Chapter 1 Introduction and Overview
Learning Objectives
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 Resolu
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
Summary
Review Questions
Further Reading
Notes
Part I Structural Neuroimaging
Chapter 2 The Physics behind Magnetic Resonance Imaging (MRI)
Learning Objectives
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
Summary
Review Questions
Further Reading
Chapter 3 Structural Imaging Methods
Learning Objectives
3.1 Structural T1-Weighted MRI
3.1.1 Quality Check
Image Artifacts
Incidental Findings
Image Acquisition Problems and Constraints
3.1.2 Finding Structure in Anatomical Images and Normalization
Volume-Based Normalization
Segmentation and Segmentation-Based Normalization
Surface Extraction and Surface-Based 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
Preprocessing
Tensor Estimation
Tractography
Useful Indices
Statistics
Interpretation
3.2.3 The Relevance of Anatomical Connectivity for Behavior and Mind
3.3 Magnetic Resonance Spectroscopy (MRS)
3.3.1 Data Acquisition
From Biological Structure to a Frequency Spectrum
Single-Voxel MRS and MRS Imaging
Water Suppression and Editing
3.3.2 Data Analysis
3.3.3 The Relevance of Molecular Indices for Behavior and Mind
Summary
Review Questions
Further Reading
Notes
Part II Hemodynamic Neuroimaging
Chapter 4 Hemodynamic Imaging Methods
Learning Objectives
4.1 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
4.2 Functional Magnetic Resonance Imaging (fMRI)
4.2.1 Blood-Oxygenation-Level Dependent fMRI
Blood Oxygenation and the Physics of fMRI
Measuring the BOLD Contrast
4.2.2 Arterial Spin Labeling fMRI
4.2.3 The Relevance of fMRI for Behavior
4.3 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
4.4 Functional Near-Infrared Spectroscopy (fNIRS)
4.5 A Comparison of Research with fMRI, PET, and fNIRS
Summary
Review Questions
Further Reading
Chapter 5 Designing a Hemodynamic Imaging Experiment
Learning Objectives
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
More Advanced Designs and Analyses
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
Summary
Review Questions
Further Reading
Notes
Chapter 6 Image Processing
Learning Objectives
6.1 Software Packages
6.2 Properties of the Images
6.3 Preprocessing Step 1: Slice Timing
6.4 Preprocessing Step 2: Motion Correction
6.5 Preprocessing Step 3: Coregistration
6.6 Preprocessing Step 4: Normalization
6.7 Preprocessing Step 5: Spatial Smoothing
Summary
Review Questions
Further Reading
Chapter 7 Basic Statistical Analyses
Learning Objectives
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 t-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
Summary
Review Questions
Further Reading
Chapter 8 Advanced Statistical Analyses
Learning Objectives
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)
The Implementation and Analysis of RS fMRI
Findings Obtained with 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
Summary
Review Questions
Further Reading
Part III Electrophysiological Neuroimaging
Chapter 9 Electromagnetic Field of the Brain
Learning Objectives
9.1 Electrophysiological Activity of the Brain
9.1.1 From Neurons to Electric Field
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
Summary
Review Questions
Further Reading
Chapter 10 Electroencephalography and Magnetoencephalography
Learning Objectives
10.1 Electroencephalography (EEG)
10.1.1 EEG Electrodes
Reference and Ground Electrodes
EOG 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
Summary
Review Questions
Further Reading
Chapter 11 Basic Analysis of Electrophysiological Signals
Learning Objectives
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
Summary
Review Questions
Further Reading
Chapter 12 Advanced Data Analysis
Learning Objectives
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
12.3.1 Autoregression
12.3.2 Granger Causality
Summary
Review Questions
Further Reading
Part IV Complementary Methods
Chapter 13 Multi-modal Imaging
Learning Objectives
13.1 The Spatial and Temporal Unfolding of Visual Category Representations
13.2 Simultaneous Application of EEG and fMRI
13.3 M/EEG Source Localization
13.4 Differentiating between Representational and Access Theories of Disorders
13.5 Clinical Diagnostics with Multi-modal Imaging
Summary
Review Questions
Further Reading
Chapter 14 Causal Methods to Modulate Brain Activity
Learning Objectives
14.1 Microstimulation and Deep Brain Stimulation
14.2 Focused Ultrasound Stimulation (FUS)
14.3 Transcranial Magnetic Stimulation (TMS)
14.4 Transcranial Current Stimulation (TCS)
Summary
Review Questions
Further Reading
Glossary
References
Index
