Optimizing the Geometry of Deep Brain Stimulating Electrodes

摘要

Deep brain stimulation (DBS) is an effective treatment for movement disorders, including essential tremor, Parkinson's disease, and dystonia, and is under investigation as a treatment for epilepsy and depression. Despite the rapid clinical growth of DBS, there has been little effort to optimize the geometry of the DBS electrode for either stimulation efficiency or selectivity. The objective of this study was to identify the electrode geometry that optimized stimulation efficiency. Due to the large number of possible electrode geometries, genetic algorithms (GA), a global heuristic search method, were used to find the optimal electrode geometry. The electrode contact was discretized into 15 equal-length segments, and the algorithm determined whether each segment was a conductor or insulator. The optimization algorithm was initially designed to minimize the stimulation voltage, and the cost function to be minimized was the sum of the voltage thresholds needed to activate 20%, 50%, and 80% of a randomly distributed population of model axons positioned around the electrode. The algorithm results demonstrated that despite the non-uniformity of the current density across the electrode, the most efficient geometry was a single segment that was 27 % shorter than the standard clinical electrode. Subsequently, the optimization was conducted to maximize the power efficiency of the electrode, and the cost function to be minimized was the sum of the power thresholds needed to activate 20%, 50%, and 80% of the randomly distributed axons. The results showed the optimal geometry was triple-band segmented electrode with insulating gaps in between. The results of this study reveal that optimal electrode geometry depends on the cost function to be optimized, and suggest that modifications, such as decreasing electrode width, may reduce power consumption and increase device longevity.