Combining Repetition Suppression and Pattern Analysis Provides New Insights into the Role of M1 and Parietal Areas in Skilled Sequential Actions.

How does the brain change during learning? In functional magnetic resonance imaging (fMRI) studies, both multivariate pattern analysis (MVPA) and repetition suppression (RS) have been used to detect changes in neuronal representations. In the context of motor sequence learning, the two techniques have provided discrepant findings: pattern analysis showed that only premotor and parietal regions, but not primary motor cortex (M1), develop a representation of trained sequences. In contrast, RS suggested trained sequence representations in all these regions. Here, we applied both analysis techniques to a five-week finger sequence training study, in which participants executed each sequence twice before switching to a different sequence. Both RS and pattern analysis indicated learning-related changes for parietal areas, but only RS showed a difference between trained and untrained sequences in M1. A more fine-grained analysis, however, revealed that the RS effect in M1 reflects a fundamentally different process than in parietal areas. On the first execution, M1 represents especially the first finger of each sequence, likely reflecting preparatory processes. This effect dramatically reduces during the second execution. In contrast, parietal areas represent the identity of a sequence, and this representation stays relatively stable on the second execution. These results suggest that the RS effect does not reflect a trained sequence representation in M1, but rather a preparatory signal for movement initiation. More generally, our study demonstrates that across regions RS can reflect different representational changes in the neuronal population code, emphasizing the importance of combining pattern analysis and RS techniques.SIGNIFICANCE STATEMENT Previous studies using pattern analysis have suggested that primary motor cortex (M1) does not represent learnt sequential actions. However, a study using repetition suppression (RS) has reported M1 changes during motor sequence learning. Combining both techniques, we first replicate the discrepancy between them, with learning-related changes in M1 in RS, but not pattern dissimilarities. We further analyzed the representational changes with repetition, and found that the RS effects differ across regions. M1's activity represents the starting finger of the sequence, an effect that vanishes with repetition. In contrast, activity patterns in parietal areas exhibit sequence dependency, which persists with repetition. These results demonstrate the importance of combining RS and pattern analysis to understand the function of brain regions.

The role of feedback in the production of skilled finger sequences.

Actions involving fine control of the hand, for example, grasping an object, rely heavily on sensory information from the fingertips. Although the integration of feedback during the execution of individual movements is well understood, less is known about the use of sensory feedback in the control of skilled movement sequences. To address this gap, we trained participants to produce sequences of finger movements on a keyboard-like device over a 4-day training period. Participants received haptic, visual, and auditory feedback indicating the occurrence of each finger press. We then either transiently delayed or advanced the feedback for a single press by a small amount of time (30 or 60 ms). We observed that participants rapidly adjusted their ongoing finger press by either accelerating or prolonging the ongoing press, in accordance with the direction of the perturbation. Furthermore, we could show that this rapid behavioral modulation was driven by haptic feedback. Although these feedback-driven adjustments reduced in size with practice, they were still clearly present at the end of training. In contrast to the directionally specific effect we observed on the perturbed press, a feedback perturbation resulted in a delayed onset of the subsequent presses irrespective of perturbation direction or feedback modality. This observation is consistent with a hierarchical organization of even very skilled and fast movement sequences, with different levels reacting distinctly to sensory perturbations.NEW & NOTEWORTHY Sensory feedback is important during the execution of a movement. However, little is known about how sensory feedback is used during the production of movement sequences. Here, we show two distinct feedback processes in the execution of fast finger movement sequences. By transiently delaying or advancing the feedback of a single press within a sequence, we observed a directionally specific effect on the perturbed press and a directionally non-specific effect on the subsequent presses.

The effect of instruction on motor skill learning.

Many motor skills are learned with the help of instructions. In the context of complex motor sequences, instructions often break down the movement into chunks that can then be practiced in isolation. Thus, instructions shape an initial cognitive representation of the skill, which in turn guides practice. Are there ways of breaking up a motor sequence that are better than others? If participants are instructed in a way that hinders performance, how much practice does it take to overcome the influence of the instruction? To answer these questions, we used a paradigm in which participants were asked to perform finger sequences as fast and accurately as possible on a keyboard-like device. In the initial phases of training, participants had to explicitly remember and practice two- or three-digit chunks. These chunks were then combined to form seven 11-digit sequences that participants practiced for the remainder of the study. Each sequence was broken up into chunks in a way such that the instruction was either aligned or misaligned with the basic execution-level constraints. We found that misaligned chunk instruction led to an initial performance deficit compared with the aligned chunk instruction. Overall, instructions still influenced the temporal pattern of performance after 10 days of subsequent training, with shorter interpress intervals within a chunk compared with between chunks. However, for the misaligned instructed sequences, this temporal pattern was altered more rapidly, such that participants could overcome the induced performance deficit in the last week. At the end of training, participants found idiosyncratic, but interindividually stable, ways of performing each sequence.NEW & NOTEWORTHY Instructions often break down motor sequences into smaller parts, such that they can be more easily remembered. Here, we show that different ways of breaking down a finger sequence can subsequently lead to better or worse performance. The initial instruction still influenced the temporal performance pattern after 10 days of practice. The results demonstrate that the initial cognitive representation of a motor skill strongly influences how a skill is learned and performed.

A critical re-evaluation of fMRI signatures of motor sequence learning.

Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.

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.