Dataset Information

Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue.

ABSTRACT:

Background and purpose

Although progress has been made in understanding the optimal anatomic structures as target areas for DBS, little effort has been put into modeling and predicting electromagnetic field properties of activated DBS electrodes and understanding their interactions with the adjacent tissue. Currently, DBS is performed with the patient awake to assess the effectiveness and the side effect spectrum of stimulation. This study was designed to create a robust and rather simple numeric and visual tool that provides sufficient and practical relevant information to visualize the patient's individual VAT.

Materials and methods

Multivariate polynomial fitting of previously obtained data from a finite-element model, based on a similar DBS system, was used. The model estimates VAT as a first-approximation sphere around the active DBS contact, using stimulation voltages and individual tissue-electrode impedances. Validation uses data from 2 patients with PD by MR imaging, DTI, fiber tractography, and postoperative CT data.

Results

Our model can predict VAT for impedances between 500 and 2000 Ω with stimulation voltages up to 10 V. It is based on assumptions for monopolar DBS. Evaluation of 2 DBS cases showed a convincing correspondence between predicted VAT and neurologic (side) effects (internal capsule activation).

Conclusions

Stimulation effects during DBS can be readily explained with this simple VAT model. Its implementation in daily clinical routine might help in understanding the types of tissues activated during DBS. This technique might have the potential to facilitate DBS implantations with the patient under general anesthesia while yielding acceptable clinical effectiveness.

SUBMITTER: Madler B

PROVIDER: S-EPMC8013266 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue.

Mädler B B Coenen V A VA

AJNR. American journal of neuroradiology 20120202 6

<h4>Background and purpose</h4>Although progress has been made in understanding the optimal anatomic structures as target areas for DBS, little effort has been put into modeling and predicting electromagnetic field properties of activated DBS electrodes and understanding their interactions with the adjacent tissue. Currently, DBS is performed with the patient awake to assess the effectiveness and the side effect spectrum of stimulation. This study was designed to create a robust and rather simpl ...[more]

PMID: 22300931

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Project description:OBJECTIVE:Computational models are a popular tool for predicting the effects of deep brain stimulation (DBS) on neural tissue. One commonly used model, the volume of tissue activated (VTA), is computed using multiple methodologies. We quantified differences in the VTAs generated by five methodologies: the traditional axon model method, the electric field norm, and three activating function based approaches-the activating function at each grid point in the tangential direction (AF-Tan) or in the maximally activating direction (AF-3D), and the maximum activating function along the entire length of a tangential fiber (AF-Max). APPROACH:We computed the VTA using each method across multiple stimulation settings. The resulting volumes were compared for similarity, and the methodologies were analyzed for their differences in behavior. MAIN RESULTS:Activation threshold values for both the electric field norm and the activating function varied with regards to electrode configuration, pulse width, and frequency. All methods produced highly similar volumes for monopolar stimulation. For bipolar electrode configurations, only the maximum activating function along the tangential axon method, AF-Max, produced similar volumes to those produced by the axon model method. Further analysis revealed that both of these methods are biased by their exclusive use of tangential fiber orientations. In contrast, the activating function in the maximally activating direction method, AF-3D, produces a VTA that is free of axon orientation and projection bias. SIGNIFICANCE:Simulating tangentially oriented axons, the standard approach of computing the VTA, is too computationally expensive for widespread implementation and yields results biased by the assumption of tangential fiber orientation. In this work, we show that a computationally efficient method based on the activating function, AF-Max, reliably reproduces the VTAs generated by direct axon modeling. Further, we propose another method, AF-3D as a potentially superior model for representing generic neural tissue activation.

Project description: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.

Project description:BackgroundMotor 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.MethodsThe 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.ResultsTissue 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.ConclusionAccurate 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.

Dataset Information

Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue.

Background and purpose

Materials and methods

Results

Conclusions

Publications

Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue.

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