Co-registration of stereotactic MRI and isofieldlines during deep brain stimulation

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Neurocapillary-Modulation

OBJECTIVES

We consider two consequences of brain capillary ultrastructure in neuromodulation. First, blood-brain barrier (BBB) polarization as a consequence of current crossing between interstitial space and the blood. Second, interstitial current flow distortion around capillaries impacting neuronal stimulation.

MATERIALS AND METHODS

We developed computational models of BBB ultrastructure morphologies to first assess electric field amplification at the BBB (principle 1) and neuron polarization amplification by the presence of capillaries (principle 2). We adapt neuron cable theory to develop an analytical solution for maximum BBB polarization sensitivity.

RESULTS

Electrical current crosses between the brain parenchyma (interstitial space) and capillaries, producing BBB electric fields (EBBB) that are >400x of the average parenchyma electric field (ĒBRAIN), which in turn modulates transport across the BBB. Specifically, for a BBB space constant (λBBB) and wall thickness (dth-BBB), the analytical solution for maximal BBB electric field (EABBB) is given as: (ĒBRAIN × λBBB)/dth-BBB. Electrical current in the brain parenchyma is distorted around brain capillaries, amplifying neuronal polarization. Specifically, capillary ultrastructure produces ∼50% modulation of the ĒBRAIN over the ∼40 μm inter-capillary distance. The divergence of EBRAIN (Activating function) is thus ∼100 kV/m2 per unit ĒBRAIN.

CONCLUSIONS

BBB stimulation by principle 1 suggests novel therapeutic strategies such as boosting metabolic capacity or interstitial fluid clearance. Whereas the spatial profile of EBRAIN is traditionally assumed to depend only on macroscopic anatomy, principle 2 suggests a central role for local capillary ultrastructure-which impact forms of neuromodulation including deep brain stimulation (DBS), spinal cord stimulation (SCS), transcranial magnetic stimulation (TMS), electroconvulsive therapy (ECT), and transcranial electrical stimulation (tES)/transcranial direct current stimulation (tDCS).

Neuroanatomical considerations for optimizing thalamic deep brain stimulation in Tourette syndrome

OBJECTIVE

Deep brain stimulation (DBS) of the centromedian thalamic nucleus has been reportedly used to treat severe Tourette syndrome, yielding promising outcomes. However, it remains unclear how DBS electrode position and stimulation parameters modulate the specific area and related networks. The authors aimed to evaluate the relationships between the anatomical location of stimulation fields and clinical responses, including therapeutic and side effects.

METHODS

The authors collected data from 8 patients with Tourette syndrome who were treated with DBS. The authors selected the active contact following threshold tests of acute side effects and gradually increased the stimulation intensity within the therapeutic window such that acute and chronic side effects could be avoided at each programming session. The patients were carefully interviewed, and stimulation-induced side effects were recorded. Clinical outcomes were evaluated using the Yale Global Tic Severity Scale, the Yale-Brown Obsessive-Compulsive Scale, and the Hamilton Depression Rating Scale. The DBS lead location was evaluated in the normalized brain space by using a 3D atlas. The volume of tissue activated was determined, and the associated normative connective analyses were performed to link the stimulation field with the therapeutic and side effects.

RESULTS

The mean follow-up period was 10.9 ± 3.9 months. All clinical scales showed significant improvement. Whereas the volume of tissue activated associated with therapeutic effects covers the centromedian and ventrolateral nuclei and showed an association with motor networks, those associated with paresthesia and dizziness were associated with stimulation of the ventralis caudalis and red nucleus, respectively. Depressed mood was associated with the spread of stimulation current to the mediodorsal nucleus and showed an association with limbic networks.

CONCLUSIONS

This study addresses the importance of accurate implantation of DBS electrodes for obtaining standardized clinical outcomes and suggests that meticulous programming with careful monitoring of clinical symptoms may improve outcomes.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Patient-Specific Simulations of Deep Brain Stimulation Electric Field with Aid of In-house Software ELMA

Deep brain stimulation (DBS) is an established technique for reduction of symptoms in movement disorders. Finite element method (FEM) simulations of the electric field magnitude (EF) are useful for estimating the affected tissue around the DBS lead and this can help optimize the therapy. This paper describes how patient-specific FEM models can be set up with the aid of the Matlab-based in-house software tool ELMA. Electrode placement is determined from two coordinates in postoperative medical imaging and electric conductivity is assigned from preoperative magnetic resonance imaging (MRI) and patient-specific DBS data. Simulations are performed using the equation for steady currents in Comsol Multiphysics (CM). The simulated EF is superimposed on the preoperative MRI for evaluation of affected structures. The method is demonstrated with patient-specific simulations in the zona incerta and a globus pallidus example containing cysts with higher conductive which causes considerable distortion of the EF. The improved software modules and precise lead positioning simplifies and reduces the time for DBS EF modelling and simulation.

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Connectivity Predicts deep brain stimulation outcome in Parkinson disease

OBJECTIVE

The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort.

METHODS

A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center.

RESULTS

In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p < 0.001). This same connectivity profile predicted response in an independent patient cohort (p < 0.01). Structural and functional connectivity were independent predictors of clinical improvement (p < 0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients.

INTERPRETATION

Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. Ann Neurol 2017;82:67-78.

Patient-Specific Electric Field Simulations and Acceleration Measurements for Objective Analysis of Intraoperative Stimulation Tests in the Thalamus

Despite an increasing use of deep brain stimulation (DBS) the fundamental mechanisms of action remain largely unknown. Simulation of electric entities has previously been proposed for chronic DBS combined with subjective symptom evaluations, but not for intraoperative stimulation tests. The present paper introduces a method for an objective exploitation of intraoperative stimulation test data to identify the optimal implant position of the chronic DBS lead by relating the electric field (EF) simulations to the patient-specific anatomy and the clinical effects quantified by accelerometry. To illustrate the feasibility of this approach, it was applied to five patients with essential tremor bilaterally implanted in the ventral intermediate nucleus (VIM). The VIM and its neighborhood structures were preoperatively outlined in 3D on white matter attenuated inversion recovery MR images. Quantitative intraoperative clinical assessments were performed using accelerometry. EF simulations (n = 272) for intraoperative stimulation test data performed along two trajectories per side were set-up using the finite element method for 143 stimulation test positions. The resulting EF isosurface of 0.2 V/mm was superimposed to the outlined anatomical structures. The percentage of volume of each structure’s overlap was calculated and related to the corresponding clinical improvement. The proposed concept has been successfully applied to the five patients. For higher clinical improvements, not only the VIM but as well other neighboring structures were covered by the EF isosurfaces. The percentage of the volumes of the VIM, of the nucleus intermediate lateral of the thalamus and the prelemniscal radiations within the prerubral field of Forel increased for clinical improvements higher than 50% compared to improvements lower than 50%. The presented new concept allows a detailed and objective analysis of a high amount of intraoperative data to identify the optimal stimulation target. First results indicate agreement with published data hypothesizing that the stimulation of other structures than the VIM might be responsible for good clinical effects in essential tremor. (Clinical trial reference number: Ref: 2011-A00774-37/AU905)

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

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

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.

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.

Modeling the current distribution across the depth electrode-brain interface in deep brain stimulation

The mismatch between the extensive clinical use of deep brain stimulation (DBS), which is being used to treat an increasing number of neurological disorders, and the lack of understanding of the underlying mechanisms is confounded by the difficulty of measuring the spread of electric current in the brain in vivo. In this article we present a brief review of the recent computational models that simulate the electric current and field distribution in 3D space and, consequently, make estimations of the brain volume being modulated by therapeutic DBS. Such structural modeling work can be categorized into three main approaches: target-specific modeling, models of instrumentation and modeling the electrode-brain interface. Comments are made for each of these approaches with emphasis on our electrode-brain interface modeling, since the stimulating current must travel across the electrode-brain interface in order to reach the surrounding brain tissue and modulate the pathological neural activity. For future modeling work, a combined approach needs to be taken to reveal the underlying mechanisms, and both structural and dynamic models need to be clinically validated to make reliable predictions about the therapeutic effect of DBS in order to assist clinical practice.

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

Deep brain stimulation (DBS) is an increasingly used clinical treatment for various neurological disorders, particularly movement disorders such as Parkinson's disease. However, the mechanism by which these high frequency electrical pulses act on neuronal activity is unclear. Once the stimulating electrode is placed in situ, an electrode–brain interface (EBI) is created. To compensate for the lack of studies on the effects of this generic depth EBI on therapeutic DBS, we constructed a three-dimensional computational model of the EBI using the finite element method, in which the structural details and biophysical properties of the EBI are preserved. Our investigations focus on the peri-electrode space as a significant element of the EBI, and its physiological and pathological modulation, in particular by brain pulsation and giant cell formation. We also consider the difference between the current fields induced by different configurations of the quadripolar electrode contacts. These results quantitatively demonstrated that the peri-electrode space is a significant element of the EBI and its biophysical properties are modulated by brain pulsation and giant cell formation, as well as by the choice of electrode contact configuration. This study leads to a fuller understanding of the EBI and its effects on the crossing electric currents, and will ultimately lead to optimisation of the therapeutic effects of DBS.