Introduction to human neuroimaging:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore
Cambridge University Press
2019
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| Series: | Cambridge fundamentals of neuroscience in psychology
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xviii, 350 Seiten Illustrationen, Diagramme |
| ISBN: | 9781316632185 9781107180307 |
Staff View
MARC
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Record in the Search Index
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| adam_text | Contents List of Figures Preface 1 Introduction and Overview page x xv 1 1.1 Brain Enthusiasm: The Relevance of Distinguishing Fact from Fiction 1.2 The Basis of Neural Signals 1.2.1 Information Transfer in Neurons 1.2.2 Signal Processing 1.2.3 Other Signals in the Brain: Molecular and Hemodynamic Signals 1.2.4 Maps in the Brain: From the Activity of Single Neurons to Signals without Single-Neuron Resolution 1.3 A Short Overview of Methods in Human Neuroscience 1.3.1 Techniques to Measure Brain Structure 1.3.2 Techniques to Measure Hemodynamic Correlates of Neural Activity 1.3.3 Techniques to Measure Electrophysiological Activity 20 22 Part I Structural Neuroimaging 29 2 The Physics behind Magnetic Resonance Imaging (MRI) 2.1 The Effect of Magnetic Fields on the Human Body 2.2 From Resonance to Imaging 2.3 How Do These Physical Principles Give Rise to an Image with Anatomical Structure? 2.4 The Hardware of a Scanner 2.5 Parameters That Are Chosen by the User 3 Structural Imaging Methods 3.1 Structural Tl-Weighted MRI 3.1.1 Quality Check 3.1.2 Finding Structure in Anatomical Images and Normalization 3.1.3 Approaches to Investigate Brain Morphometry 3.1.4 Statistical Analysis and Interpretation 3.1.5 Voxel-Based Lesion-Symptom Mapping 3.1.6 The Relevance of Brain Structure for Behavior and Mind 3.2 Diffusion Tensor Imaging (DTI) 3.2.1 Data Acquisition 3.2.2 Data Analysis 2 4 7 10 13 15 18 19 31 32 35 40 42 45 48 49 49 50 56 57 58 58 61 62 64
Contents VI 3.2.3 The Relevance of Anatomical Connectivity for Behavior and Mind 3.3 Magnetic Resonance Spectroscopy (MRS) 3.3.1 Data Acquisition 3.3.2 Data Analysis 3.3.3 The Relevance of Molecular Indices for Behavior and Mind 67 68 69 72 73 Part II Hemodynamic Neuroimaging 77 4 Hemodynamic Imaging Methods 4.1 4.2 4.3 4.4 4.5 Hemodynamics and Its Relationship to Neural Activity 4.1.1 The Hemodynamic Response Function 4.1.2 The Relationship between the HRF and Different Aspects of Neural Activity Functional Magnetic Resonance Imaging (fMRI) 4.2.1 Blood-Oxygenation-Level Dependent fMRI 4.2.2 Arterial Spin Labeling fMRI 4.2.3 The Relevance of fMRI for Behavior Positron Emission Tomography (PET) 4.3.1 The Physics of PET 4.3.2 Using PET for Measuring Neural Activity 4.3.3 Unique Contributions of PET Functional Near-Infrared Spectroscopy (fNIRS) A Comparison of Research with fMRI, PET, and fNIRS 5 Designing a Hemodynamic Imaging Experiment 5.1 Think Before You Start an Experiment 5.2 Which Conditions to Include: The Subtraction Method 5.2.1 The Subtraction Method 5.2.2 Considerations about the Subtraction Method 5.3 How to Present the Conditions: The Block Design 5.3.1 The Block Design and the Hemodynamic Response Function 5.3.2 The Block Design in Practice in fMRI and fNIRS 5.3.3 A Few Examples of Classical Studies Using a Block Design 5.4 The Event-Related Design 5.5 The Baseline or Rest Condition 5.5.1 The Role of a Baseline in Task-Based fMRI 5.5.2 Regions Activated during a Resting Baseline 5.6 Task and Stimuli in the Scanner 6 Image Processing 6.1 Software Packages 6.2
Properties of the Images 79 81 81 84 88 89 91 92 92 93 94 95 96 98 102 103 104 104 106 108 108 111 113 115 118 118 120 122 127 127 130
Contents vii 6.3 Preprocessing Step 1: 6.4 Preprocessing Step 2: 6.5 Preprocessing Step 3: 6.6 Preprocessing Step 4: 6.7 Preprocessing Step 5: Slice Timing Motion Correction Coregistration Normalization Spatial Smoothing 7 Basic Statistical Analyses 7.1 Statistical Analyses: The General Linear Model 7.1.1 Simple Linear Regression 7.1.2 Multiple Linear Regression 7.1.3 The General Linear Model Applied to fMRI Data 7.1.4 Data Cleaning prior to Applying the GLM 7.1.5 The Efficiency of a Design and Correlation between Predictors 7.2 Determining Significance and Interpreting It 7.2.1 Calculating a Simple Test Statistic: A i-Contrast 7.2.2 Correction for Multiple Comparisons, or How to Avoid Brain Activity in Dead Salmon 7.2.3 Combining Data across Participants: Second-Level Whole-Brain Analyses 7.2.4 Region-of-Interest Analyses 7.2.5 Another Statistical Caveat: Double Dipping and Circular Analyses 7.2.6 Statistical Inference 8 Advanced Statistical Analyses 131 132 135 137 137 142 142 142 143 144 145 146 148 148 151 154 155 157 159 163 8.1 Functional Connectivity: Designs and Analyses 8.1.1 Correlations in Brain Activity 8.1.2 The Interpretation of Correlations in Brain Activity 8.1.3 Modeling Directional Functional Connectivity 8.1.4 Task-Related Modulations of Connectivity 8.1.5 Resting-State fMRI (RS fMRI) 8.2 Multi-voxel Pattern Analyses 8.2.1 A Schematic Tutorial of MVPA 8.2.2 A Specific Example of MVPA 8.2.3 The Potential of MVPA to Move beyond Neophrenology 8.2.4 What Do We Measure with MVPA? 8.3 Functional MRI Adaptation 163 164 165 168 171 173 176 176 178 181 183 188
Part III Electrophysiological Neuroimaging 191 9 Electromagnetic Field of the Brain 9.1 Electrophysiological Activity of the Brain 9.1.1 From Neurons to Electric Field 193 194 194
Contents viii 9.1.2 Magnetic Field of the Neural Activity 9.1.3 From the Field to Sensors 9.2 Electromagnetic Field Signals 9.2.1 Properties of the Field Signal 9.2.2 Dimensions and Resolution of the Field Signal 9.3 Brain Dynamics vs. Mind Dynamics 10 Electroencephalography and Magnetoencephalography 10.1 Electroencephalography (EEG) 10.1.1 EEG Electrodes 10.1.2 EEG Amplifier 10.1.3 Procedure for Data Acquisition 10.2 Magnetoencephalography (MEG) 10.2.1 MEG Sensors 10.2.2 Magnetically Shielded Room 10.2.3 Procedure for MEG Data Acquisition 10.3 Comparison between EEG and MEG 11 Basic Analysis of Electrophysiological Signals 11.1 Preprocessing 11.1.1 Noise 11.1.2 Montage 11.1.3 Segmentation and Visual Inspection 11.1.4 Independent Component Analysis for Preprocessing 11.1.5 Filtering for Preprocessing 11.1.6 Resampling 11.2 Main Signal Processing 11.2.1 Spectral Analysis 11.2.2 Event-Related Potential Analysis 11.3 Statistical Tests 12 Advanced Data Analysis 12.1 Short Time Fourier Transform and Wavelet Transform 12.1.1 Short Time Fourier Transform 12.1.2 Wavelet Transform 12.1.3 STFT or Wavelet? 12.2 Phase Analysis 12.2.1 Computation of the Phase 12.2.2 Phase Synchrony 12.2.3 Network Analysis 12.2.4 Inter-trial Phase Coherence 12.2.5 Trial Averaging Revisited 12.3 Autoregression and Granger Causality 197 198 198 200 204 206 209 210 211 218 219 221 222 226 227 228 231 232 232 235 236 236 238 240 241 241 246 249 252 252 252 255 258 259 259 260 262 265 267 269
Contents ix 12.3.1 Autoregression 12.3.2 Granger Causality 269 271 Part IV Complementary Methods 275 13 Multi-modal Imaging 13.1 13.2 13.3 13.4 13.5 The Spatial and Temporal Unfolding of Visual Category Representations Simultaneous Application of EEG and fMRI M/EEG Source Localization Differentiating between Representational and Access Theories of Disorders Clinical Diagnostics with Multi-modal Imaging 14 Causal Methods to Modulate Brain Activity 14.1 14.2 14.3 14.4 Microstimulation and Deep Brain Stimulation Focused Ultrasound Stimulation (FUS) Transcranial Magnetic Stimulation (TMS) Transcranial Current Stimulation (TCS) Glossary References Index 277 278 281 284 286 289 292 293 297 298 303 309 325 343
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| spelling | Op de Beeck, Hans 1975- Verfasser (DE-588)1188360329 aut Introduction to human neuroimaging Hans Op de Beeck (KU Leuven, Belgium) ; Chie Nakatani (KU Leuven, Belgium) Cambridge ; New York ; Port Melbourne ; New Delhi ; Singapore Cambridge University Press 2019 xviii, 350 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Cambridge fundamentals of neuroscience in psychology Neurologie (DE-588)4041888-1 gnd rswk-swf Bildgebendes Verfahren (DE-588)4006617-4 gnd rswk-swf Bildgebendes Verfahren (DE-588)4006617-4 s Neurologie (DE-588)4041888-1 s DE-604 Nakatani, Chie Verfasser aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031066434&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Op de Beeck, Hans 1975- Nakatani, Chie Introduction to human neuroimaging Neurologie (DE-588)4041888-1 gnd Bildgebendes Verfahren (DE-588)4006617-4 gnd |
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| title | Introduction to human neuroimaging |
| title_auth | Introduction to human neuroimaging |
| title_exact_search | Introduction to human neuroimaging |
| title_full | Introduction to human neuroimaging Hans Op de Beeck (KU Leuven, Belgium) ; Chie Nakatani (KU Leuven, Belgium) |
| title_fullStr | Introduction to human neuroimaging Hans Op de Beeck (KU Leuven, Belgium) ; Chie Nakatani (KU Leuven, Belgium) |
| title_full_unstemmed | Introduction to human neuroimaging Hans Op de Beeck (KU Leuven, Belgium) ; Chie Nakatani (KU Leuven, Belgium) |
| title_short | Introduction to human neuroimaging |
| title_sort | introduction to human neuroimaging |
| topic | Neurologie (DE-588)4041888-1 gnd Bildgebendes Verfahren (DE-588)4006617-4 gnd |
| topic_facet | Neurologie Bildgebendes Verfahren |
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