Bio, Electro and Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium.
Department of Neurology, Institute of Neurosciences (IONS), Université Catholique de Louvain, Brussels, Belgium-Cliniques Universitaires Saint Luc, Brussels, Belgium.
J Neural Eng. 2021 Apr 6;18(5). doi: 10.1088/1741-2552/abe68f.
Finite element modelling has been widely used to understand the effect of stimulation on the nerve fibres. Yet the literature on analysis of spontaneous nerve activity is much scarcer. In this study, we introduce a method based on a finite element model, to analyse spontaneous nerve activity with a typical bipolar electrode recording setup, enabling the identification of spontaneously active fibres. We applied our method to the vagus nerve, which plays a key role in refractory epilepsy.We developed a 3D model including dynamic action potential (AP) propagation, based on the vagus nerve geometry. The impact of key recording parameters-inter-electrode distance and temperature-and uncontrolled parameters-fibre size and position in the nerve-on the ability to discriminate active fibres were quantified. A specific algorithm was implemented to detect and classify APs from recordings, and tested on six ratvagus nerve recordings.Fibre diameters can be discriminated if they are below 3m and 7m, respectively for inter-electrode distances of 2 mm and 4 mm. The impact of the position of the fibre inside the nerve on fibre diameter discrimination is limited. The range of active fibres identified by modelling in the vagus nerve of rats is in agreement with ranges found at histology.The nerve fibre diameter, directly proportional to the AP propagation velocity, is related to a specific physiological function. Estimating the source fibre diameter is thus essential to interpret neural recordings. Among many possible applications, the present method was developed in the context of a project to improve vagus nerve stimulation therapy for epilepsy.
有限元建模已广泛用于了解刺激对神经纤维的影响。然而，关于自发神经活动分析的文献要少得多。在这项研究中，我们引入了一种基于有限元模型的方法，用典型的双极电极记录设置来分析自发神经活动，从而能够识别自发活动的纤维。我们将该方法应用于迷走神经，迷走神经在难治性癫痫中起关键作用。我们基于迷走神经的几何形状开发了一个包括动态动作电位 (AP) 传播的 3D 模型。定量分析了关键记录参数-电极间距离和温度-以及不受控制的参数-纤维大小和在神经中的位置-对区分活动纤维能力的影响。实现了一种特定的算法来从记录中检测和分类 AP，并在六只大鼠迷走神经记录上进行了测试。如果电极间距离分别为 2mm 和 4mm，则纤维直径分别小于 3m 和 7m 时可以区分。纤维在神经内位置对纤维直径区分的影响有限。通过对大鼠迷走神经进行建模识别的活动纤维范围与组织学上发现的范围一致。神经纤维直径与 AP 传播速度成正比，与特定的生理功能有关。因此，估计源纤维直径对于解释神经记录至关重要。在许多可能的应用中，本方法是在一个旨在改进癫痫迷走神经刺激治疗的项目背景下开发的。
