Master Thesis: A computational model of Deep Brain Stimulation

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Deep Brain Stimulation (DBS) is an effective clinical treatment for movement disorders, such as Parkinson’s disease and is actively investigated for treatment of several neurological dysfunctions such as mood or memory disorders. However, the mechanisms of action between the applied electric fields and the underlying neural circuits remain largely enigmatic, limiting both the clinical efficacy as well as the pace of translational research of DBS. Computational modeling of DBS has recently emerged as a powerful tool for investigating both theoretical mechanisms and techniques to optimize the clinical application of the technology. Much of the field has focused on patient-specific DBS models, which had a great impact on surgical targeting and stimulation parameter selection.


The successful student is asked to develop a patient-specific 3D Finite Element Method (FEM) model of the human brain and evaluate the effects of stimulation parameter selection on the electric field generated by DBS. The student is asked to couple the results of the FEM analysis with compartmental cable models of the axon, assess the outcomes of stimulation parameter selection of various neural substrates and optimise stimulation parameters accordingly.


Project typeMaster thesis
PeriodSummer term 2022
Presence timeVirtual and/or at the Chair of Digital Health
Useful knowledgeFinite Element Method, Compartmental Cable Models, Programming, Neuroscience
Work distribution50 % modeling and 50 % programming
StudOn linkN/A
RegistrationE-mail to Dr. Andreas Rowald


Literature recommendations are provided during the meetings. The candidate is further encouraged to research relevant publications on this topic.


Final presentation and final report/thesis


Dr. Andreas Rowald
Research Group Leader
Henkestraße 91, Haus 7, 1. OG
91052 Erlangen
📞+49 9131 85-23604

Friedrich-Alexander-Universität Erlangen-Nürnberg