Motor skill learning between selection and execution.

Learning motor skills evolves from the effortful selection of single movement elements to their combined fast and accurate production. We review recent trends in the study of skill learning which suggest a hierarchical organization of the representations that underlie such expert performance, with premotor areas encoding short sequential movement elements (chunks) or particular component features (timing/spatial organization). This hierarchical representation allows the system to utilize elements of well-learned skills in a flexible manner. One neural correlate of skill development is the emergence of specialized neural circuits that can produce the required elements in a stable and invariant fashion. We discuss the challenges in detecting these changes with fMRI

Cooperation not competition: Bihemispheric tDCS and fMRI show role for ipsilateral hemisphere in motor learning

What is the role of ipsilateral motor and premotor areas in motor learning? One view is that ipsilateral activity suppresses contralateral motor cortex and, accordingly, that inhibiting ipsilateral regions can improve motor learning. Alternatively, the ipsilateral motor cortex may play an active role in the control and/or learning of unilateral hand movements. We approached this question by applying double-blind bihemispheric transcranial direct current stimulation (tDCS) over both contralateral and ipsilateral motor cortex in a between-group design during 4 d of unimanual explicit sequence training in human participants. Independently of whether the anode was placed over contralateral or ipsilateral motor cortex, bihemispheric stimulation yielded substantial performance gains relative to unihemispheric or sham stimulation. This performance advantage appeared to be supported by plastic changes in both hemispheres. First, we found that behavioral advantages generalized strongly to the untrained hand, suggesting that tDCS strengthened effector-independent representations. Second, functional imaging during speed-matched execution of trained sequences conducted 48 h after training revealed sustained, polarity-independent increases in activity in both motor cortices relative to the sham group. These results suggest a cooperative rather than competitive interaction of the two motor cortices during skill learning and suggest that bihemispheric brain stimulation during unimanual skill learning may be beneficial because it harnesses plasticity in the ipsilateral hemisphere

Mirror reversal and visual rotation are learned and consolidated via separate mechanisms: recalibrating or learning de novo?

Motor learning tasks are often classified into adaptation tasks, which involve the recalibration of an existing control policy (the mapping that determines both feedforward and feedback commands), and skill-learning tasks, requiring the acquisition of new control policies. We show here that this distinction also applies to two different visuomotor transformations during reaching in humans: Mirror-reversal (left-right reversal over a mid-sagittal axis) of visual feedback versus rotation of visual feedback around the movement origin. During mirror-reversal learning, correct movement initiation (feedforward commands) and online corrections (feedback responses) were only generated at longer latencies. The earliest responses were directed into a nonmirrored direction, even after two training sessions. In contrast, for visual rotation learning, no dependency of directional error on reaction time emerged, and fast feedback responses to visual displacements of the cursor were immediately adapted. These results suggest that the motor system acquires a new control policy for mirror reversal, which initially requires extra processing time, while it recalibrates an existing control policy for visual rotations, exploiting established fast computational processes. Importantly, memory for visual rotation decayed between sessions, whereas memory for mirror reversals showed offline gains, leading to better performance at the beginning of the second session than in the end of the first. With shifts in time-accuracy tradeoff and offline gains, mirror-reversal learning shares common features with other skill-learning tasks. We suggest that different neuronal mechanisms underlie the recalibration of an existing versus acquisition of a new control policy and that offline gains between sessions are a characteristic of latter

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

Human primary motor cortex (M1) is essential for producing dexterous hand movements. Although distinct subpopulations of neurons are activated during single-finger movements, it remains unknown whetherM1also represents sequences of multiple finger movements. Using novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques and combining evidence from both 3T and 7T fMRI data, we found that after 5 d of intense practice, premotor and parietal areas encoded the different movement sequences. There was little or no evidence for a sequence representation in M1. Instead, activity patterns in M1 could be fully explained by a linear combination of patterns for the constituent individual finger movements, with the strongest weight on the first finger of the sequence. Using passive replay of sequences, we show that this first-finger effect is due to neuronal processes involved in the active execution, rather than to a hemodynamic nonlinearity. These results suggest thatM1receives increased input from areas with sequence representations at the initiation of a sequence, but thatM1activity itself relates to the execution of component finger presses only. These results improve our understanding of the representation of finger sequences in the human neocortex after short-term training and provide important methodological advances for the study of long-term skill development

Repetita iuvant: Repetition facilitates online planning of sequential movements

Copyright © 2020 the American Physiological Society. Beyond being essential for long-term motor-skill development, movement repetition has immediate benefits on performance, increasing speed and accuracy of a second execution. While repetition effects have been reported for single reaching movements, it has yet to be determined whether they also occur for movement sequences, and what aspects of sequence production are improved. We addressed these questions in two behavioral experiments using a discrete sequence production (DSP) task in which human volunteers had to perform short sequences of finger movements. In experiment 1, we presented participants with randomly varying sequences and manipulated 1) whether the same sequence was repeated on successive trials and 2) whether participants had to execute the sequence (Go) or not (No-Go). We establish that sequence repetition led to immediate improvements in speed without associated accuracy costs. The largest benefit was observed in the middle part of a sequence, suggesting that sequence repetition facilitated online planning. This claim was further supported by experiment 2, in which we kept a set of sequences fixed throughout the experiment, thus allowing participants to develop sequence-specific learning: once the need for online planning decreased, the benefit of repetition disappeared. Finally, we found that repetition-related improvements only occurred for the trials that had been preceded by sequence production, suggesting that action selection and sequence preplanning may not be sufficient to reap the benefits of repetition. Together, these results show that repetition can enhance representations at the level of movement sequences (rather than of individual movements) and facilitate online planning. NEW & NOTEWORTHY Even for overlearned motor skills such as reaching, movement repetition improves performance. How brain processes associated with motor planning or execution benefit from repetition, however, remains unclear. We report the novel finding of repetition effects for sequential movements. Our results show that repetition benefits are tied to improved online planning of upcoming sequence elements. We also highlight how actual movement experience appears to be more beneficial than mental rehearsal for observing short-term repetition effects

Effector-independent motor sequence representations exist in extrinsic and intrinsic reference frames.

Many daily activities rely on the ability to produce meaningful sequences of movements. Motor sequences can be learned in an effector-specific fashion (such that benefits of training are restricted to the trained hand) or an effector-independent manner (meaning that learning also facilitates performance with the untrained hand). Effector-independent knowledge can be represented in extrinsic/world-centered or in intrinsic/body-centered coordinates. Here, we used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis to determine the distribution of intrinsic and extrinsic finger sequence representations across the human neocortex. Participants practiced four sequences with one hand for 4 d, and then performed these sequences during fMRI with both left and right hand. Between hands, these sequences were equivalent in extrinsic or intrinsic space, or were unrelated. In dorsal premotor cortex (PMd), we found that sequence-specific activity patterns correlated higher for extrinsic than for unrelated pairs, providing evidence for an extrinsic sequence representation. In contrast, primary sensory and motor cortices showed effector-independent representations in intrinsic space, with considerable overlap of the two reference frames in caudal PMd. These results suggest that effector-independent representations exist not only in world-centered, but also in body-centered coordinates, and that PMd may be involved in transforming sequential knowledge between the two. Moreover, although effector-independent sequence representations were found bilaterally, they were stronger in the hemisphere contralateral to the trained hand. This indicates that intermanual transfer relies on motor memories that are laid down during training in both hemispheres, but preferentially draws upon sequential knowledge represented in the trained hemisphere

Two distinct ipsilateral cortical representations for individuated finger movements.

Movements of the upper limb are controlled mostly through the contralateral hemisphere. Although overall activity changes in the ipsilateral motor cortex have been reported, their functional significance remains unclear. Using human functional imaging, we analyzed neural finger representations by studying differences in fine-grained activation patterns for single isometric finger presses. We demonstrate that cortical motor areas encode ipsilateral movements in 2 fundamentally different ways. During unimanual ipsilateral finger presses, primary sensory and motor cortices show, underneath global suppression, finger-specific activity patterns that are nearly identical to those elicited by contralateral mirror-symmetric action. This component vanishes when both motor cortices are functionally engaged during bimanual actions. We suggest that the ipsilateral representation present during unimanual presses arises because otherwise functionally idle circuits are driven by input from the opposite hemisphere. A second type of representation becomes evident in caudal premotor and anterior parietal cortices during bimanual actions. In these regions, ipsilateral actions are represented as nonlinear modulation of activity patterns related to contralateral actions, an encoding scheme that may provide the neural substrate for coordinating bimanual movements. We conclude that ipsilateral cortical representations change their informational content and functional role, depending on the behavioral context

Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns

Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded feature spaces can be estimated from the data. We present here a number of different ways in which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in an open-source software toolbox

Encoding of sensory prediction errors in the human cerebellum.

A central tenet of motor neuroscience is that the cerebellum learns from sensory prediction errors. Surprisingly, neuroimaging studies have not revealed definitive signatures of error processing in the cerebellum. Furthermore, neurophysiologic studies suggest an asymmetry, such that the cerebellum may encode errors arising from unexpected sensory events, but not errors reflecting the omission of expected stimuli. We conducted an imaging study to compare the cerebellar response to these two types of errors. Participants made fast out-and-back reaching movements, aiming either for an object that delivered a force pulse if intersected or for a gap between two objects, either of which delivered a force pulse if intersected. Errors (missing the target) could therefore be signaled either through the presence or absence of a force pulse. In an initial analysis, the cerebellar BOLD response was smaller on trials with errors compared with trials without errors. However, we also observed an error-related decrease in heart rate. After correcting for variation in heart rate, increased activation during error trials was observed in the hand area of lobules V and VI. This effect was similar for the two error types. The results provide evidence for the encoding of errors resulting from either the unexpected presence or unexpected absence of sensory stimulation in the human cerebellum