Subthalamic Nucleus Deep Brain Stimulation: Accurate Axonal Threshold Prediction with Diffusion Tensor Based Electric Field Models

Abstract

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the therapy of choice for medically intractable Parkinson's Disease. However, the physiological mechanisms responsible for the therapeutic effects of DBS remain unknown, and quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is lacking. Recently our group has developed various computational techniques to study the neural response to DBS. The goal of this study was to incrementally incorporate increasing levels of complexity into our computer models of STN DBS and address activation of the corticospinal tract (CST). Our model system was customized to an STN DBS patient and CST thresholds were calculated with electric field models that ranged from an electrostatic, homogeneous, isotropic model to one that explicitly incorporated the capacitance of the electrode-tissue interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to clinical CST thresholds defined from electromyographic recordings from eight muscle groups in the arm and leg of the STN DBS patient. Coupled evaluation of the model and clinical data showed that accurate prediction of axonal thresholds required our most detailed model. In addition, the simplifications and assumptions typically utilized in neurostimulation models substantially overestimate neural activation.