Expressions of multiple neuronal dynamics during sensorimotor learning in the motor cortex of behaving monkeys

Distinct Kinematic Adjustments over Multiple Timescales Accompany Locomotor Skill Development in Mice

Robust locomotion is critical to many species' survival, yet the mechanisms by which efficient locomotion is learned and maintained are poorly understood. In mice, a common paradigm for assaying locomotor learning is the rotarod task, in which mice learn to maintain balance atop of an accelerating rod. However, the standard metric for learning in this task is improvements in latency to fall, which gives little insight into the rich kinematic adjustments that accompany locomotor learning. In this study, we developed a rotarod-like task called the RotaWheel in which changes in paw kinematics are tracked using high-speed cameras as mice learn to stay atop an accelerating wheel. Using this device, we found that learning was accompanied by stereotyped progressions of paw kinematics that correlated with early, intermediate, and late stages of performance. Within the first day, mice sharpened their interlimb coordination using a timed pause in the forward swing of their forepaws. Over the next several days, mice reduced their stride length and took shorter, quicker steps. By the second week of training, mice began to use a more variable locomotor strategy, where consecutive overshoots or undershoots in strides were selected across paws to drive forward and backward exploration of the wheel. Collectively, our results suggest that mouse locomotor learning occurs through multiple mechanisms evolving over separate time courses and involving distinct corrective actions. These data provide insights into the kinematic strategies that accompany locomotor learning and establish an experimental platform for studying locomotor skill learning in mice.

Corticospinal correlates of fast and slow adaptive processes in motor learning

Recent computational theories and behavioral observations suggest that motor learning is supported by multiple adaptation processes, operating on different timescales, but direct neural evidence is lacking. We tested this hypothesis by applying transcranial magnetic stimulation over motor cortex in 16 human subjects during a validated reach adaptation task. Motor-evoked potentials (MEPs) and cortical silent periods (CSPs) were recorded from the biceps brachii to assess modulations of corticospinal excitability as indices for corticospinal plasticity. Guided by a two-state adaptation model, we show that the MEP reflects an adaptive process that learns quickly but has poor retention, while the CSP correlates with a process that responds more slowly but retains information well. These results provide a physiological link between models of motor learning and distinct changes in corticospinal excitability. Our findings support the relationship between corticospinal gain modulations and the adaptive processes in motor learning. NEW & NOTEWORTHY Computational theories and behavioral observations suggest that motor learning is supported by multiple adaptation processes, but direct neural evidence is lacking. We tested this hypothesis by applying transcranial magnetic stimulation over human motor cortex during a reach adaptation task. Guided by a two-state adaptation model, we show that the motor-evoked potential reflects a process that adapts and decays quickly, whereas the cortical silent period reflects slow adaptation and decay.

Added value of money on motor performance feedback: Increased left central beta-band power for rewards and fronto-central theta-band power for punishments

Monetary rewards and punishments have been shown to respectively enhance retention of motor memories and short-term motor performance, but their underlying neural bases in the context of motor control tasks remain unclear. Using electroencephalography (EEG), the present study tested the hypothesis that monetary rewards and punishments are respectively reflected in post-feedback beta-band (20-30 Hz) and theta-band (3-8 Hz) oscillatory power. While participants performed upper limb reaching movements toward visual targets using their right hand, the delivery of monetary rewards and punishments was manipulated as well as their probability (i.e., by changing target size). Compared to unrewarded and unpunished trials, monetary rewards and the successful avoidance of punishments both entailed greater beta-band power at left central electrodes overlaying contralateral motor areas. In contrast, monetary punishments and reward omissions both entailed increased theta-band power at fronto-central scalp sites. Additional analyses revealed that beta-band power was further increased when rewards were lowly probable. In light of previous work demonstrating similar beta-band modulations in basal ganglia during reward processing, the present results may reflect functional communication of reward-related information between the basal ganglia and motor cortical regions. In turn, the increase in fronto-central theta-band power after monetary punishments may reflect an emphasized cognitive need for behavioral adjustments. Globally, the present work identifies possible neural substrates for the growing behavioral evidence showing beneficial effects of monetary feedback on motor learning and performance.

Disruption of M1 Activity during Performance Plateau Impairs Consolidation of Motor Memories

Upon exposure to a new sensorimotor relationship, motor behaviors iteratively change early in adaptation but eventually stabilize as adaptation proceeds. Behavioral work suggests that motor memory consolidation is initiated upon the attainment of asymptotic levels of performance. Separate lines of evidence point to a critical role of the primary motor cortex (M1) in consolidation. However, a causal relationship between M1 activity during asymptote and consolidation has yet to be demonstrated. The present study investigated this issue in male and female participants using single-pulse transcranial magnetic stimulation (TMS) to interfere with postmovement activity in M1 in two behavioral phases of a ramp-and-hold visuomotor adaptation paradigm. TMS was either provided after each trial of the ramp phase of adaptation when a gradual increase in the visuomotor rotation caused movements to be changing, or after each trial of the hold phase of adaptation when the rotation was held constant and movements tended to stabilize. Consolidation was assessed by measuring performance on the same task 24 h later. Results revealed that TMS did not influence adaptation to the new visuomotor relationship in either condition. Critically, however, TMS disruption of M1 activity selectively impaired consolidation of motor memories when it was provided during the hold phase of adaptation. This effect did not take place when TMS was delivered over adjacent dorsal premotor cortex or when motor behaviors in late adaptation were prevented from plateauing. Together, these data suggest that the impaired consolidation stemmed from interference with mechanisms of repetition-dependent plasticity in M1.SIGNIFICANCE STATEMENT The present work demonstrates that TMS disruption of M1 activity impairs the consolidation of motor memories selectively when performance reaches asymptotic levels during sensorimotor adaptation. These findings provide evidence for a causal contribution of M1 to motor memory formation when movements tend to repeat, likely through mechanisms of repetition-dependent plasticity.

Minimum Winfree loop determines self-sustained oscillations in excitable Erdös-Rényi random networks

The investigation of self-sustained oscillations in excitable complex networks is very important in understanding various activities in brain systems, among which the exploration of the key determinants of oscillations is a challenging task. In this paper, by investigating the influence of system parameters on self-sustained oscillations in excitable Erdös-Rényi random networks (EERRNs), the minimum Winfree loop (MWL) is revealed to be the key factor in determining the emergence of collective oscillations. Specifically, the one-to-one correspondence between the optimal connection probability (OCP) and the MWL length is exposed. Moreover, many important quantities such as the lower critical connection probability (LCCP), the OCP, and the upper critical connection probability (UCCP) are determined by the MWL. Most importantly, they can be approximately predicted by the network structure analysis, which have been verified in numerical simulations. Our results will be of great importance to help us in understanding the key factors in determining persistent activities in biological systems.

The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning

Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on the relative stability of memories based on these movement characteristics, especially in comparison to the initial adaptation. Here, we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity. Following training, subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning. The compensatory force patterns were largely based on the perturbation kinematic (e.g., velocity), but also showed a small contribution from the other motion kinematic (e.g., position). However, the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training, suggesting a difference in stability. Next, we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals. We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning. We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model. Specifically, we show that (1) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and (2) for combined motion-state perturbations, the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain, even when the position-dependent gain at the end of training was significantly greater. Collectively, these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position.

Human motor adaptation of limb movement in response to force perturbations has been shown to be motion-state dependent. That is, the compensatory response to these disturbances is correlated and proportional to the temporal changes in the position, velocity, and acceleration during the motion. Despite a growing understanding of this adaptation process, there is little information on the relative stability of this learning when based on these different temporal features of movement. Here we modified a previous computational model of motor adaptation to predict the decay of the compensatory response associated to different motion states, specifically learning based on temporal variations in limb position and velocity. We confirmed the simulated behavior by examining the decay of the temporal force output after subjects were trained to compensate for movement disturbances based on different combinations and magnitudes of these two motion states. Both simulation and behavioral results show that velocity-based learning decays at a slower rate than position-based, even when learning is significantly biased towards the latter at the end of training. Collectively, these results suggest that motion-state learning based on movement velocity is more stable than that based on limb position.

Stimulating the cerebellum affects visuomotor adaptation but not intermanual transfer of learning

When systematic movement errors occur, the brain responds with a systematic change in motor behavior. This type of adaptive motor learning can transfer intermanually; adaptation of movements of the right hand in response to training with a perturbed visual signal (visuomotor adaptation) may carry over to the left hand. While visuomotor adaptation has been studied extensively, it is unclear whether the cerebellum, a structure involved in adaptation, is important for intermanual transfer as well. We addressed this question with three experiments in which subjects reached with their right hands as a 30° visuomotor rotation was introduced. Subjects received anodal or sham transcranial direct current stimulation on the trained (experiment 1) or untrained (experiment 2) hemisphere of the cerebellum, or, for comparison, motor cortex (M1). After the training period, subjects reached with their left hand, without visual feedback, to assess intermanual transfer of learning aftereffects. Stimulation of the right cerebellum caused faster adaptation, but none of the stimulation sites affected transfer. To ascertain whether cerebellar stimulation would increase transfer if subjects learned faster as well as a larger amount, in experiment 3 anodal and sham cerebellar groups experienced a shortened training block such that the anodal group learned more than sham. Despite the difference in adaptation magnitude, transfer was similar across these groups, although smaller than in experiment 1. Our results suggest that intermanual transfer of visuomotor learning does not depend on cerebellar activity and that the number of movements performed at plateau is an important predictor of transfer.