Development of accurate computational models for patient-specific deep brain stimulation

Deep brain stimulation (DBS) is a surgical treatment that involves implanting electrodes within the brain to alleviate the symptoms of various neurological disorders. One such disorder, Parkinson's disease (PD), is a neurodegenerative disease whose primary symptoms are tremor, rigidity, bradykinesia, and gait instability. DBS uses an implanted pulse generator (IPG) to stimulate deep cortical structures using high frequency stimulation (HFS). This therapeutic stimulation helps manage some of the patient's symptoms and subsequently improves their quality of life.;We implemented and compared five different patient-specific electric field models to assess the degree of model complexity that was needed to make accurate predictions of neural response to DBS. We observed that the more simplistic models, ones that did not account for a tissue encapsulation layer around the electrode or a non-homogeneous tissue medium, generated excessive, non-realistic predictions of axonal activation.;In this study, we also determined if current-controlled devices with independent current sources provided a more targeted stimulation. We investigated the impact of using current steering between multiple electrode contacts to selectively activate different target neural populations typically associated with therapeutic benefit, while avoiding populations associated with side effects. We found that current steering allowed for increased selectivity in activating different neural populations of interest, but at a price of a much larger stimulation parameter search space.;Finally, we developed a novel predictor function that performed better than an AF-based approach in quantifying the spread of neural activation. This new artificial neural network (ANN)-based predictor function could also estimate volumes of tissue activated (VTAs) for multiple electrode geometries and multi-contact stimulation configurations without needing explicit computer simulations. Collectively, the analysis of these different components gave us the methodology and the tools to accurately predict neural activation by DBS on a patient-specific basis. In turn, an application of these tools can be used to develop novel electrode designs and optimize clinical therapeutic stimulation.