Master Thesis: A computational model of Deep Brain Stimulation
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.
|Summer term 2022
|Virtual and/or at the Chair of Digital Health
|Finite Element Method, Compartmental Cable Models, Programming, Neuroscience
|50 % modeling and 50 % programming
|E-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