EEG - fMRI: Physiological Basis, Technique, and Applications

Description

This book provides the most up-to-date and comprehensive source of information on all aspects of EEG-fMRI, a neuroimaging technique for synchronous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The reader will find in-depth information on the physiological principles of the EEG and fMRI signals, practical aspects of data measurement, artifact reduction, data analysis, and applications. All the main areas of the technique’s application are the subject of one or multiple chapters:  sleep research, cognitive neuroscience, and clinical neurology and psychiatry. In addition to providing a thorough update, this second edition offers five entirely new chapters covering important areas of research that have emerged during the past 5 years, including noninvasive brain stimulation during fMRI, resting-state functional connectivity, real-time fMRI, and neurofeedback. Written by the most prestigious experts in the field, the text is enhanced by numerous high-quality illustrations. This book will be valuable for neuroradiologists, neuroscientists, physicists, engineers, electrophysiologists, (neuro) medical scientists, neurologists, and neurophysiologists. Chapter 30 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Preface to the Second Edition Reference Contents Part I: Background 1: Principles of Multimodal Functional Imaging and Data Integration 1.1 Introduction 1.2 Modes of Data Integration 1.3 Multimodal Data Acquisition Strategies: Degree of Synchrony 1.4 Multimodal Data Integration Strategies 1.4.1 Spatial Coregistration 1.4.2 Asymmetric Integration 1.4.3 Symmetrical Data Fusion 1.5 Summary References 2: EEG: Origin and Measurement 2.1 Introduction to the Electrophysiology of the Brain 2.2 Origin of EEG and MEG: Cellular Sources 2.3 Main Types of Rhythmical EEG/MEG Activities: Phenomenology and Functional Significance 2.3.1 Sleep EEG Phenomena 2.3.2 Theta Rhythms 2.3.3 Alpha Rhythms of Neocortex and Thalamus 2.3.4 Beta and Gamma Activity of the Neocortex 2.3.5 DC and Ultraslow Potentials 2.4 Origin of the EEG/MEG II: Generators, Volume Conduction and Source Estimation 2.5 Localisation Methods Applied to Spontaneous Oscillatory Activities: Alpha, Mu and Sleep Spindles 2.6 Conclusions References 3: The Basics of Functional Magnetic Resonance Imaging 3.1 The Basics of MR Imaging 3.1.1 Spins in an External Magnetic Field 3.1.2 The Magnetic Resonance Effect 3.1.3 Spatial Encoding in MR Imaging 3.1.3.1 Frequency Encoding 3.1.3.2 Phase Encoding 3.1.3.3 Slice Selection 3.1.4 Relaxation Times T1 and T2 3.1.5 The Relaxation Time T2* and Gradient Echoes 3.1.6 k-Space 3.1.7 Echo Planar Imaging (EPI) 3.1.8 Spin Echoes 3.1.9 The Specific Absorption Rate (SAR) 3.2 The Cerebral Blood Flow (CBF) 3.2.1 Definition, Order of Magnitude and Measurement 3.2.2 Arterial Spin Labelling Measurements 3.2.3 Labelling Methods 3.2.4 Quantification Problems in ASL 3.3 The Cerebral Blood Volume (CBV) 3.3.1 Definition, Order of Magnitude and Measurement 3.3.2 Contrast Agent-Based Methods 3.3.2.1 Dynamic Imaging 3.3.2.2 Steady-State Imaging 3.3.3 Contrast Agent-Free Method: Vascular Space Occupancy Measurement 3.4 The BOLD Effect and Functional MRI References 4: Locally Measured Neuronal Correlates of Functional MRI Signals 4.1 Blood Oxygenation Level-Dependent (BOLD) Signals 4.2 Extracellular Neurophysiological Signals 4.3 Relationship Between Neuronal Activity and fMRI Signals 4.4 Correlations Between Neurophysiological Signals and fMRI Responses 4.5 What Is the Neural Origin of fMRI Responses? 4.6 Neuronal Correlates of Negative BOLD Responses 4.7 Neuronal Correlates of Spontaneous Fluctuations in fMRI Signals 4.8 Dissociations Between BOLD Responses and Neurophysiological Activity References 5: What Can fMRI Add to the ERP Story? 5.1 Introduction 5.2 ERP Generator Localization 5.3 The Inverse Problem of EEG 5.4 Does fMRI Help to Solve the Inverse Problem? 5.5 Further Aspects 5.6 Serial Processing vs. Parallel and Reciprocal Network Activity 5.7 Subcortical Processing 5.8 Conclusions References 6: The Added Value of EEG-fMRI in Imaging Neuroscience 6.1 Introduction 6.2 The EEG-fMRI Integrated Source Space 6.3 Data Integration Strategies for EEG-fMRI Studies 6.4 Illustration of the Integration of fMRI and EEG in the Temporal Domain 6.5 Illustration of the Integration of fMRI and EEG in the Spatial Domain 6.6 Direct Integration of fMRI and Intra-cranial EEG in the Spatial Domain 6.7 Discussion References Part II: Technical and Methodological Aspects of Combined EEG-fMRI Experiments 7: EEG Instrumentation and Safety in the MRI Environment 7.1 Introduction 7.2 EEG Instrumentation 7.2.1 Electrodes 7.2.1.1 Electrode Materials 7.2.1.2 Electrode Lead Arrangement 7.2.1.3 Electrode Lead Movement 7.2.2 EEG Recording System 7.2.2.1 Filters 7.2.2.2 Sampling Rate 7.2.2.3 Signal Range 7.2.2.4 Signal Resolution 7.2.3 RF Emissions 7.2.4 Miscellaneous Factors 7.2.5 Summary 7.3 Safety 7.3.1 Safety Limits 7.3.2 Static Field 7.3.3 Gradient Fields 7.3.4 Eddy Currents 7.3.5 RF Fields 7.3.6 Implanted Electrodes 7.3.7 Summary References 8: EEG Quality: The Pulse Artifact 8.1 Introduction 8.2 Biophysical Mechanisms 8.2.1 Hypothesized Sources 8.2.2 Experimental Evidence 8.3 Data Acquisition Considerations 8.4 Artifact Reduction Methods 8.4.1 Temporal Waveform-Based Methods 8.4.2 Spatiotemporal Pattern-Based Methods 8.4.3 Sensor-Based Methods 8.4.4 Artifact Reduction Evaluation 8.5 Conclusion References 9: EEG Quality: The Image Acquisition Artefact 9.1 Origin of the Image Acquisition Artefact 9.2 Characteristics of the Image Acquisition Artefact 9.2.1 Characterisation of the Cooling Pump Artefact 9.2.1.1 Cooling Pump Artefact Prevention 9.2.1.2 Cooling Pump Artefact Removal 9.3 Avoiding Image Acquisition Artefacts: Interleaved EEG–fMRI Protocols 9.4 Reduction of Image Acquisition Artefacts 9.4.1 Reduction at the Source 9.4.1.1 Stepping-Stone Sampling 9.4.2 Synchronisation of EEG and fMRI Data Acquisitions 9.5 Correction of the Image Acquisition Artefact Using EEG Post-Processing 9.5.1 Artefact Template Subtraction 9.5.2 Computing and Correcting Timing Errors 9.5.3 Temporal Principal Component Analysis 9.5.4 Independent Component Analysis 9.5.5 Filtering in the Frequency Domain 9.5.6 Between Prevention and Correction: Prospective Motion Correction and EEG Artefacts 9.6 Evaluation of Correction Methods References 10: Image Quality Issues 10.1 fMRI Pulse Sequences 10.2 GE-EPI 10.2.1 Image Blurring 10.2.2 Geometric Distortion 10.2.3 Signal Dropout 10.2.4 Image Ghosting 10.2.5 RF Interference 10.3 Other Sources of Image Artefact in fMRI 10.3.1 Bulk Head Motion 10.3.2 Physiological Noise 10.4 The Impact of EEG Recording on MR Image Quality 10.4.1 Main Static Magnetic Field (B0) Effects 10.4.2 Transverse Rotational Magnetic Field (B1) Effects 10.4.3 Impact on SNR 10.5 fMRI Quality Assurance (QA) 10.5.1 Quantification of SNR and Temporal SNR 10.5.2 The Weisskoff Test 10.5.3 Coherent Noise Testing 10.6 Summary and Conclusions References 11: EEG-fMRI at Ultrahigh Magnetic Fields: B0 ≥ 3 Tesla 11.1 Introduction 11.2 Safety Considerations 11.2.1 Physical Principles and Relevant Safety Guidelines 11.2.2 Safety Studies at High Fields 11.2.3 Safe Imaging with High-Density EEG Nets 11.3 EEG Recording and Quality 11.3.1 Pulse-Related Artefact 11.3.2 Other Noise Sources at High Field 11.3.3 EEG Noise Removal Strategies at High Field 11.4 Image Quality 11.5 Example of an Application of EEG-fMRI at 7 T: Auditory Steady-State Response (ASSR) 11.6 Conclusions References 12: Experimental Design and Data Analysis Strategies 12.1 Introduction 12.2 Data Acquisition and Experimental Design 12.2.1 Interleaved EEG and fMRI Acquisitions: Triggered and Sparse Scanning 12.2.2 Simultaneous EEG and fMRI Acquisitions: Continuous Scanning 12.2.3 Experimental Protocol 12.2.3.1 Resting-State EEG–fMRI: Spontaneous Brain Activity 12.2.3.2 Stimulus-Driven Paradigms 12.3 Analysis of Simultaneously Acquired EEG–fMRI Data 12.3.1 Model-Based Analysis of fMRI Time-Series Data 12.3.1.1 Preprocessing 12.3.1.2 The General Linear Model (GLM) and Statistical Inference 12.3.2 EEG-Derived GLM: Use of Event on sets and Illustration in Epilepsy 12.3.3 EEG-Derived GLM: Parametric Design and Single Trial 12.3.4 EEG-Derived GLM: EEG Spectrum 12.3.5 Multivariate Analysis 12.3.6 EEG-informed fMRI Functional Connectivity 12.3.7 Use of intracerebral EEG in the context of concurrent fMRI recordings 12.4 EEG and fMRI Localization: Modes of Integration 12.4.1 Comparison of Independently Derived Results 12.4.2 fMRI as a Spatial Constraint for EEG Source Reconstruction 12.4.3 Towards Symmetrical Models of EEG and fMRI Fusion 12.5 Unresolved Problems and Caveats 12.5.1 Relationship Between Neuronal Activity, EEG and fMRI Signals 12.5.2 Specific Issues Related to Spontaneous Brain Activity 12.5.2.1 HRF 12.5.2.2 Experimental Efficiency of Paradigm-less fMRI 12.5.3 The Impact of Data Acquisition and Processing Artefacts on fMRI Data Analysis 12.5.3.1 Artefacts in the Signals 12.5.3.2 Artefacts Introduced by EEG Preprocessing 12.6 Summary and Outlook References 13: Real-Time fMRI Neurofeedback with Simultaneous EEG 13.1 Introduction 13.2 Regulation of Amygdala BOLD Activity and Frontal EEG Asymmetry 13.3 Regulation of Thalamic BOLD Activity and Alpha EEG Rhythm 13.4 Simultaneous Real-Time fMRI and EEG Neurofeedback 13.5 Real-Time Independent Component Analysis for EEG-fMRI 13.6 Conclusions References 14: Non-invasive Brain Stimulation with Multimodal Acquisitions 14.1 Brain Imaging: Possibilities and Limitations 14.2 Invasive and Non-invasive Brain Stimulation 14.2.1 The Physics and Physiology of Single-Pulse Transcranial Magnetic Stimulation (TMS) 14.2.2 From Single-Pulse to Repetitive TMS: Stimulation Protocols 14.2.3 Clinical Applications of TMS 14.3 The Multimodal Approach: Combinations of Brain Stimulation and Brain Imaging 14.3.1 Brain Imaging Before Brain Stimulation 14.3.2 Brain Imaging After Brain Stimulation 14.3.3 Simultaneous Brain Stimulation and Brain Imaging 14.3.3.1 Technical Challenges and Practical Implementation 14.3.3.2 TMS Affects Networks, Not Just a Local Region 14.3.3.3 TMS Network Effects Depend on Brain State 14.3.3.4 TMS Network Effects Are Functionally Relevant 14.4 New Developments 14.4.1 Closed-Loop Neuroscience 14.4.2 Simultaneous TMS-fMRI-EEG 14.5 Conclusions References Part III: Applications of EEG-fMRI 15: Brain Rhythms 15.1 Multimodal Studies of Brain Rhythms 15.1.1 Considerations for the Study of Rest 15.1.2 From Unimodal to Multimodal Approaches to the Resting State 15.1.3 Unimodal Approaches to Resting State 15.1.3.1 Resting State in fMRI 15.1.3.2 Spontaneous Neural Activity in Electrophysiology 15.1.4 Multimodal Studies of Rest 15.1.4.1 Direct and Indirect Measurement of Neural Activity by (f)MRI 15.1.4.2 Functional Imaging Studies of “Brain Oscillations” 15.1.4.3 Endogenous Brain Oscillations in Healthy Subjects 15.1.4.4 Similar Electrical Oscillations, Different fMRI Networks 15.1.4.5 Similar fMRI Networks, Different Electrical Oscillations 15.1.4.6 Brain Rhythms and Connectivity 15.1.4.7 Brain Oscillations and Networks During Sleep 15.1.4.8 Endogenous Brain Oscillations in Patients with Epilepsy 15.2 Multimodal Approaches in a Task Setting 15.2.1 General Considerations 15.2.2 Multimodal Measurements in a Task Context: Examples 15.2.3 Linking Neuronal Oscillations to Haemodynamic Changes 15.3 Conclusions References 16: Sleep 16.1 FMRI in Sleep Research 16.1.1 Sleep 16.1.2 Imaging Sleep 16.1.3 EEG and fMRI in Sleep Research 16.2 fMRI During Sleep: Technical Challenges 16.2.1 Sleep Recording 16.2.1.1 Multimodality of Sleep Recording 16.2.1.2 Referentiation of Recordings 16.2.1.3 Extended Recording Time: Electrophysiological Recordings 16.2.2 MR Imaging 16.2.2.1 Extended Recording Time: fMRI Recordings 16.2.2.2 Movement 16.2.3 Effect on Participant 16.2.3.1 Participant Not Falling or Staying Asleep 16.2.3.2 Extended Recording Time: Subjective Discomfort 16.2.4 Effect on Study Protocols 16.2.4.1 Drop-Out Rate 16.2.4.2 Specific Suppression of Sleep Stages 16.2.4.3 Selection Bias 16.2.4.4 No Control Over Sleep State 16.2.4.5 No Whole-Night Recordings 16.2.4.6 Fluctuation of Microstates 16.2.5 Possible Solutions 16.2.5.1 Habituation 16.2.5.2 Sleep Deprivation 16.2.5.3 MR Recording Techniques 16.3 FMRI in Sleep: Results 16.3.1 Sensory Processing During Sleep 16.3.1.1 NREM Sleep Acoustic Stimulation Visual Stimulation Olfactory Stimulation 16.3.1.2 REM Sleep 16.3.2 EEG-Informed fMRI 16.3.2.1 Falling Asleep 16.3.2.2 Graphoelements: Spindles, K-Complexes, and Slow Oscillations 16.3.2.3 REM Sleep 16.3.2.4 Lucid REM Sleep 16.3.3 Network Analysis 16.3.4 Animal Data 16.4 Summary and Outlook References 17: EEG–fMRI in Adults with Focal Epilepsy 17.1 Introduction 17.2 Interictal EEG–fMRI 17.2.1 What Is an Interictal Spike? 17.2.2 Interictal Epileptiform Activity in Presurgical Assessment 17.2.3 Methodology 17.2.3.1 Data Acquisition 17.2.3.2 Data Analysis 17.2.4 Relevance of the Observed BOLD Changes 17.2.5 Clinical Utility 17.2.6 The Influence of Lesions 17.2.7 Simultaneous intracranial EEG-fMRI 17.3 Ictal EEG–fMRI 17.3.1 Limitations of Ictal EEG–fMRI 17.3.1.1 Unpredictable Nature of Seizures 17.3.1.2 Seizure-Related Motion 17.3.2 Detection of Ictal Activity 17.3.3 Statistical Analysis of Ictal Haemodynamic Changes 17.3.4 Application of Ictal EEG–fMRI 17.3.4.1 Localisation Potential of Ictal EEG–fMRI 17.3.4.2 Mechanism of Epilepsy 17.4 Conclusions References 18: EEG-fMRI in Generalised Epilepsy: Adults 18.1 Idiopathic Generalised Epilepsy 18.1.1 Definition and Classification of Generalised Epilepsy Syndromes 18.1.2 Diagnosing IGE 18.1.3 IGE Comorbidity 18.2 Cortical and Subcortical Generators of Generalised Spike and Wave Activity 18.3 What EEG fMRI Has to Tell Us About Generators of Generalised Spike and Wave in Adults 18.3.1 EEG-fMRI Provides a Topographic Map of Structures Involved in the Generation of GSW 18.3.1.1 The Thalamus and Cortex in GSW 18.3.1.2 The Thalamus Non-Thalamic Subcortical Contributions 18.3.1.3 Cortical BOLD: The Importance of the Default Mode Network Cortical Change in Adults Beyond the DMN 18.3.1.4 Cortical Change in Lennox-Gastaut Syndrome 18.3.1.5 Connectivity Analysis Insights into the Mechanisms of GSW 18.4 Conclusions References 19: EEG-fMRI in Children with Epilepsy 19.1 EEG-fMRI in Children with Epilepsy 19.2 Methodological Issues Specific to Paediatric EEG-fMRI Studies 19.2.1 Patient Selection and Scanning 19.2.2 Modelling IED-Related BOLD Changes in Children: Variability and Developmental Changes 19.3 Results of EEG-fMRI Studies in Paediatric Epilepsy 19.3.1 Self-Limited Focal Epilepsies 19.3.2 Symptomatic and Cryptogenic Focal Epilepsies 19.3.3 Idiopathic Generalized Epilepsies 19.3.4 Epileptic Encephalopathies 19.4 Summary and Future Perspectives References 20: EEG-fMRI in Psychiatry 20.1 Introduction 20.2 Anxiety Disorder 20.3 Attention Deficit Hyperactivity Disorder (ADHD) 20.4 Depression, Posttraumatic Stress Disorder (PTSD) and Neurofeedback 20.5 Schizophrenia 20.6 Obsessive-Compulsive Disorder (OCD) 20.7 Dementia References 21: Combining Electroencephalography and Functional Magnetic Resonance Imaging in Pain Research 21.1 Introduction 21.2 Combining EEG and fMRI in Pain Research: General Issues 21.3 Combining EEG and fMRI in Pain Research: Practical Issues 21.3.1 Selectivity of the Nociceptive Input in EEG-fMRI Studies 21.3.2 Delivery of Nociceptive Stimuli in EEG/fMRI Environment 21.3.3 Experimental Design 21.4 Studies Combining EEG and fMRI in Pain Research 21.5 Future Directions: EEG-Driven Analysis of fMRI-BOLD Responses to Nociceptive Stimulation 21.5.1 Single-Trial Estimation of the Magnitude of Stimulus-Evoked EEG Responses 21.5.2 Correlation Between EEG and fMRI Responses at Single-Trial Level References 22: Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging of the Human Auditory System 22.1 Introduction 22.2 Specifics of Auditory Recordings 22.2.1 Interference of the Static Magnetic Field 22.2.2 Interference of Transient Magnetic Fields 22.2.3 BOLD Response to Scanner Noise 22.2.4 Sparse Sampling 22.2.5 Silent fMRI Acquisition 22.2.6 Adjusting Auditory Stimulus Frequencies 22.2.7 Active Noise Cancellation 22.3 Simultaneous EEG and fMRI in Auditory Experiments 22.4 Evaluation Methods of Concurrent Auditory EEG-fMRI 22.5 Conclusion References 23: Visual System 23.1 Simultaneous EEG-fMRI of the Visual System: Signal Quality 23.2 fMRI-Informed EEG of the Visual System 23.2.1 Localising VEPs 23.2.2 Visual Attention and Other Cognitive Processes 23.3 EEG-Informed fMRI of the Visual System 23.3.1 Spontaneous EEG Oscillations 23.3.2 Task-Related EEG Activity 23.4 Uninformed EEG–fMRI and Other Approaches 23.4.1 Event-Related Oscillations (EROs) 23.4.2 Visual Attention and Other Cognitive Processes 23.5 Investigating Neurovascular Coupling in the Visual System by EEG–fMRI 23.6 Outlook References 24: Cognition 24.1 Advantages and Disadvantages of Simultaneous EEG–fMRI Recordings of Cognitive Functions 24.2 Attention 24.2.1 Oddball Paradigm 24.2.2 Mismatch Negativity 24.2.3 Preparatory Attention 24.3 Executive functions 24.3.1 Cognitive Flexibility 24.3.2 Performance Monitoring 24.3.3 Decision-Making 24.3.4 Behavioral Inhibition 24.3.5 Working Memory 24.4 Memory 24.5 Limitations and Outlook References 25: Neuronal Models for EEG–fMRI Integration 25.1 From Correlating Measurements to Models of Neuronal Population Activity 25.2 Direct Correlations Between Field Potentials and BOLD 25.3 Neuronal Population Activity and the BOLD Signal 25.4 Neuronal Population Activity and Field Potential Measurements 25.5 Theoretical Predictions for BOLD and Field Potential Measurements 25.6 Theories About Field Potential Components 25.6.1 Broadband Power Changes 25.6.2 Peaks in the Field Potential Power Spectrum in the Range from 30 to 80 Hz 25.6.3 Low Frequency Alpha Oscillations 25.6.4 Measured Field Potential Data Will Be a Summation Across All Underlying Processes 25.7 Predicting Empirical Data with This Modeling Framework 25.8 Discussion 25.9 Conclusion References Part IV: Modelling 26: BOLD-Response and EEG Gamma Oscillations 26.1 Introduction 26.2 Methodical Issues 26.3 Gamma activity and BOLD Response 26.3.1 Covariation of High Frequency Oscillations and BOLD Signal 26.3.2 Gamma Activity and BOLD Response: Variation Across Subjects 26.3.3 Gamma Activity and BOLD Response: Further Reports 26.3.4 Single-Trial Coupling of Auditory Evoked Gamma Band Response and BOLD Signal 26.4 Conclusions References 27: EEG–fMRI in Animal Models 27.1 Introduction 27.2 Advantages of EEG–fMRI in Animal Models 27.3 Limitations and Technical Challenges of EEG–fMRI in Animal Models 27.4 Anesthesia 27.5 Movement: Curarization and Habituation 27.6 Physiology 27.7 MRI Compatible Electrodes 27.8 fMRI Signal Generation 27.8.1 Measurement of CMRO2 by MR Spectroscopy 27.8.2 Estimation of CMRO2 by Calibrated BOLD 27.8.3 CBV 27.8.4 CBF 27.9 Signal Artifact and Artifact Removal 27.10 Data Analysis 27.11 Sequential EEG–fMRI Studies in Animals 27.12 Applications of Simultaneous EEG–fMRI in Animals 27.13 Epilepsy 27.14 Absence Seizure Models 27.15 Generalized Tonic-Clonic Seizure Models 27.16 Partial Seizure Models 27.17 Sleep 27.18 Sensory–Motor Stimulation Models 27.19 Relating fMRI Signals to Electrophysiological Recordings 27.20 Future Directions 27.21 Conclusions References 28: EEG–fMRI Information Fusion: Biophysics and Data Analysis 28.1 Introduction 28.2 EEG–fMRI Information Fusion: Limitations 28.2.1 Coupling of Electrophysiological and Hemodynamic Responses 28.2.2 Experimental Limitations 28.3 EEG–fMRI Information Fusion: Solutions 28.3.1 Information Fusion: Definition 28.3.2 Asymmetrical vs. Symmetrical Approaches 28.3.3 EEG to fMRI Approaches 28.3.4 fMRI to EEG Approaches 28.3.5 Symmetrical EEG–fMRI Approaches 28.3.5.1 Model-Driven Approaches 28.3.5.2 Data-Driven Approaches 28.4 Conclusion References 29: Sparse and Data-Driven Methods for Concurrent EEG–fMRI 29.1 Introduction 29.2 Sparse Sampling of the Hemodynamic Response Function 29.3 Leveraging Sparsity for Artifact Removal 29.4 Data Driven Methods for EEG–fMRI Integration 29.4.1 Tensor Factorization 29.4.2 Canonical Polyadic Decomposition (CPD) 29.4.3 Tucker Decomposition 29.4.4 Coupled Matrix–Tensor Factorization (CMTF) 29.5 Canonical Correlation Analysis 29.6 Conclusion References 30: Integrating EEG–fMRI Through Brain Simulation 30.1 Introduction 30.2 Brain Network Models 30.3 EEG and fMRI Forward Models 30.4 Evoked Potentials 30.5 Resting-State 30.6 EEG–fMRI (Anti)Correlation 30.7 From EEG–fMRI to Neural Activity 30.8 From EEG–fMRI to Neural Mechanisms 30.9 Outlook: Diagnosis and Therapy References Index