190 Chapter 2 Diomagnetism 2.4 Neuromagnetism

2.4 N euromagnetism b.

THOMAS ELBERT

~ ~ ~ ~ The Brain is just the weight of God ­ ~ : For - Heft them - Pound for Pound ­ : : ~. : ~ And they will differ - if they do - ~ ~ ~ As syllable from Sound - :

EMILY DICKINSON, 1862 Fig. 2.58: Example of simultaneous EEG (left) 2.4.1 Introduction to Magnetencephalography are attached on places as indicated (F: frontal, C: central, P: parietal, T: According to Emily Dickinson, in 1862, brain function cannot be measured by physical voltage measured between these posi scales. Nevertheless, as we near the end of the 20th century our attempts to measure, (c) At the sametinie interval, the IT "pound for pound", pieces of brain structure and function have not been abated. We a function of time, horn 37 sensor I, carry on, despite an ongoing struggle between the frustration of our inability to un­ left temporal region as indicated. A derstand the functioning of the brain and the illusion that we may, at some point, becomes also visible in t,he EEG. SI be able to fit the pieces of our knowledge into the grand picture. Indeed, with every (Data courtesy to Dr. C. Wienbruch observation of normal and abnormal brain processes, the chances of determining the etiology of pathological conditions increases and the prospects of developing better diagnoses and treatment procedures improves. Measuring brain function as precisely originate from the volume currents, like Elec' as possible in its fluctuation in time and space on different scales constitutes the en­ evoked or event-related potentials (EP, ERP) trance for the understanding of brain functioning on a holistic, systemic level. When decades ago, and furthermore, have become activational brain patterns have simple spatial configurations, magndoencephalogra­ cognitive and behavioral neuroscience. T-,:eE phy allows the macroscopic description of active neural sources with high spatiaf and electrodes attached to the surface of the sca nearly arbitrary temporary resolution. No other method currently available provides measures the voltage fluctuationsori the sur comparable informationI. trades. For our purposes it is significant t The present chapter provides an introduction to Magnetoencephalography (MEG) body not only evokes an electric potential di and presents selected examples of studies employing MEG and the MEG-based meth­ also elicits a measurable magnetic field. AI ods used to locate active regions in the brain (magnetic source imaging -MSI). Reviews magnetic counterparts are found in measure of basic work on perceptual processing and studies of movement-related activity in­ Fig. 2.58), and evoked or event-related magi clude the ones by Hari (1990); Hari & Ilmoniemi (1986); Hoke (1988); Williamson & Biomagnetic fields have a very low amp Kaufman (1987). Clinical applications are summarized by Lewine & Orrison (1995); true for the magnetic field which appears a Makela et al. (1998), and Naatanen et al. (1994) outline the potential of MEG for the activity, the amplitude of the MEG lies ar studies of human cognition. almost 5-6 a-rders of magnitude less than th; activity generated by power lines, cars or I fields of a sensory stimulation lie somewher 2.4.1.1 Overview Tesla), one order of magnitude smaller. The sour.ces of biomagnetic signals result mostly from processes of neuronal or muscu­ Measuring the extremely weak biomagr lar excitation. The signals originate from an intracellular current flow with a relatively free of contact with the subject or patient, high current density. The excited portion of the nerve endings or muscle tissue repre­ for technical equipment. Biomagnetic me1 sents a local source of current. At different locations the current penetrates through the scientific and clinical importance that the cell membrane such that the circuit can be closed over the volume conductor Le., the clear advantages that they hold over t by current pathways through extracellular body tissue. The bioelectric potentials that alone. A major advantage lies in the fa( are vertical to the body's surface essentia 1 ]0 the future, optical methods may promise similar or even greater poteritial but non-invasive whereas the electric potential distribution optical imaging of human brain function remains to be developed.

/ 2.4 Neuromagnetism 191

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Fig. 2.58: Example of simultaneous EEG (left) and MEG (right) recordings. (a) Electrodes are attached on places as indicated by the black dots and also at the earlobes (F: frontal, C: central, P: parietal, T: temporil.l sites). (b) The EEG refers to the voltage measured between these positions and the eadobes as a function of time. (c) At.the same time interval, theto:agnetiCfield{MEG) was meaSured, also as a functio~ -ofiime"-from 37simsor locations. (d) Sensors were 'locatedover the left temporal region as indicated. A clear spike can be detected in the MEG and becomes also visible in the EEG.-Spikes are comlllon in patients with epilepsy. (Data courtesy to Dr. C. Wienbruch) originate from the volume currents, like Electroencephalogram (EEG - Fig. 2.58) and evoked or event-related potentials (EP, ERP), were integrated into clinical diagnostics decades ago, and furthermore, have become a fundamental parameter of research in cognitive and behavioral nenroscience. TheEEG refers tothevoltage derived from two electrodes attached to the surface of thescalp,-w1i.ifetn-e Electrocorticogram (ECoG) measures t.he voltage fluctua.t.ions on the surface of the brain, using intracranial elec­ trodes. For our purposes it is significant that the current which runs through t.he body not only evokes an electric potential dist.ribution on the surface of the body, but also elicits a measurable magnetic field. Analogous to the electric potentials, these magnetic counterparts are found in measures of the Magnetoencephalogram (MEG ­ Fig. 2.58), and evoked or event-related magnetic fields (EF, ERF). Biomagnetic fields have a very low amplitude (see Fig. 2.1). This is part,icularly true for the magnetic field which appears as a result of neuronal act.ivity. For brain activity, the amplitude of the MEG lies around 1 pT (1 picoTesla = 10-12 Tesla), almost 5-6 orders of magnitlldeless than that of urban noise (resulting from magnetic activity generated by power lines, cars or elevators). The evoked cortical magnetic fields of a sensory stimulation lie somewhere around 100 IT (100 femtoTesla "" 10-13 Tesla), one order of magnitude smaller. Measuring the extremely weak biornagnetic fields is completely non-invasive and free of contact with the subject or patient, but it requires a great initial investment for technical equipment. Biornagnetic methods, however, would never have gained .the scientific and clinical importance that they increasingly have if it were not for the clear advantages that they hold over the measurements of bioelectric potentials alone. A maj\Jr advantage lies in the fact that magnetic field components which are vertical to the body's surface essentially result from intracellular current flow, whereas the electric potential distribution is brought to the surface by the volume

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/ 2.4 Neuromagnetism 192 Chapter 2 Biomagnetism --~----"------1 current and is therefore still measurable at considerable distances from the SOurce. Log size T~e IIleasurem~nt (One example of this is the measurement of EKG from extremities: I m - is ·successful even at a great distance from the heart, such as from the distal section Brain of the extremities.) Consequently, the volume currents are considerably distorted, as 0.1 m - I·.cm body tissue varies greatly in its degree of conductivity, and may be even markedly Map - anisotropic-like, for instance, in muscle tissue. As muscles also cover areas of the mm - scalp, and as conductivities vary greatly for scalp, skull, cerebro-spinal fluid and brain Column 0.1 mm - with their complex geometries, neuronal sources can be modeled only to a very limited extent when information is based on EEG alone. Under most conditions, biomagnetic Neuron 10 I'm - measurements allow for the determination of the source of biological activity with a Dendrile I I'm - better spatial resolution (up to only a few mm) than is possible with the measurement Synapse 0.1 I'm - i of electric potentials. This is particularly true for the source of magnetic fields which ms are evoked by various sensory modalities within the primary representational zones of cerebral cortex. In many instances, this acitivity can be modeled as a single current dipole. The accuracy of the localization of the "equivalent current dipoles" (ECD) Fig. 2.59: Temporal and spatial re lies somewhere below one half of a centimeter (Liitkenhi:iner et al., 1990, 1996) and is function: EEG Electroen< thus not only considerably better than EEG-based source analysis but also superior bination of functional ma to the localizations of brain-imaging methods which are based on blood flow measures functional Magnetic ResOI such as PET (Positron Emission Tomography) or SPECT (Single Photon Emission phy (see Sections 3.2.2 an, Computed Tomography). The accuracy of source localization is not identical with the accuracy of separating different, simultaneously active sources. While the localization The advantages of MEG-ba; accuracy, as seen above, lies somewhere around a few millimeters (particularly the source imaging is accompanied relative localization accuracy), the ability to separate many different sources is about a series of requirements in order one order lower. . measure the extremely weak magnl Currents flowing perpendicularly to the surface of the head emit magnetic fields fields. Since the discovery of with a relatively small signal strength outside the body. They are, so to speak, mag­ Josephson Effect and the developm netically silent, but do create a pronounced electrical potential which can be measured of the SQUID, which resulted fr on the surface. Because magnetic fields and electric potentials contain complementary this discovery, these requirements h, information with respect to their source(s), the ability to measure both types of signals been fulfilled. The Josephson Efl simultaneously provide constraints on source localization that are not available when is a phenomenon which only appe using one type of signal alone. with superconductors. In order In comparison to other imaging methods (see Fig. 2.59 and Section 2.4.9), it is facilitate superconduction, the of particular importance to note that MEG and evoked magnetic.Jields, as well as tectors must be enclosed within EEG and evoked potentials, are not only capable of valuable spatial resolution, but, almost mari~sized, helium-filled "1 in comparison to PET, SPECT, and fMRI (functional Magnetic Resonance Imaging), war" which - in "whole head systeIl they possess a much higher temporal resolution, so that it is possible to follow dynamic - is shaped such that it surrounds aspects of functional processes in real time. One further advantage is that it is also scalp, Le., covering the entire n possible to track neural source localizations even if the activity to be localized is not rocranium (see Figs. 2.13 and 2,( accompanied by secondary phenomena such as the changes of the regional blood flow Such a highly sensitive measurem or with local changes of the metabolic activity, as is required with fMRI, PET and device not only detects the desired 2 . SPECT tivity coming from the brain, but G records the ever-present environm 2 Biomagnetic techniques do not belong to the catergory of imaging techniques like eT, MRl Or PET, which, on the basis of physical principles alone, allow for the construction of cross sections, tal noise which arises from the USE i.e. tomograms. The reconstruction of the distribution of sources and their respective intensities from electra-magnetic devices. Therefofl the measurements of the magnetic and electric maps outside of the brain is, however, possible if spatial second requirement for MEG meas\: constraints are included in the mOdelling. Such constraints can be constructed if neurophysiological knowledge is taken into consideration. The simplest type of constraint is the assumption of one or a ments is to protect the sensors fr few focal sources. More complex models restrict the sources to the (de)polarization of gray matter. disturbing environmental magnl fields much stronger than those be 2.4 Neuromagnetism 193

Log size

Brain

Map

Column 0.1

Neuron 10 I'm - Dendrite I I'm -

Synapse 0.1 I'm - i ms sec win hours days Log time

Fig. 2.59: Temporal and spatial resolution of imaging techniques for the study of brain function: EEG Electroencephalography, MSI: Magnetic Source Imaging (Com­ bination of functional mangetoencephalography with structural MRT); fMRT: functional Magnetic Resonance Tomography; PET: Positron Emission Tomogra­ phy (see Sections 3.2.2 and 2.4.9)

The advantages of MEG-based source imaging is accompanied by a series of requirements in order to measure the extremely weak magnetic fields. Since the discovery of the Josephson Effect and the development of the SQUID, which resulted from this discovery, these requirements have been fulfilled. The Josephson Effect is a phenomenon which only appears with superconductors. In order to facilitate superconduction, the de­ tectors must be enclosed within an almost man-sized, helium-filled "De­ war" which - in "whole head systems" - is shaped such that it surrounds the scalp, Le., covering the entire neu­ rocranium (see Figs. 2.13 and 2.60). Such a highly sensitive measurement Fig. 2.60: An MEG "whole-head system" device not only detects the desired ac­ typically includes 120 to 150 mag­ tivity coming from the brain, but also netic sensors that embody pick­ up coils and SQUIDs, housed in records the ever-present environmen­ a bath of liquid helium. Helium tal noise which arises from the use of boils at a temperature close to ab­ electro-magnetic devices. Therefore, a solute zero and thus keeps the sen­ second requirement for MEG measure­ sors in a superconducting. state. ments is to protect the sensors from The element that contains the sen­ disturbing environmental magnetic sors, the socalled Dewar, insulates fields much stronger than those being the liquid by means of a VacCUffi. ~'.'.

194 Chapter 2 Biomagnetism 2.4 Neuromagnetism

measured. For this reason, MEG measurements are carried out in a magnetically where the first magnetically shielde shielded room which attenuates the magnetic current fields of the environment (see et al., 1970). A significant portior Section 2.2 for details on biomagnetic instrumentation). ' Williamson and Lloyd Kaufman (I The combination of MEG with MRI information into a merged graphic data set is 1980; Williamson & Kaufman, 191 called Magnetic Source Imaging. While previously practical work just superimposed 1970s the majority of the differen the location of an equivalent current dipole determined from MEG data onto the discovered (Hoke, 1988; Hoke et ai corresponding MRT section, current techniques have been developed that also use work remains to be done to investi MRI information to constrain source configurations to gray matter of the cortex. It the beginning of the 1990s, only ab has proven to be useful, particularly in clinical practice for reconstructing the cortical in this huge undertaking. The 1\1 sheet by surface rendering and to display anatomy, pathological tissue and functional SQUIDs (see Section 2.2.3). With activation in a single image. been a substantial increase in the n brain activity. Apart from Helsinki 2.4.1.2 History and the Relation of Magnetoencephalography to Other Japan have the highest density witl Methods for the Noninvasive Study of Brain Function 2.2.7). With the discovery of x-rays by Rontgen (Rontgenstrahlung) in the year 1895 it be­ came possible to noninvasively image structures within the body for the first time. 2.4.2 The Generation of X-ray diagnostic allowed for the imaging of the structure of bones as well as - by Brain means of contrasting agents - blood vessels, the gastro-intenstinal tract, and other tracts containing fluids within the body. Using powerful computers, it is possible to 2.4.2.1 Functional Neuroanat reconstruct the three-dimensional appearance of an object or sections (Greek: Tomos) of an organ using only the shadow that the object casts into the different directions of The principal building blocks of thE space. Such procedures of computer-assisted tomography (CAT) have not only been more abundant by a factor of 10. developed on the basis of x-rays but also for the imaging of organs using radioactively the maintenance of proper concent labeled substances that are inhaled or injected into the body (like PET, or SPECT), and other substances between capi or for imaging of organ sections that result from the magnetic resonance of protons the information processing units. (Magnet-Resonance-Tomography = MRT; see Chapter 3). When a neuron fires, it sends a Galavani's observations, made as early as the first half of the 19th century, demon­ ization along its axons that travel i strate that electrical activity serves as the basis of nerve and muscular activity. In the with its neighboring cells, the synar: 1870s the physiologist Richard Caton, from Liverpool, discovered that "feeble currents will travel across the 50mm wide sy of var'ying degree pass through the multiplier when the electrodes are placed on two points rotransmitter then changes the men of the external surface, Or one electrode on the gray matter and one on the surface of the ion channels through the cell mem skull" (1875, p. 278). Caton also observed responses evoked by external stimuli which tential and consequently extra- and led him to the following conclusion: "When any part of the gray matter is in a state of 1995, for an introduction). The r: functional activity, its electric wrrent voltage usually exhibits negative variation." outflow of negative (excitatory: El In a further and essential step, the psychiatrist Hans Berger fist described electrical extracellular space. These ions will recordings from the human scalp, which he named electroencephalogram (EEG), in soma or passively diffuse through t 3 1929 . Although it was already assumed at this time that electrical currents also K+ - through adjacent glial cells. generate magnetic fields, i.e., an MEG, no measurement technique existed until forty such PSPs also create measurable V< years later. the EEG. In 1963, Baule and McFee measured the Magnetocardiogram (MCG) for the first Ions are pumped back through t time, thus proving that it is possible to record magnetic activity generated within drite and the soma, and consequent the body (Baule & McFee, 1963). The first measurements of cerebral magnetic fields from the dendrites towards the son were reported by David Cohen in 1968, at the Massachusetts Institute of Technology, Savart law, the intracellular currer When tens of thousands of such pc 3 "Da ieh aus spraehliehen Griinden das Wort 'Eleetroeerebrogramm', das sieh aus grieehisehen und lateinisehen Bestandteilen zusammensetzt, fur barbariseh halte, moehte ich flir diese von mir hier zum magnetic flux becomes measurable erstenmal beim Mensehen naehgewiesene Kurve in Anlehnung an den Namen 'Elektrokardiogramm' stimulus generally creates a magne den Namen 'Elektroenkephalogramm' vorsehlagen." (Berger, 1929). that about 1 million synapses have

/ n 2.4 Neuromagnetism 195

y where the first magnetically shielded room was built in 1967 (Cohen, 1968, 1972; Cohen et al., 1970). A significant portion of the initial work on MEG was done by Samuel Williamson and Lloyd Kaufman at New York University (Kaufman & Williamson, is 1980; Williamson & Kaufman, 1981; Williamson et al., 1979). In the course of the :cl 1970s the majority of the different types of biomagnetic signals were subsequently le discovered (Hoke, 1988; Hoke et al., 1994; Lewine & Orrison, 1995). However, much le work remains to be done to investigate and understand these signals in detail. Until It the beginning of the 1990s, only about two dozen laboratories world-wide participated al in this huge undertaking. The MEG measurement method is now fully based on al SQUIDs (see Section 2.2.3). With the advent of the whole-head systems, there has been a substantial increase in the number of laboratories investigating neuromagnetic brain activity. Apart from Helsinki as a center in biomagnetic research, Germany and Japan have the highest density with more than a dozen systems each (see also Section 2.2.7). e­ le. 2.4.2 The Generation of Electromagnetic Fields in the Human by Brain er to 2.4.2.1 Functional Neuroanatomy IS) The principal building blocks of the brain are neurons and glial cells, the latter being of more abundant by a factor of 10. The glia are important for structural support, for en the maintenance of proper concentrations of ions, and for the transport of nutrients !ly and other substances between capillary blood vessels and brain tissue. Neurons are l') , the information processing units. illS When a neuron fires, it sends a transient change in its electrical membrane polar­ ization along its axons that travel into its many axonal branches. At special contacts Ill­ he with its neighboring cells, the synapses, the neuron will secret a neurotransmitter that will travel across the 50mm wide synaptic cleft to the neighboring neurons. The neu­ IIts rotransmitter then changes the membrane properties of the postsynaptic cell, opening IIts ion channels through the cell membrane, causing slow changes in its membrane po­ the ich tential and consequently extra- and intracellular current flows (see, e.g. Kandel et al., , of 1995, for an introduction). The postsynaptic potentials (PSP) arise from the net outflow of negative (excitatory: EPSP) or positive (inhibitory: IPSP) charges into ca! extracellular space. These ions will be actively pumped back along the dendrite and in soma or passively diffuse through the membrane of the neuron or - as in the case of !so K+ - through adjacent glial cells. The extracellular currents that result from many rty such PSPs also create measurable variations in the scalp potentials, thereby producing the EEG. irst Ions are pumped back through the cell membrane at various points along the den­ Illn drite and the soma, and consequently, an intracellular branch, Le., current that flows Ms from the dendrites towards the soma closes the current loop. As given by the Biot­ tgy, Savart law, the intracellular current causes, like any current flow, a magnetic field. When tens of thousands of such postsynaptic currents are synchronously active, the ond magnetic flux becomes measurable as a magnetically evoked response. An external nun nm' stimulus generally creates a magnetic response of a magnitude which would suggest that about 1 million synapses have been synchronously activated in cerebral cortex.

" 196 Chapter 2 Biomagnetism 2.4 Nellromagnetism

The transmembrane ionic and displacement currents contribute little to the extracra­ nial magnetic field because they are radially symmetric around neuronal processes (Swinney & Wikswo, 1980). In principle, the extracellular volume currents may also contribute to magnetic activity, but generally this contribution is negligibly small when only the magnetic field normal to the head's surface is measured. If the conductive medium in which the neurons are embedded is of infinite dimensions and uniform con­ ductivity, the extracellular currents are symmetric in such a way that the magnetic field generated by each current path is canceled by the magnetic field generated by other extracellular current elements. Because neurons are embedded in a medium with a more complex conductivity pattern, boundary effects distort the extracellular current pattern away from this symmetry. A higher electrical conductivity in one region will cause an increase of current density parallel to the boundary in the region of lower conductivity. As illus­ trated in Fig. 2.61, the secondary sources will align perpendicular to the conductivity boundary, with their magnetic field tangential to it. Therefore, if only sources per­ pendicular to the boundary surface (e.g. the head's surface) are measured, secondary sources would make no contribution (Cohen et al., 1970). This means that the normal (axial) component of neuromagnetic signals (and only this one is measured by most MEG devices) mostly reflects intracellular currents flowing in dendritic trees towards the soma.

Fig. 2.62: The figure illustrates pyra relative position The mien titude of cell bodies countl densely packed the pyram neurons in the cortex. (Frl

Cortical neurons· can be divided cells constitute about 85% (Figs. 2.1 close to the soma and are densely c cases the axon is directed inward, tm Fig. 2.61: The pattern of volume currents from a focal source (current dipole denoted by to the cortical surface along a straig the arrow) is perturbed from its symmetry by electrical conductivity barriers the axon may ascend into upper cort (left). As the principle of superposition holds, this pattern is equivalent to one white matter before it enters cortex 1 4 that would be produced by the current dipole in an infinite homogeneous medium 10 axon terminals is quite large in « plus a set of radial currents positioned at the conductivity barrier (left). As the star-like across the soma, an apica latter set of secondary currents flowing perpendicularly to the barrier does not layers with horizontal elongations. ] contribute to the axial magnetic field, the magnetic field that is measured in this case reflects only the impressed current component. • They possess many spines. S, that bear plastic synapses, i.1 larize the postsynaptic membl About lOll neurons in the human brain are involved in transmission and processing prerequisite for learning. of information. EEG and MEG thus result from the summed mass activity of tens of 5 thousands (> 10 ) of neurons. Synchronous activity of such large number of neurons is • local and long-range connectic common in the cerebral cortex, where more than 105 pyramidal cells can be found per mm2 and areas in the order of 1-5 mm2 are activated by even the simplest stimulus • at the soma there are probabl, (Okada, 1983). • on the dendritic tree there are 2.4 Neuromagnetism 197

Fig. 2.62: The figure illustrates pyramidal cells from one Golgi preparation in their correct relative position The microphotograph inserted at the top right shows the mul­ titude of cell bodies counterstained with a Nisslstain. This insert illustrates how densely packed the pyramidal cells are; a cell type that constitutes 85% of the neurons in the cortex. (From: Braitenberg & Schiiz, 1991)

Cortical neurons can be divided into two basic types: The excitatory pyramidal cells constitute about 85% (Figs. 2.62 and 2.63). In these neurons, the dendrites start close to the soma and are densely covered with spines (> 1 spine/j.tm). In nearly all cases the axon is directed inward, towards the white matter and travels perpendicularly to the cortical surface along a straight line until the first divisions begin. From there, the axon may ascend into upper cortical layers or then, myelinated, constitute a part of white matter before it enters cortex again. The volume that is innervated by the nearly 104 axon terminals is quite large in each of these cases. A basal dendritic tree extends star-like across the soma, an apical dendritic tree stretches out till the uppermost layers with horizontal elongations. Pyramidal cells bare the following characteristics: .• They possess many spines. Spines are bubble-like extensions on the dendrites that bear plastic synapses, i.e., synapses that may alter their power to depo­ larize the postsynaptic membrane. The presence of these spines seems to be a prerequisite for learning. • local and long-range connections • at the soma there are probably only inhibitory synapses • on the dendritic tree there are predominantly (95%) excitatory synapses

" 198 Chapter 2 Biomagnetism 2.4 Neuromagnetism ------

• the axon terminals have only excitatory synapses -40 mV, a pyramidal cell may ex Eh-aitenberg & Schuz (1991) estirr Stellate cells are, generally, of inhibitory nature. The dendrites, mostly without thors considered the quantitative spines, are arranged around the cell body in star-like (stellate) manner. In contrast to synapses per neuron, the number the pyramidal cells, the dendritic trees ofstellate cells do not have a preferred direction of axonal branches per neuron (4 with respect to the cortical surface. The axon leaves the soma in an arbitrary direction mm3 (4.1 km) or just the distribu and branches immediately and in many small volumes. Stellate cells bare the following cortex, they could not detect qua, characteristics: macroscopic bundles that support that connects the Wernicke and E • few or no spines connections between neurons seen • only local connections mated length of dendrites per neu to 400 m per mm3 cortex). Hence • only type II synapses (inhibitory) on the axon the majority of which (75%) are I( The sensory input into the hun all cortical neurons. In other wor tributed into a vast network before it does not seem astonishing that behavior to a much greater extent \ When many pyramidal cells I when their apical dendrites are de surface and at the scalp fed by thf The magnetic fields induced by th, the intracellular the MEG. Stellate cells do not cor current that flows activated cells tend to cancel each perpendicularIy to Vice versa, it seems reasonable to g the cortical surface produces the MEG generated by pyramidal cells, resul points toward an increase in cortica

B ::;:;110 I/2nr be a reduction of excitability or e)l IPSPs at the apical dendrites would however, that inhibitory synapses c Fig. 2.63: Three pyramidal cells are outlined on the right of this graph. The den~ritic trees the cell body, as only when close to with their spines appear thicker than the axonal branches below. Depolarization veto to a depolarization traveling f of pyramidal cells, caused by excitatory post-synaptic potentials (EPSP), results the soma draw current from both t in an outflow of negative charges into extracellular space near the synapses. The lation of the two current branches generation of EEG (through volume currents) and MEG (induced by the intracel­ lular currents) results from current flows initiated mainly by EPSPs. One neuron are unlikely to contribute much to can generate a magnetic field of some 0.002 IT and thus up to 50,000 neurons are therefore probably due to a red must be depolari~ed synchronously in order to explain a typical sensory evoked 1987; Rockstroh et al., 1989; Elbel magnetic field. of ongoing c~ain-like activation of et al., 1989). For our purpose, it does not seem important to differentiate further types, like, e.g. the Martinotti cells, that send spirally axons upward from deeper layers of the 2.4.2.2 Cell Assemblies cortex. It is, however, important to note that the basic composition of the different cell types is very similar throughout the cortex. This fact suggests that similar schemata Ultimately, it will only be possible and principles for the processing of information as well as for the generation of electro­ that generates MEG and EEG on tl: magnetic activity apply for the whole cortex. A neuron may receive as many as 1000 activity in a plastic network ofexcit signals, Le. depolarization/hyperpolarization of the membrane potential by neighbor­ assemblies (Braitenberg, 1978; Bra ing neurons at any given time. If the depolarization at the axon hillock exceeds some fundamental to models concerned v

/ 2.4 Neuromagnetism 199

-40 mV, a pyramidal cell may excite as many as 5000-10.000 cells. For the mouse, BTaitenberg & Schiiz (1991) estimated a number of 7000-8000 cells. When these au­ thors considered the quantitative characteristics of connectivity, like the number of synapses per neuron, the number of synapses per axon (180/mm), the total length of axonal branches per neuron (4 cm) or the total length of all axons within a given mm3 (4.1 km) or just the distribution of different cell types in different regions of the cortex, they could not detect qualitative differences between the different regions. If macroscopic bundles that support long range connections (like the fasciculus arcuatus that connects the Wernicke and Broca's areas) are not taken into consideration, the connections between neurons seem to be quite stochastic in nature. The total esti­ mated length of dendrites per neuron can be estimated to be 3~5 mm (corresponding to 400 m per mm3 cortex). Hence, there are about 2 synapses on a fLm of a dendrite, the majority of which (75%) are located on a spine. The sensory input into the human cerebral cortex comprises only about 1/1000 of all cortical neurons. In other words, every sensory signal that reaches cortex is dis­ tributed into a vast network before it results in a behavioral output. And consequently, it does not seem astonishing that the current status of the network determines this behavior to a much greater extent than the particular sensory event. When many pyramidal cells become synchronously activated, and particularly when their apical dendrites are depolarized, a negativity is generated at the cortical surface and at the scalp fed by the volume currents, generating the EEG (Fig. 2.63). The magnetic fields induced by the many tiny intracellular current flows summate to the MEG. Stellate cells do not contribute: Due to their lack of a spatial orientation, activated cells tend to cancel each other's contribution to macroscopic measurements. Vice versa, it seems reasonable to generally assume that surface negativity, i.e. activity generated by pyramidal cells, results from depolarization of dendritic trees and hence points toward an increase in cortical excitability (Elbert, 1992, 1993). Positivity would be a reduction of excitability or excitation. It has often been argued that summated IPSPs at the apical dendrites would also produce surface positivity. It should be noted, however, that inhibitory synapses are probably not common at greater distances from the cell body, as only when close to the axon hillock can they throw in their inhibitory veto to a depolarization traveling from the dendrites towards the soma. PSP close to the soma draw current from both the soma and the dendritic tree with partial cancel­ lation of the two current branches at distant sites of measurement. Therefore, IPSPs are unlikely to contribute much to this process. Surface-positive waves like the P300 are therefore probably due to a reduction in cortical excitability (Elbert & Rockstroh, 1987; Rockstroh et al., 1989; Elbert, 1993) and P300 would indicate an interruption of ongoing c~ain-like activation of cell assemblies (Birbaumer et al., 1990; Rockstroh et al., 1989).

2.4.2.2 Cell Assemblies ("--- Ultimately, it will only be possible to achieve an understanding of mass cooperation that generates MEG and EEG on the basis ofabraintheory that allows for modeling of activity in a plastic network of excitatory (and inhibitory) neurons. The concept of cell assemblies (Braitenberg, 1978; Braitenberg & Schiiz, 1991; Hebb, 1949) has become fundamentCLl.~o.rnodels concerned with the functioning of the brain even though many

" 200 Chapter 2 Biomagnetism 2.4 Neuromagnetism neuroscientists were initially reluctant to accept this position. Hebb (1949)postulated phy of its connections and fi that short-term memory is represented in reverberatory circuits, as described earlier "Gestalt" of an assembly in by Lorente de No. Once activated, these circuits can maintain excitation, as they are the cells or its transmitters. formed by a set of highly interconnected neurons, each of which receives excitation from, and gives excitation to, other members of the same set. If a sufficiently large 3. This specificity of an asseml number of neurons in one such cell assembly is activated, then the whole set will become frequency of fast changing ele active and produce the function for which it has been sculptured, which includes calling components. It must be fast up stored information and outputs to use it. explosively as a whole: a wl A key concept related to memory storage is that the structure of these cell as­ within the fraction of a secOJ semblies is flexible and can be changed rapidly to update the context of the stored meaning-aspects of that delu information. This requires the continued strengthening of connections between simul­ taneously active neurons (Hebb's rule "...cells that fire together wire together."), an The possibility of "observing" c assumption which has long been considered the physiological basis for the acquisition on the number of neurons that an of learning and storage of memory. It is thought that increasing the level of postsynap­ number is not known. Only theOJ tic activity within neural networks will, in turn, increase the ability of simultaneously many neurons might be involved i active synapses to depolarize the postsynaptic membrane, while insufficient activation range from a few thousand to a few is thought to weaken them. Hebbian models of memory were suggested by quite a about oneflfth of all members wou number of theorists including Palm (1982) and more recently Brown and associates expect more than twenty thousand (1990),~The synapses in the assemblies can be upregulated and down regulated by both delectability with MEG. One of th homosynaptic and heterosynaptic events and by a variety of chemical reactions. Kan­ cortical sensory coding is how act del and associates have suggested that the long-term information storage mechanism neurons leads to a unique and glot may involve the modulation of genetic material (i.e. gene expression) to manufacture formation such that a certain objec' a protein within the cell that perpetuates or fixes the specific synaptic gain of a par­ elements is recognized, or how a 0 ticular synapse in the cell. Probably all of the synapses on dendritic spines are subject there is no single area in the cortE to both short-term and long-term modification in their relative gains, a finding which been suggested that the active neur suggests that three out of four cortical synapses are plastic (Braitenberg & Schiiz, to one cell assembly by synchronou 1991). The build-up and strengthening of a cell assembly requires that a large portion percepjcs ..WQuldthenbe.possible th of synapses in' the neuropil that are not relevant for the incoming information event a major.focus .has-been-aILthe.roIE be shut off, otherwise connections would form too randomly. This means that the ",vent .aIld thereby th~. rolethat syJ excitability of the neuropil must somehow be reduced for a fraction of a second or learning, and formation of-memory so before a relevant event can be stored (Birbaumer et al., 1990; Elbert, 1987; Elbert cally represented .in activity patterr et al., 1992b; Elbert, 1993). stimulus will activate a population ( Let us now review the consequences of such a Hebbian view for. the interpretation of a population code. Neurons whic of brain activity, especially that which can be recorded non-invasively as event-related stimulus must be differentiated frOJ responses and also observed in its behavioral consequences: populations in order to avoid false 1. The development of cell assemblies depends on plastic ("Hebbian") excitato.ry stim~li. A solutipn to this so-calle cell systems with a rapid rise time for their construction. The system ideally of 'seIisdr1

phy of its connections and firing patterns, or, so to speak, in the topographical "Gestalt" of an assembly in its phase space, not in the properties of its parts, the cells or its transmitters.

3. This specificity of an assembly is best reflected in the spatial distribution and frequency offast changing electrical activities, such as the EEG and event-related components. It must be fast because assemblies must have the ability to ignite( explosively as a whole: a whisper can turn on a full blown paranoid delusion within the fraction of a second, including all, or nearly all, sensory, motor, and meaning-aspects of that delusion.

The possibility of "observing" cell assemblies in action via EEG and MEG depends on the number of neurons that are coherently active in a certain brain region. This number is not known. Only theoretical considerations provide estimates as to how many neurons might be involved in a cell assembly. Aertsen et al. (1995) suggest a range from a few thousand to a few ten thousand members of a cell assembly, whereby about one fifth of all memher-s would be active at a time. Correspondingly, we cannot expect more than twenty thousand neurons to be active, a number that may just reach delectability with MEG. Oneofthefundamental problems in the neurophysiology of cortical sensory coding is how activity beginning in spatially separated clusters of neurons leads to a unique and globally coherent percept, how the brain processes in­ formation such that a certain object which can be comprised of many different sensory elements is recognized, or how a certain "Gestalt" comes into our a\\[areness. Since there is no single area in the cortex where all I?rQclC~sing pathways converge,'lt has been suggested that the active neuronal clusters peUahiing tQ) a certain object are tied, \""< to one cell assembly by synchronous oscillations. ~irnultaI}~QusJl,djyation ofdifferent \ percepts_would_then-be-possible through different.oscillatoryJrequenc\es. Ther~fm:e, n amajorlocus.has-been-oILthe_wle_oLoscillatory brilin responses evoked by ~ given ~vent and thereby th~()leJhat synchrony and plasticity play in sensory processing, l~arnii1g~ andfof!nationoLmemory. Within the cerebral cortex, information is typi­ cally represented ·in activity patterns of large populations of neurons. Any particular stimulus will activate a population of neurons which describes its properties by means of a population code. Neurons which belong to a population activated by a particular stimulus must be differentiated from those related to other simultaneously activated populations in order to avoid false conjunctions between features related to different stimllli. A sol4tion to this so-called "binding problem" (the problem of how a subset of 'seJs~'rj/'h'lforrl{~fi3n is selected to form the representation of a given object) seems to be based on temporal coding (Abeles, 1982; von der Malsburg & Schneider, 1986), i.e., when neurons responding to the same stimulus in the visual field synchronize their discharges with a precision of a few milliseconds. Indeed, animal research suggests that a temporal synchrony between the response of different neurons could be a mechanism to bind neurons dynamically into functional groups. The firing of cell assemblies in a related .Qr synchronol.\s manner has been related to synchronous oscillations in single cell firing and local field potentials (ECoGLof cats and monkeys (for summary see, e.g., Pantev et al., 1995a). These coherent oscillatory brain activities areprorninent in thegaffillla-band (from 20 to oveilOO Hz), for instance, when animals (or humans) are presented with perceptual tasks. -

" 202 Chapter 2 Biomagnetism 2.4 Neuromagnetism ------1

2.4.3 Types of MEG Signals and Techniques of Signal Analyses cluster. The difference suggests t cL.t; ,(. "'("'," from the precentral motor cortex Neural mass activity produces irregular time series, as can be seen in 'EEG, MEG or somatosensory areas. Support for ECoG. Through visual inspection, it is already apparent that these time series cannot come from their different reactivi simply result from an uncoordinated, arbitrary firing of single neurons. Indeed, we already smaller 2 s before a volun expect that neurons must cooperate and partially synchronize their firing patterns so after movement. It is thus sirr in order to produce meaningful output. A substantial body of research attempts to visual input and processing. Th< a) systematically track this code of coordinated activity with linearand stQc:;)lastic of movement further confirm the statistical techniques like power spe-Ct~ar~J1~lyses (Hari & Salmelin, 1996) and b) to -trace the dynamics ofthe system, e.g., to "freeze" them in a state space, aspace which underlie this component. is spanned by the system's variables (Elbert et al., 1994a). Suppression of the 20 Hz rh) Its spatial locations follow the hon this Il-rhythm component after me 2.4.3.1 Spontaneous Activity Analyzed by Means of Spectral Analyses greatest activity in the Vertex reg Hans Berger was the first to observe the rhythmic oscillations in EEG recordings, Functional investigations of this : which he then labeled according to the Greek alphabet with Alpha (8-12 Hz), as the i.e. when the subject is waiting' most prominent activity, Beta (12-20 Hz) and Gamma (> 20 Hz) covering the higher 1993). frequency bands. Only when more sophisticated technology allowed for the exami­ nation of the lower frequency ranges and the EEG was recorded during sleep stages, 2.4.3.2 Brain Activity Anal~ was slower activity divided into Delta (0.5-4 Hz) and Theta (4-8 Hz). Grey Waiter Theory suggested using Theta relating to Thalamus, when he observed that the appearance of such waves in the course of normal development was related to the maturation The ongoing stream of neuronal of the thalamo-cortical system. Spontaneous rhythms in the Theta and Alpha fre­ presents itself as an endless variat quency band reflect a complex interplay for signaling between the cerebral cortex and irregular motion in only one chan the thalamus, which is an essential relay station along the sensory pathways from traces of brain activity - in this the periphery to the brain and which is also involved in functional loops that coordi­ however, were selected such that t nate excitability in distant cortical areas (Elbert, 1993). It has been suggested that the selected time period. Both tral thalamo-cortical oscillations contribute to the generation of distinct brain waves. the conditions during the two obse Greek letters are not only used to denote the different frequency bands, but in completely different functions evol addition, they' indicate typical rhythms or waves that are prominent within these Such behavior is well-known f frequency bands. Correspondingly, alpha-waves have frequencies within the alpha systems as sensitive dependence 0 band, while oscillations with signal power in several frequency bands have their own dependency in any given determir letter, such as the Il-rhythms with its components in the alpha- and beta-band. definition of chaos means absence ( The alpha waves seem to be primarily generated in those cortical areas that relate of chaos is based on non-linear rr to visual input and processing. They are prominent when the subject has the eyes established during the late 19th c, closed or is in a drowsy state, not paying much attention to the sensory (visual) input. accessible until the work of Lorenz I It therefore has been argued that the alpha-waves represent an idling phenomenon that behaviorin a deterministic system prevents the build-up of excitation in visual neuronal networks (visual hallucinations) behavior totally ruled by (determ when the eyes are closed and that ascertains that vision is at its peak efficiency as of thetime-seues=and~me-ser soon as the eyes are opened. For the same reason, a car motor is sometimes left idling chaos._ during a cold winter in Finland, as alii Lounasmaa has put it. As physiological systems are r The Il-waves are strongest in amplitude over the central (Rolandic) fissure and toolsdeveloped by chaos theory fo Fourier analysis suggests that it includes two components, one centered around 10 Hz, for ~xamining EEG andMEc::jsc the other around 20 Hz. This is already indicated by their 'comb shape', Le. the approach). If we do not view EEC similarity of theses waves to the appearance of repeated "p,": Ilj),j),. MEG investigations of the brain's engine, but (at leas by Hari, Salmelin and coworkers have contributed much to delineate the origin of these tic regulatory processes of neuron rhythms (Hari & Salmelin, 1996). The sources of the j),-rhythm cluster over the central chaos theory to characterize and ( fissure, with somewhat more anterior dominance for the 20-Hz than for the lO-Hz theoretical premises cannot be fuIJ 2.4- Neuromagnetism 203 -"'------=-=-=- cluster. The difference suggests that the 20-Hz activity receives major contributions from the precentral motor cortex, whereas the lO-Hz component seems to reside in somatosensory areas. Support for the functional segregation of these two components come from their different reactivity to movements. The level of the 10-Hz rhythm is already smaller 2 s before a voluntary movement and then returns only within 1 s or so after movement. It is thus similar to the alpha waves that also disappear during visual input and processing. The insensitivity of the source locations for the type of movement further confirm the idea of a widespread idling in sensory areas that underlie this component. Suppression of the 20 Hz rhythm starts later and its rebound may be earlier. Its spatial locations follow the homuncular organization, with most lateral locations of this {L-rhythm component after movements offace and lips, foot movements, producing greatest activity in the Vertex region and movements of digits and hands in between. rtmctlonal investigations of this rhythm suggest that it appears in a "Wait" state, i.e. when the subject is waiting to make a specific response (Kristeva-Feige et al., 1993).

2.4.3.2 Brain Activity Analyzed by Means of Tools Derived frOIu Chaos Theory

The ongoing stream of neuronal mass activity represented in EEG or MEGtraces presents itself as an endless variation of spatio-temporal pattern to the observer. An irregular motion in only one channel is illustrated in Figure 2.64, in which two such traces of brain activity - in this case MEG - were superimposed. The two traces, however, were selected such that there was high resemblance during the beginning of the selected time period. Both traces result from the same spatial sensor position, and the conditions during the two observations are very much the same. Nevertheless, two completely different functions evolve quickly, as time progresses. Such behavior is well-known from the considerations of non-linear deterministic systems as sensitive dependence on initial conditions. If we observe such a sensitive dependency in any given deterministic system, we call it "chaos". While the classic definition of chaos means absence of order and unpredictability, the scientific definition of chaos is based on non-linear mathematics. Although its principles were already. established during the late 19th century by Poincare, they were not mathematically accessible until the work ofLorenz (Lorenz, 1963). Today, C9110S is_defined as stochastic behavior in a deterministic system, or more colloquially: Ch!1oS is apparently lawless behavior totally ruled by (deterministic)Jil0'J3. Unpredicta.Qilityiii_thede.y~lopment of theBiii:e-~n~ffemesensitivityto i;itial conditions are the fingerprints of chaos~ . As physiological systems !lre non-linear systems, it has.been sugg~st~(Lthat the toolsdeveloped by chaos theQry for the analyses of time series might also prove useful for ~xamining.EEG andMEG (see e.g. Elbert et al., 1994a, for a summary of this approach). If we do not view EEG and MEG as a stochastic signal, e.g., as the noise of the brain's engine, but (at least partially) as a measure of lawful and determinis­ tic regulatory processes of neuronal mass activity, we can apply the measures from chaos theory to characterize and quantify the brain mechanisms at work. Even if the theoretical premises cannot be fulfilled, we may use the theory of chaos to define and

"

./ 204 Chapter 2 Biomagnetism 2.4 Neuroma':.gn_e_t_is_m -1

phase space. Depending on the to as dimensional complexity. The Lyapunov exponent is two adjacent trajectories. Posit quickly with time. As any pres sion, the error will consequently a measure for the predictabilit} Another method of evaluat time series is the '01itu(jl tnfOTn how rn(inY....9its can_be predicte, measured.history of a function It is also possible to estim Kaplan & Glass (1992) developf on the fact that, in a determin 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 o.~ the region in the phase space.. nearly the same orientation. 1 time (sec) serve as an indicator of the detf elements provides an estimator Fig. 2.64: Two traces were selected from one MEG-channel such that a high cross­ The most promising applical correlation existed at their beginning. As can be seen, the original correlation the site of epileptic foci and, pc decays exponentially with time. tigation of the determinism inc values of determinism in interic minism derived from MEG timf quantify complexity as it appears in a given time series. We cannot directly construct because the latter picks up mo a phase space for that part of the brain which generates the EEG, since we do not and the lower the system's dimE know its specific equations or its underlying principles of generation. A revQllltiQni1~y come. Genuine epileptic patterr cliSGQvery irrthe 1980s was the possibility and development of ail algorithm to recon­ underlying dynamics by means, fltrucLan equivalentstateJ5pace. GbYr~n"u~~gle time series such as EE;C;(~) from just a very low fractal dimension (so olleJocatiQn, how i;;. itP9ssibk tQ.resurrect the dynamics of the generatmg system, Mal-EEG. Further evidence for a system which possibly inclUdes a large number of independent variables? Takens seizures came from studying th, (1981) and Packard et al. (1980) proposed a reconstruction of the state space by means (Iasemidis et al., 1990; IasemidiE of time delays Elt: The values measured at fixed time delays EEG(t), EEG(t+Elt), ECoG and discovered abrupt at EEG(t+2Elt) are treated as though they characterized new variables. Other tech­ attack at electrodes close to th niques of reconstructing the phase space are summarized in Elbert et al. (1994a). using ECoG and MEG (Elbert Once a topological equivalent attractor has been reconstructed, we can describe its features using measures such as the 1JlJ'9~§.t_LyqllU'Tl,I}!L~ponent(LLE) or the fractal The consistency of results tb dimension of its texture. sures like LLE and MSI (Elbert This reasoning, however, relies on the assumption that EEG and MEG are gen­ clinical diagnosis: As the collaI: erated by a deterministic system. Furthermore, the mathematical computations can cations close to the focus, it m only provide meaningful results in low-dimensional systems. But even if these assump­ the MEG provides information tions arc not strictly fulfilled, we can use such measures to quantify the complexity or be developed further such that the patterns in a time series and characterize different brain states correspondingly. available (see also Section 2.4.8: This is of particular interest if pathological brain states are to be diagnosed. Alterations that can best be Attractors are most commonly characterized by the fractal dimension, D2, of the in a number of neurological pa attractor, a measure of the density of points within a certain volume element of the (Reviews e.g. Elbert et al., 199'

/ n 2.4 Neuromagnetism 205

phase space. Depending on the particular algorithm used, this measure is also referred to as dimensional complexity. The Lyapunov exponent is a quantitative measure describing the separation of two adjacent trajectories. Positive LLE indicate that neighboring trajectories diverge quickly with time. As any present condition can be specified only with limited preci­ sion, the error will consequently inflate over time. The LLE can therefore be considered a measure for the predictability within a time series. Another method of evaluating complexity and predictability of the EEG/MEG time series is the 1J1-1l:tu(),ljn[QL11](lti()1!.JY:T!rt{()T!. Thlsmethod refers tQthe~uestionof how m

It is also possible to estimate the determinism inherent in a given time series. Kaplan & Glass (1992) developed a direct test for determinism. Their method is based· on the fact that, in a deterministic system, the trajectory's tangent is a function of the region in the phase space. All tangents in a given region of the phase spacehave nearly the same orientation. The average vector in a volume element can therefore serve as an indicator of the determinism in this region, and the sum across all volume elements provides an estimator of the determinism inherent in the time series.

IS­ The most promising application of non-linear measures is in the field of diagnosing ~n the site of epileptic foci and, possibly, the prediction of a seizure. Already the inves­ tigation of the determinism indicates that epileptic patients have generally elevated values of determinism in interictual MEG and EEG (Muhlnickel et al., 1994). Deter­ minism derived from MEG time series is consistently greater than for EEG, probably let because the latter picks lip more widespread activity. The greater the determinism LOt and the lower the system's dimensionality, the more adequate non-linear analyses be- . I[y come. Genuine epileptic patterns comprised the first EEG activity examined for their 0­ underlying dynamics by means of chaos theory: TIabloyantz & Destexhe (1986) report 1st a very low fractal dimension (somewhat greater than 2) for the case of strongly Petit­ m, Mal-EEG. Further evidence for the appearance of a chaotic dynamic during epileptic ns seizures came from studying the LLE (2.9 ± 0.6 bits/sec). Iasemidis and Sackellares JlS (Iasemidis et al., 1990; Iasemidis & Sackellares, 1991) analyzed intracranially recorded t) , ECoG and discovered abrupt attenuation in the LLE prior to and during an epileptic :h­ attack at electrodes close to the focus. We were able to confirm these observations a). using ECoG and MEG (Elbert et al., 1997a) as illustrated in Figure 2.65. its tal The consistency of results that are obtained by applying different non-linear mea­ sures like LLE and MSI (Elbert et al., 1997a) suggests that these measures may assist clinical diagnosis: As the collapse of LLE is confined to or at least starts only at lo­ ~n- cations close to the focus, it may help to define epileptogenic pacemaker zones. As :an the MEG provides information that bears some similarity to ECoG, the method may Ip­ be developed further such that more powerful non-invasive diagnostic tools become or ~y. available (see also Section 2.4.8). Alterations that can best be characterized through non-linear measures may exist ilie in a number of neurological [Jathologies, such as M. Alzheimer and other dementia ilie (Reviews e.g. Elbert et al., 1994a, 1997a).

-' 206 Chapter 2 Biomagnetism 2.4 Neuromagnetism ~~-~-~---I

withi!1Jhis array. The event is repe. 20 locked signal (ensemble) average is 18 of the epoch. If MEG(t)n denotes t] at time t and trial n, ~he sir;na.~a.ve] 16 N ~ 14 ERF(t). = M EG(t) = N6'" 1 n=l 12 If MEG(t)n is considered the sUI w 10 ground MEG and measurement ern .....I .....I ratio (SNR) in proportion to -IN. 1 8 and SNR can be calculated as follow et al., 1988): 6

4 . 1 2 slgna power = (TSignal = T11: 2 o 5 7.5 10 12.5 15 17.5 20 22.5 25 TIME (min) ._.~") noise power = (T~OiBe TU Fig. 2.65: A clear collapse of the LLE can be seen around minute 16, where a seizure had occurred in a patient with temporal lobe epilepsy. The LLE-indent, how-·· ever, remains restricted to distinct locations (thick gray trace). Transient abrupt Var alterations often preceed the attack (in this case at minute 15) and the corre­ sponding sites show reductions in the LLE already 10 minutes before the seizure. The LLE recovers to normal values very quickly after the seizure. (From Elbert signal - to - noise ratio = et al., 1997a) lJt6ono;n("'. ). O~of th~_~~sump_t~onsof signal aver 2.4.3.3 Signal Analysis of Event-Related Fields Th!§_assumPtion~s."iQ~teR,.,wh~n' th' habittI1l:1esor when its mt~n'cyl%if~~ Any external or internal event gives rise to a ch~w!ttl~rj,~~ic pattern in the stream comP?Il:..nts related to certain cognitiv of EEG and MEG. This event-related signal is ~mDeda.ed in the ongoing "sponta­ cognitive processes are under investig neous" fluctuations and must be extracted by averaging techniques. Event-related exceeded, will actually worsen the sign fields (ERF) and event-related potentials (ERP) are theoretically relevant because One.Wllyof dealing with. coIIlllonent !< they provide ways of testing theories of abnormal brain functioning that no other triaL i!!1q align trials on these signals method can offer. For example, unlike ordinary behavioral tests of cognitive process­ (1967) proposed an iterative procedur ing, event-related activities give an index of the processing of task-irrelevant events, each single trial by moving a template distracting stimuli, or events subjects have been told to ignore. Th~_t()pographic dis­ along the trial to find the latency of r tribution and method of source localization of ERPs and ERFs gives clues as to what formed by aligning trials on the identi parts of the brain are active cluring a particl.!lar cognitive activity. The empirical rele­ as a new template. If the SNR is toe vance'ofERPs is attested to by the fact that ERP abnormalities have been repeatedly simply reflect random noise. demonstrated in neurological and psychiatric disorders. Another assumption of signal ave Both ERPs and ERFs benefit greatly from signal averaging and filtering to enhance random noise. This is only an appr their Sigrial-to-noise ratio. Data are generally digitized at a fixed rate to fill a data when considering the block of alpha ac array, and a stimulus or other synchronizing event defines the time epoch ofinterest perturbation" (Makeig, 1993; Pantev e •,!, • 2.4 Neuromagnetism 207

within this array. T)1e event is repeated (each repetition is called atrial), and a time­ locke(f~ignal (ensemble) average is calculated across trial epochs for each time point of the epoch. If MEG(t)n denotes the magnetic field strength at some sensor location attime t and trial n, t~~gnaLa~erage isdefined as

1 N F?RF(t) = MEG(t) = N L MEG(t)n· (2.10) n=1

If MEG(t)n is considered the sum of true signal ERF(t) and random noise (back­ ground MEG and measurement error), signal averaging improves the signal-to-noise ratio (SNR) in proportion to ,,(N. Unbiased estimates of signal power, noise power, and SNR can be calculated as follows (Mocks et al., 1984; Roth et al., 1995; 'Duetsky et al., 1988):

. 21fT 2 1 2 ' :) szgnal power = O'Signal = T J M EG(t) dt - TO'Noise (2.11) o

T .----"--") noisepower=O'~OiSe T(N1_1)'~ (fo (MEG(t)n- MEG(t)2)dt) re (2.12) ~- ' R; Variance M EG(t) pt e­ 'e, signal - to - noise ratio 2 / 2 . (2.13) ,rt O'Signal O'Noi.e·

, I,' \J.eHxlOly'\((\~']pJCI'iO(C!) One of th

" , =-20=-8=----_~ ~9_h_'apter 2 Biomagnetism :j 2.4 Neuromagnetism

pips, for instance, reliably produced momentary increases in spectral power 'in the 2-8 Hz and 10-40 Hz bands. The phase of the latter activity varies from trial to trial I such that it cannot be extracted by averaging (see Section 2.4.3.4). ' 1 N Before components of the ERF are extracted, it is useful to apply SNR enhancing RMS= N LMEG(t)~ filters that incorporate assumptions about frequency, timing, and spatial distribution n=l of the component of interest. For example,the P300co ponentmay be e)(p~cted to rn Further, Global Dissimilarity I hayg a frequency lower than 2 Hz, to peak in a range of 280 to 400 ms (in, a simple calculated by subtracting two mal auditory choice reaction time task in young adults). Filters are useful whenever the et al., 1992). frequency of the noise is different from that of the signal. Digital frequency filters have the advantage over analog filters of being able to operate without introducing Once the ERF is obtained, sc distorting phase shifts into the signal (Elbert, 1991). The most commonly used digital are extracted, often based on the filter has been the moving average or boxcar filter, in which each point of the signal oretical concept of the response b is replaced by an average of that point and a certain number of prior and subsequent composed by different sub·entities points (Cook & Miller, 1992). It should, however, be kept in mind that analog filters called components..A component still have a place in data acquisition prior to digital filtering, A low-pass analog filter be defined as electric or magnetic with a half power frequency below but close to half the sampling rate prevents aliasing. tlvity associated with a specific ne A high-pass analog filter minimizes irrelevant baseline shifts that arise from electrode logical or psychological process, for drifts in the case of EEG, but may also be present in MEG recordings contaminated ample, as part of a motor act sud by environmental noise '(e.g. cars, elevators etc.). moving one's finger, a part of sew For the EEG, current source density maps (also called surface Laplacian or radial processing such as a response to a t current estimate maps; Fig. 2.66) act as spatial filters emphasizing localized compo­ pip, or a step in the information 1 nents with a high spatial frequency (i.e. more shallow sources). For this to work well, of cessing such as categorizing a stiml course, electrodes must be placed with a high spatial frequency (preferably more than as target or non-target (Fig. 2.67). one hundred, when the whole scalp is to be covered (Junghi:ifer et al., 1997). Maps can statistical sense, a component expl: be based on unaveraged activity such as epileptic spikes or on signal averages. The experimental variance. Basically, equation for calculating current source density is concept of a component rests on idea that information processing be divided into discrete steps of in 'tf2V,' tf2V) mation transformation, each of wl (2.14) 1= P ( Ox2 + oy2 activates a subprocessor and with distinct set of regions within the br: where V is the voltage, x and y the surface location on the x-y plane, and p the re­ Some leads or sensors will pick 2 sistivity. In addition, p = k . d where d is the distance between electrodes and k is activity from those structures bel a constant for all electrodes within a subject. In calculating the Laplacian, surface than others, particularly when SoUl contours can be generated by a method called spherical spline interpolation, which is are multiple with overlapping in based on physical principles for minimizing the deformation energy of a thin sphere ences. Ever since the stimulating c constrained to pass through known points (Junghi:ifer et al., 1997; Perrin et al., 1990, tributions of Emmanuel Donchin d 1987). It should be noted, however, that an error of measurement for electrode loca­ ing the 1970s, the concept of corn tions as little as a few mm will distort the interpolation and introduce "ghost" sources nents has been a controversial one. when the Laplacian is calculated. the one hand, the neuronal struCtl For MEG, the spatial gradient (e.g. calculated as the diffElrence between two adja­ which are the physiological substI cgnt sensors) can serve as a similar measure that also provides a spatial filter, focusing of psychological entities such as "se the. signal to nearby sources. The Finish Neuromag system uses directlyplanar gra­ tive attention" or "context updati diometers to n:~cord the MEG, i.e. provides the difference in magnetic field between are not known and on the other ha two adjacent locations. the definition of the psychological s c It is generally useful to obtain a measure for the signal power which is commonly units is often insufficient. EHPlE .calculated as the root II.l~an.s.qU:i.J~JItMSl.a(;Eos.sal1cl~anll~ls_aIlg~Qrnetimesreferred insufficiently defined entities. Neve . to_.as_glnba~ power (although po~~r-=wouldjmp!yJh

1 N RMS= N 'L MEG(t)~. (2.15) n=l

Further,. Global Dissimilarity (GD) is defined as the RMS of the difference maps calculated by subtracting two maps normalized with respect to their RMS (Brandeis et al., 1992). Once the ERF is obtained, scores Scalp - Potential are extracted, often based on the the­ oretical c.oncept of the response being composed by different sub-entities, so­ called components..A component can 0.5 be defined as electric or magnetic ac­ tivity associated with a specific neuro­ o logical or psychologiCal process, for ex­ ample, as part of a motor act such as -0.5 moving one's finger, a part of sensory processing such as a response to a tone pip, or a step in the information prCl­ Scalp - Laplacian .... cessing such as categorizing a stimulus 15 as target or non-target (Fig. 2.67). In a 10 statistical sense, a component explains 5 experimental variance. Basically, the o concept of a component rests on the idea that information processing can ·5 be divided into discrete steps of infor­ -10 mation transformation, each of which I) activates a subprocessor and with it a distinct set of regions within the brain. e­ Some leads or sensors will pick up Fig. 2.66: Comparison of spline- is activity from those structures better interpolated scalp potential and related current source density ce than others, particularly when sources (CSD) for a 65-channel recording. is are multiple with overlapping influ- While the scalp potential suggests re ences. Ever since the stimulating con- just a single source (tangential 0, tributions of Emmanuel Donchin dur- current beloW-the vertex), the ... ing the 1970s, the concept of compCl- CSD points at a second source es nents has been a controversial one. On with a more radial orientation the one hand, the neuronal structures between Fp1 und F7. It is also a­ which are the physiological substrate obvious that a less dense elec- ~g of psychological entities such as "selec- trode array cannot resolve these 'a­ tive attention" or "context updating" sources. For the scalp potentials, en are not known and on the other hand, the scale on the right corresponds . . f h . hi' I b to lOlLV/unit. thed efimtlOn 0 t e psyc 0 oglca su - uy units is often insufficient. ERP/ERF-components are in the crossing point between ed insufficiently defined entities. Nevertheless, it is possible to operationalize components, to e.g. via the variance explained across distinct experimental conditions.

" 210 Chapter 2 Biomagnetism 2.4 Neuromagnetism

, Wavef(}rm subtmetioncan he 11 duce the effects of component over of two_slIIiilar pitches are given in quently than the other. The ERF t the sensory effects of the tone and L t By subtracting the ERF to the frel .I Bereltschafts- I the sensory effects are removed leav potential/field I the sensory responses to the two t 'Gi' 011 I effects are additive, an assumption Cl I ,-.C'J ~~ I quency specific temporal recovery c OIl, Cl ... I the NIQO to be smaller in response C'J'- I ~= , Principal Components Analysis ;,~ t I :1.'-' I I measurement which uses the time pc '-'''Cl I I sensors, and.differentexperimentai ~r!~- I I ;:: u I onset of terms, PCA identifies orthogonal Q+:l ~ ~ I behavioral space defined.by .. the .. variables. GI u Cl I response "varimax" procedure. However va E~ stimulus u El I many factors are to be extracted ar ~ Q.. I II Furthermore, each experiment give: .I .3 .5 s time no establishecLcriterion for decidin! Thus, it is uncertain how many stab! these components with ones previoll Fig. 2.67: Schematic categorization of endogenous components of the event-related po­ Template correlation assesses thE tential/field in response to a stimulus (left) and prior to a voluntary response waveform to be evaluated. The temr (right). The Nd depends on attentional processes and may be related to memory ponent shape or on signal averages. traces. The MMN (mismatch negativity, mismatch field) appears when there is by the various procedures? Paramet a mismatch between the expected stimulus and the actual stimulus perceived, underlyiI!gumeliability. Fabiani et I like when a deviant is embedded in a string of standard stimuli. Task relevant latency estimates of averages to be b events that interrupt the ongoing processing may give rise to a P300-like compo­ reliability was similar to peak-pickin nent. Preparation for an event, like the preparation for the execution of a motor response are preceeded by a surface negative shift in the EEG (Bereitschaftspo­ QLP300 were most reliable (betweel tential) and a magnetic field change over central and precentral regions. a full cycle 2Hz cQsinusoidal wave. as good as using the output of a vel averages of frequent trials from infre the probability effect than when th Measurement procedures include peak-picking, area measurement, W-aveforIllsgb­ Test-retest reliabilities of both ampli traction, Principal Components Analysis as well as template correlation, and.9ipole sessions, probably due to changes in rPodeling (see Coles et al., 1986; Rockstroh et al., 1989; Roth et al., 1995, for reviews). result, other components such as the _.:::' Peak-picking means finding maxima or minima in specified latency ranges and deter­ lying between 0.15 to 0.27 (Boutros mining peak latency and amplitude with respect to a pre-stimulus baseline. This is et al., 1990). the simplest method of component evaluation, but can be biased when latency ranges are selected after an inspection of the data, and is perhaps unduly restricted in that it considers only peaks among other waveform features. It should at least be avoided 2.4.3.4 Event-Related Spectral to base the extracted value on only one point, which may he influenced by noise or Animal studies suggest that the ass overlapping components. The median over a time interval specified in the range of temporal coincidence of neural activif maximal signal power seems to be preferable. This is mand,\tory with multiple sen­ intracranial, single-cell recordings Obl sors where the maxima generally appear at different times.-< Area is ffifasured in a activity, several studies have examinE specifiedlatency range, and thus is based on multiple points. Like peak-picking, area by object and/or movement recogni measurement can be biased and influenced by overlapping components.

,/ 2.4 Neurornagnetism 211

" Wavehrmsubtraction ca.n be used before peak-picking or area measurement to re­ duce the effects of component overlap. For example, consider a paradigm where tones oftwosiII!ilar pitches are given in an unpredictable sequence and_one occurs less fre­ quently than the other. The ERF to thtl rare tone can be considered a combination of the sensory effects of the tone and the cognitive effects of the tone being a rare deviant. By subtracting the ERF to the frequent tones from the ERF to the infrequent tones, the sensory effects are removed leaving behind the cognitive effects. This assumes that the sensory responses to the two tones are identical and that cognitive and sensory effects are additive, an assumption which is not always warranted. For example, fre­ quency specific temporal recovery of the auditory NlOO, a non-cognitive effect, causes the NI00 to be smaller in response to frequent stimuli than to rare stimuli. -) Principal Components Analysis (PCA) constitute another approach to component measurement whichllsesthe time points on waveforms from different subjeets,gLfferent sensors, and_ different experimental conditions to define components. In statistical terms, peA. identifies orthogona..l axes of maximal variance in a multidimensional space defined by the variables. Generally, these ~es- are rotated according to the "varimax" procedure. However, variO\ls criteria can be determined in terms of how manyfactors ar(lto be extracted and how many rotations of the factors are possible. Furthermore, each experiment gives slightly different factor structures, and there is no established criterion for deciding whether these differences are significant or not. Thus, it is uncertain how many statistical components to interpret, and how to identify these components with ones previously described. \, Template correlation assesses the similarity of a template of the component to the 0­ se waveform to be evaluated. The template may be based on prior knowledge of the com­ ry ponent shape or on signal averages. How reliable are the component scores extracted is by the various procedures? Parametric studies have illuminated some of the variables Id, underlyiI!Kunreliability. Fabiani et al. (1987) found the split-half reliabilities of P300 lit latency estimates of averages to be between 0.63 and 0.88, and in most paradigms, the 10­ reliability was similar to peak-picking and template correlation. Amplitude estimates ~r ofP300 were most reliable (between 0.90 and 0.96) when based on covariance with 10- a full cycle 2 Hz cosinusoidal wave. Taking measurements at pz alone was almost as good as using the output of a vector filter based on Fz, Cz, and Pz. Subtracting averages of frequent trials from infrequent trials led to more reliable measurement of the probability effect than when the two types of trials were measured separately. !b­ Test-retest reliabilities of both amplitude and latency were lower between than within pIe sessions, probably due to changes in P300 over time. In contrast to this encouraging 'S) . result, other components such as the P50 produced unsatisfying reliability coefficients .er­ lying between 0.15 to 0.27 (Boutros et al., 1991; Kathmann & Engel, 1990; Thretsky I is et al., 1990). ges bat 2.4.3.4 Event-Related Spectral Perturbations :led i or Animal studies suggest that the association of information may be formed through lof temporal coincidence of neural activity in the gamma band. To bridge the gap between en­ intracranial, single-cell recordings obtained from animal experiments and human brain D a activity, several studies have examined to what extent this oscillatory activity induced II"e3 by object and/or movement recognition can also be non-invasively measured in the

" 212 Chapter 2 Biomagnetisrn 2.4 Neurornagnetism human EEG and MEG. For instance, visual and auditory evoked and induced transient Time-domain average gamma-band responses (GBR) have been demonstrated in human E,EG and MEG to simple stimuli and coherent patterns (e.g., Lutzenbergex et al., 1995; Pantev et al., 1995b). Using a similar visual stimulus design, as developed for animal research, MiilIer et al. (1996) detected evoke.d GBRinJLhighJrequenc)' range (30:c'85 Hz). This Epoch activity appeared in response to coherently moving bars primarily over posterior areas Epoch: contralateral to the stimulated hemifield, while no Comparable response was evoked by incoherent patterns (bars moving in opposite directions). It can be assumed that this Epoch: topographical pattern reflected activity in the underlying MT (VS) which is associated with movement encoding. This work demonstrates the similarity of induced oscillatory responses in animals and humans. However, considerable data processing is mandatory in order to extract this in­ Epoch 1 formation from EEG, which constitutes a widespread summed activity from many simultaneously active networks. MEG, and its planar gradient in particular, repre­ sents activity from more circumscribed regions, so that signals' are more comparable Time- and phase- locked to ECoG derivation (Okada & Xu, 1993). evoked gamma-band cortical activity The simplest method of analysis for extracting local oscillatory information from EEG and MEG,consistsin_estimatingthetemporal change .in.spectral.density by calculating the FFT in a window which slides across the time series. An example of the resulting "landscape" is provided in Fignre 2.68. . Frequency-domain average Dennis Gabor realized that it is artificial and suboptimal to insist on a series of infinitely extended basis functions given that all real signals result from a flow of information, that is, a constant change across time. Instead of composing the signals by means of an inadequate series of functions and then restricting the arising TIme- and phase- nonlocked problems hy multiplication with a window function, Gabor directly built the window induced gamma-band into functions of finite length. A finite signal is better represented by a sum of finite cortical activity Gabor functions. The resulting evolutionary spectrum provides an ideal compromise between frequency and time resolution. One problem, however, is that there will be Fig. 2.68: Example of evoked anI indefinitely many Gabor-bases, requiring procedures to select a favorable base. As the resents the resulting res choice of different bases will hamper the comparability, it might be useful to search bandpass from 24-48 Hz for a base which fits the MEG time series under a variety of experimental conditions while the remaining acti and varying subjects. however, an external eve which are not phase-lock Another result of previous work in the field of event-related spectral changes if the spectral power is c (Pfurtscheller & Neuper, 1992; Makeig, 1993; Kristeva-Feige et al., 1993; Feige et al., ery single trial and the f 1994a) suggests that specific oscillatory generators may appear or vanish in the course window across a trial wi of the event-related response. Consequently, it is of interest to examine the spatial power as displayed in th, distribution of these event-related alterations. The scalp distribution of event-related responses is not sufficient to determine the coherence of active oscillatory generators. One and the same topographical distribution may be produced by several coherent or a change in the original coupling, incoherent, simultaneously active generators. In order to uncover the functional mean­ the appearance of a new generate ing of an oscillatory response, a procedure is required which can simultaneously map one. Therefore, it is necessary to several non-coherently overlapping spectral field components. The procedure which of an oscillatory activity. we have developed (Feige et al., 1994b,c) reaches this goal by taking the correlation be­ A generator within the cortex tween all possihle pairs of channels into account. Other procedures, particula~ly those with a fixed phase relation betwe, developed for EEG analyses, often rely solely on the degree of similarity (correlation) the generator is focal, the MEG pt ofa pair of signal channels, and interpret this measure.in terms of coupling strength In a more extended network, dela between two cortical areas. Alterations in this correlation, however, may be caused by we are ahle to detect any fixed f 2.4 Neuromagnetism 213

Time-domain average Stimulus onset T

Epoch 1

Epoch 2 Wt'/JfW~MMIJ!.P~\cfQ'M~'/i4~'\'iN!1JWlffl Epoch 3

Time- and phase-locked evoked gamma-band cortical activity -200 -100 0 lW :ID 3D 41l 1lI lID 'Xl) lID Tnne (mS)

Frequency-domain average 1.2 Norm. 1 Spectral Power .8 Time- and phase- nonlocked induced gamma-band cortical activity

Fig. 2.68: Example of evoked and induced gamma-band responses. The thick line rep­ resents the resulting response when the auditory evoked field is filtered with a bandpass from 24~48 Hz. The time- and phase-locked activity is then extracted, while the remaining activity (gray lines) is treated as noise. In many instances, however, an external event may induce changes in the spectral density functions which are not phase-locked to the stimulus onset. This activity can be extracted !S if the spectral power is calculated in a small time window of, e.g., 300 ms for ev­ L, ery single trial and the power is averaged across trials subsequently. Sliding the le window across a trial will produce the type of time dependency in the spectral at power as displayed in the lower part of the Figure. (From Pantev et al., 1994) Id s. or a change in the original coupling, Le., a change in the common generator, but also by n­ the appearance of a new generator which oscillates independently from the previous ~ one. Therefore, it is necessary to simultaneously determine similarity and amplitude ch of an oscillatory activity. e­ A generator within the cortex will produce a field distribution across the sensors se with a fixed phase relation between the signals picked up by the different sensors. If 0 D) the generator is focal, the MEG phase differences between the sensors can be 0 or 180 • ~h In a more extended network, delays caused by transmission times might require that by we are able to detect any fixed phase relationships. An oscillatory network is then

" 214 Chapter 2 Biomagnetism 2.4 Neuromagnetism defined as a generator whose activity variation exhibits an oscillatory component. Synchronous activity in the The localizing power derives from the assumption of an external reference phase to demonstrated using coherently IT which the ongoing MEG is "phase aligned" before averaging, simply by sliding the time compared to pseudowords (Fig. ~ windows to match latency with respect to the event. Any signal occurring phase-locked in its spatial extension between to the reference phase will survive the averaging, while random-phase signals will cancel response to function words that t each other. However, as there is no external phase reference available, the reference hemisphere, including both Wern phase for averaging must be derived from the channels themselves. Feige's method, indicates synchronous activity reI called "phase-aligned spectrum" , consists of averaging the complex Fourier spectra of a speech perception. number of time windows after phase aligning them to the spectra of the corresponding time windows of a phase reference channel. For k channels, a k x k matrix will result from phase-aligning each channel with each other channel. The underlying coherence pattern is extracted from this matrix by means of the singular value decomposition LH with suitable rotations, if more than one generator is to be assumed. We have evaluated content words this procedure with simulated and real data. Figure 2.69 illustrates the validity by application to simulated data. 12 I .8

C) PAS real First three SVD map' A) Sample time course ofone channel part _ for simulated dipole I ~ ~dtb .. simulation •• I. ~~ £ • ~ ( ~.J, .e•• pseudo words ~ ~I·...... " rl* .n, "DC! ...to.all • zoo 40{1 tOp '" loot ,,",1~'J

B) Field maps ofthe two simulated D) PAS oscilating dipoles imaginary Norm. part Spectral simulation Power

Fig. 2.70: Normalized power as a fu The channel with the I Fig. 2.69: Simulated oscillatory generators and phase alignment spectrum (PAS). Two each hemisphere, respecti dipolar sources were chosen as independent oscillatory generators with a fixed non-words (lower diagram position and orientation, but a time-varying amplitude. Local coherence back­ (right side). Note the "2 ground noise was added through the activity of 500 dipoles with random .ori­ Pulvermiiller et al., 1994) entation and random distribution on a spherical surface with a stochastically fluctuating amplitude. Additional white noise was added to each recording chan­ These results illustrate that ani nel. A) Sample time course of One of the channels for the first of two simulated vestigation of induced GBR in hu dipoles, whose begin, phase and duration were randomized. B) Field distribu­ available. When relating the subj tion for the two simulated dipoles. C) and D): PAS matrix (real and imaginary tical correlates of basic processes part) for the two oscillatory generators plus the 500 random dipoles. Each small recognition can be explored. contour plot represents the PAS for OnC reference channel. The first two of three We can conclude that oscillator SVD maps on the upper right recover the two simulated oscillating dipoles nicely. mokeys and man with techniques (Figure courtesy of Dr. B. Feige) ECoG, MEG and EEG. The increa

/ 2.4 Neuromagnetism 215

Synchronous activity in the gamma-band (GBR) in humans has not only been demonstrated using coherently moving visual stimuli, but also in response to words as compared to pseudowords (Fig. 2.70;Pulvermiiller et al., 1994). This activity differed in its spatial extension between function words and content words. It was only in response to function words that the observed responses remained restricted to the left hemisphere, including both Wernicke and Broca's area. This result suggests that GBR indicates synchronous activity related to the functional meaning of word and, thus, to speech perception.

LH RH content words content words

12 12 I 1.00 I 1.00 .8 .8

pseudo words pseUdo words

Norm. Spectral Power

Fig. 2.70: Normalized power as a function of frequency and time (data from one subject). I The channel with the largest late evoked magnetic response was selected for each hemisphere, respectively. Responses to content words (upper diagrams) and o non-words (lower diagrams) over the left hemisphere (left side) and over the right d (right side). Note the "25 Hz-valley" in the diagram on the lower left. (From l- Pulvermiiller et al., 1994) 1­ Iy These results illustrate that animal experiments should be complemented by the in­ 11- vestigation of induced GBR in humans, where information about perception is readily ~ available. When relating the subject's response to oscillatory events, the electrocor­ u­ ry tical correlates of basic processes of sensory encoding such as object or movement all recognition can be explored. ee We can conclude that oscillatory event-related brain responses are studied in both Iy. mokeys and man with techniques ranging from single cell recordings to mass action, ECoG, MEG and EEG. The increasing data base contradicts the view that oscillations

-.

/ 215 Chapter 2 Biomagnetism 2.4 Neuromagnetism ------~------are not used for coding but are simply a by-product of the functional architecture of is allowed to move with time. A any neural network. In other words, they are not just the noise of the brain engine, modeled ECD to the real measure but do bear functional significance. We now also know that oscillations serve more the application of the model. MOl than one purpose and that their function includes the following: anatomical structures. It is know or white matter do not incorporat 1. Oscillations reflect idling in neural mass systems which would prevent the system neuromagnetic data, only current from activating themselves when there is little or no input into the network (like surface of gray matter contribute for Alpha activity, see Section 2.4.3.1). Determining which source is be 2. Faster oscillatory synchronization is linked to learning and plasticity: is possible if additional physiologi the possible source configurations. (a) oscillations are used to form memories (Section 2.4.2.2) sume that white matter does not pI (b) oscillations might tie neural assemblies in various brain regions to form one outside of the cranium, and that tJ percept (feature binding). source of the activity generates a c: bralcortex. The spatial organizati, Controversial are further suggestions: Magnetic Resonance Imaging. By 3. The stimulus itself might be coded in non-linear dynamic patterns that appear as this reconstruction, considerable cc oscillations to the eye ofthe naive observer. The attractor governing the dynamic to obtain information concerning e pattern is related to stimulus encoding - everything that is not coherent to this for every time point. This procedur signal is dismissed as noise (Section 2.4.3.2). advanced than the other imaging tion (Dale & Sereno, 1993; Fuchs e 4. The existence of oscillations creates phase, Le., a variable that might serve as 1996; Kineses et al., 1998). Only a clock signal. (If we assume that the contribution of a particular cell to a cell the neurophysiological basis of psye assembly is measured by the number of spikes it provides to the ensemble, then these processes are occurring. Wh a time interval needs to be set over which spikes are counted. Oscillations might fields and electric potentials) is pra provide the clock signal needed to update the contribution of the individual sampling rate, the spatial resolutic neurons.) channels (EEG plus MEG). The im; from some number n of cortical pat the procedure which we currently fa 2.4.4 Magnetic Source Imaging (MSI): Determining the Loca­ each of which can slide across the tion of Neural Activity multiplied by the parameters of eal number of channels. Physical modi The measurement of electric and magnetic activity outside lL volume conductor, as per variable a saturation of informe found in the body, does not allow for a definite conclusion about the position and this number of channels, however, tl strength of the source of the activity at this time. In terms of images, the activity channel. only delivers a two dimensional shadow of three dimensional activity to the surface. Individual anatomical informatic In other words, the spatial imaging of the active generator structures constitutes the struct a realistic head model, to gel attempt to determine a solution of electromagnetic equations which, by their nature, tion, and, finally, to project the cor is not unique. This inverse problem, first formulated by Helmholtz in the middle of anatomical structures (Hoke et al., ] the last century, results from the fact that an infinite number of source configurations 3D MRI tomograms. If the sources within the body can produce exactly the same distribution of electric and magnetic and if EEG and MEG are simultanee activity on its surface. bined to obtain a solution for the el Only if additional information is provided does it become possible to construct in principle - uniqne. The resultin constraints such that only one solution remains which satisfies the equations, Le., vation has the unique advantage th the spatio-temporal distribution of electromagnetic generators becomes unique..The metabolism, but images the electric simplest, and still most powerful constraint is the assumption of a focal source which of a second, Static activity, averagt ~p~r6xi'rriation can then be modeled with good! by a current dipole - regardless of the fMRI , is replaced by a moving imagl real shape of neuronal fields. The location of the equivalent current dipole (ECD) of the functional sequence in patter 2.4 Neuromagnetism 217

is allowed to move with time. A high goodness of fit of the field produced by the modeled EeD to the real measurement « 0.95) provides a reasonable justification for the application of the model. More sophisticated approaches utilize the knowledge of anatomical structures. It is known, for instance, that regions occupied by ventricles or white matter do not incorporate active structures. More explicitly, for most of the neuromagnetic data, only current dipoles with an orientation perpendicularly to the surface of gray matter contribute to electromagnetic activity on a macroscopic scale. Determining which source is being activated in the brain at a specific point in time is possible if additional physiological-anatomical constraints can be made that limit the possiblesource configurations. To solve the physical~q'uatIbrisit is sufficient to as­ sume that white matter does not produce any significant electrical or magnetic activity outside of the cranium, and that the polarization in gray matter which represents the source of the activity generates a current flow perpendicular to t~e surface of the cere­ braf~ortex. The spatial organization of the cerebral cortex can be reconstructed from Magnetic Resonance Imaging. By limiting the possible source space of the models to this reconstruction, considerable constraints are provided which may make it possible to obtain information concerning expansion, position and strength of cortical sources for every time point. This procedure, which is currently in development, could be more advanced than the other imaging techniques due to its fine temporal-spatial resolu­ tion (Dale & Sereno, 1993; Fuchs et al., 1994; Liitkenhoner et al., 1995; Liitkenhoner, 1996; Kincses et al., 1998). Only with this procedure will it be possible to record the neurophysiological basis of psychological processes in the temporal range in which these processes are occurring. While the time resolution of the measures (magnetic fields and electric potentials) is practically unlimited and only restricted through the sampling rate, the spatial resolution will generally be determined by the number of channels (EEG plus MEG). The image of the non-invasively measured activity created from some number n of cortical patches is represented by a system of n equations. In the procedure which we currently favor, we allow for a number of depolarized patches, each of which can slide across the cortical surface. Ideally, the number of patches multiplied by the parameters of each patch should be considerably smaller than the number of channels. Physical modeling suggests that with around 150-200 channels per variable a saturation of information sets in (EEG and MEG) is achieved. Up to i this number of channels, however, the source resolution improves with each additional y channel. Individual anatomical information is necessary for three different reasons: to con­ e struct a realistic head model, to generate constraints for a realistic source configura­ ., tion, and, finally, to project the computed source location(s) onto the corresponding anatomical structures (Hoke et al., 1994). The necessary information is available from 3D MRI tomograms. If the sources can be restricted to a number of cortical patches, and if EEG and MEG are simultaneously measured from many different sites and com­ bined to obtain a solution for the electromagnetic equations, this solution becomes ­ :t in principle - unique. The resulting pattern of patches that indicates cortical acti­ ,.. , vation has the unique advantage that it does not display a slowly changing cerebral K\ metabolism, but images the electric neuronal activation which varies within fractions :b of a second. Static activity, averaged across minutes as in PET, or across seconds in le fMRI , is replaced by a moving image on a millisecond timescale, allowing the detection ») of the functional sequence in patterns of neural mass activation.

,/ 218 Chapter 2 Biomagnetism 2.4 Neuromagnetism

For the modeling of MEG/EEG data, three constituents become relevant: the neurons and they sum up to gen model of the volume conductor, the neuroanatomically based co11straints, and the (see Section 2.4.2). Macroscopi source model. These three aspects can be treated separately but need to be combined matter. The white matter, whi, for the imaging of a particular data set. assumed not to contribute to EE planar area with a diameter of I 2.4.4.1 Modeling the Volume Conductor an extended layer with a diame 1987). Subcortical structures al Volume conductor models may be divided into realistic approaches and into models away from the surface, which dil with a simple, basic geometry. The first type of modeling requires excessive numerical 4 to 7 times (Lutzenberger et al. calculations, in contrast to simple models like a sphere or a spheroid (Cuffin & Cohen, of a subcortical structure is syn HJ77a,b; Munck De, 1989; De Munck et al., 1989). Such simple geometric forms, how­ elements have the same orienta ever, are only rough approximations of the real headshape. For the case of modeling would still amount to only 1 magnetic data alone, a local homogeneous sphere works remarkably well, which can be %( Thus, an intracranial polarized I, explained by the fact that the body is transparent to low-frequency magnetic fields. Liitkenhoner et al. (1995, 1996) compared simple and realistic modeling, and demon­ when reasonably extensive in tl1 deeper structure (Braun et al., 1 strated a high degree of similarity for the early sensory components. If the electrit activity. Hence, event-related I' data are included, the different conductivities of brain, cerebra-spinal fluid, skull and arising from a polarization of till skin come into play. For a spherical model with four concentric layers, analytical solu­ tions have been provided by Cuffin & Cohen (1979); Berg & Scherg (1994) developed Thus, the availability of a 1" a very fast computation model. More recently, solutions have also been developed for prerequisite for a realistic source eccentric sphere models of the head (Cuffin, 1991). from MRI data have been desCl Provided enough computing power is available, there is, however, no need for an­ F\lchs et al., 1994; LiitkenhOner , alytical solutions, and, hence, there is no limitation for the possible shapes of volume conductor model in two steps. conductors. Given the assumption that the volume conductor consists of a homoge­ successively from each of the 128 I nous core and layers of homogenous conductivity, it is possible to employ a boundary Then, a triangulated 3D surface element model (HamiWiinen & Sarvas, 1989; Meijs et al., 1985), If this assumption For the surface reconstructic of homogenous conductivities within the different layers is not valid, finite element by Steinstrater (1991). In the fil (Bertrand et al., 1991) or finite-difference-models (Stok et al., 1986) become neces­ voxels are distinguished: those Wf sary. In the latter case, a discretization of volume elements is required. In the case complement (non-brain-voxels), , of boundary element models, a discretization of the surfaces is sufficient, and thus a first two sets of voxels can be achi two- instead of a three-dimensional problem saves computation time. into three substeps: preprocessil Menninghaus et al. (1994) investigated the absolute error if a single dipole is local­ matter (brain voxels) with a floc ized within a spherical model. A magnetic field was produced by current dipoles placed determine the non-brain voxels. ] within a realistic phantom head which was filled with saline solution. For depths of the to either the brain or the non-br, dipole between 1.2 cm and 3.3 cm, measured from the inner surface of the skull, the results in a binary matrix in whic locali~ation error ranged from 3.7 mm to 7.9 mm. Using a realistic boundary element voxels have the value zero. In th, model, the error proved to be independent of the depth, reaching an average of only and smoothed using an algorithrr 1.9 mm. In Liitkenhoner et al. 1995, 1996, triangles with about 400,000 Verl 2.4.4.2 Neuroanatomical and Neurophysiological Constraints and Recon­ struction of Cortex 2.4.4.3 The Source Models

EPSP or IPSP cause currents to penetrate through the neuronal membrane. This Among the source models, the mo will give rise to an intracellular current loop through the dendritic tree penetrating as it is the dominant term in the the membrane at different locations, and also to the volume current which closes the areas of less than 2-3 cm in dia loop. As the pyramidal cells in the cortex are oriented in parallel, with the apical current dipole (Lutzenberger et dendritic tree closer to the surface than to the soma, thousands of tiny current loops parameters, three for its locatiO! are created by the depolarization (and sometimes also hyperpolarization) of pyramidal of magnetic modeling within a l:: 2.4 Neuromagnetism 219 neurons and they sum up to generate the macroscopically measurable EEG and MEG (see Section 2.4.2). Macroscopically, this becomes evident as a polarization of gray matter. The white matter, which includes the axons and glia cells, can generally be assumed not to contribute to EEG and MEG (Rockstroh et al., 1989). A small active, planar area with a diameter of 0.1 cm produces only 5% of the activity produced by an extended layer with a diameter of 1 cm (Braun et al., 1990; Lutzenberger et al., 1987). Subcortical structures are not only small in size, but are also located farther away from the surface, which diminishes their contributions to surface signals another 4 to 7 times (Lutzenberger et al., 1987; Rockstroh et al., 1989). Even if a large portion of a subcortical structure is synchronously active and even if we assume that active elements have the same orientation in space, their contribution to scalp potentials would still amount to only 1% of the signals generated by extended cortical sources. Thus, an intracranial polarized layer of 1 mV might produce a scalp potential of 20 /lV, when reasonably extensive in the cortex, but only 0.2 J.1- V when limited in size in a deeper structure (Braun et al., 1990). This attenuation is even stronger for magnetic activity. Hence, event-related responses with larger amplitudes can be modeled as arising from a polarization of the cortex. Thus, the availability of a reconstruction of the cortical surface constitutes the prerequisite for a realistic source model. Algorithms suitable to extract these surfaces from Mill data have been described by a number of groups (Dale & Sereno, 1993; Fuchs et al., 1994; Liitkenhoner et al., 1995). We construct the surface of the volume conductor model in two steps. First, rough 2D contours of the brain are extracted successively from each of the 128 MRI slices using an interactive segmentation software. Then, a triangulated 3D surface is generated. For the surface reconstruction. of the cortex, we use a 3D algorithm developed by Steinstrater (1994). In the first step (rough segmentation), three different sets of voxels are distinguished: those which can be attributed to the brain (brain voxels) or its complement (non-brain-voxels), and those for which no clear assignment to one of the first two sets of voxels can be achieved (remaining voxels). This first step is subdivided into three substeps: preprocessing (elimination of artifacts), definition of the white matter (brain voxels) with a flood-filling procedure, and distance transformation to determine the non-brain voxels. In the second step, the remaining voxels are assigned to either the brain or the non-brain voxels using a concurrent growing process. This results in a binary matrix in which all brain voxels have the value 1 and all non-brain voxels have the value zero. In the third step, the surface of the brain is triangulated and smoothed using an algorithm similar to that described by Dale & Sereno (1993). In Liitkenhoner et al. 1995,1996, the cortical surface was represented by about 800,000 triangles with about 400,000 Vertex points.

2.4.4.3 The Source Models

Among the source models, the model of an equivalent current dipole plays a central role as it is the dominant term in the multipole expansion (Katila & Karp, 1983). Active areas of less than 2-3 cm in diameter can be described well by a single equivalent current dipole (Lutzenberger et al., 1987). A current dipole is determined by six parameters, three for its location and three for the dipole strength, two in the case of magnetic modeling within a sphere. Such a moving dipole model has been used

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/ 220 Chapter 2 Biomagnetism ,------~------2.4 NeuromagIletism

in most of the earlier publications and has proven to be very useful for the modeling has been modified for mntinuous of eariy sensory evoked responses. A typical analysis is described in detail by, e.g., A survey for alternative approach Liitkenhoner et al. (1990); Liitkenhoner (1996)4. Given that a priori informat In more recent approaches, several simultaneously active dipoles with a fixed loca­ sources to a defined volume, a mi tion and orientation have been assumed (Baumgartner et al., 1991). The concept of imization of the squared average the fixed dipole was introduced by Scherg & von Cramon (1985, 1986). In this concept, minimum norm estimation. The n the inverse problem is solved simultaneously for a whole time segment during which pected difference between estimat the location and the orientation of dipoles remain invariant and only the magnitudes 1991). are allowed to vary across time. Michael Scherg called this widely used method BESA One basic question concerns th (brain electrical source analysis Scherg, 1989; Scherg et al., 1989; Scherg, 1990a,b). tify them as two different centers If these assumptions hold, this approach of spatio-temporal dipole modeling is more et al., 1990; Liitkenhoner, 1991, 1 precise than the modeling with several moving dipoles as the constraints arising from strated that two dipolar sources ca the assumption of invariant sources restrict the degrees of freedom of the model. The the Sources is at least approximat, MUSIC-algorithm (MUltiple Signal Identification and Classification) constitutes a and the location of the detector. related approach in which the data matrix is submitted to a singular value decom­ spatial resolution decreases from 1 position (Elbert et al., 1995b; Mosher et al., 1992, 1990). The neurophysiological the separability also depends on plausibility of spatia-temporal dipole models is moderate. Even if only focal activity other: If the two dipoles are ortho is considered, its spread along a gyrus cannot be explained well by one"stationary best, whereas the worst case is an dipole varying only in its moment across time. ' Junghofer experimentally evah Model simulations (Hari et al., 1988; Kuriki & Mizutani, 1988; Liitkenhoner, 1991; sources within a spherical phantor Liitkenhoner et al., 1996), as well as the phantom measurements mentioned above logical saline solution. In one case, b~ ~ (Menninghaus et al., 1994) suggest that, for magnetic fields generated single sinusoidal variations of 9.5 Hz and dipolar source, the accuracy of source localization is in the order of a few mllhmeters, A single dipole model produces the provided that the signal-to-noise ratio of the biogmagnetic data is sufficient and that an dipole moments crosses the zero lir appropriate volume conductor model is used. In the case oftwo or more simultaneously the estimated locations may not on active sources, such a high accuracy can be expected only if the distances between also at locations which are distant f the sources are sufficiently large. A reliable source separation becomes increasingly is important to note that the good] difficult with decreasing inter-source distances so that for distances below a certain erratic source locations. Only when limit, the critical inter-source-distance, the sources cannot be distinguished from one not occur. We also evaluated the p single equivalent source. between two simultaneously active It is generally agreed that at least the more endogenous components of the event­ approach (Elbert et al., 1995b). A related response (Fig. 2.67) originate from multiple, simultaneously active patches in stimuli which evoke activity in SI ~ the cortex. Therefore, models assuming distributed sources have gained increasing attention. These models do not rely on the attempt to explain the observed activities The idea underlying the dipole p with a few current dipoles, but estimate a current density distribution (or a discretiza- activities result from a few patches , tion of such a distribution by means of thousands of dipoles). As mentioned above, surface is available in triangular f it had already been shown by Hermann von Helmholtz (1853) that the corresponding these triangles. A dipole is assigne physical equations have an infinite number of solutions and that, hence, this inverse surface of the triangle and located problem must be solved by introducing additional constraints. Ideally, we could select the dipole moment is chosen pro the most probable current density distribution (Clarke, 1991) using a priori informa­ It is obvious that one patch can b tion about the probability of different types of density distributions. This idea was dipole. However, the number offre put forward by Wimalainen & Ilmoniemi (1984) for discrete sources. The approach parameters and onc single amplitu One question that remains to 4 Briefly, the original least-squares fit problem is transformed into a minimization problem for the single current dipole riding on the non-linear parameters (dipole coordinates) by replacing the linear parameters (components of the dipole moment) with the algebraic solutions available for their least-squares estimates, The resulting the surface - would be sufficient. minimization problem is then solved iteratively by means of Powell's method (e.g. Press et al., 1992). negative as a basic feature of the Starting values for the iteration procedure are obtained by an exhaustive search in a three-dimensional and dipole orientation, Le. it is grid (5 mm spacing between adjacent gridlines). independently.

/ I 2.4 Neuromagrtetism 221

has been modified for continuous current density distributions (Clarke, 1989, 1991). ., A survey for alternative approaches can be found in Kullmann (1991) . Given that a priori information only serves to restrict the location of possible ,­ sources to a defined volume, a minimization of the global error corresponds to a min­ )f imization of the squared average of the current density, an approach referred to as ~, minimum norm estimation. The more precise the a priori information, the less the ex­ h pected difference between estimated and real current density distribution (Ilmoniemi, !S 1991). A One basic question concerns the minimal separation of two sources in order to iden­ ). tify them as two different centers of activity. Liitkenh5ner and others (Liitkenh5ner ~e et al., 1990; Liitkenh5ner, 1991, 1996; Tan et al., 1990; Wikswo et al., 1990) demon­ m strated that two dipolar sources can be securely separated only if the distance between le the sources is at least approximately in the range of the distance between the sources a and the location of the detector. If the two sources are simultaneously active, the n­ spatial resolution decreases from the range of millimeters to centimeters. Of course, al the separability also depends on the orientation of the two dipoles relative to each ty other: If the two dipoles are orthogonal in orientation to each other, separation is the ry best, whereas the worst case is an antiparallel orientation. Jungh5fer experimentally evaluated such calculations using simultaneously active 11; sources within a spherical phantom consisting of a glass container filled with physio­ ye logical saline solution. In one case, the two dipoles were activated simultaneously with pe sinusoidal variations of 9.5 Hz and a phase delay of 90° between the different sources. rs, A single dipole model produces the correct locations when one of the two time-varying an dipole moments crosses the zero line. However, at times when both dipoles are active, ,ly the estimated-locations may not only be situated between the two correct locations but IeIl also at locations which are distant from the region of both active sources (Fig. 2.71). It ~ly is important to note that the goodness of fit may reach acceptable levels even for such un erratic source locations. Only when very high GoF-values were required did such errors me not occur. We also evaluated the possibility that under certain conditions the overlap between two simultaneously active sources can be disentangled using a MUSIC-type nt­ approach (Elbert et al., 1995b). An example for responses to simple somatosensory I in stimuli which evoke activity in SI and SI! is presented in Figure 2.72. ing The idea underlying the dipole patch model is that the source of the electromagnetic ~ies activities result from a few patches of activated cortex (Figs. 2.73,2.74). As the cortical za­ surface is available in triangular form, a patch can be constructed from a number of we, these triangles. A dipole is assigned to each one, its vector being perpendicular to the ~ng surface of the triangle and located at its center. If a uniform activation is desirable, !rse the dipole moment is chosen proportional to the surface of the respective triangle. lect It is obvious that one patch can be exploited in a similar way as a single equivalent lIla­ dipole. However, the number offree parameters is halved as there are only two location was parameters and one single amplitude! "lch One question that remains to be examined is whether simpler models - like a the single current dipole riding on the cortex with its moment oriented perpendicular to . the the surface - would be sufficient. We believe that this question must be answered in the Iting 192). negative as a basic feature of the latter model is a coupling between dipole location ional and dipole orientation, Le. it is no longer possible to choose these two quantities independently.

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222 Chapter 2 Biomagnetism ~Neuromagnetism ------~------'----~=~= I

Fig. 2.71: In a phantom model, two dipoles, fixed in location and orientation, were activated with a sinusoidal amplitude. The phase between the sinusoidal currents was 90°. The true locations of the generators are indicated by circles. These locations correspond exactly to the locations identified by the MUSIC·type approach. The single ECO identifieij correct positions for the instant of zero-crossing of the second generator. At other instances, the trajectory of the single ECO model (crosses) may be quite erratic. (Data courtesy of Dr. M. Junghiifer)

The following consideration illustrates the serious consequences of this fact. A current dipole is usually considered to be an equivalent source. Its location can be interpreted as something akin to a center of gravity of the real source. However, in the case of a current source distributed on a curved surface (as the cortex), it is a rare exception that the center of gravity would be located on the surface. Furthermore, even if the center of gravity were located on the surface, it cannot be expected that the normal direction at that surface point will be identical with the direction of the vectorial sum of all the currents in the source. In practice, this problem is aggravated by the fact that only a discretized surface is available, and therefore, the direction perpendicular to the surface is severely affected by discretization errors. Liitkenhoner's dipole patch model removes all of these problems: Expressed in physiological terms, the underlying assumptions imply that the level of neural activation is constant throughout the patch. Of course, many other activation patterns should be evaluated as well; for example, a pattern corresponding to a two-dimensional Gaussian distribution. Such a shape would provide a smooth transition between activa.ted and non-activated regions Fig. 2.72: The localization pi of the brain. of the spatial data, sensor positions. If It is evident that the dipole patch model can replace the simple current dipole vidual time average, model and may be integrated into analyses with the MUSIC algorithm. The simulta­ independent single neous activation of several patches accounts for simultaneously active sources. For the within the time int, analysis of slower SEF components one patch might be restricted to, e.g., movement is possible (see El b in SIl, while the other could account for activities in SI. cations which resul In general, there are two remaining useful alternatives: in many cases, the simple the MUSIC-like mu model of one equivalent current dipole assumed in each hemisphere can be sufficient. sections. b) Evoked In other cases, restricting the number of polarized patches of the cortex will produce a red segments in the valid model. Despite rapid progress in the field, the further development and validation fit was achieved at of source models and its integration with spatio-temporal modeling approaches remains is marked with a gl a major research goal. the best fit of ECD development of two which turned out tr Etbert et al., 1995t

./ ism 2.4 Neuromagnetism------=.223

l

"Lted 0 ,90 , tions The f the nodel , -, b) I . Time Course of I one Selected I.. Chalmel

l. A Ul be er , in ~ rare more, I that of the Lvated ection ouer's ~, the Ighout ell; for Such a Fig. 2.72: The localization procedure uses the information contained in the time evolution 'egions of the spatial data, i.e. of the magnetic field values recorded simultaneously at M sensor positions. If the dipole moments evolve independently in time, their indi­ dipole vidual time averaged contribution to the spatia-temporal data gives rise to a time multa- independent single dipole equation. This is satisfied by each dipole being active ror the within the time interval. Thus, the localization of simultaneously active sources vement is possible (see Elbert et al., 1995b, for a description of the algorithm). a) Lo­ cations which result from the single moving ECD-model (red crosses) and from simple the MUSIC-like multidipole-dipole approach (circles) are superimposed onto MRI Ihcient. sections. b) Evoked magnetic waveform (blue) of one selected MEG-channel. The red segments in the waveform correspond to a signal-ta-noise ratio >6. The best lduce a I fit was achieved at 62 ms with a signal-ta-noise ratio of 11.7. This point in time idation \ is marked with a gray bar. The second gray bar, around 122 ms, corresponds to -emains I the best fit of ECD in the region of S-II. The bottom part illustrates the temporal I development of two stationary SOurces assumed by the MUSIC-type approach, I which turned out to correspond perfectly to S-I and SoIl-regions. (Adapted from Elbert et al., 1995b) \

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/ 224 Chapter 2 Biomagnetism 2.4 Neuromagnetism

Fig. 2.74: The dipole patch mode Fig. 2.73: Dipole patch model: The patch shown in this figure has an area of 10 mm2 sources in the postcentl and is represented by 51 triangles. The green triangle serves as the starting the direction of major ( point (seed) for the construction of a patch. Each triangle is used as a platform different sites: magenta for a current dipole oriented perpendicularly to the surface (arrows displayed in and red stimualtion at t red and located in the centers of the triangles). The larger magenta arrow can the expe'cted homuncula be interpreted as the equivalent current dipole representing the totality of the the first major peak of t: I dipoles on the patch. The yellow surface represents an extended patch with an The individual MRI sen area of 1 cm2 (dipole symbols omitted). The reverse of the cortical surface was a realistiv volume condu given a gray pigment. (From Liitkenhoner et al., 1995, with kind permission) permission)

11 2.4.5 Functional Cortical Organization as Revealed by MSI somatosensory representation (SI) procedure is particularly suited to The cortical representations of the sensory perception relates in an orderly way to and face area but may also work f, the spatial arrangements of receptors in the periphery. The cortical maps of the The SEFs are induced by pneUl visual space, the body surface, or tone frequency (cochlear place) can be individually nologies Inc.). These stimulators aj determined by means of magnetic source imaging. In a similar manner, it is possible the mechanoreceptors of a defined to map motoric organization along the central sulcus and possibly also in other areas The first major peak of these ta of the frontal lobe. hemisphere; see Fig. 2.75) and the localize the generator of this SEF ( 2.4.5.1 Somatosensory System in area 3b contralateral to the sti response is observed within 30-7C The following protocol describes a standard scan that we typically apply in Konstanz about 50 ms; the face latency is ap to map the somatosensory cortex using MSI of ERF components. The first major peak between 7-15 nAm. Signal power f, of the ERF evoked by a light superficial pressure stimulator is located in the primary across 37 local sensors). In genera]

,/ 2.4 Neuromagnetism 225

Fig. 2.74: The dipole patch model demonstrates how somatosensory stimulation activates

,2 sources in the postcentral sulcus. The four different arrows (5nAm) illustrate the direction of major current flow activated by somatosensory stimulation at 'g different sites: magenta represents the lip, blue the first, green the fifth digit, m and red stimualtion at the collarbone. The locations (base of the arrows) follow in the expected homuncular map in area 3b. (A single cortical patch was fitted to iLIl the first major peak of the somatosensory evoked magnetic field - see Fig. 2.75). be The individual MRI served to reconstruct the cortex and provided the basis for an a realistiv volume conductor model. (From Liitkenhoner et al., 1995, with kind IllS permission)

somatosensory representation (SI) with most of its activity in Brodman's area 3b. The procedure is particularly suited to locate the representations of all finger tips, the lip to and face area but may also work for toes (see Fig. 2.74). we The SEFs are induced by pneumatic stimulators (developed by Biomagnetic Tech­ l1ly nologies Inc.). These stimulators apply a pressure to the skin and consequently activate ble the mechanoreceptors of a defined area of the skin (approximately 8 mm in diameter). eas The first major peak of these tactile evoked fields show a fairly dipolar pattern (per hemisphere; see Fig. 2.75) and the single moving dipole reflects an adequate model to localize the generator of this SEF component in most cases. This generator is located in area 3b contralateral to the stimulated site. This main deflection of the evoked response is observed within 30-70 ms poststimulus. For the fingers, the latency is about 50 ms; the face latency is approximately 35 ms. Dipolemoments typically range l.eak between 7-15 nAm. Signal power for the contralateral source reaches 50-100 IT (RMS ary across 37 local sensors). In general, these values are somewhat lower for the toe, and

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/ 226 Chapter 2 Biomagnetism 2.4 Neuromagnetism also lower for stimulations in the face area. For stimulation sites at the trunk, e.g. the the M20 is more lateral (some, shoulder or collar bone, signals can be too small to obtain reliabl~ measurements unless nerve stimulation. When the i thousands of stimulations are averaged. Data are recorded with a bandpass filtered seconds, SII becomes active, ge between 0.1 and 200 Hz. The stimulation consists of 500-1000 trials. The stimulus 1992). Their ampliutes are larg frequency can be as high as 2/s. the magnetic wave at 100 ms prc 1992). Painful stimuli can be appliec types of stimulation elicit seven clition to primary somatosensor) (SII; Hari & Ilmoniemi, 1986; H 1986) and cingulate gyrus (Kit~ latencies greater than 200 ms ha dividual variation. Attentional. latencies, modifying the response whether chronic noxious stimula1 pain, specifically, to an increaSE sory cortical representation. ME 1995a, 1997) suggest that the p cause alterations in the central I process. Patients who experienc, of time seem to produce more ex somatosensory stimuli are applie(

~.4.5.2 Auditory System

In a similar fashion as for the s( been used to detect aspects of j in the auditory cortex. Evoked Fig. 2.75: The set of waveforms shows the magnetically evoked responses that appear at 1993). The NlOOm (M100) sour different sensors in response to a light touch of the tip of the left thumb. The have been localized in the vicinity black arrows indicates the direction and location of the major current flow that areas) bilaterally on superior tern] has created the field maxima around 50 ms 1990). These sources of the N1mJ deeper locations, while in AI, th, For the source analysis of the field, peak responses are filtered using a low-pass frequencies are progressively mon of 20 Hz and a high-pass of 1 Hz. Several ways of source modeling are possible: The location of the Pa-map in pI The simplest approach is the moving ECD. Alternatively, a two-dipole model (for secondary auditory areas, is in ac( stimulation sites of the hand, one contralateral dipole may be enough) can be fitted A typical auditory scan to ma using e.g. BESA with one dipolar source per hemisphere as start condition. The fitting mal audiological status (air condu interval should be 15-30 ms around the maximum, whereby 2/3 of the time window 10 dB hearihg level in the range fr are before and 1/3 after the peak. Alternatively, the source analysis can be based tained prior to the investigation. on the dipole patch-model.. The resulting homuncular organization is illustra.ted in responses can be used to map aSj: Figure 2.74. the transient responses are of inte Another standard procedure to evoke the sensory fields uses electric stimuli to and decay time, cosine function, the median or ulnar nerves at the wrist. Stimulus intensity is set above the motor sented bilaterally. The interstimul threshold. Two distinct peaks arise from the contralateral primary somatosensory The carrier frequencies are 500:10( cortex SI (at 21-22 ms with polarity reversal at 32 ms and at around 40 ms). In added depending on time constrair agreement with the homuncular organization, Vanni et al. (1996) demonstrated that related epochs of 300 ms (includil stored for further analysis.

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the M20 is more lateral (some 7 mm) along the central sulcus for median than ulnar nerve stimulation. When the interval between stimuli is extended in the range of seconds, SIr becomes active, generating waves around 90-100 ms (Hari et al., 1990, 1992). Their ampliutes are largest if the stimulus is increased to the pain level, and the magnetic wave at 100 ms probably corresponds to the electric P100 (Kekoni et al., 1992). Painful stimuli can be applied through electrical or through CO 2 -laser pulses. Both types of stimulation elicit several stages of cortical activation (Kakigi, 1994). In ad­ dition to primary somatosensory activation, bilateral secondary somatosensory areas (SIr; Hari & Ilmoniemi, 1986; Hari et al., 1983; Howlandet al., 1995; Huttunen et al., 1986) and cingulate gyrus (Kitamura et al., 1995) are activated. Components with latencies greater than 200 ms have more complex source locations with large intrain­ dividual variation. Attentional processes probably play an increasing role at longer latencies, modifying the response to pain. Using MEG, it is now possible to investigate whether chronic noxious stimulation leads to changes in the cortical representation of pain, specifically, to an increased representation of the painful area in somatosen­ sory cortical representation. MEG-investigations by Flor and colleagues (Flor et al., 1995a, 1997) suggest that the persistent incoming stimulation of chronic pain may cause alterations in the central representation of the body parts related to the pain process. Patients who experience chronic noxious stimulation over extended periods of time seem to produce more extensive activation of cortical assemblies when phasic somatosensory stimuli are applied to body sites related to their chronic pain.

2.4.5.2 Auditory System

In a similar fashion as for the somatosensory system, magnetic source imaging has been used to detect aspects of functional - in particular tonotopic - organization in the auditory cortex. Evoked fields are illustrated in Figure 2.76 (Pantev et al., 1993). The N100m (M100) sources of the auditory evoked magnetic fields (AEFs) ~t have been localized in the vicinity of the primary auditory cortex (primary association le U. areas) bilaterally on superior temporal surfaces (Hari et al., 1980; Pantev et al., 1989, 1990). These sources of the N1m/N1 wave exhibit higher frequencies at progressively deeper locations, while in AI, the source of the middle latent Pam/Pa wave, higher ss frequencies are progressively more superficially located (Pantev et al., 1994, 1995b). ,e: The location of the Pa-map in primary auditory cortex, and its NI-mirror image in [)r secondary auditory areas, is in accord with observations from animal studies. ~ A typical auditory scan to map N1-tontopy may include the following: The nor­ llg mal audiological status (air conduction and bone conduction thresholds no more than JW 10 dB hearihg level in the range from 250 to 8000 Hz) of the subjects should be ascer­ ed tained prior to the investigation. The transient or the steady-state auditory evoked in responses can be used to map aspects of the tonotopy of the auditory cortex. When the transient responses are of interest, short tone-bursts of 50 ms duration (3 ms rise to and decay time, cosine function, and 60 dB nHL (normative hearing level) are pre­ or sented bilaterally. The interstimulus interval is randomized between 600 and 800 ms. ry The carrier frequencies are 500:1000, 4000 Hz for short scans; 250 and 2000 Hz maybe In added depending on time constraints. Using a bandwidth from 0.1 to 200 Hz, stimulus­ I3t related epochs of 300 ms (including 100 ms prestimulus baselines) are recorded and stored for further analysis.

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/ 2 Biomagnetism 2.4 Neuromagnetism 228 Chapter ------~----

Steady-state responses (SSRs; ISI transient GBF maximum amplitude when tone l ~, SSFs sources determined for the 85 that ( ~ (BP 24-48 Hz) dency tonotopy resembling ("'-'o""~ sented more deeply within the SUI 4s tonotopy of the middle latency P laterally Pantev et al., 1996). ~ A solid review of magnetoenCf -~ 25 vided by Jacobson (1994). ~ 1 5 200fT'-~ 50 fT ,--t----,------,---- 2.4.5.3 Visual System -200 0 200 400 -200 0 200 400 In comparison to the other modali1 Noteworthy is the approach by Sal activation related to viewing, lar MLF (BP 24-48 Hz) tbat during the conversion from v (BP .1-100 H(~" /,.,,/\~ bilaterally from the occipital visui 0.1 5 As for the other modalities tr ;'~"" steady-state visual evoked field'(S: 10 fT 10fTI A I , by a repetitive visual stimulus preo '''~?'''I''''I ffll +i'"pilij "''1,,111 1''''1''''1 The SSVEP can be recorded from 80 -20 0 20 40 60 80 -20 0 20 40 60 same fundamental fre( time (ms) having the higher harmonics (Muller et al., 1 SSF

" " " 1Hz , , ' IT 50 ~,: 'VXJ\/ ""',: ", 0.026 s posterior W" ,J ,,' _ extremum anterior , NW!':', ,:-', :", 0.028 s extremum 10fT :,,: ~,: :,:' ~-, ,,,,,+,,,,,,4"1"'~"'" -50 -10 -20 0 20 40 60 80 o 100 m. time (ms) Fig. 2.11: In a study by Miiller e rate (ISI = Inter­ Fig. 2.16: The auditory evoked fields change with increasing repetition an array of six light-em (MLF, Stimulus-Interval): the NI-wave (upper left), the middle latency field shielded recording cham in the middle left) and the steady-state field (SSF, bottom left) can be seen 3 mm fiber optic guides extracted wide-band response. The transient gamma-band field (GBF, right) is forms evoked by differer with kind by the 24-48 Hz bandpass (BP). (Reproduced from Pantev et a!., 1993, localizations on the bott, permission)

2.4.5.4 Movement-Related J identified the Using Inter-Stimulus-Intervals of different lengths, Lu et al. (1992) of information Characteristic neural activity can cortical area whose activity reflects the decay of passive sensory storage of the neuronal acti­ movements. Generally, the onset about auditory stimuli (echoic memory). The lifetime for decay the psychophysically tromyography, are used as a fiduc vation trace in the primary auditory cortex was found to predict 1983). First (1:-0.5 sec prior to I determined duration of memory for the loudness of a tone.

/ ~ism 2.4 Neuromagnetism 229

Steady-state responses (SSRs) or steady-state fields (SSFs) - Figure 2.76 - show ,f maximum amplitude when tone pulses are presented at repetition rates near 40 Hz. SSFs sources determined for the different carrier frequencies display a "medial" ten­ dency tonotopy resembling that of the NI (sources for the higher frequencies repre­ sented more deeply within the supratemporal sulcus) opposite the "lateral" tendency tonotopy of the middle latency Pa (sources for the higher frequencies situated more laterally Pantev et al., 1996). A solid review of magnetoencephalographic studies of the auditory system is pro­ vided by Jacobson (1994).

2.4.5.3 Visual System In comparison to the other modalities, less work has been done using visual stimulation. Noteworthy is the approach by Salmelin et al. (1994) demonstrating how to disentangle activation related to viewing, language and visualization. The work demonstrated that during the conversion from visual to symbolic representation activity progressed bilaterally from t·he occipital visual cortex toward temporal and frontal lobes. As for the other modalities, transient or steady-state responses can be used. The steady-state visual evoked field (SSVEF) is a continuous brain response that is elicited by a repetitive visual stimulus presented at a fixed rate of 5-6 Hz or greater (Fig. 2.77). The SSVEP can be recorded from the scalp as a nearly sinusoidal oscillatory waveform having the same fundamental frequency as the driving stimulus and often including higher harmonics (Miiller et al., 1997).

15Hz 1Hz 12Hz fTso fT ,.

-so .1OJ~~~- o '100,... o 100 m.

Fig. 2.77: In a study by Miiller et al. (1997), flickering visual stimuli were generated by = Inter- an array of six light-emitting diodes (LEDs) located outside the magnetically i (MLF, shielded recording chamber. The stimuli were delivered into the chamber via D in the 3 mm £iber optic guides that were attached to each LED. The averaged wave­ !X1:racted forms evoked by different driving frequencies are displayed on the top, source ,iith kind localizations on the bottom. (Courtesy of Dr. M. Miiller and O. Flofimann) 2.4.5.4 Movement-Related Activity Hied the lrmation Characteristic neural activity can be detected prior and during the performance of ,nal aeti­ movements. Generally, the onset of spontaneous movements detected, e.g., by elec­ hysically tromyography, are used as a fiduciary point for backward averaging (Weinberg et al., 1983). First (1:--0.5 sec prior to movement onset) the Breitschaftsfeld, a slow-rising

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/ 230 Chapter 2 Biomagnetism 2.4 Neuromagnetism ------bilateral shift, appears over frontal and central regions including generators in the sup­ plementary motor area (BMA) (Deecke et al., 1985). During the later pre-movement period, the activity becomes predominantly contralateral to the moving limb (Cheyne et al., 1995). EMG Jerk-locked back-averaging may become a useful tool to explore cortical hyper­ EMG excitability in patients with involuntary movements. It can be demonstrated, for instance, that myoclonic jerks in patients with progressive myoclonus epilepsy are cortically initiated (Makela et al., 1998). In 1964 Grey Waiter and colleagues (Waiter, 1964) discovered that a slow change Cz-EEG in the EEG baseline developed during the warned foreperiod of a reaction time task when a warning stimulus indicated that a second GO-signal would occur a few seconds later. Further investigations soon revealed that a shift towards negativity appears in the interval between two contingent events, particularly if the second event requires a distinct response (Fig. 2.78). Although the phenomenon may be a composite of various subcomponents, it has thereafter been referred to as "contingent negative variation", CNV. Generally, the CNV has been divided into an early aspect, related to the processing of sensory input, and a terminal CNV, related to action preparation (Rockstroh et al., 1989). The sig­ MEG nificance of such slow potentials with respect to information processing has received considerable interest as they may represent physiological correlates of psychological constructs such as expectancy, preparation and attention. We have argued that slow brain potentials can serve as indicators of the regulation of excitability in cortical neu­ ral cell assemblies (Birbaumer et al., 1990; Elbert & Rockstroh, 1987; Elbert et al., 1992a). If we assume that brain structures are able to adjust firing thresholds in o advance, threshold control could be considered a mechanism for directing attention to future action. The question arises whether the anticipatory negativity, and thus the tuning of controlled processing, would be restricted to motor areas, or whether it also appears in sensory and association areas. In sensory areas, tuning might facilitate Fig. 2.78: CNVjCMV: Averages r, selecting sensory input patterns. For the case of auditory stimuli, it is difficult to disen­ paradigm. One type of s' tangle the sources in the sensory and motor areas by means of the EEG, as activation cates that the subject has of the auditory cortex will project to frontocentral areas as much as the preactiva­ During other trials (NOGI tion of motor programs will. The first examinations of the magnetic counterpart of The EMG (pars indices rn, the CNV, the contingent magnetic variation (CMV, Fig. 2.78), suggest that both,the responded only during the early CMV and the terminal CMV, in particular, are generated by distributed sources toid is more negative in tl in motor, sensory and association areas (Elbert et al., 1994c). areas indicates that the diJ areas. (Data from Elbert I

2.4.5.5 Presurgical Functional Mapping ECoG mapping of sensory evoked n In the neurosurgical treatment of brain neoplasms, vascular malformations such as motor cortex, functional mapping u aneurysms, but also for neurosurgical removal of epileptic zones, precise localization available prior to initiation of a pa: of eloquent cortex is essential to minimize 'neurological deficits yet allow for the max­ site selection and the resection of imal removal of nearby dysfunctional tissue (Lewine & Orrison, 1995). Thmors, for surgical time can be shortened. example, may distort brain anatomy so as to render localization of sensory represen­ Typically, scan protocols for th, tations or motor areas impossible on the basis of anatomic landmarks. According to that outlined in the sections above Lewine and Orrison, "functional mapping of the sensorimotor cortex is now part of the tunity to evaluate the precision of routine clinical practice of several clinical MEG facilities" (p. 389). Mapping of the Orrison, 1995; Sutherling et al., 19! auditory cortex is also becoming increasingly relevant. As opposed to the alternatives, and noninvasive methods has been e

./ 2.4 Neuromagnetism----=------231

EMG EMG

I M~~* It\ [2 PT I sec + o 2 4

Fig. 2.78: CNV/CMV: Averages recorded from a single subject during a two-stimuli paradigm. One type of stimulus (GO - black lines) presented at time 0 indi­ cates that the subject has to respond to an imperative signal presented 4 slater. During other trials (NOGO - blue lines) no overt motor response was required. The EMG (pars indices m. flexor dig. longum, top traces) shows that the subject responded only during the GO-condition. The EEG recorded from C z • right mas­ toid is more negative in the GO-condition. The MEG, recorded over temporal areas indicates that the differentiation results - at least in part - from temporal areas. (Data from Elbert et al., 1994c)

ECoG mapping of sensory evoked responses or the direct electrical stimulation of the motor cortex, functional mapping using MSI has the advantage that the knowledge is available prior to initiation of a particular surgical approach. Thus, the craniotomy site selection and the resection of pathological tissue can be carefully planned and surgical time can be shortened. Typically, scan protocols for the elicitation of transient responses correspond to that outlined in the sections above. In several instances, there has been the oppor­ tunity to evaluate the precision of MSI localization (Gallen et al., 1993; Lewine & Orrison, 1995; Sutherling et al., 1988). "In all cases the agreement between invasive and noninvasive methods has been excellent, with the MEG median nerve source local-

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,/ 232 2.4 Neuromagnetism izing within the brain tissue directly beneath the position identified by surface ECoG reorganization (Rossini et al., l' monitoring of the somatosensory evoked potential" (Lewine & Orrisan, 1995, p. 392). important to have predictive par ~edian The sources of the somatosensory evoked fields (SEFs) to nerve stimuli between homologous areas in th are localized on the posterior wall of the central sulcus (Vanni et al., 1996; Wood et al., 1985). The information of this localization can be very useful in neurosurgery (Fig. 2.79) since the primary motor cortex, to be protected during operations, lies on 2.4.6 Cortical Represe the anterior wall (Miikelii et al., 1998). The central nervous system and to reorganize itself after alteratic first Subsection, 2.4.6.1, address tivity patterns and, in particular as a result of alterations in the tion, 2.4.6.2, focuses on changes attempts to uncover indicators 0 and behavioral functions.

2.4.6.1 Cortical Reorganiza put

Peripheral lesions or deprivation representations. For instance in will not remain unresponsive: bu normally respond to the now-abs( to Occur after a period of extensiv receptive field in the cortex. In' Fig. 2.79: Sources of SEF P30m deflection overlied on 3-D MR image of a patient after removal of right frontal glioma. Two years after operation, the patient's symp­ activated area has been found to toms reappeared and reoperation was planned. Sagittal section shows that tumor have been made not only during infiltrates the primary motor cortex, immediately anterior to the SEF sources. these findings have been based tG Because of high probability of postoperative hemiparesis, reoperation was can­ cortical organization and reorgan celled. (From Miikelii, 1998; unpublished data, courtesy of Dr. Forss) MEG/MSI in humans, which has fl between cortical reorganization ar The averaged position of the estimated AEF-NlOOm dipoles lies on the upper Studies have demonstrated such i surface of temporal lobes with a vertical 2mm standard (Nakasato et al., 1995). This pain to cortical reorganization in h precision allows to identify auditory cortex, information that can assist for surgery between somatosensory training a especially on the left temporal lobe, because the left auditory cortex is surrounded by et al., 1995a). the language-related areas. Disappearance of the NlOOm response (Miikelii & Hari, Alterations in afferent input lea 1992; Miikelii et al., 1991) have been reported in patients with temporal lobe lesions. cal reorganization, resulting in altel Currently MEG techniques have been developed to localize areas relevant for the studies by Merzenich and colleagu( perception of vowels (Eulitz et al., 1995) as well as higher speech-related cortical areas Merzenich, 1990; Merzenich & Jen~ directly. Visual stimuli are equally applicable in the functional localization of eloquent et al., 1992c,d,a,b, 1993) demonstn cortical regions using, e.g., recordings of pattern reversal VEF (Makela et al., 1998). results in an altered representation The parameters of function used in presurgical evaluation need to be stable across can .also be observed in humans by populations of patients and age-matched controls. N20m stability has been evaluated cortIcal reorganization has primarib in detail (Rossini et al., 1994a). The median nerve, thumb and little finger of the two ~mputation of an upper extremity· hands were stimulated. N20m localization, distance between homologous ECDs in the tron, whereby the representational two hemispheres, distance between thumb and little finger ECDs and the difference represented by the hand and digit in this distance in the two hemispheres were calculated. In patients 2-7 months after focus of cortical activation elicited hemispheric stroke at least one of these parameters was aberrant, implying functional centimeters toward the receptive fi,

,

./ ''11,'-~'" ,Jl'''''ii'!;'\.'------.' -:' 2.4 Neuromagnetism 233

reorganization (Rossini et al., 1994b). In the study of plasticity (sce below), it is also important to have predictive parameters that describe the intra-individual relationship between homologous areas in the two hemispheres.

2.4.6 Cortical Representational Plasticity The central nervous system and the cerebral cortex, in particular, have the potential to reorganize itself after alterations of its input from peripheral neural structures. The first Subsection, 2.4.6.1, addresses questions concerning the capacity of neuronal ac­ tivity patterns and, in particular, of the ability of sensory and motor maps to change as a result of alterations in the effectiveness of afferent inputs. The second Subsec­ tion, 2.4.6.2, focuses on changes in functional maps resulting from brain lesions and attempts to uncover indicators of reorganization that accompany recovery of mental and behavioral functions.

2.4.6.1 Cortical Reorganization as a Consequence of Altered Afferent In­ put

Peripheral lesions or deprivation from afferent input alter the organization of central representations. For instance, in the case of amputation the representational cortex will not remain unresponsive, but neighboring regions invade the area which would normally respond to the now-absent afferents. Plasticity has also been demonstrated to occur after a period of extensive training which has repetitively activated a certain receptive field in the cortex. In the case of such repetitive activity, the size of the activated area has been found to enlarge. It is notworthy that these observations have been made not only during developmental stages, but also in adults. While these findings have been based to a large extent on animal experiments, functional cortical organization and reorganization have also been studied using non-invasive MEG/MSI in humans, which has further allowed for an examination of the relationship between cortical reorganization and perceptual, behavioral and cognitive processes. Studies have demonstrated such important findings as the relationship of phantom pain to cortical reorganization in human amputees (Flor et al., 1995b) or the relation between somatosensory training and homuncular organization in musicians (Elbert et al., 1995a). Alterations in afferent input lead to functional cortical modifications, i.e. to corti­ cal reorganization, resulting in alterations in the cortical responses to stimuli. Animal studies by Merzenich and colleagues (Jenkins et al., 1990; Menninghaus et al., 1994; Merzenich, 1990; Merzenich & Jenkins, 1995; Merzenich et al., 1987, 1984; Recanzone et al., 1992c,d,a,b, 1993) demonstrated as early as 1984 that an amputation of digits results in an altered representation of the hand in area 3b. Today such plastic changes can also be observed in humans by means of non-invasive investigations. In humans, cortical reorganization has primarily been demonstrated for the somatosensory cortex. Amputation of an upper extremity results in alterations of the homuncular organiza­ tion, whereby the representational zone of the face shifts towards the zones formerly represented by the hand and digits. MEG-based source imaging revealed that the focus of cortical activation elicited by facial stimulation was shifted up to several centimeters toward the receptive field which. would normally receive input from the

./ 234 __C_ha--,pter 2 Biomagnetism 2.4 Neuromagnetism now amputated hand and fingers (Elbert et al., 1994bj Yang et al., 1994). A similar tendency was observed for the receptive field of the upper arm (stump). Additional rc­ sponsiveness of these reorganized cortical areas was evidenced by a~ enhanced evoked electroencephalographic potential and magnetic field compared to stimulations on the intact side. Observed alterations provide evidence for extensive plastic reorganization in the adult sensory cortex of humans following nervous system injury. Knecht and co-workers (Knecht et al.) 1995, 1996) investigated perceptual thresh­ olds in patients with amputation of the arm. The sensory discrimination thresholds in the face were lower on the side of the amputation, as compared to the intact side, a find­ ing which suggests that the alteration of cortical representation may have perceptual consequences. Flor et al. (1995b) determined, by use of non-invasive neuromagnetic recording techniques, the extent of cortical reorganization in the primary somatosen­ sory cortex in upper extremity amputees with varying degrees of phantom limb pain. The magnitude of cortical reorganization showed a highly significant positive associa­ tion with the amount ofphantom limb pain experienced by the amputees (Fig. 2.80). These data indicate that phantom limb pain is related to and may be a consequence of plastic changes in the primary somatosensory cortex. The intuitive hypotheses that cortical reorganization constitutes an adaptive mechanism thus need not be true. Increased use of the other hand will lead to an expansion of the representational cortical zones and to a reduction of receptive fields (Elbert et al., 1995a, 1997b; Rock­ stroh et al., 1998). Violinists and other string players provide a good model for the study. of the effects of differential afferent input to the two sides of the brain in hu­ mans. During practice or performance, the second to the fifth digits (D2-D5) of the left hand are continuously engaged in fingering the strings, a task involving a great deal of manual dexterity and enhanced sensory stimulation, while the thumb grasps the neck of the instrument and remains relatively stationary. The right hand, in ma­ nipulating the bow, engages in a task involving less individual finger movement and fluctuation in tactilc and pressure input than D2-D5 of the left hand. By using MEG­ based source imaging, we demonstrated that the brains of string players are different than the brains of normal control subjects in that the representation of the digits of the left hand of string players was substantially expanded compared to the digits of the left hand in normal control subjects (Elbert et al., 1995a). For the thumb, which has the less active task of holding the neck of the instrument, the expansion was not as great as for the digits involved in the fingering. The amount of cortical reorganization was strongly correlated with age at inception of musical practice (and Fig. 2.80: The amount of reorganiza with years of practice) for the digits involved in the fingering, but not for the thumb. upper extremity amputees Results suggest that ther!l is use-dependent plasticity of cortical zones representing termined non-invasively by different parts of the body permitting a rapid reallocation of available central nervous magnitude of cortical reorg system circuitry to conform to the current needs of the individual. Another MEG association with the degrel study, (Mogilner et al., 1993) demonstrated the reorganization of the somatosensory The figure illustrates <.lata cortex after surgical separation of webbed fingers (treatment of syndaktylie). Using limb pain (marked by blal MSI we could investigate (Sterr et al., 1998) the opposite effect: repetetive chronic perienced no phantom \iml represent the locations (El synchronous and behaviorally relevant stimulation to several digits resulted in a fusion t.he lip projected onto a s( and in a disordered arrangement of digit representation. At the same time subjects hemispheric asymmetry in consistently mislocalized light pressure stimuli applied to the finger tips. tom pain patient. This st.u In another MEG study artificial syndactylism effects in adult humans were inves­ may be a consequence of pI tigated. The index and middle finger of the non-dominant hand were taped together FIar et al., 1995b)

/ 2.4 Neuromagnetism 235 ------

Fig. 2.80: The amount of reorganization in the primary somatosensory cortex in thirteen upper extremity amputees with varying degrees of phantom limb pain was de­ termined non-invasively by the use of neuromagnetic recording techniques. The magnitude of cortical reorganization showed a very strong and significant positive association with the degree of phantom limb pain experienced by the amputees. The figure illustrates data from a representative subject with intense phantom limb pain (marked by black filled symbols) and from another subject who ex­ perienced no phantom limb pain (marked by white filled symbols). The squares represent the locations (EeD) of the digits, the circles represent the location of the lip projected onto a schematic coronal section of the brain. A pronounced hemispheric asymmetry in the location of the lips can be observed in the phan­ tom pain patient. This study indicates that phantom limb pain is related to and may be a consequence of plastic changes in primary somatosensory cortex. (From Flor et al., 1995b)

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./ 236 Chapter 2 Biomagnetism 2.4 Neuromagnetism for three weeks and MEG recordings were performed before, immediately after, and holds true, the -t;xtension of acti, three weeks after the period of syndactylism. Using current den~ity reconstruction another pathway, i.e. another lel analysis (Puchs & Diissel, 1992), Ziemus et al. (1997) demonstrated that this form of eye, for instance, results in the e peripheral, passive, synchronous co-activation induced a change in the current den­ the intact eye (Stryker, 1991). Dl sity pattern, showing two different effects: either enlargement of both or shrinking of neighboring face area (Elbert et I one finger-representation at the cost of the other. These plasticity effects appeared ing in a hearing loss of a particul; to be reversible in the follow-up MEG recordings. In equivalent MEG measurements fields into the cortex representing of a group of control subjects the current density pattern remained unchanged (see In regions in which different Fig. 2.81). ticity may lead to changes across results in an extension of the nei respond to visual processing (R, Syndactylism Control result from various types of neU! input, axional sprouting, or Hebh 1995). In humans, suggestive e, ported: RosIer, Roder et al. (1993 a haptIC mental rotation task wh blind people and blindfolded sight blind subjects showed a pronounc Index finger 3rd finger Index finger 3rd finger blindfolded, sighted subjects exhi ~ontal predominance of the negat Jects and a similar occipital predc rotation task proper. Adventitious negative potential over both the fl gest that reorganization occurs rat development 'varies depending on uh). Furthermore, it is apparent t Fig. 2.81: Subject before (above) and after (below) three weeks of artificial syndactylism: in mediating reorganization. index finger representation increased; area of middle finger in SI with shrunken Studies relating behavioral and current density. In the control subject (pictures on the right side) the current sparse. One reason for this lies in th, density of both finger representations appears reduced after three weeks of syn­ or consequences of neuronal plastici dactylism. (From Ziemuset al., 1997) studies. It is therefore necessary te employing MSl. Consistent with earlier work in animals (e.g. Clark et al., 1988), we conclude that synchronous stimulation creates a fusion of cortical representational zones while asyn­ chronous stimulation leads to separation. These processes may underlie certain mys­ 2.4.6.2 Cprtical and Behavior terious illnesses such as the focal dystonia of the hand in musicians (Byl et al., 1996, ~o: the field of neurological rehabili 1995). tJCIty and functional reorganizatior Such cortical plasticity is thought to result from changes in synaptic efficacy that ically, future MSI research should f follow Hebb's rule (Rauschecker, 1995). Consequently, peripheral lesions can demon­ recovery and plasticity of function strate hidden pathways which would normally not be sufficiently activated to result in :ortical functions in sensory and me a firing of the postsynaptic neuron (Merzenich, 1990). Even simple behavioral train­ I~farct, and the impact of particula ing may result in similar phenomena and produce an enlargement of the cortical area tJon. In several laboratories, for in~ that is activated by a particular task (Recanzone et al., 1993). In extreme cases, ex­ ~ovement are currently employed tl tensions spanning an area of several millimeters have been observed in the relatively In patients who had suffered from ~ small brains of monkeys (Pons et al., 1991). The growth or the sprouting ofaxons by Edward Taub (Taub et al. 1993' has been considered another basic mechanism underlying plastic alterations (Darlan­ the various ways in which sU~h beh: Smith & Gilbert, 1994; Garraghty & Kaas, 1992). Yet regardless of which mechanism and plasticity.

/ 2.4 Neuromagnetism 237

holds true, the extension of active pathways or brain regions occurs at the expense of another pathway, i.e. another less active region. Consequently, the deprivation of an eye, for instance, results in the extension of the neighboring stripe with dominance of the intact eye (Stryker, 1991). Deafferentation of the hand leads to an extension of the neighboring face area (Elbert et al., 1994b; Pons et al., 1991). Cochlear lesions result­ ing in a hearing loss of a particular frequency band cause extensions of those receptive fields into the cortex representing neighboring frequencies (Robertson & Irvine, 1989). In regions in which different sensory representations are processed, cortical plas­ ticity may lead to changes across the borders of a modality. Thus, visual deprivation results in an extension of the neighboring non-visual regions which do not normally respond to visual processing (Rauschecker, 1995). Such intermodal plasticity may result from various types of neuronal mechanisms among which activation of silent input, axional sprouting, or Hebb-like mechanisms have been discussed (Rauschecker, 1995). In humans, suggestive evidence for intermodal plasticity has also been re­ ported. Rosier, Roder et al. (1993), for instance, compared event-related potentials in a haptic mental rotation task, which involved the tactile discrimination of letters, in blind people and blindfolded sighted subjects. While the tactile stimuli were encoded, blind subjects showed a pronounced occipital activity (a negative slow wave), while blindfolded, sighted subjects exhibited frontal predominance of activity. A similar frontal predominance of the negative slow wave occurred in blindfolded sighted sub­ jects and a similar occipital predominance in congenitally blind subjects during the rotation task proper. Adventitiously blind subjects, however, exhibited a pronounced negative potential over both the frontal and the occipital cortex. These results sug­ gest that reorganization occurs rather quickly following vision loss, but that temporal development 'varies depending on the process (such as encoding or processing stim­ uli). Furthermore, it is apparent that frontal structures may play an important role in mediating reorganization. Studies relating behavioral and perceptual elements to cortical reorganization are sparse. One reason for this lies in the difficulty to operationalize the functional meaning or consequences of neuronal plasticity for perception, cognition and behavior in animal studies. It is therefore necessary to complement animal studies with humans studies employing MSI.

2.4.6.2 C.ortical and Behavioral Plasticity Following Brain Lesions

For the field of neurological rehabilitation it is of utmost interest to examine the plas­ ticity and functional reorganization following brain lesions in humans. More specif­ ically, future MSI research should focus on the investigation of processes underlying recovery and plasticity of function, such as the organization and reorganization of cortical functions in sensory and motor impairment following ischemic or hemorrhagic infarct, and the impact of particular rehabilitation procedures on cortical reorganiza­ tion. In several laboratories, for instance, constraint-induced facilitation of impaired movement are currently employed to overcome non-use of an affected upper extremity 'I in patients who had suffered from stroke, following the training procedure developed s by Edward Taub (Taub et al., 1993). To extend the training, it is important to target the various ways in which such behavioral plasticity relates to cortical reorganization D and plasticity.

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/ 238 Chapter 2 Biomagnetism 2.4 Neuromagnetism

So far, research on plasticity and reorganizatioll following brain lesions has been epileptogenic zones as accurately a focused on the following functions: vide the necessary information. 'I the skull and implantation of electr 1. Recovery of sensory and motor function: Brain damage such as that resulting cost intensive, but also puts the pi from cerebrovascular accidents can lead to a variety of alterations: electrodes can only scan a limited a (a) complete destruction of a given brain structure combined with total loss of By choosing an inadequate implant activity in this area; able for diagnosis. It is thus highly (b) remaining structural integrity, but decrease of brain activity; would allow for the non-invasive dei the epileptic focus. (c) disintegration of distributed cooperative activities, i.e., alteration of spatio­ Two approaches have been ta1 temporal patterning of brain activity secondary to focal damage.. ized during interictual periods and In all three cases it is important to gain detailed information about the extent Methohexital, a short-term barbitu of the necrotic center versus the surrounding area of reduced perfusion (penum­ epileptogenic acitivity is generated bra) and the outer edematous zones in focal brain damage. In addition, it is ure 2.58 provides an example of a si important to obtain information about the functional status of each particular area. While the structural information can routinely be determined by MR.I, 2.4.7.1 Localization of Interict procedures determining the status of function of the particular area remain to The usefulness of localizing epilepti be developed further. Such methods can be based on MSI. periods is still debated, at least as f, 2. The assessment of functionality in areas directly affected by strokes and their According to Liiders et al. (1993), iIl1 neighboring regions can, for instance, be evaluated by somatosensory evoked zone, which in general is larger the magnetic fields to sensory alterations (Maclin et al., 1994). The location of seizure onset corresponds best to t the estimated EeD was always in non-infarcted tissue, in the region of the so­ resected in order to achieve seizure matosensory cortex. Only recently has access been made to the study of cortical concept of irritative and epileptoge plasticity and reorganization in the somatosensory cortex, in particular (see pre­ and ECoG recordings is well definel vious Section). MEG recordings with respect to focu (Brockhaus et al., 1997). Furtherrn 3. Recovery of attention and language function is another field where MSI offers represent an inconstant and infreque interesting perspectives. Research on aphasia suggests that symptoms do not Nevertheless, it is now establishel exclusively reflect impaired sensory, input-related functions of the lesioned areas in the evaluation of patients when it but are also consequences of impaired cognitive processes that are not directly show independent spiking versus con linked to speech perception and language production. Quick visual inspection is generally ~ between spikes generated in the two In summa-ry, recent studies suggest that brain lesions following trauma not only lead to a number of cases that demonstrate impairment of sensory functions related to the particular area destroyed by the lesion, can have a direct and positive effect 1 but also to impairment in higher cognitive functions related to input processing. It study on 50 patients with focal epilE has further been suggested that reorganization, Le., restitutional and substitutional a 37-channel gradiometer already pr, processes, may take place following stroke and during rehabilitation. However, most convexity foci (Sm5th et al., 1995).. importantly, studies suggest that the areas and the amount of reorganization either evaluation. In patients with orbitofro through spontaneous recovery or through training remains to the specified. It is this be mandatory before localization bee uncertainty that requires future research. 2.4.7.2 Non-invasive Localizati 2.4.7 Application of MSr to Epilepsy Activity About 0.25% of the population suffer from drug-resistent focal epilepsy, Le. anticon­ As indicated above, interictual activit vulsants are not sufficiently effective or even contraindicated to control seizures. It zone, i.e., the region the removal of' is estimated that about 10-15% of these patients would profit from surgical removal is generated in the irritative zone (I of the epileptogenic tissue. The resection of brain tissue requires careful presurgi­ necessarily overlap with the epileptog cal diagnosis that determines the location of the relevant pathological tissue, i.e., the

/ ~. I 2.4 Neuromagnetism 239

epileptogenic zones as accurately as possible. To date, only invasive procedures pro­ vide the necessary information. The surgical intervention that includes opening of the skull and implantation of electrodes for the diagnostic procedure alone is not only cost intensive, but also puts the patient at a considerable risk. Furthermore, depth electrodes can only scan a limited area of the brain in, generally, only one hemisphere. By choosing an inadequate implantation site, important information may not be avil­ able for diagnosis. It is thus highly desirable to advance MSI-based procedures which would allow for the non-invasive detection of epilepitic activity and the localization of the epileptic focus. Two approaches have been taken: a) epileptiform transients have been local­ ized during interictual periods and b) seizure-like activity has been induced applying Methohexital, a short-term barbiturate that puts the patient into light coma while epileptogenic acitivity is generated at the same time (Brockhaus et al., 1997). Fig­ ure 2.58 provides an example of a single epileptic spike.

2.4.7.1 Localization of Interictual Epileptic Activity The usefulness of localizing epileptic transients that are recorded during interictual periods is still debated, at least as far as the primary epileptogenic area is concerned. According to Liiders et al. (1993), interictal spike activity mainly reflects the irritative zone, which in general is larger than the primary epileptogenic zone. The area of seizure onset corresponds best to the primary epileptogenic zone which has to be resected in order to achieve seizure free outcome after surgery. According to the concept of irritative and epileptogenic zone, the value of interictal spikes in EEG and ECoG recordings is well defined, but the significance of interictal spikes in the MEG recordings with respect to focus localization has not been established until now (Brockhaus et al., 1997). Furthermore, in most epilepsy patients, interictal spikes represent an inconstant and infrequent phenomenon. Nevertheless, it is now established that whole-head MEG recordings can be useful in the evaluation of patients when it is difficult to determine if the two hemispheres show independent spiking versus correlated spiking (with one side leading the other). Quick visual inspection is generally sufficient to determine the temporal relationship between spikes generated in the two hemispheres. Lewine & Orrison (1995) present a number of cases that demonstrate that "MEG evaluation of epileptiform activity can have a direct and positive effect on patient care" (p. 403). Furthermore, a recent study on 50 patients with focal epilepsy showed that MEG recordings using simply a 37-channel gradiometer already provided good localizing results for patients with convexity foci (Sm.ith et al., 1995). Results were judged on the basis of presurgical evaluation. In patients with orbitofrontal or deep foci, whole-head recordings seem to be mandatory before localization becomes meaningful and clinically helpful.

2.4.7.2 Non-invasive Localization of Barbiturate-Induced Epileptiform Activity As indicated above, interictual activity generally does not result from the epileptogenic zone, i.e., the region the removal of which would abolish the seizures, but generally is generated in the irritative zone (EIger, 1992) that often surrounds but does not necessarily overlap with the epileptogenic zone. Localization of the epileptogenic zone

./ 240 Chapter 2 Biomagnetism 2.4 Neuromagnetism requires its stimulation while EEG and MEG are recorded. Most of the techniques in drug-induced spike densities in t that provoke epileptic seizures will result in movements, rendering MEG registration a correct lateralization of the epilet difficult. Short-acting barbiturates such as Methohexital, however, put the patient a similar lateralizing power of the at rest while stimulating the epileptogenic zone at the same time (Brockhaus et al., trates the time course of the signal 1997; Wienbruch, 1997). The patterns induced by this technique allow the localiza­ displayed in Fig. 2.58). The contou tion of the epileptogenic zone, particularly in patients with temporal lobe epilepsy. a singel spike, source configuration A substantial body of literature report that epileptiform discharges are induced by in spatial location. This is confirm various barbiturates, such as Methohexital, Amobarbital, Thiopental and the nar­ spike activity (Fig. 2.83). In patienl cotic Propofol. Among them, Methohexital seems one with a high potency to activate source must often be assumed. Co epileptiform discharges in EEG and ECoG. Hufnagel and coworkers (1992) showed that the magnetic fields were predomina Methohexital increases the spike density and, furthermore, activates a distinct type of by 37-channel gradiometersystem u. epileptiform pattern, best described as spike-burst-suppression pattern (SBS). SBS is primary epileptogenic focus could n characterized by a high amplitude spike burst followed by a suppression in the EEG I Magnetometers and whole-head-systl ECoG consisting of isoelectrical activity or subdelta background activity. Spikes and is known about the source configUJ SBS induced by Methohexital in the ECoG recorded with temporal subdural strip focal epilepsies. In order to assess tl electrodes have a high validity for the localization of the primary epileptogenic zone. perform simultaneous ECoG and Ml that ECoG only detects a limited pa -4.3 pT ,..- 4.8 pT Brockhaus et al. (1997) evaluated the ECoG are necessarily restricted to 1 quality of localization of The source is where the elcectrodes , the epileptogenic area using spontaneous and Methohexital"induced spikes. The MEG was recorded from 15 pa­ tients with temporal lobe epilepsy using a 2.5 37-channel-first-order­ gradiometer. All pa­ tients underwent sep­ arate electrocortico­ graphic (ECoG) record­ 0.5 ings of Methohexital­ o +---.,..---,--,..--,-----,----,-----r------, induced epileptiform -ID -5 10 15 20 25 discharges. No adverse effects during or after the short narcosis were noticed by either the patient or the inve~tiga­ tors. Patients fell asleep 15 16 17 about 1 minute after Fig. 2.82: Contourplots for the two extrema in signal inten- application of Metho- (RMS) sity that typically appear in the course of hexital and recovered Fig. 2.83: The localizations in the c< a spike. The plots demonstrate that the source within 10 minutes. No localization of the first pea configuration must have changed while the spike clinical excitatory effects panel the localization for t reverses its polarity. like nausea or vomiting upper to the lower bank of were observed nor were any seizures elicited. Spike densities in MEG and simultaneous spreading excitation is SUP] EEG recordings increased significantly after Methohexital application. Differences black lines).

./ 2.4 Neuromagnetism 241 in drug-induced spike densities in the MEG recordings between both sides allowed a correct lateralization of the epiletogenic area in 11 of the 15 patients, confirming a similar lateralizing power of the MEG and ECoG recordings. Figure 2.82 illus­ trates the time course of the signal intensity for a typical focal spike (like the one displayed in Fig. 2.58). The contourplots already demonstrate that in the course of a singel spike, source configurations do not simply reverse in polarity, but change in spatial location. This is confirmed when a moving ECD is used to localize the spike activity (Fig. 2.83). In patients with temporal lobe epilepsy, an unstable deep source must often be assumed. Consequently, it is not surprising that extrema of the magnetic fields were predominantly located at the margins of the area covered by 37-channel gradiometersystem used in the Brockhaus et al. study. Hence, the primary epileptogenic focus could reliably be identified in only 4 of the 15 patients. Magnetometers and whole-head-systems should improve this ratio considerably. Little is known about the source configuration corresponding to epileptiform activity in focal epilepsies. In order to assess the localizing power of MSI, it would be useful to perform simultaneous ECoG and MEG recordings. It should be mentioned, however, that ECoG only detects a limited part of the total activity - sources identified by the ECoG are necessarily restricted to locations where electrodes have been implanted: The source is where the elcectrodes are!

Fig. 2.83: The localizations in the course of a single spike: The upper panel shows the localization of the first peak in signal intensity (single EeD-model); the lower panel the localization for the second peak. The localization moves from the upper to the lower bank of the supratemporal gyrus. The interpretation of a spreading excitation is supported by the reversal in current flow (indicated by black lines).

./ 242 Chapter 2 Biomagnetism 2.4 Neuromagnetism

In sum, current attempts deserve further investigation, whereby the induction of dominate the recorded signal duri epileptiform activity by means of short-acting barbiturates will at least work in patients 1995; Lewine & Orrison, 1995). ThE with more shallow sources. . the MEG signal in the theta or dE sorting for a high goodness of fit (: 2.4.8 Localization of Dysfunctional Structures and Structures and low confidence volume. The USI analyses has also been suggested (V Related to Abnormal Information Processing ofslow waves is generally lower and I 2.4.8.1 Localization Based on Abnormal Low Frequency Waves of brain tissue is rare as opposed tc When this type of analysis is ap The investigation of brain functioning in cases of lesions of circumscribed brain struc­ activity can be detected in about tures or neural pathways constitutes a central paradigm for basic neuroscientific re­ particularly in those with cortical search. At the same time, the improved understanding of the consequences of lesions in subcortical lesions as well as in pat the various brain regions provides a basis for innovative therapeutic possibilities. Neu­ tivity may be present in patients '" ropsychological testing combined with functional brain imaging with a high tempora1 neuropsychological deficits (Lewine and spatial resolution will open new ways of observing dynamic alterations of cerebral liminary data suggest that the free organization which occurs after brain damage as well as the accompanying dynamic waves changes in the course of reh, process of lesion-induced impairment and recovery of mental functioning. Figure 2.84 sitution (as opposed to substitutio illustrates that structural information, easily provided by MRI and CT-scans, is not function). sufficient to describe the regions affected by brain injury. A dysfunctional area can Clusters of locations of slow wa be defined through the mapping of abnormal brain waves which often surround the plasms (MiikeUi et al., 1998). In th structurally obvious lesion. tumor as completely as possible w Sources of focal slow waves can be tumor (Fig. 2.85). Mirror clusters, E seen in about 30% of patients with

5.5 8.7 n.l Hz \ / /'

eyes closed / // eyes open

Fig. 2.84: This patient experienced the abrupt onset of a dense right hemiparesis and aphasia due to a stroke, as visually observable on the above CT scans. Squares correspond to the locations of the primary receptive somatosensory area, as de­ termined by the. first major peak (30-70 ms) in the magnetically evoked field. Circles indicate the location of equivalent dipoles of large local slow waves inthe I I delta band (1-6 Hz). Note that these locations circumscribe a dysfunctional re­ I I gion around the structurally visible lesion in the MRl. Typical lesions producing o 10 20 30B focal slow activity include cerebral infarcts, contusions, local infections, tumors, developmental defects, degenerative defects of subdural hematomas. (Courtesy Fig. 2.85: Left: 'Spectra of spontane( of Dr. C. Gallen) after a stroke in the left ani frequencies. Right: The Ml The abnormal waves commonly appear below the alpha band, mostly in the 1-6 Hz range. The field patterns of this activity are usually complex as multiple generator Lewine and colleagues (1995; 19! areas are simultaneously active. At some time instances, however, a single source may a sample of 50 unselected epileptic r

,/ 2.4 Neuromagnetism 243 dominate the recorded signal during abnormal low-frequency events (Lewine et al., 1995; Le.wine & Orrison, 1995). These instances can be identified by bandpass-filtering the MEG signal in the theta or delta range and then applying the ECD model and sorting for a high goodness of fit (>0.9-0.95), high signal power (RMS >200-400 IT) and low confidence volume. The use of clustering algorithms and principal component analyses has also been suggested (Vieth et al., 1996). In healthy subjects the frequency of slow waves is generally lower and co-localization ofthe dipolar sources in about 1 cm3 of brain tissue is rare as opposed to cases with brain pathologies (Vieth et al., 1996). When this type of analysis is applied, abnormal clusters of sources of spontaneous activity can be detected in about 80% of patients after cerebrovascular accidents, particularly in those with cortical lesions. The ratio drops to 50% in patients with subcortical lesions as well as in patients after head trauma. Extensive slow wave ac­ tivity may be present in patients with large infarcts and persistent neurological and neuropsychological deficits (Lewine et al., 1995; Lewine & Orrison, 1995). Some pre­ liminary data suggest that the frequency and Clustering of abnormal low frequency waves changes in the course of rehabilitation and may thus serve as an index of re­ sitution (as opposed to substitution that may also be responsible for a recovery of function). Clusters of locations of slow waves are also observed in about 70% of brain neo­ plasms (Makela et al., 1998). In the surgical resection it is important to remove the tumor as completely as possible without, however, damaging healthy brain tissue. Sources of focal slow waves can be found in the cortex immediately adjacent to the tumor (Fig. 2.85). Mirror clusters, corresponding foci in the healthy hemispheres, are seen in about 30% of patients with neoplasms and with cerebrovascular insults.

5,5 8,7 1Ll Hz \ / /

eyes closed / eyes open

I I I I o 10 20 30Hz

Fig. 2,85: Left: ·Spectra of spontaneous parieto-occipital activity in a 23-year old patient after a stroke in the left anteromedial thalamus. Note the increase in the 1-6 Hz frequencies. Right: The MRI showing the lesion. (From Miikelii et aI., 1998)

Lewine and colleagues (1995; 1995) also found focal slow wave activity in 80% of a sample of 50 unselected epileptic patients. Such activity can be pff~sent even in the

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./ 244 Chapter 2 Biomagnetism 2.4 Neur()magnetism ------absence of structural lesions or interictal spikes, however, multiple foci are identified of the event-related response. 1 in more than half of the patients. It seems that the site with the most intense focus the magnetic counterpart of thE is generally the one with the side and lobe of pathology. ' orie~tation (Reite et al., 1989), Functional disturbance foreruns structural changes e.g., in ischemia; thus it may spatial regulation of cortical exc be possible to localize functional perturbations not visible in morphological imaging deviant temporal COurse of dipol techniques before they become permanent. Observations of significant amounts of slow ~ared to controls provides furthf activity during the first four weeks following transient ischemic attacks and minor head mterest for the explanation of sel point in this direction (Miikelii et al., 1998). of the pe~k latency of the magnE Clusters also occur in patients with schizophrenia and dementia and substance w~en Schizophrenic patients Iistel abuse, when no structural lesions can be detected. It remains to be seen if these Without hallucinations (Tiihonen clusters can reliably identify dysfunctional brain regions. N10?m peak latency produced b) mUSIC maskers), suggests "that tJ 2.4.8.2 Magnetoencephalographic Localization of Abnormal Information not by external input from the ea Processing and Deviant Cognition 1992, p. 257), and may be an app

Recent research has also begun to examine MEG correlates of cognitive deviances char­ 2.4.9 The Relation ofM acteristic for impairments that may contribute to neuropsychological and to psychi­ atric symptoms and deficits. The majority of work so far has focused on schizophrenia Non-invasive Stud (Rockstroh et al., 1997b; Roth et al., 1995). A substantial body of research has eluci­ Brain imaging techniques can be di dated the covariation between deviant cognitive processes and event-related potentials. brain function (Fig. 2.59) and thOSE For instance, hypotheses regarding cortical functional characteristics in schizophrenia about brain activation in time and (e.g., frontocortical vs. temporal lobe activity) have been tested by means of event­ related potentials and their source modeling (Rockstroh et al., 1997a). Source model­ Methods for structural imagiIJ ing based on electrical data alone, however, did not prove sufficient for uncovering the structures with aberrant patterns of activity occurring with psychopathological pro­ • X-raY~Computerized Tomogr. cesses. With the addition of magnetoencephalographic data, a major step forward is raphy) to be expected as the combination of EEG and MEG will allow, for instance, the quan­ tification 'of the relative weight of deviant patterns in frontal and temporal structures. • Magnetic Resonance Tomogn Considerable effort in the last 10-20 years has been devoted to the use of event-related potentials in the attempt to uncover characteristically deviant cognitive processes in Methods for functional imaginl various cases with psychopathology. For instance, smaller amplitudes of later aspects • functional Magnetic Resonano of the event-related potentials (ERPs), in particular reduced 'amplitudes of N100, P300 changes ~n blood flow param and CNV, were reported in schizophrenic patients during acute episodes (summary of oxygenation (BOLD) evidence by Cohen et al., 1991). The findings are so reliable that they have even been discussed as "markers" of the disease. Larger amplitudes of early aspects of the ERP • Positron Emission Tomograp£ before 100 ms have been attributed to inefficient inhibition of irrelevant sensory input, (rCBF) or metabolic rates (I probably even on a subcortical level. It has been argued that this lack of inhibition thr.ough ,-rays that result froIT leads to an information o~erload which subsequently impairs controlled processing and or mhaled. which then becomes manifest in attenuated late ERP-components. An exception to • ~ingle- Photon-Emission-Compl the rule of attenuated ERP amplitudes is the negative potential shift following an m regional cerebral blood flow t imperative stimulus and the response. In forewarned reaction tasks, a postimperative Xenon inhaled by the subject. negativity (PINV) is commonly observed in schizophrenic patients, but rarely found in healthy control subjects. However, in healthy subjects, a PINV can be induced by • Non-invasive optical spectos~ro specific experimental manipulations such as unexpected changes in the contingencies opment, see Section 2.4.9.3) between stimulus, response, and response outcome (for summary see Cohen et al., 1991; Rockstroh et al., 1989). • MEG and EEG-basedsourceloc with MRT) Only a few magnetoencephalographic studies have been performed with schizophrenic patients. These have concentrated mainly on earlier components

/ 2.4 Neuromagnetism 245 ----"'--~------~~------of the event-related response. They point to reduced interhemispheric asymmetry of the magnetic counterpart of the N100 and - compared to controls - deviant dipole orientation (Reitc et al., 1989), a finding which supports the notion of an atypical spatial regulation of cortical excitability in schizophrenics (Rockstroh et al., 1989). A deviant temporal course of dipole orientation for N100 in schizophrenic patients com­ pared to controls provides further evidence (Roth et al., 1995). Finally, of particular interest for the explanation of schizophrenic symptoms is the finding of a :!O-ms delay of the peak latency of the magnetic counterpart of the NlOO (N100m) that occurred when schizophrenic patients listened to tones while hallucinating compared to periods without hallucinations (Tiihonen et al., 1992). This effect, which parallels a change in N100m peak latency produced by distracting external stimulation (such as speech or music maskers), suggests "that the auditory cortex is activated during hallucinations not by external input from the ear but by endogenous neural firing" (Tiihonen et al., 1992, p. 257), and may be an approach to uncover generators of hallucinations.

2.4.9 The Relation of MEG and MSr to Other Methods for the Non-invasive Study of Brain Function Brain imaging techniques can be divided into the non-invasive methods for the study of brain function (Fig. 2.59) and those of brain structure. The former provide information about brain activation in time and space.

Methods for structural imaging: • X-ray-Computerized Tomography (CT, or CAT for Computer-Assisted Tomog­ raphy)

• Magnetic Resonance Tomography (MRT - see Chapter 3)

Methods for functional imaging: • functional Magnetic Resonance Tomography (fMRT - see Section 3.2.2) measures changes in blood flow parameters, in particular alterations in regional blood oxygenation (BOLD) • Positron Emission Tomography (PET), measures regional cerebral blood flow (rCBF) or metabolic rates (e.g. of glucose) by tracking decaying positrons through ,-rays that result from this decay. The radioactive substance is injected or inhaled. • Single-Photon-Emission-Computer Tomography (SPECT), measures alterations in regional cerebral blood flow through scanning of ,-rays emitted by radiocative Xenon inhaled by the subject. • Non-invasive optical spectoscropy (includes a variety of techniques under devel­ opment, see Section 2.4.9.3) • MEG and EEG-based source localization; MSI (MEG localization in combination with MRT)

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/ 246 ChapteT 2 Biomagnetism 2.4 Neuwmagnetism

2.4.9.1 Methods Mapping Alterations in Regional Cerebral Blood Flow: 2.4.9.2 EEG and MEG Provi, PET, SPECT, fMRT While approximative theoretical COl When MEG is compared to measures of regional cerebral blood flow, it is often men­ cesses seem to underly EEG and ?I tioned that PET, SPECT and flvIRI, but not MEG, rely on physical principles that information in real measurements a allow the unequivocal reconstruction of spatial pattern. In fact, all of these methods mal information. This may be coun require considerable data processing and assumptions about links of a certain mea­ similar in appearance to EEG signa sure to neurophysiological processes. Depending on a particular type and procedure tical differences are noteworthy (for that is chosen for the data analyses, each of these imaging techniques can produce a et al., 1993): variety of outcomes for one and the same original data set. The interpretations then vary with the assumptions of what physiological process might have caused the alter­ • In the past, most of the EEG ations extracted by the signal analysis. Therefore, it is not surprising that some of to the international 10-20 Sy1 the papers in the field may remind the reader more of a psychoanalytic approach than to compute the CSD, high re, of straightforward science. It is desirable to replicate results in different laboratories original system has become il and, most of all, to validate the outcome using different imaging methods, a procedure EEG electrodes cover only a sometimes referred to as multi-modal imaging. between two electrodes and t A first study compared magnetic and metabolic brain activity during a verb gener­ shallow sources that have the ation task (Eulitz et al., 1994). While PET results revealed activation of Broca's and electrodes are 'needed (when e Wernicke's areas and their homologues in the right hemisphere, MEG results could to avoid spatial aliasing (Gevin not be explained by limiting sources to theses structures but, in addition, indicated Srinivasan et al., 1996). sources in the primary and secondary auditory cortices, as well as in parietal regions. • Application of larger numbers Clear interhemispheric pattern differences between MEG and PET, which may be due fixation may produce artefact to variant physiological mechanisms underlying rCBF increase and magnetic activity, results in distortion of the int suggest that PET data will most likely not provide physiologically meaningful con­ erroneous ghost sources when I straints and, therefore, may not necessarily validate results of the distributed source analysis of MEG data. • Artefacts, except subject move The following differences between MSI and PETIfMRT need to be kept in mind: MEG, as EEG may be contam and volume-conducted EKG. S • Procedures that image blood flow parameters generally require difference maps EEG than for MEG. Subject between two conditions,in order to account for the structural patterns. Assume whereas, in EEG, it might be I a resting condition is subtracted from an active task condition. The resulting implications for the design of s blood flow pattern may then tell us where the task is processed. It may, however, be determined by the type of rest (like lying in a small tube without distractors) • EEG and MEG require quite ( or by the non-linear inteTaction between the two conditions. calization. While for pure MEC accurate one compartment BE • The link between blood flow and neural acitvity is not well understood. It is 1989), for EEG, three concentri certainly non-linear and it is possible that blood flow changes only occur for model are necessary. Also, con certain types or strengths of neural activations. On the other hand, not every than MEG (Haueisen et al., 19! neural mass activity will create the kind of open fields that can be detected by EEG or MEG. • Unlike in EEG, in MEG it is • sessions because it is hardly p • The relationship between cytoarchitecture and brain morphology is variable. the same position with respect Since the recorded magnetic fie • Blood flow does not change quickly enough to allow a temporal resolution that the source, it is therefore erron would be high enough to monitor cognitive processing in real time. a readjustment of the MEG d! One can compute from the mee • PET and SPECT expose the subject to radioactive radiation. configuration for all recordings strength (e.g. dipole strength) f 2.4 Neuromagnetism 247

2.4.9.2 EEG and MEG Provide Different Information While approximative theoretical considerations suggest that similar physiological pro­ cesses seem to underly EEG and MEG, the signals generally provide quite different information in real measurements and therefore are ideally combined to extract maxi­ mal information. This may be counterintuitive since the neuromagnetic signals can be similar in appearance to EEG signals. Comparing EEG and MEG, the following prac­ tical differences are noteworthy (for review see also Anogianakis et al., 1992; Wikswo et al., 1993):

• In the past, most of the EEG studies have used electrode placements according to the international 10-20 system (Fig. 2.58). In order to localize sources or to compute the CSD, high resolution EEG with many more points than in the original system has become increasingly useful. In contrast to MEG sensors, EEG electrodes cover only a relatively small area compared to the distance between two electrodes and thus, EEG is vulnerable to spatial aliasing. For shallow sources that have the highest spatial frequencies, a hundred or more electrodes are 'needed (when equally spaced across the head's surface) in order to avoid spatial aliasing (Gevins et al., 1990; Gevins, 1993; Junghofer et al., 1997; Srinivasan et al., 1996).

• Application of larger numbers of EEG electrodes is cumbersome and improper fixation may produce artefacts. Improper measurement of electrode location results in distortion of the interpolated surface potential and consequently in erroneous ghost sources when CSD is calculated.

• Artefacts, except subject movements, are generally more severe in EEG than in MEG, as EEG may be contaminated by movement of electrodes, electrode drift and volume-conducted EKG. Similarly, ocular artefacts are also more severe for EEG than for MEG. Subject movements can cause severe artefacts in MEG, whereas, in EEG, it might be no problem at all. The latter fact has important implications for the design of studies.

• EEG and MEG require quite different volume conductor models for source lo­ calization. While for pure MEG analysis a simple sphere or a more realistic and accurate one compartment BEM model are sufficient (Hamiilainen & Sarvas, 1989), for EEG, three concentric spheres or a realistic three compartment BEM model are necessary. Also, conductivity changes in the head affect EEG more than MEG (Haueisen et al., 1997).

• Unlike in EEG, in MEG it is not easy to compare signal amplitudes between sessions because it is hardly possible to readjust the MEG system to exactly the same position with respect to the human head and the sources within it. Since the recorded magnetic field strength strongly depends on the distance to the source, it is therefore erroneous to compare magnetic field amplitudes after a readjustment of the MEG device. There are two solutions to this problem. One can compute from the measured fields a magnetic field for a virtual sensor configuration for all recordings (Burghoff et al., 1997) or one can use the source strength (e.g. dipole strength) for further analysis.

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/' 248 Chapter 2 Biomagnetism 2.4 Neuromagnetism ~------

As mentioned in Section 2.4.2, the same brain processes may be differentially im­ aged by MEG and EEG. A study by Eulitz et al. (1997) quantitatively illustrated this point. From a theoretical point of view, such differences are plausible. EEG results from the extracellular volume currents triggered mainly by postsynaptic potentials. MEG is thought to arise from the intracellular branch of this process Le., from the currents that flow from the dendrite to the soma. ·Thereby, MEG is mainly sensitive to currents flowing tangentially to the surface of the scalp and to a lesser degree - about 10% - to radial sources. As a consequence EEG and MEG are affected differently by averaging. If sources vary across trials and appear in different cerebral regions from trial to trial, their impact on the event-related brain responses will be suppressed by averaging. Therefore, this "biological noise" is more strongly reduced for tangential sources than for radial ones (as tangential sources in opposing walls of a sulcus may partially cancel, leaving only the radially directed currents in the average). Sources in. the primary and secondary sensory projection areas such as the Brodman areas 3b (so­ matosensory), 41/42 (auditory) or 17 (visual) are primarily tangentially oriented and are consistently evoked in each trial. Consequently, for such sources activated early in information processing, the signal-to-noise ratio is considerably higher for MEG measurements than for EEG measurements. When higher processing stages are inves­ tigated, the corresponding sources may become more distributed and currents flowing simultaneoulsy in opposing walls of a sulcus may partially cancel each other out. The remaining equivalent current dipole may have a stronger radial than tangential orien­ tation (Lutzenberger et al., 1987) and thus will appear with a relatively greater weight in EEG than MEG responses. Therefore, complementary information such as EEG should be used in addition to MEG when brain activations beyond the primary and Fig. 2.86:' Sources from somatm secondary projection areas are being studied. This statement is supported by studies somatosensory stimul, indicating superadditive information in MEG and EEG (e.g., Pflieger et al., 1998, cortical sheet. The pa also own unpublished observations), and lower error bounds for source localizations ments (from 60 to 12 based on. simultaneous EEG and MEG measurements (Mosher et al., 1993). However, orthogonal slice comb a recent theoretical study (Malmivuo et al., 1997) claimed that EEG and MEG record (using the procedure the electric activity of the brain in a very similar way. We believe that more theoretical temporal covariance IT research must be performed to fully resolve this contradiction. from the averaged reSj .ance matrix and the fo of each source dipole. 2.4.9.3 Perspectives inverse operator as a Animated images of brain activity in three dimensions with a resolution on the mil­ then used to calculate' to be locally perpendi( lisecond time scale are still somewhat visionary but can already be performed by means M. Sereno, A. Dale an, of MSr under certain conditions. An example of such a sequence of cortical activa­ tions is presented in Figure 2.86. MSr is o.nly possible for the less complex source configurations, but no other method can currently compete with the spatio-temporal tissue thus allow for excellent resolution. This might change when £MRr methods are developed that allow the imag­ made to optically image brain ing of Sodium-ions (Na+). As the sodium currents are related to neural firing, such a (VilIringer & Chance, 1997). C technique would allow direct tracking of neural activity. The nuclear resonance from can be developed to usefully n ~a+ is relatively weak, but averaging, like in the case of evoked responses, might allow expected from new techniques for extracting the brain responses that occur consistently to an external event. MSl and £MRl remain the most Furthermore, mass activation of neural cell assemblies alters the optical properties study of brain function. Their ( of brain tissue in which these cells are embedded. Changes include hemoglobin the way to even more wonders in oxygenation, cytochrome-c-oxidase redox state and light scattering reflecting either membrane potential (fast signal) or cell swelling (slow). Studies of exposed brain

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Fig. 2.86:- Sources from somatosensory (left), auditory (right) and combined auditory and somatosensory stimulation (middle column) are superimposed onto the inflated cortical sheet. The panels from top to bottom correspond to different time seg­ ments (from 60 to 120 ms). In this subject, the cortical sheet was found by orthogonal slice combination, flood-filling, and deformable template refinement (using the procedure described in Sereno & Dale, 1992). The sensor spatio­ temporal covariance matrix is then estimated using a finite series of time steps from the averaged response. An Eigenvalue decomposition of the sensor covari­ .ance matrix and the forward solution are used to obtain estimates of the variance of each source dipole. These are inserted into the equation for the optimal linear inverse operator as a priori source variance estimates. The inverse operator is then used to calculate a solution for each time step. Source dipole are restricted to be locally perpendicular to the cortical sheet. (Collaborative study with Drs. M. Sereno, A. Dale and C. Pantev; unpublished) tissue thus allow for excellent temporo-spatial rsolution. Attempts are now being made to optically image brain activity in human subjects through the intact skull (Villringer & Chance, 1997). Currently, it is unclear to what extend these methods can be developed to usefully map brain activity. Another innovative leap can be expected from new techniques employing nuclear magnetic resonance. Till then, MSI and fMRI remain the most valuable sources of information for the non-invasive study of brain function. Their current usefulness and potential promise will show us the way to even more wonders in neuroscience, materializing Emily Dickinson's verse.

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Acknowledgements Brockhaus, A., Lehnertz, K., W M., & Eiger, C. E. (1997). P, The author wishes to thank Hannes Nowak and Jens Hauei~en for their helpful methohexital-induced epileptifo scientific comments on earlier versions of the manuscript and Christina Robert, and ceph. Cl'in. Neurophysiol., 102( Lisa Green for language editing. Research was supported by grants from the Deutsche Brown, T: H., Kairiss, E. W., & Forschungsgemeinschaft and the Stiftung Volkswagenwerk. mechanisms and algorithms. AI Burghoff, M., Steinhoff, U., & Hal obtained with multi-squid-systE Supercond., 7(2), 3465-3468. Byl, N., Wilson, F., Merze~ich, M. References Sensory dysfunction associated Abeles, M. (1982). Local Cortical Circuits. An Eleetrophysiological Study. Springer, Berlin. dystonia: a comparative study. Aertsen, A., Erb, M., Schiiz, A., & Palm, G. (1995). In C. Pantev, T. Elbert, & Byl, N. N., Merzenich, M. M., & J B. Liitkenhiiner, (Eds.), Oscillatory Event-Related Brain Dynamics, Coherent assembly paralleled by degradation ofrepl dynamics in the cortex: mulit-neuron recordings, network simulations and anatomical con­ for Neuroscience, Annual Meetil siderations, pp. 59-84. Plenum Publishing Cooperation, New York. Cheyne, D., 'Weinberg, H., Gaetz, Anogianakis, G., Badier, J., Barrett, G., Erne, S., Fenici, R., Fenwick, P., Grandori, F., Hari, predicting side movement: Neur R., Ilmoniemi, R., Mauguiere, F., Lehmann, D., Perrin, F., Peters, M., Romani, G.-L., fields. Neurosci. Lett., 188, 1-4. & Rossini, P. (1992). A consensus statement on the relative merits of EEG and MEG Clark, S. A., AlIard, T., Jenkins, V (editorial). Electroenceph. Clin. Neurophysiol., 82, 317 - 319. body-surface map in adult cortE 444-445. Babloyantz, A. & Destexhe, A. (1986). Low dimensional chaos in an instance of epilepsy. Proc. Nat!. Acad. Sci. USA, 83, 3513-3517. Clarke, C.J. S. (1989). Probabili Baule, G. M. & McFee, R. (1963). Detection of the magnetic field of the heart. Am. Heart. Problems, 5, 999-1012. J., 55, 95-96. . Clarke, C. J. S. (1991). In J. Ne Baumgartner, C., Sutherling, W. W., Di, S., & Barth, D. S. (1991). Spatiotemporal mod­ localization and 3D modelling, . eling of cerebral evoked magnetic fields to median nerve stimulation. Electroenceph. Clin. continuous current sources, pp. : partment of Physics. Neurophysiol., 79, 27-35. Berg, P. & Scherg, M. (1994). A fast method for forward computation of multiple-shell Cohen, D. (1968). Magnetoencepha spherical head models. Electroenceph. Clin. Neurophysiol., 90, 58-64. rhythm currents. Science, 161, , Bertrand, 0., Thevenet, M., & Perrin, F. (1991). In J. Nenonen, H. M. Rajala, & T. Katila, Cohen, D. (1972). Magnetoenceph< (Eds.), Biomagnetic Localization and 3D Modelling, volume Report TKK-F-A689, 3D fi­ a superconducting magnetometel nite element method in hrain electrical activity studies, pp. 154-171. Helsinki: Helsinki Cohen, D., Edelsack, E. A., & Zim University of Technology, Department of Physics. a shielded room with a supercon 16, 278-280. Birbaumer, N., Elbert, T., Canavan, A., & Rockstroh, B. (1990). Slow potentials of the cerebral cortex and behavior. Physiological Reviews, 70, 1--41. Cohen, L.-G., Bandinelli, S., Sato Boutros, N. N., Overall, J., & Zouridakis, G. (1991). Test-retest reliability of the P50 mid­ detection of somatosensory stilll latency auditory evoked response. Psychiatry Research, 39, 181-192. Clin. Neurophysiol., 81(5), 366-3 Braitenberg, V. (1978). I-n R. .Heim & G. Palm, (Eds.), Theoretical Approach to Complex Coles, M. G. H., Gratton, G., Kra Systems, Cell assemblies in the cerebral cortex. Springer, Berlin, Heidelberg. E. Donchin, & S. W. Porges, (E( tions, Principles of signal acquisil Braitenberg, V. & Schiiz, A. (1991). Anatomy of the Cortex. Statistics and Geometry. (19~ Springer, Berlin. Cook, E. W. & Miller, G. A. chophysiologists. Psychophysiolo, Brandeis, D., Naylor, H., Halliday, R., Callaway, E., & Yano, L. (1992). Scopolamine effects on visual information processing, attention, and event-related potential map latencies. Cllffin, B. N. (1991). Eccentric sph; 871-878. Psychophysiology, 29, 315-336. Braun, C., Lutzenberger, W., Miltner, W., & Elbert, T. (1990). In C. H. M. Brunia, A. W. K. Cuffin, B. N. & Cohen, D. (1977a). Gaillard, & A. Kok, (Eds.), Psychophysiological Brain Research, volume 1, Can subcortical shapes. IEEE-BME 'I'rans. Biom structures generate potentials large in amplitude?, pp. 31-35. Tilburg University Press, Cuffin, B. N. & Cohen, D. (1977b). Tilburg. sources. J. Appl. Phys., 48, 3971

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