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Title Page

Abstract

Contents

1. Introduction 14

2. Closed-Loop Neuromodulation for Parkinson's Disease: Current State and Future Directions 18

2.1. Introduction 18

2.2. Mechanisms of Neuromodulation 20

2.2.1. Effects of L-DOPA in the brain 20

2.2.2. Effects of DBS in the brain 23

2.3. Neuromodulation Control Paradigm 26

2.3.1. Control paradigm 26

2.3.2. Biomarkers 28

2.3.3. Stimulation algorithms 33

2.4. Future Directions 37

1) Cancellation of stimulation-related artifacts 38

2) AI in closed-loop DBS 40

3) Multi-modal closed-loop systems 41

2.5. Conclusion 43

3. Development of Tonic Dopamine Measurement Method 45

3.1. Introduction of Tonic Dopamine Measurement Techniques 45

3.2. Genetically Encoded Fluorescent Dopamine Sensor 47

3.2.1. Introduction of Fluorescent Sensor 47

3.2.2. ICoRD: Iterative Correlation-based ROI Detection Method 48

3.2.3. Microfluidic System for Investigating Anticipatory Medication Effects on Dopamine Homeostasis in Dopaminergic Cells 71

3.3. Fast-scan cyclic voltammetry (FSCV) 85

3.3.1. Introduction of FSCV 85

3.3.2. Second Derivative-based Background Drift Removal (SDBR) for Tonic Dopamine Measurement in FSCV 86

4. Advanced FSCV for Prolonged Measurement of Tonic Dopamine: Implications for Pharmacokinetic Analysis of Levodopa-Induced Dyskinesia 103

4.1. Introduction 104

4.2. Results 106

4.3. Discussion 115

4.4. Methods 122

5. Dopamine-related Biomarkers for PD 127

5.1. Relationship between Striatal Tonic Dopamine and Firing Activity of the Subthalamic Nucleus in PD 127

5.1.1. Introduction 127

5.1.2. Method 128

5.1.3. Result and Discussion 131

5.1.4. Conclusion 134

5.2. Simultaneous Estimation of Tonic Dopamine and Serotonin with High Temporal Resolution In Vitro Using Deep Learning 134

5.2.1. Introduction 135

5.2.2. Methods 137

5.2.3. Results and Discussion 139

5.2.4. Conclusion 142

6. Conclusion 143

References 146

요약문 184

List of Tables

table 2.1. Electical, neurochemical, and behavioral biomarkers of PD for closed-loop systems. 28

table 3.1. CNMF parameters. 60

table 3.2. Processing time (minutes) of ICoRD and CNMF on simulated calcium imaging data (512 x 512 x number of frames). 61

table 3.3. Comparison between SDBR and the tonic dopamine measurement methods using FSCV. 102

table 4.1. Technical features of the advanced FSCV system for in vivo dopamine measurement compared with other methods. 119

List of Figures

Figure 2.1. Illustration of the effects of L-DOPA on neurons and circuits. 21

Figure 2.2. Illustration of the effects of DBS on neurons and circuits. 24

Figure 2.3. Comparison of open-loop and closed-loop DBS. 27

Figure 2.4. Illustration of electrical, neurochemical and behavioral biomarkers of PD. 29

Figure 2.5. Closed-loop algorithms to control the electrical stimulation. 33

Figure 2.6. Closed-loop L-DOPA infusion systems for PD. 37

Figure 2.7. Schematic diagram of a potential multi-modal system. 42

Figure 3.1. Schematic representation of the proposed ICoRD algorithm for neural signal extraction. 51

Figure 3.2. Demixing of two overlapped neurons (Neuron A, Neuron B) in simulation data using ICoRD. 53

Figure 3.3. Detailed flowchart of the proposed ICoRD algorithm. 55

Figure 3.4. Simulated calcium dynamics at a severe noise level (-30 dB). 56

Figure 3.5. Results of the proposed ICoRD method and CNMF in simulated calcium imaging datasets. 60

Figure 3.6. Trend of correlation coefficient with final estimate, ground-truth correlation coefficient, and information difference during iterations in the simulation data with an SNR of -30 dB. 63

Figure 3.7. Application to in vivo two-photon calcium imaging data. 65

Figure 3.8. Comparison of estimated SNR with different SNR estimation methods and the ground-truth SNR. 66

Figure 3.9. ROI and the extracted calcium signal obtained by ICoRD and CNMF in neuron 1 with white Gaussian noise of different powers added. 68

Figure 3.10. Schematic illustration of the novel closed-loop control system. 76

Figure 3.11. 3D perspectives of the microfluidic device. 78

Figure 3.12. Developed LabVIEW UI. 79

Figure 3.13. In vitro PD modeling and evaluation of dopamine homeostasis using the closed- loop control system. 82

Figure 3.14. Fundamental of fast-scan cyclic voltammetry. 87

Figure 3.15. In vitro setup for FSCV measurement. 89

Figure 3.16. In vitro experiment for sensitivity test of FSCV. 89

Figure 3.17. Selectivity test of SDBR method using dopamine (6 µM), ascorbic acid (AA, 200 µM), 3,4-dihydroxyphenylacetic acid (DOPAC, 20 µM), and L-DOPA (5 µM). 91

Figure 3.18. A verification experiment for the measurement of dopamine using FSCV. 92

Figure 3.19. Result of verification experiment for the measurement of dopamine using FSCV. 92

Figure 3.20. Background subtraction and SDBR results according to dopamine concentration and charging current in a standard normal distribution (SND) shape voltammogram model. 95

Figure 3.21. An example of phasic dopamine signaling the in vivo experiment. 96

Figure 3.22. In vitro test of SDBR to record the tonic and phasic dopamine with standard FSCV. 97

Figure 3.23. Measurement of tonic dopamine level change using SDBR during in vivo pharmacological stimulation. 99

Figure 3.24. Anatomical insertion location of the putamen. 100

Figure 3.25. Measurement of tonic dopamine level change in primate during L-DOPA injection. 101

Figure 3.26. Abstract figure of SDBR. 101

Figure 4.1. Animal model for levodopa-induced dyskinesia. 107

Figure 4.2. Long-range monitoring of tonic dopamine levels using an advanced FSCV system. 110

Figure 4.3. Profiling dyskinetic behavior in response to acute L-DOPA administration along LID progression. 112

Figure 4.4. Long-range tracing of tonic dopamine dynamics in response to acute L-DOPA administration to the LID model using the advanced FSCV system. 114

Figure 4.5. Correlation analysis between dyskinetic behavior and dopamine dynamics along LID induction. 116

Figure 4.6. Calculation procedure for the slope of dopamine level change. 117

Figure 4.7. Pharmacokinetic analysis of LID with the advanced FSCV system. 121

Figure 5.1. Schematic representation of proposed multi-modal measurement. 128

Figure 5.2. Multi-modal recording in the Rat PD model. 129

Figure 5.3. Example of multimodal recording results. 132

Figure 5.4. Relationship between firing rate of STN and tonic dopamine level of striatum. 133

Figure 5.5. Experimental and data processing pipeline for estimating dopamine and serotonin simultaneously using FSCV. 138

Figure 5.6. Schematic representation of proposed deep learning model based on temporal convolutional network (TCN). 139

Figure 5.7. In vitro test for simultaneous prediction of dopamine and serotonin. 140

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