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@ARTICLE{Payonk:273941,
      author       = {Payonk, Jan Philipp and Bathel, Henning and Arbeiter, Nils
                      and Kober, Maria and Fauser, Mareike and Storch, Alexander
                      and van Rienen, Ursula and Zimmermann, Julius},
      title        = {{I}mproving computational models of deep brain stimulation
                      through experimental calibration.},
      journal      = {Journal of neuroscience methods},
      volume       = {414},
      issn         = {0165-0270},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2024-01415},
      pages        = {110320},
      year         = {2025},
      abstract     = {Deep brain stimulation has become a well-established
                      clinical tool to treat movement disorders. Nevertheless, the
                      knowledge of processes initiated by the stimulation remains
                      limited. To address this knowledge gap, computational models
                      are developed to gain deeper insight. However, their
                      predictive power remains constrained by model uncertainties
                      and a lack of validation and calibration.Exemplified with
                      rodent microelectrodes, we present a workflow for validating
                      electrode model geometry using microscopy and impedance
                      spectroscopy in vitro before implantation. We address
                      uncertainties in the tissue distribution and dielectric
                      properties and outline a concept for calibrating the
                      computational model based on in vivo impedance spectroscopy
                      measurements.The standard deviation of the volume of tissue
                      activated across the 18 characterized electrodes was
                      approximately $32.93\%,$ underscoring the importance of
                      electrode characterization. Thus, the workflow significantly
                      enhances the model predictions' credibility of neural
                      activation exemplified in a rodent model.Computational
                      models are frequently employed without validation or
                      calibration, relying instead on manufacturers'
                      specifications. Our approach provides an accessible method
                      to obtain a validated and calibrated electrode geometry,
                      which significantly enhances the reliability of the
                      computational model that relies on this electrode.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.},
      keywords     = {Deep Brain Stimulation: methods / Deep Brain Stimulation:
                      standards / Animals / Calibration / Computer Simulation /
                      Microelectrodes / Rats / Dielectric Spectroscopy: methods /
                      Electrodes, Implanted: standards / Male / Brain: physiology
                      / Models, Neurological / Computational modeling (Other) /
                      Deep brain stimulation (Other) / Dielectric properties
                      (Other) / Encapsulation tissue (Other) / Impedance
                      spectroscopy (Other) / Uncertainty quantification (Other)},
      cin          = {AG Storch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000014},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39549963},
      doi          = {10.1016/j.jneumeth.2024.110320},
      url          = {https://pub.dzne.de/record/273941},
}