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
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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. Indeed, decomposition of the MEG sensor signals into activity of specifi c neural sources enhances and clarifi es stimulus and task effects. Sources of early cortical somatosensory responses, for example, may be localized very precisely because of the lack of simultaneous activation from elsewhere in the cortex. Relative locations of sources within subjects can also be determined fairly accurately, such as the distance between areas in the somatosensory cortex that are activated by electrical stimulation of the thumb versus little fi nger. It is important to keep in mind that MEG (or EEG) data allow estimation of the centre of an active brain area but do not provide detailed information about its shape. The precise appearance of the resulting activity map is determined by the specifi c analysis method employed. Chapter 5 (Baillet) outlines the general concepts and assumptions for the various approaches that are currently used in MEG source analysis. Many of those methods are applicable to EEG data as well or were initially developed in that domain. Because of the inverse problem, some constraints are needed in order to proceed from the distribution of magnetic fi eld to confi guration of neural sources. A widely used and robust approach is to represent the neu- ral sources by a physiologically plausible and mathematically simple model, an equivalent current dipole. Chapter 6 (Salmelin) discusses the practical application of this method to analysis of both evoked responses and oscilla- tory background activity. Chapter 7 (Jensen & Hesse) focuses on another approach in which constraints are set to the solution of the inverse problem by accepting the result that accounts for the measured signals with the small- est overall amount of electric current. Chapter 8 (Hämäläinen et al.) illus- trates a conceptually similar approach, but with minimization of the overall power, in which the activity is additionally constrained to the cortex based on individual structural magnetic resonance images (MRIs) and the process may be further guided by functional MRI (fMRI) activation maps. Chapter 9 (Gross et al.) details the use of spatial fi lters (‘beamformers’) in the detection of interconnected neural networks. Chapter 10 (Pantazis & Leahy) outlines x Introduction recent advances in performing fMRI-type statistical analysis on distributed MEG maps (cf. Chapters 7 to 9) keeping in mind, however, that time courses of activity in different brain areas determined from MEG signals are highly correlated with each other, which is not the case for the voxel time courses in fMRI data. Chapter 11 (Poline et al.) addresses the combined use of different neuroimaging techniques. Most analysis approaches are initially tested on very simple sensory or motor paradigms, with one or maximally two experimental conditions, and typically on a single subject. In reality, neuroscience questions can be far more complex, usually with multiple experimental conditions in multiple subjects. Chapters 5 through 10 touch upon these issues as well. The reader will appre- ciate that all approaches have their advantages and disadvantages, and the choice between different analysis methods in the end should best be deter- mined by the particular research question of interest. Modeling the sources as focal equivalent current dipoles is a mathematically transparent approach, with the minimum number of assumptions, but it generally requires manual intervention and benefi ts from expertise. Distributed source reconstruction approaches tend to be more automated and require less manual intervention and may, therefore, be freer from potential analyst bias. Their possible risk lies in their mathematical complexity and hidden assumptions. However, it should be realized that no method is inherently better than the others and none of them can truly circumvent the inverse problem. Ideally, and certainly when in doubt, the reader would be well advised to use more than one method to analyze MEG data and to compare between them. Chapters 12 through 15 (Kakigi & Forss; Salmelin; Mäkelä; Kringelbach et al.) provide a set of examples of MEG studies ranging from basic sensory processing to cognitive tasks, and to the use of MEG in a clinical setting. The topics we chose to describe here obviously refl ect only a very small part of the various research questions that MEG has been applied to. Nevertheless, we hope they will give a general idea of the possibilities of the MEG method. To conclude, the reader should appreciate that the whole fi eld of MEG research is very much a dynamic and growing one. In this book, a selection of scientists with long experience in MEG, each with their own view and voice, seek to provide some seasoned practical tools for the recording and analysis of MEG data and to offer their insights into various conceptual and technical issues that continue to be discussed and developed. We hope this will be helpful and informative to new MEG users. Happy MEG recordings! Peter Hansen Morten Kringelbach Riitta Salmelin Helsinki and Oxford Contributors
Tipu Z. Aziz, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK Sylvain Baillet, COGIMAGE laboratory, Centre de Recherche sur le Cerveau et la Moelle, CNRS, INSERM, University Pierre & Marie Curie, Paris and Department of Neurology, Medical College of Wisconsin/Froedtert Hospital, Milwaukee, USA Nina Forss, Brain Research Unit, Low Temperature Laboratory, Aalto University, Helsinki, Finland Line Garnero, Inserm, Paris, France Alex L. Green, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK Joachim Gross, Centre for Cognitive Neuroimaging, Department of Psychology, University of Glasgow, Scotland Matti S. Hämäläinen, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA Peter C. Hansen, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK Christian Hesse, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Ole Jensen, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Ryusuke Kakigi, National Institute for Physiological Sciences, Okazaki, Japan
xi xii Contributors
Morten L. Kringelbach, Dept. of Psychiatry, University of Oxford, UK and Aarhus University, Denmark. Jan Kujala, Brain Research Unit, Low Temperature Laboratory, Aalto University, Helsinki, Finland Pierre-Jean Lahaye, Inserm, Paris, France Richard M. Leahy, Signal and Image Processing Institute, University of Southern California, CA, USA Fa-Hsuan Lin, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA Fernando H. Lopes da Silva, Centre of Neurosciences, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands Jyrki P. Mäkelä, BioMag laboratory, HUSLAB, Helsinki University Central Hospital John C. Mosher, Los Alamos National Laboratory, NM, USA Dimitrios Pantazis, Signal and Image Processing Institute, University of Southern California, CA, USA Lauri Parkkonen, Brain Research Unit, Low Temperature Laboratory, Aalto University, Helsinki, Finland Jean-Baptiste Poline, Inserm, Paris, France Riitta Salmelin, Brain Research Unit, Low Temperature Laboratory, Aalto University, Helsinki, Finland Alfons Schnitzler, Department of Neurology, Heinrich Heine University, Dusseldorf, Germany 1
Electrophysiological Basis of MEG Signals
Fernando H. Lopes da Silva
• MEG signals recorded at the scalp are generated by synchronous activity of tens of thousands of neurons • Mainly postsynaptic currents in apical dendrites contribute to MEG signals • MEG signals are highly sensitive to currents tangential to the skull, originating in the cortical sulci • MEG fi elds refl ect the primary neuronal currents directly, with minimum distortion from different layers – brain tissue, skull, scalp – with different electric conductivities • Estimation of MEG sources implies the construction of computational models of the biophysical sources and of the volume conductor
Magnetoencephalography (MEG) is the technique of measuring the magnetic fi elds generated by brain activity, and was pioneered by Cohen (1968 ). An important feature of MEG/EEG is the ability to record varying signals generated by the brain in relation to states of activity, whether determined by intrinsic processes—e.g., different states of sleep and alertness—or in relation to motor acts and sensory events (Hari, 2005 ). In this overview, we will focus on the basic physiological and biophysical aspects of how magnetic signals are generated in the brain. We start, however, with a brief description of the main features of MEG as a method to study brain functions in man.
1 2 MEG: An Introduction to Methods
Main Features of MEG MEG provides information about the dynamics of the activities of popula- tions of neurons of the cerebral cortex. The time resolution capabilities of MEG and EEG are unrivalled. Nevertheless, MEG, like EEG, has a basic limitation in that the neuronal signals are only recorded from the scalp. Consequently, there is no unique solution to the problem of reconstructing where exactly the sources of these signals are localized within the brain. This problem is commonly referred to as the inverse problem. The standard approach to overcome the non-uniqueness of the inverse problem in MEG/ EEG is to introduce constraints on the possible solutions, in order to exclude all solutions except the one that is most suitable to describe the data. Thus, the functional localization of brain sources of MEG/EEG signals depends to some degree on the models used and on the corresponding assumptions, and therefore will have some degree of uncertainty. This contrasts with the spatial accuracy of MRI brain images, where the MRI technology affords a truer 3-dimensional reconstruction. It explains partly why the development of clinical applications of MEG has been slow, since the research on how to optimize estimates of the solutions of the inverse problem has been arduous, and has only recently reached a stage where con- sensual strategies are emerging. These inherent diffi culties, along with the fact that MEG needs rather costly facilities, account for the restraint of medical specialists in promoting this new methodology, and the hesitation of hospital administrators in supporting the necessary investments in material and human resources. Nonetheless, MEG has proven a most valuable tool in research, not only of neurocognitive processes (Salmelin et al., 1994 ) but also in clinical settings (Van ‘t Ent et al., 2003 ), as presented further in this volume.
Some Basic Notions of Cellular Neurophysiology Neurons generate time-varying electrical currents when activated. These are ionic currents generated at the level of cellular membranes; i.e, they consist of transmembrane currents. We can distinguish two main forms of neuronal activation (Lopes da Silva & van Rotterdam, 2005 ): the fast depolarization of the neuronal membranes that results in the action potential mediated by sodium and potassium voltage-dependent ionic conductances gNa and gK(DR) , and the more protracted change of membrane potential due to synaptic acti- vation mediated by several neurotransmitter systems. The action potential consists of a rapid change of membrane potential, such that the intracellular potential jumps suddenly from negative to positive, and quickly, in 1 or 2 mil- liseconds, returns to the resting intracellular negativity. In this way, a nerve impulse is generated that has the remarkable property of propagating along axons and dendrites without loss of amplitude. The contribution of other Electrophysiological Basis of MEG Signals 3 ionic conductances to the magnetic fi eld is discussed below, in relation to new insights obtained using computer models. Regarding the slower postsynaptic potentials, two main kinds have to be distinguished: the excitatory (EPSPs) and the inhibitory (IPSPs) potentials, which depend on the type of neurotransmitter and corresponding receptor, and their interaction with specifi c ionic channels and/or intracellular second messengers. Generally speaking, at the level of a synapse in the case of the EPSP, the transmembrane current is carried by positive ions inwards. In the case of the IPSP, it is carried by negative ions inwards or positive ions outwards. Thus the positive electric current is directed to the extracellular medium in the case of an EPSP, and it is directed from the inside of the neuron to the outside in the case of an IPSP ( Figure 1–1 ).
Post-synaptic Extra-cellular Potentials
EPSP IPSP
−
+ −
+
− − + +
Figure 1–1. Intra- and extracellular current fl ow in an idealized pyrami- dal neuron due to different types of synaptic activation. EPSP: excitatory synapse at the level of the apical dendrite; the generated current of posi- tive ions fl ows inwards, causing depolarization of the cell. This results in an active sink at the level of synapse. The extracellularly measured EPSP has a negative polarity at the level of the synapse. At the soma there is a passive source, and the potential has a reversed polarity. IPSP: inhibitory synapse at the level of the soma. A current of negative ions fl ows inwards causing hyperpolarization of the cell; this results in an active source at the level of the synapse. The extracellularly measured IPSP has a positive polarity at the level of the soma. At the level of the distal apical dendrite there is a passive sink, and the potential has a reversed polarity. (Adapted from Niedermeyer & Lopes da Silva, 2005). 4 MEG: An Introduction to Methods
As a consequence of these currents in the extracellular medium, an active sink is generated at the level of an excitatory synapse, whereas an active source occurs in the case of an inhibitory synapse. The fl ow of these compensating extracellular currents depends on the electrical properties of the local tissue. Glial cells occupy an important part of the space between neurons, and are coupled to one another by gap junctions. The conductivity of the latter is very sensitive to changes in pH on extracellular K+ and Ca 2+ and can therefore be modulated under various physiological and pathological conditions (Huang et al., 2005 ). Furthermore, the volume of the extracellular space may change under various physiological and pathological conditions, which will also be refl ected in changes of tissue conductivity. From the biophysics we may state that there is no accumulation of charge anywhere in the medium, in that currents fl owing in or out of the neuronal membranes at the active synaptic sites are compensated by currents fl owing in the opposite direction elsewhere along the neuronal membrane. This is described in more precise terms in the next section. Consequently, in the case of an EPSP, in addition to the active sink at the level of the synapse, distrib- uted passive sources occur along the soma-dendritic membrane. The oppo- site occurs in the case of an IPSP: in addition to the active source at the level of the synapse, distributed passive sinks are formed along the soma-dendritic membrane. Therefore we may state that synaptic activity at a given site of the soma- dendritic membrane of a neuron causes a sink–source confi guration in the extracellular medium around the neurons. In the context of the present dis- cussion, the most important point to take into consideration is the geometry of the neuronal sources of electrical activity that gave rise to the scalp EEG or MEG signal. The neurons that give the main contribution to the MEG or the EEG are those that form “open fi elds” according to the classic description of Lorente de Nó (1947 ); i.e., the pyramidal neurons of the cortex, since the lat- ter are arranged in palisades with the apical dendrites aligned perpendicularly to the cortical surface ( Figure 1–2 ). This means that longitudinal intracellular currents fl ow along dendrites or axons, and thus generate magnetic fi elds around them—just as happens in a wire, according to the well known right- hand rule of electromagnetism (see also FAQ, Q1). Pyramidal neurons of the cortex, with their long apical dendrites, generate coherent magnetic fi elds when activated with a certain degree of synchrony. We may say that these neurons behave as “current dipoles,” the activity of which can be detected by sensors placed at a small distance from the skull. Later in this chapter we will consider this issue further in quantitative terms, based on recent computational model studies. In order to make the next step toward an understanding of how MEG signals recorded outside the skull are generated, we also have to take into consideration the folding of the cortex. The fact that the cortex is folded, forming gyri and sulci, implies that some populations of neurons have apical Electrophysiological Basis of MEG Signals 5
Spatial Organization of Assemblies of Neurons in the CNS According to Lorente de Nó (1947)
A
Closed fields B
C + −
Open D and mixed fields
Figure 1–2. Examples of closed (A, B), open (C) and open–closed (D) fi elds according to Lorente de Nó. The isopotential lines resulting from the acti- vation of the neuronal population are shown on the right side. (Adapted from Niedermeyer & Lopes da Silva, 2005). dendrites that are perpendicular to the overlying skull, i.e., those that are at the top of a gyrus; whereas others are parallel to the skull, i.e., those that are on the wall of a sulcus. The point to note in this respect is that the orientation of the neurons with respect to the skull infl uences the resulting MEG signal recorded outside the skull. In fact the MEG “sees” only those magnetic fi elds that have a component perpendicular to the skull. These magnetic fi elds are generated by neuronal currents that have a component oriented tangentially to the skull. In contrast, those currents that are oriented radially to the skull do not generate a magnetic fi eld outside the head (Figure 1–3 ).
A Few Basic Notions of Biophysics EEG and MEG signals vary in time, but this variation is relatively slow; hence, induction effects can be neglected. This means that Maxwell equations need not be applied, and the behavior of the electric and magnetic fi elds can be described by the classic Ohm’s, Ampère’s and Coulomb’s laws. For a rigorous treatment of the biophysics of the generation of electric and magnetic fi elds in the brain 1 one has just to assume that the neural generator, for example a synaptic ionic current, is represented by the impressed current density J i such 6 MEG: An Introduction to Methods
Radial and Tangential sources
Qd
propagation Direction of Direction B B
I Qr
VI 0
A B B
Qr Qd
Direction of propagation 0 0 0
Figure 1–3. Schematic drawing of a piece of cortex showing the crown of a gyrus and a sulcus. Two cylinders are drawn to indicate the relation between the direction of the intracellular current and the resulting mag- netic fi elds around the apical dendrite. Sources at the top of a gyrus cause radial fi elds that are not detectable by MEG. Sources in the fi ssures cause tangential fi elds that can be detected at the MEG.