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001     273941
005     20250115165650.0
024 7 _ |a 10.1016/j.jneumeth.2024.110320
|2 doi
024 7 _ |a pmid:39549963
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024 7 _ |a 0165-0270
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024 7 _ |a 1872-678X
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037 _ _ |a DZNE-2024-01415
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Payonk, Jan Philipp
|b 0
245 _ _ |a Improving computational models of deep brain stimulation through experimental calibration.
260 _ _ |a Amsterdam [u.a.]
|c 2025
|b Elsevier Science
336 7 _ |a article
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336 7 _ |a ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a 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.
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650 _ 7 |a Computational modeling
|2 Other
650 _ 7 |a Deep brain stimulation
|2 Other
650 _ 7 |a Dielectric properties
|2 Other
650 _ 7 |a Encapsulation tissue
|2 Other
650 _ 7 |a Impedance spectroscopy
|2 Other
650 _ 7 |a Uncertainty quantification
|2 Other
650 _ 2 |a Deep Brain Stimulation: methods
|2 MeSH
650 _ 2 |a Deep Brain Stimulation: standards
|2 MeSH
650 _ 2 |a Animals
|2 MeSH
650 _ 2 |a Calibration
|2 MeSH
650 _ 2 |a Computer Simulation
|2 MeSH
650 _ 2 |a Microelectrodes
|2 MeSH
650 _ 2 |a Rats
|2 MeSH
650 _ 2 |a Dielectric Spectroscopy: methods
|2 MeSH
650 _ 2 |a Electrodes, Implanted: standards
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Brain: physiology
|2 MeSH
650 _ 2 |a Models, Neurological
|2 MeSH
700 1 _ |a Bathel, Henning
|b 1
700 1 _ |a Arbeiter, Nils
|b 2
700 1 _ |a Kober, Maria
|b 3
700 1 _ |a Fauser, Mareike
|b 4
700 1 _ |a Storch, Alexander
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700 1 _ |a van Rienen, Ursula
|b 6
700 1 _ |a Zimmermann, Julius
|b 7
773 _ _ |a 10.1016/j.jneumeth.2024.110320
|g Vol. 414, p. 110320 -
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|t Journal of neuroscience methods
|v 414
|y 2025
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856 4 _ |u https://pub.dzne.de/record/273941/files/DZNE-2024-01415%20Data.zip
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