Accepted Manuscript

Effects of different electrical brain stimulation protocols on subcomponents of motorskill learning

George Prichard, Cornelius Weiller, Brita Fritsch, Janine Reis

PII: S1935-861X(14)00164-8

DOI: 10.1016/j.brs.2014.04.005

Reference: BRS 540

To appear in: Brain Stimulation

Received Date: 2 January 2014

Revised Date: 9 April 2014

Accepted Date: 11 April 2014

Please cite this article as: Prichard G, Weiller C, Fritsch B, Reis J, Effects of different electrical brainstimulation protocols on subcomponents of motor skill learning, Brain Stimulation (2014), doi: 10.1016/j.brs.2014.04.005.

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Original Article

Effects of different electrical brain stimulation protocols on

subcomponents of motor skill learning

George Prichard1, 2, 3, Cornelius Weiller1, Brita Fritsch1*, Janine Reis1*

1Department of Neurology, Albert-Ludwigs-University Freiburg, Germany.

2Faculty of Behavioral and Social Sciences, University of Groningen, The Netherlands.

3 Institute of Cognitive Neuroscience, University College London, United Kingdom.

* These authors contributed equally

Corresponding Author: Janine Reis, janine.reis@uniklinik-freiburg.de, Dept of Neurology, University Hospital Freiburg, Breisacher Straße 64, 79106 Freiburg, Germany phone: +49 (0)761 270 50010 Fax: +49 (0)761 270 53900

Running Title: tDCS, tRNS and motor skill learning

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Abstract

Background: Noninvasive electrical brain stimulation (NEBS) with

transcranial direct current (tDCS) or random noise stimulation (tRNS)

applied to the primary motor cortex (M1) can augment motor learning.

Objective: We tested whether different types of stimulation alter particular

aspects of learning a tracing task over three consecutive days, namely skill

acquisition (online/within session effects) or consolidation (offline/between

session effects).

Methods: Motor training on a tracing task over three consecutive days was

combined with different types and montages of stimulation (tDCS, tRNS).

Results: Unilateral M1 stimulation using tRNS as well as unilateral and

bilateral M1 tDCS all enhanced motor skill learning compared to sham

stimulation. In all groups, this appeared to be driven by online effects

without an additional offline effect. Unilateral tDCS resulted in large skill

gains immediately following the onset of stimulation, while tRNS exerted

more gradual effects. Control stimulation of the right temporal lobe did not

enhance skill learning relative to sham.

Conclusions: The mechanisms of action of tDCS and tRNS are likely

different. Hence, the time course of skill improvement within sessions could

point to specific and temporally distinct interactions with the physiological

process of motor skill learning. Exploring the parameters of NEBS on

different tasks and in patients with brain injury will allow us to maximise the

benefits of NEBS for neurorehabilitation.

Keywords: tDCS, tRNS, brain stimulation

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Introduction

A substantial portion of our lives is spent learning new motor skills: from

walking to writing, driving and sports. Motor skills are the primary

mechanism for interaction with the world around us; hence, defective motor

skills resulting from neurological diseases are a severe impairment.

Noninvasive electrical brain stimulation (NEBS) applied transcranially to the

motor cortex (M1) has been shown to improve motor skill learning in

healthy individuals [1,2] and in chronic stroke patients [3–5]. Transcranial

direct current stimulation (tDCS) with an anode over M1 and a cathode over

the contralateral supraorbital area, in combination with motor training

resulted in greater skill gains compared to sham in healthy subjects. Using

tasks of different complexity, experiments have shown both within session

(online) improvements in a single-day [6,7], as well as between session

(offline) improvements observed with multi-session training [8,9]. In an

attempt to maximize stimulation benefits, recent studies utilized a bilateral

M1 montage, with an anode over the M1 contralateral and a cathode

ispilateral to the training hand. The basic idea of this approach is the

modulation of interhemispheric inhibition [10–12], that is strengthening the

facilitatory effect on one M1 with anodal tDCS, while reducing the inhibitory

influence of the other M1 by cathodal tDCS [12,13].

While tDCS uses a direct current flowing in one direction necessitating an

anode and cathode with potentially different local effects, transcranial

random noise stimulation (tRNS) uses an alternating current with a

randomly changing frequency and current direction, removing

anode/cathode-specific effects. High frequency tRNS (100-640Hz) applied

to M1 has also been shown to facilitate implicit motor sequence learning

[14]. Both anodal tDCS and tRNS enhance M1 excitability [14–16],

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although it is likely there are differences in the mechanism of action of a

constant current versus changing currents applied to the cortex [17,18]. It

has been suggested that the concept of stochastic resonance may apply to

all forms of NEBS: stimulation-induced noise introduced to a neuronal

system may provide a signal processing benefit in the brain by altering the

signal-to-noise ratio [19,20]. While synchronization with task-relevant

activity may play a particular role for tRNS, additional homeostatic

mechanisms induced by a constant noise input may apply for tDCS [19,21].

Despite the huge amount of separate investigations of tDCS and tRNS

effects assessed in a single session, there are only two direct comparisons

between these stimulation types: For visuomotor learning, neither tRNS nor

anodal tDCS applied to M1 combined with a brief single training session

improved learning relative to sham stimulation [22]. On an orientation

discrimination task Pirulli et al. [23] found the effect of stimulation type

applied to the visual cortex varied depending on the timing of stimulation,

with tRNS more effective if applied during practice, whereas tDCS induced

better discrimination when applied before practice. However, these results

directly contrast with results from the motor learning domain, where anodal

tDCS applied to M1 before learning a serial reaction time task was found to

inhibit or leave unaffected subsequent learning [7,24], showing NEBS

effects are current type, site and task specific.

Given that these studies probed aspects of learning in a single session and

one was not directly related to motor learning, it is currently unknown

whether tDCS and tRNS would exert distinct effects onto specific

subcomponents of motor skill learning, i.e. within session (online)

improvements or between session (offline) effects, only assessable when

training for more than one session. Disentangling how different forms of

NEBS interact with the stages of the learning process is of great value both

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for understanding the mechanisms of motor skill learning as well as to

maximize clinical benefits of NEBS. Here, we directly contrast the effects of

tDCS and tRNS on repeated motor learning sessions in an exploratory

study to test the efficacy of these different stimulation types.

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Methods and Materials

This study was in accordance with the Declaration of Helsinki amended by

the 59th WMA General Assembly, Seoul, October 2008 and was approved

by the local Ethics Committee of the University of Freiburg.

Participants

Subjects were invited for participation by bulletin board announcements at

the university as well as by word-of-mouth and social media. 91 healthy,

adult participants (39 males, mean age = 25.7, SD = 4.6 years) were

recruited to the experiment and given a small monetary reimbursement. All

participants gave written informed consent, and met safety criteria for TMS

and tDCS [25]. All were right handed as assessed by the Edinburgh

Handedness Inventory (mean score = 93.4, SD = 10.7). Inclusion required

a normal neurological or psychiatric medical history. Subjects were also

screened for symptoms of depression using the Beck Depression Inventory

([26], score > 12 leading to exclusion). In addition, all subjects were

screened for the brain-derived neurotrophic factor (BDNF) val66met

polymorphism known to affect motor skill learning [27] and the distribution

of genotypes (val66val, Met carriers) within each stimulation group was

monitored to avoid a confounding effect in a particular stimulation group.

Subjects were not excluded due to their genotype.

The Tracing Task

We created a complex, continuous word/shape tracing task with the

Psychophysics Toolbox package [28,29] for Mathworks MATLAB, on the

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basis of established hand tracing tasks [30–32]. Participants were required

to trace over a series of words and shapes on a Wacom Bamboo Fun

graphics tablet with a stylus held in the non-dominant (left) hand. The non-

dominant hand was selected in order to increase task difficulty for a steeper

learning curve. The template letters were presented separately on a

monitor (Figure 1A and 1B). Participants could move the cursor to a ready

position by moving the stylus without touching the tablet; touching initiated

the trial and started a time bar showing how long was left for the trial. Each

trial allowed 2 seconds per real letter or shape letter (e.g. 10 seconds for a

5 letter word). This timing was selected from speed accuracy trade-off data

from a behavioural pilot, finding 2 seconds per letter difficult but not

frustrating. Instructions were to trace as accurately as possible over the

template using all of the given time and lift the stylus on finishing a trial,

allowing us to measure the time taken per trial.

Template words consisted of the most common 3-5 letter German words

selected from a free database (compiled from subtitles by Invoke IT

Limited, http://invokeit.wordpress.com/frequency-word-lists/) which were

screened to remove emotionally salient words. Words were printed in a

freely available cursive font (League Script,

www.theleagueofmoveabletype.com). For template shapes, an alphabet of

random shapes was drawn in Inkscape (http://www.inkscape.org/), where

each shape corresponded to each letter of the alphabet. Using this, each

real word was converted into a ‘shape word’, where each real letter was

replaced by a made-up shape. For instance, the word ‘der’ (Figure 1A) was

turned into a shape-word with shape-letters corresponding to each real

letter (Figure 1B). A single trial consisted of tracing over one template (one

real word or one shape-word).

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- Figure 1 near here -

Scoring Method

To measure participants’ performance, we devised a scoring method which

allowed for intuitive feedback and analysis. In brief, the final score can be

interpreted as 'percentage correct': a participant’s trace which perfectly

matches the template receives a score of 100; any deviation from this

(drawing off the template lines) reduces the score. Drawing very little or

consistently far away from the target lines results in a score at or

approaching zero.

In order to calculate these scores, both the trace and template (target) data

was converted into an image (Figure 1 C and D). The sum of the

differences between the two images was used as scoring method (Figure

1E). In order to introduce a margin of error, both images were blurred with a

Gaussian kernel (size: 50x50 pixels; standard deviation: 12 pixels; Figure 1

C and D) - this allows minor deviations, and makes the score worse the

further the trace deviates from the target, with a cut-off if deviations are too

far. At this point, a perfect trace gives 0 and deviations are arbitrarily high.

Therefore, we set the sum of the pixels in the template image as a

threshold upper score (e.g. writing nothing is the baseline for worst score).

The score was thresholded at this number, then divided by it and

subtracted from 1. This gives a fraction (with 0 as the worst score and 1 as

the best), which we turned into a percentage.

Study design

The study design is shown in Figure 2. We chose a parallel study design.

Subject allocation to one of the four stimulation conditions followed a fully

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balanced randomization list prepared prior to the experiment. The electrode

montage was known to both participant and experimenter; however both

participants and the experimenter were blinded to the type (tRNS or tDCS)

and verity of stimulation (sham or not sham) by stimulation codes entered

into the electrical stimulator. All participants were naïve to the task and all

practiced the same version of the task, except for the fact that the order of

words/shape words within each training block was automatically pseudo-

randomized.

- Figure 2 near here -

Participants practiced the tracing task over 3 consecutive days at the same

time of day (+/- 1 hour). After locating the motor cortex using TMS on day 1,

the position was marked with a skin safe marker for use on day 2. NEBS

electrodes were secured in the appropriate location using bandages. On

day 1 and 2 participants completed 12 blocks of 15 trials (180 trials total) of

the tracing task, with an enforced 15s break between each block where

they were shown their average score over the last block for motivational

purposes. Stimulation was switched on after completion of block 1. Training

usually lasted longer than 20 minutes (35-40 minutes); thus, training

continued after the offset of stimulation. This approach was chosen to

mimic previous study designs [7–9] for comparability, as well as for safety

reasons (no tRNS studies are published to our knowledge with stimulation

durations above 20 minutes). On day 3, participants completed 8 blocks

without stimulation to test for any delayed carry-over effects of stimulation.

Motor cortex localisation

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For all participants transcranial magnetic stimulation (TMS) using a

Magstim 200 and a custom-made figure-of-8-shaped coil (outer diameter

7cm, all Magstim, UK) was used to locate the left and right motor cortex for

NEBS electrode placement (including participants in the control group). The

“hot spot” was defined as the best location to consistently elicit motor

evoked potentials (MEPs) in the contralateral first dorsal interosseus

muscle.

Noninvasive Electrical Brain Stimulation

All stimulations except sham consisted of 20 minutes of stimulation with 15

seconds at the beginning and end slowly ramping up or down the current to

avoid discomfort or phosphenes. In the sham group, current was ramped

up then down over 30 seconds and current type (tRNS or tDCS) was

randomly selected. All electrical stimulation was applied using a neuroConn

DC-MR stimulator (Ilmenau, Germany). For tDCS 1mA of direct current

(assumed current density 0.0625 mA/cm2) was applied using carbon

electrodes covered by a square 16cm2 sponge. High frequency tRNS was

applied as in [14]. Random current levels were generated for each sample

(sampling rate 1280Hz, maximum frequency 640Hz), and a digital high-

pass filter was applied at 100Hz, resulting in a maximal current density of

0.0625 mA/cm2.

Electrode positions are shown in Figure 2 (unilateral M1 stimulation for both

tDCS and tRNS, bilateral M1 stimulation for tDCS). A non-M1 tRNS

stimulation group was included to address regional specificity of tRNS on

motor skill learning. In this group the right electrode was placed on the

position T6 of the 10-20 EEG system (tRNS:T6-SO). The temporal-parietal-

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occipital junction (T6) was selected as an area where current flow would

not directly affect motor areas [33].

Psychophysical assessment

Before stimulation on day 1 and after finishing the experiment on day 3

subjects completed the positive and negative affect scale (PANAS, [34]) in

order to detect any effects of stimulation on mood. Before and after each

session on every day, participants rated their mental fitness on a visual-

analogue scale (VAS) from 1-10, where 1 was defined as ‘perfect mental

fitness’ and 10 was ‘extremely mentally unfit’. Each day we recorded

potential side effects and sleep duration the previous night. At the end of

day 3 participants were asked to guess whether they received real or sham

stimulation in order to check whether the blinding procedure was effective.

Analysis of Subcomponents of Motor Skill Learning

Subcomponents of motor skill learning were assessed by taking score

differences at various time points. This allowed us to break overall learning

down into online and offline effects, with immediate effect as a separate

measure (see Figure 2).

Overall learning was defined as the score difference on the first block of

day 3. Online (within day) effects were defined as the sum of score

differences between the first and last block of days 1 and 2 (Day1Block12-

Day1Block1) + (Day2Block12-Day2Block1). Offline (between day) effects

were defined as the sum of the differences between the last and first blocks

of consecutive days (Day2Block1-Day1Block12) + (Day3Block1-

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Day2Block12). Immediate effects – initial changes after stimulation – were

the sum of the differences between the first two blocks on stimulation days

(Day1Block2-Day1Block1) + (Day2Block2-Day2Block1). Overall effects

(used for graphs only) were scores from Day3Block1 – Day1Block1. Day 3

learning was defined as the last block of day 3 minus the first block

(Day3Block8-Day3Block1) and was used to see if stimulation caused any

non-transient changes in learning in the absence of continued stimulation.

Statistical analysis

To ensure that participants performed the task as instructed (keeping

movement speed at 2 seconds per letter), a repeated measures ANOVA

was performed on “movement speed” with factor GROUP (sham, tDCS:M1-

SO, tDCS:M1-M1 and tRNS:M1-SO) as the between-subject variable and

factor TIME as within-subject variable. This was needed to control for an

additional shift in the speed-accuracy trade-off, e.g. sacrificing accuracy for

higher speed.

An ANCOVA with accuracy skill score on Day3Block1 as the dependent

variable, factor group (sham, tDCS:M1-SO, tDCS:M1-M1 and tRNS:M1-

SO) as the independent variable and baseline skill score (Day1Block1) as a

covariate was performed to test for total (overall) learning. Separate

ANOVAs were performed for online, offline and immediate effects with the

score change as the dependent variable and factor group (sham, tDCS:M1-

SO, tDCS:M1-M1 and tRNS:M1-SO) as the independent variable. Another

ANCOVA tested total day one learning (Day1Block12 as dependent

variable, Day1Block1 as covariate) to allow for a direct comparison with

previous single-session NEBS studies; a further ANOVA tested day 3

learning for any lasting effects of stimulation on learning (Day3Block8-

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Day3Block1). Post hoc pairwise t-tests were used to discern where

differences lay. Since the project was set up as an exploratory experiment,

this would not necessarily require multiple test adjustment. However, to

control for a potential type I error without increasing the probability of

creating a type II error, we predefined two independent hypotheses for the

main experiment tested with Bonferroni-Holm adjustment for multiple

comparisons [35]. We hypothesized that 1) all 3 stimulation types show

greater overall learning than sham (3 pairwise comparisons) and 2) the 3

stimulation types differ with regard to overall learning and learning

subcomponents (3 pairwise comparisons). For the tRNS:T6-SO control

experiment added a posteriori, an ANCOVA with score on Day3Block1 as

the dependent variable, factor group (sham, tRNS:T6-SO) as the

independent variable and baseline skill score (Day1Block1) as a covariate

was calculated. Subcomponents of learning were not assessed.

ANOVAs for demographical data (Edinburgh Handedness score, age) were

performed with group (sham, tDCS:M1-SO, tDCS:M1-M1, tRNS:M1-SO

and tRNS:T6-SO) as a factor. Repeated measures ANOVAs with factor

group and time were used to assess changes in sleep, PANAS and VAS

scores. Chi-squared tests were used to test for binary differences between

each group on condition guess, gender and val66met polymorphism.

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Results

Demographics

91 healthy German-speaking participants took part in the experiment. One

participant was excluded from analysis (including demographics) due to an

error in stimulation parameters (tDCS:M1-SO group). No further

participants were excluded. All participants tolerated the stimulation well. 8

subjects out of 90 reported mild headache after stimulation on day 1 only;

this is likely to be caused by TMS rather than NEBS, as they did not

reoccur on day 2 (no TMS), and 2 of these 8 were in the sham group. 1

subject in the tRNS:M1-SO group reported a mild transient headache under

the SO electrode on day 2 which passed before stimulation ended.

No significant differences between groups were found for age or

handedness (Table 1).

There was a significant effect of time on negative PANAS (p<0.001) scores,

with participants showing lower scores (less depressed/negative) on both

measures at the end of the experiment. However, there was no significant

effect of group or group x time. Positive PANAS scores showed no

significant changes. Mental fitness VAS scores significantly decreased after

each session (p<0.001), however there was no significant difference

between groups. There were no significant effects of day, group or group x

time on sleep (Table 2).

Separate chi-square analyses showed no significant differences between

groups on BDNF val66met polymorphism, gender or condition guess (Table

1). Though not significantly different from other groups, participants who

received tRNS guessed they had been stimulated less often than subjects

receiving sham stimulation (Table 1).

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Adherence to task demands

Within the allowed movement speed range (max. 2 sec per letter),

participants slightly slowed down their movement speed (from 1.89 sec per

letter on day 1 to 1.92 s per letter on day 3; 30 ms change on average) over

the course of the experiment (rmANOVA: p <0.0001 for factor TIME).

However, there was no significant effect of GROUP (p = 0.761) and no

interaction (p = 0.419). In order to assess whether this needed to be

accounted for when calculating the subjects’ accuracy score, we estimated

the percentage of variance in scores that was accounted for by time taken

(normalised to number of letters per trial) to be 2% (R2 = 0.020). This low

explained variance, and lack of a significant difference between groups

indicates that improving scores over time was not just due to participants

slowing down and/or moving along their initial speed-accuracy trade-off, so,

we did not apply any corrections for movement speed.

Motor Cortex Stimulation

Overall Learning: The ANCOVA on day 3 block 1 scores showed a

significant effect of group [F(3, 68) = 3.097, p = 0.033] with an effect size

(eta2) of 0.12 and an observed power of 70% (Figure 3). Post-hoc tests

(hypothesis 1) showed this was due to significantly higher scores achieved

in tRNS:M1-SO (p=0.024), tDCS:M1-M1 (p = 0.030), and tDCS:M1-SO (p

= 0.047) compared to sham. No significant differences in skill at d3Block1

were observed between the 3 different motoric stimulation groups

(hypothesis 2, all p-values between 0.8 and 1).

- Figure 3 near here –

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Subcomponents of motor skill learning: In order to provide a direct

comparison with the majority of previous motor learning studies, an

ANCOVA was performed on day 1 online effects (block 12) with each

motoric stimulation group as a factor and day 1 block 1 as a covariate. This

was not significant [F(3,68) = 1.666, p = 0.183].

An ANOVA on online effects was not significant [F(3,68) = 1.068, p =

0.386], though Figure 3 suggests a trend; nor was there a significant

difference between stimulation groups for offline effects [F(3,68) = 0.164, p

= 0.920]. There was a significant effect of stimulation group on immediate

effects [F(3,68) = 4.054, p = 0.010], this showed a particularly large effect

size (eta2 = 0.152) and an observed power of 82%. Post-hoc t-tests

(hypothesis 1) revealed this was due to an increased immediate score for

tDCS:M1-SO compared to sham (p = 0.002). There was also a significant

difference between tDCS:M1-SO and tRNS:M1-SO (p= 0.013; Figure 3). An

ANOVA on day 3 learning (last block of day 3 minus first block of day 3)

was not significant [F(3,68) = 0.454, p = 0.715]; it does not appear that

electrical stimulation has online effects on days subsequent to stimulation.

Control experiment

Since all M1 stimulation types yielded a significant effect on overall motor

skill learning compared to sham and the montages utilized typically affect a

relatively wide cortical and subcortical network [36], we were concerned

that effects of NEBS may be regionally unspecific. We added a tRNS

stimulation group not targeting the motor cortex (tRNS:T6-SO) in order to

establish whether stimulation excluding M1 would also enhance overall skill

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learning. An ANCOVA on skill achieved by the beginning of day 3 found no

significant difference between sham and tRNS:T6-SO stimulation [F(1,33) =

2.226, p = 0.15] (Figure 4).

- Figure 4 near here -

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oDiscussion

Motor cortex electrical stimulation accelerates prolonged motor skill

learning with different tDCS montages as well as with tRNS. Enhancement

of overall learning is mainly driven by online (within session) improvements.

No significant differences are found between tDCS and tRNS. Non-M1

stimulation does not significantly enhance motor skill learning, confirming

the key role of the primary motor cortex in motor skill learning and region

specific effects of stimulation.

The Tracing Task

Previous NEBS studies used the implicit serial reaction time task

[6,14], or an explicit reaction time task [7], recording reaction time while

learning a sequence. In contrast, the tracing task requires complex,

synergistic and continuous movements of multiple hand and arm muscles,

as well as hand-eye coordination. It is similar to a visuomotor task we

introduced earlier - the sequential visual isometric pinch task (SVIPT, [8]):

for both tasks skill depends on both speed and movement accuracy. With

practicing the tracing task subjects improved their accuracy at one fixed

(predetermined) movement speed in the task’s speed-accuracy trade-off.

In contrast to the SVIPT, in which both factors were freely changeable by

the subject, improving accuracy at the cost of significantly slower speeds

(seconds) was not possible for the tracing task. As depicted by the learning

curve of sham stimulated subjects (Figure 3), skill on the tracing task is

continuously increased within training sessions (online), followed by a small

loss in skill between training days (offline). The small loss of skill between

sessions may represent a small warm-up decrement [31,37,38], since

subjects do not return to naïve levels of performance and the level of the

previous day is reinstated within the first 15 -30 trials of training.

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Stimulation parameters

All types of M1 stimulation significantly increased motor skill

learning compared to sham, visible in Figure 3. This is the first

demonstration of improved motor skill learning over multiple days by

unilateral M1-tRNS and bilateral M1-tDCS. Surprisingly, from our data there

is no strong evidence for one type of active M1 stimulation being more

effective for enhancing overall motor skill learning than any other.

Subcomponents of Skill Learning

With regard to specific subcomponents of multi session motor skill

learning this is the first study directly comparing the effects of different

stimulation types. We observed a tendency (visible in Figure 3, upper) for

improved day 1 online effects for all these stimulations, which is in line with

previous single session studies using unilateral M1-tDCS [6,7,39], tRNS

[14] or bilateral M1-tDCS [40] on reaction time tasks. Although not

statistically significant, all types of M1 stimulation appeared to increase

online (within session) rather than offline (between session) effects. This

result is different from our previous findings when using the SVIPT [8,9]:

tDCS (as tDCS:M1-SO) enhanced motor skill learning over five days due to

offline/consolidation effects, which was a cumulative effect over several

days. There are several explanations due to task differences for this

discrepancy. First, skill on the tracing task may be more difficult to acquire

as there are multiple possible word/shape templates to trace, each

requiring the coordination of multiple muscle groups in the hand and arm,

rather than a single sequence executed with a small muscle group.

Second, subjects were potentially rewarded for improvements since a score

was presented after every training block [41]. Finally, the tracing task is

trained at a fixed high speed, whereas on the SVIPT both speed and

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accuracy can be improved. It is therefore conceivable that unilateral M1-

tDCS may have differential online or offline effects on particular aspects of

motor tasks, e.g. either accuracy or speed. Indeed, task-dependency of

tDCS effects has been described for similar tasks [42].

A large immediate effect of stimulation was present in the tDCS:M1-

SO group visible as skill increase directly after the onset of stimulation in

block 2 on day 1 and 2 (Figure 3). This was clearly different from the

tRNS:M1-SO group. To our knowledge, an effect this fast has not been

mentioned previously, though may be visible in previous papers [6,8]. So is

the immediate effect a rapid acceleration of learning, an instantaneous

performance boost, or both? The immediate boost for tDCS:M1-SO could

be due to tDCS interacting with frontal networks involved in executive

function/working-memory [43,44], which tRNS and bilateral M1 tDCS may

not effect in the same way, due to different current parameters and

electrical field distributions, respectively. In favour of DC and RN currents

having opposing effects on frontal networks, visuomotor task-related

activity in the left frontal cortex was decreased after high frequency tRNS,

but not after tDCS [22]. Interestingly, the acceleration of learning rapidly

declined, resulting in DC:M1-SO having online/overall effects comparable

to other stimulation groups.

Potential mechanisms

Our results suggest that either tDCS:M1-SO has mechanistically

different effects to other forms of stimulation (particularly tRNS), or it may

affect the same learning mechanism over a different time course. tDCS has

been shown to rapidly affect MEPs after only a few minutes [15]; this

appears to be slightly more delayed for tRNS [45], though the two

stimulations have not been directly compared with regard to the

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electrophysiological effects. Intuitively, one would assume that a constant

polarized current exerts different effects on the cellular and molecular level

than a frequently changing current. It has been suggested that the

polarization of neurons leading to subthreshold shifts in membrane

potential by tDCS may subsequently change neuronal firing rates and alter

spike timing which would then catalyse synaptic transmission and

behavioural effects [21,27,46,47]. Indeed, on a synaptic level, anodal tDCS

induces long-term potentiation when combined with a second input [27], a

mechanism that may be mimicked by training in the presence of anodal

tDCS in humans. Accordingly, behavioural improvements often cannot be

detected without training [27,48], suggesting an activity-dependent process

that requires synaptic co-activation. Terney et al. suggest tRNS acts by

repeatedly opening sodium channels causing membrane depolarisation

[14] as has been described for weak changing currents applied to neurons

in vitro [49,50]. To our knowledge, however, there is no direct research on

the cellular/molecular effects of tRNS. Hence, itis currently unknown what

neural mechanisms differentiate tDCS and tRNS. In comparison to tDCS, in

the conceptual framework of stochastic resonance, tRNS (i.e., externally

applied random noise) may have a higher likelihood to optimize task-

specific brain activity (endogenous noise) due to amplification of weak

signals [19,21]. Hence, it should be kept in mind that excitability alteration

may not be the only factor positively interacting with the physiological

process of motor skill learning.

Control Group

The non-M1 control group (tRNS:T6-SO) in which stimulation was

active though not over M1 showed no improvement in overall learning

compared to sham (Figure 4). T6 was chosen in order to minimise current

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flow through motor areas, as used by Antal et al. [33], tRNS was chosen

because of better blinding of participants. Although the current flow or effect

of tRNS may be non-focal, spreading over more than the area directly

under the electrodes, the lack of effect of T6 stimulation supports the view

that stimulation effects are region specific.

Limitations

It should be noted that due to the size of the electrodes used and

the placement of the return electrode we cannot with complete certainty

attribute these learning improvements solely to M1; it is also likely that

motor areas adjacent to M1 or prefrontal areas are affected by stimulation,

contributing to our results. More focal brain stimulation techniques may help

in ensuring learning gains can be related to specific brain areas, for

instance high-definition tDCS [51] or repetitive TMS [52]. Moreover, the

nondominant (left) hand was used for training and the right M1 stimulated in

our study. Results may vary when training the dominant (right) hand, given

a predominant left-hemispheric representation for movements and motor

memories and potentially greater responsiveness of the left M1 to NEBS

[53,54].

Conclusion

NEBS applied to M1 enhances motor skill learning over multiple

sessions; on the tracing task this appears to be due to strengthening of skill

acquisition within session when training the nondominant hand. Strikingly,

the direct comparison of different stimulation protocols revealed no clear

advantage of a particular stimulation type or montage with regard to overall

learning. Hence, as long as M1 is targeted, current type and electrical field

distribution within the brain seem to have a subordinate role with regard to

the behavioural outcome. This information may be important for the design

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of future clinically-oriented NEBS trials. Improvement of motor skill learning

induced by NEBS may also be effective for various other activities requiring

fine motor skills. Hence, the combination of NEBS and task-specific motor

training could be a promising approach to enhance general motor function

in patients with motor deficits, e.g. after stroke.

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Acknowledgements

Thanks to Mark Nieuwenstein for comments on an earlier version of the

manuscript and to Michel Rijntjes for helpful comments on the task design.

Financial Disclosures

All authors of this manuscript report no biomedical financial interests or

potential conflicts of interest.

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Tables

Demographics 1

Table 1: Demographics for group size, age, Edinburgh Handedness Inventory, gender and BDNFval66met carriers. ANOVA scores for age, Edinburgh Handedness Inventory. ± shows standard deviation. Gender shows percentage of males per group, met carriers shows percentage per group with val66met polymorphism.

n Age ± Edinburgh

± Males (%)

Met carriers

(%)

Condition Guess

Sham 18 25.7 5.5 92.5 14.0 38.9 38.8 38.9

tDCS:M1-SO

18 24.7 3.1 90.6 9.4 50 44.4 55.6

tDCS:M1-M1

18 26.5 6.3 96.9 7.5 38.9 33.3 47.1

tRNS:M1-SO

18 26.0 3.4 93.1 11.3 44.4 22.2 33.3

tRNS:T6-SO 18 25.6 4.5 93.9 10.4 44.4 38.8 16.7

p 0.832 0.494 0.959 0.160

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Demographics 2

Table 2: Values for PANAS, VAS and sleep scores at each collection point, as well as results of repeated measures ANOVAs. VAS: visual-analogue scale for

ratings on perceived mental fitness ranging from 1(perfect mental fitness) to 10 (extremely mentally unfit.)1

PANAS pre +

PANAS pre -

VAS 1 pre

VAS 1 post

Sleep 1 VAS 2 pre

VAS 2 post

Sleep 2 VAS 3 pre

VAS 3 post

Sleep 3 PANAS post +

PANAS post -

Sham 32.8 15.8 3.4 4.1 7.4 3.3 4.0 7.3 3.1 3.6 7.1 31.7 14.4

DC:M1-SO

31.8 14.9 2.9 4.3 7.1 2.9 3.6 7.2 2.7 3.3 7.2 31.6 13.4

DC:M1-M1

33.6 14.9 3.2 3.8 7.2 3.2 3.6 6.9 3.3 3.7 7.0 34.3 13.7

RN:M1-SO

32.2 15.6 2.8 3.4 7.1 3.3 3.8 6.9 3.2 3.4 6.8 31.4 13.4

RN:T6-SO

33.0 15.4 3.6 3.6 6.5 3.5 3.7 6.8 3.8 3.7 6.8 33.3 13.2

p (time) <0.001 0.882 0.552 <0.001

p (time* group)

0.237 0.907 0.523 0.802

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Figure legends

Figure 1: The tracing task and scoring method. A and B: An example trial;

black lines show the template word (A) or shape (B). Red lines show the

trace. B shows the corresponding shape-word to the real word ‘der’. C: A

template shape after blurring (black lines in B). D: A traced shape after

blurring (red lines from B). E: The subtractive image (each pixel in C

subtracted from each pixel in D). Yellow shows pixels with a value of 0.

Blue and red show pixels which increase the raw score (decreasing the

intuitive score), where the template was not matched (blue) and the trace

deviated from the template (red).

Figure 2: Study design. Upper: Subjects practiced the tracing task for

three consecutive days. Electrode placements in each condition are shown

on the right side. Red = anode, green = cathode, blue = non-polarised

tRNS electrode. For sham stimulation electrode positions randomly varied

between the M1 stimulation conditions. . Adapted from Reis and Fritsch

(2011) with permission. Lower: training day and measurements of learning

subcomponents are shown. Day 1 and 2 consisted of 12 training blocks (15

words/shapes per block). Stimulation was started after block 1. Please refer

to methods for a description of learning subcomponents.

Figure 3: Upper: Improvement in skill score per stimulation group. From

stimulation onset the average score in each stimulation group is higher than

sham at every time point, showing the early and persistent effects of

stimulation. Data presented as mean, error bars represent S.E.M. Lower:

Separated components of motor learning. Scatter data points show

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individual difference scores (points), mean (central line) and standard error

of the mean (bars) for each group. Improvements due to stimulation are

visible for overall learning. Plot shows overall measure as described in

methods; p-values calculated on ANCOVA with correction for baseline

performance. A trend is visible for online effects, no clear changes for

offline effects. Significant improvement in immediate effect is shown for

tDCS:M1-SO compared to sham and tRNS:M1-SO. Level of significance:

*p<0.05, **p<0.01. Data presented as mean, error bars represent S.E.M..

Figure 4: Non-M1 control stimulation. Improvement in skill score over time

for sham and tRNS:T6-SO control group is shown. There is no significant

difference in overall learning (measured in the ANCOVA for D3Block1).

Data presented as mean, error bars represent S.E.M.

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