Nicotinic signaling is required for motor learning but not rehabilitation after spinal cord injury

Currently, therapeutic intervention for spinal cord injury is limited. Many approaches rely on strengthening the remaining substrate and driving recovery through rehabilitative training. As compared to learning novel compensatory strategies, rehabilitation focuses on restoring movements lost to injury. Whether rehabilitation of previously learned movements after spinal cord injury requires the molecular mechanisms of motor learning, or if it engages previously trained motor circuits without requiring novel learning. Our findings implicate the latter mechanism, as we demonstrate that nicotinic acetylcholine signaling is required for motor learning but is dispensable for the recovery of previously trained motor behavior after cervical spinal cord injury.

Shaping the Sensory–Motor Network by Short-Term Unresolvable Sensory–Motor Mismatch

Learning from errors as the main mechanism for motor adaptation has two fundamental prerequisites: a mismatch between the intended and performed movement and the ability to adapt motor actions. Many neurological patients are limited in their ability to transfer an altered motor representation into motor action due to a compromised motor pathway. Studies that have investigated the effects of a sustained and unresolvable mismatch over multiple days found changes in brain processing that seem to optimize the potential for motor learning (increased drive for motor adaptation and a weakening of the current implementation of motor programs). However, it remains unclear whether the observed effects can be induced experimentally and more important after shorter periods. Here, we used task-based and resting-state fMRI to investigate whether the known pattern of cortical adaptations due to a sustained mismatch can be induced experimentally by a short (20 min), but unresolvable, sensory–motor mismatch by impaired facial movements in healthy participants by transient facial tapping. Similar to long-term mismatch, we found plastic changes in a network that includes the striatal, cerebellar and somatosensory brain areas. However, in contrast to long-term mismatch, we did not find the involvement of the cerebral motor cortex. The lack of the involvement of the motor cortex can be interpreted both as an effect of time and also as an effect of the lack of a reduction in the motor error. The similar effects of long-term and short-term mismatch on other parts of the sensory–motor network suggest that the brain-state caused by long-term mismatch can be (at least partly) induced by short-term mismatch. Further studies should investigate whether short-term mismatch interventions can be used as therapeutic strategy to induce an altered brain-state that increase the potential for motor learning.

Aging-Related Changes in Cortical Sources of Sleep Oscillatory Neural Activity Following Motor Learning Reflect Contributions of Cortical Thickness and Pre-sleep Functional Activity

Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

Always Pay Attention to Which Model of Motor Learning You Are Using

This critical review considers the epistemological and historical background of the theoretical construct of motor learning for a more differentiated understanding. More than simply reflecting critically on the models that are used to solve problems—whether they are applied in therapy, physical education, or training practice—this review seeks to respond constructively to the recent discussion caused by the replication crisis in life sciences. To this end, an in-depth review of contemporary motor learning approaches is provided, with a pragmatism-oriented clarification of the researcher’s intentions on fundamentals (what?), subjects (for whom?), time intervals (when?), and purpose (for what?). The complexity in which the processes of movement acquisition, learning, and refinement take place removes their predictable and linear character and therefore, from an applied point of view, invites a great deal of caution when trying to make generalization claims. Particularly when we attempt to understand and study these phenomena in unpredictable and dynamic contexts, it is recommended that scientists and practitioners seek to better understand the central role that the individual and their situatedness plays in the system. In this way, we will be closer to making a meaningful and authentic contribution to the advancement of knowledge, and not merely for the sake of renaming inventions.

Observing errors in a combination of error and correct models favors observational motor learning

Background Imitative learning is highly effective from infancy to old age; however, little is known about the effects of observing errors during imitative learning. This study aimed to examine how observing errors affected imitative learning performance to maximize its effect. Methods In the pre-training session, participants were instructed to pinch at a target force (8 N) with auditory feedback regarding generated force while they watched videos of someone pinching a sponge at the target force. In the pre-test, participants pinched at the target force and did not view a model or receive auditory feedback. In Experiment 1, in the main training session, participants imitated models while they watched videos of pinching at either the incorrect force (error-mixed condition) or target force (correct condition). Then, the exact force generated was measured without receiving auditory feedback or viewing a model. In Experiment 2, using the same procedures, newly recruited participants watched videos of pinching at incorrect forces (4 and 24 N) as the error condition and the correct force as the correct condition. Results In Experiment 1, the average force was closer to the target force in the error-mixed condition than in the correct condition. In Experiment 2, the average force in the correct condition was closer to the target force than in the error condition. Conclusion Our findings indicated that observing error actions combined with correct actions affected imitation motor learning positively as error actions contained information on things to avoid in the target action. It provides further information to enhance imitative learning in mixed conditions compared to that with correct action alone.

Individual Optimal Attentional Strategy in Motor Learning Tasks Characterized by Steady-State Somatosensory and Visual Evoked Potentials

Focus of attention is one of the most influential factors facilitating motor performance. Previous evidence supports that the external focus (EF) strategy, which directs attention to movement outcomes, is associated with better motor performance than the internal focus (IF) strategy, which directs attention to body movements. However, recent studies have reported that the EF strategy is not effective for some individuals. Furthermore, neuroimaging studies have demonstrated that the frontal and parietal areas characterize individual optimal attentional strategies for motor tasks. However, whether the sensory cortices are also functionally related to individual optimal attentional strategy remains unclear. Therefore, the present study examined whether an individual’s sensory processing ability would reflect the optimal attentional strategy. To address this point, we explored the relationship between responses in the early sensory cortex and individuals’ optimal attentional strategy by recording steady-state somatosensory evoked potentials (SSSEP) and steady-state visual evoked potentials (SSVEP). Twenty-six healthy young participants first performed a motor learning task with reaching movements under IF and EF conditions. Of the total sample, 12 individuals showed higher after-effects under the IF condition than the EF condition (IF-dominant group), whereas the remaining individuals showed the opposite trend (EF-dominant group). Subsequently, we measured SSSEP from bilateral primary somatosensory cortices while presenting vibrotactile stimuli and measured SSVEP from bilateral primary visual cortices while presenting checkerboard visual stimuli. The degree of increasing SSSEP response when the individuals in the IF-dominant group directed attention to vibrotactile stimuli was significantly more potent than those in the EF-dominant individuals. By contrast, the individuals in the EF-dominant group showed a significantly larger SSVEP increase while they directed attention to visual stimuli compared with the IF-dominant individuals. Furthermore, a significant correlation was observed such that individuals with more robust IF dominance showed more pronounced SSSEP attention modulation. These results suggest that the early sensory areas have crucial brain dynamics to characterize an individual’s optimal attentional strategy during motor tasks. The response characteristics may reflect the individual sensory processing ability, such as control of priority to the sensory inputs. Considering individual cognitive traits based on the suitable attentional strategy could enhance adaptability in motor tasks.

Mechanisms of Human Motor Learning Do Not Function Independently

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.