SEARCH

SEARCH BY CITATION

Keywords:

  • cognitive processes;
  • consolidation;
  • learning;
  • memory systems;
  • performance

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References

Implicit and explicit memory systems for motor skills compete with each other during and after motor practice. Primary motor cortex (M1) is known to be engaged during implicit motor learning, while dorsal premotor cortex (PMd) is critical for explicit learning. To elucidate the neural substrates underlying the interaction between implicit and explicit memory systems, adults underwent a randomized crossover experiment of anodal transcranial direct current stimulation (AtDCS) applied over M1, PMd or sham stimulation during implicit motor sequence (serial reaction time task, SRTT) practice. We hypothesized that M1-AtDCS during practice will enhance online performance and offline learning of the implicit motor sequence. In contrast, we also hypothesized that PMd-AtDCS will attenuate performance and retention of the implicit motor sequence. Implicit sequence performance was assessed at baseline, at the end of acquisition (EoA), and 24 h after practice (retention test, RET). M1-AtDCS during practice significantly improved practice performance and supported offline stabilization compared with Sham tDCS. Performance change from EoA to RET revealed that PMd-AtDCS during practice attenuated offline stabilization compared with M1-AtDCS and sham stimulation. The results support the role of M1 in implementing online performance gains and offline stabilization for implicit motor sequence learning. In contrast, enhancing the activity within explicit motor memory network nodes such as the PMd during practice may be detrimental to offline stabilization of the learned implicit motor sequence. These results support the notion of competition between implicit and explicit motor memory systems and identify underlying neural substrates that are engaged in this competition.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References

Acquisition of serial (or sequential) behavior is critical to activities of daily living. It is a process in which multiple elements of movement are integrated through motor practice into a single behavior (Doyon, 2008). Motor sequence acquisition through practice involves at least two distinct, yet interrelated processes in the nervous system: online processes leading to improvements in skill performance during practice, and offline processes that lead to either stabilization of the skill performance over time (memory stabilization) or improvement in skill performance between training sessions (offline learning) (Robertson & Cohen, 2006). Sequence learning is implemented by a network of cortical and subcortical structures that are engaged during practice as well as after practice (Doyon et al., 1997, 2003; Karni et al., 1998; Robertson et al., 2001; Press et al., 2005).

Acquisition of serial behavior may involve implicit or explicit learning. Implicit sequence learning refers to improvement in performance of the sequence without overt information about the elements of a sequence. In contrast, explicit sequence learning is accompanied by explicit conscious recollection of each element and its order in the sequence (Squire, 1986; Vidoni & Boyd, 2007; Robertson, 2009). There are multiple differences in the explicit and implicit memory systems, including the neural substrates that implement implicit and explicit learning. Using positron emission tomography, Honda and colleagues demonstrated that anatomically distinct networks were associated with implicit and explicit sequence learning. Implicit sequence learning was primarily associated with activity in the contralateral sensory and M1 (Pascual-Leone et al., 1994). In contrast, when learners developed explicit knowledge about the practiced sequence, activation in the dorsal premotor cortex (PMd), dorsolateral prefrontal cortex and supplementary motor area correlated strongly with conscious recall of the sequence (Honda et al., 1998; Vidoni & Boyd, 2007; Robertson, 2009).

Implicit and explicit memory systems are complex and often compete to mediate task performance. Learning a word-list (explicit memory task) immediately after implicit motor sequence practice enhanced learning of the motor sequence (Brown & Robertson, 2007a). This suggested that sequence-related information in the explicit memory system probably competes with implicit memory system, and blocking that sequence-related explicit information (with a word-list) allows the implicit memory system to maximize motor learning. Here we investigated the neural basis of competition between the implicit and explicit systems during implicit motor sequence learning. We used anodal transcranial direct current stimulation (AtDCS) to modulate the excitability of distinct neural structures known to be engaged in implicit (primary motor cortex, M1) and explicit (PMd) memory systems during implicit motor sequence practice. The effect of AtDCS on M1 and PMd was assessed with online and offline changes in motor performance. AtDCS in conjunction with motor practice has been used to upregulate activity in specific cortical areas involved in motor practice to influence acquisition of motor skills compared with that which can be achieved by practice alone. We hypothesized that compared with sham stimulation, AtDCS over M1 will enhance online and offline learning of the implicit motor sequence. In contrast, because PMd is known to be engaged in explicit knowledge of motor sequences, upregulating PMd with AtDCS during practice will attenuate online and offline learning of the implicit motor sequence.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References

Participant

Thirteen right-handed healthy adults consented to participate in the experimental protocol approved by the Institutional Review Board of the Northwestern University. None of the participants had any history of neurological, psychiatric illness or any contraindications to transcranial magnetic stimulation (TMS) or tDCS. All participants used their non-dominant (left) hand for practice of the sequences.

Experimental design

Each participant attended three experimental sessions separated by at least 8 days (Fig. 1). Each experimental session consisted of two consecutive days. On day 1 of each experimental session, TMS was used to identify the hotspot for the first dorsal interosseous (FDI) muscle (see below for details). Participants were then tested for baseline performance on the motor sequence. Following baseline assessment, the participants received AtDCS over PMd or M1 or sham AtDCS. Once the participants were comfortable with tDCS (∼2 min later), motor sequence practice was begun. The order of PMd, M1 and sham tDCS was counterbalanced across the three experimental sessions and across participants. On day 2 of each session, the participants returned for a test of retention of the learned motor sequence.

image

Figure 1.  Experimental design. In a randomized crossover design, participants practiced a new motor sequence (S) at each session. During practice, participants received AtDCS over the M1, or PMd or sham stimulation. Motor performance on the practiced sequence (S) and on a random sequence (R) was assessed at baseline, at the end of acquisition on Day 1 and at a retention test on Day 2.

Download figure to PowerPoint

Implicit learning task

A modified version of the serial reaction time task (SRTT) (Nissen & Bullemer, 1987) was used for implicit or procedural learning. Stimuli were presented in a horizontal array at one of the four locations on a computer screen. Each of the positions on the screen corresponded to four keys (V, B, N, M) on the keyboard. Participants sat comfortably in front of the computer with fingers (little, ring, middle and index) of the left hand on the four keys (V, B, N, M), respectively. For each trial, when a cue appeared on the screen, the participants responded as quickly as possible by pressing the corresponding key. The stimulus remained on the screen until the correct response was made. Unbeknown to the participant a ten-item sequence was repeatedly presented. This allowed them to acquire the sequence in an implicit manner.

At each experimental session, participants practiced one of the three ten-item implicit sequences (4-1-2-4-3-2-1-4-1-3; 3-2-4-3-1-4-2-3-4-1; 2-1-3-2-4-3-1-3-2-4) of comparable difficulty and with minimal carryover between sequences. A different sequence was practiced at each experimental session and the order of the sequences was counterbalanced across the 13 subjects. On Day 1 of each experimental session, participants practiced a new sequence of 700 trials [50 baseline trials + 600 practice trials + 50 at the end of acquisition (EoA)]. EoA performance was assessed approximately 5 min after practice with tDCS ended. On Day 2 of the experimental session, the participants were tested for retention of the practiced sequence. Fifty random trials were also presented at baseline, EoA and at Day 2 of retention, to control for changes in the reaction time due to changes in visuospatial processing speed over practice. Within these random trials there was no repeating sequence. A more specific measure of implicit sequence learning was obtained by contrasting the sequential response times against those response times for the random trials.

To ensure that the participants did not have explicit knowledge of the motor sequences, they were asked if they noticed any pattern after Day 2 of testing. Three of 12 participants reported that they thought that some pattern was repeating, but could not explicitly recall more than three serial elements of the sequence when asked to reproduce it (i.e. no free recall). One additional participant was able to recall five items of the ten-item sequence on the free-recall test, and was therefore excluded from further analysis.

TMS and tDCS

TMS was employed to localize the M1 location for FDI muscle. Participants were seated in a comfortable chair with the forearm supported in a prone position and hand resting on an arm support. Single TMS pulses were applied over the right motor cortex with a 70-mm figure-of-eight coil attached to a Magstim Rapid Stimulator (The Magstim Company, Wales, UK). The coil was held tangentially to the scalp with the handle pointing posteriorly away from the midline at an angle of ∼45 °. Cortical current induced from this position is directed approximately perpendicular to the central sulcus (Brasil- Neto et al., 1992; Mills et al., 1992). A ‘hot-spot’ for FDI was determined as the site at which the largest motor evoked potential was obtained from FDI at lowest TMS intensity. This hotspot overlies the area of the M1 that more heavily projects to the FDI, and was the site for M1 tDCS. For the premotor cortex, the tDCS active electrode was positioned 3 cm anterior and 1 cm medial to the hot-spot (Boros et al., 2008).

tDCS was delivered at 1 mA current intensity using a constant-current stimulator (Dupel Iontophoresis System, Empi, MN, USA) using an 8-cm2 saline-soaked anode and a self-adhesive carbonized cathode (48 cm2) placed over the forehead above the contralateral orbit. For active tDCS conditions, the current was ramped up over 10 s, held constant at 1 mA for 15 min and then ramped down over 10 s. For the sham tDCS, the current was ramped up for 10 s and then the machine was switched off. All the participants tolerated tDCS very well and there no adverse effects were reported.

Data analysis

Only reaction times (RTs) for correct trials were included in the analysis. RTs longer than 2.5 standard deviations from a participant’s mean were removed, along with any RTs longer than 3000 ms. Mean RT was calculated for 50-trial blocks of practiced and random sequences for baseline, EoA and retention. For the practice session performance, mean RT was calculated for 100-trial practice blocks. Implicit sequence-specific performance was measured as the difference in the mean response time between the sequence and random trials. Sequence specific performance was assessed at baseline, at the EoA and at retention. Offline learning was quantified as the change in implicit sequence-specific performance from the EoA to retention testing on Day 2. Offline learning encompasses multiple post-practice processes (e.g. consolidation) that contribute to stabilization and enhancement of motor memory.

Statistical analysis

A repeated-measures anova (anovaRM) with independent factor Stimulation Condition (M1-AtDCS, PMd-AtDCS, and Sham) and dependent factor Time (baseline, EoA and retention) was employed to assess implicit motor sequence-specific learning over time. Additionally, a similar anovaRM with repeated measures on practice blocks was used to evaluate the stimulation condition-dependent changes in sequence performance during practice. A Bonferroni correction was used for post-hoc tests to determine the locus of significant stimulation condition by time interactions. Changes in motor sequence performance online and offline were compared for the three stimulation conditions using an anovaRM with repeated measures on time. Statistical significance was pre-set at = 0.05.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References

Baseline and acquisition phase

Figure 2 illustrates the performance on the sequence trial blocks and random trial blocks at baseline, during practice, at EoA and at retention. At baseline, anovaRM did not reveal a significant difference in the implicit sequence performance between the three stimulation conditions (= 0.773). During practice, there was a significant effect of practice, which indicated participants improved performance with practice (< 0.001) irrespective of the stimulation condition. A main effect of AtDCS on implicit sequence performance during practice was revealed (F2,33 = 3.879, = 0.031). Post-hoc analysis revealed that AtDCS M1 significantly improved practice performance compared with sham tDCS (= 0.032). Although AtDCS applied over PMd also improved practice performance, the effect did not reach statistical significance (= 0.064). At the end of the acquisition phase, although there was no statistically significant difference in performance between the three stimulation conditions (= 0.08), there was a tendency for M1 and PMd to reveal better performance compared with sham stimulation.

image

Figure 2.  Performance (mean reaction time ± SEM) for the trials of the practiced sequence (S) and random sequence (R) at baseline, during practice of S, at the end of acquisition (EoA) and at retention (R). While practice and AtDCS had an effect on the practiced sequence performance and retention, there was no effect on the random sequence (R).

Download figure to PowerPoint

Retention

At retention, there was a statistically significant effect of the stimulation condition (= 0.002; Fig. 2; retention block). Post-hoc analysis using Bonferroni correction revealed that AtDCS over M1 significantly improved retention performance of the implicit sequence compared with AtDCS applied over PMd (= 0.003) or sham stimulation (= 0.008).

Offline learning

Change in sequence-specific performance over the retention interval (i.e. from EoA to retention) revealed a significant effect of stimulation condition (anovaRM, = 0.043). Post-hoc analysis revealed that AtDCS applied over PMd significantly attenuated offline learning compared with AtDCS over M1 (= 0.028) or sham stimulation (= 0.031; Fig. 3).

image

Figure 3.  Sequence-specific motor learning was calculated as a ratio of the reaction time (RT) for the practiced sequence and RT for the random sequence. At baseline, there was no significant difference between the stimulation conditions. From EoA to retention, there was significant attenuation of sequence-specific motor performance in the PMd AtDCS condition (*). At retention, the M1-AtDCS condition had significantly better sequence-specific learning compared with the PMd AtDCS and sham stimulation condition (**).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References

We investigated the online and offline changes in motor performance resulting from AtDCS applied over M1 and PMd during practice of an implicit motor sequence. AtDCS applied over M1 enhanced practice performance compared with sham stimulation and also supported offline stabilization of the motor sequence. In contrast, PMd stimulation with AtDCS during practice attenuated offline stabilization of the motor sequence compared with sham and M1 stimulation.

Role of M1 in online and offline learning

Imaging studies during practice of implicitly acquired motor sequences have indicated that M1 is actively engaged during acquisition to promote online changes in performance (Pascual-Leone et al., 1994; Doyon et al., 1997; Honda et al., 1998). Recently, non-invasive brain stimulation techniques have allowed the exploration and modulation of motor learning by enhancing or suppressing the excitability of M1 (Reis et al., 2008; Bolognini et al., 2009; Stagg et al., 2011b). Similar to previously reported findings, we observed that AtDCS over M1 during motor practice enhanced online changes in motor performance of an implicit motor sequence (Nitsche et al., 2003; Kang & Paik, 2011). AtDCS over M1 improved the performance of the practiced sequence during acquisition as well as at the EoA. The benefit of AtDCS over M1 was specific to the practiced sequence and did not change the performance of the random sequence. This indicates that the tDCS online learning effect is implemented by modulation of learning-related mechanisms, and not by an overall change in general motor behavior. While AtDCS is predominantly known to increase motor cortical excitability by altering the membrane potential (Stagg & Nitsche, 2011), behavioral effects on sequence learning may involve a decrease in gamma-aminobutyric acid (Stagg et al., 2011a) and brain-derived neurotrophic factor-dependent synaptic plasticity (Fritsch et al., 2010).

Even after practice ends, M1 is actively engaged in post-practice processes that help stabilize (memory stabilization) or enhance (offline learning) sequence performance over the retention interval. Our hypothesis was similar to those proposed for previous studies (Reis et al., 2009; Tecchio et al., 2010) – enhancing M1 activity with AtDCS will enhance online and offline learning of the practiced sequence. Our findings did not support the offline component of our hypothesis. In the current study, although AtDCS over M1 during practice supported offline stabilization of motor performance, it did not enhance offline learning compared with sham stimulation. These differences may arise from difference in our methods compared with the other studies. Reis et al. (2009) used a sequential visual isometric pinch task over multiple days of practice, which probably engage different control mechanisms than the SRTT task practiced over a single day. Tecchio et al. (2010) employed AtDCS to upregulate M1 activity after practice to enhance consolidation of the practiced implicit sequence. This post-practice application of AtDCS may have specifically enhanced consolidation processes and improved offline learning. Nevertheless, our findings support the previously reported role of M1 in offline memory stabilization (Kantak et al., 2010; Kang & Paik, 2011).

Premotor cortex – role in online and offline changes in motor performance

To our knowledge, our study is the first to investigate the effects of AtDCS applied over PMd during practice on performance and learning of an implicit SRTT sequence. Contrary to our hypothesis, AtDCS applied over PMd did benefit motor performance during practice and at EoA compared with sham stimulation. Although not statistically significant, the effect size was high, indicating that the effect was likely to be real and may have reached significance with a larger sample size. There may be multiple mechanisms that may implement this effect. Although we used a smaller anode than those previously used, evidence exists that AtDCS applied over PMd is known to increase the excitability within the M1 via corticocortical connections (Boros et al., 2008). Although it is not clear how explicit and implicit systems interact during practice at a neural substrate level, the behavioral evidence for the effect of explicit knowledge on implicit motor performance is also mixed. Although PMd is thought to be predominantly a part of the explicit memory system, there is evidence that it may be engaged during early performance of any sequence learning task that links the visuospatial cues to compatible responses, an important characteristic of our task (Grafton et al., 1998, 2002; van der Graaf et al., 2006).

Our findings are different from those observed by Boyd & Linsdell (2009) who observed that enhancing PMd excitability during the immediate post-practice period led to better offline learning of a continuous tracking task. In our study, we applied AtDCS during practice of the implicit sequence task, therefore not directly affecting the motor memory consolidation phase. It is likely that AtDCS over PMd during practice led to a motor memory representation that did not demonstrate offline stabilization.

Mechanisms for performance and learning may differently engage M1 and PMd

Although somewhat beneficial to online practice performance of the implicit motor sequence learning task, AtDCS over PMd attenuated offline stabilization of the implicit motor sequence compared with sham and M1 AtDCS. This emphasizes the well-known performance–learning distinction which suggests that factors that enhance practice performance may not always enhance retention of motor skills (Kantak & Winstein, 2011). Even after practice ends, functional properties and representation of the skill continue to evolve in the brain and help stabilize motor performance over the retention interval (online learning). One interpretation is that online and offline processes may differentially engage M1 and PMd during implicit sequence acquisition. While practice performance benefitted from AtDCS applied over PMd and M1, retention benefitted from AtDCS over M1 alone. This suggests that M1 is critical for both online and offline processes of implicit sequence acquisition. By contrast, PMd may be actively engaged primarily during online performance changes. An alternative explanation to help explain the attenuation of retention following PMd-AtDCS may relate to recently reported interactions between implicit and explicit memory systems during the immediate post-practice period.

Competitive interaction between implicit and explicit memory systems during post-practice period

Memory systems for implicit and explicit motor skills have been shown to compete during the post-practice consolidation (Poldrack & Packard, 2003; Brown & Robertson, 2007a,b). Offline improvements in the implicit motor skill sequence were blocked by learning an explicit or declarative skill (e.g. learning a word-list) immediately after implicit skill practice. Furthermore, a decrease in implicit motor skill over the retention interval was proportional to the amount of declarative learning. This suggests that offline mechanisms that support implicit motor memory stabilization may be blocked by explicit memory (Brown & Robertson, 2007a). In our study, we did not provide explicit information to our participants. Furthermore, we also eliminated one participant who had explicit recall of the practiced sequence. In this study, we specifically focused on the effects of tDCS on neural substrates (M1 and PMd) during implicit sequence learning. While M1 is known to be preferentially engaged in implicit motor learning, PMd is shown to be specifically active during explicit learning. Galea et al. (2010) have demonstrated that inhibitory theta burst TMS to the dorsolateral prefrontal cortex enhanced motor memory consolidation by disrupting the explicit system, providing the first evidence for the competitive interaction at the level of neural substrates. The current study extends that understanding of the neural structures that underlie this competition between the implicit and explicit motor memory systems and provides evidence for differential involvement of M1 and PMd in implicit sequence learning. We used AtDCS to up-regulate excitability of PMd, a neural substrate that is known to be engaged in explicit motor skill learning. This short-term increased activation of the explicit memory system probably competes with immediate offline mechanisms of the implicit memory system that support memory stabilization for skill retention. This may, in part, explain why AtDCS over PMd attenuated offline performance stabilization compared with sham and M1 AtDCS stimulation.

Our finding that AtDCS over PMd attenuated retention but not practice performance probably suggests a competition between the implicit and explicit memory neural substrates that is temporally specific to the immediate post-practice consolidation phase. Recent research indicates that while implicit and explicit memory systems may interact and compete immediately post-practice wakeful hours, these systems operate independently during sleep (Robertson, 2009). Indeed when AtDCS was applied over PMd during rapid eye movement sleep, improved implicit skill learning was evident (Nitsche et al., 2010). In the current study, we did not apply tDCS during the post-practice consolidation phase, thereby limiting our ability to make direct inferences about the effects on consolidation phase. However, future research with time-specific application of tDCS may help to provide clear insight into the temporal evolution of implicit–explicit interactions. Another limitation of this study is that we only modulated two specific motor areas (M1 and PMd). There is evidence that both implicit and explicit learning involve a wide and distinct network other than these two substrates. It is unclear how these networks interact with each other and what factors affect this interaction.

In conclusion, we assessed the role of M1 and PMd in implicit motor learning using AtDCS employed to enhance activity within the neural substrates during motor practice. Our results indicate that M1 is a critical neural substrate that implements online improvements in performance and offline stabilization for implicit motor sequence learning. In contrast, enhanced PMd activity during practice may be detrimental to offline stabilization of implicit motor sequence learning. These results support the distinction between performance and learning mechanisms. In addition, they indicate a differential engagement of M1 and PMd for practice and retention of implicit motor sequence. Finally, our results add further support to the notion of competition between the implicit and explicit motor memory systems specifically during the post-practice consolidation phase. More research is needed to elucidate the time course and differential role of specific neural substrates during implicit and explicit motor learning.

Abbreviations
AtDCS

anodal transcranial direct current stimulation

EoA

end of acquisition

FDI

first dorsal interosseous

M1

primary motor cortex

PMd

dorsal premotor cortex

RT

reaction time

SRTT

serial reaction time task

TMS

transcranial magnetic stimulation

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. References
  • Bolognini, N., Pascual-Leone, A. & Fregni, F. (2009) Using non-invasive brain stimulation to augment motor training-induced plasticity. J. Neuroeng. Rehabil., 6, 8.
  • Boros, K., Poreisz, C., Munchau, A., Paulus, W. & Nitsche, M.A. (2008) Premotor transcranial direct current stimulation (tDCS) affects primary motor excitability in humans. Eur. J. Neurosci., 27, 12921300.
  • Boyd, L.A. & Linsdell, M.A. (2009) Excitatory repetitive transcranial magnetic stimulation to left dorsal premotor cortex enhances motor consolidation of new skills. BMC Neurosci., 10, 72.
  • Brasil- Neto, J.P., McShane, L.M., Fuhr, P., Hallett, M. & Cohen, L.G. (1992) Topographic mapping of the human motor cortex with magnetic stimulation: factors affecting accuracy and reproducibility. Electroencephalogr. Clin. Neurophysiol., 85, 916.
  • Brown, R.M. & Robertson, E.M. (2007a) Inducing motor skill improvements with a declarative task. Nat. Neurosci., 10, 148149.
  • Brown, R.M. & Robertson, E.M. (2007b) Off-line processing: reciprocal interactions between declarative and procedural memories. J. Neurosci., 27, 1046810475.
  • Doyon, J. (2008) Motor sequence learning and movement disorders. Curr. Opin. Neurol., 21, 478483.
  • Doyon, J., Gaudreau, D., Laforce, R. Jr, Castonguay, M., Bedard, P.J., Bedard, F. & Bouchard, J.P. (1997) Role of the striatum, cerebellum, and frontal lobes in the learning of a visuomotor sequence. Brain Cogn., 34, 218245.
  • Doyon, J., Penhune, V. & Ungerleider, L.G. (2003) Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia, 41, 252262.
  • Fritsch, B., Reis, J., Martinowich, K., Schambra, H.M., Ji, Y., Cohen, L.G. & Lu, B. (2010) Direct current stimulation promotes BDNF-dependent synaptic plasticity: potential implications for motor learning. Neuron, 66, 198204.
  • Galea, J.M., Albert, N.B., Ditye, T. & Miall, R.C. (2010) Disruption of the dorsolateral prefrontal cortex facilitates the consolidation of procedural skills. J. Cogn. Neurosci., 22, 11581164.
  • van der Graaf, F.H., Maguire, R.P., Leenders, K.L. & de Jong, B.M. (2006) Cerebral activation related to implicit sequence learning in a double serial reaction time task. Brain Res., 1081, 179190.
  • Grafton, S.T., Hazeltine, E. & Ivry, R.B. (1998) Abstract and effector-specific representations of motor sequences identified with PET. J. Neurosci., 18, 94209428.
  • Grafton, S.T., Hazeltine, E. & Ivry, R.B. (2002) Motor sequence learning with the nondominant left hand. A PET functional imaging study. Exp. Brain Res., 146, 369378.
  • Honda, M., Deiber, M.P., Ibanez, V., Pascual-Leone, A., Zhuang, P. & Hallett, M. (1998) Dynamic cortical involvement in implicit and explicit motor sequence learning. A PET study. Brain, 121, 21592173.
  • Kang, E.K. & Paik, N.J. (2011) Effect of a tDCS electrode montage on implicit motor sequence learning in healthy subjects. Exp. Transl. Stroke Med., 3, 4.
  • Kantak, S.S. & Winstein, C.J. (2011) Learning-performance distinction and memory processes for motor skills: a focused review and perspective. Behav. Brain Res., 228, 219231.
  • Kantak, S.S., Sullivan, K.J., Fisher, B.E., Knowlton, B.J. & Winstein, C.J. (2010) Neural substrates of motor memory consolidation depend on practice structure. Nat. Neurosci., 13, 923925.
  • Karni, A., Meyer, G., Rey-Hipolito, C., Jezzard, P., Adams, M.M., Turner, R. & Ungerleider, L.G. (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc. Natl. Acad. Sci. USA, 95, 861868.
  • Mills, K.R., Boniface, S.J. & Schubert, M. (1992) Magnetic brain stimulation with a double coil: the importance of coil orientation. Electroencephalogr. Clin. Neurophysiol., 85, 1721.
  • Nissen, M.J. & Bullemer, P. (1987) Attentional requirements of learning: evidence from performance measures. Cogn. Psychol., 19, 132.
  • Nitsche, M.A., Schauenburg, A., Lang, N., Liebetanz, D., Exner, C., Paulus, W. & Tergau, F. (2003) Facilitation of implicit motor learning by weak transcranial direct current stimulation of the primary motor cortex in the human. J. Cogn. Neurosci., 15, 619626.
  • Nitsche, M.A., Jakoubkova, M., Thirugnanasambandam, N., Schmalfuss, L., Hullemann, S., Sonka, K., Paulus, W., Trenkwalder, C. & Happe, S. (2010) Contribution of the premotor cortex to consolidation of motor sequence learning in humans during sleep. J. Neurophysiol., 104, 26032614.
  • Pascual-Leone, A., Grafman, J. & Hallett, M. (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge. Science, 263, 12871289.
  • Poldrack, R.A. & Packard, M.G. (2003) Competition among multiple memory systems: converging evidence from animal and human brain studies. Neuropsychologia, 41, 245251.
  • Press, D.Z., Casement, M.D., Pascual-Leone, A. & Robertson, E.M. (2005) The time course of off-line motor sequence learning. Brain Res. Cogn. Brain Res., 25, 375378.
  • Reis, J., Robertson, E., Krakauer, J.W., Rothwell, J., Marshall, L., Gerloff, C., Wassermann, E., Pascual-Leone, A., Hummel, F., Celnik, P.A., Classen, J., Floel, A., Ziemann, U., Paulus, W., Siebner, H.R., Born, J. & Cohen, L.G. (2008) Consensus: ‘Can tDCS and TMS enhance motor learning and memory formation?’ Brain Stimul., 1, 363369.
  • Reis, J., Schambra, H.M., Cohen, L.G., Buch, E.R., Fritsch, B., Zarahn, E., Celnik, P.A. & Krakauer, J.W. (2009) Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc. Natl. Acad. Sci. USA, 106, 15901595.
  • Robertson, E.M. (2009) From creation to consolidation: a novel framework for memory processing. PLoS Biol., 7, e19.
  • Robertson, E.M. & Cohen, D.A. (2006) Understanding consolidation through the architecture of memories. Neuroscientist, 12, 261271.
  • Robertson, E.M., Tormos, J.M., Maeda, F. & Pascual-Leone, A. (2001) The role of the dorsolateral prefrontal cortex during sequence learning is specific for spatial information. Cereb. Cortex, 11, 628635.
  • Squire, L.R. (1986) Mechanisms of memory. Science, 232, 16121619.
  • Stagg, C.J. & Nitsche, M.A. (2011) Physiological basis of transcranial direct current stimulation. Neuroscientist, 17, 3753.
  • Stagg, C.J., Bachtiar, V. & Johansen-Berg, H. (2011a) The role of GABA in human motor learning. Curr. Biol., 21, 480484.
  • Stagg, C.J., Jayaram, G., Pastor, D., Kincses, Z.T., Matthews, P.M. & Johansen-Berg, H. (2011b) Polarity and timing-dependent effects of transcranial direct current stimulation in explicit motor learning. Neuropsychologia, 49, 800804.
  • Tecchio, F., Zappasodi, F., Assenza, G., Tombini, M., Vollaro, S., Barbati, G. & Rossini, P.M. (2010) Anodal transcranial direct current stimulation enhances procedural consolidation. J. Neurophysiol., 104, 11341140.
  • Vidoni, E.D. & Boyd, L.A. (2007) Achieving enlightenment: what do we know about the implicit learning system and its interaction with explicit knowledge? J. Neurol. Phys. Ther., 31, 145154.