Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, United States of America.
J Neural Eng. 2019 Apr;16(2):026001. doi: 10.1088/1741-2552/aaefcf. Epub 2018 Nov 9.
Advancement in prosthetic limb technology requires corresponding improvements in the capability of the amputee to naturally control the device via original motor pathways while simultaneously receiving haptic feedback via sensory pathways. Recording efferent axonal activity using a peripheral neural interface (PNI) allows a good tradeoff between invasiveness and selectivity while possibly preserving the phenomenology of controlling the original limb. One such PNI, the thin-film transverse intrafascicular multichannel electrode (tfTIME), has been shown to be successful in controlling powered prosthetics. However, the tfTIME is highly susceptible to stimulation artifact; thus, using such a PNI to both record efferent motor signals while concurrently stimulating afferent sensory axons in the same nerve is problematic. The micro-channel sieve electrode could also provide a stable, selective, neural interface with larger signal-to-noise levels that are less susceptible to concurrent stimulation artifact or other external noise effects.
This study uses a computational model to compare recording levels of simulated ENGs across neural drive levels as well as basic control signals derived from the ENGs in both tfTIME and micro-channel sieve PNIs. A motor neuron pool model generated axon firing rates at a given neural drive. The time course of the corresponding extracellular currents of the myelinated motor axons were determined using core conductor axon models. Finite element models determined the contribution of the extracellular current from nodes of Ranvier on potentials recorded using each interface. Contributions from each node were combined to create the final ENG.
ENGs recorded using the micro-channel sieves were shown to have much higher amplitudes compared to ENGs recorded using the tfTIMEs. Signal amplitudes also varied less as a function of axonal placement and spike timing, resulting in more consistent signals with amplitudes determined predominantly by neural drive.
Simulation results suggest that the micro-channel sieve provides higher quality control signals over tfTIME PNIs in decoding ENGs. Coupling these results with concurrent stimulation results of the companion paper (Part B: stimulation) suggests that the micro-channel sieve is an optimal bidirectional PNI.
义肢技术的进步需要提高截肢者通过原始运动通路自然控制设备的能力，同时通过感觉通路接收触觉反馈。使用外围神经接口 (PNI) 记录传出轴突活动可以在侵入性和选择性之间取得很好的平衡，同时可能保留控制原始肢体的现象学。一种这样的 PNI，即薄膜横向束内多通道电极 (tfTIME)，已被证明在控制动力假肢方面是成功的。然而，tfTIME 非常容易受到刺激伪影的影响；因此，使用这样的 PNI 同时记录传出运动信号并同时刺激同一神经中的传入感觉轴突是有问题的。微通道筛电极也可以提供稳定、选择性的神经接口，具有更大的信噪比，并且不易受到并发刺激伪影或其他外部噪声影响。
本研究使用计算模型比较了 tfTIME 和微通道筛 PNIs 中模拟的 ENGs 在神经驱动水平以及基本控制信号方面的记录水平。运动神经元池模型在给定神经驱动下产生轴突放电率。使用核心导体轴突模型确定有髓运动轴突的相应细胞外电流的时程。有限元模型确定来自 Ranvier 节点的细胞外电流对使用每个接口记录的电位的贡献。将每个节点的贡献组合起来创建最终的 ENG。
与使用 tfTIME 记录的 ENG 相比，使用微通道筛记录的 ENG 显示出更高的幅度。信号幅度也随轴突放置和尖峰时间的变化较小，导致具有主要由神经驱动决定的幅度的更一致信号。
模拟结果表明，在解码 ENG 方面，微通道筛比 tfTIME PNI 提供更高质量的控制信号。将这些结果与伴侣论文（B 部分：刺激）中的并发刺激结果结合起来表明，微通道筛是一种理想的双向 PNI。
