目的:这项研究的主要目的是记录来自脊髓内上升和下降束的感觉和运动信息,以解码在跑步机上行走时的后肢运动学。
方法:两种不同的实验范式(即,在当前研究中使用了主动和被动)。在积极的实验中,训练五只猫两足行走,而他们的手保持在跑步机的前框架上保持平衡或四足行走。在被动实验中,实验人员被动地移动了肢体。使用植入L3-L4脊柱节段的背柱(DC)和侧柱(LC)中的微线阵列记录局部场电位(LFP)活性。LFP的振幅和频率分量形成了特征集,并使用弹性网络正则化来解码后肢关节角度。
结果:结果表明,在跑步机上行走过程中,从DC和LC区域记录的信号的信息内容之间没有显着差异,但是在麻醉猫的后肢被动运动过程中,DC的信息含量明显高于LC。此外,在运动过程中,使用来自DC的记录信号获得的解码性能与来自LC的解码性能相当。但是,例如,使用DC中的记录通道获得的解码性能明显优于使用从LC记录的信号获得的解码性能。长期分析表明,可以在2-3个月内实现鲁棒的解码性能,而性能不会显着下降。
结论:这项工作提出了一种有希望的方法来开发一种自然和强大的运动神经假体装置,该装置使用下降的神经信号来执行运动和上升的神经信号作为控制运动的反馈信息。
OBJECTIVE: The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord for decoding the hindlimb kinematics during walking on the treadmill.
METHODS: Two different experimental paradigms (i.e., active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3-L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set and the elastic net regularization was used to decode the hindlimb joint angles.
RESULTS: The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. But, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2-3 months without a significant decrease in performance.
CONCLUSIONS: This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information for control of the movement.