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12Functional BrainMapping Using
Intracranial Source
Imaging
CONTENTS
12.1 Introduction
12.2 Neurophysiological SignalsOrigin of Activity • Localization of the Sources
12.3 Mathematical Modeling
Source Models • Volume Conductor Models • SourceEstimation Procedure
12.4 Neurophysiological RecordingsElectroencephalography — EEG • Magnetoencephalography
— MEG
12.5 Data AnalysisSurface Maps • Source Imaging Procedure • Mapping-Based on
EEG or MEG
12.6 Application Examples Auditory Evoked Responses • Somatosensory Evoked
Responses • Visual Evoked Responses • Epileptogenic Spike
Localization • Language Mapping
12.7 Concluding Remarks
References
12.1 Introduction
The treatment of certain complex neurological diseases, such as pharmacologically intractable epilepsy,
brain tumors, and arteriovenous malformations, often includes surgical intervention. In all cases, accurate
localization of the lesion to be resected is of paramount importance, and the margin between therapeutic
treatment and debilitating side effects due to surgery is very narrow. Several additional factors contribute
to the success of surgery, chief among which is the accurate identification of the cortical areas that are
responsible for certain brain functions, such as sensation, movement, and speech. This latter procedure,known as functional brain mapping, is critical, because resection of such vital brain areas can have
devastating results.
George ZouridakisUniversity of Houston
Darshan IyerUniversity of Houston
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A number of noninvasive functional imaging modalities, including functional magnetic resonance
imaging (fMRI) (Binder et al., 1997; Cuenod et al., 1995), positron emission tomography (PET) (O’Leary
et al., 1996; Peterson et al., 1988), regional cerebral blood flow (rCBF) (Friberg, 1993), and single photon
emission computed tomography (SPECT) (Gomez-Tortosa et al., 1994), can map brain functions with
varying degrees of success. However, the most reliable approach to map the human brain still relies on
direct electrical stimulation of the exposed cortex (Ojemann et al., 1989), a procedure that is highly
invasive and unpleasant, and it is performed mostly intraoperatively on an awake patient.
Recently, however, completely noninvasive procedures have been successfully used for brain mapping
(Breier et al., 2000; Ebersole and Wade, 1990; Gallen et al., 1994; Hamalainen et al., 1993; Papanicolaou et
al., 1999; Zouridakis et al., 1998b). These procedures rely on the fact that performance of certain brain
functions activates only a small population of cortical neurons whose activation gives rise to electromagnetic
signals that can be recorded externally. The electrical aspects of brain activation are recorded in an electro-
encephalogram (EEG) by placing a set of electrodes on the scalp, while the corresponding magnetic aspects
can be captured in a magnetoencephalogram (MEG) by placing an array of coils close to the head.
Electrical or magnetic source imaging (SI) refers to the localization of the intracranial sources that
give rise to externally recorded electrical or magnetic signals, respectively. This procedure is a form of functional imaging and combines the neurophysiological EEG or MEG data with structural MRI scans.
That is, while MRI alone provides information on the brain’s anatomy, SI provides information about
its function. Moreover, SI provides very high temporal and spatial resolution, which makes it far superior
than any other functional imaging modality in that respect.
In this chapter we introduce the basic principles that make EEG- and MEG-based SI procedures
possible, and the equipment used to accomplish it. Additionally, we provide a few concrete examples that
demonstrate the usefulness of SI as a valuable clinical tool for functional brain mapping.
12.2 Neurophysiological Signals
12.2.1 Origin of Activity
When considering surface neurophysiological activity, two major aspects must be distinguished. The
first one is the characterization of the sources underlying the observed signals. The human brain is
composed of vast numbers of electrically active neurons and other supporting cells (e.g., glial cells)
that are assembled in functional groups. In particular, the outer surface of the brain, the cerebral
cortex, consists of a thin and highly compact intricate network of cells arranged in layers (Kandel et
al., 1991). The average thickness of the cortex is 2.5 mm, and its density reaches an impressive 10 5
cells per millimeter which form approximately 1015 synapses. Conceptually, the building block of the
cerebral cortex is a column, a structure of neurons arranged radially to the head surface, with a diameterof about 0.5 to 3 mm (Kelly, 1991). A specific type of neurons in a column, called pyramidal cells,
have a linear structure with dendrites that are arranged parallel to each other. In the resting state, each
of these neurons is negatively polarized relative to the surrounding electrolyte solution, because of the
activity of the cell membrane. When a cell is in the active state, large quantities of positive and negative
ions — namely, sodium (Na+), potassium (K+), and chloride (Cl–) — cross the cell membrane, moving
from the intracellular to the extracellular fluid, and vice versa. For all practical purposes, this ion
movement is equivalent to a current flow, and it is responsible for all electrophysiological signals
recorded externally. In particular, the electrical potentials and the magnetic fields recorded on the
scalp as the EEG and MEG, respectively, result mainly from the temporal and spatial summation of
postsynaptic activity generated along the apical dendrites of the pyramidal neurons. These fieldsrepresent the synchronous activation of a large number of neurons, estimated to be between 10 4 and
105 cells (Okada et al., 1992; Williamson and Kaufman, 1987).
The second aspect of surface neurophysiological activity is the influence of the tissues surrounding
the active neurons, the so-called volume conductor effects. A detailed analysis of the relationship between
the intracranial sources and the extracranially recorded signals is beyond the scope of this chapter, but
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excellent reviews can be found elsewhere (Hamalainen et al., 1993; Williamson and Kaufman, 1987).
Briefly, neural activity can be represented mathematically as a current density in a closed volume of finite
conductivity. Outside this volume, the conductivity and current density are zero. The intracellular
currents give rise to an electric field outside the cells which, in turn, results in currents that flow passively
through the rest of the conducting medium. Thus, the total current density can be divided into two
components, primary (intracellular) and secondary (extracellular). The primary currents are more inter-
esting in neurophysiology, because they are associated directly with cell activation. Under specific con-
ditions (Lewine, 1990), that are fairly well approximated in many practical applications, it can be assumed
that the electromagnetic fields recorded on the scalp as the familiar EEG and MEG are associated mainly
with the primary (intracellular) currents, and thus they represent cell activation.
The relationship between the intracranial sources and the extracranially recorded biological signals
can be computed as a function of the distance between the observation point and the source location.
Both the magnetic field and the potential are inversely proportional to the square of this distance.
12.2.2 Localization of the SourcesDetermining the extracranial potentials and magnetic fields that result from the intracranial current
sources is known as the forward problem. Conversely, the inverse problem requires localization of the
intracranial current sources that give rise to the externally recorded electrical potentials (EEG) and
magnetic fields (MEG). Unfortunately, the inverse problem has no unique solution, as the corresponding
mathematical equations can be satisfied by an infinite number of possible source configurations. To
overcome this limitation, it is necessary to solve a “constrained inverse problem” and make assumptions
for both the sources and the surrounding head tissues (Morse and Feshbach, 1963). These assumptions
must be neurophysiologically based, otherwise the results computed and their interpretation may be
completely erroneous (Jayakar et al, 1991; Nunez et al., 1991). A typical approach involves the so-called
“quasi-static” approximation (Plonsey and Heppner, 1967) under which all potentials and currents atany given instant in time are determined by the properties of the sources at that time only. Moreover,
the various brain tissues are assumed to be linear and completely characterized by their electrical con-
ductivity, which is frequency independent. The medium can be inhomogeneous, if its conductivity is
different for different compartments, and anisotropic, if the conductivity of the tissues is different along
different directions in the three-dimensional space.
12.3 Mathematical Modeling
12.3.1 Source ModelsOver the past several years, many different approaches have been developed to identify the sources that
explain the fields recorded on the head surface. The most common approach has been to describe the
activation of a well-localized (Nunez et al., 1991) small population of neurons at a particular point in
time as a single equivalent current dipole. Indeed, when a neuron is activated, ion currents flow through
it and this “electrical generator” can be modeled as a small current dipole, with a moment of about 10 –13
A m. A schematic diagram of such an arrangement is shown in Figure 12.1.
However, to identify the sources that account for a complex field distribution over a time interval, a
series of single dipoles is required (Supek and Aine, 1993). These dipoles can move in position and
orientation over time, and when displayed on a single image, they appear as a moving dipole.15,16 Another
approach is to consider current distributions in the brain (Ioannides et al., 1990; Morse and Feshbach,1963; Okada et al., 1992; Wang et al., 1992), and estimate several fixed dipoles at discrete points within
a region of interest. Spatiotemporal source modeling (Baumgartner et al., 1991; Scherg, 1992; Scherg
and von Cramon, 1985) is yet another approach that takes into consideration overlapping activity of
multiple generators. In this case, dipoles are fixed in location and orientation, but they can vary over
time in strength and polarity to explain the temporal evolution of the recorded fields.
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In general, solutions for multiple-dipole sources are much less reliable than those for a single dipole
(Cuffin, 1998), because in the former case the solutions provided are very sensitive to noise. Thus, most
clinical applications still use single-dipole approaches.
12.3.2 Volume Conductor ModelsVolume conductor models have evolved from extremely simple (Rush and Driscoll, 1978) to very realistic
and computationally intensive ones (Fender, 1991). The first and simplest model was that of an infinite,
homogeneous, isotropic medium. The next sophistication level considered a homogeneous but bounded
medium (specifically, a sphere) as a head model (Wilson and Bayley, 1950). Later on, to account for the
various tissue layers within the head, several concentric-sphere models were developed (Ruth and Driscoll,
1968; Schneider, 1972; Witner et al., 1972) that included three concentric spheres to account for the scalp,
skull, and the brain. More recently, however, additional approaches, such as the eccentric-sphere model
(Mejis and Peters, 1987) and the four-shell model, have been proposed. The latter uses a cerebrospinal
fluid layer underneath the skull, and it is especially useful for hydrocephalic patients (Nishijo et al., 1996).
However, more realistic head models can be obtained if, instead of using arbitrary spheres, MRIs are
employed to determine the curvature of a particular subject’s head and fit the spheres of the multicom-
partment model (Lopes da Silva et al., 1991). More realistic models computed entirely from MRIs (Mejis
and Peters, 1987) are implemented using numerical techniques, such as the boundary element method
(BEM) (Mejis et al., 1989) and the finite element method (FEM). Finally, realistic head models that
account also for the effects of anisotropy in the electrical conductivity have been developed (de Munck,
1988; Zhou and van Oosterom, 1991).
12.3.3 Source Estimation Procedure
Initially, inverse source localization procedures were implemented on spherical models (Rush andDriscoll, 1978; Smith et al., 1993) and they were later extended to work on realistic geometry. In the
case of a single-current dipole, to identify the intracranial sources that explain the fields recorded on
the head surface, the procedure takes the measured values from all sensors at a given instant in time
and searches, using iterative minimization techniques, for an equivalent dipole within the head that
could generate such fields (Henderson et al., 1975; Kavanagh et al., 1978; Schneider, 1972; Sidman et
FIGURE 12.1 A well-localized small population of neurons, with pyramidal cells oriented radially to the head
surface. The sum of currents (arrows on the cells) flowing in the apical dendrites can be modeled as a single equivalent
current dipole.
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al., 1978). Techniques such as the brain electrical source analysis (BESA) (Jirsa et al., 2000) and CURRY
(Neuroscan, El Paso, TX) are iterative with automatic picking of dipole sources and define one dipole
for each point in time.
In more general cases, the localization procedure starts with some initial estimate of the distributed
sources and then recursively enhances the strength of some of the elements, while decreasing the strength
of the rest of the elements until they become zero. In the end, only a small number of elements will
remain nonzero, yielding a localized solution. This method is implemented, for example, in the FOCUSS
(Gorodnitsky et al., 1995) and LORETA (Pascual-Marqui et al., 1994) algorithms.
Another approach to source localization employs a spatiotemporal model, under the assumption that
there are several dipolar sources that maintain their position and orientation fixed, while they vary only
their amplitude as a function of time. Rather than fitting dipoles to measurements from one instant in
time, dipoles are fitted by minimizing the residual least-square error over the entire time interval (Scherg
and von Cramon, 1985). More advanced spatiotemporal approaches, such as the multiple signal classi-
fication algorithm (MUSIC) (Mosher et al., 1992), have also been developed, whereby the algorithm
searches for a single dipole through the three-dimensional head volume. To localize the source, the user
must search the head volume for local peaks. Extensions of this algorithm automate this search througha recursive approach (Mosher and Leahy, 1963).
12.4 Neurophysiological Recordings
12.4.1 Electroencephalography — EEG
The EEG is a record of the electrical aspects of neurophysiological activity. More specifically, a one-channel
EEG represents the potential difference between two sites on the head, and requires two electrodes connected
to the input of a differential amplifier — one active and one for reference. These electrodes are typically
gold-plated or sintered Ag-AgCl, and they are attached to the scalp with a special glue (collodion) orelectrolyte gel. Typically, the active electrode is placed close to the structures of interest, whereas the reference
electrode is placed at a distant location (usually on the left or linked mastoids, behind the ears). This type
of connection is called monopolar, or referential. When both the active and the reference electrodes are
placed close to the structures of interest, the placement is called bipolar or differential. The advantage of
this latter connection is that far-field activity common to both electrodes is cancelled and thus sharp
localizations of events can be obtained. Alternatively, the reference electrode can be connected to a circuit
that measures the average activity of all electrodes to obtain a recording with an average reference. The
common electrical connection for the electrical circuitry of the recording equipment is called the ground,
and all voltages are measured with reference to it. The patient is also connected to this point through a
separate electrode typically placed on the patient’s forehead. The potential differences in each channel areamplified by high-gain, differential amplifiers, and then it is either displayed on a monitor, saved on a hard
disk, or simply plotted on paper, depending on the technology used.
For clinical recordings, electrodes are arrange on the head in a montage that follows the 10–20 inter-
national placement system and its extensions. Routine clinical equipment employs about 21 electrodes,
whereas more recent systems specialized primarily for research may include hundreds of electrodes. In
this case, arrays of electrodes are arranged on a cap for convenience, and these densely sampled recordings
provide the so-called dense-array EEG, or dEEG. A system that uses 256 channels is described next.
12.4.1.1 EEG Recording Device
The latest dEEG systems incorporate several unique features: they offer high spatial sampling with up to256 recording channels; they are portable, and often the complete set of 256 amplifiers is battery operated
and is about the size of a book; they can use active electrodes for noise cancellation, in which case each
electrode incorporates a micropreamplifier that preconditions the EEG signals before transmission to
the main amplifiers through the electrode leads, and this eliminates the need for a shielded room; and
finally, they provide very high sampling rates of up to 5 kHz per channel.
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The active electrode is a sensor with a very low output impedance. By integrating the first amplifier
stage with a regular electrode, extremely low-noise measurements are now possible without any skin
preparation. The noise levels achieved can be as low as the thermal noise of the electrode impedance,which is the theoretically minimum level. Active electrodes eliminate all problems associated with high
electrode impedances, cable shielding, capacitive coupling between the cables, and sources of interference,
as well as any artifacts due to cable or connector movements. The amplifiers of a dEEG system (ActiveTwo,
BioSemi, the Netherlands) available in our lab, along with an active electrode and a cap with the electrode
holders, are shown in Figure 12.2.
12.4.2 Magnetoencephalography — MEG
The MEG measures the extracranial magnetic fields produced by intracranial electrical currents. Neuro-
magnetic signals are many orders of magnitude weaker than the ambient magnetic noise, which is due
to the earth’s field and to the presence of ferromagnetic objects and electrical instrumentation. Typicalscalp-recorded magnetic fields have a peak amplitude of about 100 fT (Gomez-Tortosa et al, 1994; Lewine,
1990), whereas environmental electromagnetic noise in a hospital (power lines, elevators, MRI magnets,
etc.) may be as high as 1 T (1015 fT) in extreme cases (Lewine, 1990). Therefore, to detect this kind of
biological activity, it is necessary to use highly sensitive instrumentation and, at the same time, attempt
to eliminate extraneous magnetic fields.
12.4.2.1 MEG Recording Device
MEG measurements were practically impossible before the introduction of superconductive instru-
mentation. The latest generation of biomagnetometers are composed of large arrays of pick-up coils,
each of which is connected to a SQUID (superconducting quantum interference device) that acts asa very low noise, ultrahigh gain, current-to-voltage converter. The SQUIDs and induction coils are
immersed in liquid helium to maintain a superconducting state. This type of device can detect even
very small changes in magnetic flux, such as the one resulting from neurophysiological activity. Figure
12.3 shows a schematic diagram of a multisensor MEG system along with a detection coil and a SQUID
of a single channel.
FIGURE 12.2 Active electrodes, electrode cap, and amplifiers of a portable dEEG system with 256 channels.
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To reduce the amount of extraneous magnetic noise, MEG systems are operated in specially designed
magnetically shielded rooms (Gallen et al., 1994). Additional improvements in the quality of the MEG
signals are obtained by selecting an appropriate type for the detection coils. Coils can be single-loop
magnetometers that measure the magnetic field directly, and first-, second-, or third-order gradiometers
that consist of two or more loops that measure spatial changes of magnetic field (Flynn, 1994). Various
examples of coils for both planar and axial geometry are shown in Figure 12.4.
A gradiometer (Figure 12.4d) can selectively cancel out environmental magnetic noise, depending on its
“baseline,” i.e., the distance between the two coils; because the magnetic fields decrease very rapidly with
distance from the source, fields generated by a neural source close to the gradiometer will be detected withdifferent strengths in each loop, and therefore they will induce currents of different magnitude in the two
loops, thus resulting in a nonzero net current in the gradiometer. On the other hand, fields generated far
away from the gradiometer will be practically uniform in space and will result in zero net current.
Because of the sensitivity of MEG recordings to noise, patients implanted with electrically active
medical devices such as cardiac pacemakers, neurostimulators, and infusion pumps cannot be studied
with MEG, due to the electrical interference from the implanted devices. Also, equipment entering the
room must be screened for large metallic components or electromagnetic activity that could introduce
artifactual signals.
The cost and complexity of instrumentation led to the initial development of MEG systems containing
only a few channels. Recently, however, whole-head systems with large arrays comprising 248 magne-
tometers have been developed (model 360WH, 4DNeuroimaging, San Diego, CA). An example of a 148-
channel system is shown in Figure 12.5. The need for cryogenics, and for a shielded room and a rigid
helmet-type sensor limit the scope of applications of the MEG-based mapping procedures.
FIGURE 12.3 Schematic diagram of a multisensor MEG system (left) along with a detection coil and SQUID in a
single channel (right).
FIGURE 12.4 MEG detection coils include planar (a) magnetometers and (b) gradiometers, and axial (c) magne-
tometers and (d) gradiometers. In a gradiometer, a uniform field B creates currents J and –J of opposite polarity a
produces a zero net signal.
Squid
Coil
Single SensorDewar
Liquid Helium
Skull
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12.5 Data Analysis
12.5.1 Surface Maps
Since both EEG and MEG activity have a common basis, their analyses can be carried out using similar
methods. During the last 30 years, new techniques, such as color mapping of activity distribution over
the scalp (Desmedt et al., 1987; Duffy et al., 1979), have shifted the focus from an analysis of waveforms,
derived at selected scalp sites, to the spatial distribution over the entire scalp, at particular points in time.
The recording sensors typically span a two-dimensional surface over the skull, and each sensor measures
a time series. When these measurements are interpolated and color-coded to provide a spatially contin-uous representation of the measurements, a so-called topographic scalp map is obtained at each time
point. Figure 12.6 shows the location of the sensors on the head and a three-dimensional map of the
electrical fields corresponding to the peak of the N1 auditory component.
Topographic analysis is either done qualitatively, by interpreting focal maxima and minima in the
map, or quantitatively by dipole localization methods (Brandt, 1992). Alternatively, a combined spa-
tiotemporal approach, where topographic information over different instances in time, called a space-
time series, is taken for source localization.
Recently, much effort has been made in the development of source imaging techniques based on high-
resolution or dense-array EEG (dEEG). For example, techniques such as scalp Laplacian mapping and
cortical imaging attempt to restore the high-frequency content of brain electrical activity that is smearedand distorted by the low-conductivity skull (Babiloni et al., 1996; He et al., 1995; Hjorth, 1975; Le et al.,
1994). These techniques can enhance the spatial resolution of the EEG by deconvolving the low-pass
spatial filtering effect of the head volume conduction (Freeman, 1980; He et al., 1996; Sidman et al.,
1992). Since a surface cortical map lies closer to the actual sources, its potential distribution may provide
more direct view of the neural sources (Fender, 1991; Jayakar et al., 1991).
FIGURE 12.5 A whole-head MEG system with 148 recording channels operated in a magnetically shielded room.
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12.5.2 Source Imaging Procedure
The SI procedure consists of several steps that culminate in the display of functional information obtained
using MEG or EEG onto high-resolution anatomic images typically obtained by MRI. For this final step,
it is necessary to translate EEG/MEG locations into MRI coordinates.
The precise location of the measurement points on the scalp is determined electronically withreference to a Cartesian coordinate system anchored on three landmarks (fiducial points) on each
subject’s head: two external ear canal points and the nasion. The line passing through the two pre-
auricular points defines the y-axis of the system. The line perpendicular to the y-axis passing through
the nasion defines the x-axis, and the line perpendicular to the x-y plane passing through the x-y
origin defines the z-axis.
The EEG/MEG anatomic reference frame is established electronically: a set of three receivers trian-
gulate the signal from a stylus-type transmitter placed successively at several reference points on the
subject’s head (typically the three fiducial points, the vertex, and the inion). Additionally, small vitamin
E-containing capsules visible on MRI are placed on the fiducials. The locations of the common markers
on the MR images and the EEG/MEG measurements serve for translating EEG/MEG locations intothe MRI reference frame. Validation studies have shown that the accuracy of this approach can be as
high as a few millimeters (Gallen et al., 1994). The same stylus transmitter can be used to define the
curvature of the head by tracing its surface.
After the anatomic reference frame has been established, the fiducial points registered, and the patient’s
head digitized, the actual recordings are obtained. The latter consist of time-varying measurements at
each detector position. The data undergo further analyses that allow the sources underlying the recorded
activity to be localized.
The equivalent current dipole (ECD) model yields a description of the instantaneous current dipole
in terms of its location, strength, and orientation, along with an estimate of its reliability (confidence
volume). Even though there is some variation across labs regarding acceptance criteria for the dipolesolutions, a high correlation between data and model and a small confidence volume are always desirable.
It is, therefore, possible to select a set of “best-fitting dipoles” to describe the data.
Once the best fitting dipoles have been identified, they are co-registered onto the subject’s MRI scan.
The resulting images are then printed on MRI films with different symbols and colors, each corresponding
to a distinct type of neurophysiological activity.
FIGURE 12.6 Example of three-dimensional topographic map. (Left) Arrangement of sensors on the head; (right)
a three-dimensional map of the electrical fields obtained at the peak of the N1 auditory component.
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12.5.3 Mapping-Based on EEG or MEG
Both theory and experiment suggest that the MEG offers no significant advantage over the EEG (Cohen
and Cuffin, 1991). MEG systems, however, are very expensive (total cost about $3 million); they require
special cryogenic equipment, a magnetically shielded room, daily monitoring and maintenance, and they
are available only in a handful of places around the world. Thus, the clinical usefulness of MEG can bevery limited. On the contrary, in addition to the unique features mentioned earlier, the latest EEG systems
incorporate several advantages: they are readily available in practically all clinical settings, and even the
most sophisticated systems are much less expensive than MEG (total cost about $150,000). Therefore,
successful brain mapping based on EEG can have a significant impact on patient care.
12.6 Application Examples
In general, the neurophysiological signals recorded on the surface of the head can be separated into two
categories, spontaneous and elicited. For instance, epileptogenic discharges, such as interictal spikes, are
events of the former type. The latter type of activity, known as evoked potentials in the case of EEG andevoked fields in the case of MEG, results from external stimulation of a specific sensory pathway, such
as the auditory, the somatosensory, or the visual.
In the next sections we give examples from our studies during the past few years that illustrate the
process of measuring surface activity and localizing the underlying sources.
12.6.1 Auditory Evoked Responses
The most prominent component of an evoked response obtained from transient auditory stimulation
is the N1, which occurs at approximately 100 msec after stimulus onset. Due to the magnitude and
consistency of the response, the sources underlying the N1 can be easily localized. Typically, neuro-
physiological signals are collected in “trials,” each of which consists of a few hundred milliseconds of
activity prior to and following the stimulus onset. Single-trial responses are contaminated by noise,
and to improve the signal-to-noise ratio, many trials are collected and averaged (typically between 100
and 500), using the stimulus onset as a time reference. The averaging approach relies on the assumption
that the neuronal responses to all stimuli are identical. Additionally, to eliminate artifacts from periodic
events, such as arterial pulsations or the 60-Hz power line interference, the interstimulus interval is
randomly varied within a predefined range.
A variety of stimuli can elicit this component. We used pure tones of 1-kHz frequency and 50 msec
duration (10-msec rise/fall and 30-msec plateau) that were delivered binaurally at an intensity of 80 dB
nHL, and a mean stimulus rate of 0.4/sec (Iyer et al., 2002). Figure 12.7 shows an example of the resulting
evoked response. Each of the superimposed tracings represents the activity recorded on one of the 256
channels, while the highlighted area denotes the peak of the N1 component. The three-dimensional
surface maps superimposed on the subject’s MRI were reconstructed at the N1 peak, while the two dipoles
indicate the cortical areas activated at that time.
The estimated sources underlying the N1 component were consistently found to be on the floor
of the Sylvian fissure, i.e., in the area of the primary auditory cortex, as shown in Figure 12.8. The
sources depicted correspond to activity during the highlighted interval around the N1 peak, as
shown in Figure 12.7.
12.6.2 Somatosensory Evoked Responses
Localization of the central sulcus and the adjacent precentral and postcentral gyri is a fundamental
objective in most mapping studies involving patients, when brain lesions are located in the parietal or
frontal cortex. In a recent MEG study (Zouridakis et al., 1999), we used tactile stimulation to map the
somatosensory cortex. Stimuli, i.e., bursts of compressed air, were delivered to a plastic diaphragm clipped
to the patient’s fingertip, lip, or toe (Benzel et al., 1993). Approximately 500 single trials were required,
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each composed of 100 msec of prestimulus and 100 msec of poststimulus activity. The interstimulus
interval was approximately 500 msec. The average responses were digitally filtered with a bandpass filter
between 2 and 40 Hz to eliminate high frequency noise, low frequency artifacts, and baseline drifts. At
each time point between 30 and 75 msec following stimulus onset, dipole coordinates were obtained
using a single-dipole model, and the ones with the best correlation between the measured and predictedfields were selected as the source location. Following this approach detailed mapping of the sensory
homunculus can be achieved. In the case of EEG-based mapping, evoked responses are typically obtained
using electrical stimulation. Figure 12.9 shows an example mapping, where left cortical areas are activated
after stimulation of the subject’s right toe, little finger, index finger, thumb, and lower lip. The topographic
arrangement of the areas identified resembles the “homunculus” typically seen in textbooks.
FIGURE 12.7The N1 evoked potential resulting from auditory stimulation as recorded from 256 channels aroundthe head (left), surface distribution of potentials at the N1 peak, and cortical areas activated during the highlighted
portion of the N1 (right).
FIGURE 12.8 Localization of the auditory N1 component in the primary auditory area resulting from 1-kHz tone
stimuli.
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12.6.3 Visual Evoked Responses
In cases in which a lesion involves the occipital cortex, visual evoked responses can be used to assess
preoperatively potential postoperative complications. Figure 12.10 shows an example of the cortical areas
activated after stimulation of a patient’s left hemifield of view with a checkerboard pattern (check size
1.4!, pattern reversal rate 1/sec). The maximum magnetic responses were obtained in the right hemi-
sphere, at a latency of approximately 130 msec; this is indicated by a square in the figure.
12.6.4 Epileptogenic Spike Localization
Ictal events, such as spikes, are very important for localizing epileptogenic regions in the brain. During the
past several years, MEG has been used in clinical settings as a noninvasive method for localizing the sources
of ictal activity (Sutherling and Barth, 1989), and more recently, due to the availability of large-array sensors,
FIGURE 12.9 The cortical areas activated after stimulation of a patient’s right toe (1), little finger (2), index finger
(3), thumb (4), and lower lip (5) follow the well-known topography of a textbook “homunculus.”
FIGURE 12.10 Cortical areas activated after stimulation of a patient’s left hemifield of view with a checkerboard
pattern stimulus.
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the sources of interictal spikes (Aung et al., 1995; Papanicolaou et al., 1999). Several studies have suggested
that noninvasive source imaging may reduce dependence on invasive electrophysiology for localization of
epileptic foci (Papanicolaou et al., 1999; Smith et al., 1993; Zouridakis et al., 1999). Most MEG epilepsy studies report localizations of interictal activity only, because the large artifacts from muscle activity typically
associated with seizures make localization of ictal sources extremely difficult. As an example, 2 sec of
simultaneous MEG and EEG activity is shown in Figure 12.11 where, in addition to the artifacts resulting
from the electrical activity of the heart, an interictal epileptic spike can be seen in both modalities.
Source localizations of several spikes obtained during this study are overlaid on MRI scans in Figure
12.12, where they are shown as triangles. In this case, a focal area of interictal epileptic activity over the
lateral surface of the left posterior temporal lobe can be seen.
12.6.5 Language Mapping
Identifying brain regions involved in language functions is often an integral part of the evaluation that
brain surgery patients. At present, this task is accomplished mostly through invasive techniques (Ojemann
et al., 1989), but recently noninvasive techniques based on MEG have also been used. For example, using
a task for continuous recognition of either printed or spoken single words, evoked responses were
recorded for 1 sec after the onset of a word and the corresponding intracranial generators were modeled
as single-current dipoles. The number of dipoles obtained in each hemisphere was used to quantify the
extent of cerebral activation (Breier et al., 2000; Zouridakis et al., 1998a). The areas thus identified
included the posterior part of the superior temporal gyrus, and the supramarginal and angular gyri. A
characteristic MRI slice with the localized sources superimposed is shown in Figure 12.13, where the
circles and triangles correspond to two repetitions of the same experimental task.
12.7 Concluding Remarks
The previous paragraphs give some examples of how SI is used for research and clinical purposes. Both
MEG and EEG large-array systems have received Food and Drug Administration (FDA) approval for
FIGURE 12.11 An epileptic interictal spike detected simultaneously on the MEG and EEG records, along with
ECG artifacts.
MEG spike
EEG spike
ECG
I
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clinical use, and they are gradually becoming available in many centers around the world. As new
technological and computational advances come into existence, the clinical relevance of this technique
becomes more apparent. In particular, in the case of epilepsy surgery, the usefulness of SI as a noninvasive
tool to preoperatively delineate the extent of a lesion (to be resected) and of the eloquent cortex (to bepreserved) has already been recognized (Breier et al., 2000; Gallen et al., 1994; Hamalainen et al., 1993;
Papanicolaou et al., 1999; Zouridakis et al., 1998a, 1998b).
With the development of larger sensor arrays and of general source modeling algorithms, it is possible
that future clinical applications of source imaging may be extended to include brain injury and stroke
assessment, dementias, developmental disorders, as well as further characterization of higher cortical
FIGURE 12.12 A cluster of epileptic interictal spikes localized in the left posterior temporal lobe.
FIGURE 12.13 Cortical areas involved with receptive language. The circles and triangles correspond to two repeti-
tions of the same experimental task.
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areas involved in attention, memory, and cognition. As a result, these procedures may lead to safer, faster,
and more cost-effective clinical interventions.
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