Deep brain stimulation (DBS) in the ventral intermediate nucleus of thalamus (Vim) is known to exert a therapeutic effect on postural and kinetic tremor in patients with essential tremor. For DBS leads implanted near the caudal border of Vim, however, there is an increased likelihood that one will also induce paresthesia side-effects by stimulating neurons within the sensory pathway of the ventral caudal (Vc) nucleus of thalamus. The aim of this computational study was to 1) investigate the neuronal pathways modulated by therapeutic, sub-therapeutic, and paresthesia-inducing DBS settings in three patients with essential tremor, and 2) determine how much better of an outcome could have been achieved had these patients been implanted with a DBS lead containing directionally-segmented electrodes (dDBS). Multi-compartment neuron models of the thalamocortical, cerebellothalamic, and medial lemniscal pathways were first simulated in the context of patient-specific anatomies, lead placements, and programming parameters from three ET patients who had been implanted with Medtronic 3389 DBS leads. The models showed that in these patients, complete suppression of tremor was associated most closely with activating an average of 62% of the cerebellothalamic afferent input into Vim (n=10), while persistent paresthesias were associated with activating 35% of the medial lemniscal tract input into Vc thalamus (n=12). The dDBS lead design demonstrated superior targeting of the cerebello-thalamo-cortical pathway, especially in cases of misaligned DBS leads. Given the close proximity of Vim to Vc thalamus, the models suggest that dDBS will enable clinicians to more effectively sculpt current through and around thalamus in order to achieve a more consistent therapeutic effect without inducing side effects.

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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.