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MEG: an Introduction to Methods This Page Intentionally Left Blank MEG: an Introduction to Methods MEG: An Introduction to Methods This page intentionally left blank MEG: An Introduction to Methods Edited by Peter C. Hansen Morten L. Kringelbach Riitta Salmelin 1 2010 1 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offi ces in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 2010 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data MEG : an introduction to methods / [edited by] Peter C. Hansen, Morten L. Kringelbach, and Riitta Salmelin. p. ; cm. Includes bibliographical references and index. ISBN 978-0-19-530723-8 1. Magnetoencephalography. I. Hansen, Peter C., 1963- II. Kringelbach, Morten L. III. Salmelin, Riitta. [DNLM: 1. Magnetoencephalography—methods. 2. Nervous System Diseases—diagnosis. WL 141 M496 2010] RC386.6.M36M44 2010 616.8’047547—dc22 2010002216 9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper Contents Introduction vii Contributors xi SECTION 1 BASICS Chapter 1 Electrophysiological Basis of MEG Signals 1 Fernando H. Lopes da Silva Chapter 2 Instrumentation and Data Preprocessing 24 Lauri Parkkonen Chapter 3 Measurements 65 Lauri Parkkonen and Riitta Salmelin Chapter 4 Experimental Design 75 Riitta Salmelin and Lauri Parkkonen SECTION 2 DATA ANALYSIS Chapter 5 The Dowser in the Fields: Searching for MEG Sources 83 Sylvain Baillet Chapter 6 Multi-Dipole Modeling in MEG 124 Riitta Salmelin Chapter 7 Estimating Distributed Representations of Evoked Responses and Oscillatory Brain Activity 156 Ole Jensen and Christian Hesse v vi Contents Chapter 8 Anatomically and Functionally Constrained Minimum-Norm Estimates 186 Matti S. Hämäläinen, Fa-Hsuan Lin, and John C. Mosher Chapter 9 Noninvasive Functional Tomographic Connectivity Analysis with Magnetoencephalography 216 Joachim Gross, Jan Kujala, Riitta Salmelin, and Alfons Schnitzler Chapter 10 Statistical Inference in MEG Distributed Source Imaging 245 Dimitrios Pantazis and Richard M. Leahy Chapter 11 Combining Neuroimaging Techniques: The Future 273 Jean-Baptiste Poline, Line Garnero and Pierre-Jean Lahaye SECTION 3 APPLICATIONS Chapter 12 Somatosensory and Motor Function 300 Ryusuke Kakigi and Nina Forss Chapter 13 MEG and Reading: From Perception to Linguistic Analysis 346 Riitta Salmelin Chapter 14 The Use of MEG in Clinical Settings 373 Jyrki P. Mäkelä Chapter 15 Using Magnetoencephalography to Elucidate the Principles of Deep Brain Stimulation 403 Morten L. Kringelbach, Peter C. Hansen, Alex L. Green, and Tipu Z. Aziz Author Index 424 Subject Index 429 Introduction Magnetoencephalography (MEG) is a neuroimaging technique that uses an array of sensors positioned over the scalp that are extremely sensitive to minuscule changes in the magnetic fi elds produced by small changes in the electrical activity within the brain. It is, therefore, a direct measurement of neural activity. MEG as technique for investigating neural function in the brain is not new but was originally pioneered in the late 1960’s. However, it is only since the early 1990’s, with the introduction of high-density (200+) detector grids covering the whole head, that the full potential of MEG has begun to be realized. At the present time, MEG has grown to be a signifi cant neuroimaging technique, with an increasing number of users and with the number of scientifi c papers utilizing MEG increasing year on year. The casual reader of this book may well be wondering what the great attraction of MEG is. Why bother with MEG when alternative neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), exist and seem to be attractive? To begin to answer this question, let us consider an example of what the amazingly com- plicated human brain is actually capable of achieving in a real world situation. Imagine driving home from work in your car and suddenly experiencing a car in the opposite lane swerving into your lane. You somehow manage to avoid a crash but are left wondering how your brain made this possible. Consider all of the complex sensory processing, object recognition, eval- uation, executive planning, cognitive decisions, motor events and strong emotions evinced in the previous sequence of events that have occurred in a time window of perhaps 750 ms or less. With MEG, the sequence of neural events can be readily tracked with at least 750 snapshots of sensor data, from which one can reconstruct 750 three-dimensional plots of current distribution in the brain. In fMRI, neural activation in this same time window, indirectly measured via local changes in the level of blood oxygenation, is typically com- pressed to one measured brain volume. Although fMRI localizes the active vii viii Introduction areas with high spatial accuracy, it provides no real-time information of neural involvement and is therefore not ideal to track brain activity related to the kind of rapid decision-making that made it possible for you to survive the near crash. This book is aimed at everyone interested in MEG. The reader may wish to use MEG because there is a specifi c neuroscience question which they think could best be answered with MEG, or because MEG happens to be the func- tional neuroimaging method that is available. Either way, one needs to be aware of the possibilities and limitations of the method in order to produce reliable and meaningful results. This book is intended to provide the reader with some basic tools for planning and executing MEG experiments and for analyzing and interpreting the data. The reader will appreciate this is a rapidly evolving fi eld, and the chapters presented here are by no means exhaustive. Nevertheless, we hope that the practical approach we have sought in these chapters will be helpful when pursuing MEG studies. The great strength of all neurophysiological methods, EEG as well as MEG, is that the detected signal directly refl ects real-time information trans- fer between neurons. Neural activation induces an electric current that gener- ates in turn corresponding electric and magnetic fi elds which are measured. Analysis of EEG data has traditionally been limited to dealing with the timing information available to the electrodes stuck to the scalp as the electric fi eld changes at each electrode. However, MEG analysis has, from the onset, sought instead to localize the neural sources of electric current within the brain by using the time courses of the changes in the magnetic fi eld recorded by an array of detectors. The reason for this difference of approach is straightfor- ward. Electric fi elds (and therefore EEG) are strongly infl uenced by large changes of electric conductivity between the brain, skull and scalp, creating marked distortion. Currently, it is not feasible to noninvasively determine the geometry and conductivity of these different structures accurately and effi - ciently in individual subjects. The magnetic fi eld (and therefore MEG) are affected signifi cantly less by the same structures, causing far less distortion. Because of the concentric organization of these physiological structures, con- ductivity varies primarily along the radial direction. As a result, the magnetic fi eld recorded outside of the head is essentially the same as the one that would be recorded on the exposed brain surface, making reconstruction far more accurate. As always, benefi ts come with cost. MEG is most sensitive to tangen- tially oriented currents that are located close to the sensors, i.e., in the cortex, whereas EEG is equally sensitive to signals from neural sources in any orienta- tion and potentially can reach to deeper structures. In practice, though, the heightened sensitivity of MEG to tangential currents is a great asset in source localization, as it markedly simplifi es the ‘inverse problem’, i.e., the complex relationship between current sources and electromagnetic fi elds. The electro- physiological basis of MEG and the basic mathematical formulation are described in Chapter 1 (Lopes da Silva). Knowledge of instrumentation is essential for understanding what is actually being measured in MEG and how it is done. For example, one needs Introduction ix to be aware of the different types of detection coils that can be used for record- ing the MEG data, as this infl uences the appearance of the signals at the sensor level. If conclusions are drawn solely based on the sensor signals, without proceeding to some form of source reconstruction, it is crucial to know the structure of the detector coils. Chapter 2 (Parkkonen) describes the principles of MEG hardware, and Chapter 3 (Parkkonen & Salmelin) provides guide- lines for performing successful measurements. These chapters also discuss the important steps involved in preprocessing MEG data, including evaluation of possible artifacts and the effects of fi ltering. Chapter 4 (Salmelin & Parkkonen) focuses on the design of MEG experiments and considers the selection of parameters also with respect to that in behavioral and fMRI neuroimaging studies. The unique power of MEG is that it combines the localization of active brain areas with reasonable spatial accuracy, together with the extraction of the time courses of activation in those areas with excellent temporal accuracy. Source analysis and reconstruction is now usually an integral part of the anal- ysis of MEG data.
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