation and likely represent
a rebound effect after a prior pause of 120 s. This might also
explain the analogous phenomenon in block 3 of the left hand,
at the beginning of sham stimulation.
The significant ITI enhancements and increased number of
finger taps after tRNS and sham stimulation (between blocks 9–
12) show that the utilized FT task was efficient in inducing motor
learning. These unspecific effects on motor le arning gain special
significance when interpreted with corresponding CSE results:
although the FT t ask improvements in the right hand were also
obser ved in the sham condition, facilitation of CSE occurred
only after tRNS. This implies that electrical stimulation might
be associated with an enhanced potential for learning ( Koeneke
et al., 2006 ). Motor learning is known to occur as a result of
motor training (for a review see, L jubisavljevic, 2006 ), and to be
closely associated with CSE facilitation and ITI improvements
in simple tapping tasks ( Koeneke et al., 2006 ). Further studies
also emphasize the robust relation between motor learning and
excitability enhancements, e.g., CSE levels return to baseline
once subjects overlearn a task ( Muellbacher et al., 2001 ) and
improvement retention is disrupted when CSE is specifically
suppressed over M1 ( Muellbacher et al., 2002 ).
The robust beha vioral improvements in the FT t ask a fter
stimulation could not be differentiated (i.e., tRNS, sham),
possibly due to a ceiling effect. In the young, he althy
participants of this study, underlying motor learning processes
are likely to be already optimized. Additionally, maximum
task-related activation of M1 is thought to leave no room
for further performance gains, especially in early st ages of
motor learning ( Bezzola et al., 2012 ). Other me asures of
FT task performance, e.g., force and tapping duration might
expose tRNS-specific beha vioral gains with higher sensitivity
( Muellbacher et al., 2001 ; Rönnefarth et al., 2018 ). Providing
evidence for neuromodulation of motor learning would be
particularly relevant in the context of novel inter ventions
following brain injury ( P ascual-Leone et al., 2005 ).
Motor Inhibition
Unlike the simple motor training task, random noise stimulation
in the inhibitory control task (GNG task) left CSE unchanged
in both the tRNS and sham conditions, suggestive of an
underlying inhibitory task-dependent brain state counteracting
the facilitatory tRNS effects reported in idle subjects ( Terney
et al., 2008 ). We hypothesized a decrease in CSE after GNG
and tRNS, reflecting enhanced motor inhibition. Methodological
limitations and task complexity might have contributed to the
absence of a clearer MEP decrease:
Firstly, CSE measurements after tRNS were not obtained on
a trial-by-trial basis during GNG task execution and do not
trace the time course of transient inhibitory state fluctuations
per trial. The GNG task is a hallmark for motor inhibition
encompassing periods of response preparation and response
inhibition reflected by changes in CSE, for a review see
Greenhouse et al. (2015) and Bestmann and Duque (2016) .
As sub je cts enga ge in the task and prepare to respond, motor
inhibition, characterized by reduced MEP s, prevents a premature
response ( Greenhouse et al., 2015 ). The warning cue further
enhances inhibitory processes ( Boulinguez et al., 2009 ; Criaud
et al., 2012 ) and the specificity of suppression to the muscles
involved in the task ( Greenhouse et al., 2012 ). If a “no-go” target
cue appears, response inhibition acts as an active breaking process
leading to global suppression of motor cortical activity with
concurrent MEP suppression ( Stinear et al., 2009 ; Greenhouse
et al., 2012 ; Ma cDonald et al., 2014 ; Bestmann and Duque,
2016 ). Since CSE was investigated with single pulse n TMS
after task execution, any potential transient enhancement of
motor inhibition during the GNG t ask would not be detected
in our paradigm.
Secondly, inhibition is interrupted by “go” cues requiring
motor activation with concurrent brief facilitation of CSE
( Stinear et al., 2009 ; MacDonald et al., 2014 ). These short but
frequent motor responses might ha ve contributed to the absence
of a clear MEP suppression. Yet, rare “no-go” trials ( < 20%)
are required to ensure sufficient inhibition-related activity and
a 9% “no-go” probability has been shown to induce suc h
activity ( Schoffelen et al., 2005 ; Wessel, 2018 ). As becomes
apparent, the inhibitory state associated with the GNG task
is comparably more complex than the FT task. It includes
the subcomponents response preparation, response inhibition,
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response activation and poses the methodological challenge
of tracking these dynamically overlapping state changes with
sufficient temporal resolution.
Inhibitory Contr ol
Considering limitations arising from using single pulse n TMS
to measure CSE after task completion, R T and task accuracy
data acquired online, during the GNG task, ser ve as an easily
assessable , more adequate parameter. R T and task accuracy are
beha viorally rele vant and trace dynamic state changes with a
higher temporal resolution. R T s were significantly slowed in
the tRNS condition, especially after electrical stimulation, while
task accuracy was enhanced. Slowing of R T s in “go” trials is
commonly used as a surrogate parameter for motor inhibition
and is positively correlated to task accuracy ( Bezdjian et al., 2009 ;
Leotti and Wager, 2010 ). Response slowing is associated with
suppression of MEP s, very similar to mechanisms involved in
response inhibition ( J ahfari et al., 2010 ).
The speed-accuracy trade-off is modulated by intraindividual
inhibitory control: patients with impulse control disorders such
as attention deficit and hyperactivity disorder (ADHD) and
in patients who stutter , the speed-accuracy trade-off is shifted
toward deficient inhibitory control with faster R T s and lower t ask
accuracy ( Bezdjian et al., 2009 ; E ggers et al., 2013 ). In turn, longer
R T s and better task accuracy as signs of enhanced inhibitory
control are achieved in patients with ADHD by pharmacological
agents such as Moda finil ( Turner et al., 2004 ). This phenomenon
can likewise be obser ved in healthy subjects depending on gender
(enhanced in female) and motivation ( Bezdjian et al., 2009 ; Leotti
and Wager, 2010 ). Consequently, we propose slowed R T s and
enhanced task accuracy during and after tRNS to result from
strengthened motor inhibition and inhibitory control outlasting
stimulation. Our data suggests t hat tRNS impede s movement
initiation by stabilizing the existing task-dependent brain state
and delaying response initiation ( Schmidt et al., 2013b ). Future
tRNS studies could try to modulate and optimize the speed-
accuracy-tradeoff via task difficulty and in patients with deficient
inhibitory control.
CONCLUSION
We provide evidence that tRNS-induced neuromodulation in
the motor system is dependent on the task during stimulation
such that CSE is enhanced in a FT task and inhibitory control
is improved in a GNG task. Results confirm our hypothesis that
transcranially applied random noise stimulation enhances the
endogenous task-dependent brain state of he althy sub jects. To
our knowledge, we are the first to show CSE facilitation after
online tRNS during a FT task. We argue in favor of online
tRNS to a void contradictory results and expose task specific
regulatory processes to be modulated by transcranial stimulation
techniques. Further confirmation of tRNS ’ mechanism of action
is required to limit variability as a result of task dependency and
to potentiate its neuroplastic effects in health and dise ase.
DA T A A V AILABILITY
The datasets generated for this study are availa ble on request to
the corresponding author.
AUTHOR CONTRIBUTIONS
SS, MS, and SB conceived the principle idea of the work. AJ ,
SS, MS, SB , LH, AK, MR , and RF designed the experiments.
MS developed the software for experimental procedures
and electrical stimulation. AJ , LH, and AK performed
the measurements. AJ , SS, LH, LK, and RB-P conducted
computational and statistical analyses of the d ata. All authors
participated in the interpretation of the d ata. The manuscript was
drafted by A J and critically re vised and approved by all authors.
FUNDING
This work was supported by the German Research Foundation,
DFG grant BR 1691/8-1 and OB 102/22-1.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relations hips that could
be construed as a potential conflict of interest.
Copyright © 2019 Jooss, H a berbosch, Köhn, Rönne farth, Bat he-Peters, Kozarzewsk i,
Fleischmann, Sc holz, Schmid t and Brandt. Th is is an open-access ar t icle distributed
under the terms of the Cre a tive Commons A ttribution License (CC BY). The use,
distribution or reproduct ion in other forums is permitted, provided the original
author(s) and the copyrigh t owner(s) are credited and tha t the orig inal public at ion
in th is journal is cited, in accordance with accepted ac ademic pract ice. No use,
distribution or reproduct ion is permitted whic h does not comply with these terms.
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