Deep brain stimulation (DBS) is a highly effective therapeutic option for Parkinson’s disease (PD). However, it can take 50 or more hours to obtain stimulation settings that optimally treat a patient’s symptoms. Additionally, axial symptoms, such as gait, are not adequately treated in the long term. In my work I explore the use of quantified motor behavior outcomes to reduce the time needed to obtain optimal stimulation parameters, and to develop a novel stimulation delivery approaches to better treat gait. First, I tested a Bayesian optimization approach to quickly and accurately model the input/output response of rigidity to stimulation frequency. I found, for PD patients that have a high degree of rigidity, Bayesian optimization models their response needing fewer samples than a traditional trial-and-error approach. Next, I tested a novel closed-loop stimulation delivery approach that delivered short duration pulse trains at specific phases of gait. I found that the patients that respond strongly to this type of stimulation delivery have a worse gait with their clinical settings. Overall, many patients saw small changes to their gait with this approach. Lastly, I analyzed the effects of turning stimulation on and off on gait. I found that a repeated measures of gait with short duration, 1 minute, wash-in and -out can detect significant changes. This is in contrast to previous reports that significant changes are only seen between 30-60 minutes. Through these studies I demonstrate the use of quantified motor behavior outcomes to improve DBS for PD.
University of Minnesota Ph.D. dissertation. June 2020. Major: Biomedical Engineering. Advisor: Théoden Netoff. 1 computer file (PDF); vi, 113 pages.
Using Quantified Motor Behavior Outcomes to Improve Deep Brain Stimulation in Parkinson’s Disease.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.