Computerized stimulation parameter adjustment of deep brain stimulation minimizing side effects and power consumption for Parkinson's disease

Deep brain stimulation (DBS) is broadly applied for neuropsychiatric diseases and thus determining its mechanism is of interest, especially in terms of the neural structure surrounding the DBS probe and the volume of tissue activated (VTA) during DBS. For re-operations for battery replacement, a major issue is reducing treatment power consumption without compromising clinical benefits. To avoid side effects and to minimize power consumption, optimized adjustment of the stimulation parameters is required. This study thus proposes a scheme for determining the optimal stimulation parameters. An electromagnetic finite element model for a patient-specific physiological brain model is first established using magnetic resonance imaging (MRI) data. Using finite element analysis (FEA), varied stimulation parameters are applied to the electromagnetic model for VTA estimation. Optimal electrode contacts) are selected based on the estimated VTA to avoid side effects. Moreover, a nonlinear programming method for optimizing the stimulation voltage and the pulse width is applied to minimize power consumption in DBS. The effectiveness of the model parameters was verified using five Parkinson's disease patients. The results demonstrate that the estimates of the VTA are consistent with the observations within the desired region of the brain while avoiding side effects and reducing power consumption by 13% on average. The proposed method allows clinicians and researchers to efficiently select the optimal stimulation parameters. Moreover, it provides valuable information for closed-loop stimulation protocols in DBS.