The functional and structural neural correlates of dynamic balance impairment and recovery in persons with acquired brain injury.

Dynamic balance control is associated with the function of multiple brain networks and is impaired following Acquired Brain Injury (ABI). This study aims to characterize the functional and structural correlates of ABI-induced dynamic balance impairments and recovery following a rehabilitation treatment. Thirty-one chronic participants with ABI participated in a novel rehabilitation treatment composed of 22 sessions of a perturbation-based rehabilitation training. Dynamic balance was assessed using the Community Balance and Mobility scale (CB&M) and the 10-Meter Walking Test (10MWT). Brain function was estimated using resting-state fMRI imaging that was analysed using independent component analysis (ICA), and regions-of-interest analyses. Brain morphology was also assessed using structural MRI. ICA revealed a reduction in component-related activation within the sensorimotor and cerebellar networks post-intervention. Improvement in CB&M scale was associated with a reduction in FC within the cerebellar network and with baseline FC within the cerebellar-putamen and cerebellar-thalamic networks. Improvement in 10MWT was associated with baseline FC within the cerebellar-putamen and cerebellar-cortical networks. Brain volume analysis did not reveal structural correlates of dynamic balance, but dynamic balance was correlated with time since injury. Our results show that dynamic balance recovery is associated with FC reduction within and between the cerebellar and sensorimotor networks. The lack of global structural correlates of dynamic balance may point to the involvement of specific networks in balance control.

Formation of Long-Term Locomotor Memories Is Associated with Functional Connectivity Changes in the Cerebellar-Thalamic-Cortical Network.

Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioral and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall, reflecting a retrieval of previous motor actions, and faster relearning, reflecting an increased sensitivity to errors. Although these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also demonstrated recently in other kinds of adaptation such as in locomotor adaptation. Little is known about the neural processes that underlie these distinct aspects of memory. We hypothesize that recall and faster relearning reflect different learning processes that operate at the same time and depend on different neuronal networks. Seventeen subjects performed a multisession locomotor adaptation experiment in the laboratory, together with resting-state and localizer fMRI scans, after the baseline and the locomotor adaptation sessions. We report a modulation of the cerebellar-thalamic-cortical and cerebellar-basal ganglia networks after locomotor adaptation. Interestingly, whereas thalamic-cortical baseline connectivity was correlated with recall, cerebellar-thalamic baseline connectivity was correlated with faster relearning. Our results suggest that separate neuronal networks underlie error sensitivity and retrieval components. Individual differences in baseline resting-state connectivity can predict idiosyncratic combination of these components. SIGNIFICANCE STATEMENT: The ability to shape our motor behavior rapidly in everyday activity, such as when walking on sand, suggests the existence of long-term motor memories. It was suggested recently that this ability is achieved by the retrieval of previous motor actions and by enhanced relearning capacity. Little is known about the neural mechanisms that underlie these memory processes. We studied the modularity in long-term motor memories in the context of locomotor adaptation using resting-state fMRI. We show that retrieval and relearning effects are associated with separate locomotor control networks and that intersubject variability in learning and in the generation of motor memories could be predicted from baseline resting-state connectivity in locomotor-related networks.

Overcoming motor "forgetting" through reinforcement of learned actions.

The human motor system rapidly adapts to systematic perturbations but the adapted behavior seems to be forgotten equally rapidly. The reason for this forgetting is unclear, as is how to overcome it to promote long-term learning. Here we show that adapted behavior can be stabilized by a period of binary feedback about success and failure in the absence of vector error feedback. We examined the time course of decay after adaptation to a visuomotor rotation through a visual error-clamp condition--trials in which subjects received false visual feedback showing perfect directional performance, regardless of the movements they actually made. Exposure to this error-clamp following initial visuomotor adaptation led to a rapid reversion to baseline behavior. In contrast, exposure to binary feedback after initial adaptation turned the adapted state into a new baseline, to which subjects reverted after transient exposure to another visuomotor rotation. When both binary feedback and vector error were present, some subjects exhibited rapid decay to the original baseline, while others persisted in the new baseline. We propose that learning can be decomposed into two components--a fast-learning, fast-forgetting adaptation process that is sensitive to vector errors and insensitive to task success, and a second process driven by success that learns more slowly but is less susceptible to forgetting. These two learning systems may be recruited to different degrees across individuals. Understanding this competitive balance and exploiting the long-term retention properties of learning through reinforcement is likely to be essential for successful neuro-rehabilitation.

How is a motor skill learned? Change and invariance at the levels of task success and trajectory control.

The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed-accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-to-noise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms.