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.

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.

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.