REVIEW „
Magnetoencephalography as a Research Tool in
Neuroscience: State of the Art
ANDREAS A. IOANNIDES
Laboratory for Human Brain Dynamics
RIKEN Brain Science Institute, Saitama, Japan
Magnetoencephalography (MEG) is a noninvasive neuroimaging method for detecting, analyzing, and interpreting the magnetic field generated by the electrical activity in the brain. Modern hardware can capture the
MEG signal at hundreds of points around the head in a snapshot lasting only a fraction of a millisecond. The
sensitivity of modern hardware is high enough to permit the extraction of a clean signal generated by the brain
well above the noise level of the MEG hardware. It is possible to identify signatures of superficial and often
deep generators in the raw MEG signal, even in snapshots of data. In a more quantitative way, tomographic
images of the electrical current density in the brain can be extracted from each snapshot of MEG signal, providing a direct correlate of coherent collective neuronal activity. A number of recent studies have scrutinized
brain function in the new spatiotemporal window that real-time tomographic analysis of MEG signals has
opened. The results have allowed the variability in a single area to be seen in the context of activity in other
areas and background rhythmic activity. In this view, normal brain function is seen as a cascade of extremely
fast events and the unfolding of specialized processes, segregated in space and time and organized into
well-defined stages of processing. NEUROSCIENTIST 12(6):524–544, 2006. DOI: 10.1177/1073858406293696
KEY WORDS
Magnetoencephalography (MEG), Magnetic field tomography (MFT), MEG spikes, Oscillations, Connectivity
Our brains are made up of neurons of varying sizes and
shapes that are intricately connected to each other.
Functionally, these neurons form transient coherent networks in an ever-changing tapestry of alliances using
chemical and electrical synapses and producing changes in
local electrical and chemical properties. It is possible to
detect, with sensors outside the head, changes relating to
either electrical activity (instantaneous reflections of neural events) or changes in metabolic consumption of nutrients and oxygen (caused by neuronal demand and
mediated by blood supply). Established neuroimaging
methods relying on well-understood physical laws allow
the noninvasive study of much of the human brain in
spatial scales from a fraction of a millimeter to many centimeters and temporal scales from a fraction of a millisecond to many hours. The central point of this review is that
although other neuroimaging techniques cover some part
of this spatiotemporal window, only magnetoencephalography (MEG) can cover the entire range, beginning with
tomographic descriptions of almost the entire brain,
extracted from a snapshot less than a millisecond long
(see Box 1 and Figure 1).
MEG deals with the detection, analysis, and interpretation of the minute magnetic fields generated by the brain
(see Box 2 and Figure 2). The MEG signal is directly
related to instantaneous changes in electrical activity in
the brain. Modern MEG hardware and software can
Address correspondence to: Andreas A. Ioannides, Laboratory for Human
Brain Dynamics, Brain Science Institute, Riken, 2-1 Hirosawa, Wako-shi,
Saitama 351-0198, Japan (e-mail:[email protected]).
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Volume 12, Number 6, 2006
Copyright © 2006 Sage Publications
ISSN 1073-8584
disentangle the signal generated by the brain from ambient and biological noise, over a remarkably wide range of
frequencies: from near DC levels to thousands of cycles
per second (Fig. 3). Information about brain activity can
therefore be extracted from the same MEG signal at multiple time scales, from a fraction of a millisecond to many
hours, provided that signal processing and analysis methods are appropriate. To move from a description of the
MEG signal to descriptions of generators in the brain, one
must solve the inverse problem (see Box 3). The mathematical properties of the inverse problem demand that a
nonlinear problem be solved independently for each time
slice of data (Taylor and others 1999). This singles out
magnetic field tomography (MFT; Ioannides and others
1990) as the most appropriate algorithm for delivering a
tomographic description of activity from a single map of
MEG data. Nevertheless, the analysis of MEG data continues to rely on oversimplified assumptions, partly
because of the computational demands of single-trial
tomographic analysis and partly for historical rather than
scientific reasons (Ioannides 1994).
This review illustrates how tomographic estimates of
activity from single snapshots of MEG data begin to fully
use the capabilities of MEG. The results open up new
ways of studying brain function using methods already
developed for data mining (see Box 4). The first part of the
review uses tomographic analysis of average MEG data to
simplify the comparison with the raw signal and the
results from conventional analysis methods. The full
potential of MEG is realized only with tomographic
reconstructions of the real-time (single-trial) MEG data.
Magnetoencephalography in Neuroscience
Box 1. Methods of Neuroimaging: The MRI Family, Electroencephalogram, and
Magnetoencephalogram
Magnetic resonance imaging (MRI) uses radiofrequency (RF) pulses modulating a strong, steady magnetic field
to locally disturb the spin of protons and detect the radiation emitted as the excited nuclei return to their ground
states. MRI has constantly spawned new innovations of the basic technology that revolutionized the visualization
of brain anatomy, chemistry, and function. The same basic MRI hardware can be used for different types of imaging, often by simply using different pulse sequences. MRI, as originally introduced, provides a three-dimensional
image of proton density, primarily water molecules. MRI is suitable for visualizing soft tissue and has developed
into the method of choice for imaging anatomical structure in the brain, so we will refer to it hereafter structural
MRI (sMRI). In recent years, diffusion tensor MRI (DT-MRI) has advanced rapidly. DT-MRI allows measurements
of the local flow of material, mainly water, and hence the determination of diffusion properties (the coefficients of
the local diffusion tensor) in each brain voxel (Le Bihan and others 2001). DT-MRI can be used to visualize white
matter tracts and hence to visualize the neural pathways connecting different brain areas.
In the early 1980s, at about the same time as MRI became widely available, rapid development of emission computed tomography (ECT), especially in positron emission tomography (PET), started the modern era of noninvasive
brain functional imaging (Posner and Raichle 1994). PET relies on the detection of emissions from radioactively
labeled molecules introduced as a bolus into the bloodstream. After each bolus, the concentration of the radioactively labeled molecules increases for a few minutes, particularly in metabolically active areas. Injections of a bolus
are given in separate sessions, some just before the subject executes a task (the active sessions), and some while
the subject rests (the baseline sessions). Computing the difference or some statistical measure of the detected
radioactivity emanating from each brain voxel provides a map of the activated areas. Apart from the technical difficulties of producing and handling short-lived radioactive material, ECT techniques have two other limitations. They
are too slow, and safety issues limit the number of times a person can be a subject as well as the number of bolus
injections given per session (hence the independent task and baseline sessions). Until recently, it was possible to
obtain maps of brain activity only by combining measurements from many subjects.
In the early 1990s, yet another MRI innovation allowed images weighted by blood oxygenation level to be
obtained in as little as a few tens of milliseconds (Kwong and others 1992; Ogawa and others 1992). Because
blood oxygenation is finely controlled and responds promptly to local energy demand, voxel-by-voxel estimation
of changes in blood oxygenation represent maps of brain activation and are referred to as functional MRI (fMRI).
Although fMRI is a major advance over ECT because it is faster and less invasive (no injection of radioactive substances), it still relies on indirect measures of neuronal activity. Increases and decreases in measured activity are
difficult to interpret because the processes that regulate oxygen supply are complex and not fully understood.
Ultimately, the measurements still rely on blood supply. Although the data sampling can be extremely fast—on
the order of 20 milliseconds—the single shot fMRI map of changes accumulated over the last few seconds is
too blurred to make out any neural activity. Yet one more twist in the MRI story has unfolded recently with the
development of functional DT-MRI, measuring a local water phase transition that precedes the activationtriggered vascular responses by several seconds (Le Bihan and others 2006). Although the details of the swelling
in the activated cells are as yet unclear, this new capability of sensing intrinsic cellular events holds the promise
of an MRI-based functional imaging capability with temporal resolution well within 1 second.
The electrical activity within and among neurons in the brain generates electromagnetic disturbances that
propagate outside the head at the speed of light. The electroencephalogram (EEG) and magnetoencephalogram
(MEG) provide two complementary measures of these electromagnetic disturbances, namely, the electrical
potential on the scalp (the EEG signal) and the magnetic field just outside the head (the MEG signal). The activity of a single neuron produces too weak a signal to be detected outside the head; the MEG and EEG signals
require a large number of neurons to be aligned in space and electrically active at the same time. Estimates of
how many neurons are needed to generate a detectable MEG signal vary, depending on what assumptions are
made about the underlying generation mechanism (Hamalainen and others 1993; Ioannides and others 2005).
Modern hardware allows recording of EEG and MEG signals simultaneously from hundreds of independent positions and, at least in the case of MEG, with minimal cross-talk. EEG and MEG maps can be routinely collected
with sampling rates of many kilohertz, providing direct correlates of changes of electrical activity in the brain with
a time resolution of a fraction of a millisecond. The EEG is a blurred image of the generators because of the high
electrical resistance of the intervening tissue, especially the skull. In contrast, the MEG signal is more directly
related to the generators because the magnetic fields propagate with little attenuation from the generators to the
sensors. MEG signal quality can be gauged by comparing single-shot images extracted from average or even
raw MEG signal and single-shot fMRI images (Fig. 1).
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Fig. 1. Comparison of single-shot images of magnetoencephalography (MEG) and fMRI. A, The top two rows
show magnetic field tomography (MFT) solutions from
average MEG data elicited by the onset of a checkerboard pattern that covered the lower left part of the visual
field. The green traces show the outline of the calcarine
sulcus. The pink area shows the modulus (absolute magnitude), and the thin yellow arrow shows the direction of
the peak current density for tomographic (MFT) solutions
(see Box 3) derived from the instantaneous average MEG
signal (66 milliseconds after stimulus onset) for three
runs. The modern arrow corresponds to the equivalent
current dipoles (ECD) solution when it is within a centimeter of the displayed sMRI slice. Although the MFT
solution is remarkably stable, the ECD estimate varies
considerably because other labile activity from other
areas contributes as much as V1 to the MEG signal. B, In
contrast, the reconstruction from a single-shot fMRI
acquisition is dominated by contributions from blood
vessels and other artifacts such that signatures of neuronal activity can be seen only after statistical manipulation of many such single-shot acquisitions. C, The MFT
reconstructions derived from single-shot time slices of
single trials. The activity in V1 can be clearly identified in
these real-time single-shot MFT solutions.
The latter parts of the review summarize recent results and
the implications of the fascinating view they reveal of the
dynamic organization of brain function.
The Dynamic Range of the MEG Signal
The MEG signal is minute, but the exquisite sensitivity of
superconducting quantum interference devices (SQUIDs)
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combined with the noise elimination hardware and software tools disentangles the signal generated by the brain.
The clean signal has sufficiently good dynamic range to
allow a tomographic reconstruction from a snapshot of
data. Such a reconstruction is demonstrated in Figure 1
for the activity in the primary visual cortex. The displayed
results show that the MFT reconstructions represent an
absolute measure of the instantaneous current density
vector. The image is informative in a quantitative way
with no need for reference to some baseline activation. In
contrast, the equivalent snapshot of fMRI data (representing the accumulation of seconds of activity) cannot be
interpreted on its own. Many such images must be combined before correlates of neuronal activity are identified.
Two related points are demonstrated by the next example
(Fig. 3). First, the huge dynamic range of the MEG signal
is maintained over long times, more than 4 minutes in the
case of Figure 3. The raw signal is free of noise contamination from the environment, except from narrow-band,
50-Hz noise from the power line. During the 4 minutes,
the subject goes through three repetitions of a sequence of
three eye states: closed, open (no fixation), and fixation on
a target. The second-order blind identification (SOBI)
algorithm (Tang 2002) is applied to the signal, producing
151 independent components (ICs). The signal for each
IC (or for a set of ICs) was projected back to the space of
MEG sensors, and the corresponding source was imaged
using MFT. Individual ICs or combinations of ICs corresponding to artifacts were identified from the correlation
of their time course with that of auxiliary channels (e.g.,
electrooculogram, electrocardiogram, etc.), their frequency spectra, or their localization signatures. The figure
shows that SOBI gathers the strong noise contributions
into a few ICs that can be studied separately or simply
eliminated from the signal. Although the raw signal generated by brain activity is one to two orders of magnitude
weaker, it is recovered with high fidelity and is still a couple of orders of magnitude stronger than the noise level of
the hardware (less than 10 fT). The second point is made
by the last row of the figure. The clean, low-pass–filtered
MEG signal is computed in two ways and compared with
the raw low-pass MEG signal (blue trace). The black trace
is the signal, first processed and cleaned by SOBI and
then low-pass filtered. The red trace is the signal after initially low-pass filtering the data and then running SOBI
and removing unwanted components. The order clearly
makes a difference, and this is because low-pass filtering
spreads the data in time and thus makes it impossible for
SOBI to bunch them together in just a few ICs. A similar
problem arises with heavy filtering and averaging of the
MEG signal before source localization. In this case, small
transient activations are eliminated, whereas other slower
components are spread in time.
Neural Sources: MFT Images and Signatures
in the Raw Data
We next consider MFT reconstructions for electrical stimulation of the right median nerve. This stimulation produces one of the most stereotypical MEG signals, the
M20 response. The M20 is generated by activity in the
Magnetoencephalography in Neuroscience
Box 2. Magnetoencephalography Recordings and Signal Processing
The strength of the MEG signal is minute. The earth’s magnetic field is about a billion times as strong, and even the
usual urban environment at frequency ranges that overlap the ones of interest in MEG is still many orders of magnitude stronger than the strongest MEG signal from a normal human brain. A prerequisite for useful MEG measurements
is therefore the availability of sensors that can detect the weak magnetic fields generated by the brain and methods
that can exclude the large ambient fields, separating the signal of interest from any interfering signals from the environment as well as irrelevant signals from the subject’s own body. Figure 2 provides examples of the hardware and
imaging tools that must be combined in an MEG experiment.
The basic MEG measurement relies on the detection of the electrical current in a small loop of wire, typically
about 1 cm across, induced by the change in the magnetic field component perpendicular to the loop surface.
Measurement of the induced current determines the value of the change in the magnetic field. Usually a set of coils
is used, arranged as a gradiometer to emphasize nearby signals from the brain at the expense of distant sources.
The detection of the minute current generated by the magnetic field changes in the brain is measured by coupling
the coil or gradiometer to an extremely sensitive superconducting quantum interference device (SQUID). As the
name implies, SQUIDs rely on superconductivity for their exquisite sensitivity, and together with their sensing coils,
they must be kept at extremely low temperatures, just a few degrees above absolute zero. To achieve this, sensing coils and SQUIDs are kept in a thermos-like container called a dewar, which is filled with liquid helium. In modern systems, the bottom of the dewar is shaped into a helmet with well more than 100 (nowadays a few hundred)
sensing coils evenly distributed on its inner surface. Just a few centimeters away, on the other side of the insulating layer, at normal room temperature, the subject can safely place his or her head inside the helmet. Each sensing coil samples the local magnetic field, and the full set of sensing coils can be scanned a few thousand times per
second. Each scan produces an independent measurement of the instantaneous magnetic field just outside the
head.
The second requirement (that of separating the signal of interest from ambient and other interfering signals) is
achieved by a combination of passive shielding, use of gradiometer design either in hardware or in software, and/or
by using additional reference channels. A figure-eight gradiometer is effective at eliminating noise and has been
used in some MEG systems (Makela and others 1993). However, the same effect can be obtained with software by
using a mathematical transformation of the signal of conventional sensors (Ioannides 1987), which can also be used
as a general noise elimination algorithm. Other signal processing techniques (e.g., independent component analysis) coupled to the use of information from auxiliary channels such as the electrooculogram and electrocardiogram
effectively eliminate biological and other artifacts.
Fig. 2. A, The magnetoencephalography hardware viewed from just outside the shielded room. B, A subject with
his head in position inside the helmet-like base of the dewar. C, The relative position of the brain and sensors, showing the projections of the sensor close to the superimposed axial sMRI slice. The asterisks show the position of the
center of the coil closest to the head, and the line shows the symmetry axis of the coil, which coincides with the
symmetry axis of the first-order gradiometer (line joining the centers of the two coils). The red and green colors denote
sensors on the near or far side of the displayed sMRI slice. D, The error in co-registration before and after correction
for distortion of soft tissue (see text). The most noticeable difference is around the cheeks, and the error is probably
due to padding during the sMRI acquisition. E, The position during each run is measured by activating head
localization coils placed near the nasion and left and right preauricular points. The electrodes for recording the
horizontal and vertical (over only one eye) electrooculogram are also seen in the image.
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Fig. 3. Second-order blind identification (SOBI) analysis of a 4-minute, 20-second–long record of magnetoencephalography (MEG) data sampled at 2084 Hz (more than 500,000 time slices). Raw MEG (MLF12, MRF12, and MRT32) and/or
auxiliary channels (electrooculogram [EOG], electrocardiogram [ECG]) are displayed with the corresponding independent
components activation curves (ICs) for (A) eye movements and (B) heart activity. C, The raw MEG signal for one MEG
channel is displayed in the time and frequency domains with the corresponding MEG signal for the same channel after
back-projection of the 3 ICs with the strong power line signal. D, The raw MEG signal with two versions of a 4-Hz
low-pass-filtered clean signal, differing only in the order of SOBI and low-pass filtering operations. MFT = magnetic field
tomography.
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Magnetoencephalography in Neuroscience
Box 3. Forward and Inverse Problem, Generator Models, and Magnetic Field Tomography
The determination of the electroencephalography (EEG) and magnetoencephalography (MEG) signal from the
knowledge of the sources, the electrical properties of their biological environment, and the configuration of the
measuring devices is known as the forward problem. The estimation of generator strength, location, and time course
from the EEG and MEG signal is known as the inverse problem. The laws of electromagnetism define how the forward and inverse problems should be tackled and what assumptions about the generators are justified a priori.
The forward problem is linear and has a unique solution. In other words, the electric and magnetic field generated by any combination of instantaneous current elements is uniquely defined for each current element, and the
total is simply the sum of individual contributions, one from each element. In the case of continuous primary current density, the instantaneous electric and magnetic field can be computed by integrating the contributions from
each small-volume element in the source space. The source space for MEG includes only regions in which gray
and possibly white matter exist; any intervening regions and boundaries are not part of the source space as long
as they do not generate primary currents.
In contrast to the forward problem, the inverse problem has no unique solution (von Helmholtz 1853), but this is
much less of a problem in practice than what appears from the dry mathematical statement of the inverse problem. A unique solution to the inverse problem can be obtained as a probabilistic estimate under suitable constraints. Until recently, the most popular constraint reduced the generators to one or more point-like sources, or
current dipoles. These point-like sources are interpreted as representative of their neighborhood and are referred
to as equivalent current dipoles. Another popular constraint assumes that the continuous current density can be
written as a linear sum of functions, each defining the sensitivity profile, or lead field, of the sensors. The lead fields
decay fast away from each sensor, so a compensating weight function usually is used to ameliorate biases toward
superficial sources. These methods lead to a solution with smallest length and are known as minimum norm (MN)
or weighted minimum norm (wMN) solutions. Because the wMN leads to a linear system of equations, standard
pseudoinverse techniques can be used to define the inverse operator that can then be applied directly to the data.
Using a linear inverse solution makes the computations simpler in other ways too. Linear noise elimination techniques can be superimposed so that linear signal–processing operations (e.g., using the covariance matrix to
account for noise) can be applied at the level of the signal. Unfortunately, close examination of the mathematical
properties of the lead fields shows that neither current dipoles nor linear solutions are justified a priori. The key
point is rather subtle: There is no justification for expressing the full primary current density vector as a weighted
sum of lead fields. The laws of electromagnetism allow only the direction of the primary current density to be represented by a weighted linear sum of lead fields, and this leads inevitably to a nonlinear relationship between the
measurements and the distribution of generators. This conclusion was reached on the basis of simulation studies
leading to the standard form of magnetic field tomography (MFT; Ioannides and others 1990). A mathematical
analysis of lead fields showed that the expansion of the direction of the unknown current density as a weighted
sum of lead fields, as used by standard MFT, is the optimal (nonlinear) relationship between generators and signals for yielding tomographic description of the generators (Taylor and others 1999). Allowing the weights to
depend on some general power of the modulus of the unknown current density leads to a generalized version of
MFT, which describes most of the other popular distributed source algorithms (Ioannides and Taylor 1999; Taylor
and others 1999). The use of a priori weights was introduced in the first version of MFT, and their possible use was
noted for introducing constraints such as restricting the source space to the cortical surface or biasing the solutions according to results from other methods (Ioannides and others 1990). It has not been used with MFT because
the reconstructions are in general accurate enough without this constraint. It is then possible to compare the MFT
solutions with the anatomy as a direct test of the localization accuracy.
contralateral primary somatosensory cortex (S1), peaking
about 22 milliseconds after stimulus onset. The columns
in Figure 4 show, from left to right, the butterfly display
of the MEG signal time course, the signal topography at
the M20 peak, the MFT localization for the M20, and two
region of interest (ROI) activation curves. The first activation curve is obtained from the MFT solutions for the S1
ROI. The second activation is obtained by a data-driven
approach using the main feature (negative-positive dipolar pattern seen as blue-red patches on each map) of the
M20 topography. The average of the five channels around
the negative peak is subtracted from the average of the
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five channels around the positive peak, producing a virtual
channel (VC) for the S1 activation. In summary, the
columns of Figure 4 show in turn the raw MEG signal, the
signal topography, MFT solutions, and their activation
curves together with a VC. These results are shown in rows
from bottom to top, for two single trials, for two average
runs, and for the grand average across four subjects. Two
points are worth noting: First, it is evident that the virtual
channel represents the S1 activation well, as would be
expected from a superficial and dominant source. Second,
both MFT and the virtual channel provide a reasonable
representation of the time course shape, despite the
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Box 4. Post–Magnetic Field Tomography and Exploratory Data Analysis
Magnetic field tomography (MFT) analysis generates independent tomographic estimates of the current density
vector for each snapshot of data. Because each snapshot is obtained independently, the MFT solutions and
quantities derived from them can be analyzed further using statistics. Specifically, statistical parametric mapping
(SPM) can be performed just like in fMRI. Different active conditions can be compared to each other or each one
with its baseline (taken as the control condition), or a variable of the active condition can be varied parametrically. The crucial difference, however, is that statistical parametric maps based on MFT reconstructions from
magnetoencephalography (MEG) data can be obtained for latency windows only a few milliseconds long. Sliding
this window in time can therefore describe changes in activity as a function of time.
For each time slice and condition in the experiment, the activity within a region of interest (ROI) can be summarized by computing the integral over the ROI volume of either the modulus of the current density vector or its
projection along some direction. The activation time course for an ROI can then be obtained by simply forming
the time series for successive activations. Time series can be analyzed by a variety of techniques to determine
frequency spectra, average measures of power or phase, and signal-to-noise ratio (SNR; Laskaris and Ioannides
2001; Ioannides, Kostopoulos, and others 2002). The availability of simultaneous regional activations from different areas allows the computation of measures of linkage between those areas, for example, by computing the
correlation or a nonlinear alternative, mutual information, between time-shifted segments of these areas’ activations (Ioannides, Liu, Kwapien, and others 2000).
Modern data-mining techniques provide powerful ways of analyzing sequences of MFT solutions, ROI activation time series, or simply linear combinations of MEG channels (Laskaris and others 2004). Each time point in
the sequence can be represented as a dimension so that each regional response (in the predefined latency range)
casts a point image in a multidimensional space. Representative prototypes and selective averages can be computed in a reduced feature space, providing a data-driven, principled summary of the variation in the data
(Laskaris and Ioannides 2001). Three potentially significant theoretical developments of these ideas have been
achieved recently (Laskaris and Ioannides 2002). First, by embedding time-delay images of responses into the
reduced feature space, pattern analysis and nonlinear dynamical methods were combined in a common analysis framework. Second, by tracing the evolution of controlled cases (e.g., by parametrically varying the strength
of a stimulus), the reduced space could be “sign posted,” leading to what were termed semantic geodesic maps.
Finally, by quantifying the similarity of the trajectories in different spaces—each representing responses from a
given brain area—powerful nonlinear measures of the coupling between brain regions could be computed.
We introduced examples of powerful generalizations of these ideas in the main text to describe collective brain
excitations. The responses from each area are used to define a dimension in a generalized space. Using data samples from either the prestimulus period or some other control condition, a global space is defined and then reduced
to fewer dimensions by standard multidimensional scaling techniques. Embedding all responses (latencies, trials,
conditions, etc.) in the feature space defines a trajectory for the evolution of activity for each condition that can be
visualized in a parsimonious way. Representative prototypes and selective averages can be computed in the
reduced feature space, providing a data-driven, principled summary of the similarities and differences in regional
responses within and across conditions, just as was done in single time series data earlier (Laskaris and Ioannides
2001). Because, by construction, the origin of the global space is the most representative point for the baseline (or
control condition), the distance of the corresponding point on the trajectory from the origin, r0(t), provides an objective measure of the collective excitation. Similarly, Dij(t), the distance between coincident or same-latency points in
two trajectories, is a measure of the dissimilarity of the collective responses for conditions i and j at that latency.
These measures can be averaged across subjects and/or grouped according to condition similarity, providing robust
but flexible time-dependent summaries of collective excitations and how they vary across conditions.
Most events in the brain are dictated more by endogenous influences than by exogenous stimuli. The real-time
MFT solutions provide equal access to events triggered by internal or external precursors. We developed a family
of algorithms for analyzing endogenously generated organization (EGO). EGO analysis has been particularly
successful recently in identifying and studying very transient and reproducible events in the real-time MFT solutions, what we called MEG spikes. The concept of spike-triggered averaging was adapted for the EGO analysis of
MEG spikes. Toward the end of the review, we will discuss the first demonstration of recovery of the underlying
state-dependent rhythms and activity levels by MEG-spike-triggered averaging.
approximate nature of the signal averaging. Although the
shape is preserved, amplitude is reduced, especially for
the grand average across subjects.
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The same analysis is repeated in Figure 5, but this time
highlighting a relatively deep generator at the base of the
ventral occipital cortex. For this analysis, the MEG data
Magnetoencephalography in Neuroscience
Fig. 4. The first column shows a butterfly plot of the magnetoencephalography (MEG) signal elicited by median nerve
stimulation of the right hand. The vertical lines mark the time of stimulus onset (blue) and the M20 peak (22 milliseconds;
red). The second column shows the MEG signal topography at the M20 peak. The third column shows the magnetic field
tomography (MFT) solution for the M20 at two axial slices on the left and their zoomed images on the right. The last column shows the virtual channel (VC) output together with the MFT activation for a region of interest (ROI) defined functionally from the MFT solutions of the M20. For display purposes, each VC and activation curve is normalized so that the
maximum is one; the relative magnitude across figures is printed separately for VCs and activation curves. The first four
rows from the bottom show results for one subject, the two bottom rows for two single trials, and the two rows above for
the average of two runs. The top row shows the grand average results after averaging two runs from four subjects. The VC
is defined from the M20 topography of the first average run (map marked with an asterisk on the third row, second column). The same channels are used for all VC computations, with no correction for differences in head location and shape
even when the MEG signal from different runs and subjects is averaged. The MEG channels used for the VC definition are
highlighted inside the solid and dashed ellipses marking the negative and positive extrema of the dipolar pattern.
were elicited by face stimuli presented to the lower left
quadrant of the visual field. We focus on the activation
from the right fusiform gyrus (FG). This generator is
rather deep and not so well covered by the MEG sensor
array, so there is no a priori reason for its activity to produce a clear dipolar pattern in the MEG signal topography.
Even if a dipolar pattern is produced, it could be easily
masked by stronger contributions to the MEG signal from
more superficial sources. We therefore defined, in addition
to the virtual channel derived from the topography of the
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average MEG signal of one run, a second virtual channel
derived from the properties of the MFT solution.
Specifically, we computed the signal topography for a
current dipole located at the center of the fusiform activation of the MFT solution and with current dipole moment
along the local current density direction. We used this signal topography to define the second virtual channel.
Remarkably, even in this case, the agreement between
MFT and the virtual channels is good, especially for
latencies between 130 and 200 milliseconds, when face
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Fig. 5. Summary results for brain activity elicited by a face stimulus presented in the lower left quadrant of the visual field.
The arrangement of the figure is as in Figure 4, with the two bottom rows showing 2 single trials and the next two averages from two runs, each with 30 trials. The top row shows the grand average of 21 runs: 3 runs for each of seven subjects. In this case, the red vertical line shows the peak latency for the M170 (184 millisecond) component, frequently
reported to show selectivity for face stimuli. The magnetic field tomography (MFT) solutions are shown in an axial and coronal slice through the peak activity in the FG. For this case, two virtual channels (VCs) are used. The first is defined from the
magnetoencephalography (MEG) map topography of the first average signal. The second VC is defined from a computergenerated signal, with the topography shown at the top of the third column. This signal is generated by computing the forward problem with a current dipole source placed at the peak of the MFT fusiform activation for the M170 and in the same
direction as the current density vector. The same channels are used for all VC computations, with no correction for differences in head location and shape even when the MEG signal from different runs and subjects is averaged.
stimuli elicit a distinctive MEG signal (Sams and others
1997). The response is evident for both the MFT solution
and the virtual sensors, even after averaging the
MEG signal of all runs for seven subjects. Compared
to the somatosensory stimulation presented before, the
MFT solutions elicited by faces are less stable in terms of
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localization, and they become weaker faster with averaging across trials and subjects. The MFT solutions after
adjustment for changes in head position are nevertheless
remarkably stable for averages of even a few trials (data
not shown; Ioannides 2001). Note also that the single-trial
maps and activations are stronger and much more variable
Magnetoencephalography in Neuroscience
than what the average would imply. This variability is
usually interpreted as physiological noise, but the figure
indicates otherwise. First, the fusiform activations in single trials are identified intermittently over wider periods
than what the averages would suggest, and they are more
transient than the average. The fusiform activation in the
first single trial (bottom row) even has opposite polarity
from the average at the time of the M170. The conclusion
from the results of Figures 4 and 5 is that averaging across
trials, runs, and subjects can reveal stereotypical
responses, and these can be obtained surprisingly well
even with simple linear combinations of signals. These
stereotypical responses represent the real-time activations
well in only a few cases, typically for early responses in
primary sensory areas evoked by strong stimuli. For late
components, the stereotypical responses are poor descriptions of the underlying real-time dynamics.
The Localization Accuracy of MEG
The simplest way to test the accuracy of a method is with
phantom or computer-generated data. Such tests have
repeatedly demonstrated accuracy of a few millimeters for
MEG, including tests with a realistic human skull phantom (Leahy and others 1998). In general, however, there
is no guarantee that the assumed source generator model
used in a phantom or computer-generated test adequately
describes real events in the brain. The equivalent current
dipoles (ECD) model, for example, appears reasonable
in some applications but blatantly fails to fit data, even
those of Figure 1, where the activity of interest (in V1)
is focal. The influence of other sources (such as V5) can
shift the solution for a single ECD by centimeters
(Tzelepi and others 2001). The obvious test is to compare MEG localization with the results of a technique
that can localize accurately, ideally by simultaneously
recording the two signals. Unfortunately, the only available technique for accurate and noninvasive localization
of brain function is fMRI. It is obviously not possible to
simultaneously record fMRI and MEG, so the next best
option is to record the same responses to identical stimuli in the same subjects by both methods and compare
the results. Many such studies have been performed, but
until recently, the results were inconclusive. It is possible to localize real brain activity with fMRI with submillimeter accuracy (Cheng and others 2001). This,
however, can be achieved only with high fields, after
contributions from large vessels are eliminated, and in
reasonable times over a limited region along the acquisition slices. In the case of MEG, the localization accuracy
is compromised by the choice of model and the preprocessing of the stimuli. After all these choices are made,
it is still unclear what the appropriate measure of MEG
activity is to compare with the fMRI activations. The
available choices include instantaneous measures of
activity and activity filtered within a frequency band
and/or integrated over a latency range. We used statistical parametric maps comparing short 6-millisecond segments of stimulation with similar segments of the
control condition (subject fixating on a cross with the
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same luminosity as in the active condition but with no
stimuli delivered). The active window was stepped by
one time slice (1.6 milliseconds) for the next statistical
parametric mapping (SPM) comparison.
For fMRI, we employed hardware (Varian Unity Inova
4-T whole-body MRI system) and an analysis that had
already demonstrated submillimeter localization accuracy
(Cheng and others 2001) to serve as a gold standard for
the comparison. For stimuli, we used circular flickering
checkerboard patterns covering part of one of the two
lower visual field quadrants on a homogeneous gray background. The visual field quadrant to be stimulated was
selected individually for each of the four subjects used in
the experiment so that the available visual field (excluding
the foveal region up to about 3 degrees) corresponded to
a relatively smooth portion of the dorsal V1 for that subject. Two preliminary fMRI experiments were performed
for each subject to determine the optimal location and orientation of the fMRI acquisition slices for the main experiment (Cheng and others 2001). The results from the two
techniques were compared by simply superimposing the
contours of statistically significant activity elicited by the
stimuli. With fMRI, clear activations were observed, and
after eliminating contributions from large vessels, the
expected V1 activation was delineated from the activations of other areas, including V2 (whose boundaries had
already been determined by the preliminary experiments).
For MEG, a significant increase in activity was identified
in two early waves, one between 40 and 50 milliseconds
and the other beginning between 50 and 60 milliseconds
and leading to the dominant M70 peak. The beginning of
the first wave was localized in V1 in three of the four subjects, and the beginning of the second wave was localized
in all four subjects. In each case, the brief V1 activation
was followed within a few milliseconds by widely distributed activation that included, in addition to V2 activation, strong activations in V5 and the putative V6
homologue. The two early V1 activations were remarkably close to each other and to the fMRI V1 activation,
with a center-to-center distance of the corresponding
statistical parametric maps of about 3 mm (Moradi and
others 2003). The accuracy attained in this study was
comparable to the (sMRI)/MEG co-registration accuracy,
derived from the position of the sensors relative to the
head (i.e., the brain).
We have developed a new co-registration algorithm that
uses, in addition to the old magnetic stylus recordings,
data obtained from the reflection of a low-power infrared
laser. The analysis also corrects for distortions that often
arise because of differences in acquisition of sMRI and
MEG (Hironaga and Ioannides 2002). With the new
method, we can determine the position of the sensors
relative to a fixed space defined by the sMRI of the subject with an accuracy of about 1 mm. We repeated and
extended the MEG part of the experiment using stimuli
confined to parafoveal and peripheral locations in
each of the four quadrants of the visual field. Three new
subjects were used, and each subject was measured three
times on three separate days. Using the new co-registration
procedure and our standard MFT-SPM analysis, we
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533
demonstrated localization accuracy and reproducibility of
1 to 2 mm throughout V1 (Poghosyan and Ioannides,
unpublished data).
There are no localization accuracy tests for areas outside V1 with comparable sophistication, so the best that
can be done is to make inferences based on theoretical
arguments and the known anatomy and physiology.
From a theoretical point of view, localization within V1
is no easier than localization elsewhere on the cortical
mantle. MFT results are consistent with this expectation
for extrastriate cortex activation (Poghosyan and
Ioannides, unpublished data) and for activations elicited
by somatosensory and auditory stimuli and associated
with motor execution and inhibition. The accuracy is
also good for relatively deep areas on the border of the
MEG array such as the FG. In a number of MEG experiments with faces, we have identified activity at foci with
Talairach coordinates (Talairach and Tournoux 1988)
matching those reported for face-selective response by
fMRI studies. As already demonstrated (Fig. 5),
fusiform activity generates an MEG signal that has a signature that can be seen in the raw MEG maps, even in
single trials. We will discuss brain activations elicited by
face stimuli later in this review.
The MFT solutions have always shown a constant interplay between superficial and deep activity. Activations in
the amygdalae (Ioannides, Poghosyan, and others 2004)
and orbitofrontal cortex (Ioannides, Liu, Theofilou, and
others 2000) were identified when stimuli expected to
activate these areas were used. Recently, MFT studies of
saccadic eye movements demonstrated localization in the
brain stem and cerebellum consistent in location and timing with known anatomy and physiology (Ioannides,
Corsi-Cabrera, and others 2004; Ioannides and Fenwick
2005; Ioannides and others 2005).
The Timing of Early Visual Responses
MFT analysis of visual responses consistently identifies activations earlier than most MEG and electroencephalography (EEG) reports in the literature but is more
in agreement with what is known from animal physiology.
For example, in the localization studies for V1 described
above, we identify activity in V1 and the extrastriate cortex earlier than what is usually reported in EEG studies
(Di Russo and others 2002). In a different experiment,
we have identified arousal/attentional modulation of
V1 activity within 100 milliseconds by contrasting the
responses to identical stimuli viewed passively or in
a GO/NOGO task (Poghosyan and others 2005). The
earliest attentional modulation of V1 reported in other
electrophysiological studies was between 140 and 250
milliseconds (Noesselt and others 2002). It is important to
note that in our GO/NOGO experiment, an earlier activation was identified around 40 milliseconds in V1, which
was not modulated by attention (i.e., it was the same
in both the active GO/NOGO task and during passive viewing of identical visual stimuli). In fact, the earliest attentionrelated modulation was identified in the right inferior
parietal lobule (IPL). As demonstrated schematically in
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Fig. 6. A very fast right inferior parietal lobule (R-IPL)
activity is identified within 50 milliseconds of the onset of
a visual cue for a GO/NOGO task. The figure shows
schematically how the R-IPL activity could influence the
strong activity in V1 around 100 milliseconds. No R-IPL
activity is present when subjects view the same visual
stimuli passively.
Figure 6, our results agree with the prevailing view of topdown attentional control and support studies suggesting
involvement of the IPL (Corbetta and others 2000;
Kastner and Ungerleider 2000). Early top-down influence
was also seen in another recent study. In this case, faces
presented as stimuli in the upper visual field elicited activity 70 to 80 milliseconds after stimulus onset in the
FG, which in turn influenced activity in V1/V2 a few
milliseconds later (Liu and Ioannides 2006).
Our results agree in general with other studies for late
components—for example, the ones evoked by visual
stimuli around 100 milliseconds and later (Tzelepi and
others 2001; Liu and Ioannides 2006). The discrepancies
are either for early responses or early modulations, and
these are usually weaker components that can be easily
missed, depending on how the data are analyzed. First,
source models are often very different; we consistently
used MFT, allowing for activity throughout the brain.
Many EEG and MEG studies use single or multiple current dipoles. In the cases in which we used current
dipoles, we also failed to reliably identify the early activations, although a direct comparison is difficult because
we typically used fewer trials in each run. The post-MFT
operations such as the computation of statistical parametric maps provide high sensitivity and can easily identify
weak activations. If current dipoles or the statistical properties of the signal (e.g., the covariance matrix) have
already been used as part of the source reconstruction, no
additional statistical manipulation can be consistently
applied to the solutions. The failure to identify early activations may have to do with the way the data are processed
Magnetoencephalography in Neuroscience
Fig. 7. The occluded stimulus (small insert in the middle
of the figure) follows 300 milliseconds after the simple figure (top insert) also presented for 50 milliseconds. In the
prime condition (top row of top insert), the simple figure
was either a global, local, or mosaic interpretation of the
occluded figure. In the control condition (lower part of top
insert), the simple figure was a square or two small black
patches. Activation time courses for regions of interest
(ROIs) in left and right V1/V2, lateral occipital cortex, and
fusiform gyrus were computed from magnetic field
tomography solutions for responses elicited by the
occluded figure for separate runs for each condition. A
parsimonious interpretation of global brain excitation was
derived through multidimensional scaling of the computed time courses for the ROIs. The average global excitation for prime responses is weaker between 130 and
200 milliseconds than the responses to the control
(unprimed) identical stimuli.
before the source reconstruction analysis. It is our practice
to minimally alter the raw MEG signal and do so only if
there is a good reason. Specifically, we use either the full
bandwidth allowed by the Nyquist frequency of our sampling rate, or if we filter off-line, we usually use the lowpass filter at 200 Hz. In many EEG and MEG studies, the
data are low-pass filtered at less than 40 Hz. This operation
could easily eliminate short-lived early activations directly
or mask them by spreading the stronger and more sustained activations in time. Another very obvious reason for
the loss of early activations is the very conservative acceptance criterion used in many studies, of admitting activations that remain stable for at least 10 or 20 milliseconds.
If we had filtered our data at less than 40 Hz and/or required
brain activations to last for 10 milliseconds, we would have
missed the early V1 and fusiform activations and the attentional modulation of V1 within 100 milliseconds.
Visual Responses to Complex Stimuli:
Amodal Completion
We studied the process of amodal completion in a same/
different task in which test pairs were preceded by a
sequence of two figures (see Fig. 7). In the prime condition,
the first figure could be congruent to a global or local completion or a mosaic interpretation of an occluded part
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(behind a square) in the second figure. In the control (unrelated prime) condition, the first figure was either two
patches (instead of the occluded figure) or a square. We
analyzed the average MEG data for the responses to the
second, occluded figure, testing for differences depending
on what the preceding (first) stimulus was. We concluded
two separate analyses.
The first analysis emphasized the information at the
level of MEG signals (Plomp and others 2006). Sensors
with high consistent responses were identified, and the
latencies and amplitudes of peaks in the MEG signals
were compared for the different preceding stimuli. First,
slightly reduced latencies and increased amplitudes were
found for prime (local, global, or mosaic) compared
with control stimuli. Peak latencies occurred at 123 and
128 milliseconds, respectively, for prime and control
conditions. Second, within the prime conditions, the
mosaic interpretation gave rise to the largest amplitude
and the fastest response. The mean latencies for mosaic,
local, and global peaks were 119, 124, and 125 milliseconds, respectively. The results singled out the mosaic
interpretation as one that can rapidly emerge in visual
processing when context favors it.
The second analysis used MFT solutions and statistical parametric maps of the same data with ROIs in the
left and right V1/V2, lateral occipital cortex (LOC), and
FG (Liu and others 2006). In this analysis, we found significantly reduced activation in the right fusiform cortex
between 120 and 200 milliseconds after occluded figure
onset for mosaic, local, or global preceding stimuli as
compared with the preceding control figures. This was
interpreted as a neural correlate of priming by the preceding congruent figure, suggesting that in early stages
of perception, the right FG acts as a hub for different
occluded figure interpretations.
The results of the two studies stand independently but
are difficult to interpret within a unified framework. The
first analysis is independent of localization because the
same sensors are used for each subject, so the technique
pools processes in which either different areas are activated or the same set of areas is activated but at different
latencies in different subjects. The second analysis relied
strictly on localization. SPM results were pooled together
only if they were identified in the same areas and latencies
in all subjects. The latter analysis revealed, however,
significant spatiotemporal differences between local,
global, and mosaic interpretations within individual subjects, at the latencies identified in the first study, but without common trends. To carry the analysis further, we
adapted methods from time-series analysis (Laskaris and
Ioannides 2002) to describe collective excitations of many
brain areas in a unified framework (see Box 4). We used
the moduli of the current density vector (as computed by
MFT) in the previously defined six areas (one for V1/V2,
LOC, and FG in each hemisphere). The set of points corresponding to activity in the prestimulus period for all
conditions was used to explore this global 6-dimensional
space and to reduce it to a 4-dimensional feature space.
We computed the average across subjects of r0(t) and
Dij(t) for each condition (see Box 4 for the definition of
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535
these quantities). The Dij(t) identified differences between
the main prime conditions from 90 milliseconds, with
strongest peaks at about 100 and 120 milliseconds for
the difference between global and either local or mosaic
interpretations (data not shown). The time course of r0(t)
averaged across all runs, the 10 subjects, and condition
category (prime or control) showed clearly the priming
effect in the same latency range (120–200 milliseconds)
as in our second study (Fig. 7). The new framework
describes the global excitation associated with each previous stimulus by its corresponding r0(t) and quantifies the
dissimilarities between them by Dij(t). A parsimonious
explanation of the results is consistent with the idea that
the preceding stimulus sets up processing of the current
stimulus more efficiently in many areas and reduces the
load on the “hub”, the right FG, that coordinates processing during the period of the priming effect.
From Average to Real-Time Tomographic
Analysis of MEG Data
It was stated from the outset that the real power of MEG
lies in its ability to map activity throughout the brain in
real time, over a huge range of temporal scales. Events
that last few milliseconds can be tracked and related to
processes that last hours. Yet all examples discussed so far
used MFT solutions obtained from average MEG data.
We used MFT analysis of average data for two reasons.
First, the single-trial analysis is demanding on both computer resources and post-MFT analysis. Second, the spatiotemporal scales that open up are not accessible by any
other method, so before venturing into unknown terrain, it
is critical to establish that the methodology employed
reproduces the results that have been established by
nearly a century’s work in electrophysiology. The linearity of the forward problem provides a stringent test of how
well the single-trial MFT solutions represent real events in
the brain. The linearity of the forward problem trivially leads
MFT
to the following equation: <J (r,t; single trial)>single trials =
MFT
J (r,t; average), where <JMFT(r,t; single trial)>single trials
is the expectation value (average) of the current density
vector field of single-trial MFT solutions and JMFT(r,t;
average) is the current density vector field of the MFT of
the average MEG signal. Because MFT uses a nonlinear
algorithm, the result is not guaranteed a priori. How well
the equation holds is therefore a measure of how accurate
the MFT single-trial reconstruction is. In all our studies,
we found that this equation holds remarkably well
(Ioannides 2001). Linearity can also be used to check how
well the average describes events in single trials. The contribution of a generator to the average signal will be similar to the single trial activation if the generator activity is
reproducible in each single trial. For radial gradiometers
it is difficult to isolate the contribution of a given generator in the raw signal for one MEG channel because the
signal is affected by many generators. A consistent presence of the contribution of any one generator will be lost
amongst the many other contributions from generators
that are activated differently in each single trial. Virtual
channels, as defined and used earlier, offer an acceptable
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alternative because they can describe well the activity of a
focal generator while reducing others. Furthermore, comparison of the virtual channel and activation curves
derived from MFT solutions provide a further test of how
well the signal that survives the averaging process represents a focal activation. For strong evoked responses in
primary sensory areas, the average captures the structure
of events in single trials reasonably well (Fig. 4). Even
averaging across subjects naïvely appears to provide a
good approximation, albeit with much loss in amplitude.
The conclusion, however, is very different for weak stimuli or late components (Fig. 5). In this case, the waveform
captured by the average is a poor representation of the single-trial events. Nevertheless, a current dipole approximating the focal FG activity of the MFT solution (derived
from the average MEG data) produces an MEG topography that can be recognized in the raw MEG signal. What
is remarkable is how similar the virtual channels derived
either from the raw signal or from the current dipole
approximation to the MFT solution are to each other, and
to the activation time course for the FG derived from the
MFT solutions. There is clearly a tangible connection
between the MFT estimate of brain activity and patterns
in the raw signal, and these patterns can be approximately
extracted by simple methods even for single trials.
When we use the average MEG signals, we rarely use
hundreds of trials and often as few as five. There are two
reasons for this intermediate approach. First, we wish to
study subjects that are comfortable during the experiment
and in as natural an environment as possible. We do not
generally employ a bite bar or other head fixation methods. We use short runs instead so that head movement is
minimized. Second, using a large number of trials in the
average does not help with accurate localization because it
often increases what we term area cross-talk. As we will
discuss in more detail later, the high variability from trial
to trial is far from being random noise—it is the source of
information. If we average too many trials, we get a
smooth signal, but we lose detail in time (both within the
latency range of each trial and across trials) and space. We
lose accuracy in space because activations in nearby areas
(say in V1 and V2) follow each other in quick succession,
and this, coupled to the latency jitter, is bound to mix them
in the average. Therefore, we use averaging of few trials to
reduce the computations while maintaining some of the
variability. The post-MFT statistics will then sort out what
is noise and what is signal (e.g., which activations are consistently higher in active compared to baseline conditions).
More discussion about the advantages and disadvantages
of analysis of average and single-trial MEG data can be
found elsewhere (Ioannides 1994; Ioannides 2001).
Real-Time Analysis of Responses to
Simple Sensory Stimuli
We have studied the responses to simple stimuli in three
modalities: auditory (Liu and Ioannides 1996; Liu and
others 1998; Laskaris and Ioannides 2001), somatosensory (Ioannides, Kostopoulos, and others 2002), and
visual (Laskaris and others 2003). The results from these
Magnetoencephalography in Neuroscience
studies converge on the following conclusions. In each
modality, the early entry to the system is fast and often
associated with weak activations. The most prominent
early response in the auditory and visual signal (between
60 and 100 milliseconds) is associated with excitation of
a number of areas and not, as is often assumed, just the
primary sensory cortex. In fact, if the variance of the
signal is used directly to classify the responses, then
much of the signal appears to come from oscillations in
the α band, which precede and outlast the stimulus
(Laskaris and Ioannides 2001; Laskaris and others 2003).
In the case of the visual stimulus, the generators appear
to be in polymodal areas (Laskaris and others 2003). An
apt analogy is that of a Greek dance. The evoked activity
enters the cortical arena as a new dancer while the
orchestra in the polymodal areas plays on its α tune.
Every dancer (area) adjusts a little, and the new dancer
fits into the flow. This analogy also explains well why
averaging is often misleading. One must take into
account background activity—not just a static background as suggested by optical imaging (Arieli and others 1996) but rather a continuously varying one,
especially in the α band (Ioannides and others 1998). If
the “beat” is overlooked, then the view across single trials appears noisy, but in reality, the smooth average is a
mirage produced by a sandwich of histories (Liu and
Ioannides 1996). The average captures well the first entry
into the primary sensory areas, but this is often weak.
Later activations appear noisy, but if the single trial
responses from one brain area are clustered into groups
of similar responses, (e.g., by the methods described in
Box 4), then the network structure that links the brain
area in a larger network of areas becomes apparent as the
linked responses from different nodes in this network are
revealed (Ioannides, Kostopoulos, and others 2002).
The response in an area can be influenced by input
other than external stimuli, as recently demonstrated by
a tactile discrimination experiment (Liu and others
2003). In this study, multidimensional scaling techniques were used to classify the activation of the primary
sensory cortex (S1) during electrical stimulation of the
right-hand digits 2, 3, and 4. The subjects were required
to estimate the frequency of a comparison stimulus
(stim2: 21–29 Hz) delivered after a standard stimulus
(stim1: 21 Hz). The results are summarized in Figure 8.
During the first half of stim2, (50–250 milliseconds after
stim2 onset), the points in the feature space describing
the S1 activity clustered according to the frequency
of stim2, but in the second half of the stimulation
(800–1000 milliseconds after stim2 onset), the clustering was also influenced by the subjects’ intended
response. Thus, the S1 activity was influenced by the
decision, presumably made in other areas, despite the
conflict with the ongoing stimulation.
Study of Large-Scale Networks
Neuroimaging data and invasive electrophysiology have
consistently shown that the brain activity for specialized
operations is segregated into distinct regions. The aim
of much contemporary research is to understand how a
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Fig. 8. Characterization of primary sensory cortex (S1)
response while a stimulus (stim2) is applied to fingers (2,
3, and 4) of the right hand (see text for details). The S1
activity following stim2 is displayed on the left for the
beginning of stimulation (50–250 milliseconds after stim2
onset) and on the right at the end of the stim2 period
(800–1000 milliseconds). In each case, the minimal spanning tree (MST) representation of the single-trial time
series is displayed (Laskaris and Ioannides 2001). The
MST shows the single-trial set as an ordered list, with
each node (symbol) representing the time series of one
single trial over a predefined latency range. An edge of
the MST connects nearby nodes, and it is an objective
representation of the similarity of the two single-trial time
courses associated with the nodes on either side of
the edge. The MST map in the left panel shows that at
the beginning of stimulation, the single-trial distribution is
concordant with the stimulus input. The MST map on the
right shows that by the end of stimulation, both stimulus
input and the subject’s perception influence the S1
activity. This is the first evidence from neuroimaging of
perception-related correlates in the activity of human S1.
complex task is first divided among segregated areas and
then how the output is integrated. We have applied tomographic analysis of MEG signals to this problem, focusing
much of our effort on two tasks that involved specialized
areas widely distributed in the brain, namely, saccadic eye
movements and the recognition of objects, particularly
faces and especially the emotional expression of faces.
We first summarize results for facial emotion processing
in normal and schizophrenic subjects.
Face and Facial Emotion Recognition
Electrophysiological recordings in animals (Desimone
and others 1984; Tanaka 1993) and human patients
(Allison and others 1994) have demonstrated that neurons in the ventral temporal cortex respond preferentially to classes of objects. Recent fMRI studies have
confirmed the object sensitivity of the ventral temporal
cortex and marked responsiveness to faces (Puce and
others 1995). In most subjects, fMRI statistical parametric maps contrasting faces and other objects highlight a
well-circumscribed region called the face fusiform
area (FFA; Kanwisher and others 1997). In the past
few years, many EEG and MEG studies have identified
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activations between 100 and 200 milliseconds that show
some specificity to faces, and a negative component
N170 and its MEG analogue M170 have been widely
accepted as indices of early face processing. In some
studies, the signal from individual MEG sensors was
analyzed, focusing on sensors of interest (SOIs) that
showed face sensitivity (Liu and others 2002). In a
recent study, multidimensional scaling analysis was
applied to a number of sensitive sensors (Furey and
others 2006). In many recent EEG and MEG studies, the
N170, M170, and SOI peaks were associated with activity in the fusiform. As we will see below, although the
fusiform is definitely activated between 100 and 200
milliseconds, particularly with face stimuli, the MEG
(and by inference the EEG) reflect a network activity with
the fusiform one of the main hubs for some of the time.
Primates, and particularly humans, have evolved the
ability to recognize the facial expression of emotion on
another’s face because this is one of the skills most relevant to human communication and social cognition.
Studies with brain-lesioned patients, using EEG, MEG,
and particularly fMRI, have associated the amygdalae,
inferior frontal cortex, middle temporal cortex, FG,
and anterior cingulate with the neural network activated
during recognition of facial expressions across different
emotions (Sprengelmeyer and others 1998). Schizophrenic
patients show deficits in the recognition of facial expressions of emotion (Morrison and others 1988). During a
facial expression recognition task, schizophrenic subjects show reduced EEG activity in frontal electrodes
(Streit, Wolwer, and others 2001). Likewise, PET and
fMRI activations have consistently identified reduced
activity in brain areas known to be involved in facial
expression recognition (Andreasen and others 1997).
MFT analysis was used to study brain activity and connectivity during face and facial affect recognition tasks in
normal and schizophrenic subjects. The data were collected with two different MEG machines at the Juelich
Research Center, Germany. In parallel studies, we mapped
activity using either the average MEG signal, to connect
with the large number of earlier studies, or real-time data,
to gain a deeper understanding. Face stimuli evoked the
expected activations in visual and fusiform areas. Stronger
activations were found for emotional faces compared to
either neutral or blurred faces in several cortical and subcortical brain regions including the orbitofrontal cortex
and amygdalae (Streit and others 2003). The early studies
prepared the ground for the main study of the series. In
this study, the full-head BTi Magness hardware was used
to record the MEG signal from 15 partly remitted schizophrenic inpatients and 12 healthy controls. A nearly identical experimental protocol to that of our earlier studies
was used. The analysis of average MEG signals elicited
from the brain in response to facial emotions identified
weaker activations within a spatiotemporally well-defined
network of brain regions in schizophrenic patients (Streit,
Ioannides, and others 2001). These results were consistent
with earlier electrophysiological, fMRI, and PET studies
of patients and controls.
The single-trial analysis of the sets of MEG data produced new insights for both normal processing and the
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way it fails in pathology. The earliest of two studies with
normal subjects used data from the BTi twin Magnes system (2 × 37 channels), which offered only partial coverage of the two hemispheres. In the next study, real-time
ROI activity was analyzed (Liu and others 1999). It
showed that all the object stimuli excited the areas studied, so any face specificity was a relative increase in the
fusiform rather than a qualitative change in activity.
Amygdala activation was found to vary according to emotion. Exactly the same experiment was repeated soon after
the first BTi whole-head helmet system (148 channels)
became available, initially with just one normal subject.
The single-trial data from this experiment were analyzed
extensively using post-MFT SPM and mutual information
(MI) analysis (Ioannides, Liu, Kwapien, and others 2000;
Ioannides 2001). Statistical parametric maps were computed, contrasting the responses elicited by face stimuli
with the responses elicited by the other five object categories. This is the same contrast as the one used in the
fMRI study that identified the FFA originally (Kanwisher
and others 1997). This post-MFT SPM analysis identified
a face-selective increase in activity in the fusiform
between 130 and 150 milliseconds after stimulus onset.
Lateral fusiform activity started as an activity in the anterior hippocampus, and amygdala activity diminished.
This lateral fusiform activity preceded and outlasted the
main FFA activity by about 10 milliseconds. The singletrial activations from the posterior calcarine cortex
(referred to here as V1/V2), the left and right FFA, and the
left and right amygdalae were used for the MI analysis.
We computed MI between each pair of areas using all
single-trial time series and contrasted the resulting MI
maps for different tasks (object or emotion recognition)
and for categories within each task. The MI analysis
showed that through feed-forward and feedback linkages,
the computational load associated with the task of identifying objects and emotions is spread across both space
(different ROIs and hemispheres) and time (different
latencies and delays in couplings between areas). Within
200 milliseconds, processing for different objects shows
different connectivity structure, beginning with right hemisphere V1/V2 and FG coupling. Processing of different
emotions shows different connectivity structure in the right
hemisphere FG and amygdala coupling, particularly at
latencies after 200 milliseconds (Ioannides, Liu, Kwapien,
and others 2000). In the main study, only right-handed
subjects were included, and the data from all were used for
the MFT analysis of the average data (already discussed
above). The data from seven normal and five schizophrenic subjects survived the tight selection criteria for the
single-trial analysis.
The single-trial analysis showed differences between
males and females, so the analysis was restricted to only
males: four normal and four schizophrenic subjects
(Ioannides, Poghosyan, and others 2004). The only new
brain area identified from the single-trial SPM that
showed a different pattern of activation between healthy
and schizophrenic individuals when responses to emotional and neutral faces were contrasted was the right
amygdala Specifically, the statistical parametric maps
showed a highly significant increase in right amygdala
Magnetoencephalography in Neuroscience
activity of patients for emotional faces during the first
100 ms after stimulus onset (Ioannides, Poghosyan, and
others 2004). More detailed inspection of the data
showed that the presentation of emotional faces in the
emotional face recognition task induced an irregular,
persistent, and sustained change in the right amygdala of
patients but not in control subjects. The detailed singletrial analysis showed that the reduced activations identified in the average MEG signal of schizophrenic
subjects was due to high variability across single trials
rather than reduced activity in each single trial. Finally,
in control subjects, measures of linkage demonstrated
distinct stages of processing of emotional faces within a
well-defined network of brain regions. Activity in each
node of the network, confined to 30- to 40-millisecond
latency windows, is linked to earlier and later activations
of other nodes in part of the overall network. In schizophrenic subjects, no such well-defined stages of processing were observed. Instead, the activations, although
strong, were poorly linked to each other, manifesting
only isolated links between pairs of areas (Ioannides,
Poghosyan, and others 2004). The most striking
result was a weak and early modulation of the right
amygdala between 30 and 50 milliseconds for the
emotion recognition runs of all controls, which led to
a modulation of V1 activity 40 milliseconds later
(Ioannides, Poghosyan, and others 2002). No such early
modulation and interaction between amygdala and V1
was identified in schizophrenic subjects. Early modulation of the amygdala has been demonstrated for fear
conditioning with auditory stimuli (Quirk and others
1995), and together with psychophysical and neuroimaging data, they suggest that a fast low road to the
amygdala might exist in the visual system. This modulation can influence the representation of emotional
events, especially when related to threat (Vuilleumier
2005). Our results are consistent with such a circuit as
well as with the timing of normal extraction of emotional content from visual information. They show that
the process is very fast, enough to influence early visual
processing and percept formation within 100 milliseconds of stimulus onset and thereby influence the following stages of processing. The overactive amygdalae in
schizophrenic patients and the absence of the early modulation suggest that a failure of this mechanism may
play an important role in schizophrenia.
How Fast Thinking Really Is
Recent studies at the cellular level have provided evidence for synchronous synaptic input to dendrites and
volleys of near-simultaneous action potentials. MEG
studies have also identified high-frequency oscillations
well above 200 Hz after averaging large numbers of
somatosensory-evoked responses (Haueisen and others
2001). Inspection of real-time MFT solutions shows transient events, lasting just a few milliseconds, which we
call MEG spikes. These events are ubiquitous in the brain
and are easy to explain away as noise; neither their power
nor their frequency changes much with task demands or
Volume 12, Number 6, 2006
stimulus properties. They are not, however, likely to be
noise because they often appear in quick succession in
task-relevant areas far from each other on the cortical
mantle (e.g., left and right frontal eye fields in saccadic
tasks). We designed an experiment to investigate MEG
spikes and their role in planning, preparation, and execution or inhibition of saccades (Ioannides and others
2005). The saccadic task was specified by either a single
visual cue or a sequence of cues, each providing partial
information. We used the highest sampling rate (2 kHz)
available with our hardware. Tomographic analysis
of real-time MEG data identified MEG spikes that were
widely distributed across the cortex, cerebellum, and
brain stem during cue presentations and saccades. As
expected, stimulus properties and task demands did not
change either their frequency or strength. More detailed
analysis, however, showed that stimulus properties and
task demands influenced the distribution of directions of
the MEG-spike current-density vector and the fine temporal organization across brain areas. The results showed
that very quickly after the onset of a visual cue providing
relevant information for an impending saccade, brain
activity changed accordingly and competing motor programs for yet undecided future actions were activated.
These motor programs were maintained until cues with
new information resolved the uncertainty. MI analysis
showed further that the MEG spikes were organized into
feed-forward and corollary discharge sequences that
could, when combined with the slower activity-linked
processing in discrete brain areas over long periods,
lasting hundreds of milliseconds.
Oscillatory activity provides a good candidate for
linking MEG spike activity across time. To test this idea,
we measured the MEG signal while subjects relaxed with
eyes closed, opened their eyes, and fixated on a cross
in front of them. We have shown earlier in this review
(Fig. 3) some of the data and the SOBI analysis for identifying and eliminating the large artifacts. From each one
of the three conditions, six segments of 4-second MEG
data were analyzed with MFT. Endogenously generated
organization (EGO) analysis was applied to these segments, identifying and aligning MEG spikes in the left
and right FG. The aligned MFT solutions for each condition and ROI were averaged. The activation curves for the
left and right fusiform ROIs were computed from each
MEG-spike-triggered average. The results are displayed
in Figure 9. Low-pass filtering of the data at less than 40
Hz (Fig. 9A) shows clear evidence that MEG spikes ride
on the crest of oscillations in the α band and that the
baseline level of each condition is different. The eyesopen condition shows little evidence for oscillations, and
its baseline level is between that of the other two conditions. The very transient nature of the MEG spikes is difficult to see in the raw MEG signal (Fig. 9B), so the raw
signal is displayed on two zoomed ranges (after baseline
subtraction) in Figures 9C and D. The alignment according to MEG spikes of the fusiform of one hemisphere
shows no evidence for MEG spikes in the fusiform of the
other hemisphere and no oscillations (except possibly for
the eyes-closed condition).
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539
Fig. 9. Endogenously generated organization
(EGO) analysis of the 4-minute data of Figure 3. The
single-trial magnetic field tomography (MFT) solutions were averaged after alignment to magnetoencephalography (MEG) spikes for 250 milliseconds on
either side of each MEG spike. Separate MEGspike-triggered averaging was performed for the
eyes-closed, eyes-open, and fixate conditions.
Activation curves were then computed for regions of
interest (ROIs) in the left and right fusiform from each
average. A, The raw MEG-spike-triggered averages
after low-pass filtering at 40 Hz. B, The raw MEGspike-triggered averages. C, Zoomed view (–50
to +50 milliseconds) of the raw MEG-spiketriggered averages after baseline removal (–200 to
–100 milliseconds). D, Zooming further on the MEG
spike, showing just 5 milliseconds on either side
(baseline removed as in c). FG = fusiform gyrus. In
each part of the figure (A, B, C, and D) the top and
bottom rows show results after EGO alignment of
MEG spikes of the left and right FG, respectively. The
left and right columns respectively show the resulting
average timecourses for the left and right FG.
540
THE NEUROSCIENTIST
Magnetoencephalography in Neuroscience
The New View of Brain Function
Conclusion and Outlook
The results of real-time tomographic analysis of MEG
signals paint a very different view of brain function than
the one that is implied by the conventional results of
averaging and heavy filtering of data (Fig. 10). In the
old view, a stimulus sets in motion a sequence of activations in different brain areas that tends to propagate
every few tens of milliseconds from one area to the next.
Any background rhythms are just uninteresting noise
that must be eliminated. The new view is rather different: Because the cortex is always active, any stimulusevoked effect must be seen as a perturbation of ongoing
activity in many brain areas. To understand the influence
of the stimulus on the activity of one area, or even on a
primary sensory area, the interaction between the effect
of the stimulus and the local background activity must
be taken into account together with indirect stimulusevoked effects mediated by other areas that can very
quickly reach even primary sensory areas. We have
already described results showing that within 100 milliseconds, the processing in V1 is modulated by attention (Poghosyan and others 2005) and activity from the
fusiform (Liu and Ioannides 2006) and amygdala
(Ioannides, Poghosyan, and others 2002).
The effect oscillations in the α band have been encountered every time the single-trial activity in an area was
organized into similar patterns, either by exploratory data
analysis or EGO analysis. Exploratory data analysis has
shown that much of the energy in the MEG signal is due
to oscillations of generators in polymodal areas that guide
the effect of the stimulus in the primary sensory areas
(Laskaris and Ioannides 2001; Laskaris and others 2003).
Figure 9 shows that MEG spikes are bunched at the peaks
of local oscillations in the α band. If MEG spikes are a
manifestation of highly synchronous axon potentials, as
already suggested (Ioannides and others 2005), then their
association with the α band oscillations assumes greater
significance because it offers a possible noninvasive window into mechanisms for working memory and attention
that have, until now, been studied only in animal experiments (Fries and others 2001; Lee and others 2005).
An earlier study showed that eliminating stimulusevoked responses from single trials in auditory, linguistic, and GO/NOGO experiments left an oscillatory
activity with a clear peak in the α range. This α oscillation changed consistently with training, suggesting that
as the subject becomes familiar with the task, the activity in widespread brain areas participates in the processing, including areas not originally involved (Ioannides
and others 1998). This interpretation is consistent with
MI analysis of the single-trial data. For example, the MI
analysis of the face and face-affect recognition data
showed that a complex task is tackled in the brain by
dividing the workload across areas and time. The apparent randomness of the trial-to-trial variability becomes a
manifestation of the high-level organization of stages of
processing.
The challenge for the future is to understand the neuronal basis of this new, highly dynamic view of brain
function. In particular, we would like to understand how
transient events such as MEG spikes are generated, how
they are organized, how they relate to neuronal properties (fluctuations in membrane potential and up and
down states, gap junctions, etc.), and how they are modified in pathology. Clearly there is a need to combine the
results of real-time MFT analysis with other methods.
The popular ways of restricting MEG solutions to fMRI
foci of activity will not help much, however, because
events mapped by MEG may simply not scale up to the
time scales of fMRI. The MRI family can, however, be
invaluable in other ways. Anatomical information from
sMRI coupled with probabilistic cytoarchitectonic maps
(Barnikol and others 2006) can provide detail for individual subjects that can push the MFT resolution further,
possibly to the point that functional units can be mapped
within specialized areas. A most exciting prospect for
the future is the true combination of the diffusion tensor
(DT)–MRI family with MEG. Conventional DT-MRI
can provide information about connectivity and thus link
with the functional information from mutual information
analysis of MFT solutions. If functional DT-MRI breaks
through the 1-second barrier for temporal resolution, it
can provide tomographic maps of activity that can be
combined with MEG maps to better describe events in
roughly similar time scales with each technique. In conclusion, real-time tomographic analysis of MEG data,
together with other new neuroimaging tools, can help us
better understand how our consciousness is founded on
dances of fleeting activations across the entire brain. Our
moment-to-moment consciousness is shielded from this
never-ending avalanche of activations that would otherwise overwhelm us, but our mental stability may in the
end rest on their flawless operations.
Volume 12, Number 6, 2006
Acknowledgments
The work on face processing was initiated through a collaboration with the late Marcus Streit of Düsseldorf
University, and the first experiments were conducted at the
Institute of Medicine, Research Center Jülich, Germany.
The bulk of the analysis of the face data and all other studies was completed at the Laboratory for Human Brain
Dynamics (LHBD), Brain Science Institute (BSI), RIKEN,
Japan. The contribution of LHBD members is acknowledged, with special thanks to Lichan Liu, Vahe Poghosyan,
Peter Fenwick, and Nikos Laskaris. Collaborations within
BSI are also acknowledged for the fMRI/MEG study with
Keiji Tanaka, Allen Waggoner, and Kang Cheng (who also
provided the fMRI image in Fig. 1) and for the occlusion
study with Cees van Leeuwen and Gijs Plomp. Finally,
I thank Daniel Palomo, Mari Aoki, Yuka Okazaki, Afiza
Abu Bakar, and Christos Papadelis for help during the
preparation of the article.
THE NEUROSCIENTIST
541
Fig. 10. A, A view of brain function consistent
with averaging. The response to be analyzed is not
influenced by background brain activity. The influence of a stimulus excites a dormant primary area
(PA). B, A view of brain function more consistent
with the results of real-time tomographic analysis
of magnetoencephalography (MEG) data. The
brain is always active, and any new stimulus must
be seen as a disturbance of ongoing activity that
could influence one of many interacting networks as
it invades and integrates with background activity.
Even early activation of primary sensory areas may
be strongly modulated by ongoing activity or by
early, automatic, and largely unconscious processing of the stimulus. C, Activity evoked by the stimulus and endogenous activity interact over different
time scales, which can be as fast as a few milliseconds (e.g., MEG spikes) or state-dependent effects
lasting many seconds (e.g., background rhythms
depending on whether the subject has his or her
eyes open, closed, or fixating). IC stands for independent component.
542
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Magnetoencephalography in Neuroscience
References
Allison T, McCarthy G, Nobre A, Puce A, Belger A. 1994. Human
extrastriate visual-cortex and the perception of faces, words, numbers, and colors. Cereb Cortex 4:544–54.
Andreasen NC, Oleary DS, Flaum M, Nopoulos P, Watkins GL,
Ponto LLB, and others. 1997. Hypofrontality in schizophrenia: distributed dysfunctional circuits in neuroleptic-naive patients. Lancet
349:1730–4.
Arieli A, Sterkin A, Grinvald A, Aertsen A. 1996. Dynamics of ongoing activity: explanation of the large variability in evoked cortical
responses. Science 273:1868–71.
Barnikol UB, Amunts K, Dammers J, Mohlberg H, Fieseler T,
Malikovic A, and others. 2006. Pattern reversal visual evoked
responses of V1/V2 and V5/MT as revealed by MEG combined
with probabilistic cytoarchitectonic maps. Neuroimage 31:86–108.
Cheng K, Waggoner RA, Tanaka K. 2001. Human ocular dominance
columns as revealed by high-field functional magnetic resonance
imaging. Neuron 32:359–74.
Corbetta M, Kincade JM, Ollinger JM, McAvoy MP, Shulman GL.
2000. Voluntary orienting is dissociated from target detection in
human posterior parietal cortex. Nat Neurosci 3:292–7.
Desimone R, Albright TD, Gross CG, Bruce C. 1984. Stimulus-selective
properties of inferior temporal neurons in the macaque. J Neurosci
4:2051–62.
Di Russo F, Martinez A, Sereno MI, Pitzalis S, Hillyard SA. 2002.
Cortical sources of the early components of the visual evoked
potential. Hum Brain Mapp 15:95–111.
Fries P, Reynolds JH, Rorie AE, Desimone R. 2001. Modulation of
oscillatory neuronal synchronization by selective visual attention.
Science 291:1560–3.
Furey ML, Tanskanen T, Beauchamp MS, Avikainen S, Uutela K, Hari R,
and others. 2006. Dissociation of face-selective cortical responses by
attention. Proc Natl Acad Sci U S A 103:1065–70.
Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. 1993.
Magnetoencephalography: theory, instrumentation, and applications
to noninvasive studies of the working human brain. Reviews of
Modern Physics 65:413–97.
Haueisen J, Schack B, Meier T, Curio G, Okada Y. 2001. Multiplicity
in the high-frequency signals during the short-latency somatosensory evoked cortical activity in humans. Clin Neurophysiol
112:1316–25.
Hironaga N, Ioannides AA. 2002. Accurate co-registration for MEG
reconstructions. In: Nowak H, Haueisen J, GieBler F, Huonker R,
editors. Proceedings of the 13th international conference on biomagnetism. Berlin: Verlag. p 931–3.
Ioannides AA. 1987. Graphical solutions and representations for the
biomagnetic inverse problem. 1. Orlando (FL): Academic Press.
p 205–16.
Ioannides AA. 1994. Estimates of brain activity using magnetic field
tomography and large scale communication within the brain. In:
Wan H-M, Popp F-A, Warnke U, editors. Bioelectrodynamics and
biocommunication. Singapore: World Scientific. p 319–53.
Ioannides AA. 2001. Real time human brain function: observations
and inferences from single trial analysis of magnetoencephalographic signals. Clin Electroencephalogr 32:98–111.
Ioannides AA, Bolton JPR, Clarke CJS. 1990. Continuous probabilistic solutions to the biomagnetic inverse problem. Inverse Probl
6:523–42.
Ioannides AA, Corsi-Cabrera M, Fenwick PBC, Portilla YD, Laskaris
NA, Khurshudyan, and others. 2004. MEG tomography of human
cortex and brainstem activity in waking and REM sleep saccades.
Cereb Cortex 14:56–72.
Ioannides AA, Fenwick PB. 2005. Imaging cerebellum activity in real
time with magnetoencephalographic data. Prog Brain Res
148:139–50.
Ioannides AA, Fenwick PBC, Liu LC. 2005. Widely distributed magnetoencephalography spikes related to the planning and execution
of human saccades. J Neurosci 25:7950–67.
Ioannides AA, Kostopoulos GK, Laskaris NA, Liu LC, Shibata T,
Schellens M, and others. 2002. Timing and connectivity in the
human somatosensory cortex from single trial mass electrical
activity. Hum Brain Mapp 15:231–46.
Volume 12, Number 6, 2006
Ioannides AA, Liu LC, Kwapien J, Drozdz S, Streit M. 2000. Coupling
of regional activations in a human brain during an object and face
affect recognition task. Hum Brain Mapp 11:77–92.
Ioannides AA, Liu LC, Theofilou D, Dammers J, Burne T, Ambler T,
and others. 2000. Real time processing of affective and cognitive
stimuli in the human brain extracted from MEG signals. Brain
Topogr 13:11–9.
Ioannides AA, Poghosyan V, Dammers R, Streit M. 2004. Real-time
neural activity and connectivity in healthy individuals and schizophrenia patients. Neuroimage 23:473–82.
Ioannides AA, Poghosyan V, Liu LC, Streit M. 2002. Early amygdala
activations in normal and schizophrenic subjects. Society for
Neuroscience Abstracts. p 521.5.
Ioannides AA, Taylor JG. 1999. Minimum norm, magnetic field
tomography and FOCUSS. In: Yoshimoto T, Kotani M, Kuriki S,
Karibe H, Nakasato N, editors. Recent advances in biomagnetism.
Sendai (Japan): Tohoku University Press. p 228–31.
Ioannides AA, Taylor JG, Liu LC, Gross J, Muller-Gartner HW. 1998.
The influence of stimulus properties, complexity, and contingency
on the stability and variability of ongoing and evoked activity in
human auditory cortex. Neuroimage 8:149–62.
Kanwisher N, McDermott J, Chun MM. 1997. The fusiform face area:
a module in human extrastriate cortex specialized for face perception. J Neurosci 17:4302–11.
Kastner S, Ungerleider LG. 2000. Mechanisms of visual attention in
the human cortex. Annu Rev Neurosci 23:315–41.
Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM,
Poncelet BP, and others. 1992. Dynamic magnetic-resonanceimaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 89:5675–9.
Laskaris NA, Fotopoulos S, Ioannides AA. 2004. Mining information
from event-related recordings: a user-friendly framework for intelligent single-trial analysis of multichannel encephalographic recordings. IEEE Signal Processing Magazine 21:66–77.
Laskaris NA, Ioannides AA. 2001. Exploratory data analysis of
evoked response single trials based on minimal spanning tree. Clin
Neurophysiol 112:698–712.
Laskaris NA, Ioannides AA. 2002. Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics
of single trial evoked responses. Clin Neurophysiol 113:1209–26.
Laskaris NA, Liu LC, Ioannides AA. 2003. Single-trial variability in
early visual neuromagnetic responses: an explorative study based
on the regional activation contributing to the N70m peak.
Neuroimage 20:765–83.
Leahy RM, Mosher JC, Spencer ME, Huang MX, Lewine JD. 1998. A
study of dipole localization accuracy for MEG and EEC using a
human skull phantom. Electroencephalogr Clin Neurophysiol
107:159–73.
Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N,
and others. 2001. Diffusion tensor imaging: concepts and applications. J Magn Res Imaging 13:534–46.
Le Bihan D, Urayama S, Aso T, Hanakawa T, Fukuyama H. 2006.
Direct and fast detection of neuronal activation in the human brain
with diffusion MRI. Proc Natl Acad Sci U S A 103:8263–8.
Lee H, Simpson GV, Logothetis NK, Rainer G. 2005. Phase locking of
single neuron activity to theta oscillations during working memory
in monkey extrastriate visual cortex. Neuron 45:147–56.
Liu LC, Fenwick PB, Laskaris NA, Schellens M, Poghosyan V,
Shibata T, and others. 2003. The human primary somatosensory
cortex response contains components related to stimulus frequency
and perception in a frequency discrimination task. Neuroscience
121:141–54.
Liu J, Harris A, Kanwisher N. 2002. Stages of processing in face perception: an MEG study. Nat Neurosci 5:910–6.
Liu LC, Ioannides AA. 1996. A correlation study of averaged and single trial MEG signals: the average describes multiple histories each
in a different set of single trials. Brain Topogr 8:385–96.
Liu LC, Ioannides AA. 2006. Spatiotemporal dynamics and connectivity pattern differences between centrally and peripherally presented faces. Neuroimage 31:1726–40.
Liu LC, Ioannides AA, Muller-Gartner HW. 1998. Bi-hemispheric
study of single trial MEG signals of the human auditory cortex.
Electroencephalogr Clin Neurophysiol 106:64–78.
THE NEUROSCIENTIST
543
Liu LC, Ioannides AA, Streit M. 1999. Single trial analysis of neurophysiological correlates of the recognition of complex objects and
facial expressions of emotion. Brain Topogr 11:291–303.
Liu LC, Plomp G, van Leeuwen C, Ioannides AA. 2006. Neural correlates of priming on occluded figure interpretation in human
fusiform cortex. Neuroscience 141:1585–97.
Makela JP, Ahonen A, Hämäläinen M, Hari R, Ilmoniemi R, Kajola M,
Knuutila J, and others. 1993. Functional differences between auditory cortices of the two hemispheres revealed by whole-head neuromagnetic recordings. Hum Brain Mapp 1:48–56.
Moradi F, Liu LC, Cheng K, Waggoner RA, Tanaka K, Ioannides AA.
2003. Consistent and precise localization of brain activity in human
primary visual cortex by MEG and fMRI. Neuroimage 18:595–609.
Morrison RL, Bellack AS, Mueser KT. 1988. Deficits in facial-affect
recognition and schizophrenia. Schizophr Bull 14:67–83.
Noesselt T, Hillyard SA, Woldorff MG, Schoenfeld A, Hagner T,
Jancke L, and others. 2002. Delayed striate cortical activation during spatial attention. Neuron 35:575–87.
Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H,
and others. 1992. Intrinsic signal changes accompanying sensory
stimulation—functional brain mapping with magnetic-resonanceimaging. Proc Natl Acad Sci U S A 89:5951–5.
Plomp G, Liu LC, van Leeuwen C, Ioannides AA. 2006. The “mosaic
stage” in amodal completion as characterized by MEG responses.
J Cogn Neurosci 2006;18:1394–405.
Poghosyan V, Shibata T, Ioannides AA. 2005. Effects of attention and
arousal on early responses in striate cortex. Eur J Neurosci 22:225–34.
Posner MI, Raichle ME, editors. 1994. Images of mind. New York:
Scientific American Library.
Puce A, Allison T, Gore JC, Mccarthy G. 1995. Face-sensitive regions
in human extrastriate cortex studied by functional MRI. J
Neurophysiol 74:1192–9.
Quirk GJ, Repa JC, Ledoux JE. 1995. Fear conditioning enhances
short-latency auditory responses of lateral amygdala neurons—
parallel recordings in the freely behaving rat. Neuron 15:1029–39.
544
THE NEUROSCIENTIST
Sams M, Hietanen JK, Hari R, Ilmoniemi RJ, Lounasmaa OV. 1997.
Face-specific responses from the human inferior occipito-temporal
cortex. Neuroscience 77:49–55.
Sprengelmeyer R, Rausch M, Eysel UT, Przuntek H. 1998. Neural
structures associated with recognition of facial expressions of basic
emotions. Proc R Soc Lond B Biol Sci 265:1927–31.
Streit M, Dammers J, Simsek-Kraues S, Brinkmeyer J, Wolver W,
Ioannides A. 2003. Time course of regional brain activations during
facial emotion recognition in humans. Neurosci Lett 342:101–4.
Streit M, Ioannides A, Sinnemann T, Wolwer W, Dammers J, Zilles K, and
others. 2001. Disturbed facial affect recognition in patients with schizophrenia associated with hypoactivity in distributed brain regions: a
magnetoencephalographic study. Am J Psychiatr 158:1429–36.
Streit M, Wolwer W, Brinkmeyer J, Ihl R, Gaebel W. 2001. EEGcorrelates of facial affect recognition and categorisation of blurred
faces in schizophrenic patients and healthy volunteers. Schizophr
Res 49:145–55.
Talairach J, Tournoux P, editors. 1988. Co-planar stereotaxic atlas of
the human brain. Stuttgart (Germany): G. Thieme.
Tanaka K. 1993. Neuronal mechanisms of object recognition. Science
262:685–8.
Tang AC. 2002. Independent components of magnetoencephalography: localization. Neural Comput 14:1827–58.
Taylor JG, Ioannides AA, Muller-Gartner HW. 1999. Mathematical
analysis of lead field expansions. IEEE Trans Med Imaging
18:151–63.
Tzelepi A, Ioannides AA, Poghosyan V. 2001. Early (N70m) neuromagnetic signal topography and striate and extrastriate generators following pattern onset quadrant stimulation. Neuroimage 13:702–18.
von Helmholtz H. 1853. ber einige Gesetze der Vertheilung elektrischer Stroéme in koéperlichen Leitern, mit Anwendung auf
die thierisch-elektrischen Versuche. Ann Phys Chem 89:211–33,
353–77.
Vuilleumier P. 2005. How brains beware: neural mechanisms of emotional attention. Trends Cogn Sci 9:585–94.
Magnetoencephalography in Neuroscience