Model-based optimization of electrode designs for deep brain stimulation

Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite its success, the clinical efficacy of DBS can be improved; for example, by reducing the number of surgeries required to replace batteries or to correct misplaced leads, which increase the risks and cost of the therapy. Our objective was to design novel electrode designs that increase the efficiency and selectivity of DBS. We coupled computational models of cylindrical stimulation electrodes with cable models of axons of passage (AOP), terminating axons (TA), and local neurons (LN); and we used engineering optimization to design electrodes for stimulating these elements. Compared with the clinical Model 3387 electrode, optimal electrodes consumed 48-67% less power. Similar gains in selectivity were evident with the optimized electrodes, which reduced the activation of non-targeted elements from 34-71 % with the 3387 array to only 1-36 %, while activating 100% of the targeted elements. Overall, both the geometry and polarity of the electrode had a profound impact on the efficiency and selectivity of stimulation. Thus, model-based design is a powerful tool that can be used to increase the efficacy of DBS by increasing electrode performance.