Fathi Yaser, Erfanian Abbas
Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Front Neurosci. 2022 Mar 25;16:801818. doi: 10.3389/fnins.2022.801818. eCollection 2022.
To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
迄今为止，解码肢体运动学信息主要依赖于从外周神经、背根神经节（DRG）、腹根、脊髓灰质和感觉运动皮层记录的神经信号。在本研究中，我们证明了从脊髓内的外侧柱和背侧柱记录的神经信号具有在运动过程中解码后肢运动学的潜力。实验使用完整的猫进行。训练猫在仅后肢的条件下在移动带上行走，同时它们的前肢保持在跑步机的前体上。使用植入猫脊髓左右两侧背侧柱和外侧柱的微电极阵列记录的局部场电位信号来解码双侧后肢关节角度。结果表明，对侧后肢运动学的解码精度与同侧运动学相同。有趣的是，在仅后肢运动期间，可以从脊髓一侧的外侧柱准确解码双腿的后肢运动学。结果表明，使用从背侧柱和外侧柱记录的神经信号获得的解码性能之间没有显著差异。时频分析结果表明，所有频段的事件相关同步（ERS）和事件相关去同步（ERD）模式都可以揭示运动过程中神经信号的动态变化。运动的开始和结束可以通过ERD/ERS模式清楚地识别。互信息（MI）分析结果表明，θ频段比其他频段包含更多的肢体运动学信息。此外，θ功率随着运动速度的提高而增加。
