SIMULATION OF DEEP BRAIN STIMULATION

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Parkinson's Disease (PD) is a significant health problem for the aging population of the U.S. Despite the success of levodopa-based treatment for PD, many patients develop disabling motor side effects over time. One alternative for these patients is deep brain stimulation (DBS), a therapy in which a neurostimulation system is implanted in the brain during stereotactic surgery. This treatment has been FDA approved for approximately ten years and its effectiveness in treating the motor symptoms of PD has been well established. However, DBS is not without its own side effects profile. Namely, it is heavily dependent on the intuitive skill of the clinician and no atlas exists for clinicians to share therapeutic or side effect outcomes. In this project, we propose to integrate these methods into a knowledge base that is viewable as an interactive, 3D atlas and to use this framework to examine neuropsychological outcomes in DBS patients with PD. The central hypothesis is that cognitive and psychological outcomes in these patients are correlated with stimulation- induced activation of specific brain regions. Therefore, one long-term objective is to better manage such outcomes by using the knowledge base created in this study to prescribe stimulation protocols that avoid activating these regions. Another potential result of this work is the ability to predict neuropsychological outcomes in DBS patients, which would help alleviate some of the long-term treatment costs this group experiences. To perform the proposed research we must solve two general computing problems: how to effectively generate large, complex, multi-resolution, finite element models, and how to accurately visualize the model results. We anticipate that this study will generate a knowledge-base of clinical outcomes to be correlated with stimulation-induced activation of particular brain regions. The fundamental goal of this project is to characterize neuropsychological outcomes in PD patients who are receiving STN DBS. In order to create the greatest potential benefit with the simplest study design, we propose to use information gathered under existing standard of care for patient evaluation. Specifically, we propose to use pre- and post- operative imaging along with pre- and post-operative neuropsychological evaluation results. The novel component of this work is the integration of state-of-the-art clinical and research methods to improve the health of PD patients by characterizing neuropsychological outcomes.