A method for predicting the outcomes of combined pharmacologic and deep brain stimulation therapy for Parkinson's...

Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson's disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multilinear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p < 0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.