Customizing Deep Brain Stimulation to the Patient Using Computational Models

Bilateral subthalamic (STN) deep brain stimulation (DBS) is effective in improving the cardinal motor signs of advanced Parkinson's disease (PD); however declines in cognitive function have been associated with this procedure. The aim of this study was to assess cognitive-motor performance of 10 PD patients implanted with STN DBS systems during either clinically determined stimulation settings or settings derived from a computational model. Cicerone DBS software was used to define the model parameters such that current spread to non-motor areas of the STN was minimized. Clinically determined and model defined parameters were equally effective in improving motor scores on the traditional clinical rating scale (UPDRS-III). Under modest dual-task conditions, cognitive-motor performance was worse with clinically determined compared to model derived parameters. In addition, the model parameters provided a 66% reduction in power consumption. These results indicate that the cognitive-motor declines associated with bilateral STN can be mitigated, without compromising motor benefits, utilizing stimulation parameters that minimize current spread into non-motor regions of the STN.