Master Thesis: A computational model of transcutaneous Vagus Nerve Stimulation
Background
Vagus nerve stimulation has emerged as a promising nonpharmacological intervention for a range of medical conditions. Invasive vagus nerve stimulation via implanted stimulators is employed as a noninvasive treatment for drug-resistant epilepsy, congestive heart failure, and major depression. Noninvasive application of electrical stimulation offers advantages such as reduced infection risk and enhanced usability. However, a limitation of current vagus nerve stimulation approaches lies in the predominantly empirical selection of stimulation regions within the ear and patterns, often resulting in uncertain variations in therapeutic and physiological effects. This may lead to overstimulation, potentially activating pain-related Aδ fibers, or conversely, under-stimulation.
Aim
In this project we aim to investigate the mechanisms underlying non-invasive so-called transcutaneous Vagus Nerve Stimulation (tVNS) and optimize its clinical application using computational modeling. The successful student will develop a highly realistic human digital twin model and perform biophysical simulations, which combine finite element methods and artificial representations of neural structures. Our objective is to comprehensively explore the multidimensional parameter space of tVNS in-silico, with the aim of guiding therapy optimization and patient stratification.
We intend to assess the interaction between the electric field and both targeted and non-targeted neural structures within the region of stimulation. By leveraging the insights gained from this analysis, we aim to develop guidelines for the application of tVNS, facilitating its effective and safe utilization in clinical settings.
Data
| Project type | Master thesis |
| ECTS | 30 |
| Language | English/German |
| Period | Start upon agreement |
| Presence time | Virtual and/or at the Chair of Digital Health |
| Useful knowledge | Python Programming, Finite Element Method, Compartmental Cable Models, Neuroscience |
| Work distribution | 50 % modeling and 50 % programming |
| StudOn link | N/A |
| Registration | E-mail with CV and transcript of records to contact person at the bottom |
Literature
Literature recommendations are provided during the meetings. The candidate is further encouraged to research relevant publications on this topic.
Examination
Final presentation and final report/thesis
Contact
Vincent Gemar
Henkestraße 91, Haus 7, 1. OG
91052 Erlangen
Germany
✉vincent.gemvin.gemar@fau.de