Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
Int J Neural Syst. 2023 Apr;33(4):2350022. doi: 10.1142/S0129065723500223. Epub 2023 Mar 15.
Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.
外周神经系统的电刺激是治疗多种疾病的一种很有前途的治疗选择；然而，其对组织的影响和刺激的安全性仍知之甚少。为了设计既能增强治疗效果又不会造成组织损伤的刺激方案，我们根据最新的计算能力和机器学习技术，使用神经的超高分辨率横截面图像构建了外周神经和刺激袖带的计算模型。我们使用未受刺激（健康）和过度刺激（受损）的大鼠坐骨神经来构建神经模型，以探索神经损伤如何影响诱导电流密度分布。使用我们内部的计算、准静态、平台和导纳法（AM），我们估计了神经内的感应电流分布，并将其与健康和受损神经进行了比较。我们还估计了健康和受损神经样本中局部细胞损伤的程度。当神经受损时，如主要由神经纤维包装减少所证明的那样，电流比在健康样本中更深地穿透到过度刺激的神经中。由于外周神经电刺激的安全限制仍然参考香农准则来区分安全和不安全的刺激，这项工作展示的能力是朝着开发针对外周神经的具体安全标准迈出的重要一步，这些标准利用了最新的计算生物电磁学和机器学习进展，如基于 Python 的 AM 和基于 CNN 的神经图像分割。
