Objective.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.Approach.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 ratin-vivovagus nerve recordings.Main results.Fibre diameters can be discriminated if they are below 3μm and 7μm, 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.Significance.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.
Keywords: fibre discrimination; neural computational modelling; recording setup design; spontaneous activity identification; vagus nerve recording.
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