Du Jinze, Morales Andres, Kosta Pragya, Bouteiller Jean-Marie C, Martinez Gema, Warren David, Fernandez Eduardo, Lazzi Gianluca
Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Institute for Technology and Medical Systems Innovation (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
Int Work Conf Interp Nat Artif Comput. 2022 May-Jun;13258:526-535. doi: 10.1007/978-3-031-06242-1_52. Epub 2022 May 24.
Although electrical stimulation is an established treatment option for multiple central nervous and peripheral nervous system diseases, its effects on the tissue and subsequent safety of the stimulation are not well understood. Therefore, it is crucial to design stimulation protocols that maximize therapeutic efficacy while avoiding any potential tissue damage. Further, the stimulation levels need to be adjusted regularly to ensure that they are safe even with the changes to the nerve due to long-term stimulation. Using the latest advances in computing capabilities and machine learning approaches, we developed computational models of peripheral nerve stimulation based on very high-resolution cross-sectional images of the nerves. We generated nerve models constructed from non-stimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to examine how the current density distribution is affected by nerve damage. Using our in-house numerical solver, the Admittance Method (AM), we computed the induced current distribution inside the nerves and compared the current penetration for healthy and damaged nerves. Our computational results indicate that when the nerve is damaged, primarily evidenced by the decreased nerve fiber packing, the current penetrates deeper inside the nerve than in the healthy case. As safety limits for electrical stimulation of biological tissue are still debated, we ultimately aim to utilize our computational models to determine refined safety criteria and help design safer and more efficacious electrical stimulation protocols.
尽管电刺激是治疗多种中枢神经系统和周围神经系统疾病的既定治疗选择，但其对组织的影响以及刺激的后续安全性尚未得到充分了解。因此，设计能够在避免任何潜在组织损伤的同时最大化治疗效果的刺激方案至关重要。此外，需要定期调整刺激水平，以确保即使由于长期刺激导致神经发生变化，刺激水平仍然安全。利用计算能力和机器学习方法的最新进展，我们基于神经的超高分辨率横截面图像开发了周围神经刺激的计算模型。我们生成了由未受刺激（健康）和过度刺激（受损）的大鼠坐骨神经构建的神经模型，以研究电流密度分布如何受到神经损伤的影响。使用我们内部的数值求解器——导纳法（AM），我们计算了神经内部的感应电流分布，并比较了健康神经和受损神经的电流穿透情况。我们的计算结果表明，当神经受损时，主要表现为神经纤维排列减少，电流在神经内部的穿透比健康情况下更深。由于生物组织电刺激的安全极限仍存在争议最终，我们旨在利用我们的计算模型来确定精确的安全标准，并帮助设计更安全、更有效的电刺激方案。
