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.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR012553-13
Application #
8363718
Study Section
Special Emphasis Panel (ZRG1-BST-J (40))
Project Start
2011-08-01
Project End
2012-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
13
Fiscal Year
2011
Total Cost
$88,819
Indirect Cost
Name
University of Utah
Department
Type
Organized Research Units
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
Tong, Xin; Edwards, John; Chen, Chun-Ming et al. (2016) View-Dependent Streamline Deformation and Exploration. IEEE Trans Vis Comput Graph 22:1788-801
Erem, Burak; Martinez Orellana, Ramon; Hyde, Damon E et al. (2016) Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals. Phys Rev E 93:042218
Burton, Brett M; Tate, Jess D; Good, Wilson et al. (2016) The Role of Reduced Left Ventricular, Systolic Blood Volumes in ST Segment Potentials Overlying Diseased Tissue of the Ischemic Heart. Comput Cardiol (2010) 43:209-212
Raj, Mukund; Mirzargar, Mahsa; Preston, J Samuel et al. (2016) Evaluating Shape Alignment via Ensemble Visualization. IEEE Comput Graph Appl 36:60-71
Gao, Yi; Zhu, Liangjia; Cates, Joshua et al. (2015) A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM J Imaging Sci 8:1007-1029
Gillette, Karli; Tate, Jess; Kindall, Brianna et al. (2015) Generation of Combined-Modality Tetrahedral Meshes. Comput Cardiol (2010) 2015:953-956
Erem, B; Hyde, D E; Peters, J M et al. (2015) COMBINED DELAY AND GRAPH EMBEDDING OF EPILEPTIC DISCHARGES IN EEG REVEALS COMPLEX AND RECURRENT NONLINEAR DYNAMICS. Proc IEEE Int Symp Biomed Imaging 2015:347-350
Coll-Font, J; Erem, B; Štóví?ek, P et al. (2015) A STATISTICAL APPROACH TO INCORPORATE MULTIPLE ECG OR EEG RECORDINGS WITH ARTIFACTUAL VARIABILITY INTO INVERSE SOLUTIONS. Proc IEEE Int Symp Biomed Imaging 2015:1053-1056
Erem, Burak; van Dam, Peter M; Brooks, Dana H (2014) Identifying model inaccuracies and solution uncertainties in noninvasive activation-based imaging of cardiac excitation using convex relaxation. IEEE Trans Med Imaging 33:902-12
Fu, Zhisong; Lewis, T James; Kirby, Robert M et al. (2014) Architecting the Finite Element Method Pipeline for the GPU. J Comput Appl Math 257:195-211

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