Milot Marie-Hélène, Marchal-Crespo Laura, Beaulieu Louis-David, Reinkensmeyer David J, Cramer Steven C
École de réadaptation, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Pavillon Gérald-Lasalle, 3001, 12e Avenue Nord, Sherbrooke, QC, J1H 5N4, Canada.
Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems IRIS, ETH Zurich, TAN E3 Tannenstrasse 1, 8092, Zurich, Switzerland.
Exp Brain Res. 2018 Nov;236(11):3085-3099. doi: 10.1007/s00221-018-5365-5. Epub 2018 Aug 21.
To promote motor learning, robotic devices have been used to improve subjects' performance by guiding desired movements (haptic guidance-HG) or by artificially increasing movement errors to foster a more rapid learning (error amplification-EA). To better understand the neurophysiological basis of motor learning, a few studies have evaluated brain regions activated during EA/HG, but none has compared both approaches. The goal of this study was to investigate using fMRI which brain networks were activated during a single training session of HG/EA in healthy adults learning to play a computerized pinball-like timing task. Subjects had to trigger a robotic device by flexing their wrist at the correct timing to activate a virtual flipper and hit a falling ball towards randomly positioned targets. During training with HG/EA, subjects' timing errors were decreased/increased, respectively, by the robotic device to delay or accelerate their wrist movement. The results showed that at the beginning of the training period with HG/EA, an error-detection network, including cerebellum and angular gyrus, was activated, consistent with subjects recognizing discrepancies between their intended actions and the actual movement timing. At the end of the training period, an error-detection network was still present for EA, while a memory consolidation/automatization network (caudate head and parahippocampal gyrus) was activated for HG. The results indicate that training movement with various kinds of robotic input relies on different brain networks. Better understanding the neurophysiological underpinnings of brain processes during HG/EA could prove useful for optimizing rehabilitative movement training for people with different patterns of brain damage.
为促进运动学习,已使用机器人设备通过引导期望动作(触觉引导-HG)或人为增加运动误差以促进更快学习(误差放大-EA)来改善受试者的表现。为了更好地理解运动学习的神经生理基础,一些研究评估了在EA/HG期间激活的脑区,但尚无研究对这两种方法进行比较。本研究的目的是使用功能磁共振成像(fMRI)来调查在健康成年人学习玩类似电脑弹球的定时任务的单次HG/EA训练期间,哪些脑网络被激活。受试者必须在正确的时间弯曲手腕触发机器人设备,以激活虚拟挡板并将下落的球击向随机定位的目标。在HG/EA训练期间,机器人设备分别减少/增加受试者的定时误差,以延迟或加速他们的手腕运动。结果表明,在HG/EA训练期开始时,一个包括小脑和角回的错误检测网络被激活,这与受试者识别其预期动作与实际运动时间之间的差异一致。在训练期结束时,EA仍存在错误检测网络,而HG激活了一个记忆巩固/自动化网络(尾状核头部和海马旁回)。结果表明,使用各种机器人输入进行运动训练依赖于不同的脑网络。更好地理解HG/EA期间脑过程的神经生理基础可能有助于优化针对不同脑损伤模式患者的康复运动训练。