It has famously been claimed that it takes 10,000 hours to become an expert at something. But while most of us will never become concert pianists, we can all learn new motor skills and improve existing ones – from touch-typing to tennis – by practicing. What happens in the brain to produce these improvements in performance?

Researchers have tried to answer this question by scanning the brains of people as they practice motor skills, but the results have proved inconsistent. Some studies find that specific brain areas become more active as people practice. This could indicate that these areas are ‘storing’ new skills. But others report that brain activity decreases with practice. This might indicate that practice instead makes certain brain areas work more efficiently. It is also unclear where in the brain these learning-related changes occur. Some studies suggest that most occur in the primary motor cortex, or M1 – the area that sends commands to muscles. Others suggest that most changes take place outside of M1, in areas that plan movements.

Berlot et al. set out to resolve these inconsistencies by scanning the brains of healthy volunteers as they learned to play six 9-digit sequences on a keyboard. Each volunteer completed about 4,000 training trials over 5 weeks, and had their brain scanned four times. As the weeks passed, the volunteers became faster and more accurate at playing the sequences. However, the activity of their primary motor cortex did not change. By contrast, the activity of areas involved in planning movements decreased throughout training. The patterns of activity for each individual sequence reorganized throughout learning in the areas outside of the M1. This happened most quickly during the early stages of training when the volunteers showed the fastest improvements in performance.

Overall, these findings suggest that when we learn a new skill, activity in the brain areas supporting that skill decrease as the brain becomes more efficient. Increases in brain activity are thus unlikely to reflect the acquired skill. Instead, more subtle changes, in which the brain uses more specific patterns of activity to encode the skill, may underlie improved performance. This may also be true for other types of learning, such as acquiring a new language.

Humans have the remarkable ability to learn complex sequences of movements. While behavioural improvements in sequence learning tasks are easily observable, the underlying neural processes remain elusive. Understanding the neural underpinnings of motor sequence learning could provide clues about more general mechanisms of plasticity in the brain. This motivation has led numerous functional magnetic resonance imaging (fMRI) studies to investigate the brain changes related to motor sequence learning. However, there is little agreement about how and where in the brain learning-related changes are observable. Previous studies include reports of signal increases across various brain regions (Floyer-Lea and Matthews, 2005; Grafton et al., 1995; Hazeltine et al., 1997; Karni et al., 1995; Lehéricy et al., 2005; Penhune and Doyon, 2002), as well as signal decreases (Jenkins et al., 1994; Peters et al., 2017; Toni et al., 1998; Ungerleider et al., 2002; Wiestler and Diedrichsen, 2013), nonlinear changes in activation (Ma et al., 2010; Xiong et al., 2009), spatial shifts in activity (Lehéricy et al., 2006; Steele and Penhune, 2010), changes in multivariate patterns (Wiestler and Diedrichsen, 2013; Wymbs and Grafton, 2015), and changes in inter-regional functional connectivity (Bassett et al., 2015; Bassett et al., 2011; Doyon et al., 2002; Mattar et al., 2016). Additionally, some experiments have matched the speed of performance (Karni et al., 1995; Penhune and Doyon, 2002; Steele and Penhune, 2010; Lehéricy et al., 2005; Seidler et al., 2002; Seidler et al., 2005), while others have not (Bassett et al., 2015; Lutz et al., 2004; Wiestler and Diedrichsen, 2013; Wymbs and Grafton, 2015). Given that fMRI analysis has many degrees of freedom, these inconsistencies may not be too surprising. However, the implicit pressure in the publication system to report findings may also have contributed to a lack of coherency. To address this issue, we designed a comprehensive longitudinal study of motor sequence learning that allowed us to systematically reinvestigate previous findings. In order to increase transparency, we pre-registered the design, as well as all tested hypotheses on the Open Science Framework (Berlot et al., 2017; https://osf.io/etnqc), and make the full dataset available to the research community (Berlot, 2020; OpenNeuro accession number ds002776).

The main aim of our study was to systematically evaluate different ideas of how learning-related changes are reflected in the fMRI signal. In the context of motor sequence learning, the most commonly examined brain region is the primary motor cortex (M1). Previous reports of increased M1 activation after long-term learning have been interpreted as additional recruitment of neuronal resources for trained behavior, taken to suggest the skill is represented in M1 (Floyer-Lea and Matthews, 2005; Karni et al., 1995; Karni et al., 1998; Lehéricy et al., 2005; Penhune and Doyon, 2002; for a review see Dayan and Cohen, 2011; Figure 1a). Since then, several pieces of evidence have suggested that sequence-specific memory may not reside in M1 (Beukema et al., 2019; Wiestler and Diedrichsen, 2013; Yokoi and Diedrichsen, 2019). However, some of these reports studied skill acquisition over a course of a few days, while human skill typically evolves over weeks (and months) of practice. Therefore, including several weeks of practice might be more suitable to test whether, and at what time point, M1 develops skill-specific representations.

Figure 1

Download asset Open asset

Outside of M1, learning-related activation changes have been reported in premotor and parietal areas (Grafton et al., 2002; Hardwick et al., 2013; Honda et al., 1998; Penhune and Doyon, 2002; Tamás Kincses et al., 2008; Vahdat et al., 2015), with activation increases commonly interpreted as increased involvement of these areas in the skilled behavior. Yet, recent studies have mostly found that, as the motor skill develops, activation in these areas predominantly decreases (Penhune and Steele, 2012; Wiestler and Diedrichsen, 2013; Wu et al., 2004). Such reductions are harder to interpret as they could reflect a reduced areal involvement in skilled performance or, alternatively, more energy efficient implementation of the same function (Figure 1b; Picard et al., 2013; Poldrack et al., 2005). To complicate things further, regional activity increases and decreases could occur simultaneously in the same area (Figure 1c; Steele and Penhune, 2010). In such a scenario, the net activation in the region would not change, yet, the trained sequences would engage slightly different subpopulations of the region than untrained sequences.

A variant of this idea is that each specific sequence becomes associated with dedicated neuronal subpopulation (and hence fMRI activity pattern). Such a representation would form the neural correlate of sequence-specific learning – the part of the skill that does not generalize to novel, untrained motor sequences (Karni et al., 1995). Sequence-specific activation patterns should change early in learning (Figure 1d), when behavior improves most rapidly, and stabilize later, once the skill has consolidated and an optimal pattern is established (Peters et al., 2017). One possible way in which sequence-specific patterns could reorganize is by becoming more distinct from one another (Figure 1e; Wiestler and Diedrichsen, 2013). Having a distinctive code for each sequence might be of particular importance to the system in a trained state, allowing it to produce different dynamical sequences, while avoiding confusion or ‘tangling’ of the different neural trajectories (Russo et al., 2018).

To systematically examine the cortical changes associated with motor sequence learning, we carried out a longitudinal study over 5 weeks of training with 4 sessions of high-field (7 Tesla) fMRI scans. Behavioural performance in the first three scanning sessions was imposed to the same speed of performance. This allowed us to inspect whether examined fMRI metrics reflect brain reorganization, independent of behavioral change. However, controlling for speed incurs the danger of not tapping into neural resources that are necessary for skilled performance (Orban et al., 2010; Poldrack, 2000). We therefore compared the fMRI session with paced performance at the end of behavioural training with one acquired with full speed performance (Figure 2). This manipulation allowed us to systematically assess the role of speed on the fMRI metrics of learning, thereby addressing an important methodological problem faced by virtually every study on motor learning.

Figure 2 with 1 supplement see all

Download asset Open asset

Here we present a large longitudinal motor sequence learning study that allowed us to systematically investigate several previously proposed fMRI signatures of motor learning, including one new hypothesis concerning the change in multivariate activity patterns with learning. The existing literature, with its diversity of experimental protocols and analysis approaches, does currently not provide a consistent picture of learning-related changes. This inconsistency is exacerbated by the fact that most papers prioritize making new claims over re-examining previously established findings. Consequently, it is very hard to assess the replicability of most past findings. We address this issue here by a) producing a well-powered, longitudinal data set that tackles some of the methodological inconsistencies (i.e. speed matching), b) pre-registering both design and hypotheses, and c) making data and analysis pipelines openly available, such that other hypotheses and analyses techniques can be freely tested.

Our findings reveal that parietal and premotor areas show widespread decreases in overall activation, as well as reorganization of sequence-specific patterns early in learning. Additionally, we observed that sequence specific patterns in these areas (as well as the striatum) were more distinct during full speed performance. In contrast to this set of results, none of these learning-specific metrics were detected in M1, even after 5 weeks of training.

On the one hand, our lack of any observable change in M1 activation contradicts some prior results, where increased activation in M1 was observed for matched performance after learning (Karni et al., 1995; Matsuzaka et al., 2007; Penhune and Doyon, 2002; Steele and Penhune, 2010; Vahdat et al., 2015), and does not align with reports of M1 stimulations influencing consolidation or storage of motor skills (in motor sequence tasks: Kang and Paik, 2011; Nitsche et al., 2003; Reis et al., 2009; Waters-Metenier et al., 2014; in other motor tasks: Classen et al., 1998; Galea et al., 2011; Hadipour-Niktarash et al., 2007). We also found no support for a combination of increases and decreases of activation with training, which would lead to an overall change of the mean activity pattern (Steele and Penhune, 2010).

Instead, our results suggest that the pattern of neural activity in M1 does not change as participants become more skilled at producing motor sequences. This is consistent with a recent line of evidence demonstrating that M1 does not change activation with learning (Huang et al., 2013), and primarily encodes single movement elements, rather than sequences (Yokoi et al., 2018; Russo et al., 2019). Somewhat more surprisingly, we also observed no difference in overall M1 activation during full speed performance, when performance was considerably faster for trained sequences. This suggests that the activity increases related to faster movement speeds are compensated for by the shorter duration spent on the task.

Primary somatosensory cortex in many ways paralleled the results observed in M1. We observed no overall activation change, or change in the sequence-specific pattern correlation across sessions. The only exception was the observed shift in the mean activation pattern across sessions. One possible explanation is that feedback-related sensory activity in S1 undergoes some plastic changes with learning. This is consistent with a recent study demonstrating that S1, but not M1, is involved during consolidation of motor skills (Kumar et al., 2019; for a review on somatosensory plasticity in motor learning see Ostry and Gribble, 2016).

In contrast to the limited evidence of learning-related changes in primary somatosensory and primary motor areas, higher order association areas (e.g. parietal and premotor cortices) displayed an array of learning-related changes. First, activation decreased in areas involved in sequence execution, with larger decreases for trained as compared to untrained sequences. This result contrasts with other previous studies reporting increases in activation in premotor areas with learning (Grafton et al., 2002; Honda et al., 1998; Penhune and Doyon, 2002; Vahdat et al., 2015). Partially responsible for these inconsistencies may be a publication bias, favoring reports of signal increases over signal decreases with learning. For example, a recent metanalysis reanalyzed evidence for signal increases in the main text, while moving the (matched) evidence for signal decreases into the supplementary materials (Hardwick et al., 2013). Our data corroborate a number of recent studies reporting reduced activation in task-evoked premotor and parietal areas (Steele and Penhune, 2010; Wiestler and Diedrichsen, 2013; Wu et al., 2004).

The only activation increases for trained relative to untrained sequences were observed in areas that were suppressed below baseline during sequence execution. This has also been previously reported in a motor sequence learning study (Tamás Kincses et al., 2008), where deactivation was larger during performance of trained than random sequences. These areas include the precuneous, temporal parietal junction and the cingulate, regions commonly assigned to the default mode network (Raichle et al., 2001; Shulman et al., 1997). This group of regions is more activated during rest than during task performance, and has been associated with functions such as episodic memory retrieval and attention to internal states (Andrews-Hanna et al., 2010; Gusnard et al., 2001). Our observation of decreased inhibition of the default mode network likely reflects central attentional resources being freed up, allowing participants to engage in other mental processes (e.g., daydreaming) while performing the task. Thus, this release from initial deactivation is possibly task-irrelevant, reflecting increased automaticity with learning (Shamloo and Helie, 2016).

Overall, changes in average activation are relatively hard to interpret, as they could reflect a combination of numerous factors. As a more direct fMRI metric of plasticity, we suggest to inspect changes in the sequence-specific activity patterns, since these constitute a more likely fMRI correlate of the sequence-specific performance advantage observed after training. In this project, this provided us with two key insights of how activation patterns reorganize in association areas with learning. First, activity patterns associated with each of individual trained sequences, changed to a greater extent earlier in learning, and stabilized later. This finding resonates with several animal studies suggesting that the emergence of skilled behavior is associated with early plasticity and later stabilization of neuronal activity patterns (Makino et al., 2017; Peters et al., 2017). Here we report a similar effect in humans, and advance these findings by demonstrating that this reorganization occurs at the level of sequence-specific patterns. In past studies using rodent models, sequence-specific patterns could not be dissociated from the overall activity pattern, as the animals were only trained on production of a single sequence. Additionally, by pacing participants’ speed, we were able to cleanly dissociate changes in the organization of activity patterns from changes in the behavioral performance or variability. Second, activation patterns became more distinct for trained sequences at full speed. This indicates that the engagement of specific neuronal subpopulations for different sequences is particularly important when pushing the limit of performance.

While our study focused on the role of cortical areas in motor sequence learning, we also examined activation in the striatum, which has been suggested to play a critical role in skilled performance (Graybiel and Grafton, 2015; Kawai et al., 2015; Otchy et al., 2015). In contrast to previous fMRI studies (Coynel et al., 2010; Lehéricy et al., 2006; Reithler et al., 2010), we did not find clear evidence for differences in overall activity, or shifts of the overall activity pattern with learning. Nonetheless, we observed distinguishable striatal activation patterns for different trained sequences at full speed, in line with a recent report showing distinguishable striatal patterns for performance of consolidated motor sequences (Pinsard et al., 2018). While by itself the finding of differential sequence-specific activity patterns is not evidence for a causal role of the striatum in the production of skilled behaviors, it is a necessary condition for such a functional role. Therefore, our results here are in line with the proposed involvement of the striatum in motor sequence learning. Additionally, our results suggest that full speed performance might be particularly important for further studies of striatal multivariate activation to counteract the generally lower signal-to-noise ratio in this region.

An important feature of our design was that we collected imaging data in the trained state, both when performance was clamped to the initial speed, and when participants performed as fast as possible. Previous studies have usually included only one of these two options, making direct comparisons difficult (see Lutz et al., 2004 for an examination of various execution speeds on BOLD activity and Orban et al., 2010 in a motor learning context). Our results provide two important insights: first, in terms of the overall fMRI activation, the pattern of results remained the same for paced vs. full speed performance. This indicates that, in this specific case, the increased motor demands and the decreased time on task averaged out. In general, however, these two factors may not balance perfectly – therefore paced performance may be a better choice when comparing overall activation across sessions. Second, even though slow and paced performance in the trained state activated sequence-specific activation patterns, these were much stronger when performing at maximal speeds. Thus, for questions regarding the fine-grained patterns, it might be more suitable to challenge the system fully.

Of course, our list of inspected fMRI metrics of learning was not exhaustive. For instance, we did not investigate whether various fMRI correlates of learning predict behavioral outcomes, or how functional connectivity and network metrics change with learning, partly because of the absence of specific predictions. Pre-registration of hypothesis are especially important for these analyses, since the search space of possible tests becomes exponentially larger (e.g. correlating all possible brain metrics with all possible behavioral metrics; or using various metrics to assess inter-regional relationships). However, we hope that our dataset can serve as a resource for other researchers to (re-)test novel predictions about learning related changes.

fMRI data and analysis pipelines have been deposited to OpenNeuro, under the accession number ds002776. Analysis code is available on GitHub at https://github.com/eberlot/motor_sequence_learning.git (copy archived at https://github.com/elifesciences-publications/motor_sequence_learning).

1. 1. Berlot E
   2. Nicola JP
   3. Diedrichsen J

   (2020) OpenNeuro

   ID ds002776. Motor sequence learning.

   https://openneuro.org/datasets/ds002776

1. 1. Andrews-Hanna JR
   2. Reidler JS
   3. Sepulcre J
   4. Poulin R
   5. Buckner RL

   (2010) Functional-anatomic fractionation of the brain's default network

   Neuron 65:550–562.

   https://doi.org/10.1016/j.neuron.2010.02.005

   • PubMed
   • Google Scholar
2. 1. Ashby FG
   2. Turner BO
   3. Horvitz JC

   (2010) Cortical and basal ganglia contributions to habit learning and automaticity

   Trends in Cognitive Sciences 14:208–215.

   https://doi.org/10.1016/j.tics.2010.02.001

   • PubMed
   • Google Scholar
3. 1. Bassett DS
   2. Wymbs NF
   3. Porter MA
   4. Mucha PJ
   5. Carlson JM
   6. Grafton ST

   (2011) Dynamic reconfiguration of human brain networks during learning

   PNAS 108:7641–7646.

   https://doi.org/10.1073/pnas.1018985108

   • Google Scholar
4. 1. Bassett DS
   2. Yang M
   3. Wymbs NF
   4. Grafton ST

   (2015) Learning-induced autonomy of sensorimotor systems

   Nature Neuroscience 18:744–751.

   https://doi.org/10.1038/nn.3993

   • PubMed
   • Google Scholar
5. Report

   1. Berlot E
   2. Popp N
   3. Diedrichsen J

   (2017) Neural Correlates of Long-Term Motor Sequence Learning

   Open Science Framework.

   https://osf.io/ueswg/

   • Google Scholar
6. 1. Berlot E
   2. Popp NJ
   3. Diedrichsen J

   (2018) In search of the engram, 2017

   Current Opinion in Behavioral Sciences 20:56–60.

   https://doi.org/10.1016/j.cobeha.2017.11.003

   • Google Scholar
7. Software

   1. Berlot E

   (2020) motor_sequence_learning, version 00b2e9a

   Github.

   https://github.com/eberlot/motor_sequence_learning
8. 1. Beukema P
   2. Diedrichsen J
   3. Verstynen TD

   (2019) Binding during sequence learning does not alter cortical representations of individual actions

   The Journal of Neuroscience 39:6968–6977.

   https://doi.org/10.1523/JNEUROSCI.2669-18.2019

   • PubMed
   • Google Scholar
9. 1. Classen J
   2. Liepert J
   3. Wise SP
   4. Hallett M
   5. Cohen LG

   (1998) Rapid plasticity of human cortical movement representation induced by practice

   Journal of Neurophysiology 79:1117–1123.

   https://doi.org/10.1152/jn.1998.79.2.1117

   • PubMed
   • Google Scholar
10. 1. Coynel D
    2. Marrelec G
    3. Perlbarg V
    4. Pélégrini-Issac M
    5. Van de Moortele PF
    6. Ugurbil K
    7. Doyon J
    8. Benali H
    9. Lehéricy S

    (2010) Dynamics of motor-related functional integration during motor sequence learning

    NeuroImage 49:759–766.

    https://doi.org/10.1016/j.neuroimage.2009.08.048

    • PubMed
    • Google Scholar
11. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical Surface-Based analysis

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • Google Scholar
12. 1. Dayan E
    2. Cohen LG

    (2011) Neuroplasticity subserving motor skill learning

    Neuron 72:443–454.

    https://doi.org/10.1016/j.neuron.2011.10.008

    • PubMed
    • Google Scholar
13. 1. Diedrichsen J
    2. Yokoi A
    3. Arbuckle SA

    (2018) Pattern component modeling: a flexible approach for understanding the representational structure of brain activity patterns

    NeuroImage 180:119–133.

    https://doi.org/10.1016/j.neuroimage.2017.08.051

    • PubMed
    • Google Scholar
14. 1. Doyon J
    2. Song AW
    3. Karni A
    4. Lalonde F
    5. Adams MM
    6. Ungerleider LG

    (2002) Experience-dependent changes in cerebellar contributions to motor sequence learning

    PNAS 99:1017–1022.

    https://doi.org/10.1073/pnas.022615199

    • PubMed
    • Google Scholar
15. 1. Fischl B
    2. Rajendran N
    3. Busa E
    4. Augustinack J
    5. Hinds O
    6. Yeo BT
    7. Mohlberg H
    8. Amunts K
    9. Zilles K

    (2008) Cortical folding patterns and predicting cytoarchitecture

    Cerebral Cortex 18:1973–1980.

    https://doi.org/10.1093/cercor/bhm225

    • PubMed
    • Google Scholar
16. 1. Floyer-Lea A
    2. Matthews PM

    (2005) Distinguishable brain activation networks for short- and long-term motor skill learning

    Journal of Neurophysiology 94:512–518.

    https://doi.org/10.1152/jn.00717.2004

    • PubMed
    • Google Scholar
17. 1. Friston KJ
    2. Holmes AP
    3. Worsley KJ
    4. Poline J-P
    5. Frith CD
    6. Frackowiak RSJ

    (1994) Statistical parametric maps in functional imaging: a general linear approach

    Human Brain Mapping 2:189–210.

    https://doi.org/10.1002/hbm.460020402

    • Google Scholar
18. 1. Galea JM
    2. Vazquez A
    3. Pasricha N
    4. de Xivry JJ
    5. Celnik P

    (2011) Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns

    Cerebral Cortex 21:1761–1770.

    https://doi.org/10.1093/cercor/bhq246

    • PubMed
    • Google Scholar
19. 1. Grafton ST
    2. Hazeltine E
    3. Ivry R

    (1995) Functional mapping of sequence learning in normal humans

    Journal of Cognitive Neuroscience 7:497–510.

    https://doi.org/10.1162/jocn.1995.7.4.497

    • PubMed
    • Google Scholar
20. 1. Grafton ST
    2. Hazeltine E
    3. Ivry RB

    (2002) Motor sequence learning with the nondominant left hand a PET functional imaging study

    Experimental Brain Research 146:369–378.

    https://doi.org/10.1007/s00221-002-1181-y

    • PubMed
    • Google Scholar
21. 1. Graybiel AM

    (2000) The basal ganglia

    Current Biology 10:R509–R511.

    https://doi.org/10.1016/S0960-9822(00)00593-5

    • Google Scholar
22. 1. Graybiel AM
    2. Grafton ST

    (2015) The striatum: where skills and habits meet

    Cold Spring Harbor Perspectives in Biology 7:a021691.

    https://doi.org/10.1101/cshperspect.a021691

    • PubMed
    • Google Scholar
23. 1. Gusnard DA
    2. Akbudak E
    3. Shulman GL
    4. Raichle ME

    (2001) Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function

    PNAS 98:4259–4264.

    https://doi.org/10.1073/pnas.071043098

    • PubMed
    • Google Scholar
24. 1. Hadipour-Niktarash A
    2. Lee CK
    3. Desmond JE
    4. Shadmehr R

    (2007) Impairment of retention but not acquisition of a visuomotor skill through time-dependent disruption of primary motor cortex

    Journal of Neuroscience 27:13413–13419.

    https://doi.org/10.1523/JNEUROSCI.2570-07.2007

    • PubMed
    • Google Scholar
25. 1. Hardwick RM
    2. Rottschy C
    3. Miall RC
    4. Eickhoff SB

    (2013) A quantitative meta-analysis and review of motor learning in the human brain

    NeuroImage 67:283–297.

    https://doi.org/10.1016/j.neuroimage.2012.11.020

    • PubMed
    • Google Scholar
26. 1. Hazeltine E
    2. Grafton ST
    3. Ivry R

    (1997) Attention and stimulus characteristics determine the locus of motor-sequence encoding a PET study

    Brain 120:123–140.

    https://doi.org/10.1093/brain/120.1.123

    • PubMed
    • Google Scholar
27. 1. Hikosaka O
    2. Nakahara H
    3. Rand MK
    4. Sakai K
    5. Lu X
    6. Nakamura K
    7. Miyachi S
    8. Doya K

    (1999) Parallel neural networks for learning sequential procedures

    Trends in Neurosciences 22:464–471.

    https://doi.org/10.1016/S0166-2236(99)01439-3

    • PubMed
    • Google Scholar
28. 1. Honda M
    2. Deiber MP
    3. Ibáñez V
    4. Pascual-Leone A
    5. Zhuang P
    6. Hallett M

    (1998) Dynamic cortical involvement in implicit and explicit motor sequence learning a PET study

    Brain 121:2159–2173.

    https://doi.org/10.1093/brain/121.11.2159

    • PubMed
    • Google Scholar
29. 1. Huang Y
    2. Zhen Z
    3. Song Y
    4. Zhu Q
    5. Wang S
    6. Liu J

    (2013) Motor training increases the stability of activation patterns in the primary motor cortex

    PLOS ONE 8:e53555.

    https://doi.org/10.1371/journal.pone.0053555

    • PubMed
    • Google Scholar
30. 1. Hutton C
    2. Bork A
    3. Josephs O
    4. Deichmann R
    5. Ashburner J
    6. Turner R

    (2002) Image distortion correction in fMRI: a quantitative evaluation

    NeuroImage 16:217–240.

    https://doi.org/10.1006/nimg.2001.1054

    • PubMed
    • Google Scholar
31. 1. Jenkins IH
    2. Brooks DJ
    3. Nixon PD
    4. Frackowiak RS
    5. Passingham RE

    (1994) Motor sequence learning: a study with positron emission tomography

    The Journal of Neuroscience 14:3775–3790.

    https://doi.org/10.1523/JNEUROSCI.14-06-03775.1994

    • PubMed
    • Google Scholar
32. 1. Jenkinson M
    2. Beckmann CF
    3. Behrens TE
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
33. 1. Kang EK
    2. Paik NJ

    (2011) Effect of a tDCS electrode montage on implicit motor sequence learning in healthy subjects

    Experimental & Translational Stroke Medicine 3:2–7.

    https://doi.org/10.1186/2040-7378-3-4

    • PubMed
    • Google Scholar
34. 1. Karni A
    2. Meyer G
    3. Jezzard P
    4. Adams MM
    5. Turner R
    6. Ungerleider LG

    (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning

    Nature 377:155–158.

    https://doi.org/10.1038/377155a0

    • PubMed
    • Google Scholar
35. 1. Karni A
    2. Meyer G
    3. Rey-Hipolito C
    4. Jezzard P
    5. Adams MM
    6. Turner R
    7. Ungerleider LG

    (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex

    PNAS 95:861–868.

    https://doi.org/10.1073/pnas.95.3.861

    • PubMed
    • Google Scholar
36. 1. Kawai R
    2. Markman T
    3. Poddar R
    4. Ko R
    5. Fantana AL
    6. Dhawale AK
    7. Kampff AR
    8. Ölveczky BP

    (2015) Motor cortex is required for learning but not for executing a motor skill

    Neuron 86:800–812.

    https://doi.org/10.1016/j.neuron.2015.03.024

    • PubMed
    • Google Scholar
37. 1. Kumar N
    2. Manning TF
    3. Ostry DJ

    (2019) Somatosensory cortex participates in the consolidation of human motor memory

    PLOS Biology 17:e3000469.

    https://doi.org/10.1371/journal.pbio.3000469

    • PubMed
    • Google Scholar
38. 1. Lashley K

    (1950)

    Society for Experimental Biology, Physiological Mechanisms in Animal Behavior

    454–482, Insearch of the engram, Society for Experimental Biology, Physiological Mechanisms in Animal Behavior, Academic Press.

    • Google Scholar
39. 1. Lehéricy S
    2. Benali H
    3. Van de Moortele PF
    4. Pélégrini-Issac M
    5. Waechter T
    6. Ugurbil K
    7. Doyon J

    (2005) Distinct basal ganglia territories are engaged in early and advanced motor sequence learning

    PNAS 102:12566–12571.

    https://doi.org/10.1073/pnas.0502762102

    • PubMed
    • Google Scholar
40. 1. Lehéricy S
    2. Bardinet E
    3. Tremblay L
    4. Van de Moortele PF
    5. Pochon JB
    6. Dormont D
    7. Kim DS
    8. Yelnik J
    9. Ugurbil K

    (2006) Motor control in basal ganglia circuits using fMRI and brain atlas approaches

    Cerebral Cortex 16:149–161.

    https://doi.org/10.1093/cercor/bhi089

    • PubMed
    • Google Scholar
41. 1. Lutz K
    2. Koeneke S
    3. Wüstenberg T
    4. Jäncke L

    (2004) Asymmetry of cortical activation during maximum and convenient tapping speed

    Neuroscience Letters 373:61–66.

    https://doi.org/10.1016/j.neulet.2004.09.058

    • Google Scholar
42. 1. Ma L
    2. Wang B
    3. Narayana S
    4. Hazeltine E
    5. Chen X
    6. Robin DA
    7. Fox PT
    8. Xiong J

    (2010) Changes in regional activity are accompanied with changes in inter-regional connectivity during 4 weeks motor learning

    Brain Research 1318:64–76.

    https://doi.org/10.1016/j.brainres.2009.12.073

    • PubMed
    • Google Scholar
43. 1. Makino H
    2. Ren C
    3. Liu H
    4. Kim AN
    5. Kondapaneni N
    6. Liu X
    7. Kuzum D
    8. Komiyama T

    (2017) Transformation of Cortex-wide emergent properties during motor learning

    Neuron 94:880–890.

    https://doi.org/10.1016/j.neuron.2017.04.015

    • PubMed
    • Google Scholar
44. 1. Matsuzaka Y
    2. Picard N
    3. Strick PL

    (2007) Skill Representation in the Primary Motor Cortex After Long-Term Practice

    Journal of Neurophysiology 97:1819–1832.

    https://doi.org/10.1152/jn.00784.2006

    • Google Scholar
45. Preprint

    1. Mattar M
    2. Wymbs NF
    3. Bock AS
    4. Aguirre GK
    5. Grafton ST
    6. Bassett DS

    (2016) Predicting future learning from baseline network architecture

    bioRxiv.

    https://doi.org/10.1101/056861

    • Google Scholar
46. 1. Nitsche MA
    2. Schauenburg A
    3. Lang N
    4. Liebetanz D
    5. Exner C
    6. Paulus W
    7. Tergau F

    (2003) Facilitation of implicit motor learning by weak transcranial direct current stimulation of the primary motor cortex in the human

    Journal of Cognitive Neuroscience 15:619–626.

    https://doi.org/10.1162/089892903321662994

    • PubMed
    • Google Scholar
47. 1. Oosterhof NN
    2. Wiestler T
    3. Downing PE
    4. Diedrichsen J

    (2011) A comparison of volume-based and surface-based multi-voxel pattern analysis

    NeuroImage 56:593–600.

    https://doi.org/10.1016/j.neuroimage.2010.04.270

    • PubMed
    • Google Scholar
48. 1. Orban P
    2. Peigneux P
    3. Lungu O
    4. Albouy G
    5. Breton E
    6. Laberenne F
    7. Benali H
    8. Maquet P
    9. Doyon J

    (2010) The multifaceted nature of the relationship between performance and brain activity in motor sequence learning

    NeuroImage 49:694–702.

    https://doi.org/10.1016/j.neuroimage.2009.08.055

    • PubMed
    • Google Scholar
49. 1. Ostry DJ
    2. Gribble PL

    (2016) Sensory plasticity in human motor learning

    Trends in Neurosciences 39:114–123.

    https://doi.org/10.1016/j.tins.2015.12.006

    • PubMed
    • Google Scholar
50. 1. Otchy TM
    2. Wolff SBE
    3. Rhee JY
    4. Pehlevan C
    5. Kawai R
    6. Kempf A
    7. Gobes SMH
    8. Ölveczky BP

    (2015) Acute off-target effects of neural circuit manipulations

    Nature 528:358–363.

    https://doi.org/10.1038/nature16442

    • Google Scholar
51. 1. Penhune VB
    2. Doyon J

    (2002) Dynamic cortical and subcortical networks in learning and delayed recall of timed motor sequences

    The Journal of Neuroscience 22:1397–1406.

    https://doi.org/10.1523/JNEUROSCI.22-04-01397.2002

    • PubMed
    • Google Scholar
52. 1. Penhune VB
    2. Steele CJ

    (2012) Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning

    Behavioural Brain Research 226:579–591.

    https://doi.org/10.1016/j.bbr.2011.09.044

    • PubMed
    • Google Scholar
53. 1. Peters AJ
    2. Lee J
    3. Hedrick NG
    4. O'Neil K
    5. Komiyama T

    (2017) Reorganization of corticospinal output during motor learning

    Nature Neuroscience 20:1133–1141.

    https://doi.org/10.1038/nn.4596

    • PubMed
    • Google Scholar
54. 1. Picard N
    2. Matsuzaka Y
    3. Strick PL

    (2013) Extended practice of a motor skill is associated with reduced metabolic activity in M1

    Nature Neuroscience 16:1340–1347.

    https://doi.org/10.1038/nn.3477

    • PubMed
    • Google Scholar
55. Preprint

    1. Pinsard B
    2. Boutin A
    3. Gabitov E
    4. Lungu O
    5. Benali H
    6. Doyon J

    (2018) Consolidation alters motor sequence-specific distributed representations

    bioRxiv.

    https://doi.org/10.7554/eLife.39324

    • Google Scholar
56. 1. Poldrack RA

    (2000) Imaging brain plasticity: conceptual and methodological issues--a theoretical review

    NeuroImage 12:1–13.

    https://doi.org/10.1006/nimg.2000.0596

    • PubMed
    • Google Scholar
57. 1. Poldrack RA
    2. Sabb FW
    3. Foerde K
    4. Tom SM
    5. Asarnow RF
    6. Bookheimer SY
    7. Knowlton BJ

    (2005) The neural correlates of motor skill automaticity

    Journal of Neuroscience 25:5356–5364.

    https://doi.org/10.1523/JNEUROSCI.3880-04.2005

    • PubMed
    • Google Scholar
58. 1. Raichle ME
    2. MacLeod AM
    3. Snyder AZ
    4. Powers WJ
    5. Gusnard DA
    6. Shulman GL

    (2001) A default mode of brain function

    PNAS 98:676–682.

    https://doi.org/10.1073/pnas.98.2.676

    • PubMed
    • Google Scholar
59. 1. Reis J
    2. Schambra HM
    3. Cohen LG
    4. Buch ER
    5. Fritsch B
    6. Zarahn E
    7. Celnik PA
    8. Krakauer JW

    (2009) Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation

    PNAS 106:1590–1595.

    https://doi.org/10.1073/pnas.0805413106

    • PubMed
    • Google Scholar
60. 1. Reithler J
    2. van Mier HI
    3. Goebel R

    (2010) Continuous motor sequence learning: cortical efficiency gains accompanied by striatal functional reorganization

    NeuroImage 52:263–276.

    https://doi.org/10.1016/j.neuroimage.2010.03.073

    • PubMed
    • Google Scholar
61. 1. Russo AA
    2. Bittner SR
    3. Perkins SM
    4. Seely JS
    5. London BM
    6. Lara AH
    7. Miri A
    8. Marshall NJ
    9. Kohn A
    10. Jessell TM
    11. Abbott LF
    12. Cunningham JP
    13. Churchland MM

    (2018) Motor cortex embeds Muscle-like commands in an untangled population response

    Neuron 97:953–966.

    https://doi.org/10.1016/j.neuron.2018.01.004

    • PubMed
    • Google Scholar
62. Preprint

    1. Russo AA
    2. Khajeh R
    3. Bittner SR
    4. Perkins SM
    5. Cunningham JP
    6. Abbott LF
    7. Churchland MM

    (2019) Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries compatible with different classes of computation

    bioRxiv.

    https://doi.org/10.1101/650002

    • Google Scholar
63. 1. Seidler RD
    2. Purushotham A
    3. Kim SG
    4. Uğurbil K
    5. Willingham D
    6. Ashe J

    (2002) Cerebellum activation associated with performance change but not motor learning

    Science 296:2043–2046.

    https://doi.org/10.1126/science.1068524

    • PubMed
    • Google Scholar
64. 1. Seidler RD
    2. Purushotham A
    3. Kim SG
    4. Ugurbil K
    5. Willingham D
    6. Ashe J

    (2005) Neural correlates of encoding and expression in implicit sequence learning

    Experimental Brain Research 165:114–124.

    https://doi.org/10.1007/s00221-005-2284-z

    • PubMed
    • Google Scholar
65. 1. Shamloo F
    2. Helie S

    (2016) Changes in default mode network as automaticity develops in a categorization task

    Behavioural Brain Research 313:324–333.

    https://doi.org/10.1016/j.bbr.2016.07.029

    • PubMed
    • Google Scholar
66. 1. Shulman GL
    2. Fiez JA
    3. Corbetta M
    4. Buckner RL
    5. Miezin FM
    6. Raichle ME
    7. Petersen SE

    (1997) Common blood flow changes across visual tasks: ii. decreases in cerebral cortex

    Journal of Cognitive Neuroscience 9:648–663.

    https://doi.org/10.1162/jocn.1997.9.5.648

    • PubMed
    • Google Scholar
67. 1. Steele CJ
    2. Penhune VB

    (2010) Specific increases within global decreases: a functional magnetic resonance imaging investigation of five days of motor sequence learning

    Journal of Neuroscience 30:8332–8341.

    https://doi.org/10.1523/JNEUROSCI.5569-09.2010

    • PubMed
    • Google Scholar
68. 1. Tamás Kincses Z
    2. Johansen-Berg H
    3. Tomassini V
    4. Bosnell R
    5. Matthews PM
    6. Beckmann CF

    (2008) Model-free characterization of brain functional networks for motor sequence learning using fMRI

    NeuroImage 39:1950–1958.

    https://doi.org/10.1016/j.neuroimage.2007.09.070

    • PubMed
    • Google Scholar
69. 1. Toni I
    2. Krams M
    3. Turner R
    4. Passingham RE

    (1998) The time course of changes during motor sequence learning: a whole-brain fMRI study

    NeuroImage 8:50–61.

    https://doi.org/10.1006/nimg.1998.0349

    • PubMed
    • Google Scholar
70. 1. Ungerleider LG
    2. Doyon J
    3. Karni A

    (2002) Imaging brain plasticity during motor skill learning

    Neurobiology of Learning and Memory 78:553–564.

    https://doi.org/10.1006/nlme.2002.4091

    • PubMed
    • Google Scholar
71. 1. Vahdat S
    2. Lungu O
    3. Cohen-Adad J
    4. Marchand-Pauvert V
    5. Benali H
    6. Doyon J

    (2015) Simultaneous Brain-Cervical cord fMRI reveals intrinsic spinal cord plasticity during motor sequence learning

    PLOS Biology 13:e1002186.

    https://doi.org/10.1371/journal.pbio.1002186

    • PubMed
    • Google Scholar
72. 1. Walther A
    2. Nili H
    3. Ejaz N
    4. Alink A
    5. Kriegeskorte N
    6. Diedrichsen J

    (2016) Reliability of dissimilarity measures for multi-voxel pattern analysis

    NeuroImage 137:188–200.

    https://doi.org/10.1016/j.neuroimage.2015.12.012

    • PubMed
    • Google Scholar
73. 1. Waters-Metenier S
    2. Husain M
    3. Wiestler T
    4. Diedrichsen J

    (2014) Bihemispheric transcranial direct current stimulation enhances effector-independent representations of motor synergy and sequence learning

    The Journal of Neuroscience 34:1037–1050.

    https://doi.org/10.1523/JNEUROSCI.2282-13.2014

    • PubMed
    • Google Scholar
74. 1. Wiestler T
    2. Diedrichsen J

    (2013) Skill learning strengthens cortical representations of motor sequences

    eLife 2:e00801.

    https://doi.org/10.7554/eLife.00801

    • PubMed
    • Google Scholar
75. 1. Wu T
    2. Kansaku K
    3. Hallett M

    (2004) How self-initiated memorized movements become automatic: a functional MRI study

    Journal of Neurophysiology 91:1690–1698.

    https://doi.org/10.1152/jn.01052.2003

    • PubMed
    • Google Scholar
76. 1. Wymbs NF
    2. Grafton ST

    (2015) The human motor system supports Sequence-Specific representations over multiple Training-Dependent timescales

    Cerebral Cortex 25:4213–4225.

    https://doi.org/10.1093/cercor/bhu144

    • PubMed
    • Google Scholar
77. 1. Xiong J
    2. Ma L
    3. Wang B
    4. Narayana S
    5. Duff EP
    6. Egan GF
    7. Fox PT

    (2009) Long-term motor training induced changes in regional cerebral blood flow in both task and resting states

    NeuroImage 45:75–82.

    https://doi.org/10.1016/j.neuroimage.2008.11.016

    • PubMed
    • Google Scholar
78. 1. Yin HH
    2. Mulcare SP
    3. Hilário MR
    4. Clouse E
    5. Holloway T
    6. Davis MI
    7. Hansson AC
    8. Lovinger DM
    9. Costa RM

    (2009) Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill

    Nature Neuroscience 12:333–341.

    https://doi.org/10.1038/nn.2261

    • PubMed
    • Google Scholar
79. 1. Yokoi A
    2. Arbuckle SA
    3. Diedrichsen J

    (2018) The role of human primary motor cortex in the production of skilled finger sequences

    The Journal of Neuroscience 38:1430–1442.

    https://doi.org/10.1523/JNEUROSCI.2798-17.2017

    • PubMed
    • Google Scholar
80. 1. Yokoi A
    2. Diedrichsen J

    (2019) Neural organization of hierarchical motor sequence representations in the human neocortex

    Neuron 103:1178–1190.

    https://doi.org/10.1016/j.neuron.2019.06.017

    • PubMed
    • Google Scholar
81. 1. Yousry TA
    2. Schmid UD
    3. Alkadhi H
    4. Schmidt D
    5. Peraud A
    6. Buettner A
    7. Winkler P

    (1997) Localization of the motor hand area to a knob on the Precentral Gyrus a new landmark

    Brain 120:141–157.

    https://doi.org/10.1093/brain/120.1.141

    • PubMed
    • Google Scholar

Author details

1. Eva Berlot

   The Brain and Mind Institute, University of Western Ontario, Ontario, Canada

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2605-1741

2. Nicola J Popp

   The Brain and Mind Institute, University of Western Ontario, Ontario, Canada

   Contribution

   Conceptualization, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Jörn Diedrichsen

   1. The Brain and Mind Institute, University of Western Ontario, Ontario, Canada
   2. Department of Computer Science, University of Western Ontario, Ontario, Canada
   3. Department of Statistical and Actuarial Sciences, University of Western Ontario, Ontario, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing - review and editing

   For correspondence

   jdiedric@uwo.ca

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-0264-8532

• 4,013

  views

• 515

  downloads

• 67

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.