The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pontedera, Italy.
Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Nat Protoc. 2020 Oct;15(10):3129-3153. doi: 10.1038/s41596-020-0377-6. Epub 2020 Sep 28.
Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON ( https://neuron.yale.edu/neuron/ ) to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve-electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided.
外周神经接口在最近的过去已成功用于恢复残疾患者的感觉运动功能和自主神经系统的神经调节。出于伦理、临床和经济原因，这些神经接口的优化至关重要。特别是，混合模型 (HM) 构成了模拟直接神经刺激和优化植入式电极设计的几乎所有方面的有效框架：电极的类型（例如，束内与束外）、它们的插入位置和使用的刺激方案。它们基于有限元方法（用于计算电刺激引起的神经内电压分布）和计算框架（如 NEURON（https://neuron.yale.edu/neuron/））的组合使用，以确定电场对神经结构的影响。它们已经为不同的应用提供了有用的结果，但这种强大方法的整体可用性仍然受到该过程固有复杂性的限制。在这里，我们展示了一种用于外周神经接口的 HM 的通用、模块化和可扩展框架，其中可以轻松确定和实施回答不同类型研究问题所需的正确逼近程度。HM 工作流程分为以下任务：识别和表征给定神经节段束内的纤维亚群，确定束几何形状的不同逼近程度，在这些几何形状内定位纤维并参数化电极几何形状和神经-电极界面的几何形状。依次检查这些任务，并描述其实施中最相关问题的解决方案。最后，提供了一些与常见外周神经接口模拟相关的示例。
