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000273941 037__ $$aDZNE-2024-01415
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000273941 1001_ $$aPayonk, Jan Philipp$$b0
000273941 245__ $$aImproving computational models of deep brain stimulation through experimental calibration.
000273941 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
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000273941 520__ $$aDeep 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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000273941 650_7 $$2Other$$aComputational modeling
000273941 650_7 $$2Other$$aDeep brain stimulation
000273941 650_7 $$2Other$$aDielectric properties
000273941 650_7 $$2Other$$aEncapsulation tissue
000273941 650_7 $$2Other$$aImpedance spectroscopy
000273941 650_7 $$2Other$$aUncertainty quantification
000273941 650_2 $$2MeSH$$aDeep Brain Stimulation: methods
000273941 650_2 $$2MeSH$$aDeep Brain Stimulation: standards
000273941 650_2 $$2MeSH$$aAnimals
000273941 650_2 $$2MeSH$$aCalibration
000273941 650_2 $$2MeSH$$aComputer Simulation
000273941 650_2 $$2MeSH$$aMicroelectrodes
000273941 650_2 $$2MeSH$$aRats
000273941 650_2 $$2MeSH$$aDielectric Spectroscopy: methods
000273941 650_2 $$2MeSH$$aElectrodes, Implanted: standards
000273941 650_2 $$2MeSH$$aMale
000273941 650_2 $$2MeSH$$aBrain: physiology
000273941 650_2 $$2MeSH$$aModels, Neurological
000273941 7001_ $$aBathel, Henning$$b1
000273941 7001_ $$aArbeiter, Nils$$b2
000273941 7001_ $$aKober, Maria$$b3
000273941 7001_ $$aFauser, Mareike$$b4
000273941 7001_ $$0P:(DE-2719)9000306$$aStorch, Alexander$$b5$$udzne
000273941 7001_ $$avan Rienen, Ursula$$b6
000273941 7001_ $$aZimmermann, Julius$$b7
000273941 773__ $$0PERI:(DE-600)1500499-5$$a10.1016/j.jneumeth.2024.110320$$gVol. 414, p. 110320 -$$p110320$$tJournal of neuroscience methods$$v414$$x0165-0270$$y2025
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