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E-grāmata: Introduction to Human Neuroimaging

2.89/5 (9 ratings by Goodreads)
(Katholieke Universiteit Leuven, Belgium), (Katholieke Universiteit Leuven, Belgium)
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Introduction to Human NeuroimagingPalielināt
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Developed specifically for students in the behavioral and brain sciences, this is the only textbook that provides an accessible and practical overview of the range of human neuroimaging techniques. Methods covered include functional and structural magnetic resonance imaging, positron emission tomography, electroencephalography, magnetoencephalography, multimodal imaging, and various brain stimulation methods. Experimental design, image processing, and statistical inference are also addressed, with chapters for both basic and more advanced data analyses. Key concepts are illustrated through research studies on the relationship between brain and behavior, and practice questions are included throughout to test knowledge and aid self-study. Offering just the right amount of detail for understanding how major imaging techniques can be applied to answer neuroscientific questions, and the practical skills needed for future research, this is an essential text for advanced undergraduate and graduate students in psychology, neuroscience, and cognitive science programs taking introductory courses on human neuroimaging.

Recenzijas

'This masterfully written text threads the needle between being a cutting-edge and comprehensive state-of-the-art reference on all human neuroimaging methods, and a highly accessible and perspective-building book that one will want to read from cover to cover. I look forward to purchasing a copy for everyone in my lab.' Peter Bandettini, Director of the Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Health 'Well written, nicely illustrated, and authoritative, this book is certainly to be recommended both to those starting out in the field and to those wanting to deepen and widen their neuroimaging knowledge.' Vincent P. Giampietro, King's College London 'This is a valuable reference for those interested, and currently utilizing, human neuroimaging in their research. It provides a strong neurophysiological basis for the methods, a physics basis for the instrumentation, and modern analytical tools useful for the quantification of data. I highly recommend this book!' Lewis A. Wheaton, Georgia Institute of Technology

Papildus informācija

An accessible primer for courses on human neuroimaging methods, with example research studies, color figures, and practice questions.
List of Figures
x
Preface xv
1 Introduction and Overview
1(28)
1.1 Brain Enthusiasm: The Relevance of Distinguishing Fact from Fiction
2(2)
1.2 The Basis of Neural Signals
4(14)
1.2.1 Information Transfer in Neurons
7(3)
1.2.2 Signal Processing
10(3)
1.2.3 Other Signals in the Brain: Molecular and Hemodynamic Signals
13(2)
1.2.4 Maps in the Brain: From the Activity of Single Neurons to Signals without Single-Neuron Resolution
15(3)
1.3 A Short Overview of Methods in Human Neuroscience
18(11)
1.3.1 Techniques to Measure Brain Structure
19(1)
1.3.2 Techniques to Measure Hemodynamic Correlates of Neural Activity
20(2)
1.3.3 Techniques to Measure Electrophysiological Activity
22(7)
Part I Structural Neuroimaging
29(48)
2 The Physics behind Magnetic Resonance Imaging (MRI)
31(17)
2.1 The Effect of Magnetic Fields on the Human Body
32(3)
2.2 From Resonance to Imaging
35(5)
2.3 How Do These Physical Principles Give Rise to an Image with Anatomical Structure?
40(2)
2.4 The Hardware of a Scanner
42(3)
2.5 Parameters That Are Chosen by the User
45(3)
3 Structural Imaging Methods
48(29)
3.1 Structural T1-Weighted MRI
49(12)
3.1.1 Quality Check
49(1)
3.1.2 Finding Structure in Anatomical Images and Normalization
50(6)
3.1.3 Approaches to Investigate Brain Morphometry
56(1)
3.1.4 Statistical Analysis and Interpretation
57(1)
3.1.5 Voxel-Based Lesion-Symptom Mapping
58(1)
3.1.6 The Relevance of Brain Structure for Behavior and Mind
58(3)
3.2 Diffusion Tensor Imaging (DTI)
61(7)
3.2.1 Data Acquisition
62(2)
3.2.2 Data Analysis
64(3)
3.2.3 The Relevance of Anatomical Connectivity for Behavior and Mind
67(1)
3.3 Magnetic Resonance Spectroscopy (MRS)
68(9)
3.3.1 Data Acquisition
69(3)
3.3.2 Data Analysis
72(1)
3.3.3 The Relevance of Molecular Indices for Behavior and Mind
73(4)
Part II Hemodynamic Neuroimaging
77(114)
4 Hemodynamic Imaging Methods
79(23)
4.1 Hemodynamics and Its Relationship to Neural Activity
81(7)
4.1.1 The Hemodynamic Response Function
81(3)
4.1.2 The Relationship between the HRF and Different Aspects of Neural Activity
84(4)
4.2 Functional Magnetic Resonance Imaging (fMRI)
88(4)
4.2.1 Blood-Oxygenation-Level Dependent fMRI
89(2)
4.2.2 Arterial Spin Labeling fMRI
91(1)
4.2.3 The Relevance of fMRI for Behavior
92(1)
4.3 Positron Emission Tomography (PET)
92(4)
4.3.1 The Physics of PET
93(1)
4.3.2 Using PET for Measuring Neural Activity
94(1)
4.3.3 Unique Contributions of PET
95(1)
4.4 Functional Near-Infrared Spectroscopy (fNIRS)
96(2)
4.5 A Comparison of Research with fMRI, PET, and fNIRS
98(4)
5 Designing a Hemodynamic Imaging Experiment
102(25)
5.1 Think Before You Start an Experiment
103(1)
5.2 Which Conditions to Include: The Subtraction Method
104(4)
5.2.1 The Subtraction Method
104(2)
5.2.2 Considerations about the Subtraction Method
106(2)
5.3 How to Present the Conditions: The Block Design
108(7)
5.3.1 The Block Design and the Hemodynamic Response Function
108(3)
5.3.2 The Block Design in Practice in fMRI and fNIRS
111(2)
5.3.3 A Few Examples of Classical Studies Using a Block Design
113(2)
5.4 The Event-Related Design
115(3)
5.5 The Baseline or Rest Condition
118(4)
5.5.1 The Role of a Baseline in Task-Based fMRI
118(2)
5.5.2 Regions Activated during a Resting Baseline
120(2)
5.6 Task and Stimuli in the Scanner
122(5)
6 Image Processing
127(15)
6.1 Software Packages
127(3)
6.2 Properties of the Images
130(1)
6.3 Preprocessing Step 1: Slice Timing
131(1)
6.4 Preprocessing Step 2: Motion Correction
132(3)
6.5 Preprocessing Step 3: Coregistration
135(2)
6.6 Preprocessing Step 4: Normalization
137(1)
6.7 Preprocessing Step 5: Spatial Smoothing
137(5)
7 Basic Statistical Analyses
142(21)
7.1 Statistical Analyses: The General Linear Model
142(6)
7.1.1 Simple Linear Regression
142(1)
7.1.2 Multiple Linear Regression
143(1)
7.1.3 The General Linear Model Applied to fMRI Data
144(1)
7.1.4 Data Cleaning prior to Applying the GLM
145(1)
7.1.5 The Efficiency of a Design and Correlation between Predictors
146(2)
7.2 Determining Significance and Interpreting It
148(15)
7.2.1 Calculating a Simple Test Statistic: A f-Contrast
148(3)
7.2.2 Correction for Multiple Comparisons, or How to Avoid Brain Activity in Dead Salmon
151(3)
7.2.3 Combining Data across Participants: Second-Level Whole-Brain Analyses
154(1)
7.2.4 Region-of-Interest Analyses
155(2)
7.2.5 Another Statistical Caveat: Double Dipping and Circular Analyses
157(2)
7.2.6 Statistical Inference
159(4)
8 Advanced Statistical Analyses
163(28)
8.1 Functional Connectivity: Designs and Analyses
163(13)
8.1.1 Correlations in Brain Activity
164(1)
8.1.2 The Interpretation of Correlations in Brain Activity
165(3)
8.1.3 Modeling Directional Functional Connectivity
168(3)
8.1.4 Task-Related Modulations of Connectivity
171(2)
8.1.5 Resting-State fMRI (RS fMRI)
173(3)
8.2 Multi-voxel Pattern Analyses
176(12)
8.2.1 A Schematic Tutorial of MVPA
176(2)
8.2.2 A Specific Example of MVPA
178(3)
8.2.3 The Potential of MVPA to Move beyond Neophrenology
181(2)
8.2.4 What Do We Measure with MVPA?
183(5)
8.3 Functional MRI Adaptation
188(3)
Part III Electrophysiological Neuroimaging
191(84)
9 Electromagnetic Field of the Brain
193(16)
9.1 Electrophysiological Activity of the Brain
194(4)
9.1.1 From Neurons to Electric Field
194(3)
9.1.2 Magnetic Field of the Neural Activity
197(1)
9.1.3 From the Field to Sensors
198(1)
9.2 Electromagnetic Field Signals
198(8)
9.2.1 Properties of the Field Signal
200(4)
9.2.2 Dimensions and Resolution of the Field Signal
204(2)
9.3 Brain Dynamics vs. Mind Dynamics
206(3)
10 Electroencephalography and Magnetoencephalography
209(22)
10.1 Electroencephalography (EEG)
210(11)
10.1.1 EEG Electrodes
211(7)
10.1.2 EEG Amplifier
218(1)
10.1.3 Procedure for Data Acquisition
219(2)
10.2 Magnetoencephalography (MEG)
221(7)
10.2.1 MEG Sensors
222(4)
10.2.2 Magnetically Shielded Room
226(1)
10.2.3 Procedure for MEG Data Acquisition
227(1)
10.3 Comparison between EEG and MEG
228(3)
11 Basic Analysis of Electrophysiological Signals
231(21)
11.1 Preprocessing
232(9)
11.1.1 Noise
232(3)
11.1.2 Montage
235(1)
11.1.3 Segmentation and Visual Inspection
236(1)
11.1.4 Independent Component Analysis for Preprocessing
236(2)
11.1.5 Filtering for Preprocessing
238(2)
11.1.6 Resampling
240(1)
11.2 Main Signal Processing
241(8)
11.2.1 Spectral Analysis
241(5)
11.2.2 Event-Related Potential Analysis
246(3)
11.3 Statistical Tests
249(3)
12 Advanced Data Analysis
252(23)
12.1 Short Time Fourier Transform and Wavelet Transform
252(7)
12.1.1 Short Time Fourier Transform
252(3)
12.1.2 Wavelet Transform
255(3)
12.1.3 STFT or Wavelet?
258(1)
12.2 Phase Analysis
259(10)
12.2.1 Computation of the Phase
259(1)
12.2.2 Phase Synchrony
260(2)
12.2.3 Network Analysis
262(3)
12.2.4 Inter-trial Phase Coherence
265(2)
12.2.5 Trial Averaging Revisited
267(2)
12.3 Autoregression and Granger Causality
269(6)
12.3.1 Autoregression
269(2)
12.3.2 Granger Causality
271(4)
Part IV Complementary Methods
275(34)
13 Multi-modal Imaging
277(15)
13.1 The Spatial and Temporal Unfolding of Visual Category Representations
278(3)
13.2 Simultaneous Application of EEG and fMRI
281(3)
13.3 M/EEG Source Localization
284(2)
13.4 Differentiating between Representational and Access Theories of Disorders
286(3)
13.5 Clinical Diagnostics with Multi-modal Imaging
289(3)
14 Causal Methods to Modulate Brain Activity
292(17)
14.1 Microstimulation and Deep Brain Stimulation
293(4)
14.2 Focused Ultrasound Stimulation (FUS)
297(1)
14.3 Transcranial Magnetic Stimulation (TMS)
298(5)
14.4 Transcranial Current Stimulation (TCS)
303(6)
Glossary 309(16)
References 325(18)
Index 343
Hans Op de Beeck is a Professor in the Faculty of Psychology and Educational Sciences at the Katholieke Universiteit Leuven, Belgium (KU Leuven), and a member of the Laboratory of Biological Psychology (LBP) and the Brain and Cognition Research Unit. He is the recipient of a long-term fellowship and a career development award from the international Human Frontier Science Program (HFSP), an honorary fellowship of the Belgian-American Educational Foundation (BAEF), the KU Leuven Research Council Award in 2008, and the 2012 laureate of the Royal Flemish Academy of Belgium (KVAB). Chie Nakatani is a Postdoctoral Fellow in the Brain and Cognition Research Unit at the Katholieke Universiteit Leuven, Belgium, having previously worked as a Research Scientist at the Laboratory for Perceptual Dynamics, RIKEN Brain Science Institute, Japan. She has a range of research and teaching experience with a particular focus on electroencephalography.
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