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Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.

SIGNIFICANCE STATEMENTMotor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

 

Combining functional magnetic resonance imaging (fMRI) with models of neuromotor adaptation is useful for identifying the function of different neuromotor control centers in the brain. Current models of neuromotor adaptation to force perturbations have been studied primarily in whole-arm reaching tasks that are ill-suited for MRI. We have previously developed the MR-SoftWrist, an fMRI-compatible wrist robot, to study motor control during wrist adaptation. Because the wrist joint has intrinsic dynamics dominated by stiffness, it is unclear if these models will apply to the wrist. Here, we characterize adaptation of the wrist to lateral forces to determine if established adaptation models are valid for wrist pointing. We recruited thirteen subjects to perform our task using the MR-SoftWrist. Our task included a clockwise (CW) - counterclockwise (CCW) - error clamp schedule and an alternating CW-CCW force field schedule. To determine applicability of previous models, we fit three candidate models - a single-state, two-state, and context dependent multi-state model - to behavioral data. Our results indicate that features of sensorimotor adaptation reported in the literature are present in the wrist, including spontaneous recovery, and anterograde and retrograde interference between the learning of two oppositely directed force fields. A two-state model best fit our behavioral data. Under this model, adaptation was dominated by a fast learning state with minor engagement of a slow learning state. Finally, all adaptation models tested showed a consistent over-estimation of performance error, suggesting that the control of the wrist relies not only on internal models but likely other mechanisms, like impedance control, to reject perturbations. 

Many stroke survivors suffer from hemiparesis, a condition that results in impaired walking ability. Walking ability is commonly assessed by walking speed, which is dependent on propulsive force generation both in healthy and stroke populations. Propulsive force generation is determined by two factors: ankle moment and the posture of the trailing limb during push-off. Recent work has used robotic assistance strategies to modulate propulsive force with some success. However, robotic strategies are limited by their high cost and the technical difficulty of fitting and operating robotic devices in a clinical setting. Here we present a new paradigm for goal-oriented gait training that utilizes a split belt treadmill to train both components of propulsive force generation, achieved by accelerating the treadmill belt of the trailing limb during push off. Belt accelerations require subjects to produce greater propulsive force to maintain their position on the treadmill and increase trailing limb angle through increased velocity of the accelerated limb. We hypothesized that locomotor adaptation to belt accelerations would result in measurable after effects in the form of increased propulsive force generation. We tested our protocol on healthy subjects at two acceleration magnitudes. Our results show that 79% of subjects significantly increased propulsive force generation following training, and that larger accelerations translated to larger, more persistent behavioral gains.