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Neural substrates of motor memory consolidation depend on practice structure
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Shailesh S Kantak1,2, Katherine J Sullivan1, Beth E Fisher1,2, Barbara J Knowlton3 & Carolee J Winstein1,4 Motor-skill practice drives subsequent offline activity in functionally related resting human brain networks. We investigated the manner in which offline neural networks are modulated by practice structures that affect motor-skill retention. Interference to dorsolateral-prefrontal cortex (DLPFC), but not to primary motor cortex (M1), after variable practice attenuated motor-skill retention, whereas interference to M1, but not to DLPFC, after constant practice attenuated motor-skill retention. We conclude that neural substrates of motor-memory consolidation are modulated by practice structure. Motor-memory consolidation constitutes offline processes that occur 4–6 h immediately post-practice and result in stabilization of motor memory1,2. These time-dependent consolidation processes actively engage frontoparietal areas during the post-acquisition period3. Interference to motor-memory consolidation often degrades retention of motor skills, the implication being that consolidation is critical for motor learning4,5. The structure of motor practice has a strong influence on motor skill retention6. Task practice structure can be characterized as a continuum with a simple structure, such as constant practice, on one end and a more complex structure, such as variable practice, on the other. A constant practice structure is drill-like, with multiple repetitions of the same task in a row, whereas variable practice structure is one in which a motor task can be randomly interleaved with trials of other motor tasks. Compared with constant practice, strong evidence exists that variable practice enhances long-term retention of a motor skill, although it may not show any performance benefits during or at the end of the practice phase7,8. Given that variable practice leads to better retention than constant practice (contextual interference effect), we hypothesized that differences in the practice structure may drive subsequent offline activity in different neural substrates that are critical to motor-memory consolidation. To test this hypothesis, we used 1-Hz repetitive transcranial magnetic stimulation (rTMS) immediately following constant or variable motor practice of a goal-directed arm-movement task (Supplementary Fig. 1 and Supplementary Methods) to directly interfere with post-practice
processes in M1 or DLPFC (Fig. 1). Both regions have previously been shown to be engaged in early motor-memory consolidation5,9,10. We assessed the effect of rTMS interference on motor learning through motor performance in a retention test administered 1 d after practice. Comparing learning in the M1 and DLPFC interference groups with learning in the control groups allowed us to assess the role of M1 and DLPFC in early motor-memory consolidation following practice under either constant or variable practice structures. We contrasted the retention performance to the end of acquisition (EoA) performance to provide a measure of offline performance changes that occur during consolidation (stabilization). Participants who practiced under variable conditions (variable practice) demonstrated better performance stabilization from EoA to retention than those who practiced under constant conditions (constant practice), yielding a significant effect of practice structure (2 practice condition × 2 test repeated-measures ANOVA, F1,19 = 4.632, P = 0.044). This delayed emergence of practice structure benefit (contextual interference) is an example of the learning-performance distinction, a well-known concept in cognitive neuroscience11. However, the bene ficial effect of variable practice depended on rTMS site. Post-practice rTMS over M1 or DLPFC affected memory stabilization from EoA to retention differently for the two practice conditions (significant
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Figure 1 Experimental design. Participants practiced the task on day 1 either under a constant practice condition or variable practice condition. Immediately following practice, they were tested for EoA performance. Participants were re-tested on a retention test (R) to infer learning of target A3 (criterion task) 1 d later. Participants from each practice condition (constant and variable) were randomized to a control no-rTMS group (constant practice (CP), variable practice (VP)), an M1-interference group (CP-M1, VP-M1) and a DLPFC-interference group (CP-DLPFC, VP-DLPFC). The M1-interference groups received 1-Hz rTMS over M1 and the DLPFCinterference groups received 1-Hz rTMS over DLPFC, immediately after EoA. The black dots represent measurement of corticospinal excitability.
1Motor
Behavior and Neurorehabilitation Laboratory, Division of Biokinesiology and Physical Therapy at the Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, California, USA. 2Neuroplasticity and Imaging Laboratory, Division of Biokinesiology and Physical Therapy at the Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, California, USA. 3Department of Psychology, University of California, Los Angeles, California, USA. 4Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. Correspondence should be addressed to C.J.W. (
[email protected]). Received 9 March; accepted 10 June; published online 11 July 2010; doi:10.1038/nn.2596
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Figure 2 Practice structure and offline motor-memory stabilization. EoA and retention error (RMSE) of participants in the control, M1 interference, DLPFC interference, and delayed interference (VP-DLPFC 4 h and CP-M1 4 h) groups. Left, rTMS interference to DLPFC (gray-filled circle), but not to M1 (black-filled circle), after variable practice attenuated offline motor skill stabilization between EoA and retention compared with the control group (open circle). Performance stabilization between EoA and retention was attenuated only when rTMS was applied over DLPFC immediately after variable practice (gray-filled circle), but not when applied 4 h postpractice (blue-filled square). Right, immediately after constant practice, rTMS to M1 (black-filled circle), but not to DLPFC (gray-filled circle), attenuated offline stabilization of the motor skill compared with the no-rTMS group (open circle). Performance from EoA to retention was significantly attenuated when rTMS was applied over M1 immediately after constant practice (black-filled circle), but not when applied 4 h post-practice (green-filled square). Error bars represent the s.e.m.
i nteraction: 2 practice condition × 3 rTMS site × 2 test repeatedmeasures ANOVA, F2,53 = 5.419, P = 0.007). In those participants who practiced under variable practice conditions, post-practice rTMS interference over M1 did not attenuate performance from EoA to retention compared to the no-rTMS control variable practice group (repeated-measures ANOVA, F1,19 = 0.082, P = 0.778; Fig. 2). In contrast, when rTMS was applied over DLPFC following variable practice, skill performance was significantly attenuated from EoA to retention (repeated-measures ANOVA, F1,18 = 6.47, P = 0.02; Fig. 2). An opposite pattern emerged for participants who trained under constant practice structure. Post-practice rTMS over DLPFC following constant practice did not significantly affect performance stabilization from EoA to retention compared to the no-rTMS control constant practice group (repeated-measures ANOVA, F1,17 = 0.000, P = 0.985; Fig. 2). In contrast, when rTMS was applied over M1 following constant practice, skill performance was significantly attenuated from EoA to retention (repeated-measures ANOVA, F1,18 = 7.627, P = 0.013; Fig. 2). Thus, a double dissociation was observed between practice structure (variable versus constant) and the neural locus of the rTMS interference. One possibility is that the differential effects of post-training rTMS over M1 or DLPFC could be a result of some residual or lingering effects of rTMS on later recall of the skill, rather than on the consolidation process itself. Alternatively, the interference effect may be a result of the effect on motor memory itself than on the process of consolidation. To rule out these possibilities, we randomized 12 additional participants into one of two groups that received rTMS 4-h post-practice: constant practice M1-4 h and variable practice DLPFC-4 h. The constant practice M1-4 h group practiced the skill under constant conditions and had 1-Hz rTMS applied over M1 4 h after practice. The variable practice DLPFC-4 h group practiced the task under variable conditions and rTMS was applied over DLPFC 4 h after practice. The delayed rTMS failed to interfere with retention of
the motor skill in the variable practice DLPFC-4 h (2 group (variable practice DLPFC, variable practice DLPFC-4 h) × 2 test (EoA, retention) repeated-measures ANOVA, F1,13 = 7.153, P = 0.019; Fig. 2) and constant practice M1-4 h (2 group (constant practice M1, constant practice M1-4 h) × 2 test (EoA, retention) repeated-measures ANOVA, F1,14 = 7.59, P = 0.015; Fig. 2) groups, indicating that after 4 h the practiced skill had become resistant to the interference. This finding suggests that the rTMS interference effects on memory stabilization for the constant practice M1 and variable practice DLPFC groups were temporally specific to the immediate post-practice consolidation phase and were not the result of an effect on later recall or on the motor memory itself. A second possible explanation stems from the finding that rTMS effects are dependent on the state of brain activity12. Therefore, the differences in the effects of rTMS in each practice group may be related to differences in brain activity induced by the practice structure itself. This seems unlikely in our study for two reasons. First, our motor corticospinal excitability data suggest that the state of brain activity, as assessed by post-practice motor corticospinal excitability change, was not differentially affected by practice structure. Second, the rTMS applied over M1 after both variable and constant practice significantly downregulated (paired sample t test, t19 = 4.377, P < 0.05) the motor corticospinal excitability (Supplementary Results). Memory for motor skill is thought to be organized to support at least two main components: one that represents the spatial goal of the skill and the other representing the movements needed to achieve that goal13. The consolidation of these different components depends on different brain states, with the goal-based component being consolidated over sleep and the movement-based component being consolidated over wakefulness13. Given the sensitivity of these different components to different brain states, it seems reasonable to suggest that their consolidation is dependent on different neural substrates. One recent theory suggested that consolidation of goal-based components is dependent on a circuit that included DLPFC, whereas the consolidation of movement-based components is dependent on a circuit that included M1 (ref. 13). Our results provide support for this general theory with a further refinement suggesting that consolidation of each component may be differentially modulated by practice structure. The observed double dissociation suggests that variable practice may give rise to a preponderance of the goal component of the motor skill that relies on DLPFC for processing (Supplementary Discussion). This is in accordance with the ‘schema’ theory of motor learning, which suggests that, with higher variability in practice structure, the learner acquires an abstract relationship or schema between the goal and action parameters14,15. A stronger schema is likely to emerge when the learner practices a particular skill version in the context of other versions, such as under variable practice conditions. In contrast, repetitive constant practice may predominantly give rise to a movement component of motor skill that requires processing in M1 (Supplementary Discussion) for offline memory stabilization. Our results support the notion that individual motor memory components are linked to specific neural circuits2 and advance the idea that the neural substrates for consolidation of each component may be differentially modulated by practice structure. These results are the first, to the best of our knowledge, to demonstrate that motor-memory consolidation engages distinct neural substrates that differ depending on practice structure. Practice structures that are more cognitively challenging (that is, variable) may rely on higher-order motor areas such as the dorsolateral prefrontal cortex for motor-memory consolidation, whereas less cognitively challenging constant practice structures may depend more heavily on primary advance online publication nature neuroscience
b r i e f c o m m u n i c at i o n s motor cortex to mediate motor-memory consolidation. These results deepen our understanding of motor-memory organization and provide new insights into the mechanisms supporting consolidation of motor skills. Note: Supplementary information is available on the Nature Neuroscience website. Acknowledgments We thank E.M. Robertson for his thoughtful and constructive comments on an earlier version of the manuscript. The research was supported by a grant from the North American Society for the Psychology of Sport and Physical Activity and an Oakley Fellowship from the Graduate School of the University of Southern California.
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AUTHOR CONTRIBUTIONS S.S.K. provided the theoretical framework, designed the study, conducted experiments, analyzed the data and wrote the manuscript. K.J.S., B.E.F. and B.J.K. helped with the experimental design, data analyses and manuscript writing. C.J.W. provided the theoretical framework and helped with experimental design, data analysis and manuscript writing. K.J.S., B.E.F. and C.J.W. supervised the project. COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests.
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Published online at http://www.nature.com/natureneuroscience/. Reprints and permissions information is available online at http://www.nature.com/ reprintsandpermissions/. 1. Krakauer, J.W. & Shadmehr, R. Trends Neurosci. 29, 58–64 (2006). 2. Robertson, E.M. & Cohen, D.A. Neuroscientist 12, 261–271 (2006). 3. Albert, N.B., Robertson, E.M. & Miall, R.C. Curr. Biol. 19, 1023–1027 (2009). 4. Luft, A.R., Buitrago, M.M., Ringer, T., Dichgans, J. & Schulz, J.B. J. Neurosci. 24, 6515–6520 (2004). 5. Muellbacher, W. et al. Nature 415, 640–644 (2002). 6. Schmidt, R.A. & Bjork, R.A. Psychol. Sci. 3, 207–217 (1992). 7. Shea, C.H. & Kohl, R.M. Res. Q. Exerc. Sport 62, 187–195 (1991). 8. Lee, T.D. & Simon, D. in Skill Acquisition in Sport: Research, Theory and Practice (eds Williams, A.M. & Hodges, N.J.) 29–44 (Routledge, London, 2004). 9. Robertson, E.M., Press, D.Z. & Pascual-Leone, A. J. Neurosci. 25, 6372–6378 (2005). 10. Shadmehr, R. & Holcomb, H.H. Science 277, 821–825 (1997). 11. Cahill, L., McGaugh, J.L. & Weinberger, N.M. Trends Neurosci. 24, 578–581 (2001). 12. Silvanto, J. & Pascual-Leone, A. Brain Topogr. 21, 1–10 (2008). 13. Robertson, E.M. PLoS Biol. 7, e19 (2009). 14. Braun, D.A., Aertsen, A., Wolpert, D.M. & Mehring, C. Curr. Biol. 19, 352–357 (2009). 15. Schmidt, R.A. Psychol. Rev. 82, 225–260 (1975).
