Sharma Gaurav, Friedenberg David A, Annetta Nicholas, Glenn Bradley, Bockbrader Marcie, Majstorovic Connor, Domas Stephanie, Mysiw W Jerry, Rezai Ali, Bouton Chad
Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Ave, Columbus, OH, 43201, USA.
Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Ave, Columbus, OH, 4320, USA.
Sci Rep. 2016 Sep 23;6:33807. doi: 10.1038/srep33807.
Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.
神经假体技术已被用于恢复瘫痪患者对离散（非节律性）手部运动的皮层控制。然而，此前尚未证明对起源于大脑但由脊髓中的中枢模式发生器（CPG）神经网络协调的节律性运动的皮层控制。在此，我们展示了一种人工神经旁路技术，该技术可解码皮层活动并模拟脊髓CPG功能，从而实现自主的节律性手部运动。该技术结合了从大脑记录的信号、用于解码信号的机器学习算法、CPG网络的数值模型以及用于引发节律性运动的神经肌肉电刺激系统。使用这种神经旁路，一名四肢瘫痪的参与者仅通过思维就能启动、维持节律性和离散性手指运动，并在两者之间进行切换。这些结果对于推动神经假体技术以恢复瘫痪患者的复杂运动具有重要意义。
