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Objective: To establish the sensitivity of magnetic resonance elastography (MRE) to active muscle contraction in multiple muscles of the forearm. Methods: We combined MRE of forearm muscles with an MRI-compatible device, the MREbot, to simultaneously measure the mechanical properties of tissues in the forearm and the torque applied by the wrist joint during isometric tasks. We measured shear wave speed of thirteen forearm muscles via MRE in a series of contractile states and wrist postures and fit these outputs to a force estimation algorithm based on a musculoskeletal model. Results: Shear wave speed changed significantly upon several factors, including whether the muscle was recruited as an agonist or antagonist (p = 0.0019), torque amplitude (p ≤ 0.0001), and wrist posture (p = 0.0002). Shear wave speed increased significantly during both agonist (p ≤ 0.0001) and antagonist (p = 0.0448) contraction. Additionally, there was a greater increase in shear wave speed at greater levels of loading. The variations due to these factors indicate the sensitivity to functional loading of muscle. Under the assumption of a quadratic relationship between shear wave speed and muscle force, MRE measurements accounted for an average of 70% of the variance in the measured joint torque. Conclusion: This study shows the ability of MM-MRE to capture variations in individual muscle shear wave speed due to muscle activation and presents a method to estimate individual muscle force through MM-MRE derived measurements of shear wave speed. Significance: MM-MRE could be used to establish normal and abnormal muscle co-contraction patterns in muscles of the forearm controlling hand and wrist function. 

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. 

Robot assisted gait retraining is an increasingly common method for supporting restoration of walking function after neurological injury. Gait speed, an indicator of walking function, is correlated with propulsive force, a measure modulated by the posture of the trailing limb at push-off. With the ultimate goal of improving efficacy of robot assisted gait retraining, we sought to directly target gait propulsion, by exposing subjects to pulses of joint torque applied at the hip and knee joints to modulate push-off posture. In this work, we utilized a robotic exoskeleton to apply pulses of torque to the hip and knee joints, during individual strides, of 16 healthy control subjects, and quantified the effects of this intervention on hip extension and propulsive impulse during and after application of these pulses. We observed significant effects in the outcome measures primarily at the stride of pulse application and generally no after effects in the following strides. Specifically, when pulses were applied at late stance, we observed a significant increase in propulsive impulse when knee and/or hip flexion pulses were applied and a significant increase in hip extension angle when hip extension torque pulses were applied. When pulses were applied at early stance, we observed a significant increase in propulsive impulse associated with hip extension torque.