Theoretical Optimization of Stimulation Strategies for a Directionally Segmented Deep Brain Stimulation Electrode Array

Abstract

Goal Programming deep brain stimulation (DBS) systems currently involves a clinician manually sweeping through a range of stimulus parameter settings to identify the setting that delivers the most robust therapy for a patient. With the advent of DBS arrays with a higher number and density of electrodes, this trial and error process becomes unmanageable in a clinical setting. This study developed a computationally efficient, model-based algorithm to estimate an electrode configuration that will most strongly activate tissue within a volume of interest. Methods The cerebellar-receiving area of motor thalamus, the target for treating essential tremor with DBS, was rendered from imaging data and discretized into grid points aligned in approximate afferent and efferent axonal pathway orientations. A finite-element model (FEM) was constructed to simulate the volumetric tissue voltage during DBS. We leveraged the principle of voltage superposition to formulate a convex optimization-based approach to maximize activating function (AF) values at each grid point (via three different criteria), hence increasing the overall probability of action potential initiation and neuronal entrainment within the target volume. Results For both efferent and afferent pathways, this approach achieved global optima within several seconds. The optimal electrode configuration and resulting AF values differed across each optimization criteria and between axonal orientations. Conclusion This approach only required a set of FEM simulations equal to the number of DBS array electrodes, and could readily accommodate anisotropic-inhomogeneous tissue conductances or other axonal orientations. Significance The algorithm provides an efficient, flexible determination of optimal electrode configurations for programming DBS arrays.

Thinking Small: Progress on Microscale Neurostimulation Technology

“…It may not be practical to survey the vast parameter space associated with potentially many ultra-small microelectrodes. Model based optimization approaches may facilitate discovery of optimal settings (77), but it is also entirely likely that there will be only marginal improvement in therapy for certain disorders treated with DBS given the volume of tissue required for activation to achieve results (78). Instead, the benefits of spatial precision enabled by microscale neural stimulation are more likely to be realized for emergent neuroprosthetic applications such as restoration of proprioceptive/tactile sensation or visual perception through cortical stimulation.…”

Particle swarm optimization for programming deep brain stimulation arrays

“…Conductance values for lead insulation (σ = 1×10 −12 S/m) and electrodes (σ = 1×10 6 S/m) were set according to a previous model from our group [15], approximating the conductance of silicon carbide and polyimide insulation and conductive platinum electrodes, respectively. Though more complex tissue conductance models of DBS have been developed [30]–[33], for the purposes of demonstrating the PSO algorithm, we assigned simple isotropic conductance values to the encapsulation layer (0.1 mm thick; σ = 0.18 S/m) [34] and to the bulk tissue (100 mm diameter; σ = 0.3 S/m) [35].…”

“…Quadratic tetrahedral mesh elements were generated by Delaunay triangulation with variable resolution mesh refinements set such that further refinement of the mesh yielded less than 5% changes in the activating function measure. The resulting mesh consisted of 4,104,421 domain elements, 204,990 boundary elements, and 12,708 edge elements [15], [36]. …”

“…For this study, we modeled stimulation targeting the rhesus macaque cerebellar-receiving area of motor thalamus (VPLo: ventral posterolateral pars oralis) [15], which is the homologue of the ventral intermediate nucleus (Vim) in humans and primary DBS target for treating essential tremor. Of note, clinical DBS implants targeting this nucleus can be difficult to program because of low-threshold side effects such as paresthesia, which are thought to result from the spread of stimulation into the somatosensory thalamus (VPLc: ventral posterolateral pars caudalis) [6], [37].…”

“…In such models of electrical stimulation, one typically estimates the stimulus-induced currents applied to each neuronal membrane compartment through an activating function [19], driving function [20], or weighted driving function [21] that is calculated from extracellular voltages obtained from solving finite element models. For example, Xiao and colleagues showed that one can maximize the sum of the activating function values within a region of interest using convex optimization to automate programming of DBS arrays [15]. Similarly, genetic algorithms have been developed for programming stimulation settings through peripheral nerve cuff electrodes [22].…”