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.

When combined with patient-specific tissue anisotropy and patient-specific anatomical morphologies of neural pathways responsible for therapy and side effects, orientation-selective DBS approaches show potential to significantly improve clinical outcomes of DBS therapy for a range of existing and investigational clinical indications.

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.

These results suggest that pathway targeting with patient-specific model-based optimization algorithms can efficiently identify non-trivial electrode configurations for enhancing activation of clinically relevant pathways. However, the results also indicate that inter-pathway correlations can limit selectivity for certain pathways even with directional DBS leads.

Transcranial direct current stimulation (tDCS) is increasingly researched as an adjuvant to motor rehabilitation for children with hemiparesis. The optimal method for the primary motor cortex (M1) somatotopic localization for tDCS electrode placement has not been established. The objective, therefore, was to determine the location of the M1 derived using the 10/20 electroencephalography (EEG) system and transcranial magnetic stimulation (TMS) in children with hemiparesis (CWH) and a comparison group of typically developing children (TDC). We hypothesized a difference in location for CWH but not for TDC. The 2 locations were evaluated in 47 children (21 CWH, 26 TDC). Distances between the locations were measured pending presence of a motor evoked potential. Distances between the EEG and TMS locations that exceeded the 2.5 cm × 2.5 cm rubber electrode area are reported in percentages [95% confidence interval] in CWH—nonlesioned hemisphere was 68.8% [41.3-89.0], lesioned: 85.7% [57.2-98.2]; TDC—dominant hemisphere 73.9% [51.6-89.8], nondominant: 82.6% [61.2-95.0]. Distances that exceeded the 3 × 5 cm electrode sponge area in CWH—nonlesioned was 25.0% [7.3-52.4], lesioned was 28.6% [8.4-58.1]; TDC—dominant was 52.2% [30.6-73.2], nondominant was 43.5 [23.2-65.5]). Distances that exceeded the 5 × 7 cm electrode sponge area in CWH—nonlesioned was 18.8% [4.0-45.6] and lesioned was 21.4% [4.7-50.8]; TDC—dominant was 21.7% [7.5-43.7] and nondominant was 26.1% [10.2-48.4]. Individual variability in brain somatotopic organization may influence surface scalp localization of underlying M1 in children regardless of neurologic impairment. Findings suggest further investigation of optimal tDCS electrode placement. EEG and TMS methods reveal variability in localizing M1 in children regardless of stroke diagnosis. This study was registered on clinicaltrials.gov NCT020I5338.

Reversible block of nerve conduction using kilohertz frequency electrical signals has substantial potential for treatment of disease. However, the ability to block nerve fibers selectively is limited by poor understanding of the relationship between waveform parameters and the nerve fibers that are blocked. Previous in vivo studies reported non-monotonic relationships between block signal frequency and block threshold, suggesting the potential for fiber-selective block. However, the mechanisms of non-monotonic block thresholds were unclear, and these findings were not replicated in a subsequent in vivo study. We used high-fidelity computational models and in vivo experiments in anesthetized rats to show that non-monotonic threshold-frequency relationships do occur, that they result from amplitude- and frequency-dependent charge imbalances that cause a shift between kilohertz frequency and direct current block regimes, and that these relationships can differ across fiber diameters such that smaller fibers can be blocked at lower thresholds than larger fibers. These results reconcile previous contradictory studies, clarify the mechanisms of interaction between kilohertz frequency and direct current block, and demonstrate the potential for selective block of small fiber diameters.

Objective: There is growing interest in delivering kilohertz frequency (KHF) electrical signals to block conduction in peripheral nerves for treatment of various diseases. Previous studies used different KHF waveforms to achieve block, and it remains unclear how waveform affects nerve block parameters. Approach:We quantified the effects of waveform on KHF block of the rat tibial nerve in vivo and in computational models. We compared block thresholds and onset responses across currentcontrolled sinusoids and charge-balanced rectangular waveforms with different asymmetries and duty cycles.Main Results: Sine waves had higher block thresholds than square waves, but used less power at block threshold. Block threshold had an inverse relationship with duty cycle of rectangular waveforms irrespective of waveform asymmetry. Computational model results were consistent with relationships measured in vivo, although the models underestimated the effect of duty cycle on increasing thresholds. The axonal membrane substantially filtered waveforms, the filter transfer function was strikingly similar across waveforms, and filtering resulted in post-filtered rms block thresholds that were approximately constant across waveforms in silico and in vivo. Onset response was not consistently affected by waveform shape, but onset was smaller at amplitudes well above block threshold. Therefore, waveforms with lower block thresholds (e.g., sine waves or square waves) could be more readily increased to higher amplitudes relative to block threshold to reduce onset response. We also observed a reduction in onset responses across consecutive trials after initial application of supra-block threshold amplitudes.

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