The peri-electrode space is a significant element of the electrode-brain interface in deep brain stimulation: a computational study

Impact of surrounding tissue-type and peri-electrode gap in stereoelectroencephalography guided (SEEG) radiofrequency thermocoagulation (RF-TC): a computational study

PURPOSE

To use computational modeling to provide a complete and logical description of the electrical and thermal behavior during stereoelectroencephalography-guided (SEEG) radiofrequency thermo-coagulation (RF-TC).

METHODS

A coupled electrical-thermal model was used to obtain the temperature distributions in the tissue during RF-TC. The computer model was first validated by an ex vivo model based on liver fragments and later used to study the impact of three different factors on the coagulation zone size: 1) the difference in the tissue surrounding the electrode (gray/white matter), 2) the presence of a peri-electrode gap occupied by cerebrospinal fluid (CSF), and 3) the energy setting used (power-duration).

RESULTS

The model built for the experimental validation was able to predict both the evolution of impedance and the short diameter of the coagulation zone (error < 0.01 mm) reasonably well but overestimated the long diameter by 2 - 3 mm. After adapting the model to clinical conditions, the simulation showed that: 1) Impedance roll-off limited the coagulation size but involved overheating (around 100 °C); 2) The type of tissue around the contacts (gray vs. white matter) had a moderate impact on the coagulation size (maximum difference 0.84 mm), and 3) the peri-electrode gap considerably altered the temperature distributions, avoided overheating, although the diameter of the coagulation zone was not very different from the no-gap case (<0.2 mm).

CONCLUSIONS

This study showed that computer modeling, especially subject- and scenario-specific modeling, can be used to estimate in advance the electrical and thermal performance of the RF-TC in brain tissue.

Increased electrode impedance as an indicator for early detection of deep brain stimulation (DBS) hardware Infection: Clinical experience and in vitro study

BACKGROUND

When deep brain stimulation (DBS) infections are identified, they are often too advanced to treat without complete hardware removal. New objective markers to promptly identify DBS infections are needed. We present a patient with GPi (globus pallidus interna) DBS for dystonia, where the electrode impedance unexpectedly increased 3-months post-operatively, followed by serologic and hematologic markers of inflammation at 6-months, prompting explantation surgery. We recreated these conditions in a laboratory environment to analyze the pattern of changing of electrical impedance across the contacts of a DBS lead following Staphylococcus biofilm formation.

METHODS

A stainless-steel culture chamber containing 1 % brain heart infusion agar was used. A DBS electrode was dipped in peptone water containing a strain of S. aureus and subsequently introduced into the chamber. The apparatus was incubated at 37 °C for 6 days. Impedance was measured at 24hr intervals. A control experiment without S. Aureus inoculation was used to determine changes in impedance over a period of 6-days.

RESULTS

The mean monopolar impedance on day-1 was 751.8 ± 23.8 Ω and on day-3 was 1004.8 ± 68.7 Ω, a 33.7 % rise (p = 0.007). A faint biofilm formation could be seen around the DBS lead by day-2 and florid growth by day-3. After addition of the linezolid solution, a 15.9 % decrease in monopolar impedance was observed from day 3-6 (p = 0.003).

CONCLUSION

This study gives insight into impedance trends following a hardware infection in DBS. Increased impedance outside expected norms may be valuable for early prediction of infection. Furthermore, timely management using antibiotics might reduce the frequency of infection-related explant surgeries.

Lateral cerebellothalamic tract activation underlies DBS therapy for Essential Tremor

BACKGROUND

While deep brain stimulation (DBS) therapy can be effective at suppressing tremor in individuals with medication-refractory Essential Tremor, patient outcome variability remains a significant challenge across centers. Proximity of active electrodes to the cerebellothalamic tract (CTT) is likely important in suppressing tremor, but how tremor control and side effects relate to targeting parcellations within the CTT and other pathways in and around the ventral intermediate (VIM) nucleus of thalamus remain unclear.

METHODS

Using ultra-high field (7T) MRI, we developed high-dimensional, subject-specific pathway activation models for 23 directional DBS leads. Modeled pathway activations were compared with post-hoc analysis of clinician-optimized DBS settings, paresthesia thresholds, and dysarthria thresholds. Mixed-effect models were utilized to determine how the six parcellated regions of the CTT and how six other pathways in and around the VIM contributed to tremor suppression and induction of side effects.

RESULTS

The lateral portion of the CTT had the highest activation at clinical settings (p < 0.05) and a significant effect on tremor suppression (p < 0.001). Activation of the medial lemniscus and posterior-medial CTT was significantly associated with severity of paresthesias (p < 0.001). Activation of the anterior-medial CTT had a significant association with dysarthria (p < 0.05).

CONCLUSIONS

This study provides a detailed understanding of the fiber pathways responsible for therapy and side effects of DBS for Essential Tremor, and suggests a model-based programming approach will enable more selective activation of lateral fibers within the CTT.

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.

Deep brain stimulation programming strategies: segmented leads, independent current sources, and future technology

Introduction: Advances in neuromodulation and deep brain stimulation (DBS) technologies have facilitated opportunities for improved clinical benefit and side effect management. However, new technologies have added complexity to clinic-based DBS programming.Areas covered: In this article, we review basic basal ganglia physiology, proposed mechanisms of action and technical aspects of DBS. We discuss novel DBS technologies for movement disorders including the role of advanced imaging software, lead design, IPG design, novel programming techniques including directional stimulation and coordinated reset neuromodulation. Additional topics include the use of potential biomarkers, such as local field potentials, electrocorticography, and adaptive stimulation. We will also discuss future directions including optogenetically inspired DBS.Expert opinion: The introduction of DBS for the management of movement disorders has expanded treatment options. In parallel with our improved understanding of brain physiology and neuroanatomy, new technologies have emerged to address challenges associated with neuromodulation, including variable effectiveness, side-effects, and programming complexity. Advanced functional neuroanatomy, improved imaging, real-time neurophysiology, improved electrode designs, and novel programming techniques have collectively been driving improvements in DBS outcomes.

What do intracerebral electrodes measure?

OBJECTIVE

Gain insight and improve our interpretation of measurements from intracerebral electrodes. Determine if interpretation of intracerebral EEG is dependent on electrode characteristics.

METHODS

We use intracerebral EEG measurements differing only in the recording electrodes (Dixi or homemade electrodes), and numerical simulations to determine the spatial sensitivity of intracerebral electrodes and its dependence on several parameters.

RESULTS

There is a difference in the high frequency (>20 Hz) power depending on the electrode type, which cannot be explained by the different contact sizes or distance between contacts. Simulations show that the width of the gap between electrode and brain and the extent of the generators have an effect on sensitivity, while other parameters are less important.

CONCLUSIONS

The sensitivity of intracerebral electrodes is not affected in an important way by the dimensions of the contacts, but depends on the extent of generators. The unusual insertion technique of homemade electrodes resulting in a large gap between functional brain and electrodes, explains the observed signal difference.

SIGNIFICANCE

Numerical simulation is a useful tool in the choice or design of intracerebral electrodes, and increases our understanding of their measurements. The interpretation of intracerebral EEG is not affected by differences between typical commercially available electrodes.

Influence of Virchow-Robin spaces on the electric field distribution in subthalamic nucleus deep brain stimulation

Patient MRI from DBS implantations in the subthalamic nucleus (STN) were reviewed and it was found that around 10% had Virchow-Robin spaces (VRS). Patient-specific models were developed to evaluate changes in the electric field (EF) around DBS leads. The patients (n = 7) were implanted bilaterally either with the standard voltage-controlled lead 3389 or with the directional current-controlled lead 6180. The EF distribution was evaluated by comparing simulations using patient-specific models with homogeneous models without VRS. The EF, depicted with an isocontour of 0.2 V/mm, showed a deformation in the presence of the VRS around the DBS lead. For patient-specific models, the radial extension of the EF isocontours was enlarged regardless of the operating mode or the DBS lead used. The location of the VRS in relation to the active contact and the stimulation amplitude, determined the changes in the shape and extension of the EF. It is concluded that it is important to take the patients' brain anatomy into account as the high conductivity in VRS will alter the electric field if close to the DBS lead. This can be a cause of unexpected side effects.

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.

A Study on the Feasibility of the Deep Brain Stimulation (DBS) Electrode Localization Based on Scalp Electric Potential Recordings

Deep Brain Stimulation (DBS) is an effective therapy for patients disabling motor symptoms from Parkinson's disease, essential tremor, and other motor disorders. Precise, individualized placement of DBS electrodes is a key contributor to clinical outcomes following surgery. Electroencephalography (EEG) is widely used to identify the sources of intracerebral signals from the potential on the scalp. EEG is portable, non-invasive, low-cost, and it could be easily integrated into the intraoperative or ambulatory environment for localization of either the DBS electrode or evoked potentials triggered by stimulation itself. In this work, we studied with numerical simulations the principle of extracting the DBS electrical pulse from the patient's EEG - which normally constitutes an artifact - and localizing the source of the artifact (i.e., the DBS electrodes) using EEG localization methods. A high-resolution electromagnetic head model was used to simulate the EEG potential at the scalp generated by the DBS pulse artifact. The potential distribution on the scalp was then sampled at the 256 electrode locations of a high-density EEG Net. The electric potential was modeled by a dipole source created by a given pair of active DBS electrodes. The dynamic Statistical Parametric Maps (dSPM) algorithm was used to solve the EEG inverse problem, and it allowed localization of the position of the stimulus dipole in three DBS electrode bipolar configurations with a maximum error of 1.5 cm. To assess the accuracy of the computational model, the results of the simulation were compared with the electric artifact amplitudes over 16 EEG electrodes measured in five patients. EEG artifacts measured in patients confirmed that simulated data are commensurate to patients' data (0 ± 6.6 μV). While we acknowledge that further work is necessary to achieve a higher accuracy needed for surgical navigation, the results presented in this study are proposed as the first step toward a validated computational framework that could be used for non-invasive localization not only of the DBS system but also brain rhythms triggered by stimulation at both proximal and distal sites in the human central nervous system.

Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways

OBJECTIVE

The effectiveness of deep brain stimulation (DBS) therapy strongly depends on precise surgical targeting of intracranial leads and on clinical optimization of stimulation settings. Recent advances in surgical targeting, multi-electrode designs, and multi-channel independent current-controlled stimulation are poised to enable finer control in modulating pathways within the brain. However, the large stimulation parameter space enabled by these technologies also poses significant challenges for efficiently identifying the most therapeutic DBS setting for a given patient. Here, we present a computational approach for programming directional DBS leads that is based on a non-convex optimization framework for neural pathway targeting.

APPROACH

The algorithm integrates patient-specific pre-operative 7 T MR imaging, post-operative CT scans, and multi-objective particle swarm optimization (MOPSO) methods using dominance based-criteria and incorporating multiple neural pathways simultaneously. The algorithm was evaluated on eight patient-specific models of subthalamic nucleus (STN) DBS to identify electrode configurations and stimulation amplitudes to optimally activate or avoid six clinically relevant pathways: motor territory of STN, non-motor territory of STN, internal capsule, superior cerebellar peduncle, thalamic fasciculus, and hyperdirect pathway.

MAIN RESULTS

Across the patient-specific models, single-electrode stimulation showed significant correlations across modeled pathways, particularly for motor and non-motor STN efferents. The MOPSO approach was able to identify multi-electrode configurations that achieved improved targeting of motor STN efferents and hyperdirect pathway afferents than that achieved by any single-electrode monopolar setting at equivalent power levels.

SIGNIFICANCE

These results suggest that pathway targeting with patient-specific model-based optimization algorithms can efficiently identify non-trivial electrode configurations for enhancing activation of clinically relevant pathways. However, the results also indicate that inter-pathway correlations can limit selectivity for certain pathways even with directional DBS leads.

Understanding the Effects and Adverse Reactions of Deep Brain Stimulation: Is It Time for a Paradigm Shift Toward a Focus on Heterogenous Biophysical Tissue Properties Instead of Electrode Design Only?

Deep brain stimulation (DBS) has been proven to be an effective treatment modality for various late-stage neurological and psychiatric disorders. However, knowledge on the electrical field distribution in the brain tissue is still scarce. Most recent attempts to understand electric field spread were primarily focused on the effect of different electrodes on rather simple tissue models. The influence of microanatomic, biophysical tissue properties in particular has not been investigated in depth. Ethical concerns restrict thorough research on field distribution in human in vivo brain tissue. By means of a simplified model, we investigated the electric field distribution in a broader area of the subthalamic nucleus (STN). Pivotal biophysical parameters including conductivity, permittivity and permeability of brain tissue were incorporated in the model. A brain tissue model was created with the finite element method (FEM). Stimulation was mimicked with parameters used for monopolar stimulation of patients suffering from Parkinson's disease. Our results were visualized with omnidirectional and segmented electrodes. The stimulated electric field was visualized with superimpositions on a stereotactic atlas (Morel). Owing to the effects of regional tissue properties near the stimulating electrode, marked field distortions occur. Such effects include, for example, isolating effects of heavily myelinated neighboring structures, e.g., the internal capsule. In particular, this may be illustrated through the analysis of a larger coronal area. While omnidirectional stimulation has been associated with vast current leakage, higher targeting precision was obtained with segmented electrodes. Finally, targeting was improved when the influence of microanatomic structures on the electric spread was considered. Our results confirm that lead design is not the sole influence on current spread. An omnidirectional lead configuration does not automatically result in an omnidirectional spread of current. In turn, segmented electrodes do not automatically imply an improved steering of current. Our findings may provide an explanation for side-effects secondary to current leakage. Furthermore, a possible explanation for divergent results in the comparison of the intraoperative awake patient and the postoperative setting is given. Due to the major influence of biophysical tissue properties on electric field shape, the local microanatomy should be considered for precise surgical targeting and optimal hardware implantation.

Programming for Stimulation-Induced Transient Nonmotor Psychiatric Symptoms after Bilateral Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease

Background

Stimulation-induced transient nonmotor psychiatric symptoms (STPSs) are side effects following bilateral subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease (PD) patients. We designed algorithms which (1) determine the electrode contacts that induce STPSs and (2) provide a programming protocol to eliminate STPS and maintain the optimal motor functions. Our objective is to test the effectiveness of these algorithms.

Materials and Methods

454 PD patients who underwent programming sessions after STN-DBS implantations were retrospectively analyzed. Only STPS patients were enrolled. In these patients, the contacts inducing STPS were found and the programming protocol algorithms used.

Results

Eleven patients were diagnosed with STPS. Of these patients, two had four episodes of crying, and two had four episodes of mirthful laughter. In one patient, two episodes of abnormal sense of spatial orientation were observed. Hallucination episodes were observed twice in one patient, while five patients recorded eight episodes of hypomania. There were no statistical differences between the UPDRS-III under the final stimulation parameter (without STPS) and previous optimum UPDRS-III under the STPSs (p = 1.000).

Conclusion

The flow diagram used for determining electrode contacts that induce STPS and the programming protocol employed in the treatment of these symptoms are effective.

Computational modeling of neurostimulation in brain diseases

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.

Influences of interpolation error, electrode geometry, and the electrode-tissue interface on models of electric fields produced by deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for movement disorders, but the fundamental mechanisms by which DBS has its effects remain unknown. Computational models can provide insights into the mechanisms of DBS, but to be useful, the models must have sufficient detail to predict accurately the electric fields produced by DBS. We used a finite-element method model of the Medtronic 3387 electrode array, coupled to cable models of myelinated axons, to quantify how interpolation errors, electrode geometry, and the electrode-tissue interface affect calculation of electrical potentials and stimulation thresholds for populations of model nerve fibers. Convergence of the potentials was not a sufficient criterion for ensuring the same degree of accuracy in subsequent determination of stimulation thresholds, because the accuracy of the stimulation thresholds depended on the order of the elements. Simplifying the 3387 electrode array by ignoring the inactive contacts and extending the terminated end of the shaft had position-dependent effects on the potentials and excitation thresholds, and these simplifications may impact correlations between DBS parameters and clinical outcomes. When the current density in the bulk tissue is uniform, the effect of the electrode-tissue interface impedance could be approximated by filtering the potentials calculated with a static lumped electrical equivalent circuit. Further, for typical DBS parameters during voltage-regulated stimulation, it was valid to approximate the electrode as an ideal polarized electrode with a nonlinear capacitance. Validation of these computational considerations enables accurate modeling of the electric field produced by DBS.

Variation in deep brain stimulation electrode impedance over years following electrode implantation

BACKGROUND

Deep brain stimulation (DBS) electrode impedance is a major determinant of current delivery to target tissues, but long-term variation in impedance has received little attention.

OBJECTIVES

To assess the relationship between electrode impedance and time in a large DBS patient population and characterize the relationship between contact activity and impedance.

METHODS

We collected retrospective impedance and programming data from 128 electrodes in 84 patients with Parkinson's disease, essential tremor or dystonia. Effects of time, contact activity, stimulation voltage and other parameters on impedance were assessed. We also examined impedance changes following contact activation and deactivation.

RESULTS

Impedance decreased by 73 Ω/year (p < 0.001), with 72% of contacts following a downward trend. Impedance was on average 163 Ω lower in active contacts (p < 0.001). Contact activation and inactivation were associated with a more (p < 0.001) and less (p = 0.016) rapid decline in impedance, respectively. Higher stimulation voltages were associated with lower impedance values (p < 0.001). Contact number and electrode model were also significant predictors of impedance.

CONCLUSIONS

Impedance decreases gradually in a stimulation-dependent manner. These trends have implications for long-term programming, the development of a closed-loop DBS device and current understanding of the electrode-tissue interface.

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.

Discrete pattern of burst stimulation in the ventrobasal thalamus for anti-nociception

The thalamus has been proposed to play a role in sensory modulation via switching between tonic and burst dual firing of individual neurons. Of the two firing modes, altered burst firing has been repeatedly implicated with pathological pain conditions, which suggests that maintaining a certain form of thalamic burst could be crucial for controlling pain. However, specific elements of burst firing that may contribute to pain control have not yet been actively investigated. Utilizing the deep brain stimulation (DBS) technique, we explored the effects of bursting properties in pain control by electrically stimulating the ventrobasal (VB) thalamus in forms of burst patterned to test different aspects of bursts during the formalin induced nociception in mice. Our results demonstrated that electrical stimulations mimicking specific burst firing properties are important in producing an anti-nociceptive effect and found that the ≤ 3 ms interval between burst pluses (intra-burst-interval: IntraBI) and ≥ 3 pulses per burst were required to reliably reduce formalin induced nociceptive responses in mice. Periodicity of IntraBI was also suggested to contribute to anti-nociception to a limited extent.

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.

Modeling of a segmented electrode for desynchronizing deep brain stimulation

Deep brain stimulation (DBS) is an effective therapy for medically refractory movement disorders like Parkinson’s disease. The electrodes, implanted in the target area within the human brain, generate an electric field which activates nerve fibers and cell bodies in the vicinity. Even though the different target nuclei display considerable differences in their anatomical structure, only few types of electrodes are currently commercially available. It is desirable to adjust the electric field and in particular the volume of tissue activated around the electrode with respect to the corresponding target nucleus in a such way that side effects can be reduced. Furthermore, a more selective and partial activation of the target structure is desirable for an optimal application of novel stimulation strategies, e.g., coordinated reset neuromodulation. Hence we designed a DBS electrode with a segmented design allowing a more selective activation of the target structure. We created a finite element model (FEM) of the electrode and analyzed the volume of tissue activated for this electrode design. The segmented electrode activated an area in a targeted manner, of which the dimension and position relative to the electrode could be controlled by adjusting the stimulation parameters for each electrode contact. According to our computational analysis, this directed stimulation might be superior with respect to the occurrence of side effects and it enables the application of coordinated reset neuromodulation under optimal conditions.

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.

Effects of the electrical double layer and dispersive tissue properties in a volume conduction model of deep brain stimulation

The aim of this study was to investigate the interaction of the electrode-tissue interface and dispersive tissue properties on waveforms used for deep brain stimulation. A finite element model with a distributed impedance electrical double layer was developed. Bulk tissue capacitance and dispersion were found to alter the voltage waveform under constant current stimulation. When the electrode was surrounded by conductive saline or white matter tissue, the electrical double layer was dominant under voltage controlled stimulation. However, as encapsulation tissue resistivity was increased, to emulate chronic stimulation, the voltage waveform approached that observed during constant current stimulation and the influence of the frequency dependent material properties again became dominant.

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.

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

A depth electrode-brain interface (EBI) is formed once electrodes are implanted into the human brain. We investigated the impact of the EBI on the crossing electric currents during both deep brain recording (DBR) and deep brain stimulation (DBS) over the acute, chronic and transitional stages post-implantation, in order to investigate and quantify the effect which changes at the EBI have on both DBR and DBS. We combined two complementary methods: (1) physiological recording of local field potentials via the implanted electrode in patients; and (2) computational simulations of an EBI model. Our depth recordings revealed that the physiological modulation of the EBI in the acute stage via brain pulsation selectively affected the crossing neural signals in a frequency-dependent manner, as the amplitude of the electrode potential was inversely correlated with that of the tremor-related oscillation, but not the beta oscillation. Computational simulations of DBS during the transitional period showed that the shielding effect of partial giant cell growth on the injected current could shape the field in an unpredictable manner. These results quantitatively demonstrated that physiological modulation of the EBI significantly affected the crossing currents in both DBR and DBS. Studying the microenvironment of the EBI may be a key step in investigating the mechanisms of DBR and DBS, as well as brain-computer interactions in general.