Multi-Modality Neuroimaging Study on Neurobiological Mechanisms of Acupuncture (eBook)

Editor Jie Tian is a Professor and Director of Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing.

Contents 5
1: Early fMRI Studies of Acupuncture 6
1.1 Introduction 6
1.2 Principles of fMRI 7
1.2.1 MRI Contrast 7
1.2.1.1 Blood-Oxygen-Level-Dependent (BOLD) Contrast 8
1.2.1.2 Neural Basis of BOLD Signal and Neurovascular Coupling 9
1.3 Acupoint Specificity on fMRI 9
1.3.1 Acupoint Functional Specificity on fMRI 9
1.3.2 Acupoint Locational Specificity on fMRI 12
1.3.3 Comparative Studies of Brain Responses to Acupoint Stimulation on fMRI 15
1.4 fMRI Studies of Acupuncture Sensation 18
1.5 fMRI Studies of Acupuncture Manipulation 23
1.5.1 fMRI Studies of Different Acupuncture Stimulation Modalities 23
1.5.2 Influence of Acupuncture Parameters on Evoked Brain Responses 25
1.6 fMRI Studies of Acupuncture Analgesia 26
1.7 Methodological and Statistical Issues in Early fMRI Studies of Acupuncture 29
1.8 Summary 31
References 32
2: Temporospatial Encoding of Acupuncture Effects in the Brain 36
2.1 Introduction 36
2.2 Methods of Analysis for Human Acupuncture Neuroimaging Studies 37
2.2.1 General Linear Model (GLM) Analysis 37
2.2.2 Independent Component Analysis (ICA) 38
2.2.3 Functional Connectivity Analysis 39
2.2.4 Time Series State Analysis Algorithm Based on the Change-Point Theory 40
2.3 Acupuncture Neuroimaging Studies in Humans 41
2.3.1 Sustained Effects of Acupuncture and Its Influence on fMRI 45
2.4 Brain Network Analysis Methods 49
2.4.1 Graph Theory in Brain Network Analysis 49
2.4.1.1 Common Network Parameters According to Graph Theory 49
2.4.1.2 The Topological Properties of Small-World Networks and Scale-Free Networks 51
2.4.2 The Application of Graph Theory in Neuroscience Studies 52
2.4.3 The Application of Graph Theory in Acupuncture Studies 54
2.4.4 Analytical Algorithms Based on Pattern Classification 54
2.5 The Temporospatial Encoding of Acupuncture Effects on Brain Networks 59
2.6 Summary 60
References 61
3: Targeting Mechanisms of Typical Indications of Acupuncture 66
3.1 Introduction 66
3.2 Mechanisms of Addiction 67
3.2.1 Alterations in Resting-State Functional Connectivity in Heroin Addiction 67
3.2.1.1 Alterations in Functional Network Characteristics According to Duration of Heroin Use 69
3.2.1.2 Spatial and Temporal Alterations in Resting-State Networks Related to Heroin Addiction 72
3.2.1.3 Gray Matter Loss and Resting-State Abnormalities in Abstinent Heroin-Dependent Individuals 73
3.2.2 Microstructural Abnormalities in Adolescents with Internet Addiction Disorder (IAD) 76
3.3 Mechanisms of Migraine 78
3.3.1 Regional Homogeneity Abnormalities in Patients with Interictal Migraine 78
3.3.2 Gender-Related Differences in Resting-State Networks Dysfunction in Migraine 80
3.4 Mechanisms of Functional Dyspepsia (FD) 83
3.4.1 White Matter Microstructural Changes in Functional Dyspepsia 83
3.4.2 Abnormal Resting-State Brain Activity in Patients with FD 85
3.4.3 Influence of Acupuncture Treatment on Cerebral Activity in FD Patients 87
3.5 Summary 89
References 89
4: Findings of Acupuncture Mechanisms Using EEG and MEG 95
4.1 Introduction 95
4.2 Principles of Electroencephalography (EEG) 96
4.2.1 EEG Signal Generation 96
4.2.2 EEG Recording and Measurement 96
4.2.3 EEG Rhythms 98
4.2.4 EEG Applications 99
4.3 Applications of EEG in Acupuncture Research 99
4.3.1 EEG Studies of Acupuncture in Animals 100
4.3.2 EEG Studies of Acupuncture in Humans 102
4.4 Principles of MEG 106
4.4.1 Basic Principles of MEG 107
4.4.1.1 The Current Dipole Source Model 107
4.4.2 Measurement of MEG Signal 108
4.4.3 MEG Data Processing 109
4.4.3.1 The Inverse Problem 110
4.4.3.2 MEG Resolution Issues 110
4.4.4 Characterization of MEG Data in Different Frequency Bands 111
4.4.5 Methods for MEG Data Analysis 112
4.4.5.1 Partial Least Squares (PLS) 112
4.4.5.2 Principal Component Analysis (PCA) 113
4.4.5.3 Root Mean Square (RMS) 114
4.4.5.4 Independent Component Analysis (ICA) 114
4.4.5.5 Signal Space Separation (SSS) 116
4.4.5.6 Multiple Linear Regressions 116
4.4.5.7 Analysis of Variance (ANOVA) 117
4.5 Comparison Between the MEG and EEG Techniques 117
4.6 MEG Studies of Acupuncture in Humans 119
4.6.1 Effects of Acupuncture at Network Hubs in the Human Brain 119
4.6.2 Effects of Acupuncture on Network Functional Connectivity in the Human Brain 121
4.7 Summary 124
References 125
5: Prospects of Acupuncture Research in the Future 129
5.1 Introduction 129
5.2 Dynamic Functional Network Connectivity Research 130
5.2.1 Limitations of Resting-State Functional Connectivity Analysis 130
5.2.2 Necessity of Dynamic Research 130
5.2.3 Dynamic Research Application 131
5.2.3.1 A Dynamic Variety Analysis of Whole-Brain Functional Connectivity 131
5.2.3.2 A Dynamic Variety Analysis of Specific Brain Region Functional Connectivity 135
5.3 A Framework for Future fMRI Studies of Acupuncture 140
5.3.1 Study Populations 140
5.3.2 Analytical Methodology 140
5.3.3 Research Instruments 140
5.4 Summary 141
References 141
Index 143