Anodal transcranial direct current stimulation ... - Wiley Online Library

European Journal of Neuroscience, Vol. 43, pp. 113–119, 2016

doi:10.1111/ejn.13122

COGNITIVE NEUROSCIENCE

Anodal transcranial direct current stimulation enhances the effects of motor imagery training in a finger tapping task Arnaud Saimpont,1,* Catherine Mercier,1,2 Francine Malouin,1,2 Aymeric Guillot,3,4 Christian Collet,3 Julien Doyon5 and Philip L. Jackson1,6,7 adaptation et Inte gration Sociale, Universite Laval, Que bec, Canada Centre Interdisciplinaire de Recherche en Re partement de Re adaptation, Universite Laval, Que bec, Canada De 3 Claude Bernard Lyon 1, Villeurbanne, France Centre de Recherche et d’Innovation sur le Sport, Universite 4 Institut Universitaire de France, Paris, France 5 Functional Neuroimaging Unit, CRUIGM, University of Montreal, Montreal, Canada 6 Laval, Que bec, Canada Ecole de Psychologie, Universite 7 Mentale de Que bec, Universite Laval, Que bec, Canada Centre de Recherche de l’Institut Universitaire en Sante 1 2

Keywords: human, mental practice, motor imagery, motor sequence learning, neuromodulation, primary motor cortex Edited by Gregor Thut Received 8 June 2015, revised 20 October 2015, accepted 30 October 2015

Abstract Motor imagery (MI) training and anodal transcranial direct current stimulation (tDCS) applied over the primary motor cortex can independently improve hand motor function. The main objective of this double-blind, sham-controlled study was to examine whether anodal tDCS over the primary motor cortex could enhance the effects of MI training on the learning of a finger tapping sequence. Thirty-six right-handed young human adults were assigned to one of three groups: (i) who performed MI training combined with anodal tDCS applied over the primary motor cortex; (ii) who performed MI training combined with sham tDCS; and (iii) who received tDCS while reading a book. The MI training consisted of mentally rehearsing an eight-item complex finger sequence for 13 min. Before (Pre-test), immediately after (Post-test 1), and at 90 min after (Post-test 2) MI training, the participants physically repeated the sequence as fast and as accurately as possible. An ANOVA showed that the number of sequences correctly performed significantly increased between Pre-test and Post-test 1 and remained stable at Post-test 2 in the three groups (P < 0.001). Furthermore, the percentage increase in performance between Pre-test and Post-test 1 and Post-test 2 was significantly greater in the group that performed MI training combined with anodal tDCS compared with the other two groups (P < 0.05). As a potential physiological explanation, the synaptic strength within the primary motor cortex could have been reinforced by the association of MI training and tDCS compared with MI training alone and tDCS alone.

Introduction Motor learning is an essential ability in everyday life. Remarkably, motor learning can occur not only after physical practice, but also after mental practice based on motor imagery (MI) (i.e. the mental simulation of a movement without its actual execution). The positive effects of this training method on motor performance have been reported in athletes (Driskell et al., 1994; Guillot et al., 2013) and people with physical disabilities (Dickstein & Deutsch, 2007; Malouin et al., 2013), but also in healthy volunteers (Pascual-Leone

de psychologie, as above. Correspondence: Philip L. Jackson, 6Ecole E-mail: [email protected] *Present address: Centre de Recherche et d’Innovation sur le Sport, Universite Claude Bernard Lyon 1, Villeurbanne, France

et al., 1995; Jackson et al., 2003; Gentili et al., 2006; Debarnot et al., 2011b; Saimpont et al., 2013). It has been shown that imagined and executed movements are subtended by partially overlapping neural networks, including the primary motor cortex (Porro et al., 1996; Solodkin et al., 2004; Pelgrims et al., 2011). Furthermore, it is noteworthy that similar brain plasticity occurs during motor learning by physical practice and by mental practice (PascualLeone et al., 1995; Jackson et al., 2003; Debarnot et al., 2011b). This would account for the positive impact of MI training on motor performance (Jeannerod, 2001). Non-invasive brain stimulation methods such as transcranial direct current stimulation (tDCS) can modulate cortical excitability (Nitsche & Paulus, 2000, 2001). Furthermore, when tDCS is applied for 13 min, these cortical excitability changes may last for up to 1 h

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd

114 A. Saimpont et al. after the end of stimulation (Nitsche & Paulus, 2001). Interestingly, application of anodal tDCS over the primary motor cortex concomitantly with physical practice has been shown to enhance learning in different motor tasks (Nitsche et al., 2003; Reis et al., 2009; Cuypers et al., 2013). As a potential explanation, anodal tDCS is thought to strengthen the synaptic connections involved during physical practice (Reis & Fritsch, 2011). Although MI training and anodal tDCS applied over the primary motor cortex independently enhance motor learning, only one experiment has combined these two approaches (Foerster et al., 2013). This study showed that the speed of copying a set of six words with the non-dominant hand was significantly improved after a single session of mental practice combined with anodal tDCS over the primary motor cortex, but not after mental practice alone. It was concluded that anodal tDCS enhanced the MI training effects. However, one cannot totally rule out this result being due to tDCS alone and not to the association between MI training and tDCS as tDCS alone was not tested. The aim of the present study was to examine whether a combination of MI training and anodal tDCS over the primary motor cortex would improve motor performance in a finger tapping task, more than either MI training or tDCS alone.

Table 1. Characteristics of the participants in each group Characteristic Age (years) Mean 95% CI Sex (female/male) Handedness score Mean CI 95% MI vividness score Mean CI 95%

MI + Stim

MI + Sham

Read + Stim

24.4 4.5 21.8–27.0 8/4

25.6 3.8 23.5–27.8 8/4

25.3 5.1 22.4–28.2 8/4

0.90 0.12 0.83–0.97

0.91 0.12 0.84–0.98

0.93 0.10 0.87–0.99

2.93 0.58 2.60–3.26

3.04 0.69 2.65–3.43

3.22 0.56 2.90–3.54

Groups: MI + Stim, followed MI training combined with anodal tDCS; MI + Sham, followed MI training combined with sham tDCS; Read + Stim, read a book while receiving anodal tDCS. MI vividness score (MI ability) was obtained with the short version of the Kinesthetic and Visual Imagery Questionnaire (Malouin et al., 2007); the scores may range between 1 (no image/no sensation of movements during MI) and 5 (image as clear as seeing/sensation as intense as during physically performing during MI). Handedness score was obtained with the Edinburgh Handedness Inventory (Oldfield, 1971); the scores may range between - 1 (strong left-handedness) and 1 (strong right-handedness).

a gaming keypad (Razer Nostromo, USA) connected to the computer. Fingers 2, 3, 4 and 5 of their right hand lay on the space bar of the computer.

Materials and methods Participants Thirty-six right-handed young human adults (mean age 25.1 4.4 years, 24 females) were pseudo-randomly assigned to one of three groups of 12 participants, comparable in age and male : female ratio. The participants’ characteristics (age, gender, handedness, and MI ability) are summarized in Table 1. The participants were recruited on campus through mailing lists and notices. Exclusion criteria were the existence of psychiatric, neurological (including a history of traumatic brain injury), musculoskeletal, or sleep disorders; a history of epilepsy; the use of medication affecting the level of vigilance; an uncorrected visual impairment; the wearing of a cardiac pacemaker; the presence of metallic implants in the skull; being pregnant; and having a regular manual activity involving high dexterity (such as playing the piano). All participants gave informed written consent for participating in the study and received 10$ for their participation. The experiment was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Ethics Committee of the Institut de Readaptation en Deficience Physique de Quebec (#2012-287). Experimental design We used a double-blind, sham-controlled design. The participants performed a complex finger tapping task before (Pre-test), immediately after (Post-test 1) and at 90 min after (Post-test 2) an MI training period when they either mentally repeated the sequence while receiving anodal tDCS (MI + Stim group), mentally practiced the sequence while receiving sham tDCS (MI + Sham group), or read a book while receiving anodal tDCS (Read + Stim group). Fig. 1 provides a schematic view of the experiment. The experiment took place in a quiet isolated room. The participants were seated on a chair with their forearms resting on a table in front of them. A laptop computer was placed on the table, with the screen at a distance of approximately 50 cm in front of the participant’s head. Fingers 2 (index), 3 (middle finger), 4 (ring finger) and 5 (little finger) of their left hand each lay on a different key of

Familiarization Before starting the experiment proper, the participants became accustomed to the device and task. They explicitly learned a simple eight-item finger tapping sequence, where the finger tapping followed a ‘natural’ order: 2, 3, 4, 5, 5, 4, 3, and 2. The sequence was shown twice by the experimenter and then the participants physically performed the same sequence on the gaming keypad, three times consecutively without any error. Once learned, the participants repeated the sequence during two blocks of 30 s separated by 20 s of rest. Before each block, a message was displayed on the computer screen prompting the participants to press the space bar when they were ready. The screen was otherwise all black during the blocks and black with a white fixation cross in the middle during rest. At the beginning of each block, as well as after each completed sequence, the participants had to press the space bar with their right fingers. At the end of each block, a ‘tone’ sound rang out. The participants were then invited to repeatedly imagine this simple sequence during two blocks of 30 s separated by 20 s of rest. They were instructed to mentally rehearse both the kinaesthetic sensations and visual images of their moving fingers, thus combining kinaesthetic imagery and visual imagery through a first-person perspective. They were also requested to close their eyes, keep their fingers in their resting position and remain motionless during MI. To control for MI compliance, they were asked to report the vividness of MI by means of the same five-point scale used for the Kinesthetic and Visual Imagery Questionnaire (Malouin et al., 2007). After having mentally completed each sequence they were requested to press the space bar with their right fingers. Before each block, a message was displayed on the screen prompting the participants to close their eyes and press the space bar when they felt ready to start imagining the sequence. At the end of each block, a ‘tone’ sound rang out, indicating to the participants that they could open their eyes. During both actual and mental tapping, each keypad and space bar press was recorded by means of E-PRIME software (Psychology Software

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 113–119

tDCS, primary motor cortex and mental practice 115

Fig. 1. Summary of the experimental design. Familiarization: familiarization with the device and task by means of a simple eight-item sequence. Explicit learning: repetition of the eight-item complex finger tapping three times consecutively without errors. Pre-test, Post-test 1 and Post-test 2: repetition of the complex sequence as fast and as accurately as possible during two blocks of 30 s separated by a rest period of 20 s. MI training: MI + Stim group, mental rehearsal of the complex sequence during 15 blocks of 30 s separated by rest periods of 20 s, combined with anodal tDCS over the primary motor cortex; MI + Sham group, same MI training protocol with sham tDCS; Read + Stim group, anodal tDCS while reading a book. Delay: 90 min delay to control for potential longlasting effects of tDCS.

Tools, USA). The keypad presses determined whether or not each sequence performed physically was correct. The space bar presses determined the number of sequences (either physical or mental) performed.

section for details). The participants in the Read + Stim group read a book while receiving anodal tDCS. At the end of the MI training session, the participants were asked to report the vividness of their MI for the last block of imagined sequences.

Explicit learning

Post-test 1

After familiarization, the participants explicitly learned the complex eight-item finger tapping sequence (2, 4, 5, 3, 2, 5, 3, and 4). The sequence was shown by the experimenter for as long as it took for the participants to physically perform it three times consecutively without errors.

Post-test 1 was performed immediately after MI training. The participants were required to physically repeat the complex sequence as fast and as accurately as possible for two blocks of 30 s separated by 20 s of rest, as during the Pre-test. Delay and Post-test 2

Pre-test The participants started the Pre-test immediately after having learned the complex sequence. During the Pre-test, the participants were required to physically repeat the complex sequence as fast and as accurately as possible for two blocks of 30 s separated by 20 s of rest.

Post-test 2 was performed at 90 min after Post-test 1. We selected this delay with reference to Nitsche & Paulus (2001), who showed that 13 min of anodal tDCS may modify the cortical excitability of the primary motor cortex for up to 1 h after the end of stimulation. The participants were required to physically repeat the complex sequence as fast and as accurately as possible for two blocks of 30 s separated by 20 s of rest, as during the Pre-test and Post-test 1.

Motor imagery training The stimulation was launched immediately after the Pre-test. The experimenter waited for about 1 min to ensure that the participants tolerated tDCS and then MI training was started. The participants from the MI + Stim and MI + Sham groups were asked to mentally rehearse the sequence as fast and as accurately as possible during 15 blocks of 30 s each, interspersed with rest periods of 20 s. Before starting MI training, the participants were asked to imagine the finger sequence in both the kinaesthetic and visual modalities. During the whole MI training session, the participants in the MI + Stim group received anodal tDCS, whereas those in the MI + Sham group received sham stimulation (see Transcranial stimulation

Transcranial stimulation During MI training, tDCS was delivered through two sponge electrodes (surface area 35 cm2) impregnated with a saline solution. The anode (active) electrode was positioned over the right primary motor cortex according to the international 10–20 system of electrode placement (C4). The cathode (reference) electrode was placed contralaterally over the left supraorbital region (Fp1). During active tDCS (MI + Stim and Read + Stim groups), the current ramped up for 30 s until it reached 2 mA (current density 0.057 mA/cm2). It then remained constant for 13 min, before ramping down for 30 s to 0 mA. During sham tDCS, the current also progressively


116 A. Saimpont et al. increased over the first 30 s, and then ramped down to zero. The current was ramped up in active and sham stimulation to elicit the same transient tingling sensations during the two conditions. This procedure is used extensively for blinding and has been shown to be effective in sham-controlled studies (Gandiga et al., 2006; Nitsche et al., 2008). Stimulation started at around 1 min before the beginning of MI training to ensure that all of the participants tolerated tDCS. We used a double-blind design for stimulation. The stimulation type was pre-programmed in a battery-driven stimulator (DC-Stimulator Plus, Neuroconn, Germany) by an investigator who was not the investigator who applied the stimulation to the participants. The participants were informed that they could receive either a true or a sham stimulation, but were told that they could feel similar sensations in both cases. We also advised them to stay focused on their task whatever sensations they felt. All of the participants tolerated the stimulation and none reported any significant headache before or after stimulation. At the end of the experiment, the participants were asked to determine whether they had received an active stimulation by answering ‘Yes’, ‘No’ or ‘I don’t know’.

between groups was also met (P > 0.05) at Pre-test and Post-test 1 and Post-test 2. We thus compared these data by means of a repeated-measures ANOVA with Group as between-subjects factor (MI + Stim, MI + Sham, Read + Stim) and Assessment as withinsubjects factor (Pre-test, Post-test 1, Post-test 2). To take possible baseline differences between groups into account and to better characterize the anticipated interaction between Group and Assessment, we calculated the percentage of change in the number of correct sequences between Pre-test and Post-test 1 and Posttest 2 [(Post-test values – Pre-test values/Pre-test values) 9 100] for each participant and Post-test assessment. Shapiro–Wilk tests showed that these data were approximately normally distributed (P > 0.05) for all groups at both Post-test 1 and Post-test 2. Levene tests showed that variance homogeneity between groups was met at Post-test 1 (P > 0.05) but not at Post-test 2 (P = 0.034). These data were compared by means of a repeated-measures ANOVA with Group as between-subjects factor (MI + Stim, MI + Sham, Read + Stim) and Assessment as within-subjects factor (Post-test 1, Post-test 2). Post-hoc Dunnett’s T3 tests were performed in the case of significant results.

Electromyographic recordings The electromyographic (EMG) activity was continuously recorded during MI training in the MI + Stim and MI + Sham groups, in order to compare EMG activity at rest (during the 20 s rest periods of MI training) and during MI (during the 30 s practice blocks of MI training). The EMG signals were recorded from the left first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles by means of two silver chloride pre-gelled surface electrodes of 11 mm diameter placed on the muscle belly. We placed the reference electrode on the dorsal side of the left wrist. We used the Biopac System (USA) for data recordings and the associated Acknowledgeâ software for offline analysis. The EMG signals were recorded at a frequency of 1000 Hz and band pass filtered (20–500 Hz) before analysis. Data analysis Blinding The number of participants who thought that they had received active or sham stimulation, or who did not know which type of stimulation they had received, was calculated for each group, and compared by means of a chi-square test.

Electromyographic activity For each participant in the MI + Stim and MI + Sham groups, we assessed muscle activity during mental practice by computing the root mean square of EMG activity (FDI and ADM muscles) during the 15 practice blocks of MI training. Baseline was also assessed by calculating the root mean square of EMG activity for the 14 rest periods separating the MI trials. The data of three participants (two in the MI + Stim group and one in the MI + Sham group) were not recorded due to technical problems. Shapiro–Wilk tests showed that the root mean square values for the FDI muscle were approximately normally distributed (P > 0.05) for the MI + Sham and MI + Stim groups, during the Rest and MI training conditions. Levene tests showed that variance homogeneity between groups was also met (P > 0.05) in both conditions. An ANOVA for repeated measures was thus used to compare these data. Shapiro–Wilk tests showed that the root mean square values for the ADM muscle were approximately normally distributed only for the MI + Sham group in the MI training condition. We thus performed Mann–Whitney U-tests for between-group comparisons and Wilcoxon tests for within-group comparisons of these data.

Results Explicit learning

Blinding

We calculated the number of keypad presses needed by each participant to succeed in performing the complex sequence on the gaming keypad three times consecutively without any error. Shapiro–Wilk tests showed that these data were not normally distributed (P < 0.05) in the MI + Sham and MI + Stim groups. We thus compared these data between groups by means of a Kruskall–Wallis test.

The participants’ opinion about the nature of the stimulation that they received is summarized in Table 2. The chi-square test revealed that the proportions of the participants who thought they had received active stimulation, sham stimulation or who did not know

Finger task performance For each participant, we calculated the number of correct sequences during the two blocks of 30 s at Pre-test, Post-test 1 and Post-test 2. Shapiro–Wilk tests showed that these data were approximately normally distributed (P > 0.05) for all groups at Pre-test and Post-test 1 and Post-test 2, and Levene tests showed that variance homogeneity

Table 2. Number of participants who thought that they had received active stimulation (‘Yes’), sham stimulation (‘No’), or did not perceive any information about the stimulation (‘Don’t know’) for each group Group

Yes

No

Don’t know

MI + Stim MI + Sham Read + Stim

5 2 4

2 2 1

5 8 7


tDCS, primary motor cortex and mental practice 117 did not significantly differ among groups (P = 0.67). This result showed that tDCS blinding was effective and thus that the results of the finger tapping task could not be attributed to a placebo effect. Explicit learning The mean number of keypad presses to succeed in performing the complex sequence three times consecutively without errors was 78.8 35.6 in the MI + Stim group, 78.5 31.5 in the MI + Sham group and 86.8 25.3 in the Read + Stim group. The Kruskall–Wallis test performed on these data was not significant (P = 0.41), indicating that the participants in the three groups initially learned the sequence with a similar ability. Finger task performance The ANOVA performed on the mean numbers of correct sequences showed a significant effect of Assessment (F2,33 = 68.97, P < 0.001, g2p = 0.68) as well as a significant Group 9 Assessment interaction (F4,66 = 4.22, P < 0.01, g2p = 0.20), indicating that the three groups improved their performance after MI training with, however, difference among the groups in the amount of improvement. The mean percentages of change (increase) in the number of correct sequences at Post-test 1 and Post-test 2 for the three groups are shown in Fig. 2. The ANOVA showed a significant effect of Group (F2,33 = 7.69, P < 0.01, g2p = 0.32) and the achieved power was 0.93. Post-hoc Dunnett’s T3 tests revealed that the percentage of increase was significantly greater in the MI + Stim group compared with the MI + Sham group (P < 0.05) and Read + Stim group (P < 0.05); no significant difference was found between the MI + Sham and Read + Stim groups (P = 0.77). The effect of Assessment was not significant (F1,33 = 0.46, P = 0.50) and neither was the Group 9 Assessment interaction (F2,33 = 0.72, P = 0.50), thus suggesting that the Group effect was similar at Post-test 1 and Post-test 2.

Fig. 2. Box-and-whisker plots showing the percentages of increase in the number of correct responses for the three groups at Post-test 1 and Post-test 2. The width of the boxes represents the interquartile range, the horizontal line in the boxes represents the median, the whiskers represent the minimum and maximum values without outliers, and the black circle represents an outlier (value above third quartile + 1.5 9 interquartile range). The black square shows the mean of the values. Percentages of increase in performance were significantly higher in the MI + Stim group compared with the others (P < 0.05). *Significant difference between groups.

Electromyographic activity The mean ( SD) FDI and ADM root mean square values during MI training and at Rest, for the MI + Stim and MI + Sham groups, are shown in Table 3. The ANOVA on data from the FDI did not show any significant effect of Group (F1,19 = 0.07, P = 0.78), Condition (F1,19 = 2.59, P = 0.12), or Group 9 Condition interaction (F1,19 = 1.48, P = 0.24). For the ADM data, the Mann–Whitney U-tests were not significant (P = 0.78 for MI training; P = 0.94 for Rest), and neither were the Wilcoxon tests (P = 0.21 for the MI + Sham group; P = 0.33 for the MI + Stim group). There was thus no substantial muscle activation during MI training compared with rest.

Discussion The present study provides evidence that anodal tDCS applied over the primary motor cortex during MI training can significantly enhance the ability to perform a complex sequence of finger movements, compared with anodal tDCS alone and MI training alone. Interestingly, the advantage of coupling MI training with tDCS was observed both immediately after and at 90 min after MI training. Motor imagery training combined with anodal transcranial direct current stimulation The most interesting finding of this study was that the increase in the number of sequences correctly performed was significantly greater after 13 min of MI training combined with 2 mA anodal tDCS over the primary motor cortex than with sham stimulation, or after tDCS alone. This supports the results of Foerster et al. (2013), who reported improved writing task performance (revealed by shortened movement time) after a single MI training session of 13 min combined with 2 mA anodal tDCS over the primary motor cortex. Foerster et al. (2013) showed that MI training alone did not lead to significant performance improvement, whereas the effect of tDCS alone was not investigated. Thus, it could not be totally ruled out that the effect observed when combining MI with tDCS stemmed mainly from the tDCS. Our study contributes to the exclusion of this hypothesis and adds to these findings by showing that the association between MI training and anodal tDCS over the primary motor cortex is more effective for enhancing motor performance than either method alone. Motor imagery is known to activate a large fronto-parietal network, including the primary motor cortex (Solodkin et al., 2004; Guillot et al., 2009; Pelgrims et al., 2011; Hetu et al., 2013). However, tDCS is known to enhance cortical excitability under the anode (Nitsche & Paulus, 2000, 2001). Hence, it is possible that anodal tDCS applied concomitantly with MI training enhanced activity within the primary motor cortex and strengthened the

Table 3. Mean ( SD) FDI and ADM root mean square values (mV) during MI training and rest FDI muscle

ADM muscle

Group

MI training

Rest

MI training

Rest

MI + Stim MI + Sham

6.2 4.2 5.3 3.3

4.9 2.1 5.1 2.7

16.8 12.1 13.4 10.5

15.1 10.6 12.7 8.7


118 A. Saimpont et al. learning-related synaptic connections within this region. This would, in turn, lead to enhanced performance. However, this remains a working hypothesis, and further research is required to explore this issue. Furthermore, it is important to keep in mind that tDCS does not provide focal activation of the cortex underlying the anode (Nitsche et al., 2008). It is likely that excitability in regions adjacent to the primary motor cortex may also have been altered by our stimulation protocol. MI has been extensively shown to activate both the pre-motor and supplementary motor areas, which are adjacent and highly connected with the primary motor cortex. Hence, we cannot exclude that tDCS also increased the excitability within these regions and that this could also account for our results. Interestingly, our findings also extend those of previous studies using tDCS concomitantly with motor learning by physical practice or by observation. For example, it has been reported that motor learning by physical practice combined with anodal tDCS resulted in greater performance gains compared with physical practice alone (e.g. Nitsche et al., 2003; Kantak et al., 2012; Cuypers et al., 2013). More recently, Wade & Hammond (2015) have shown that, when compared with sham stimulation, anodal tDCS applied over the pre-motor cortex during observation of a finger sequence significantly enhanced performance in a subsequent serial reaction time task involving the same sequence. Taken together, these results support the simulation theory of Jeannerod (2001, 2006), which states that motor execution, MI, and action observation share a common neural substrate (mainly a fronto-parietal network) and thus that both MI training and observational learning may contribute to substantially improve motor performance. Finally, the present results show that the benefits of increasing excitability in the primary motor cortex during MI training remained observable at 90 min after the stimulation was stopped, i.e. when the effects of tDCS on cortical excitability were presumably over (Nitsche & Paulus, 2001). Hence, combining tDCS with MI training might have elicited prolonged effects. This point needs further development, and future studies should explore the impact of associating tDCS with MI training on (i) off-line learning, which occurs after several hours without exercise and/or after a period of sleep, (ii) slow on-line learning, occurring with several sessions of practice, and (iii) retention after a long delay without any form of practice. Motor imagery training alone and stimulation alone Both MI training and anodal tDCS applied over the primary motor cortex were independently able to significantly increase motor performance. For MI training alone, our result are in line with those of previous studies investigating similar finger sequence tasks (PascualLeone et al., 1995; Nyberg et al., 2006; Olsson et al., 2008; Debarnot et al., 2009, 2010, 2011a). In particular, they directly corroborate those of Debarnot and colleagues (Debarnot et al., 2009, 2010, 2011a) who used a very similar experimental design. For tDCS alone, it has been shown that early consolidation can occur in the early minutes after the end of physical practice, and that this process involves the primary motor cortex (Muellbacher et al., 2002; Tecchio et al., 2010). In particular, Tecchio et al. (2010) have shown that 15 min of anodal tDCS over the primary motor cortex after physical practice of a complex finger sequence significantly improved early motor consolidation. In the present study, the participants physically practiced the sequence during the Pre-test and this certainly induced early consolidation effects, which may have been reinforced by tDCS.

Conclusions The main result of this study was that anodal tDCS applied over the primary motor cortex during MI significantly enhanced the beneficial effects of MI training on motor performance. There is growing interest in the use of MI (re)training as well as observational (re) learning as complementary tools for functional improvement in people with physical disabilities, who may have motor limitations preventing them from engaging in intensive physical therapy (Dickstein & Deutsch, 2007; Mulder, 2007; Malouin et al., 2013; Small et al., 2013). Future clinical studies in patients with motor impairments should explore the impact of the adjunct of anodal tDCS to MIbased and/or action observation-based motor therapy.

Conflict of interests The authors declare that they have no conflict of interests.

Acknowledgements A.S. was supported by a scholarship from the Ministere des Finances et de l’Economie du Quebec. P.L.J. was supported by a career award from the Canadian Institutes of Health Research.

Abbreviations ADM, abductor digiti minimi; EMG, electromyographic; FDI, first dorsal interosseous; MI, motor imagery; tDCS, transcranial direct current stimulation.

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