TY - JOUR
T1 - Analysing vagus nerve spontaneous activity using finite element modelling
AU - Smets, Hugo
AU - Stumpp, Lars
AU - Julémont, Nicolas
AU - Cury, Joaquin
AU - Debelle, Adrien
AU - Innocenti, Bernardo
AU - Vespa, Simone
AU - Haut, Benoit
AU - Doguet, Pascal
AU - Vanhoestenberghe, Anne
AU - Delbeke, Jean
AU - Tahry, Riëm El
AU - Nonclercq, Antoine
N1 - Publisher Copyright:
© 2021 IOP Publishing Ltd.
PY - 2021/10
Y1 - 2021/10
N2 - 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 rat in-vivo vagus nerve recordings. Main results. Fibre diameters can be discriminated if they are below 3 μm and 7 μm, 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.
AB - 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 rat in-vivo vagus nerve recordings. Main results. Fibre diameters can be discriminated if they are below 3 μm and 7 μm, 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.
KW - fibre discrimination
KW - neural computational modeling
KW - recording setup design
KW - spontaneous activity identification
KW - vagus nerve recording
UR - http://www.scopus.com/inward/record.url?scp=85104912555&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104912555&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/abe68f
DO - 10.1088/1741-2552/abe68f
M3 - Article
C2 - 33588393
AN - SCOPUS:85104912555
SN - 1741-2560
VL - 18
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 5
M1 - 056008
ER -