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自主神经刺激的计算建模:迷走神经等

Computational modeling of autonomic nerve stimulation: Vagus et al.

作者信息

Grill Warren M, Pelot Nicole A

机构信息

Department of Biomedical Engineering, Duke University.

出版信息

Curr Opin Biomed Eng. 2024 Dec;32. doi: 10.1016/j.cobme.2024.100557. Epub 2024 Aug 24.

Abstract

Computational models of electrical stimulation, block and recording of autonomic nerves enable analysis of mechanisms of action underlying neural responses and design of optimized stimulation parameters. We reviewed advances in computational modeling of autonomic nerve stimulation, block, and recording over the past five years, with a focus on vagus nerve stimulation, including both implanted and less invasive approaches. Few models achieved quantitative validation, but integrated computational pipelines increase the reproducibility, reusability, and accessibility of computational modeling. Model-based optimization enabled design of electrode geometries and stimulation parameters for selective activation (across fiber locations or types). Growing efforts link models of neural activity to downstream physiological responses to represent more directly the therapeutic effects and side effects of stimulation. Thus, computational modeling is an increasingly important tool for analysis and design of bioelectronic therapies.

摘要

自主神经的电刺激、阻断和记录的计算模型能够分析神经反应背后的作用机制,并设计优化的刺激参数。我们回顾了过去五年自主神经刺激、阻断和记录的计算建模进展,重点是迷走神经刺激,包括植入式和侵入性较小的方法。很少有模型实现了定量验证,但集成的计算流程提高了计算建模的可重复性、可重用性和可及性。基于模型的优化能够设计电极几何形状和刺激参数,以实现选择性激活(跨纤维位置或类型)。越来越多的研究致力于将神经活动模型与下游生理反应联系起来,以更直接地反映刺激的治疗效果和副作用。因此,计算建模是生物电子疗法分析和设计中越来越重要的工具。

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