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

Probabilistic stimulation mapping from intra-operative thalamic deep brain stimulation data in essential tremor

Deep brain stimulation (DBS) is a therapy for Parkinson's disease (PD) and essential tremor (ET). The mechanism of action of DBS is still incompletely understood. Retrospective group analysis of intra-operative data recorded from ET patients implanted in the ventral intermediate nucleus of the thalamus (Vim) is rare. Intra-operative stimulation tests generate rich data and their use in group analysis has not yet been explored.Objective.To implement, evaluate, and apply a group analysis workflow to generate probabilistic stimulation maps (PSMs) using intra-operative stimulation data from ET patients implanted in Vim.Approach.A group-specific anatomical template was constructed based on the magnetic resonance imaging scans of 6 ET patients and 13 PD patients. Intra-operative test data (total:n= 1821) from the 6 ET patients was analyzed: patient-specific electric field simulations together with tremor assessments obtained by a wrist-based acceleration sensor were transferred to this template. Occurrence and weighted mean maps were generated. Voxels associated with symptomatic response were identified through a linear mixed model approach to form a PSM. Improvements predicted by the PSM were compared to those clinically assessed. Finally, the PSM clusters were compared to those obtained in a multicenter study using data from chronic stimulation effects in ET.Main results.Regions responsible for improvement identified on the PSM were in the posterior sub-thalamic area (PSA) and at the border between the Vim and ventro-oral nucleus of the thalamus (VO). The comparison with literature revealed a center-to-center distance of less than 5 mm and an overlap score (Dice) of 0.4 between the significant clusters. Our workflow and intra-operative test data from 6 ET-Vim patients identified effective stimulation areas in PSA and around Vim and VO, affirming existing medical literature.Significance.This study supports the potential of probabilistic analysis of intra-operative stimulation test data to reveal DBS's action mechanisms and to assist surgical planning.

Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation

Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an emerging therapy for refractory depression. Good clinical outcomes are associated with the activation of white matter adjacent to the SCC. This activation produces a signature cortical evoked potential (EP), but it is unclear which of the many pathways in the vicinity of SCC is responsible for driving this response. Individualized biophysical models were built to achieve selective engagement of two target bundles: either the forceps minor (FM) or cingulum bundle (CB). Unilateral 2 Hz stimulation was performed in seven patients with treatment-resistant depression who responded to SCC DBS, and EPs were recorded using 256-sensor scalp electroencephalography. Two distinct EPs were observed: a 120 ms symmetric response spanning both hemispheres and a 60 ms asymmetrical EP. Activation of FM correlated with the symmetrical EPs, while activation of CB was correlated with the asymmetrical EPs. These results support prior model predictions that these two pathways are predominantly activated by clinical SCC DBS and provide first evidence of a link between cortical EPs and selective fiber bundle activation.

Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS.

OBJECTIVE

The goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings.

MATERIALS AND METHODS

We used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm).

RESULTS

The recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities.

CONCLUSIONS

Subtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.

Role of the volume conductor on simulations of local field potential recordings from deep brain stimulation electrodes

OBJECTIVE

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are commonly used in research analyses, and are beginning to be used in clinical practice. Computational models of DBS LFPs provide tools for investigating the biophysics and neural synchronization that underlie LFP signals. However, technical standards for DBS LFP model parameterization remain to be established. Therefore, the goal of this study was to evaluate the role of the volume conductor (VC) model complexity on simulated LFP signals in the subthalamic nucleus (STN).

APPROACH

We created a detailed human head VC model that explicitly represented the inhomogeneity and anisotropy associated with 12 different tissue structures. This VC model represented our "gold standard" for technical detail and electrical realism. We then incrementally decreased the complexity of the VC model and quantified the impact on the simulated LFP recordings. Identical STN neural source activity was used when comparing the different VC model variants. Results Ignoring tissue anisotropy reduced the simulated LFP amplitude by ~12%, while eliminating soft tissue heterogeneity had a negligible effect on the recordings. Simplification of the VC model to consist of a single homogenous isotropic tissue medium with a conductivity of 0.215 S/m contributed an additional ~3% to the error.

SIGNIFICANCE

Highly detailed VC models do generate different results than simplified VC models. However, with errors in the range of ~15%, the use of a well-parameterized simple VC model is likely to be acceptable in most contexts for DBS LFP modeling.

Influence of charges deposited on membranes of human hyperdirect pathway axons on depolarization during subthalamic deep brain stimulation

Objective.The motor hyperdirect pathway (HDP) is a key target in the treatment of Parkinson's disease with deep brain stimulation (DBS). Biophysical models of HDP DBS have been used to explore the mechanisms of stimulation. Built upon finite element method volume conductor solutions, such models are limited by a resolution mismatch, where the volume conductor is modeled at the macro scale, while the neural elements are at the micro scale. New techniques are needed to better integrate volume conductor models with neuron models.Approach.We simulated subthalamic DBS of the human HDP using finely meshed axon models to calculate surface charge deposition on insulting membranes of nonmyelinated axons. We converted the corresponding double layer extracellular problem to a single layer problem and applied the well-conditioned charge-based boundary element fast multipole method (BEM-FMM) with unconstrained numerical spatial resolution. Commonly used simplified estimations of membrane depolarization were compared with more realistic solutions.Main result.Neither centerline potential nor estimates of axon recruitment were impacted by the estimation method used except at axon bifurcations and hemispherical terminations. Local estimates of axon polarization were often much higher at bifurcations and terminations than at any other place along the axon and terminal arbor. Local average estimates of terminal electric field are higher by 10%-20%.Significance. Biophysical models of action potential initiation in the HDP suggest that axon terminations are often the lowest threshold elements for activation. The results of this study reinforce that hypothesis and suggest that this phenomenon is even more pronounced than previously realized.

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.

Measuring and modeling the effects of vagus nerve stimulation on heart rate and laryngeal muscles

BACKGROUND

Reduced heart rate (HR) during vagus nerve stimulation (VNS) is associated with therapy for heart failure, but stimulation frequency and amplitude are limited by patient tolerance. An understanding of physiological responses to parameter adjustments would allow differential control of therapeutic and side effects. To investigate selective modulation of the physiological responses to VNS, we quantified the effects and interactions of parameter selection on two physiological outcomes: one related to therapy (reduced HR) and one related to side effects (laryngeal muscle EMG).

METHODS

We applied a broad range of stimulation parameters (mean pulse rates (MPR), intra-burst frequencies, and amplitudes) to the vagus nerve of anesthetized mice. We leveraged the in vivo recordings to parameterize and validate computational models of HR and laryngeal muscle activity across amplitudes and temporal patterns of VNS. We constructed a finite element model of excitation of fibers within the mouse cervical vagus nerve.

RESULTS

HR decreased with increased amplitude, increased MPR, and decreased intra-burst frequency. EMG increased with increased MPR. Preferential HR effects over laryngeal EMG effects required combined adjustments of amplitude and MPR. The model of HR responses highlighted contributions of ganglionic filtering to VNS-evoked changes in HR at high stimulation frequencies. Overlap in activation thresholds between small and large modeled fibers was consistent with the overlap in dynamic ranges of related physiological measures (HR and EMG).

CONCLUSION

The present study provides insights into physiological responses to VNS required for informed parameter adjustment to modulate selectively therapeutic effects and side effects.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.

Numerical investigation of the energy distribution of Low-intensity transcranial focused ultrasound neuromodulation for hippocampus

OBJECTIVE

Ultrasonic neuromodulation as a safe and non-invasive brain stimulation method that delivers a low-intensity, focused ultrasound to nervous system tissue in a targeted area of the brain. The objective of this study is to numerically investigate the ultrasound wave propagation and the energy distribution within the brain tissues using customized single element focused ultrasound transducers (SEFT), targeting the hippocampus.

METHODS

A high resolution detailed human head model with seven tissue types was constructed from magnetic resonance imaging (MRI). A full-wave finite-difference time-domain simulation platform, Sim4life, was then used to simulate a 3D non-linear ultrasound wave equation to the specific region of interest, the hippocampus. Three customized SEFT were used to test the effect of transducer positions, and another customized transducer was used to compare the sensitivity effect on heterogeneous and homogeneous brain models. Finally, the sensitivity and performance of low intensity focusing ultrasound stimulation were evaluated.

RESULTS

An optimized application of SEFT was customized to deliver 100 W/m² intensity of energy deposition at the hippocampus region. About 85.65% of the generated volume beam was delivered to the targeted hippocampus region and the beam overlap parameter was affected by different transducer positions. Deflection angle changes of SEFT at the range of ± 5% did not have a significant effect on energy delivery and position displacement. Only 0.5% of peak pressure change was observed between heterogeneous and homogeneous brain models. The sensitivity analysis also showed that the sound speed is the most influential acoustic parameter.

SIGNIFICANCE

This study demonstrated that ultrasound neuromodulation targeting the depth brain tissue of the hippocampus could be a potential and promising alternative method to some non-acoustic brain stimulation modalities. In the numerical study of ultrasound brain stimulations, ultrasound parameters and the brain model need to be properly determined to simulate the ultrasonic neuromodulations.

The Listening Zone of Human Electrocorticographic Field Potential Recordings

Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.

Deep brain stimulation electrode modeling in rats

Deep Brain Stimulation (DBS) is an efficacious treatment option for an increasing range of brain disorders. To enhance our knowledge about the mechanisms of action of DBS and to probe novel targets, basic research in animal models with DBS is an essential research base. Beyond nonhuman primate, pig, and mouse models, the rat is a widely used animal model for probing DBS effects in basic research. Reconstructing DBS electrode placement after surgery is crucial to associate observed effects with modulating a specific target structure. Post-mortem histology is a commonly used method for reconstructing the electrode location. In humans, however, neuroimaging-based electrode localizations have become established. For this reason, we adapt the open-source software pipeline Lead-DBS for DBS electrode localizations from humans to the rat model. We validate our localization results by inter-rater concordance and a comparison with the conventional histological method. Finally, using the open-source software pipeline OSS-DBS, we demonstrate the subject-specific simulation of the VTA and the activation of axon models aligned to pathways representing neuronal fibers, also known as the pathway activation model. Both activation models yield a characterization of the impact of DBS on the target area. Our results suggest that the proposed neuroimaging-based method can precisely localize DBS electrode placements that are essentially rater-independent and yield results comparable to the histological gold standard. The advantages of neuroimaging-based electrode localizations are the possibility of acquiring them in vivo and combining electrode reconstructions with advanced imaging metrics, such as those obtained from diffusion or functional magnetic resonance imaging (MRI). This paper introduces a freely available open-source pipeline for DBS electrode reconstructions in rats. The presented initial validation results are promising.

Directional Deep Brain Stimulation in the Treatment of Parkinson's Disease

Deep brain stimulation (DBS) is a treatment modality that has been shown to improve the clinical outcomes of individuals with movement disorders, including Parkinson's disease. Directional DBS represents an advance in the field that allows clinicians to better modulate the electrical stimulation to increase therapeutic gains while minimizing side effects. In this review, we summarize the principles of directional DBS, including available technologies and stimulation paradigms, and examine the growing clinical study data with respect to its use in Parkinson's disease.

Comparison of methodologies for modeling directional deep brain stimulation electrodes

Deep brain stimulation (DBS) is an established clinical therapy, and directional DBS electrode designs are now commonly used in clinical practice. Directional DBS leads have the ability to increase the therapeutic window of stimulation, but they also increase the complexity of clinical programming. Therefore, computational models of DBS have become available in clinical software tools that are designed to assist in the identification of therapeutic settings. However, the details of how the DBS model is implemented can influence the predictions of the software. The goal of this study was to compare different methods for representing directional DBS electrodes within finite element volume conductor (VC) models. We evaluated 15 different DBS VC model variants and quantified how their differences influenced estimates on the spatial extent of axonal activation from DBS. Each DBS VC model included the same representation of the brain and head, but the details of the current source and electrode contact were different for each model variant. The more complex VC models explicitly represented the DBS electrode contacts, while the more simple VC models used boundary condition approximations. The more complex VC models required 2-3 times longer to mesh, build, and solve for the DBS voltage distribution than the more simple VC models. Differences in individual axonal activation thresholds across the VC model variants were substantial (-24% to +47%). However, when comparing total activation of an axon population, or estimates of an activation volume, the differences between model variants decreased (-7% to +8%). Nonetheless, the technical details of how the electrode contact and current source are represented in the DBS VC model can directly affect estimates of the voltage distribution and electric field in the brain tissue.

Characterization and localization of upper and lower extremity motor improvements in STN DBS for Parkinson's disease

INTRODUCTION

Subthalamic deep brain stimulation (STN DBS) may have differential effects on cardinal motor signs of Parkinson's disease (PD) in the upper and lower extremities. In addition, sites of maximally effective DBS for each sign and extremity may be distinct. Our study seeks to elucidate these structure-function relationships.

METHODS

We applied an ordinary least squares linear regression model to measure motor effects of STN DBS on upper (UE) and lower (LE) extremity tremor, rigidity, and bradykinesia. We then applied an atlas-independent electrical-field model to identify sites of maximally effective stimulation for each sign and each extremity. Distances between sites and statistical power to resolve differences were calculated.

RESULTS

In our study population (n = 78 patients), STN DBS improved all cardinal motor signs (β = 0.64, p < .05). Improvement magnitudes were tremor > rigidity > bradykinesia. Effects of STN DBS on UE versus LE signs were statistically equal for tremor and bradykinesia, but greater for UE rigidity than LE rigidity (β = 0.19, p < .05). UE maximal-effect loci were lateral, anterior, and dorsal to LE loci, but were not statistically resolved, despite sufficient statistical power to resolve differences of ≤0.48 mm (p < .05) between maximally effective loci of stimulation.

CONCLUSION

STN DBS produces differential effects on UE and LE rigidity, but not for tremor or bradykinesia. This finding is not explained by distinct UE and LE loci of maximally effective stimulation. Instead, we hypothesize that downstream effects of STN DBS on motor networks and limb biomechanics are responsible for observed differences in UE and LE responses.

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.

Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes

Objective. To formulate, validate, and apply an alternative to the finite element method (FEM) high-resolution modeling technique for electrical brain stimulation-the boundary element fast multipole method (BEM-FMM). To include practical electrode models for both surface and embedded electrodes.Approach. Integral equations of the boundary element method in terms of surface charge density are combined with a general-purpose fast multipole method and are expanded for voltage, shunt, current, and floating electrodes. The solution of coupled and properly weighted/preconditioned integral equations is accompanied by enforcing global conservation laws: charge conservation law and Kirchhoff's current law.Main results.A sub-percent accuracy is reported as compared to the analytical solutions and simple validation geometries. Comparison to FEM considering realistic head models resulted in relative differences of the electric field magnitude in the range of 3%-6% or less. Quantities that contain higher order spatial derivatives, such as the activating function, are determined with a higher accuracy and a faster speed as compared to the FEM. The method can be easily combined with existing head modeling pipelines such as headreco or mri2mesh.Significance.The BEM-FMM does not rely on a volumetric mesh and is therefore particularly suitable for modeling some mesoscale problems with submillimeter (and possibly finer) resolution with high accuracy at moderate computational cost. Utilizing Helmholtz reciprocity principle makes it possible to expand the method to a solution of EEG forward problems with a very large number of cortical dipoles.

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

OBJECTIVE

Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

METHODS

Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

RESULTS

Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

CONCLUSIONS

Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

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.

Atlas-independent, N-of-1 tissue activation modeling to map optimal regions of subthalamic deep brain stimulation for Parkinson disease

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Neuroanatomical variations among patients are obscured in atlas-based VTA modeling.

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N-of-1 neuroanatomical and VTA modeling enables patient-level precision.

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Mean optimal stimulation is dorsomedial to the STN, near its posterior half.

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Individual VTAs deviate from optimal stimulation sites to varying degrees.

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Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap.

Background

Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement.

Methods

The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model.

Results

Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA.

Conclusion

Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.

In silico Accuracy and Energy Efficiency of Two Steering Paradigms in Directional Deep Brain Stimulation

Background: In Deep Brain Stimulation (DBS), stimulation field steering is used to achieve stimulation spatial specificity, which is critical to obtain clinical benefits and avoid side effects. Multiple Independent Current Control (MICC) and Interleaving/Multi Stim Set (Interleaving/MSS) are two stimulation field steering paradigms in commercially available DBS systems. This work investigates the stimulation field steering accuracy and energy efficiency of these two paradigms in directional DBS.

Methods: Volumes of Tissue Activated (VTAs) were generated in silico using pulse widths of 60 μs and five pulse amplitude fractionalizations intended to steer the VTAs radially in 12° steps. For each fractionalization, VTAs were generated with nine pre-defined target radii. Stimulation field steering accuracy was assessed based on the VTAs rotation angle. Energy efficiency was inferred from current draw from battery values, which were calculated based on the pulse amplitudes needed to generate and steer the VTAs, as well as electrode impedance measurements of clinically implanted directional leads.

Results: For radial steering, MICC needed a single VTA. In contrast, Interleaving/MSS required the generation of two VTAs, whose union and intersection created an Interleaving/MSS VTA and an Intersection VTA, respectively. MICC VTAs were 6.8 (−3.2–11.8)% larger than Interleaving/MSS VTAs. The Intersection VTAs accounted for 26.2 (16.0–32.8)% of Interleaving/MSS VTAs and were exposed to a higher stimulation frequency. For all VTA radius-fractionalization combinations, steering accuracy was 7.0 (4.5–10.5)° for MICC and 24.0 (9.0–25.3)° for Interleaving/MSS. Pulse amplitudes were 16.1 (9.2–28.6)% lower for MICC than for Interleaving/MSS, leading to a 45.9 (18.8–72.6)% lower current draw from battery for MICC.

Conclusions: The results of this work show that in silico, MICC achieves a significantly better stimulation field steering accuracy and has a significantly higher energy efficiency than Interleaving/MSS. Although direct evidence still needs to be generated to translate the results of this work to clinical practice, clinical outcomes may profit from the better stimulation field steering accuracy of MICC and longevity of DBS systems may profit from its higher energy efficiency.

Steering the Volume of Tissue Activated With a Directional Deep Brain Stimulation Lead in the Globus Pallidus Pars Interna: A Modeling Study With Heterogeneous Tissue Properties

Objective: To study the effect of directional deep brain stimulation (DBS) electrode configuration and vertical electrode spacing on the volume of tissue activated (VTA) in the globus pallidus, pars interna (GPi). Background: Directional DBS leads may allow clinicians to precisely direct current fields to different functional networks within traditionally targeted brain areas. Modeling the shape and size of the VTA for various monopolar or bipolar configurations can inform clinical programming strategies for GPi DBS. However, many computational models of VTA are limited by assuming tissue homogeneity. Methods: We generated a multimodal image-based detailed anatomical (MIDA) computational model with a directional DBS lead (1.5 mm or 0.5 mm vertical electrode spacing) placed with segmented contact 2 at the ventral posterolateral "sensorimotor" region of the GPi. The effect of tissue heterogeneity was examined by replacing the MIDA tissues with a homogeneous tissue of conductance 0.3 S/m. DBS pulses (amplitude: 1 mA, pulse width: 60 μs, frequency: 130 Hz) were used to produce VTAs. The following DBS contact configurations were tested: single-segment monopole (2B-/Case+), two-segment monopole (2A-/2B-/Case+ and 2B-/3B-/Case+), ring monopole (2A-/2B-/2C-/Case+), one-cathode three-anode bipole (2B-/3A+/3B+/3C+), three-cathode three-anode bipole (2A-/2B-/2C-/3A+/3B+/3C+). Additionally, certain vertical configurations were repeated with 2 mA current amplitude. Results: Using a heterogeneous tissue model affected both the size and shape of the VTA in GPi. Electrodes with both 0.5 mm and 1.5 mm vertical spacing (1 mA) modeling showed that the single segment monopolar VTA was entirely contained within the GPi when the active electrode is placed at the posterolateral "sensorimotor" GPi. Two segments in a same ring and ring settings, however, produced VTAs outside of the GPi border that spread into adjacent white matter pathways, e.g., optic tract and internal capsule. Both stacked monopolar settings and vertical bipolar settings allowed activation of structures dorsal to the GPi in addition to the GPi. Modeling of the stacked monopolar settings with the DBS lead with 0.5 mm vertical electrode spacing further restricted VTAs within the GPi, but the VTA volumes were smaller compared to the equivalent settings of 1.5 mm spacing.

Deep brain stimulation of terminating axons

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic region is an established treatment for the motor symptoms of Parkinson's disease. Several types of neural elements reside in the subthalamic region, including subthalamic nucleus (STN) neurons, fibers of passage, and terminating afferents. Recent studies suggest that direct activation of a specific population of subthalamic afferents, known as the hyperdirect pathway, may be responsible for some of the therapeutic effects of subthalamic DBS.

OBJECTIVE

The goal of this study was to quantify how axon termination affects neural excitability from DBS. We evaluated how adjusting different stimulation parameters influenced the relative excitability of terminating axons (TAs) compared to fibers of passage (FOPs).

METHODS

We used finite element electric field models of DBS, coupled to multi-compartment cable models of axons, to calculate activation thresholds for populations of TAs and FOPs. These generalized models were used to evaluate the response to anodic vs. cathodic stimulation, with short vs. long stimulus pulses.

RESULTS

Terminating axons generally exhibited lower thresholds than fibers of passage across all tested parameters. Short pulse widths accentuated the relative excitability of TAs over FOPs.

CONCLUSION(S)

Our computational results demonstrate a hyperexcitability of terminating axons to DBS that is robust to variation in the stimulation parameters, as well as the axon model parameters.

Localization of motor and verbal fluency effects in subthalamic DBS for Parkinson's disease

INTRODUCTION

Subthalamic nucleus deep brain stimulation (STN DBS) improves cardinal motor symptoms of Parkinson's disease (PD) but can worsen verbal fluency (VF). An optimal site of stimulation for overall motor improvement has been previously identified using an atlas-independent, fully individualized, field-modeling approach. This study examines if cardinal motor components (bradykinesia, tremor, and rigidity) share this identified optimal improvement site and if there is co-localization with a site that worsens VF.

METHODS

An atlas-independent, field-modeling approach was used to identify sites of maximal STN DBS effect on overall and cardinal motor symptoms and VF in 60 patients. Anatomic coordinates were referenced to the STN midpoint. Symptom severity was assessed with the MDS-UPDRS part III and established VF scales.

RESULTS

Sites for improved bradykinesia and rigidity co-localized with each other and the overall part III site (0.09 mm lateral, 0.93 mm posterior, 1.75 mm dorsal). The optimal site for tremor was posterior to this site (0.10 mm lateral, 1.40 mm posterior, 1.93 mm dorsal). Semantic and phonemic VF sites were indistinguishable and co-localized medial to the motor sites (0.32 mm medial, 1.18 mm posterior, 1.74 mm dorsal).

CONCLUSION

This study identifies statistically distinct, maximally effective stimulation sites for tremor improvement, VF worsening, and overall and other cardinal motor improvements in STN DBS. Current electrode sizes and voltage settings stimulate all of these sites simultaneously. However, future targeted lead placement and focused directional stimulation may avoid VF worsening while maintaining motor improvements in STN DBS.

Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus

Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the challenge of misalignment between current sources and mesh nodes in a finite-element or resistor-network method volume conduction model needs to be further investigated. Therefore, using a previously published and validated multi-scale model of the hippocampus, the authors have formulated an algorithm for LFP estimation, and by extension, bidirectional communication between discretized and numerically solved volume conduction models and biologically detailed neural circuit models constructed in NEURON. Development of this algorithm required that we assess meshes of (i) unstructured tetrahedral and grid-based hexahedral geometries as well as (ii) differing approaches for managing the spatial misalignment of current sources and mesh nodes. The resulting algorithm is validated through the comparison of Admittance Method predicted evoked potentials with analytically estimated LFPs. Establishing this method is a critical step toward closed-loop integration of volume conductor and NEURON models that could lead to substantial improvement of the predictive power of multi-scale stimulation models of cortical tissue. These models may be used to deepen our understanding of hippocampal pathologies and the identification of efficacious electroceutical treatments.

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

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

Modeling of Electric Fields in Individual Imaging Atlas for Capsular Threshold Prediction of Deep Brain Stimulation in Parkinson's Disease: A Pilot Study

Background: Modeling of deep brain stimulation electric fields and anatomy-based software might improve post-operative management of patients with Parkinson's disease (PD) who have benefitted from subthalamic nucleus deep brain stimulation (STN-DBS).

Objective: We compared clinical and software-guided determination of the thresholds for current diffusion to the pyramidal tract, the most frequent limiting side effect in post-operative management of STN-DBS PD patients.

Methods: We assessed monopolar reviews in 16 consecutive STN-DBS PD patients and retrospectively compared clinical capsular thresholds, which had been assessed according to standard clinical practice, to those predicted by volume of tissue activated (VTA) model software. All the modeling steps were performed blinded from patients' clinical evaluations.

Results: At the group level, we found a significant correlation (p = 0.0001) when performing statistical analysis on the z-scored capsular thresholds, but with a low regression coefficient (r = 0.2445). When considering intra-patient analysis, we found significant correlations (p < 0.05) between capsular threshold as modeled with the software and capsular threshold as determined clinically in five patients (31.2%).

Conclusions: In this pilot study, the VTA model software was of limited assistance in identifying capsular thresholds for the whole cohort due to a large inter-patient variability. Clinical testing remains the gold standard in selecting stimulation parameters for STN-DBS in PD.

Effect of Biophysical Model Complexity on Predictions of Volume of Tissue Activated (VTA) during Deep Brain Stimulation

Deep brain stimulation (DBS) has evolved to an important treatment for several drug-resistant neurological and psychiatric disorders, such as epilepsy, Parkinson's disease, essential tremor and dystonia. Despite general effectiveness of DBS, however, its mechanisms of action are not completely understood. Simulations are commonly used to predict the volume of tissue activated (VTA) around DBS electrodes, which in turn helps interpreting clinical outcomes and understand therapeutic mechanisms. Computational models are commonly used to visualize the extend of volume of activated tissue (VTA) for different stimulation schemes, which in turn helps interpreting and understanding the outcomes. The degree of model complexity, however, can affect the predicted VTA. In this work we investigate the effect of volume conductor model complexity on the predicted VTA, when the VTA is estimated from activation function field metrics. Our results can help clinicians to decide what level of model complexity is suitable for their specific need.

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.

Evaluation of Epistemic Uncertainties for Bipolar Deep Brain Stimulation in Rodent Models

Rodent models are widely used in research on deep brain stimulation (DBS) for testing hypotheses of the action mechanism. However, differences in anatomy and technology for DBS in humans and rodents might lead to a non-identical effect on the neural activity. Particularly, strong deviations can be introduced by epistemic uncertainties related to the electrode implantation. In this study, the influence of encapsulation layer properties and implantation precision on axonal activation is quantified using polynomial chaos expansion. In order to improve the efficiency of computations, three truncation methods for the signal frequency spectrum are proposed and evaluated, allowing a tenfold speedup in the particular study. The results of uncertainty quantification on the axonal activity inside the targeted nucleus suggest a major effect of the encapsulation thickness, while the precision of implantation is found to be crucial due to possible direct activation in neighboring structures.

Feasibility of Interferential and Pulsed Transcranial Electrical Stimulation for Neuromodulation at the Human Scale

OBJECTIVES

Transcranial electrical stimulation (tES) is a promising tool for modulating neural activity, but tES has poor penetrability and spatiotemporal resolution compared to invasive techniques like deep brain stimulation (DBS). Interferential strategies for alternating-current stimulation (IF-tACS) and pulsed/intersectional strategies for transcranial direct-current stimulation (IS-tDCS) address some of the limitations of tES, but the comparative advantages and disadvantages of these new techniques is not well understood. This study's objective was to evaluate the suprathreshold and subthreshold membrane dynamics of neurons in response to IF-tACS and IS-tDCS.

MATERIALS AND METHODS

We analyzed the biophysics of IF-tACS and IS-tDCS using a bioelectric field model of tES. Neural responses were quantified for suprathreshold generation of action potentials in axons and for subthreshold modulation of membrane dynamics in spiking pyramidal neurons.

RESULTS

IF-tACS and IS-tDCS could not directly activate axons at or below 10 mA, but within this current range, these fields were able to modulate, albeit indirectly, spiking activity in the neuron model. IF-tACS facilitated phase synchronization similar to tACS, and IS-tDCS enhanced and suppressed spiking activity similar to tDCS; however, in either case, the modulatory effects of these fields were less potent than their standard counterparts at a matched field intensity. Moreover, neither IF-tACS nor IS-tDCS improved the spatial selectivity of their parent strategies.

CONCLUSIONS

Enhancing the spatiotemporal precision and penetrability of tES with interferential and intersectional strategies is possible at the human scale. However, IF-tACS or IS-tDCS will likely require spatial multiplexing with multiple simultaneous sources to counteract their reduced potency, compared to standard techniques, to maintain stimulation currents at tolerable levels.

ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling

Advances in computation and neuronal modeling have enabled the study of entire neural tissue systems with an impressive degree of biological realism. These efforts have focused largely on modeling dendrites and somas while largely neglecting axons. The need for biologically realistic explicit axonal models is particularly clear for applications involving clinical and therapeutic electrical stimulation because axons are generally more excitable than other neuroanatomical subunits. While many modeling efforts can rely on existing repositories of reconstructed dendritic/somatic morphologies to study real cells or to estimate parameters for a generative model, such datasets for axons are scarce and incomplete. Those that do exist may still be insufficient to build accurate models because the increased geometric variability of axons demands a proportional increase in data. To address this need, a Ruled-Optimum Ordered Tree System (ROOTS) was developed that extends the capability of neuronal morphology generative methods to include highly branched cortical axon terminal arbors. Further, this study presents and explores a clear use-case for such models in the prediction of cortical tissue response to externally applied electric fields. The results presented herein comprise (i) a quantitative and qualitative analysis of the generative algorithm proposed, (ii) a comparison of generated fibers with those observed in histological studies, (iii) a study of the requisite spatial and morphological complexity of axonal arbors for accurate prediction of neuronal response to extracellular electrical stimulation, and (iv) an extracellular electrical stimulation strength-duration analysis to explore probable thresholds of excitation of the dentate perforant path under controlled conditions. ROOTS demonstrates a superior ability to capture biological realism in model fibers, allowing improved accuracy in predicting the impact that microscale structures and branching patterns have on spatiotemporal patterns of activity in the presence of extracellular electric fields.

Patient Semi-specific Computational Modeling of Electromagnetic Stimulation Applied to Neuroprotective Treatments in Acute Ischemic Stroke

Neuroprotective effects of pulsed electromagnetic fields (PEMFs) have been demonstrated both in vivo and in vitro. Moreover, preliminary clinical studies have been conducted and suggested PEMFs as a possible alternative therapy to treat acute ischemic stroke. In this work, we show that it's possible to build-up a patient semi-specific head model, where the 3D reconstruction of the ischemic lesion of the patient under treatment is inserted in the head of the human body model "Duke" (v.1.0, Zurich MedTech AG). The semi-specific model will be used in the randomized, placebo-controlled, double-blind study currently ongoing. Three patients were modelled and simulated, and results showed that each ischemic lesion experiences a magnetic flux density field comparable to the one for which biological effects have been attested. Such a kind of dosimetric analysis reveals a reliable tool to assess the correlation between levels of exposure and the beneficial effect. Thus, once the on-going double blind study is complete it will prove if PEMFs treatment triggers a clinical effect, and we will then be able to characterize a dose-response curve with the methodology arranged in this study.

Biophysical reconstruction of the signal conduction underlying short-latency cortical evoked potentials generated by subthalamic deep brain stimulation

OBJECTIVE

Direct activation of the hyperdirect (HD) pathway has been linked to therapeutic benefit from subthalamic deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD). We sought to quantify the axonal conduction biophysics of corticofugal axons directly stimulated by subthalamic DBS and reconcile those findings with short-latency cortical evoked potential (EP) results.

METHODS

We used a detailed computational model of human subthalamic DBS to quantify axonal activation and conduction. Signal propagation to cortex was evaluated for medium (5.7 µm), large (10.0 µm), and exceptionally large (15.0 µm) diameter corticofugal axons associated with either internal capsule (IC) fibers of passage or the HD pathway. We then compared the modeling results to human cortical EP measurements that have described an exceptionally fast component (EP0) occurring ~1 ms after the stimulus pulse, a fast component (EP1) at ~3 ms, and a slower component (EP2) at ~5 ms.

RESULTS

Subthalamic stimulation of the HD pathway with large and medium diameter axons propagated action potentials to cortex with timings that coincide with the EP1 and EP2 signals, respectively. Only direct activation of exceptionally large diameter fibers in the IC generated signals that could approach the EP0 timing. However, the action potential biophysics do not generally support the existence of a cortical EP less than 1.5 ms after DBS onset.

CONCLUSIONS

The EP1 and EP2 signals can be biophysically linked to antidromic activation of the HD pathway.

SIGNIFICANCE

Theoretical reconstruction of cortical EPs from subthalamic DBS demonstrate a convergence of anatomical, biophysical, and electrophysiological results.

Comparing Current Steering Technologies for Directional Deep Brain Stimulation Using a Computational Model That Incorporates Heterogeneous Tissue Properties

Objective

A computational model that accounts for heterogeneous tissue properties was used to compare multiple independent current control (MICC), multi‐stim set (MSS), and concurrent activation (co‐activation) current steering technologies utilized in deep brain stimulation (DBS) on volume of tissue activated (VTA) and power consumption.

Methods

A computational model was implemented in Sim4Life v4.0 with the multimodal image‐based detailed anatomical (MIDA) model, which accounts for heterogeneous tissue properties. A segmented DBS lead placed in the subthalamic nucleus (STN). Three milliamperes of current (with a 90 μs pseudo‐biphasic waveform) was distributed between two electrodes with various current splits. The laterality, directional accuracy, volume, and shape of the VTAs using MICC, MSS and co‐activation, and their power consumption were computed and compared.

Results

MICC, MSS, and coactivation resulted in less laterality of steering than single‐segment activation. Both MICC and MSS show directional inaccuracy (more pronounced with MSS) during radial current steering. Co‐activation showed greater directional accuracy than MICC and MSS at centerline between the two activated electrodes. MSS VTA volume was smaller and more compact with less current spread outside the active electrode plane than MICC VTA. There was no consistent pattern of power drain between MSS and MICC, but electrode co‐activation always used less power than either fractionating paradigm.

Conclusion

While current fractionalization technologies can achieve current steering between two segmented electrodes, this study shows that there are important limitations in accuracy and focus of tissue activation when tissue heterogeneity is accounted for.

A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation

OBJECTIVE

Detailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. Predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results.

METHODS

We developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations.

RESULTS

The DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%.

CONCLUSIONS

DF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.

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.

Quantifying the axonal pathways directly stimulated in therapeutic subcallosal cingulate deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is an emerging experimental therapy for treatment-resistant depression. New developments in SCC DBS surgical targeting are focused on identifying specific axonal pathways for stimulation that are estimated from patient-specific computational models. This connectomic-based biophysical modeling strategy has proven successful in improving the clinical response to SCC DBS therapy, but the DBS models used to date have been relatively simplistic, limiting the precision of the pathway activation estimates. Therefore, we used the most detailed patient-specific foundation for DBS modeling currently available (i.e., field-cable modeling) to evaluate SCC DBS in our most recent cohort of six subjects, all of which were responders to the therapy. We quantified activation of four major pathways in the SCC region: forceps minor (FM), cingulum bundle (CB), uncinate fasciculus (UF), and subcortical connections between the frontal pole and the thalamus or ventral striatum (FP). We then used the percentage of activated axons in each pathway as regressors in a linear model to predict the time it took patients to reach a stable response, or TSR. Our analysis suggests that stimulation of the left and right CBs, as well as FM are the most likely therapeutic targets for SCC DBS. In addition, the right CB alone predicted 84% of the variation in the TSR, and the correlation was positive, suggesting that activation of the right CB beyond a critical percentage may actually protract the recovery process.

ESM-CT: a precise method for localization of DBS electrodes in CT images

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus produces variable effects in Parkinson disease. Variation may result from different electrode positions relative to target. Thus, precise electrode localization is crucial when investigating DBS effects.

NEW METHOD

We developed a semi-automated method, Electrode Shaft Modeling in CT images (ESM-CT) to reconstruct DBS lead trajectories and contact locations. We evaluated methodological sensitivity to operator-dependent steps, robustness to image resampling, and test-retest replicability. ESM-CT was applied in 56 patients to study electrode position change (and relation to time between scans, postoperative subdural air volume, and head tilt during acquisition) between images acquired immediately post-implantation (DBS-CT) and months later (DEL-CT).

RESULTS

Electrode tip localization was robust to image resampling and replicable to within ∼ 0.2 mm on test-retest comparisons. Systematic electrode displacement occurred rostral-ventral-lateral between DBS-CT and DEL-CT scans. Head angle was a major explanatory factor (p < 0.001,Pearson's r = 0.46, both sides) and volume of subdural air weakly predicted electrode displacement (p = 0.02,r = 0.29:p = 0.1,r = 0.25 for left:right). Modeled shaft curvature was slightly greater in DEL-CT. Magnitude of displacement and degree of curvature were independent of elapsed time between scans.

COMPARISON WITH EXISTING METHODS

Comparison of ESM-CT against two existing methods revealed systematic differences in one coordinate (1 ± 0.3 mm,p < 0.001) for one method and in three coordinates for another method (x:0.1 ± 0.1 mm, y:0.4 ± 0.2 mm, z:0.4 ± 0.2 mm, p < 10-10). Within-method coordinate variability across participants is similar.

CONCLUSION

We describe a robust and precise method for CT DBS contact localization. Application revealed that acquisition head angle significantly impacts electrode position. DBS localization schemes should account for head angle.

Clinical deep brain stimulation strategies for orientation-selective pathway activation

OBJECTIVE

This study investigated stimulation strategies to increase the selectivity of activating axonal pathways within the brain based on their orientations relative to clinical deep brain stimulation (DBS) lead implants.

APPROACH

Previous work has shown how varying electrode shape and controlling the primary electric field direction through preclinical electrode arrays can produce orientation-selective axonal stimulation. Here, we significantly extend those results using computational models to evaluate the degree to which clinical DBS leads can direct stimulus-induced electric fields and generate orientation-selective activation of fiber pathways in the brain. Orientation-selective pulse paradigms were evaluated in conceptual models and in patient-specific models of subthalamic nucleus (STN)-DBS for treating Parkinson's disease.

MAIN RESULTS

Single-contact monopolar or two-contact bipolar stimulation through clinical DBS leads with cylindrical electrodes primarily activated axons orientated parallel to the lead. Conversely, multi-contact monopolar stimulation with a cathode-leading pulse waveform selectively activated axons perpendicular to the DBS lead. Clinical DBS leads with segmented rows of electrodes and a single current source provided additional angular resolution for activating axons oriented 0°, ±22.5°, ±45°, ±67.5°, or 90° relative to the lead shaft. Employing multiple independent current sources to deliver unequal amounts of current through these leads further increased the angular resolution of activation relative to the lead shaft. The patient-specific models indicated that multi-contact cathode configurations, which are rarely used in clinical practice, could increase activation of the hyperdirect pathway collaterals projecting into STN (a putative therapeutic target), while minimizing direct activation of the corticospinal tract of internal capsule, which can elicit sensorimotor side-effects when stimulated.

SIGNIFICANCE

When combined with patient-specific tissue anisotropy and patient-specific anatomical morphologies of neural pathways responsible for therapy and side effects, orientation-selective DBS approaches show potential to significantly improve clinical outcomes of DBS therapy for a range of existing and investigational clinical indications.

Modeling Current Sources for Neural Stimulation in COMSOL

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

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the "gold standard" for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.

Action potential initiation, propagation, and cortical invasion in the hyperdirect pathway during subthalamic deep brain stimulation

BACKGROUND

High frequency (∼130 Hz) deep brain stimulation (DBS) of the subthalamic region is an established clinical therapy for the treatment of late stage Parkinson's disease (PD). Direct modulation of the hyperdirect pathway, defined as cortical layer V pyramidal neurons that send an axon collateral to the subthalamic nucleus (STN), has emerged as a possible component of the therapeutic mechanisms. However, numerous questions remain to be addressed on the basic biophysics of hyperdirect pathway stimulation.

OBJECTIVE

Quantify action potential (AP) initiation, propagation, and cortical invasion in hyperdirect neurons during subthalamic stimulation.

METHODS

We developed an anatomically and electrically detailed computational model of hyperdirect neuron stimulation with explicit representation of the stimulating electric field, axonal response, AP propagation, and synaptic transmission.

RESULTS

We found robust AP propagation throughout the complex axonal arbor of the hyperdirect neuron. Even at therapeutic DBS frequencies, stimulation induced APs could reach all of the intracortical axon terminals with ∼100% fidelity. The functional result of this high frequency axonal driving of the thousands of synaptic connections made by each directly stimulated hyperdirect neuron is a profound synaptic suppression that would effectively disconnect the neuron from the cortical circuitry.

CONCLUSIONS

The synaptic suppression hypothesis integrates the fundamental biophysics of electrical stimulation, axonal transmission, and synaptic physiology to explain a generic mechanism of DBS.

Characterization of the stimulus waveforms generated by implantable pulse generators for deep brain stimulation

OBJECTIVE

To determine the circuit elements required to theoretically describe the stimulus waveforms generated by an implantable pulse generator (IPG) during clinical deep brain stimulation (DBS).

METHODS

We experimentally interrogated the Medtronic Activa PC DBS IPG and defined an equivalent circuit model that accurately captured the output of the IPG. We then compared the detailed circuit model of the clinical stimulus waveforms to simplified representations commonly used in computational models of DBS. We quantified the errors associated with these simplifications using theoretical activation thresholds of myelinated axons in response to DBS.

RESULTS

We found that the detailed IPG model generated substantial differences in activation thresholds compared to simplified models. These differences were largest for bipolar stimulation with long pulse widths. Average errors were ∼3 to 24% for voltage-controlled stimulation and ∼2 to 11% for current-controlled stimulation.

CONCLUSIONS

Our results demonstrate the importance of including basic circuit elements (e.g. blocking capacitors, lead wire resistance, electrode capacitance) in model analysis of DBS.

SIGNIFICANCE

Computational models of DBS are now commonly used in academic research, industrial technology development, and in the selection of clinical stimulation parameters. Incorporating a realistic representation of the IPG output is necessary to improve the accuracy and utility of these clinical and scientific tools.

Model-Based Analysis of Electrode Placement and Pulse Amplitude for Hippocampal Stimulation

Objective:

The ideal form of a neural-interfacing device is highly dependent upon the anatomy of the region with which it is meant to interface. Multiple-electrode arrays provide a system which can be adapted to various neural geometries. Computational models of stimulating systems have proven useful for evaluating electrode placement and stimulation protocols, but have yet to be adequately adapted to the unique features of the hippocampus.

Methods:

As an approach to understanding potential memory restorative devices, an Admittance Method-NEURON model was constructed to predict the direct and synaptic response of a region of the rat dentate gyrus to electrical stimulation of the perforant path.

Results:

A validation of estimated local field potentials against experimental recordings is performed and results of a bi-linear electrode placement and stimulation amplitude parameter search are presented.

Conclusion:

The parametric analysis presented herein suggests that stimulating electrodes placed between the lateral and medial perforant path, near the crest of the dentate gyrus, yield a larger relative population response to given stimuli.

Significance:

Beyond deepening understanding of the hippocampal tissue system, establishment of this model provides a method to evaluate candidate stimulating devices and protocols.

Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson's disease

OBJECTIVES

Firstly, to identify subthalamic region stimulation clusters that predict maximum improvement in rigidity, bradykinesia and tremor, or emergence of side-effects; and secondly, to map-out the cortical fingerprint, mediated by the hyperdirect pathways which predict maximum efficacy.

METHODS

High angular resolution diffusion imaging in twenty patients with advanced Parkinson's disease was acquired prior to bilateral subthalamic nucleus deep brain stimulation. All contacts were screened one-year from surgery for efficacy and side-effects at different amplitudes. Voxel-based statistical analysis of volumes of tissue activated models was used to identify significant treatment clusters. Probabilistic tractography was employed to identify cortical connectivity patterns associated with treatment efficacy.

RESULTS

All patients responded well to treatment (46% mean improvement off medication UPDRS-III [p < 0.0001]) without significant adverse events. Cluster corresponding to maximum improvement in tremor was in the posterior, superior and lateral portion of the nucleus. Clusters corresponding to improvement in bradykinesia and rigidity were nearer the superior border in a further medial and posterior location. The rigidity cluster extended beyond the superior border to the area of the zona incerta and Forel-H₂ field. When the clusters where averaged, the coordinates of the area with maximum overall efficacy was X = -10(-9.5), Y = -13(-1) and Z = -7(-3) in MNI(AC-PC) space. Cortical connectivity to primary motor area was predictive of higher improvement in tremor; whilst that to supplementary motor area was predictive of improvement in bradykinesia and rigidity; and connectivity to prefrontal cortex was predictive of improvement in rigidity.

INTERPRETATION

These findings support the presence of overlapping stimulation sites within the subthalamic nucleus and its superior border, with different cortical connectivity patterns, associated with maximum improvement in tremor, rigidity and bradykinesia.

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.