Influence of Different Geometric Representations of the Volume Conductor on Nerve Activation during Electrical Stimulation

Gómez-Tames, José; González, José A.; Wang, Yu

doi:10.1155/2014/489240

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…For example, thresholds for electrical activation or block with an implanted cuff electrode depend on the electrode-fiber distance, and thus on the nerve diameter; further, fascicle diameters, spatial arrangement of fascicles, and perineurium thickness all significantly influence thresholds ( Koole et al, 1997 ; Grinberg et al, 2008 ; Pelot et al, 2019 ). Thus, data on nerve morphology are necessary to inform computational models to quantify these species- and nerve-specific responses to VNS ( Helmers et al, 2012 ; Gómez-Tames et al, 2014 ; Arle et al, 2016 ; Mourdoukoutas et al, 2017 ; Pelot et al, 2017 ). In addition, morphological considerations are important in selecting appropriate animal models to evaluate and characterize neural stimulation therapies; such data will inform translation of stimulation parameters across species, including from preclinical studies to clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat

et al. 2020

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It is necessary to understand the morphology of the vagus nerve (VN) to design and deliver effective and selective vagus nerve stimulation (VNS) because nerve morphology influences fiber responses to electrical stimulation. Specifically, nerve diameter (and thus, electrode-fiber distance), fascicle diameter, fascicular organization, and perineurium thickness all significantly affect the responses of nerve fibers to electrical signals delivered through a cuff electrode. We quantified the morphology of cervical and subdiaphragmatic VNs in humans, pigs, and rats: effective nerve diameter, number of fascicles, effective fascicle diameters, proportions of endoneurial, perineurial, and epineurial tissues, and perineurium thickness. The human and pig VNs were comparable sizes (∼2 mm cervically; ∼1.6 mm subdiaphragmatically), while the rat nerves were ten times smaller. The pig nerves had ten times more fascicles—and the fascicles were smaller—than in human nerves (47 vs. 7 fascicles cervically; 38 vs. 5 fascicles subdiaphragmatically). Comparing the cervical to the subdiaphragmatic VNs, the nerves and fascicles were larger at the cervical level for all species and there were more fascicles for pigs. Human morphology generally exhibited greater variability across samples than pigs and rats. A prior study of human somatic nerves indicated that the ratio of perineurium thickness to fascicle diameter was approximately constant across fascicle diameters. However, our data found thicker human and pig VN perineurium than those prior data: the VNs had thicker perineurium for larger fascicles and thicker perineurium normalized by fascicle diameter for smaller fascicles. Understanding these differences in VN morphology between preclinical models and the clinical target, as well as the variability across individuals of a species, is essential for designing suitable cuff electrodes and stimulation parameters and for informing translation of preclinical results to clinical application to advance the therapeutic efficacy of VNS.

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Section: Introductionmentioning

confidence: 99%

Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat

et al. 2020

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“…We quantified the effects of different representations of current source stimulation of neural tissue in COMSOL Multiphysics, a commercial finite element modeling software package. We chose COMSOL given its ease of use and its widespread use in computational modeling of implanted neural stimulation devices (e.g., Gómez-Tames et al, 2014 ; Joucla et al, 2014 ; Lempka et al, 2015 ; Arle et al, 2016 ; Mourdoukoutas et al, 2017 ; Gunalan et al, 2018 ), which allows easy translation of our findings into other researchers' modeling efforts. Furthermore, COMSOL offers the option of incorporating multiple physics other than electric currents into the model in a common environment, allows user-defined differential equations, and provides easy integration with MATLAB for all steps of the model implementation and solution.…”

Section: Discussionmentioning

confidence: 99%

Modeling Current Sources for Neural Stimulation in COMSOL

Pelot

Thio

Grill

2018

Front. Comput. Neurosci.

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Background: Computational modeling provides an important toolset for designing and analyzing neural stimulation devices to treat neurological disorders and diseases. Modeling enables efficient exploration of large parameter spaces, where preclinical and clinical studies would be infeasible. Current commercial finite element method software packages enable straightforward calculation of the potential distributions, but it is not always clear how to implement boundary conditions to appropriately represent metal stimulating electrodes. By quantifying the effects of different electrode representations on activation thresholds for model axons, we provide recommendations for accurate and efficient modeling of neural stimulating electrodes.Methods: We quantified the effects of different representations of current sources for neural stimulation in COMSOL Multiphysics for monopolar, bipolar, and multipolar electrode designs.Results: We recommend modeling each electrode contact as a thin platinum domain, modeling the electrode substrate with the conductivity of silicone, and either using a point current source in the center of each electrode contact or using a boundary current source. Alternatively, to avoid possible numerical instabilities associated with a large range of conductivity values (i.e., platinum and silicone) and to eliminate the small mesh elements required for thin electrode contacts, the electrode substrate can be assigned the conductivity of platinum by using insulating boundaries between the substrate and surrounding medium, and within the substrate to isolate the contacts from each other. When modeling more than one contact, we recommend using superposition by solving the model once for each contact, leaving inactive contacts floating, and superposing the resulting potentials. We computed comparable errors in activation thresholds across the different implementations in a simplified model (electrode in a homogeneous, isotropic medium), and in realistic models of rat spinal cord stimulation (SCS) and human deep brain stimulation, indicating that the recommended approaches are applicable to different stimulation targets.

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“…The temporal/spatial summation and facilitation of EPSPs (6), which were generated by descending action potentials after cortical stimulation of PNs were coupled with a motor axon model (7)(8), as shown in Fig. 1.…”

Section: Synaptic Effectmentioning

confidence: 99%

“…The degree of excitation or inhibition is determined using the electric field distribution and electrophysiological properties of the neuron. The electric field distribution is affected by anatomical geometry, [6][7][8] electrical conductivity of the tissues, [9][10][11][12] and stimulation parameters. 13 During intraoperative mapping and monitoring, a variety of parameters, such as the type of pulse (monophasic or biphasic and cathodal or anodal), stimulation probe (monopolar or bipolar), pulse duration, frequency, and intensity are available.…”

Section: Introductionmentioning

confidence: 99%

Corticomotoneuronal Model for Intraoperative Neurophysiological Monitoring During Direct Brain Stimulation

Gómez-Tames

Hirata

Tamura

et al. 2019

Int. J. Neur. Syst.

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Intraoperative neurophysiological monitoring during brain surgery uses direct cortical stimulation to map the motor cortex by recording muscle activity induced by the excitation of alpha motor neurons (MNs). Computational models have been used to understand local brain stimulation. However, a computational model revealing the stimulation process from the cortex to MNs has not yet been proposed. Thus, the aim of the current study was to develop a corticomotoneuronal (CMN) model to investigate intraoperative stimulation during surgery. The CMN combined the following three processes into one system for the first time: (1) induction of an electric field in the brain based on a volume conductor model; (2) activation of pyramidal neuron (PNs) with a compartment model; and (3) formation of presynaptic connections of the PNs to MNs using a conductance-based synaptic model coupled with a spiking model. The implemented volume conductor model coupled with the axon model agreed with experimental strength-duration curves. Additionally, temporal/spatial and facilitation effects of CMN synapses were implemented and verified. Finally, the integrated CMN model was verified with experimental data. The results demonstrated that our model was necessary to describe the interaction between frequency and pulses to assess the difference between low-frequency and multi-pulse high-frequency stimulation in cortical stimulation. The proposed model can be used to investigate the effect of stimulation parameters on the cortex to optimize intraoperative monitoring.

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Influence of Different Geometric Representations of the Volume Conductor on Nerve Activation during Electrical Stimulation

Cited by 10 publications

References 29 publications

Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat

Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat

Modeling Current Sources for Neural Stimulation in COMSOL

Corticomotoneuronal Model for Intraoperative Neurophysiological Monitoring During Direct Brain Stimulation

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