Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation

Toward a cerebello-thalamo-cortical computational model of spinocerebellar ataxia

Computational neural network modelling is an emerging approach for optimization of drug treatment of neurological disorders and fine-tuning of rehabilitation strategies. In the current study, we constructed a cerebello-thalamo-cortical computational neural network model to simulate a mouse model of cerebellar ataxia (pcd^5J mice) by manipulating cerebellar bursts through reduction of GABAergic inhibitory input. Cerebellar output neurons were projected to the thalamus and bidirectionally connected with the cortical network. Our results showed that reduction of inhibitory input in the cerebellum orchestrated the cortical local field potential (LFP) dynamics to generate specific motor outputs of oscillations of the theta, alpha, and beta bands in the computational model as well as in mouse motor cortical neurons. The therapeutic potential of deep brain stimulation (DBS) was tested in the computational model by increasing the sensory input to restore cortical output. Ataxia mice showed normalization of the motor cortex LFP after cerebellum DBS. We provide a novel approach to computational modelling to investigate the effect of DBS by mimicking cerebellar ataxia involving degeneration of Purkinje cells. Simulated neural activity coincides with findings from neural recordings of ataxia mice. Our computational model could thus represent cerebellar pathologies and provide insight into how to improve disease symptoms by restoring neuronal electrophysiological properties using DBS.

Neuron matters: neuromodulation with electromagnetic stimulation must consider neurons as dynamic identities

Neuromodulation with electromagnetic stimulation is widely used for the control of abnormal neural activity, and has been proven to be a valuable alternative to pharmacological tools for the treatment of many neurological diseases. Tremendous efforts have been focused on the design of the stimulation apparatus (i.e., electrodes and magnetic coils) that delivers the electric current to the neural tissue, and the optimization of the stimulation parameters. Less attention has been given to the complicated, dynamic properties of the neurons, and their context-dependent impact on the stimulation effects. This review focuses on the neuronal factors that influence the outcomes of electromagnetic stimulation in neuromodulation. Evidence from multiple levels (tissue, cellular, and single ion channel) are reviewed. Properties of the neural elements and their dynamic changes play a significant role in the outcome of electromagnetic stimulation. This angle of understanding yields a comprehensive perspective of neural activity during electrical neuromodulation, and provides insights in the design and development of novel stimulation technology.

Comparison between patient-specific deep brain stimulation simulations and commercial system SureTune3

Objective. Software to visualize estimated volume of tissue activated (VTA) in deep brain stimulation assuming a homogeneous tissue surrounding such as SureTune3 has recently become available for clinical use. The objective of this study is to compare SureTune3 with homogeneous and heterogeneous patient-specific finite element method (FEM) simulations of the VTA to elucidate how well they coincide in their estimates.Approach. FEM simulations of the VTA were performed in COMSOL Multiphysics and compared with VTA from SureTune3 with variation of voltage and current amplitude, pulse width, axon diameter, number of active contacts, and surrounding homogeneous grey or white matter. Patient-specific simulations with heterogeneous tissue were also performed.Main results. The VTAs corresponded well for voltage control in homogeneous tissue, though with the smallest VTAs being slightly larger in SureTune3 and the largest VTAs being slightly larger in the FEM simulations. In current control, FEM estimated larger VTAs in white matter and smaller VTAs in grey matter compared to SureTune3 as grey matter has higher electric conductivity than white matter and requires less voltage to reach the same current. The VTAs also corresponded well in the patient-specific cases except for one case with a cyst of highly conductive cerebrospinal fluid (CSF) near the active contacts.Significance. The VTA estimates without taking the surrounding tissue into account in SureTune3 are in good agreement with patient-specific FEM simulations when using voltage control in the absence of CSF-filled cyst. In current control or when CSF is present near the active contacts, the tissue characteristics are important for the VTA and needs consideration.Clinical. trial ethical approval: Local ethics committee at Linköping University (2012/434-31).

Postoperative Lead Movement after Deep Brain Stimulation Surgery and the Change of Stimulation Volume

INTRODUCTION

Lead movement after deep brain stimulation may occur and influence the affected volume of stimulation. The aim of the study was to investigate differences in lead position between the day after surgery and approximately 1 month postoperatively and also simulate the electric field (EF) around the active contacts in order to investigate the impact of displacement on affected volume.

METHODS

Twenty-three patients with movement disorders underwent deep brain stimulation surgery (37 leads). Computed tomography at the 2 time points were co-fused respectively with the stereotactic images in Surgiplan. The coordinates (x, y, and z) of the lead tips were compared between the 2 dates. Eleven of these patients were selected for the EF simulation in Comsol Multiphysics. Postoperative changes of EF spread in the tissue due to conductivity changes in perielectrode space and due to displacement were evaluated by calculating the coverage coefficient and the Sørensen-Dice coefficient.

RESULTS

There was a significant displacement (mean ± SD) on the left lead: x (0.44 ± 0.72, p < 0.01), y (0.64 ± 0.54, p < 0.001), and z (0.62 ± 0.71, p < 0.001). On the right lead, corresponding values were: x (-0.11 ± 0.61, ns), y (0.71 ± 0.54, p < 0.001), and z (0.49 ± 0.81, p < 0.05). The anchoring technique was a statistically significant variable associated with displacement. No correlation was found between bilateral (n = 14) versus unilateral deep brain stimulation, gender (n = 17 male), age <60 years (n = 8), and calculated air volume. The simulated stimulation volume was reduced after 1 month because of the perielectrode space. When considering perielectrode space and displacement, the volumes calculated the day after surgery and approximately 1 month later were partly overlapped.

CONCLUSION

The left lead tip displayed a tendency to move lateral, anterior, and inferior and the right a tendency to move anterior and inferior. The anchoring technique was associated to displacement. New brain territory was affected due to the displacement despite considering the reduced stimulated volume after 1 month. Postoperative changes in perielectrode space and small lead movements are reasons for delaying programming to 4 weeks following surgery.

Extrapolating meaning from local field potential recordings

Local field potentials (LFP) reflect the spatially weighted low-frequency activity nearest to a recording electrode. LFP recording is a window to a wide range of cellular activities and has gained increasing attention over recent years. We here review major conceptual issues related to LFP with the goal of creating a resource for non-experts considering implementing LFP into their research. We discuss the cellular activity that constitutes the local field potential; recording techniques, including recommendations and limitations; approaches to analysis of LFP data (with focus on power-banded analyses); and finally we discuss reports of the successful use of LFP in clinical applications.

Multi-Scale Computational Models for Electrical Brain Stimulation

Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed.

A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation

Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit.

Essential tremor (ET) is acknowledged to be the most common movement disorder affecting 1% of the population. Although the underlying mechanisms remain elusive, the thalamus, cortex and cerebellum are implicated in the underlying pathology. More recently, it has been shown that ET can be successfully treated by deep brain stimulation (DBS). This clinical treatment involves the surgical implantation of electrodes into the brain, through which current is applied. However, the mechanisms of how DBS achieves clinical benefit continue to be debated. A key question is whether ET can be modeled as a pathological network behavior as has been suggested previously. If so, we can then ask how DBS would modulate this brain activity. Our study combines: (i) simultaneous electrophysiological recordings from the brain and muscle; (ii) computational modelling; (iii) mathematical analysis. We found that the network supports oscillations in the tremor range, and the application of high frequency DBS switches this to low amplitude, high-frequency activity. We propose that our model can be used to predict DBS parameter settings that suppress pathological network activity and consequently tremor. In summary, we provide the first population level model of essential tremor including the effect of DBS on network behaviour.

Visualization of electrical field of electrode using voltage-controlled fluorescence release

In this study we propose an approach to directly visualize electrical current distribution at the electrode-electrolyte interface of a biopotential electrode. High-speed fluorescent microscopic images are acquired when an electric potential is applied across the interface to trigger the release of fluorescent material from the surface of the electrode. These images are analyzed computationally to obtain the distribution of the electric field from the fluorescent intensity of each pixel. Our approach allows direct observation of microscopic electrical current distribution around the electrode. Experiments are conducted to validate the feasibility of the fluorescent imaging method.

Neuron matters: electric activation of neuronal tissue is dependent on the interaction between the neuron and the electric field

In laboratory research and clinical practice, externally-applied electric fields have been widely used to control neuronal activity. It is generally accepted that neuronal excitability is controlled by electric current that depolarizes or hyperpolarizes the excitable cell membrane. What determines the amount of polarization? Research on the mechanisms of electric stimulation focus on the optimal control of the field properties (frequency, amplitude, and direction of the electric currents) to improve stimulation outcomes. Emerging evidence from modeling and experimental studies support the existence of interactions between the targeted neurons and the externally-applied electric fields. With cell-field interaction, we suggest a two-way process. When a neuron is positioned inside an electric field, the electric field will induce a change in the resting membrane potential by superimposing an electrically-induced transmembrane potential (ITP). At the same time, the electric field can be perturbed and re-distributed by the cell. This cell-field interaction may play a significant role in the overall effects of stimulation. The redistributed field can cause secondary effects to neighboring cells by altering their geometrical pattern and amount of membrane polarization. Neurons excited by the externally-applied electric field can also affect neighboring cells by ephaptic interaction. Both aspects of the cell-field interaction depend on the biophysical properties of the neuronal tissue, including geometric (i.e., size, shape, orientation to the field) and electric (i.e., conductivity and dielectricity) attributes of the cells. The biophysical basis of the cell-field interaction can be explained by the electromagnetism theory. Further experimental and simulation studies on electric stimulation of neuronal tissue should consider the prospect of a cell-field interaction, and a better understanding of tissue inhomogeneity and anisotropy is needed to fully appreciate the neural basis of cell-field interaction as well as the biological effects of electric stimulation.

Measurement of evoked potentials during thalamic deep brain stimulation

BACKGROUND

Deep brain stimulation (DBS) treats the symptoms of several movement disorders, but optimal selection of stimulation parameters remains a challenge. The evoked compound action potential (ECAP) reflects synchronized neural activation near the DBS lead, and may be useful for feedback control and automatic adjustment of stimulation parameters in closed-loop DBS systems.

OBJECTIVES

Determine the feasibility of recording ECAPs in the clinical setting, understand the neural origin of the ECAP and sources of any stimulus artifact, and correlate ECAP characteristics with motor symptoms.

METHODS

The ECAP and tremor response were measured simultaneously during intraoperative studies of thalamic DBS, conducted in patients who were either undergoing surgery for initial lead implantation or replacement of their internal pulse generator.

RESULTS

There was large subject-to-subject variation in stimulus artifact amplitude, which model-based analysis suggested may have been caused by glial encapsulation of the lead, resulting in imbalances in the tissue impedance between the contacts. ECAP recordings obtained from both acute and chronically implanted electrodes revealed that specific phase characteristics of the signal varied systematically with stimulation parameters. Further, a trend was observed in some patients between the energy of the initial negative and positive ECAP phases, as well as secondary phases, and changes in tremor from baseline. A computational model of thalamic DBS indicated that direct cerebellothalamic fiber activation dominated the clinically measured ECAP, suggesting that excitation of these fibers is critical in DBS therapy.

CONCLUSIONS

This work demonstrated that ECAPs can be recorded in the clinical setting and may provide a surrogate feedback control signal for automatic adjustment of stimulation parameters to reduce tremor amplitude.

Analysis of deep brain stimulation electrode characteristics for neural recording

OBJECTIVE

Closed-loop deep brain stimulation (DBS) systems have the potential to optimize treatment of movement disorders by enabling automatic adjustment of stimulation parameters based on a feedback signal. Evoked compound action potentials (ECAPs) and local field potentials (LFPs) recorded from the DBS electrode may serve as suitable closed-loop control signals. The objective of this study was to understand better the factors that influence ECAP and LFP recording, including the physical presence of the electrode, the geometrical dimensions of the electrode, and changes in the composition of the peri-electrode space across recording conditions.

APPROACH

Coupled volume conductor-neuron models were used to calculate single-unit activity as well as ECAP responses and LFP activity from a population of model thalamic neurons.

MAIN RESULTS

Comparing ECAPs and LFPs measured with and without the presence of the highly conductive recording contacts, we found that the presence of these contacts had a negligible effect on the magnitude of single-unit recordings, ECAPs (7% RMS difference between waveforms), and LFPs (5% change in signal magnitude). Spatial averaging across the contact surface decreased the ECAP magnitude in a phase-dependent manner (74% RMS difference), resulting from a differential effect of the contact on the contribution from nearby or distant elements, and decreased the LFP magnitude (25% change). Reductions in the electrode diameter or recording contact length increased signal energy and increased spatial sensitivity of single neuron recordings. Moreover, smaller diameter electrodes (500 µm) were more selective for recording from local cells over passing axons, with the opposite true for larger diameters (1500 µm). Changes in electrode dimensions had phase-dependent effects on ECAP characteristics, and generally had small effects on the LFP magnitude. ECAP signal energy and LFP magnitude decreased with tighter contact spacing (100 µm), compared to the original dimensions (1500 µm), with the opposite effect on the ECAP at longer contact-to-contact distances (2000 µm). Finally, acute edema reduced the single neuron and population ECAP signal energy, as well as LFP magnitude, and glial encapsulation had the opposite effect, after accounting for loss of cells in the peri-electrode space.

SIGNIFICANCE

This study determined recording conditions and electrode designs that influence ECAP and LFP recording fidelity.

Reversing the polarity of bipolar stimulation in deep brain stimulation for essential tremor: a theoretical explanation for a useful clinical intervention

The quadripolar electrodes used for deep brain stimulation are designed to give flexibility in contact configuration, optimize therapeutic effect, and minimize side-effects. A patient with essential tremor did not tolerate a bipolar setting due to the emergence of a pulling sensation in her face. However, when the polarity of the contacts was reversed, a 70% higher voltage was tolerated. Using an electric field model, we predicted that this effect was due to the proximity of the topmost contact to the internal capsule. Post-operative imaging supported this prediction. These results demonstrate how a multi-disciplinary approach allows us to optimize parameter settings.

Computational modeling of deep brain stimulation

Deep brain stimulation (DBS) is an effective clinical treatment for several medically refractory neurological disorders. However, even after decades of clinical success, explicit understanding of the response of neurons to applied electric fields remains limited, and scientific definition of the therapeutic mechanisms of DBS remains elusive. In addition, it is presently unclear which electrode designs and stimulation paradigms are optimal for maximal therapeutic benefit and minimal side-effects with DBS. Detailed computer modeling of DBS has emerged recently as a powerful technique to enhance our understanding of the effects of DBS and to create a virtual testing ground for new stimulation strategies. This chapter summarizes the fundamentals of neurostimulation modeling, presents some scientific contributions of computer models to the field of DBS, and demonstrates the application of DBS modeling tools to augment the clinical utility of DBS.

Spatiotemporal visualization of deep brain stimulation-induced effects in the subthalamic nucleus

Deep brain stimulation (DBS) is a successful surgical therapy used to treat the disabling symptoms of movement disorders such as Parkinson's disease. It involves the chronic stimulation of disorder-specific nuclei. However, the mechanisms that lead to clinical improvements remain unclear. Consequently, this slows the optimization of present-day DBS therapy and hinders its future development and application. We used a computational model to calculate the distribution of electric potential induced by DBS and study the effect of stimulation on the spiking activity of a subthalamic nucleus (STN) projection neuron. We previously showed that such a model can reveal detailed spatial effects of stimulation in the vicinity of the electrode. However, this multi-compartmental STN neuron model can fire in either a burst or tonic mode and, in this study, we hypothesized that the firing mode of the cell will have a major impact on the DBS-induced effects. Our simulations showed that the bursting model exhibits behaviour observed in studies of high-frequency stimulation of STN neurons, such as the presence of a silent period at stimulation offset and frequency-dependent stimulation effects. We validated the model by simulating the clinical parameter settings used for a Parkinsonian patient and showed, in a patient-specific anatomical model, that the region of affected tissue is consistent with clinical observations of the optimal DBS site. Our results demonstrated a method of quantitatively assessing neuronal changes induced by DBS, to maximize therapeutic benefit and minimize unwanted side effects.

Diffusion tensor imaging and neuromodulation: DTI as key technology for deep brain stimulation

Diffusion tensor imaging (DTI) is more than just a useful adjunct to invasive techniques like optogenetics which recently have tremendously influenced our understanding of the mechanisms of deep brain stimulation (DBS). In combination with other technologies, DTI helps us to understand which parts of the brain tissue are connected to others and which ones are truly influenced with neuromodulation. The complex interaction of DBS with the surrounding tissues-scrutinized with DTI-allows to create testable hypotheses that can explain network interactions. Those interactions are vital for our understanding of the net effects of neuromodulation. This work naturally was first done in the field of movement disorder surgery, where a lot of experience regarding therapeutic effects and only a short latency between initiation of neuromodulation and alleviation of symptoms exist. This chapter shows the journey over the past 10 years with first applications in DBS toward current research in affect regulating network balances and their therapeutic alterations with the neuromodulation technology.

Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the surgical therapy of choice for medically intractable Parkinson's disease. However, quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is limited. Recently, computational models of varying levels of complexity have been used to study the neural response to DBS. The goal of this study was to evaluate the quantitative impact of incrementally incorporating increasing levels of complexity into computer models of STN DBS. Our analysis focused on the direct activation of experimentally measureable fiber pathways within the internal capsule (IC). Our model system was customized to an STN DBS patient and stimulation thresholds for activation of IC axons were calculated with electric field models that ranged from an electrostatic, homogenous, isotropic model to one that explicitly incorporated the voltage-drop and capacitance of the electrode-electrolyte interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to experimental IC activation defined from electromyographic (EMG) recordings from eight different muscle groups in the contralateral arm and leg of the STN DBS patient. Coupled evaluation of the model and experimental data showed that the most realistic predictions of axonal thresholds were achieved with the most detailed model. Furthermore, the more simplistic neurostimulation models substantially overestimated the spatial extent of neural activation.

Investigating the depth electrode-brain interface in deep brain stimulation using finite element models with graded complexity in structure and solution

Deep brain stimulation (DBS) is an increasingly used surgical therapy for a range of neurological disorders involving the long-term electrical stimulation of various regions of the human brain in a disorder specific manner. Despite being used for the last 20 years, the underlying mechanisms are still not known, and disputed. In particular, when the electrodes are implanted into the human brain, an interface is created with changing biophysical properties which may impact on stimulation. We previously defined the electrode-brain interface (EBI) as consisting of three structural elements: the quadripolar DBS electrode, the peri-electrode space and the surrounding brain tissue. In order to understand more about the nature of this EBI, we used structural computational models of this interface, and estimated the effects of stimulation using coupled axon models. These finite element models differ in complexity, each highlighting a different feature of the EBI's effect on the DBS-induced electric field. We show that the quasi-static models are sufficient to demonstrate the difference between the acute and chronic clinical stages post-implantation. However, the frequency-dependent models are necessary as the waveform shaping has a major influence on the activation of neuronal fibres. We also investigate anatomical effects on the electric field, by taking specific account of the ventricular system in the human brain. Taken together, these models allow us to visualise the static, dynamic and target specific properties of the DBS-induced field in the surrounding brain regions.

In vivo impedance spectroscopy of deep brain stimulation electrodes

Deep brain stimulation (DBS) represents a powerful clinical technology, but a systematic characterization of the electrical interactions between the electrode and the brain is lacking. The goal of this study was to examine the in vivo changes in the DBS electrode impedance that occur after implantation and during clinically relevant stimulation. Clinical DBS devices typically apply high-frequency voltage-controlled stimulation, and as a result, the injected current is directly regulated by the impedance of the electrode-tissue interface. We monitored the impedance of scaled-down clinical DBS electrodes implanted in the thalamus and subthalamic nucleus of a rhesus macaque using electrode impedance spectroscopy (EIS) measurements ranging from 0.5 Hz to 10 kHz. To further characterize our measurements, equivalent circuit models of the electrode-tissue interface were used to quantify the role of various interface components in producing the observed electrode impedance. Following implantation, the DBS electrode impedance increased and a semicircular arc was observed in the high-frequency range of the EIS measurements, commonly referred to as the tissue component of the impedance. Clinically relevant stimulation produced a rapid decrease in electrode impedance with extensive changes in the tissue component. These post-operative and stimulation-induced changes in impedance could play an important role in the observed functional effects of voltage-controlled DBS and should be considered during clinical stimulation parameter selection and chronic animal research studies.

Toward a comparison of microelectrodes for acute and chronic recordings

Several variations of microelectrode arrays are used to record and stimulate intracortical neuronal activity. Bypassing the immune response to maintain a stable recording interface remains a challenge. Companies and researchers are continuously altering the material compositions and geometries of the arrays in order to discover a combination that allows for a chronic and stable electrode-tissue interface. From this interface, they wish to obtain consistent quality recordings and a stable, low impedance pathway for charge injection over extended periods of time. Despite numerous efforts, no microelectrode array design has managed to evade the host immune response and remain fully functional. This study is an initial effort comparing several microelectrode arrays with fundamentally different configurations for use in an implantable epilepsy prosthesis. Specifically, NeuroNexus (Michigan) probes, Cyberkinetics (Utah) Silicon and Iridium Oxide arrays, ceramic-based thin-film microelectrode arrays (Drexel), and Tucker-Davis Technologies (TDT) microwire arrays are evaluated over a 31-day period in an animal model. Microelectrodes are compared in implanted rats through impedance, charge capacity, signal-to-noise ratio, recording stability, and elicited immune response. Results suggest significant variability within and between microelectrode types with no clear superior array. Some applications for the microelectrode arrays are suggested based on data collected throughout the longitudinal study. Additionally, specific limitations of assaying biological phenomena and comparing fundamentally different microelectrode arrays in a highly variable system are discussed with suggestions on how to improve the reliability of observed results and steps needed to develop a more standardized microelectrode design.

Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease and shows great promise for numerous other disorders. While the fundamental purpose of DBS is to modulate neural activity with electric fields, little is known about the actual voltage distribution generated in the brain by DBS electrodes and as a result it is difficult to accurately predict which brain areas are directly affected by the stimulation. The goal of this study was to characterize the spatial and temporal characteristics of the voltage distribution generated by DBS electrodes. We experimentally recorded voltages around active DBS electrodes in either a saline bath or implanted in the brain of a non-human primate. Recordings were made during voltage-controlled and current-controlled stimulation. The experimental findings were compared to volume conductor electric field models of DBS parameterized to match the different experiments. Three factors directly affected the experimental and theoretical voltage measurements: 1) DBS electrode impedance, primarily dictated by a voltage drop at the electrode-electrolyte interface and the conductivity of the tissue medium, 2) capacitive modulation of the stimulus waveform, and 3) inhomogeneity and anisotropy of the tissue medium. While the voltage distribution does not directly predict the neural response to DBS, the results of this study do provide foundational building blocks for understanding the electrical parameters of DBS and characterizing its effects on the nervous system.

The influence of reactivity of the electrode-brain interface on the crossing electric current in therapeutic deep brain stimulation

The use of deep brain stimulation (DBS) as an effective clinical therapy for a number of neurological disorders has been greatly hindered by the lack of understanding of the mechanisms which underlie the observed clinical improvement in patients. This problem is confounded by the difficulty of investigating the neuronal effects of DBS in situ, and the impossibility of measuring the induced current in vivo. In our recent computational work using a quasi-static finite element (FEM) model we have quantitatively shown that the properties of the depth electrode-brain interface (EBI) have a significant effect on the electric field induced in the brain volume surrounding the DBS electrode. In the present work, we explore the influence of the reactivity of the EBI on the crossing electric current using the Fourier-FEM approach to allow the investigation of waveform attenuation in the time domain. Results showed that the EBI affected the waveform shaping differently at different post-implantation stages, and that this in turn had implications on induced current distribution across the EBI. Furthermore, we investigated whether hypothetical waveforms, which were shown to have potential usefulness for neural stimulation but are not yet applied clinically, would have any advantage over the currently used square pulse. In conclusion, the influence of reactivity of the EBI on the crossing stimulation current in therapeutic DBS is significant, and affects the predictive estimation of current distribution around the implanted DBS electrode in the human brain.