Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation

StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson's Disease

OBJECTIVE

Subthalamic deep brain stimulation (DBS) is an established therapy for Parkinson's disease. Connectomic DBS modeling is a burgeoning subfield of research aimed at characterizing the axonal connections activated by DBS. This article describes our approach and methods for evolving the StimVision software platform to meet the technical demands of connectomic DBS modeling in the subthalamic region.

MATERIALS AND METHODS

StimVision v2 was developed with Visualization Toolkit (VTK) libraries and integrates four major components: 1) medical image visualization, 2) axonal pathway visualization, 3) electrode positioning, and 4) stimulation calculation.

RESULTS

StimVision v2 implemented two key technological advances for connectomic DBS analyses in the subthalamic region. First was the application of anatomical axonal pathway models to patient-specific DBS models. Second was the application of a novel driving-force method to estimate the response of those axonal pathways to DBS. Example simulations with directional DBS electrodes and clinically defined therapeutic DBS settings are presented to demonstrate the general outputs of StimVision v2 models.

CONCLUSIONS

StimVision v2 provides the opportunity to evaluate patient-specific axonal pathway activation from subthalamic DBS using anatomically detailed pathway models and electrically detailed electric field distributions with interactive adjustment of the DBS electrode position and stimulation parameter settings.

Current Directions in Deep Brain Stimulation for Parkinson's Disease-Directing Current to Maximize Clinical Benefit

Several single-center studies and one large multicenter clinical trial demonstrated that directional deep brain stimulation (DBS) could optimize the volume of tissue activated (VTA) based on the individual placement of the lead in relation to the target. The ability to generate axially asymmetric fields of stimulation translates into a broader therapeutic window (TW) compared to conventional DBS. However, changing the shape and surface of stimulating electrodes (directional segmented vs. conventional ring-shaped) also demands a revision of the programming strategies employed for DBS programming. Model-based approaches have been used to predict the shape of the VTA, which can be visualized on standardized neuroimaging atlases or individual magnetic resonance imaging. While potentially useful for optimizing clinical care, these systems remain limited by factors such as patient-specific anatomical variability, postsurgical lead migrations, and inability to account for individual contact impedances and orientation of the systems of fibers surrounding the electrode. Alternative programming tools based on the functional assessment of stimulation-induced clinical benefits and side effects allow one to collect and analyze data from each electrode of the DBS system and provide an action plan of ranked alternatives for therapeutic settings based on the selection of optimal directional contacts. Overall, an increasing amount of data supports the use of directional DBS. It is conceivable that the use of directionality may reduce the need for complex programming paradigms such as bipolar configurations, frequency or pulse width modulation, or interleaving. At a minimum, stimulation through directional electrodes can be considered as another tool to improve the benefit/side effect ratio. At a maximum, directionality may become the preferred way to program because of its larger TW and lower energy consumption.

Bio-Heat Model of Kilohertz-Frequency Deep Brain Stimulation Increases Brain Tissue Temperature

OBJECTIVES

Early clinical trials suggest that deep brain stimulation at kilohertz frequencies (10 kHz-DBS) may be effective in improving motor symptoms in patients with movement disorders. The 10 kHz-DBS can deliver significantly more power in tissue compared to conventional frequency DBS, reflecting increased pulse compression (duty cycle). We hypothesize that 10 kHz-DBS modulates neuronal function through moderate local tissue heating, analogous to kilohertz spinal cord stimulation (10 kHz-SCS). To establish the role of tissue heating in 10 kHz-DBS (30 μs, 10 kHz, at intensities of 3-7 mA_peak ), a decisive first step is to characterize the range of temperature changes during clinical kHz-DBS protocols.

MATERIALS AND METHODS

We developed a high-resolution magnetic resonance imaging-derived DBS model incorporating joule-heat coupled bio-heat multi-physics to establish the role of tissue heating. Volume of tissue activated (VTA) under assumptions of activating function (for 130 Hz) or heating (for 10 kHz) based neuromodulation are contrasted.

RESULTS

DBS waveform power (waveform RMS) determined joule heating at the deep brain tissues. Peak heating was supralinearly dependent on stimulation RMS. The 10 kHz-DBS stimulation with 2.3 to 5.4 mA_RMS (corresponding to 3 to 7 mA_peak ) produced 0.10 to 1.38°C heating at the subthalamic nucleus (STN) target under standard tissue parameters. Maximum temperature increases were predicted inside the electrode encapsulation layer (enCAP) with 2.3 to 5.4 mA_RMS producing 0.13 to 1.87°C under standard tissue parameters. Tissue parameter analysis predicted STN heating was especially sensitive (ranging from 0.44 to 1.35°C at 3.8 mA_RMS ) to decreasing enCAP electrical conductivity and decreasing STN thermal conductivity.

CONCLUSIONS

Subject to validation with in vivo measurements, neuromodulation through a heating mechanism of action by 10 kHz-DBS can indicate novel therapeutic pathways and strategies for dose optimization.

Analysis of patient-specific stimulation with segmented leads in the subthalamic nucleus

Objective

Segmented deep brain stimulation leads in the subthalamic nucleus have shown to increase therapeutic window using directional stimulation. However, it is not fully understood how these segmented leads with reduced electrode size modify the volume of tissue activated (VTA) and how this in turn relates with clinically observed therapeutic and side effect currents. Here, we investigated the differences between directional and omnidirectional stimulation and associated VTAs with patient-specific therapeutic and side effect currents for the two stimulation modes.

Approach

Nine patients with Parkinson’s disease underwent DBS implantation in the subthalamic nucleus. Therapeutic and side effect currents were identified intraoperatively with a segmented lead using directional and omnidirectional stimulation (these current thresholds were assessed in a blinded fashion). The electric field around the lead was simulated with a finite-element model for a range of stimulation currents for both stimulation modes. VTAs were estimated from the electric field by numerical differentiation and thresholding. Then for each patient, the VTAs for given therapeutic and side effect currents were projected onto the patient-specific subthalamic nucleus and lead position.

Results

Stimulation with segmented leads with reduced electrode size was associated with a significant reduction of VTA and a significant increase of radial distance in the best direction of stimulation. While beneficial effects were associated with activation volumes confined within the anatomical boundaries of the subthalamic nucleus at therapeutic currents, side effects were associated with activation volumes spreading beyond the nucleus’ boundaries.

Significance

The clinical benefits of segmented leads are likely to be obtained by a VTA confined within the subthalamic nucleus and a larger radial distance in the best stimulation direction, while steering the VTA away from unwanted fiber tracts outside the nucleus. Applying the same concepts at a larger scale and in chronically implanted patients may help to predict the best stimulation area.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

BACKGROUND

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports.

OBJECTIVE

Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation.

METHODS

Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution.

RESULTS

Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings.

CONCLUSION

Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Role of Soft-Tissue Heterogeneity in Computational Models of Deep Brain Stimulation

BACKGROUND

Bioelectric field models of deep brain stimulation (DBS) are commonly utilized in research and industrial applications. However, the wide range of different representations used for the human head in these models may be responsible for substantial variance in the stimulation predictions.

OBJECTIVE

Determine the relative error of ignoring cerebral vasculature and soft-tissue heterogeneity outside of the brain in computational models of DBS.

METHODS

We used a detailed atlas of the human head, coupled to magnetic resonance imaging data, to construct a range of subthalamic DBS volume conductor models. We incrementally simplified the most detailed base model and quantified changes in the stimulation thresholds for direct activation of corticofugal axons.

RESULTS

Ignoring cerebral vasculature altered predictions of stimulation thresholds by <10%, whereas ignoring soft-tissue heterogeneity outside of the brain altered predictions between -44 % and 174%.

CONCLUSIONS

Heterogeneity in the soft tissues of the head, if unaccounted for, introduces a degree of uncertainty in predicting electrical stimulation of neural elements that is not negligible and thereby warrants consideration in future modeling studies.

Technical aspects of neurostimulation: Focus on equipment, electric field modeling, and stimulation protocols

Neuromodulation is a field of science, medicine, and bioengineering that encompasses implantable and non-implantable technologies for the purpose of improving quality of life and functioning of humans. Brain neuromodulation involves different neurostimulation techniques: transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), which are being used both to study their effects on cognitive brain functions and to treat neuropsychiatric disorders. The mechanisms of action of neurostimulation remain incompletely understood. Insight into the technical basis of neurostimulation might be a first step towards a more profound understanding of these mechanisms, which might lead to improved clinical outcome and therapeutic potential. This review provides an overview of the technical basis of neurostimulation focusing on the equipment, the present understanding of induced electric fields, and the stimulation protocols. The review is written from a technical perspective aimed at supporting the use of neurostimulation in clinical practice.

A latent force model for describing electric propagation in deep brain stimulation: a simulation study

Deep brain stimulation (DBS) is a neurosurgical method used to treat symptoms of movement disorders by implanting electrodes in deep brain areas. Often, the DBS modeling approaches found in the literature assume a quasi-static approximation, and discard any dynamic behavior. Nevertheless, in a real DBS system the stimulus corresponds to a wave that changes as a function of time. It is clear that DBS demands an approach that takes into account the time-varying behavior of the input stimulus. In this work, we present a novel latent force model for describing the dynamic electric propagation occurred during DBS. The performance of the proposed model was studied by simulations under different conditions. The results show that our approach is able to take into account the time variations of the source and the produced field. Moreover, by restricting our model it is possible to obtain solutions for electrostatic formulations, here experimental results were compared with the finite element method. Additionally, our approach allows a solution to the inverse problem, which is a valuable clinical application allowing the appropriate tuning of the DBS device by the expert physician.

Relationship between neural activation and electric field distribution during deep brain stimulation

Models and simulations are commonly used to study deep brain stimulation (DBS). Simulated stimulation fields are often defined and visualized by electric field isolevels or volumes of tissue activated (VTA). The aim of the present study was to evaluate the relationship between stimulation field strength as defined by the electric potential V, the electric field E, and the divergence of the electric field ∇(2) V, and neural activation. Axon cable models were developed and coupled to finite-element DBS models in three-dimensional (3-D). Field thresholds ( VT , ET, and ∇(2) VT ) were derived at the location of activation for various stimulation amplitudes (1 to 5 V), pulse widths (30 to 120 μs), and axon diameters (2.0 to 7.5 μm). Results showed that thresholds for VT and ∇(2) VT were highly dependent on the stimulation amplitude while ET were approximately independent of the amplitude for large axons. The activation field strength thresholds presented in this study may be used in future studies to approximate the VTA during model-based investigations of DBS without the need of computational axon models.

Electrode location and clinical outcome in hippocampal electrical stimulation for mesial temporal lobe epilepsy

PURPOSE

To study the clinical outcome in hippocampal deep brain stimulation (DBS) for the treatment of patients with refractory mesial temporal lobe epilepsy (MTLE) according to the electrode location.

METHODS

Eight MTLE patients implanted in the hippocampus and stimulated with high-frequency DBS were included in this study. Five underwent invasive recordings with depth electrodes to localize ictal onset zone prior to chronic DBS. Position of the active contacts of the electrode was calculated on postoperative imaging. The distances to the ictal onset zone were measured as well as atlas-based hippocampus structures impacted by stimulation were identified. Both were correlated with seizure frequency reduction.

RESULTS

The distances between active electrode location and estimated ictal onset zone were 11±4.3 or 9.1±2.3mm for patients with a >50% or <50% reduction in seizure frequency. In patients (N=6) showing a >50% seizure frequency reduction, 100% had the active contacts located <3mm from the subiculum (p<0.05). The 2 non-responders patients were stimulated on contacts located >3mm to the subiculum.

CONCLUSION

Decrease of epileptogenic activity induced by hippocampal DBS in refractory MTLE: (1) seems not directly associated with the vicinity of active electrode to the ictal focus determined by invasive recordings; (2) might be obtained through the neuromodulation of the subiculum.

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.

Simulation of cortico-basal ganglia oscillations and their suppression by closed loop deep brain stimulation

A new model of deep brain stimulation (DBS) is presented that integrates volume conduction effects with a neural model of pathological beta-band oscillations in the cortico-basal ganglia network. The model is used to test the clinical hypothesis that closed-loop control of the amplitude of DBS may be possible, based on the average rectified value of beta-band oscillations in the local field potential. Simulation of closed-loop high-frequency DBS was shown to yield energy savings, with the magnitude of the energy saved dependent on the strength of coupling between the subthalamic nucleus and the remainder of the cortico-basal ganglia network. When closed-loop DBS was applied to a strongly coupled cortico-basal ganglia network, the stimulation energy delivered over a 480 s period was reduced by up to 42%. Greater energy reductions were observed for weakly coupled networks, as the stimulation amplitude reduced to zero once the initial desynchronization had occurred. The results provide support for the application of closed-loop high-frequency DBS based on electrophysiological biomarkers.

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.

Effect of dispersive conductivity and permittivity in volume conductor models of deep brain stimulation

The aim of this study was to examine the effect of dispersive tissue properties on the volume-conducted voltage waveforms and volume of tissue activated during deep brain stimulation. Inhomogeneous finite-element models were developed, incorporating a distributed dispersive electrode-tissue interface and encapsulation tissue of high and low conductivity, under both current-controlled and voltage-controlled stimulation. The models were used to assess the accuracy of capacitive models, where material properties were estimated at a single frequency, with respect to the full dispersive models. The effect of incorporating dispersion in the electrical conductivity and relative permittivity was found to depend on both the applied stimulus and the encapsulation tissue surrounding the electrode. Under current-controlled stimulation, and during voltage-controlled stimulation when the electrode was surrounded by high-resistivity encapsulation tissue, the dispersive material properties of the tissue were found to influence the voltage waveform in the tissue, indicated by RMS errors between the capacitive and dispersive models of 20%-38% at short pulse durations. When the dispersive model was approximated by a capacitive model, the accuracy of estimates of the volume of tissue activated was very sensitive to the frequency at which material properties were estimated. When material properties at 1 kHz were used, the error in the volume of tissue activated by capacitive approximations was reduced to -4.33% and 11.10%, respectively, for current-controlled and voltage-controlled stimulations, with higher errors observed when higher or lower frequencies were used.