%0 Journal Article
%A Payonk, Jan Philipp
%A Bathel, Henning
%A Arbeiter, Nils
%A Kober, Maria
%A Fauser, Mareike
%A Storch, Alexander
%A van Rienen, Ursula
%A Zimmermann, Julius
%T Improving computational models of deep brain stimulation through experimental calibration.
%J Journal of neuroscience methods
%V 414
%@ 0165-0270
%C Amsterdam [u.a.]
%I Elsevier Science
%M DZNE-2024-01415
%P 110320
%D 2025
%X 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
%K Deep Brain Stimulation: methods
%K Deep Brain Stimulation: standards
%K Animals
%K Calibration
%K Computer Simulation
%K Microelectrodes
%K Rats
%K Dielectric Spectroscopy: methods
%K Electrodes, Implanted: standards
%K Male
%K Brain: physiology
%K Models, Neurological
%K Computational modeling (Other)
%K Deep brain stimulation (Other)
%K Dielectric properties (Other)
%K Encapsulation tissue (Other)
%K Impedance spectroscopy (Other)
%K Uncertainty quantification (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39549963
%R 10.1016/j.jneumeth.2024.110320
%U https://pub.dzne.de/record/273941