Predicting the effects of deep brain stimulation with diffusion tensor based electric field models.

Deep brain stimulation (DBS) is an established therapy for the treatment of movement disorders, and has shown promising results for the treatment of a wide range of other neurological disorders. However, little is known about the mechanism of action of DBS or the volume of brain tissue affected by stimulation. We have developed methods that use anatomical and diffusion tensor MRI (DTI) data to predict the volume of tissue activated (VTA) during DBS. We co-register the imaging data with detailed finite element models of the brain and stimulating electrode to enable anatomically and electrically accurate predictions of the spread of stimulation. One critical component of the model is the DTI tensor field that is used to represent the 3-dimensionally anisotropic and inhomogeneous tissue conductivity. With this system we are able to fuse structural and functional information to study a relevant clinical problem: DBS of the subthalamic nucleus for the treatment of Parkinsons disease (PD). Our results show that inclusion of the tensor field in our model caused significant differences in the size and shape of the VTA when compared to a homogeneous, isotropic tissue volume. The magnitude of these differences was proportional to the stimulation voltage. Our model predictions are validated by comparing spread of predicted activation to observed effects of oculomotor nerve stimulation in a PD patient. In turn, the 3D tissue electrical properties of the brain play an important role in regulating the spread of neural activation generated by DBS.