Working with MEG: A Practical Guide to Magnetoencephalography

Description

In 1968 at the University of Illinois, David Cohen made scientific history when he measured a neuronal oscillation known as the alpha rhythm from the brains of four human volunteers. The measurement of the alpha rhythm was not in itself a breakthrough: the German psychiatrist, Hans Berger, had discovered the rhythm decades earlier during his first recordings of human electroencephalography (EEG). What was novel about Cohen’s experiment was that it did not measure electrical potentials on the scalp as is the case for EEG. Instead, the measurements were made using a metal coil built to measure magnetic fields. Cohen had become the first person to measure the brain’s magnetic field and in doing so had pioneered an entirely new method for measuring the brain’s activity: magnetoencephalograpy (MEG). In the intervening decades MEG has grown from a highly specialised method practiced by a small number of researchers to a routine method of human neuroscience available at laboratories across the world. However, as the use of MEG has increased, so too has the number of people encountering MEG for the first time, and this has created a growing need for materials that aid the understanding of MEG for those with no prior experience. Cover Endorsement Half Title Series Information Title Page Copyright Page Dedication Table of Contents Preface Figures Part I Measuring the Brain’s Magnetic Field Chapter 1 What Is MEG? 1.1 What Does MEG Measure? 1.1.1 Introduction to Magnetic Fields 1.1.2 The Magnetic Field of a Neuron 1.1.3 The Magnetic Field of Populations of Neurons 1.1.4 The Effect of Volume Current 1.1.5 Summary 1.2 How Is MEG Measured? 1.2.1 Measuring the Brain’s Magnetic Field 1.2.2 Sampling the Magnetic Field 1.2.3 Summary Further Reading References Chapter 2 How to Collect MEG Data 2.1 MEG Data Acquisition 2.1.1 Acquiring MEG Data 2.1.2 Safety 2.1.3 Magnetic Interference Electrical Devices Ferromagnetism Physiological Processes 2.1.4 Workflow of Data Acquisition 2.2 Experimental Design 2.2.1 Event-Related Experimental Designs 2.2.2 Block Experimental Designs 2.2.3 Event Triggers 2.2.4 Summary Further Reading Part II Analysing the Data Chapter 3 Analysing Data Time Series 3.1 Data Preprocessing 3.1.1 Epoching 3.1.2 Artefact Removal 3.1.3 Temporal Filtering 3.1.4 Summary 3.2 The Time Domain 3.2.1 Data Analysis in the Time Domain 3.2.2 Summary 3.3 The Frequency and Time–frequency Domains 3.3.1 The Fourier Transform 3.3.2 The Frequency Domain 3.3.3 The Time–frequency Domain 3.3.4 Data Analysis in the Frequency and Time–frequency Domains 3.3.5 Phase 3.3.6 Summary Further Reading References Chapter 4 Analysing Spatial Information 4.1 Sensor Space 4.1.1 Interpreting the Spatial Distribution of the Magnetic Field 4.1.2 Summary 4.2 Source Space 4.2.1 The MEG Inverse Problem 4.2.2 The Forward Problem 4.2.3 Inverse Solutions: Model Fitting 4.2.4 Inverse Solutions: Spatial Filtering 4.2.5 Summary 4.3 Statistical Analysis of MEG Data 4.3.1 Statistical Inference From MEG Data 4.3.2 Mass Univariate Analysis 4.3.3 Cluster-Based Analysis 4.3.4 Summary Further Reading References Chapter 5 Applications of MEG 5.1 Event-Related Responses 5.1.1 Evoked Responses 5.1.2 Induced Responses 5.1.3 Summary 5.2 Functional Connectivity 5.2.1 Measuring Functional Connectivity 5.2.2 Phase Coherence 5.2.3 Power Envelope Correlation 5.2.4 Summary 5.3 Clinical Applications of MEG 5.3.1 Pre-Surgical Evaluation in Epilepsy 5.3.2 Summary 5.4 Concluding Remarks Further Reading References Glossary Index