Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia; Clinical Imaging Research Center, National University of Singapore, Singapore.
Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
Neuroimage. 2019 Nov 15;202:116023. doi: 10.1016/j.neuroimage.2019.116023. Epub 2019 Jul 17.
Soft robotics have come to the forefront of devices available for rehabilitation following stroke; however, objective evaluation of the specific brain changes following rehabilitation with these devices is lacking. In this study, we utilized functional Magnetic Resonance Imaging (fMRI) and dynamic causal modeling (DCM) to characterize the activation of brain areas with a MRI compatible glove actuator compared to the conventional manual therapy. Thirteen healthy volunteers engaged in a motor-visual fMRI task under four different conditions namely active movement, manual passive movement, passive movement using a glove actuator, and crude tactile stimulation. Brain activity following each task clearly identified the somatosensory motor area (SMA) as a major hub orchestrating activity between the primary motor (M1) and sensory (S1) cortex. During the glove-induced passive movement, activity in the motor-somatosensory areas was reduced, but there were significant increases in motor cortical activity compared to manual passive movement. We estimated the modulatory signaling from within a defined sensorimotor network (SMA, M1, and S1), through DCM and highlighted a dual-gating of sensorimotor inputs to the SMA. Proprioceptive signaling from S1 to the SMA reflected positive coupling for the manually assisted condition, while M1 activity was positively coupled to the SMA during the glove condition. Importantly, both the S1 and M1 were shown to influence each other's connections with the SMA, with inhibitory nonlinear modulation by the M1 on the S1-SMA connection, and similarly S1 gated the M1-SMA connection. The work is one of the first to have applied effective connectivity to examine sensorimotor activity ensued by manual or robotic passive range of motion exercise, crude tactile stimulation, and voluntary movements to provide a basis for the mechanism by which soft actuators can alter brain activity.
软机器人已成为中风后康复治疗可用设备的前沿领域；然而，缺乏使用这些设备进行康复治疗后特定大脑变化的客观评估。在这项研究中，我们利用功能磁共振成像 (fMRI) 和动态因果建模 (DCM) 来描述与传统手动治疗相比，MRI 兼容手套执行器的大脑区域激活情况。13 名健康志愿者在四种不同条件下进行了运动视觉 fMRI 任务，分别为主动运动、手动被动运动、手套执行器被动运动和粗糙触觉刺激。每个任务后的大脑活动清楚地确定了感觉运动区 (SMA) 作为协调初级运动 (M1) 和感觉 (S1) 皮层之间活动的主要枢纽。在手套引起的被动运动期间，运动感觉区域的活动减少，但与手动被动运动相比，运动皮层的活动显著增加。我们通过 DCM 从定义的感觉运动网络 (SMA、M1 和 S1) 内估计调节信号，并突出了 SMA 到感觉运动输入的双重门控。来自 S1 的本体感觉信号反映了手动辅助条件的正耦合，而在手套条件下，M1 活动与 SMA 正耦合。重要的是，S1 和 M1 都被显示出相互影响 SMA 的连接，M1 对 S1-SMA 连接的抑制性非线性调制，以及类似地 S1 对 M1-SMA 连接的门控。这项工作是应用有效连接来检查手动或机器人被动运动范围、粗糙触觉刺激和自主运动后感觉运动活动的首批工作之一，为软执行器改变大脑活动的机制提供了基础。
