Computational techniques for optimization of subthalamic nucleus deep brain stimulation in the context of Parkinson's disease

Deep brain stimulation has been successfully used in the treatment of Parkinson’s disease for more than two decades, with deep brain stimulation of the subthalamic nucleus significantly improving motor function. Symptoms of the disease (e.g., tremor, rigidity, bradykinesia) are measured by the Unified Parkinson’s Disease Rating Scale Part III, but uncertainty remains concerning the areas in and around the subthalamic nucleus that are associated with each particular symptoms included in UPDRS-III. Here, we retrospectively examine a cohort of Parkinson’s patients implanted at the Center for Neuromodulation in the Ohio State Wexner Medical Center. By combining anatomically-detailed pre-operative magnetic resonance imaging with post-operative computed tomographic imaging, we accurately determine the actual location of implanted electrode leads, then model a volume of tissue activation around each electrode to estimate the neuronal cell bodies and fiber tracts that are most likely to be affected by the stimulation parameters (i.e., voltage, pulse width, and frequency) that are programmed by clinicians during follow-up. By correlating these volumes of tissue activation with each motor function subscore, we have built three-dimensional statistical and probabilistic maps in and around the subthalamic nucleus that will allow clinicians to target lead placement and stimulation to particular deep brain regions based on patient-specific symptoms.2015 College of Arts and Sciences Undergraduate Research Scholarship (Ohio State University Colleges of Arts & Sciences)2015 Social and Behavioral Sciences Undergraduate Research Grant (Ohio State Department of Social & Behavioral Sciences)2014 University Honors & Scholars Summer Research Fellowship (Ohio State University Honors & Scholars)No embargoAcademic Major: Neuroscienc