University of Twente Student Theses
Finite Element Method Modeling of Functionally Selective Peripheral Nerve Stimulation of an in silico Myelinated Nerve Fiber Model
Huggers, T.J.P. (2024) Finite Element Method Modeling of Functionally Selective Peripheral Nerve Stimulation of an in silico Myelinated Nerve Fiber Model.
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| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 12 November 2029 |
| Abstract: | Neuromodulation of the autonomous nervous system is a promising alternative treatment for many chronic diseases. Current implantable neuromodulation solutions suffer from low spatial selectivity, limiting widespread therapeutic adaptation. Neural recruitment during electrical modulation of nerve fibers is characterized by lower activation thresholds for large diameter myelinated fibers compared to smaller diameter fibers with less myelination. In the context of vagus nerve stimulation, any stimulation that activates small diameter fibers responsible for desired therapeutic effects, will also activate substantial population of large myelinated fibers leading to unwanted side-effects, such as neck muscle contractions. This research explores anodal blocking as a potential method to selectively stimulate nerve fibers. An anodal block has been previously proposed to block large diameter fibers by inducing hyperpolarization of the membrane potential, while allowing conduction in smaller diameter fibers. In this work, a neural computational model is deployed in combination with finite element method modeling, in order to study the effects of different stimulation parameters and electrode geometry features on the effects of anodal blocking. It was demonstrated that anodal blocking efficacy depends on both geometrical and stimulation waveform parameters, and that functional selectivity in peripheral nerve stimulation can be achieved by careful selection of these parameters. Results of this work can be used to inform in vivo experiments and contribute to increasing the efficacy of neuromodulation methods. |
| Item Type: | Essay (Master) |
| Clients: | imec NL, Eindhoven, the Netherlands |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 53 electrotechnology |
| Programme: | Electrical Engineering MSc (60353) |
| Link to this item: | https://purl.utwente.nl/essays/104536 |
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