Journal Article

DZNE-2024-01415

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Improving computational models of deep brain stimulation through experimental calibration.

Payonk, J. P. ; Bathel, H. ; Arbeiter, N. ; Kober, M. ; Fauser, M. ; Storch, A.DZNE* ; van Rienen, U. ; Zimmermann, J.

2025
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.jneumeth.2024.110320

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.

Keyword(s): Deep Brain Stimulation: methods (MeSH) ; Deep Brain Stimulation: standards (MeSH) ; Animals (MeSH) ; Calibration (MeSH) ; Computer Simulation (MeSH) ; Microelectrodes (MeSH) ; Rats (MeSH) ; Dielectric Spectroscopy: methods (MeSH) ; Electrodes, Implanted: standards (MeSH) ; Male (MeSH) ; Brain: physiology (MeSH) ; Models, Neurological (MeSH) ; Computational modeling ; Deep brain stimulation ; Dielectric properties ; Encapsulation tissue ; Impedance spectroscopy ; Uncertainty quantification

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Contributing Institute(s):

1. Non-Motor Symptoms in Parkinson's disease (AG Storch)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2025

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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