Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.
Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.
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电刺激在周围神经中诱导的电流分布随神经损伤程度的不同而显著不同：利用卷积神经网络和现实神经模型进行的计算研究。
DOI：
10.1142/s0129065723500223
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发表时间：
2023-04
影响因子：
8
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中科院分区：
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电刺激周围神经系统是一种有希望的治疗选择，但它对组织的影响和刺激的安全性仍然知之甚少。为了设计在不造成组织损伤的情况下提高治疗效果的刺激方案，我们利用最新的计算能力和机器学习技术，基于极高分辨率的神经横断面图像构建了周围神经和刺激袖带的计算模型。我们利用未受刺激(健康)和过度刺激(受损)的大鼠坐骨神经建立了神经模型，以探讨神经损伤如何影响感应电流密度分布。使用我们的内部计算、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，并将其与健康神经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，主要表现为神经纤维堆积减少，电流比健康样本更深地渗透到过度刺激的神经中。由于周围神经电刺激的安全极限仍然参考Shannon标准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特定于周围神经的安全标准迈出的重要一步，并利用了计算生物电磁学和机器学习的最新进展，例如基于Python的AM和基于CNN的神经图像分割。
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.