Neuroscience 275 (2014) 47–53
CHANGES OF MOTOR-CORTICAL OSCILLATIONS ASSOCIATED WITH MOTOR LEARNING B. POLLOK, a* D. LATZ, a V. KRAUSE, a M. BUTZ b AND A. SCHNITZLER a,b
INTRODUCTION Motor skills are acquired during practice but often even continue to develop after practice sessions during so-called offline periods (Karni et al., 1998; Walker et al., 2002; Robertson et al., 2005; Hallgato et al., 2013). There is converging evidence that such consolidation requires a critical period after initial learning which varies between 1 and 6 h (Brashers-Krug et al., 1996; Shadmehr and Brashers-Krug, 1997; Shadmehr and Holcomb, 1997; Robertson et al., 2005; Janacsek and Nemeth, 2012). Nevertheless, a few studies reveal evidence for the assumption that improvement may occur even after a brief interval of 15 min (Denny et al., 1955; Rachman and Grassi, 1965) for a review see Halsband and Lange (2006) suggesting that newly learned motor skills become rapidly stabilized being less susceptible for interference with other motor skills (Muellbacher et al., 2002; Krakauer and Shadmehr, 2006). The pivotal role of the primary motor cortex (M1) for stabilization of newly learned skills has been evidenced in animal studies (Nudo et al., 1996; Kleim et al., 1998; Plautz et al., 2000) as well as in humans using transcranial magnetic stimulation (TMS) (Pascual-Leone et al., 1994; Classen et al., 1998; Muellbacher et al., 2002; Robertson et al., 2005) and continuous theta-burst stimulation (Krakauer and Shadmehr, 2006; Iezzi et al., 2010). These data indicate that disrupting M1 excitability within a time period up to 2 h after initial learning deteriorates consolidation and blocks offline improvement over day (Robertson et al., 2005). M1 seems to be particularly relevant for learning of repetitive movements (Muellbacher et al., 2001; Baraduc et al., 2004; Censor and Cohen, 2011). Mapping of the motor cortex by TMS during motor learning revealed that the motor cortical output maps become progressively larger during implicit learning and return to baseline, when knowledge becomes explicit (Pascual-Leone et al., 1994) supporting the significance of M1 particularly for implicit learning (for reviews see (Ashe et al., 2006; Halsband and Lange, 2006)). Functional reorganization associated with motor learning is most likely due to long-term potentiation (LTP)-like effects as shown in animals (Rioult-Pedotti et al., 1998, 2000; Hodgson et al., 2005) and humans (Ziemann et al., 2004; Jung and Ziemann, 2009). Motor learning is additionally associated with changes of oscillatory activity in the alpha (8–12 Hz) (Zhuang et al., 1997) and beta (13–30 Hz) frequency range (Boonstra et al., 2007; Houweling et al., 2008). Synchronized oscillatory activity represents a pivotal mechanism for neuronal
a
Heinrich-Heine University Duesseldorf, Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, D-40225 Duesseldorf, Germany b Heinrich-Heine University Duesseldorf, Medical Faculty, Department of Neurology, D-40225 Duesseldorf, Germany
Abstract—Motor learning results from practice but also between practice sessions. After skill acquisition early consolidation results in less interference with other motor tasks and even improved performance of the newly learned skill. A specific significance of the primary motor cortex (M1) for early consolidation has been suggested. Since synchronized oscillatory activity is assumed to facilitate neuronal plasticity, we here investigate alterations of motor-cortical oscillations by means of event-related desynchronization (ERD) at alpha (8–12 Hz) and beta (13–30 Hz) frequencies in healthy humans. Neuromagnetic activity was recorded using a 306-channel whole-head magnetoencephalography (MEG) system. ERD was investigated in 15 subjects during training on a serial reaction time task and 10 min after initial training. The data were compared with performance during a randomly varying sequence serving as control condition. The data reveal a stepwise decline of alpha-band ERD associated with faster reaction times replicating previous findings. The amount of beta-band suppression was significantly correlated with reduction of reaction times. While changes of alpha power have been related to lower cognitive control after initial skill acquisition, the present data suggest that the amount of beta suppression represents a neurophysiological marker of early cortical reorganization associated with motor learning. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.
Key words: event related desynchronization (ERD), humans, magnetoencephalography, serial reaction time task.
*Corresponding author. Address: Institute of Clinical Neuroscience and Medical, Psychology, Heinrich-Heine University, Universitaetsstr. 1, 40225 Duesseldorf, Germany. Tel: +49-211-81-10767; fax: +49211-81-13015. E-mail address: [email protected] (B. Pollok). Abbreviations: ERD, event-related desynchronization; ITI, inter-tap intervals; LTP, long-term potentiation; M1, primary motor cortex; MEG, magnetoencephalography; PMC, premotor cortex; SRTT, serial reaction time task; S1/M1, primary sensorimotor cortex; TMS, transcranial magnetic stimulation. http://dx.doi.org/10.1016/j.neuroscience.2014.06.008 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 47
48
B. Pollok et al. / Neuroscience 275 (2014) 47–53
communication (Buzsaki and Draguhn, 2004; Fries, 2005). By temporally linking neurons in functional assemblies synchronized oscillatory activity facilitates neuronal plasticity and therefore plays an important role for consolidation of skills and knowledge. TMS as well as behavioral studies suggest that consolidation of a newly learned movement requires at least 1 h. The neurophysiological changes within this interval have not been addressed so far. Therefore, the present study aims at investigating changes of motor cortical oscillations during acquisition and early consolidation of a motor sequence using magnetoencephalography (MEG).
EXPERIMENTAL PROCEDURES Subjects and paradigm Fifteen healthy subjects (seven male) participated in this study which was approved by the local ethics committee and complies with the Declaration of Helsinki. Data from one subject were excluded from the analysis due to poor quality of the MEG data. All participants gave their written informed consent prior to data acquisition. Mean age was 28.0 ± 2.3 years (mean ± standard error of the mean, s.e.m.). Subjects were naı¨ ve regarding the exact purpose of the study. The Edinburgh Handedness Inventory (Oldfield, 1971) revealed a mean lateralization ratio of 93.2 ± 1.5 indicating that all participants were right-handed. Subjects performed a serial reaction time task (SRTT) that is commonly used for the investigation of motor learning (Nissen and Bullemer, 1987). It was introduced to the participants as a measure of reaction times. Four response keys of a nonmagnetic custom made response-box anatomically aligned to the right hand were spatially mapped with respect to four horizontally aligned
bars presented on a back projection screen (Fig. 1) by a Panasonic PT-D7700E DLP projector (Panasonic Europe Ltd., Bracknell, U.K.). Subjects were instructed to react as quickly as possible as soon as one of the four bars changed from dark blue to light blue. The correct response triggered the presentation of the next bar after a time interval of 2 s in order to keep the overall movement rate constant. In case subjects did not press the correct button the bar remained light blue until subjects responded correctly. Visual stimuli were presented on a back projection screen. The stimulus was presented at 3.9° of angle of vision (width 16 cm, height 11 cm, distance 160 cm). Stimulus presentation and recording of reaction times were controlled with the help of EprimeÒ software (Psychology Software Tools, Sharpsburg, PA, USA) installed on a standard windows computer. Reaction times were determined by measuring each button press onset. Each subject performed three runs. In the random condition, presentation of the bars was completely randomized. Bars were presented 200 times. During sequential 1 and sequential 2 a cyclically repeating sequence of eight stimuli requiring a sequence of eight button presses was presented 25 times resulting in additional 200 button presses for each sequential run. The sequence was thumb, middle, ring, index, middle, index, ring, middle, index. The order of sequential 1 and random was counterbalanced across subjects. Both runs always followed immediately after each other. Sequential 2 always followed sequential 1 after a break of 10 min. During the break subjects remained in the magnetically shielded room without any specific task. Response times that were two standard deviations below or above mean individual reaction times were defined as outliers and excluded from further analysis.
Fig. 1. Experimental setting. (A) The four response keys of a button box were spatially mapped with respect to four horizontally aligned bars presented on the back projection screen. Subjects were instructed to press the appropriate button as soon as the associate bar turned from dark to light blue. (B) MEG was recorded during presentation of a randomly varying sequence (random) and two sequential runs. Please note that random and sequential 1 were counterbalanced across subjects and were conducted immediately after each other while sequential 2 always followed sequential 1 with a break of 10 min. Sequential 1 indicates motor learning and sequential 2 serves as a measure of early motor consolidation.
49
B. Pollok et al. / Neuroscience 275 (2014) 47–53
MEG Neuromagnetic activity was non-invasively recorded with a 306-channel whole-head MEG system (Elekta, Oy, Helsinki, Finland). The system consists of 102 magnetometers and 204 planar gradiometers. Only gradiometers were considered for data analysis. Eye blinks were controlled by vertical electrooculogram (EOG). Data were digitized at a sampling rate of 1000 Hz, filtered online with a bandpass filter of 0.03– 330 Hz, and stored digitally for offline analysis. Oscillatory activity as a function of time was analyzed by calculating event-related desynchronization (ERD) individually for each condition (random, sequential 1, sequential 2) as described previously (Pfurtscheller and Lopes da Silva, 1999). Data were averaged from 2 s before to 3 s after button press onset. Since a pure resting baseline was not given, we defined the entire interval (( 2) –3 s) as baseline. Finally, a selection of 30 sensors covering the left primary sensorimotor cortex (S1/M1) was averaged (Fig. 2). Latency and amplitude of maximal ERD at alpha and beta frequencies was determined, respectively in each individual.
sequential 1 (t(13) = 7.40; p < 0.01). Reaction times are summarized in Fig. 3A. Analysis of skill acquisition showed reduction of reaction times of 50.21 ± 10.79 ms (skill 1) and 112.72 ± 12.66 ms (skill 2) as compared to random suggesting superior learning during sequential 2 as compared to sequential 1 (t(13) = 7.40; p < 0.01). In order to determine changes of overall movement rate inter-tap intervals (ITI) were calculated. ITI was reduced to 97.84 ± 0.39% (sequential 1) and 95.01 ± 0.50% (sequential 2) as compared to random suggesting that despite faster reaction times associated with sequence learning overall movement tempo was increased marginally, only. Comparison between the last eight sequences of sequential 1 with the first eight sequences of sequential 2 revealed a significant decrease of reaction times from 376.53 ± 13.07 ms (sequential 1) to 334.40 ± 13.54 ms (sequential 2), (t(13) = 3.52, p = 0.004) suggesting that offline consolidation occurred. The comparison of the first and the last eight sequences of sequential 2 also revealed faster reaction times at the end of sequential 2 (322.54 ± 11.27 ms), but this effect was not found to be significant (t(13) = 0.87, p = 0.40.
Statistical analyses In order to ensure that offline consolidation occurred, we compared the last eight sequences of sequential 1 with the first eight sequences of sequential 2. To exclude offline learning during sequential 2, we compared the first with the last eight sequences of this condition. According to Press et al. (2005), we calculated skill acquisition in each group by subtracting reaction times during both sequential runs and random (i.e. skill 1 = random – sequential 1; skill 2 = random – sequential 2). Then, differences between skill 1 and skill 2 were calculated as a measure of improvement between both sequential sessions (Delta RT = skill 2 – skill 1). Please note that stronger improvement of reaction times during sequential 2 as compared to sequential 1 results in positive Delta RTs. Along the same line, differences were calculated for ERD amplitudes at alpha and beta frequencies, respectively. The data were controlled for Gaussian distribution by Kolmogorov Smirnov test and statistically analyzed using a repeated analysis of variance (ANOVA) with factor condition (random vs. sequential 1 vs. sequential 2), using IBM SPSS Statistics 20. For post hoc comparisons and comparison between two dependent samples t-tests were calculated. P-values were corrected for multiple testing using the sequentially rejective Bonferroni test (Holm, 1979). Correlation analysis between behavioral and MEG data was performed using Pearson’s correlation.
RESULTS Motor learning Analysis of reaction times revealed a main effect of factor condition (F(2, 26) = 55.01; p < 0.01). Post hoc tests indicated significantly faster reaction times during sequential 1 as compared to random (t(13) = 4.65; p < 0.01) and during sequential 2 as compared to
MEG data Maximal ERD was determined at 148.90 ± 35.40 ms on average. The analysis did not reveal a main effect of factor condition neither at alpha nor at beta frequency (p > 0.5). The analysis of the ERD amplitude showed a stepwise reduction at alpha frequency only (F(2,26) = 3.74; p = 0.03). T-tests showed that during sequential 2 ERD amplitude was significantly reduced as compared to random (t(13) = 2.94; p = 0.01; Fig. 3). Comparing ERD changes of skill 1 (i.e. random – sequential 1) and skill 2 (i.e. random – sequential 2) revealed neither significant differences at alpha (t(13) = 0.75; p = 0.47) nor at beta frequency t(13) = 0.10, p = 0.92). ERD differences between skill 1 and skill 2 were calculated and correlated with reaction time differences using Pearson’s statistics. The analysis showed a significant inverse correlation at beta (R = 0.67; p = 0.01; Fig. 4) but not at alpha frequency (R = 0.11; p = 0.69) indicating that superior learning is associated with stronger suppression of beta power during sequential 2.
DISCUSSION The present study aims at elucidating changes of motor cortical oscillations associated with acquisition and early consolidation of a motor sequence. The data support the hypothesis that motor learning is associated with a stepwise decline of alpha-band ERD and suggest that improvement of reaction times linearly varies with the amount of beta power suppression. Neuroimaging studies evidenced activation changes within the primary motor or somatosensory cortex associated with motor learning. But the results remain
50
B. Pollok et al. / Neuroscience 275 (2014) 47–53
Fig. 2. (A) Sensor plot of 204 gradiometers showing ERD at beta frequency (13–30 Hz) averaged across all subjects during presentation of a randomly varying sequence. The gray shaded area indicates the sensors selected for data analysis. The insert in the upper left corner denotes the channel with the largest ERD. Here, the gray area indicates the time period of ERD. (B) ERD at alpha (left) and beta (right) frequencies averaged across the selected sensors.
inconsistent. While some studies found increased activation during early learning (Oldfield, 1971; Hazeltine et al., 1997; Toni et al., 1998) that correlated with reaction times (Honda et al., 1998), others found increased M1 activation particularly in performing highly overlearned sequences (Karni et al., 1995) or several hours after training – most likely after firm consolidation of the newly
learned skill (Shadmehr and Holcomb, 1997). The present data suggest that 10 min after skill acquisition alpha ERD attenuates. This result extends previous findings showing that implicit learning is associated with a progressive increase of alpha-band power (Zhuang et al., 1997). Such alpha-band modulation has been related to lower control and attentional demands possibly occurring after skill
B. Pollok et al. / Neuroscience 275 (2014) 47–53
51
Fig. 4. Correlation between reaction time differences and ERD differences (skill 2 – skill 1) at beta (A) and alpha (B) frequencies. The data indicate that improvement of reaction times (i.e. superior learning) is associated with stronger beta power suppression during sequential 2, while alpha power suppression is not significantly correlated with motor learning.
Fig. 3. Mean reaction times (A) and mean ERD amplitude at alpha (B) and beta (C) frequencies during the three experimental runs (random, sequential 1 and sequential 2). Error bars indicate standard error of the mean (s.e.m.). Two asterisks indicate p-values < 0.01. Please note that all reported p-values are corrected for multiple testing by means of the sequentially rejective Bonferroni test.
acquisition (Orban et al., 2010). The present data strengthen the hypothesis that early consolidation of a newly learned motor skill is associated with S1/M1 alterations at alpha frequency. Most importantly, changes of alpha power were not significantly correlated with improvement of reaction times suggesting that it does not reflect a neurophysiological marker of sequence learning per se. As a main result, a significant inverse correlation between improvement of reaction times and the amount of beta power suppression was found suggesting that
superior learning (i.e. faster reaction times) is associated with stronger beta power suppression in particular during consolidation. The data support results from a study investigating bimanual force production (Boonstra et al., 2007). Beta oscillations have been related to the maintenance of a current cognitive or motor state (Engel and Fries, 2010). More precisely, keeping a certain state might be associated with increased beta power on the cost of flexible control strategies. The present data nicely fit this interpretation indicating that lower beta power suppression goes along with reduced gain of reaction times. The results are in line with the hypothesis that beta power modulation may represent a neurophysiological marker of functional reorganization associated with sequence learning (Boonstra et al., 2007) and early consolidation. Previous data suggest that activation changes of M1 particularly reflect movement tempo (van Mier et al., 1998, 2004; Turner et al., 2003; Orban et al., 2010). But, other studies keeping movement rate constant indicate that M1 changes particularly reflect motor learning rather than movement tempo (Pascual-Leone et al., 1994; Karni et al., 1995; Hazeltine et al., 1997). In the present study, we aimed to keep movement rate constant by a two-second break after each button press. The ITI decreased about 2% during sequential 1 and about 5% during sequential 2 as compared to random showing that
52
B. Pollok et al. / Neuroscience 275 (2014) 47–53
effects shown here are less likely due to increased movement tempo. We realize that motor learning is not restricted to changes of M1. Neuroimaging as well as neurophysiological studies suggest that a cerebellocortical and a striato-cortical network is engaged in motor learning (Grafton et al., 1992; Jenkins et al., 1994; Karni et al., 1995; Berns et al., 1997; Hazeltine et al., 1997; Honda et al., 1998; Toni et al., 1998; Hikosaka et al., 2002; Doyon and Benali, 2005; Ashe et al., 2006; Nyberg et al., 2006; Gobel et al., 2011). For instance, the involvement of the dorsal premotor cortex (dPMC) during early learning was shown (Toni et al., 1998). Therefore, one might speculate that effects presented here might be at least partly due to PMC activation. However, a previous study suggests that applying low-frequency repetitive (r) TMS 25 mm anterior to M1 favors motor consolidation (Robertson et al., 2005) weakening this assumption. Additionally, a shift from prefrontal to premotor and posterior parietal and cerebellar cortex occurs within 6 h after task learning (Shadmehr and Holcomb, 1997) and might therefore reflect firm rather than early consolidation. Despite the significant correlation between reduction of reaction times and beta ERD differences, approximately half of the subjects showed motor learning although suppression of beta ERD was not stronger during sequential 2 as compared to sequential 1. Thus, the amount of M1 beta power suppression should be seen as one but not the unique mechanism that favors motor consolidation. We can only speculate under which circumstances beta power suppression is beneficial, but since M1 has been particularly related to implicit learning (for reviews see (Ashe et al., 2006; Halsband and Lange, 2006)), we would argue that in subjects showing faster reaction times associated with stronger beta power suppression implicit learning might prevail, while in subjects using an explicit learning strategy rather prefrontal and premotor areas than M1 might be involved in motor learning and consolidation. Conclusion The present data suggest distinct functional roles of motor cortical alpha and beta oscillations for motor sequence learning. While changes of oscillatory activity at alpha frequency are assumed to indicate reduced attentional and control demands after initial learning, suppression of beta power might represent a neurophysiological marker of S1/M1 reorganization associated with motor learning and early consolidation. Acknowledgments—Vanessa Krause is grateful for two grants from Heinrich-Heine University (9772440, 9772467). Alfons Schnitzler acknowledges support from the Deutsche Forschungsgemeinschaft (DFG; SCHN 592/3-1, EraNet: 01EW0903) and Helmholtz Association (HelMA, HA-215). Bettina Pollok is grateful for financial support by a grant from the DFG (PO806-3) and a grant from Heinrich-Heine-University (9772558). All authors declare no conflict of interests. Funding did neither influence the study design, collection, analysis and interpretation
of the data and the writing of the report nor the decision to submit the manuscript for publication.
REFERENCES Ashe J, Lungu OV, Basford AT, Lu X (2006) Cortical control of motor sequences. Curr Opin Neurobiol 16:213–221. Baraduc P, Lang N, Rothwell JC, Wolpert DM (2004) Consolidation of dynamic motor learning is not disrupted by rTMS of primary motor cortex. Curr Biol 14:252–256. Berns GS, Cohen JD, Mintun MA (1997) Brain regions responsive to novelty in the absence of awareness. Science 276:1272–1275. Boonstra TW, Daffertshofer A, Breakspear M, Beek PJ (2007) Multivariate time-frequency analysis of electromagnetic brain activity during bimanual motor learning. Neuroimage 36:370–377. Brashers-Krug T, Shadmehr R, Bizzi E (1996) Consolidation in human motor memory. Nature 382:252–255. Buzsaki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304:1926–1929. Censor N, Cohen LG (2011) Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory. J Physiol 589:21–28. Classen J, Liepert J, Wise SP, Hallett M, Cohen LG (1998) Rapid plasticity of human cortical movement representation induced by practice. J Neurophysiol 79:1117–1123. Denny MR, Frisbey N, Weaver Jr J (1955) Rotary pursuit performance under alternate conditions of distributed and massed practice. J Exp Psychol 49:48–54. Doyon J, Benali H (2005) Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol 15:161–167. Engel AK, Fries P (2010) Beta-band oscillations–signalling the status quo? Curr Opin Neurobiol 20:156–165. Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9:474–480. Gobel EW, Parrish TB, Reber PJ (2011) Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task. Neuroimage 58:1150–1157. Grafton ST, Mazziotta JC, Presty S, Friston KJ, Frackowiak RS, Phelps ME (1992) Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J Neurosci 12:2542–2548. Hallgato E, Gyori-Dani D, Pekar J, Janacsek K, Nemeth D (2013) The differential consolidation of perceptual and motor learning in skill acquisition. Cortex 49:1073–1081. Halsband U, Lange RK (2006) Motor learning in man: a review of functional and clinical studies. J Physiol Paris 99:414–424. Hazeltine E, Grafton ST, Ivry R (1997) Attention and stimulus characteristics determine the locus of motor-sequence encoding. A PET study. Brain 120:123–140. Hikosaka O, Nakamura K, Sakai K, Nakahara H (2002) Central mechanisms of motor skill learning. Curr Opin Neurobiol 12:217–222. Hodgson RA, Ji Z, Standish S, Boyd-Hodgson TE, Henderson AK, Racine RJ (2005) Training-induced and electrically induced potentiation in the neocortex. Neurobiol Learn Mem 83:22–32. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70. Honda M, Deiber MP, 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(Pt 11):2159–2173. Houweling S, Daffertshofer A, van Dijk BW, Beek PJ (2008) Neural changes induced by learning a challenging perceptual-motor task. Neuroimage 41:1395–1407. Iezzi E, Suppa A, Conte A, Agostino R, Nardella A, Berardelli A (2010) Theta-burst stimulation over primary motor cortex degrades early motor learning. Eur J Neurosci 31:585–592.
B. Pollok et al. / Neuroscience 275 (2014) 47–53 Janacsek K, Nemeth D (2012) Predicting the future: from implicit learning to consolidation. Int J Psychophysiol 83:213–221. Jenkins IH, Brooks DJ, Nixon PD, Frackowiak RS, Passingham RE (1994) Motor sequence learning: a study with positron emission tomography. J Neurosci 14:3775–3790. Jung P, Ziemann U (2009) Homeostatic and nonhomeostatic modulation of learning in human motor cortex. J Neurosci 29:5597–5604. Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature 377:155–158. Karni A, Meyer G, Rey-Hipolito C, Jezzard P, Adams MM, Turner R, Ungerleider LG (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc Natl Acad Sci U S A 95:861–868. Kleim JA, Barbay S, Nudo RJ (1998) Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol 80:3321–3325. Krakauer JW, Shadmehr R (2006) Consolidation of motor memory. Trends Neurosci 29:58–64. Muellbacher W, Ziemann U, Boroojerdi B, Cohen L, Hallett M (2001) Role of the human motor cortex in rapid motor learning. Exp Brain Res 136:431–438. Muellbacher W, Ziemann U, Wissel J, Dang N, Kofler M, Facchini S, Boroojerdi B, Poewe W, Hallett M (2002) Early consolidation in human primary motor cortex. Nature 415:640–644. Nissen M, Bullemer P (1987) Attentional requirements of learning: evidence from performance measures. Cogn Psychol 19:1–32. Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM (1996) Usedependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. J Neurosci 16:785–807. Nyberg L, Eriksson J, Larsson A, Marklund P (2006) Learning by doing versus learning by thinking: an fMRI study of motor and mental training. Neuropsychologia 44:711–717. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113. Orban P, Peigneux P, Lungu O, Albouy G, Breton E, Laberenne F, Benali H, Maquet P, Doyon J (2010) The multifaceted nature of the relationship between performance and brain activity in motor sequence learning. Neuroimage 49:694–702. Pascual-Leone A, Grafman J, Hallett M (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge. Science 263:1287–1289. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857.
53
Plautz EJ, Milliken GW, Nudo RJ (2000) Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Mem 74:27–55. Press DZ, Casement MD, Pascual-Leone A, Robertson EM (2005) The time course of off-line motor sequence learning. Brain Res Cogn Brain Res 25:375–378. Rachman S, Grassi J (1965) Reminiscence, inhibition and consolidation. Br J Psychol 56:157–162. Rioult-Pedotti MS, Friedman D, Hess G, Donoghue JP (1998) Strengthening of horizontal cortical connections following skill learning. Nat Neurosci 1:230–234. Rioult-Pedotti MS, Friedman D, Donoghue JP (2000) Learninginduced LTP in neocortex. Science 290:533–536. Robertson EM, Press DZ, Pascual-Leone A (2005) Off-line learning and the primary motor cortex. J Neurosci 25:6372–6378. Shadmehr R, Brashers-Krug T (1997) Functional stages in the formation of human long-term motor memory. J Neurosci 17:409–419. Shadmehr R, Holcomb HH (1997) Neural correlates of motor memory consolidation. Science 277:821–825. Toni I, Krams M, Turner R, Passingham RE (1998) The time course of changes during motor sequence learning: a whole-brain fMRI study. Neuroimage 8:50–61. Turner RS, Desmurget M, Grethe J, Crutcher MD, Grafton ST (2003) Motor subcircuits mediating the control of movement extent and speed. J Neurophysiol 90:3958–3966. van Mier H, Tempel LW, Perlmutter JS, Raichle ME, Petersen SE (1998) Changes in brain activity during motor learning measured with PET: effects of hand of performance and practice. J Neurophysiol 80:2177–2199. van Mier H, Perlmutter JS, Petersen SE (2004) Functional changes in brain activity during acquisition and practice of movement sequences. Motor Control 8:500–520. Walker MP, Brakefield T, Morgan A, Hobson JA, Stickgold R (2002) Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron 35:205–211. Zhuang P, Toro C, Grafman J, Manganotti P, Leocani L, Hallett M (1997) Event-related desynchronization (ERD) in the alpha frequency during development of implicit and explicit learning. Electroencephalogr Clin Neurophysiol 102:374–381. Ziemann U, Ilic TV, Pauli C, Meintzschel F, Ruge D (2004) Learning modifies subsequent induction of long-term potentiation-like and long-term depression-like plasticity in human motor cortex. J Neurosci 24:1666–1672.
(Accepted 5 June 2014) (Available online 12 June 2014)
CHANGES OF MOTOR-CORTICAL OSCILLATIONS ASSOCIATED WITH MOTOR LEARNING B. POLLOK, a* D. LATZ, a V. KRAUSE, a M. BUTZ b AND A. SCHNITZLER a,b
INTRODUCTION Motor skills are acquired during practice but often even continue to develop after practice sessions during so-called offline periods (Karni et al., 1998; Walker et al., 2002; Robertson et al., 2005; Hallgato et al., 2013). There is converging evidence that such consolidation requires a critical period after initial learning which varies between 1 and 6 h (Brashers-Krug et al., 1996; Shadmehr and Brashers-Krug, 1997; Shadmehr and Holcomb, 1997; Robertson et al., 2005; Janacsek and Nemeth, 2012). Nevertheless, a few studies reveal evidence for the assumption that improvement may occur even after a brief interval of 15 min (Denny et al., 1955; Rachman and Grassi, 1965) for a review see Halsband and Lange (2006) suggesting that newly learned motor skills become rapidly stabilized being less susceptible for interference with other motor skills (Muellbacher et al., 2002; Krakauer and Shadmehr, 2006). The pivotal role of the primary motor cortex (M1) for stabilization of newly learned skills has been evidenced in animal studies (Nudo et al., 1996; Kleim et al., 1998; Plautz et al., 2000) as well as in humans using transcranial magnetic stimulation (TMS) (Pascual-Leone et al., 1994; Classen et al., 1998; Muellbacher et al., 2002; Robertson et al., 2005) and continuous theta-burst stimulation (Krakauer and Shadmehr, 2006; Iezzi et al., 2010). These data indicate that disrupting M1 excitability within a time period up to 2 h after initial learning deteriorates consolidation and blocks offline improvement over day (Robertson et al., 2005). M1 seems to be particularly relevant for learning of repetitive movements (Muellbacher et al., 2001; Baraduc et al., 2004; Censor and Cohen, 2011). Mapping of the motor cortex by TMS during motor learning revealed that the motor cortical output maps become progressively larger during implicit learning and return to baseline, when knowledge becomes explicit (Pascual-Leone et al., 1994) supporting the significance of M1 particularly for implicit learning (for reviews see (Ashe et al., 2006; Halsband and Lange, 2006)). Functional reorganization associated with motor learning is most likely due to long-term potentiation (LTP)-like effects as shown in animals (Rioult-Pedotti et al., 1998, 2000; Hodgson et al., 2005) and humans (Ziemann et al., 2004; Jung and Ziemann, 2009). Motor learning is additionally associated with changes of oscillatory activity in the alpha (8–12 Hz) (Zhuang et al., 1997) and beta (13–30 Hz) frequency range (Boonstra et al., 2007; Houweling et al., 2008). Synchronized oscillatory activity represents a pivotal mechanism for neuronal
a
Heinrich-Heine University Duesseldorf, Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, D-40225 Duesseldorf, Germany b Heinrich-Heine University Duesseldorf, Medical Faculty, Department of Neurology, D-40225 Duesseldorf, Germany
Abstract—Motor learning results from practice but also between practice sessions. After skill acquisition early consolidation results in less interference with other motor tasks and even improved performance of the newly learned skill. A specific significance of the primary motor cortex (M1) for early consolidation has been suggested. Since synchronized oscillatory activity is assumed to facilitate neuronal plasticity, we here investigate alterations of motor-cortical oscillations by means of event-related desynchronization (ERD) at alpha (8–12 Hz) and beta (13–30 Hz) frequencies in healthy humans. Neuromagnetic activity was recorded using a 306-channel whole-head magnetoencephalography (MEG) system. ERD was investigated in 15 subjects during training on a serial reaction time task and 10 min after initial training. The data were compared with performance during a randomly varying sequence serving as control condition. The data reveal a stepwise decline of alpha-band ERD associated with faster reaction times replicating previous findings. The amount of beta-band suppression was significantly correlated with reduction of reaction times. While changes of alpha power have been related to lower cognitive control after initial skill acquisition, the present data suggest that the amount of beta suppression represents a neurophysiological marker of early cortical reorganization associated with motor learning. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.
Key words: event related desynchronization (ERD), humans, magnetoencephalography, serial reaction time task.
*Corresponding author. Address: Institute of Clinical Neuroscience and Medical, Psychology, Heinrich-Heine University, Universitaetsstr. 1, 40225 Duesseldorf, Germany. Tel: +49-211-81-10767; fax: +49211-81-13015. E-mail address: [email protected] (B. Pollok). Abbreviations: ERD, event-related desynchronization; ITI, inter-tap intervals; LTP, long-term potentiation; M1, primary motor cortex; MEG, magnetoencephalography; PMC, premotor cortex; SRTT, serial reaction time task; S1/M1, primary sensorimotor cortex; TMS, transcranial magnetic stimulation. http://dx.doi.org/10.1016/j.neuroscience.2014.06.008 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 47
48
B. Pollok et al. / Neuroscience 275 (2014) 47–53
communication (Buzsaki and Draguhn, 2004; Fries, 2005). By temporally linking neurons in functional assemblies synchronized oscillatory activity facilitates neuronal plasticity and therefore plays an important role for consolidation of skills and knowledge. TMS as well as behavioral studies suggest that consolidation of a newly learned movement requires at least 1 h. The neurophysiological changes within this interval have not been addressed so far. Therefore, the present study aims at investigating changes of motor cortical oscillations during acquisition and early consolidation of a motor sequence using magnetoencephalography (MEG).
EXPERIMENTAL PROCEDURES Subjects and paradigm Fifteen healthy subjects (seven male) participated in this study which was approved by the local ethics committee and complies with the Declaration of Helsinki. Data from one subject were excluded from the analysis due to poor quality of the MEG data. All participants gave their written informed consent prior to data acquisition. Mean age was 28.0 ± 2.3 years (mean ± standard error of the mean, s.e.m.). Subjects were naı¨ ve regarding the exact purpose of the study. The Edinburgh Handedness Inventory (Oldfield, 1971) revealed a mean lateralization ratio of 93.2 ± 1.5 indicating that all participants were right-handed. Subjects performed a serial reaction time task (SRTT) that is commonly used for the investigation of motor learning (Nissen and Bullemer, 1987). It was introduced to the participants as a measure of reaction times. Four response keys of a nonmagnetic custom made response-box anatomically aligned to the right hand were spatially mapped with respect to four horizontally aligned
bars presented on a back projection screen (Fig. 1) by a Panasonic PT-D7700E DLP projector (Panasonic Europe Ltd., Bracknell, U.K.). Subjects were instructed to react as quickly as possible as soon as one of the four bars changed from dark blue to light blue. The correct response triggered the presentation of the next bar after a time interval of 2 s in order to keep the overall movement rate constant. In case subjects did not press the correct button the bar remained light blue until subjects responded correctly. Visual stimuli were presented on a back projection screen. The stimulus was presented at 3.9° of angle of vision (width 16 cm, height 11 cm, distance 160 cm). Stimulus presentation and recording of reaction times were controlled with the help of EprimeÒ software (Psychology Software Tools, Sharpsburg, PA, USA) installed on a standard windows computer. Reaction times were determined by measuring each button press onset. Each subject performed three runs. In the random condition, presentation of the bars was completely randomized. Bars were presented 200 times. During sequential 1 and sequential 2 a cyclically repeating sequence of eight stimuli requiring a sequence of eight button presses was presented 25 times resulting in additional 200 button presses for each sequential run. The sequence was thumb, middle, ring, index, middle, index, ring, middle, index. The order of sequential 1 and random was counterbalanced across subjects. Both runs always followed immediately after each other. Sequential 2 always followed sequential 1 after a break of 10 min. During the break subjects remained in the magnetically shielded room without any specific task. Response times that were two standard deviations below or above mean individual reaction times were defined as outliers and excluded from further analysis.
Fig. 1. Experimental setting. (A) The four response keys of a button box were spatially mapped with respect to four horizontally aligned bars presented on the back projection screen. Subjects were instructed to press the appropriate button as soon as the associate bar turned from dark to light blue. (B) MEG was recorded during presentation of a randomly varying sequence (random) and two sequential runs. Please note that random and sequential 1 were counterbalanced across subjects and were conducted immediately after each other while sequential 2 always followed sequential 1 with a break of 10 min. Sequential 1 indicates motor learning and sequential 2 serves as a measure of early motor consolidation.
49
B. Pollok et al. / Neuroscience 275 (2014) 47–53
MEG Neuromagnetic activity was non-invasively recorded with a 306-channel whole-head MEG system (Elekta, Oy, Helsinki, Finland). The system consists of 102 magnetometers and 204 planar gradiometers. Only gradiometers were considered for data analysis. Eye blinks were controlled by vertical electrooculogram (EOG). Data were digitized at a sampling rate of 1000 Hz, filtered online with a bandpass filter of 0.03– 330 Hz, and stored digitally for offline analysis. Oscillatory activity as a function of time was analyzed by calculating event-related desynchronization (ERD) individually for each condition (random, sequential 1, sequential 2) as described previously (Pfurtscheller and Lopes da Silva, 1999). Data were averaged from 2 s before to 3 s after button press onset. Since a pure resting baseline was not given, we defined the entire interval (( 2) –3 s) as baseline. Finally, a selection of 30 sensors covering the left primary sensorimotor cortex (S1/M1) was averaged (Fig. 2). Latency and amplitude of maximal ERD at alpha and beta frequencies was determined, respectively in each individual.
sequential 1 (t(13) = 7.40; p < 0.01). Reaction times are summarized in Fig. 3A. Analysis of skill acquisition showed reduction of reaction times of 50.21 ± 10.79 ms (skill 1) and 112.72 ± 12.66 ms (skill 2) as compared to random suggesting superior learning during sequential 2 as compared to sequential 1 (t(13) = 7.40; p < 0.01). In order to determine changes of overall movement rate inter-tap intervals (ITI) were calculated. ITI was reduced to 97.84 ± 0.39% (sequential 1) and 95.01 ± 0.50% (sequential 2) as compared to random suggesting that despite faster reaction times associated with sequence learning overall movement tempo was increased marginally, only. Comparison between the last eight sequences of sequential 1 with the first eight sequences of sequential 2 revealed a significant decrease of reaction times from 376.53 ± 13.07 ms (sequential 1) to 334.40 ± 13.54 ms (sequential 2), (t(13) = 3.52, p = 0.004) suggesting that offline consolidation occurred. The comparison of the first and the last eight sequences of sequential 2 also revealed faster reaction times at the end of sequential 2 (322.54 ± 11.27 ms), but this effect was not found to be significant (t(13) = 0.87, p = 0.40.
Statistical analyses In order to ensure that offline consolidation occurred, we compared the last eight sequences of sequential 1 with the first eight sequences of sequential 2. To exclude offline learning during sequential 2, we compared the first with the last eight sequences of this condition. According to Press et al. (2005), we calculated skill acquisition in each group by subtracting reaction times during both sequential runs and random (i.e. skill 1 = random – sequential 1; skill 2 = random – sequential 2). Then, differences between skill 1 and skill 2 were calculated as a measure of improvement between both sequential sessions (Delta RT = skill 2 – skill 1). Please note that stronger improvement of reaction times during sequential 2 as compared to sequential 1 results in positive Delta RTs. Along the same line, differences were calculated for ERD amplitudes at alpha and beta frequencies, respectively. The data were controlled for Gaussian distribution by Kolmogorov Smirnov test and statistically analyzed using a repeated analysis of variance (ANOVA) with factor condition (random vs. sequential 1 vs. sequential 2), using IBM SPSS Statistics 20. For post hoc comparisons and comparison between two dependent samples t-tests were calculated. P-values were corrected for multiple testing using the sequentially rejective Bonferroni test (Holm, 1979). Correlation analysis between behavioral and MEG data was performed using Pearson’s correlation.
RESULTS Motor learning Analysis of reaction times revealed a main effect of factor condition (F(2, 26) = 55.01; p < 0.01). Post hoc tests indicated significantly faster reaction times during sequential 1 as compared to random (t(13) = 4.65; p < 0.01) and during sequential 2 as compared to
MEG data Maximal ERD was determined at 148.90 ± 35.40 ms on average. The analysis did not reveal a main effect of factor condition neither at alpha nor at beta frequency (p > 0.5). The analysis of the ERD amplitude showed a stepwise reduction at alpha frequency only (F(2,26) = 3.74; p = 0.03). T-tests showed that during sequential 2 ERD amplitude was significantly reduced as compared to random (t(13) = 2.94; p = 0.01; Fig. 3). Comparing ERD changes of skill 1 (i.e. random – sequential 1) and skill 2 (i.e. random – sequential 2) revealed neither significant differences at alpha (t(13) = 0.75; p = 0.47) nor at beta frequency t(13) = 0.10, p = 0.92). ERD differences between skill 1 and skill 2 were calculated and correlated with reaction time differences using Pearson’s statistics. The analysis showed a significant inverse correlation at beta (R = 0.67; p = 0.01; Fig. 4) but not at alpha frequency (R = 0.11; p = 0.69) indicating that superior learning is associated with stronger suppression of beta power during sequential 2.
DISCUSSION The present study aims at elucidating changes of motor cortical oscillations associated with acquisition and early consolidation of a motor sequence. The data support the hypothesis that motor learning is associated with a stepwise decline of alpha-band ERD and suggest that improvement of reaction times linearly varies with the amount of beta power suppression. Neuroimaging studies evidenced activation changes within the primary motor or somatosensory cortex associated with motor learning. But the results remain
50
B. Pollok et al. / Neuroscience 275 (2014) 47–53
Fig. 2. (A) Sensor plot of 204 gradiometers showing ERD at beta frequency (13–30 Hz) averaged across all subjects during presentation of a randomly varying sequence. The gray shaded area indicates the sensors selected for data analysis. The insert in the upper left corner denotes the channel with the largest ERD. Here, the gray area indicates the time period of ERD. (B) ERD at alpha (left) and beta (right) frequencies averaged across the selected sensors.
inconsistent. While some studies found increased activation during early learning (Oldfield, 1971; Hazeltine et al., 1997; Toni et al., 1998) that correlated with reaction times (Honda et al., 1998), others found increased M1 activation particularly in performing highly overlearned sequences (Karni et al., 1995) or several hours after training – most likely after firm consolidation of the newly
learned skill (Shadmehr and Holcomb, 1997). The present data suggest that 10 min after skill acquisition alpha ERD attenuates. This result extends previous findings showing that implicit learning is associated with a progressive increase of alpha-band power (Zhuang et al., 1997). Such alpha-band modulation has been related to lower control and attentional demands possibly occurring after skill
B. Pollok et al. / Neuroscience 275 (2014) 47–53
51
Fig. 4. Correlation between reaction time differences and ERD differences (skill 2 – skill 1) at beta (A) and alpha (B) frequencies. The data indicate that improvement of reaction times (i.e. superior learning) is associated with stronger beta power suppression during sequential 2, while alpha power suppression is not significantly correlated with motor learning.
Fig. 3. Mean reaction times (A) and mean ERD amplitude at alpha (B) and beta (C) frequencies during the three experimental runs (random, sequential 1 and sequential 2). Error bars indicate standard error of the mean (s.e.m.). Two asterisks indicate p-values < 0.01. Please note that all reported p-values are corrected for multiple testing by means of the sequentially rejective Bonferroni test.
acquisition (Orban et al., 2010). The present data strengthen the hypothesis that early consolidation of a newly learned motor skill is associated with S1/M1 alterations at alpha frequency. Most importantly, changes of alpha power were not significantly correlated with improvement of reaction times suggesting that it does not reflect a neurophysiological marker of sequence learning per se. As a main result, a significant inverse correlation between improvement of reaction times and the amount of beta power suppression was found suggesting that
superior learning (i.e. faster reaction times) is associated with stronger beta power suppression in particular during consolidation. The data support results from a study investigating bimanual force production (Boonstra et al., 2007). Beta oscillations have been related to the maintenance of a current cognitive or motor state (Engel and Fries, 2010). More precisely, keeping a certain state might be associated with increased beta power on the cost of flexible control strategies. The present data nicely fit this interpretation indicating that lower beta power suppression goes along with reduced gain of reaction times. The results are in line with the hypothesis that beta power modulation may represent a neurophysiological marker of functional reorganization associated with sequence learning (Boonstra et al., 2007) and early consolidation. Previous data suggest that activation changes of M1 particularly reflect movement tempo (van Mier et al., 1998, 2004; Turner et al., 2003; Orban et al., 2010). But, other studies keeping movement rate constant indicate that M1 changes particularly reflect motor learning rather than movement tempo (Pascual-Leone et al., 1994; Karni et al., 1995; Hazeltine et al., 1997). In the present study, we aimed to keep movement rate constant by a two-second break after each button press. The ITI decreased about 2% during sequential 1 and about 5% during sequential 2 as compared to random showing that
52
B. Pollok et al. / Neuroscience 275 (2014) 47–53
effects shown here are less likely due to increased movement tempo. We realize that motor learning is not restricted to changes of M1. Neuroimaging as well as neurophysiological studies suggest that a cerebellocortical and a striato-cortical network is engaged in motor learning (Grafton et al., 1992; Jenkins et al., 1994; Karni et al., 1995; Berns et al., 1997; Hazeltine et al., 1997; Honda et al., 1998; Toni et al., 1998; Hikosaka et al., 2002; Doyon and Benali, 2005; Ashe et al., 2006; Nyberg et al., 2006; Gobel et al., 2011). For instance, the involvement of the dorsal premotor cortex (dPMC) during early learning was shown (Toni et al., 1998). Therefore, one might speculate that effects presented here might be at least partly due to PMC activation. However, a previous study suggests that applying low-frequency repetitive (r) TMS 25 mm anterior to M1 favors motor consolidation (Robertson et al., 2005) weakening this assumption. Additionally, a shift from prefrontal to premotor and posterior parietal and cerebellar cortex occurs within 6 h after task learning (Shadmehr and Holcomb, 1997) and might therefore reflect firm rather than early consolidation. Despite the significant correlation between reduction of reaction times and beta ERD differences, approximately half of the subjects showed motor learning although suppression of beta ERD was not stronger during sequential 2 as compared to sequential 1. Thus, the amount of M1 beta power suppression should be seen as one but not the unique mechanism that favors motor consolidation. We can only speculate under which circumstances beta power suppression is beneficial, but since M1 has been particularly related to implicit learning (for reviews see (Ashe et al., 2006; Halsband and Lange, 2006)), we would argue that in subjects showing faster reaction times associated with stronger beta power suppression implicit learning might prevail, while in subjects using an explicit learning strategy rather prefrontal and premotor areas than M1 might be involved in motor learning and consolidation. Conclusion The present data suggest distinct functional roles of motor cortical alpha and beta oscillations for motor sequence learning. While changes of oscillatory activity at alpha frequency are assumed to indicate reduced attentional and control demands after initial learning, suppression of beta power might represent a neurophysiological marker of S1/M1 reorganization associated with motor learning and early consolidation. Acknowledgments—Vanessa Krause is grateful for two grants from Heinrich-Heine University (9772440, 9772467). Alfons Schnitzler acknowledges support from the Deutsche Forschungsgemeinschaft (DFG; SCHN 592/3-1, EraNet: 01EW0903) and Helmholtz Association (HelMA, HA-215). Bettina Pollok is grateful for financial support by a grant from the DFG (PO806-3) and a grant from Heinrich-Heine-University (9772558). All authors declare no conflict of interests. Funding did neither influence the study design, collection, analysis and interpretation
of the data and the writing of the report nor the decision to submit the manuscript for publication.
REFERENCES Ashe J, Lungu OV, Basford AT, Lu X (2006) Cortical control of motor sequences. Curr Opin Neurobiol 16:213–221. Baraduc P, Lang N, Rothwell JC, Wolpert DM (2004) Consolidation of dynamic motor learning is not disrupted by rTMS of primary motor cortex. Curr Biol 14:252–256. Berns GS, Cohen JD, Mintun MA (1997) Brain regions responsive to novelty in the absence of awareness. Science 276:1272–1275. Boonstra TW, Daffertshofer A, Breakspear M, Beek PJ (2007) Multivariate time-frequency analysis of electromagnetic brain activity during bimanual motor learning. Neuroimage 36:370–377. Brashers-Krug T, Shadmehr R, Bizzi E (1996) Consolidation in human motor memory. Nature 382:252–255. Buzsaki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304:1926–1929. Censor N, Cohen LG (2011) Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory. J Physiol 589:21–28. Classen J, Liepert J, Wise SP, Hallett M, Cohen LG (1998) Rapid plasticity of human cortical movement representation induced by practice. J Neurophysiol 79:1117–1123. Denny MR, Frisbey N, Weaver Jr J (1955) Rotary pursuit performance under alternate conditions of distributed and massed practice. J Exp Psychol 49:48–54. Doyon J, Benali H (2005) Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol 15:161–167. Engel AK, Fries P (2010) Beta-band oscillations–signalling the status quo? Curr Opin Neurobiol 20:156–165. Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9:474–480. Gobel EW, Parrish TB, Reber PJ (2011) Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task. Neuroimage 58:1150–1157. Grafton ST, Mazziotta JC, Presty S, Friston KJ, Frackowiak RS, Phelps ME (1992) Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J Neurosci 12:2542–2548. Hallgato E, Gyori-Dani D, Pekar J, Janacsek K, Nemeth D (2013) The differential consolidation of perceptual and motor learning in skill acquisition. Cortex 49:1073–1081. Halsband U, Lange RK (2006) Motor learning in man: a review of functional and clinical studies. J Physiol Paris 99:414–424. Hazeltine E, Grafton ST, Ivry R (1997) Attention and stimulus characteristics determine the locus of motor-sequence encoding. A PET study. Brain 120:123–140. Hikosaka O, Nakamura K, Sakai K, Nakahara H (2002) Central mechanisms of motor skill learning. Curr Opin Neurobiol 12:217–222. Hodgson RA, Ji Z, Standish S, Boyd-Hodgson TE, Henderson AK, Racine RJ (2005) Training-induced and electrically induced potentiation in the neocortex. Neurobiol Learn Mem 83:22–32. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70. Honda M, Deiber MP, 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(Pt 11):2159–2173. Houweling S, Daffertshofer A, van Dijk BW, Beek PJ (2008) Neural changes induced by learning a challenging perceptual-motor task. Neuroimage 41:1395–1407. Iezzi E, Suppa A, Conte A, Agostino R, Nardella A, Berardelli A (2010) Theta-burst stimulation over primary motor cortex degrades early motor learning. Eur J Neurosci 31:585–592.
B. Pollok et al. / Neuroscience 275 (2014) 47–53 Janacsek K, Nemeth D (2012) Predicting the future: from implicit learning to consolidation. Int J Psychophysiol 83:213–221. Jenkins IH, Brooks DJ, Nixon PD, Frackowiak RS, Passingham RE (1994) Motor sequence learning: a study with positron emission tomography. J Neurosci 14:3775–3790. Jung P, Ziemann U (2009) Homeostatic and nonhomeostatic modulation of learning in human motor cortex. J Neurosci 29:5597–5604. Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature 377:155–158. Karni A, Meyer G, Rey-Hipolito C, Jezzard P, Adams MM, Turner R, Ungerleider LG (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc Natl Acad Sci U S A 95:861–868. Kleim JA, Barbay S, Nudo RJ (1998) Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol 80:3321–3325. Krakauer JW, Shadmehr R (2006) Consolidation of motor memory. Trends Neurosci 29:58–64. Muellbacher W, Ziemann U, Boroojerdi B, Cohen L, Hallett M (2001) Role of the human motor cortex in rapid motor learning. Exp Brain Res 136:431–438. Muellbacher W, Ziemann U, Wissel J, Dang N, Kofler M, Facchini S, Boroojerdi B, Poewe W, Hallett M (2002) Early consolidation in human primary motor cortex. Nature 415:640–644. Nissen M, Bullemer P (1987) Attentional requirements of learning: evidence from performance measures. Cogn Psychol 19:1–32. Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM (1996) Usedependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. J Neurosci 16:785–807. Nyberg L, Eriksson J, Larsson A, Marklund P (2006) Learning by doing versus learning by thinking: an fMRI study of motor and mental training. Neuropsychologia 44:711–717. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113. Orban P, Peigneux P, Lungu O, Albouy G, Breton E, Laberenne F, Benali H, Maquet P, Doyon J (2010) The multifaceted nature of the relationship between performance and brain activity in motor sequence learning. Neuroimage 49:694–702. Pascual-Leone A, Grafman J, Hallett M (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge. Science 263:1287–1289. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857.
53
Plautz EJ, Milliken GW, Nudo RJ (2000) Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Mem 74:27–55. Press DZ, Casement MD, Pascual-Leone A, Robertson EM (2005) The time course of off-line motor sequence learning. Brain Res Cogn Brain Res 25:375–378. Rachman S, Grassi J (1965) Reminiscence, inhibition and consolidation. Br J Psychol 56:157–162. Rioult-Pedotti MS, Friedman D, Hess G, Donoghue JP (1998) Strengthening of horizontal cortical connections following skill learning. Nat Neurosci 1:230–234. Rioult-Pedotti MS, Friedman D, Donoghue JP (2000) Learninginduced LTP in neocortex. Science 290:533–536. Robertson EM, Press DZ, Pascual-Leone A (2005) Off-line learning and the primary motor cortex. J Neurosci 25:6372–6378. Shadmehr R, Brashers-Krug T (1997) Functional stages in the formation of human long-term motor memory. J Neurosci 17:409–419. Shadmehr R, Holcomb HH (1997) Neural correlates of motor memory consolidation. Science 277:821–825. Toni I, Krams M, Turner R, Passingham RE (1998) The time course of changes during motor sequence learning: a whole-brain fMRI study. Neuroimage 8:50–61. Turner RS, Desmurget M, Grethe J, Crutcher MD, Grafton ST (2003) Motor subcircuits mediating the control of movement extent and speed. J Neurophysiol 90:3958–3966. van Mier H, Tempel LW, Perlmutter JS, Raichle ME, Petersen SE (1998) Changes in brain activity during motor learning measured with PET: effects of hand of performance and practice. J Neurophysiol 80:2177–2199. van Mier H, Perlmutter JS, Petersen SE (2004) Functional changes in brain activity during acquisition and practice of movement sequences. Motor Control 8:500–520. Walker MP, Brakefield T, Morgan A, Hobson JA, Stickgold R (2002) Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron 35:205–211. Zhuang P, Toro C, Grafman J, Manganotti P, Leocani L, Hallett M (1997) Event-related desynchronization (ERD) in the alpha frequency during development of implicit and explicit learning. Electroencephalogr Clin Neurophysiol 102:374–381. Ziemann U, Ilic TV, Pauli C, Meintzschel F, Ruge D (2004) Learning modifies subsequent induction of long-term potentiation-like and long-term depression-like plasticity in human motor cortex. J Neurosci 24:1666–1672.
(Accepted 5 June 2014) (Available online 12 June 2014)
