Luận văn: Mô hình 3D Hiệu quả DBS & Phát triển Công cụ Phân tích STN

Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2011 3D FUNCTIONAL MODELING OF DBS EFFICACY AND DEVELOPMENT OF ANALYTICAL TOOLS TO EXPLORE FUNCTIONAL STN Deepak Kumbhare Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.edu/etd Part of the Biomedical Engineering and Bioengineering Commons © The Author Downloaded from https://scholarscompass.edu/etd/2531 This Thesis is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact libcompass@vcu.com © Deepak Kumbhare, 2011 All Rights Reserved i www.com 3D FUNCTIONAL MODELING OF DBS EFFICACY AND DEVELOPMENT OF ANALYTICAL TOOLS TO EXPLORE FUNCTIONAL STN A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. by DEEPAK KUMBHARE Bachelor of Engineering, Rajiv Gandhi Technical University, Bhopal, India, 2007 Director: OU BAI, PH.

Assistant Professor, Department of Biomedical Engineering Virginia Commonwealth University Richmond, Virginia May, 2011 ii www.com Acknowledgments I would like to express my gratitude to my adviser, Dr. Ou Bai for his continuous support throughout in my study and research. His guidance and encouragement have helped me pursuing this exciting line of research. Throughout the entire project he has encouraged me to develop independent thinking and research skills.

I am extremely thankful to Dr. Kathryn Holloway for giving me the opportunity to work on this research and providing the clinical data for analysis. Her in-depth knowledge and logical way of thinking has been of great help to me. Her extensive discussions around my work and constructive comments have been very helpful throughout this project.

I would like to thank Dr. Ramakrishnan, whose help, suggestions, patience and encouragement has helped me at all times during this research project. I would also like to thank all my committee members, Dr. Ou Bai, Dr.

Kathryn Holloway and Dr. Din-Yu Fei for all the guidance and support throughout this project. Their detailed review, constructive criticism and excellent advice have helped me immensely in completion of my thesis project and improving my skills for technical writing. I would also like to thank my lab mates Dandan Huang, Kai Qian, Vaishnavi Karnad and Mason Montgomery for your help and support in my study.

Also, I would like to thank my friends who have been a constant source of encouragement throughout my graduate study. Last and most importantly, I would like to thank my parents for their enduring love and support throughout my life in achieving my dreams.com Contents Acknowledgments. iv List of Abbreviations. vii List of Tables.

viii List of Figures. xi CHAPTER 1 : Introduction and Background .2 Deep Brain Stimulation .3 Brain Atlas [13]: Schaltenbrand-Wahren/Talairach-Tournoux brain atlas registration .4 Visual Representation of Clinical Scores and Description of Problem:. 8 CHAPTER 2: Patients and Data. Surgical Methodology and Data Collection.

Score Coordinates and Change Score Values: .3 Data Description and Categorization. 15 CHAPTER 3: Interpolations and Computational Modeling. 32 CHAPTER 4: Monopolar and Bipolar DBS Modeling. 41 CHAPTER 5: Variability and Statistical Optimization.

49 CHAPTER 6: Additional Tools for Visualization and Analysis. 51 GRAPHIC USER INTERFACE. 53 CONCLUSION AND FUTURE WORKS. 54 LIST OF REFERENCES.

55 APPENDIX 1: DBS Intra-operative Rating. 58 APPENDIX 2: GRAPHIC USER INTERFACE (GUI): USER MANUAL .com List of Abbreviations PD - Parkinson’s Disease DBS - Deep Brain Stimulation UPDRS - Unified Parkinson's Disease Rating Scale STN - Subthalamic Nucleus GPi - Globus Pallidus AC - Anterior Commissure PC - Posterior Commissure ME - Microelectrode MER - Microelectrode Recording MSI - MacroStim Interface CT - Computed Tomography MRI - Magnetic Resonance Imaging MIDW - Monopolar Inverse Weighted Distance IDW - Inverse Distance Weighted Method RWI - Roving Window Interpolation BSI - Bipolar Simulation Interpolation ROI - Region of Interest RMSE - Root Mean Square Error CV - Cross Validation GUI - Graphic User Interface vii www.com List of Tables Table 1: Interpolation Errors……………………………………………………………………31 Table 2: Effect of varying radius in MIDWI: Normalized RMSE………………………………32 Table 3: Effect of varying power of inverse distance weight in MIDWI: Normalized RMSE….32 Table 4: Cross Validation: Degree of Prediction ………………………………………………33 Table 5: Simulation results……………………………………………………………………. Table 6: DBS Intra-operative Rating…………………………………………………………….com List of Figures Figure 1: The distribution observation points and percentage change scores values ……….18 Figure 2: The 3 D distribution of the hot spots for DBS in the R.18 Figure 3: Shows interpolation applied in 1D space……………………………………………20 Figure 4: Shows interpolation applied in 2D and 3D space……………………………………21 Figure 5: The contour plots comparing the three interpolation techniques …………………. Figure 6: (a) Predictability and RMSE versus Power of IDW; (b) Predictability and RMSE Versus radius of search window…………………….34 Figure 7: Monopolar and bipolar electrode leads…………………………………………….38 Figure8: Monopolar VS Bipolar electric field……………………………………………….39 Figure 9: Medtronic DBS lead 3387 and 3389……………………………………………….39 Figure 10: mono VS bipolar interpolation………………………………………………….42 Figure 11: Contours comparing efficacy distribution of PD symptoms……………………….45 Figure 12: Direct data interpolation (left); Result of bootstrapping: Mean (middle) and standard deviation contours (right)………………………………………………………………….50 Figure13: 3D realization of high and low yield locations…………………………………….56 Figure 14: MATLAB GUI comparing PD symptoms……………………………………….56 Figure 15: Guide Quick start window……………………………………………………….59 Figure 16: Snapshot of the matlab figure for screen set…………………………………….59 Figure 17: GUI: selecting, accepting and loading data……………………………………….60 Figure 18: GUI: Comparison of different data categories………………………………….com Figure 19: Load new data set……………………………………………………………….

20: Selects plane and coordinates……………………………………………………62 Fig.64 Fig 24: Visualize hot region…….com 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 0.1219 and xi www. Degree of prediction for the above methods was found to be 0.

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. 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.com CHAPTER 1 : Introduction and Background 1.1 Parkinson’s Diseases Parkinson's disease (PD) [1] [2, 3]is a neurodegenerative disorder characterized by progressive loss of muscle control and coordination. This leads to variety of movement problems such as shaking of the limbs and head (tremors), muscle stiffness (rigidity), slowness in movement execution (Bradykinesia), difficulty with walking and impaired balance. Later in the course of the disease, cognitive and behavioral problems may arise.

As symptoms worsen, it may become difficult to walk, talk, and complete simple tasks[1]. PD is more common in the elderly with most cases occurring after the age of 50 years. Sometimes PD occurs in younger adults. It affects both men and women.

Causes: PD occurs when by some unknown causes, dopamine-producing nerve cells are slowly destroyed, which reduces dopamine levels in the parts of the brain. Dopamine acts as a chemical messenger between two brain areas - the substantia nigra and the corpus striatum - to produce smooth, controlled movements. When the amount of dopamine is too low, communication between them becomes ineffective impairing the movement. This leads to the loss of muscle function.

The damage gets worse with time. Other cells in the brain also degenerate to some degree and may contribute to non-movement related symptoms of Parkinson's disease. The cause[4] of PD or dopamine destruction is still unknown but many researchers suspect it may be caused by a combination of genetic factors and environmental factors.com The 3 key symptoms of Parkinsonism are tremor, bradykinesia and rigidity[5]. The disorder may affect one or both sides of the body.

How much function is lost can vary. The symptoms of PD are mild at first and will progress over time. Characteristic motor symptoms include the following: 1. Tremors[6]: Tremor is an involuntary muscle contraction and relaxation of body parts, which includes trembling in fingers, hands, arms, feet, legs, jaw, or head.

Tremors most often occur while the individual is resting. Tremors may worsen when an individual is excited, tired, or stressed. Rigidity: Rigidity is the stiffness of the limbs and trunk, which may increase during movement[7]. It may produce muscle aches and pain.

Rigidity results when there is an increase in muscle tone that causes resistance to passive movement throughout the whole range of motion. Bradykinesia[8]: It is characterized by slowness of voluntary movement. Rather than being slowness in initiation (akinesia), bradykinesia describes slowness in the execution of movement. Over time, it may become difficult to initiate movement and to complete movement.

Bradykinesia together with stiffness can also affect the facial muscles and result in an expressionless, "mask-like" appearance. Parkinsonian gait While the main symptoms of Parkinson's disease are movement-related, progressive loss of muscle control and continued damage to the brain can lead to secondary symptoms. These vary in severity, and not every individual will experience all of them. Some of the secondary symptoms include anxiety, insecurity, stress and increased sweating; confusion, memory loss, 2 www.com and dementia; constipation; difficulty swallowing and excessive salivation; diminished sense of smell; skin problems; slowed, quieter speech, and monotone voice; and urinary urgency[1].

Diagnosis: The diagnosis of PD is based on the patient’s symptoms and a physical examination. A neurological exam may include an evaluation of coordination, walking, and fine motor tasks involving the hands. Several guidelines have been published to assist in the diagnosis of PD, which include the Unified Parkinson's Disease Rating Scale (UPDRS) [9, 10]. Tests are used to measure mental capacity, behavior, mood, daily living activities, and motor function.

They can be very helpful in the initial diagnosis, to rule out other disorders, as well as in monitoring the progression of the disease to make therapeutic adjustments. Brain scans and other laboratory tests are also sometimes carried out, mostly to detect other disorders resembling Parkinson's disease. The Unified Parkinson's Disease Rating Scale is a rating scale used to follow the longitudinal course of Parkinson's disease. The UPDRS has long been the major rating scale that is used to assess severity of symptoms of Parkinson's disease.