DOI

https://doi.org/10.25772/YZVD-S843

Defense Date

2011

Document Type

Thesis

Degree Name

Master of Science

Department

Biomedical Engineering

First Advisor

Ou Bai

Second Advisor

Kathryn Holloway

Third Advisor

Ding-Yu Fei

Abstract

Introduction: Exploring the brain for optimal locations for deep brain stimulation (DBS) therapy is a challenging task, which can be facilitated by analysis of DBS efficacy in a large number of patients with Parkinson’s disease (PD). The Unified Parkinson's Disease Rating Scale (UPDRS) scores indicate the DBS efficacy of the corresponding stimulation location in a particular patient. The spatial distribution of these clinical scores can be used to construct a functional model which closely models the expected efficacy of stimulation in the region. Designs and Methods: In this study, different interpolation techniques were investigated that can appropriately model the DBS efficacy for Parkinson’s disease patients. These techniques are linear triangulation based interpolation, ‘roving window’ interpolation and ‘Monopolar inverse weighted distance’ (MIDW) interpolation. The MIDW interpolation technique is developed on the basis of electric field geometry of the monopolar DBS stimulation electrodes, based on the DBS model of monopolar cathodic stimulation of brain tissues. Each of these models was evaluated for their predictability, interpolation accuracy, as well as other benefits and limitations. The bootstrapping based optimization method was proposed to minimize the observational and patient variability in the collected database. A simulation study was performed to validate that the statistically optimized interpolated models were capable to produce reliable efficacy contour plots and reduced false effect due to outliers. Some additional visualization and analysis tools including a graphic user interface (GUI) were also developed for better understanding of the scenario. Results: The interpolation performance of the MIDW interpolation, the linear triangulation method and Roving window method was evaluated as interpolation error as 0.0903, 0.1219 and0.3006 respectively. Degree of prediction for the above methods was found to be 0.0822, 0.2986 and 0.0367 respectively. The simulation study demonstrate that the mean improvement in outlier handling and increased reliability after bootstrapping based optimization (performed on Linear triangulation interpolation method) is 6.192% and 12.8775% respectively. The different interpolation techniques used to model monopolar and bipolar stimulation data is found to be useful to study the corresponding efficacy distribution. A user friendly GUI (PDRP_GUI) and other utility tools are developed. Conclusion: Our investigation demonstrated that the MIDW and linear triangulation methods provided better degree of prediction, whereas the MIDW interpolation with appropriate configuration provided better interpolation accuracy. The simulation study suggests that the bootstrapping-based optimization can be used as an efficient tool to reduce outlier effects and increase interpolated reliability of the functional model of DBS efficacy. Additionally, the differential interpolation techniques used for monopolar and bipolar stimulation modeling facilitate study of overall DBS efficacy using the entire dataset.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

August 2011

Download

Share

COinS