Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

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.

Deep brain stimulation (DBS) therapy is a potent tool for treating a range of brain disorders. High frequency stimulation (HFS) patterns used in DBS therapy are known to modulate neuronal spike rates and patterns in the stimulated nucleus; however, the spatial distribution of these modulated responses are not well understood. Computational models suggest that HFS modulates a volume of tissue spatially concentrated around the active electrode. Here, we tested this theory by investigating modulation of spike rates and patterns in non-human primate motor thalamus while stimulating the cerebellar-receiving area of motor thalamus, the primary DBS target for treating Essential Tremor. HFS inhibited spike activity in the majority of recorded cells, but increasing stimulation amplitude also shifted the response to a greater degree of spike pattern modulation. Modulated responses in both categories exhibited a sparse and long-range spatial distribution within motor thalamus, suggesting that stimulation preferentially affects afferent and efferent axonal processes traversing near the active electrode and that the resulting modulated volume strongly depends on the local connectome of these axonal processes. Such findings have important implications for current clinical efforts building predictive computational models of DBS therapy, developing directional DBS lead technology, and formulating closed-loop DBS strategies.

The cerebellar-receiving area of the motor thalamus is the primary anatomical target for treating essential tremor with deep brain stimulation (DBS). While neuroimaging studies have shown that higher stimulation frequencies in this target correlate with increased cortical metabolic activity, less is known about the cellular-level functional changes that occur in primary motor cortex (M1) with thalamic stimulation and how these changes depend on the frequency of DBS. In this study we used a preclinical animal model of DBS to collect single-unit spike recordings in M1 before, during, and after DBS targeting the cerebellar-receiving area of motor thalamus (VPLo, nucleus ventralis posterior lateralis pars oralis). The effects of VPLo-DBS on M1 spike rates, inter-spike interval entropy, and peristimulus phase-locking were compared across stimulus pulse train frequencies ranging from 10-130 Hz. While VPLo-DBS modulated the spike rates of 20-50% of individual M1 cells in a frequency-dependent manner, the population-level average spike rate only weakly depended on stimulation frequency. In contrast, the population-level entropy measure showed a pronounced decrease with high-frequency stimulation, caused by a subpopulation of cells that exhibited strong phase-locking and general spike-pattern regularization. Contrarily, low-frequency stimulation induced an entropy increase (spike-pattern disordering) in a relatively large portion of the recorded population, which diminished with higher stimulation frequencies. These results also suggest that changes in phase-locking and spike-pattern entropy are not necessarily equivalent pattern phenomena, but rather that they should both be weighed when quantifying stimulation-induced spike-pattern changes.

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