Improving computational models of deep brain stimulation through experimental calibration
By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.
Authors
- Henning Bathel
- Ursula van Rienen (ORCID: https://orcid.org/0000-0003-1042-2058)
- Alexander Storch (ORCID: https://orcid.org/0000-0002-1133-9216)
- Maria Kober (ORCID: https://orcid.org/0000-0003-1034-1960)
- Mareike Fauser (ORCID: https://orcid.org/0000-0002-9275-1391)
- Nils Arbeiter (ORCID: https://orcid.org/0009-0006-9072-6121)
- Julius Zimmermann (ORCID: https://orcid.org/0000-0003-1486-0426)
- Jan Philipp Payonk (ORCID: https://orcid.org/0009-0005-8213-8736)
Institutions
- University of Rostock (DE)
Publication Details
- Journal
- Journal of Neuroscience Methods
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1016/j.jneumeth.2024.110320
- Primary Topic
- Neurological disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft