Farrens Andria J, Schmidt Kristin, Cohen Hannah, Sergi Fabrizio
IEEE Trans Neural Syst Rehabil Eng. 2023;31:1287-1296. doi: 10.1109/TNSRE.2023.3242601. Epub 2023 Feb 16.
MRI-compatible robots provide a means of studying brain function involved in complex sensorimotor learning processes, such as adaptation. To properly interpret the neural correlates of behavior measured using MRI-compatible robots, it is critical to validate the measurements of motor performance obtained via such devices. Previously, we characterized adaptation of the wrist in response to a force field applied via an MRI-compatible robot, the MR-SoftWrist. Compared to arm reaching tasks, we observed lower end magnitude of adaptation, and reductions in trajectory errors beyond those explained by adaptation. Thus, we formed two hypotheses: that the observed differences were due to measurement errors of the MR-SoftWrist; or that impedance control plays a significant role in control of wrist movements during dynamic perturbations. To test both hypotheses, we performed a two-session counterbalanced crossover study. In both sessions, participants performed wrist pointing in three force field conditions (zero force, constant, random). Participants used either the MR-SoftWrist or the UDiffWrist, a non-MRI-compatible wrist robot, for task execution in session one, and the other device in session two. To measure anticipatory co-contraction associated with impedance control, we collected surface EMG of four forearm muscles. We found no significant effect of device on behavior, validating the measurements of adaptation obtained with the MR-SoftWrist. EMG measures of co-contraction explained a significant portion of the variance in excess error reduction not attributable to adaptation. These results support the hypothesis that for the wrist, impedance control significantly contributes to reductions in trajectory errors in excess of those explained by adaptation.
磁共振成像(MRI)兼容的机器人提供了一种研究复杂感觉运动学习过程(如适应)中所涉及的脑功能的方法。为了正确解释使用MRI兼容机器人测量的行为的神经相关性,验证通过此类设备获得的运动表现测量结果至关重要。此前,我们描述了手腕对通过MRI兼容机器人MR-SoftWrist施加的力场的适应情况。与手臂伸展任务相比,我们观察到适应的最终幅度较低,并且轨迹误差的减少幅度超过了由适应所解释的范围。因此,我们形成了两个假设:观察到的差异是由于MR-SoftWrist的测量误差;或者在动态扰动期间,阻抗控制在手腕运动控制中起重要作用。为了检验这两个假设,我们进行了一项两阶段的平衡交叉研究。在两个阶段中,参与者在三种力场条件下(零力、恒定力、随机力)进行手腕指向。在第一阶段,参与者使用MR-SoftWrist或UDiffWrist(一种不兼容MRI的手腕机器人)执行任务,在第二阶段使用另一种设备。为了测量与阻抗控制相关的预期协同收缩,我们收集了四块前臂肌肉的表面肌电图。我们发现设备对行为没有显著影响,从而验证了用MR-SoftWrist获得的适应测量结果。协同收缩的肌电图测量解释了不归因于适应的额外误差减少中很大一部分方差。这些结果支持了这样的假设,即对于手腕来说,阻抗控制对轨迹误差的减少有显著贡献,其减少幅度超过了由适应所解释的范围。