Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences.

OBJECTIVE: With a growing appreciation for interindividual anatomical variability and patient-specific brain connectivity, advanced imaging sequences offer the opportunity to directly visualize anatomical targets for deep brain stimulation (DBS). The lack of quantitative evidence demonstrating their clinical utility, however, has hindered their broad implementation in clinical practice. METHODS: Using fast gray matter acquisition T1 inversion recovery (FGATIR) sequences, the present study identified a thalamic hypointensity that holds promise as a visual marker in DBS. To validate the clinical utility of the identified hypointensity, we retrospectively analyzed 65 patients (26 female, mean age = 69.1 ± 12.7 years) who underwent DBS in the treatment of essential tremor. We characterized its neuroanatomical substrates and evaluated the hypointensity's ability to predict clinical outcome using stimulation volume modeling and voxelwise mapping. Finally, we determined whether the hypointensity marker could predict symptom improvement on a patient-specific level. RESULTS: Anatomical characterization suggested that the identified hypointensity constituted the terminal part of the dentatorubrothalamic tract. Overlap between DBS stimulation volumes and the hypointensity in standard space significantly correlated with tremor improvement (R2  = 0.16, p = 0.017) and distance to hotspots previously reported in the literature (R2  = 0.49, p = 7.9e-4). In contrast, the amount of variance explained by other anatomical atlas structures was reduced. When accounting for interindividual neuroanatomical variability, the predictive power of the hypointensity increased further (R2  = 0.37, p = 0.002). INTERPRETATION: Our findings introduce and validate a novel imaging-based marker attainable from FGATIR sequences that has the potential to personalize and inform targeting and programming in DBS for essential tremor. ANN NEUROL 2022;91:613-628.

Mechanisms of pallidal deep brain stimulation: Alteration of cortico-striatal synaptic communication in a dystonia animal model.

Pallidal deep brain stimulation (DBS) is an important option for patients with severe dystonias, which are thought to arise from a disturbance in striatal control of the globus pallidus internus (GPi). The mechanisms of GPi-DBS are far from understood. Although a disturbance of striatal function is thought to play a key role in dystonia, the effects of DBS on cortico-striatal function are unknown. We hypothesised that DBS, via axonal backfiring, or indirectly via thalamic and cortical coupling, alters striatal function. We tested this hypothesis in the dtsz hamster, an animal model of inherited generalised, paroxysmal dystonia. Hamsters (dystonic and non-dystonic controls) were bilaterally implanted with stimulation electrodes in the GPi. DBS (130 Hz), and sham DBS, were performed in unanaesthetised animals for 3 h. Synaptic cortico-striatal field potentials, as well as miniature excitatory postsynaptic currents (mEPSC) and firing properties of medium spiny striatal neurones were recorded in brain slice preparations obtained immediately after EPN-DBS. The main findings were as follows: a. DBS increased cortico-striatal evoked responses in healthy, but not in dystonic tissue. b. Commensurate with this, DBS increased inhibitory control of these evoked responses in dystonic, and decreased inhibitory control in healthy tissue. c. Further, DBS reduced mEPSC frequency strongly in dystonic, and less prominently in healthy tissue, showing that also a modulation of presynaptic mechanisms is likely involved. d. Cellular properties of medium-spiny neurones remained unchanged. We conclude that DBS leads to dampening of cortico-striatal communication, and restores intrastriatal inhibitory tone.

Deep brain stimulation by optimized stimulators in a phenotypic model of dystonia: Effects of different frequencies.

Deep brain stimulation (DBS) of the globus pallidus internus (GPi, entopeduncular nucleus, EPN, in rodents) has become important for the treatment of generalized dystonia, a severe and often intractable movement disorder. It is unclear if lower frequencies of GPi-DBS or stimulations of the subthalamic nucleus (STN) are of advantage. In the present study, the main objective was to examined the effects of bilateral EPN-DBS at different frequencies (130 Hz, 40 Hz, 15 Hz) on the severity of dystonia in the dtsz mutant hamster. In addition, STN stimulations were done at a frequency, proven to be effective by the present EPN-DBS in dystonic hamsters. In order to obtain precise bilateral electrical stimuli with magnitude of 50 µA, a pulse width of 60 µs and defined frequencies, it was necessary to develop a new optimized stimulator prior to the experiments. Since the individual highest severity of dystonic episodes is known to be reached within three hours after induction in dtsz hamsters, the duration of DBS was 180 min. During DBS with 130 Hz the severity of dystonia was significantly lower within the third hour than without DBS in the same animals (p < 0.05). DBS with 40 Hz tended to exert antidystonic effects after three hours, while 15 Hz stimulations of the EPN and 130 Hz stimulations of the STN failed to show any effects on the severity. DBS of the EPN at 130 Hz was most effective against generalized dystonia in the dtsz mutant. The response to EPN-DBS confirms that the dtsz mutant is suitable to further investigate the effects of long-term DBS on severity of dystonia and neuronal network activities, important to give insights into the mechanisms of DBS.

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.

Neural Tissue Degeneration in Rosenthal's Canal and Its Impact on Electrical Stimulation of the Auditory Nerve by Cochlear Implants: An Image-Based Modeling Study.

Sensorineural deafness is caused by the loss of peripheral neural input to the auditory nerve, which may result from peripheral neural degeneration and/or a loss of inner hair cells. Provided spiral ganglion cells and their central processes are patent, cochlear implants can be used to electrically stimulate the auditory nerve to facilitate hearing in the deaf or severely hard-of-hearing. Neural degeneration is a crucial impediment to the functional success of a cochlear implant. The present, first-of-its-kind two-dimensional finite-element model investigates how the depletion of neural tissues might alter the electrically induced transmembrane potential of spiral ganglion neurons. The study suggests that even as little as 10% of neural tissue degeneration could lead to a disproportionate change in the stimulation profile of the auditory nerve. This result implies that apart from encapsulation layer formation around the cochlear implant electrode, tissue degeneration could also be an essential reason for the apparent inconsistencies in the functionality of cochlear implants.

Numerical Simulations as Means for Tailoring Electrically Conductive Hydrogels Towards Cartilage Tissue Engineering by Electrical Stimulation.

Cartilage regeneration is a clinical challenge. In recent years, hydrogels have emerged as implantable scaffolds in cartilage tissue engineering. Similarly, electrical stimulation has been employed to improve matrix synthesis of cartilage cells, and thus to foster engineering and regeneration of cartilage tissue. The combination of hydrogels and electrical stimulation may pave the way for new clinical treatment of cartilage lesions. To find the optimal electric properties of hydrogels, theoretical considerations and corresponding numerical simulations are needed to identify well-suited initial parameters for experimental studies. We present the theoretical analysis of a hydrogel in a frequently used electrical stimulation device for cartilage regeneration and tissue engineering. By means of equivalent circuits, finite element analysis, and uncertainty quantification, we elucidate the influence of the geometric and dielectric properties of cell-seeded hydrogels on the capacitive-coupling electrical field stimulation. Moreover, we discuss the possibility of cellular organisation inside the hydrogel due to forces generated by the external electric field. The introduced methodology is easily reusable by other researchers and allows to directly develop novel electrical stimulation study designs. Thus, this study paves the way for the design of future experimental studies using electrically conductive hydrogels and electrical stimulation for tissue engineering.

Evaluation of electric field distribution in electromagnetic stimulation of human femoral head.

Electromagnetic stimulation is a common therapy used to support bone healing in the case of avascular necrosis of the femoral head. In the present study, we investigated a bipolar induction screw system with an integrated coil. The aim was to analyse the influence of the screw parameters on the electric field distribution in the human femoral head. In addition, three kinds of design parameters (the shape of the screw tip, position of the screw in the femoral head, and size of the screw insulation) were varied. The electric field distribution in the bone was calculated using the finite element software Comsol Multiphysics. Moreover, a validation experiment was set up for an identical bone specimen with an implanted screw. The electric potential of points inside and on the surface of the bone were measured and compared to numerical data. The electric field distribution within the bone was clearly changed by the different implant parameters. Repositioning the screw by a maximum of 10 mm and changing the insulation length by a maximum of 4 mm resulted in electric field volume changes of 16% and 7%, respectively. By comparing the results of numerical simulation with the data of the validation experiment, on average, the electric potential difference of 19% and 24% occurred when the measuring points were at a depth of approximately 5 mm within the femoral bone and directly on the surface of the femoral bone, respectively. The results of the numerical simulations underline that the electro-stimulation treatment of bone in clinical applications can be influenced by the implant parameters.

Would an endosteal CI-electrode make sense? Comparison of the auditory nerve excitability from different stimulation sites using ESRT measurements and mathematical models.

UNLABELLED: Regarding potential endosteal cochlear implant electrodes, the primary goal of this paper is to compare different intra- and extra-cochlear stimulation sites in terms of current strengths needed for stimulating the auditory nerve. Our study was performed during routine cochlear implantation using needle electrodes for electric stimulation and by visually recording electrically elicited stapedius reflexes (ESRT) as a measure for the stimulus transfer. Of course this rather simple setup only allows rough estimations, which, however, may provide further arguments whether or not to proceed with the concept of an endosteal electrode. In addition, a mathematical model is being developed. In a pilot study, intra-operative electric stimuli were applied via a needle electrode commonly used for the promontory stimulation test. Thus, stapedius reflex thresholds (ESRTs), electrically elicited via the needle from different points inside and outside the cochlea served as indicators for the suitability of different electrode positions towards the modiolus. Tests were performed on 11 CI-recipients. In addition, the extension of electrical fields from different stimulation sites is simulated in a mathematical cochlea model. In most patients ESRT measurements could be performed and evaluated. Thus an "endosteal" stimulation seems possible, although the current intensities must be higher than at intraluminal stimulation sites. Moreover, our model calculations confirm that the extension of electric fields is less favourable with increasing distance from the electrode to the ganglion nerve cells. In terms of hearing, the concept of an endosteal electrode should only be promoted, if its superiority for hearing preservation can be proven, e.g. in animal experiments. However, for other indications like the electric suppression of tinnitus, further research seems advisable. LEVELS OF EVIDENCE: N/A.

Addressing Model Uncertainties in Finite Element Simulation of Electrically Stimulated Implants for Critical-Size Mandibular Defects.

OBJECTIVE: Electrical stimulation is known to enhance bone healing. Novel electrostimulating devices are currently being developed for the treatment of critical-size bone defects in the mandible. Previous numerical models of these devices did not account for possible uncertainties in the input data. We present the numerical model of an electrically stimulated minipig mandible, including optimization and uncertainty quantification (UQ) methods that allow us to determine the most influential parameters. METHODS: Uncertainties in the optimized finite element model are quantified using the polynomial chaos method that is implemented in the open-source Python toolbox Uncertainpy. The volumes of understimulated, beneficially stimulated, and overstimulated tissue are considered quantities of interest because they may significantly impact the expected healing success. Further, the current is a substantial quantity, limiting the lifetime of a battery-driven stimulation unit. With sensitivity analyses, the most critical parameters in the numerical model can be identified. Thus, we can learn which parameters are particularly relevant, for example, when conceptualizing the stimulation unit or planning the manufacturing process. RESULTS: The results of this study show that the parameters of the electrode-tissue interface (ETI), as well as the conductivity within the defect volume, have the most significant impact on the model results. CONCLUSIONS: The UQ results suggest that careful characterization of the ETI and the dielectric tissue properties is crucial to reduce these uncertainties. SIGNIFICANCE: The numerical model regarding uncertainties yields important implications for reliable implant design and clinical translation.

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model.

Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target.Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin-Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions.Main results.This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson's disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling.Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.

Understanding the impact of modiolus porosity on stimulation of spiral ganglion neurons by cochlear implants.

Moderate-to-profound sensorineural hearing loss in humans is treatable by electrically stimulating the auditory nerve (AN) with a cochlear implant (CI). In the cochlea, the modiolus presents a porous bony interface between the CI electrode and the AN. New bone growth caused by the presence of the CI electrode or neural degeneration inflicted by ageing or otological diseases might change the effective porosity of the modiolus and, thereby, alter its electrical material properties. Using a volume conductor description of the cochlea, with the aid of a 'mapped conductivity' method and an ad-hoc 'regionally kinetic' equation system, we show that even a slight variation in modiolus porosity or pore distribution can disproportionately affect AN stimulation. Hence, because of porosity changes, an inconsistent CI performance might occur if neural degeneration or new bone growth progress after implantation. Appropriate electrical material properties in accordance with modiolar morphology and pathology should be considered in patient-specific studies. The present first-of-its-kind in-silico study advocates for contextual experimental studies to further explore the utility of modiolus porous morphology in optimising the CI outcome.

Subthalamic nucleus but not entopeduncular nucleus deep brain stimulation enhances neurogenesis in the SVZ-olfactory bulb system of Parkinsonian rats.

Introduction: Deep brain stimulation (DBS) is a highly effective treatment option in Parkinson's disease. However, the underlying mechanisms of action, particularly effects on neuronal plasticity, remain enigmatic. Adult neurogenesis in the subventricular zone-olfactory bulb (SVZ-OB) axis and in the dentate gyrus (DG) has been linked to various non-motor symptoms in PD, e.g., memory deficits and olfactory dysfunction. Since DBS affects several of these non-motor symptoms, we analyzed the effects of DBS in the subthalamic nucleus (STN) and the entopeduncular nucleus (EPN) on neurogenesis in 6-hydroxydopamine (6-OHDA)-lesioned hemiparkinsonian rats. Methods: In our study, we applied five weeks of continuous bilateral STN-DBS or EPN-DBS in 6-OHDA-lesioned rats with stable dopaminergic deficits compared to 6-OHDA-lesioned rats with corresponding sham stimulation. We injected two thymidine analogs to quantify newborn neurons early after DBS onset and three weeks later. Immunohistochemistry identified newborn cells co-labeled with NeuN, TH and GABA within the OB and DG. As a putative mechanism, we simulated the electric field distribution depending on the stimulation site to analyze direct electric effects on neural stem cell proliferation. Results: STN-DBS persistently increased the number of newborn dopaminergic and GABAergic neurons in the OB but not in the DG, while EPN-DBS does not impact neurogenesis. These effects do not seem to be mediated via direct electric stimulation of neural stem/progenitor cells within the neurogenic niches. Discussion: Our data support target-specific effects of STN-DBS on adult neurogenesis, a putative modulator of non-motor symptoms in Parkinson's disease.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation.

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin-Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

Subthalamic nucleus deep brain stimulation induces functional deficits in norepinephrinergic neurotransmission in a Parkinson's disease model.

BACKGROUND: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is a successful treatment option in Parkinson's disease (PD) for different motor and non-motor symptoms, but has been linked to postoperative cognitive impairment. AIM: Since both dopaminergic and norepinephrinergic neurotransmissions play important roles in symptom development, we analysed STN-DBS effects on dopamine and norepinephrine availability in different brain regions and morphological alterations of catecholaminergic neurons in the 6-hydroxydopamine PD rat model. METHODS: We applied one week of continuous unilateral STN-DBS or sham stimulation, respectively, in groups of healthy and 6-hydroxydopamine-lesioned rats to quantify dopamine and norepinephrine contents in the striatum, olfactory bulb and dentate gyrus. In addition, we analysed dopaminergic cell counts in the substantia nigra pars compacta and area tegmentalis ventralis and norepinephrinergic neurons in the locus coeruleus after one and six weeks of STN-DBS. RESULTS: In 6-hydroxydopamine-lesioned animals, one week of STN-DBS did not alter dopamine levels, while striatal norepinephrine levels were decreased. However, neither one nor six weeks of STN-DBS altered dopaminergic neuron numbers in the midbrain or norepinephrinergic neuron counts in the locus coeruleus. Dopaminergic fibre density in the dorsal and ventral striatum also remained unchanged after six weeks of STN-DBS. In healthy animals, one week of STN-DBS resulted in increased dopamine levels in the olfactory bulb and decreased contents in the dentate gyrus, but had no effects on norepinephrine availability. CONCLUSIONS: STN-DBS modulates striatal norepinephrinergic neurotransmission in a PD rat model. Additional behavioural studies are required to investigate the functional impact of this finding.

Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images.

Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.

Experimental and numerical methods to ensure comprehensible and replicable alternating current electrical stimulation experiments.

Electrical stimulation has received increasing attention for decades for its application in regenerative medicine. Applications range from bone growth stimulation over cartilage regeneration to deep brain stimulation. Despite all research efforts, translation into clinical use has not yet been achieved in all fields. Recent critical assessments have identified limited documentation and monitoring of preclinical in vitro and in vivo experiments as possible reasons hampering clinical translation. In this work, we present experimental and numerical methods to determine the crucial quantities of electrical stimulation such as the electric field or current density. Knowing the stimulation quantities contributes to comprehending the biological response to electrical stimulation and to finally developing a reliable dose-response curve. To demonstrate the methods, we consider a direct contact electrical stimulation experiment that stands representative for a broad class of stimulation experiments. Electrochemical effects are addressed and methods to integrate them into numerical simulations are evaluated. A focus is laid on affordable lab equipment and reproducible open-source software solutions. Finally, clear guidelines to ensure replicability of electrical stimulation experiments are formulated.

Discrimination between the effects of pulsed electrical stimulation and electrochemically conditioned medium on human osteoblasts.

BACKGROUND: Electrical stimulation is used for enhanced bone fracture healing. Electrochemical processes occur during the electrical stimulation at the electrodes and influence cellular reactions. Our approach aimed to distinguish between electrochemical and electric field effects on osteoblast-like MG-63 cells. We applied 20 Hz biphasic pulses via platinum electrodes for 2 h. The electrical stimulation of the cell culture medium and subsequent application to cells was compared to directly stimulated cells. The electric field distribution was predicted using a digital twin. RESULTS: Cyclic voltammetry and electrochemical impedance spectroscopy revealed partial electrolysis at the electrodes, which was confirmed by increased concentrations of hydrogen peroxide in the medium. While both direct stimulation and AC-conditioned medium decreased cell adhesion and spreading, only the direct stimulation enhanced the intracellular calcium ions and reactive oxygen species. CONCLUSION: The electrochemical by-product hydrogen peroxide is not the main contributor to the cellular effects of electrical stimulation. However, undesired effects like decreased adhesion are mediated through electrochemical products in stimulated medium. Detailed characterisation and monitoring of the stimulation set up and electrochemical reactions are necessary to find safe electrical stimulation protocols.

Numerical study on the effect of capacitively coupled electrical stimulation on biological cells considering model uncertainties.

Electrical stimulation of biological samples such as tissues and cell cultures attracts growing attention due to its capability of enhancing cell activity, proliferation, and differentiation. Eventually, a profound knowledge of the underlying mechanisms paves the way for innovative therapeutic devices. Capacitive coupling is one option of delivering electric fields to biological samples that has advantages regarding biocompatibility. However, its biological mechanism of interaction is not well understood. Experimental findings could be related to voltage-gated channels, which are triggered by changes of the transmembrane potential. Numerical simulations by the finite element method provide a possibility to estimate the transmembrane potential. Since a full resolution of the cell membrane within a macroscopic model would lead to prohibitively expensive models, we suggest the adaptation of an approximate finite element method. Starting from a basic 2.5D model, the chosen method is validated and applied to realistic experimental situations. To understand the influence of the dielectric properties on the modelling outcome, uncertainty quantification techniques are employed. A frequency-dependent influence of the uncertain dielectric properties of the cell membrane on the modelling outcome is revealed. This may have practical implications for future experimental studies. Our methodology can be easily adapted for computational studies relying on experimental data.