Functional Magnetic Resonance Imaging
Mark S. Cohen, Ph.D.
Susan Y. Bookheimer, Ph.D.
UCLA Brain mapping center
Los Angeles, CA 90095
The authors wish to thank Steven Hartmann of Vanderbilt University for his assistance in creating this html document
This article, which appeared originally in Trends in Neurosciences is to be used only
for academic research purposes and is not to be reproduced in any form without
express written permission of the publisher.
Introduction
A remarkable feature of the vertebrate brain is the anatomical specialization of cortical regions for the processing of
different types of information. Since the late nineteenth century, it has been recognized that restricted lesions of the
human brain result in location-specific sensory, motor or cognitive deficits [1] . Few tools are yet available to
understand how activities in these distinct neural processing regions are orchestrated to perform complex tasks such as
reading, memory or spatial visualization. High resolution structural data collected by magnetic resonance imaging
(MRI) has an established place in the neurosciences; the presence and localization of lesions correlated with, for
example, behavioral or cognitive deficits, suggest structural-functional relationships in cognitive skills. Until recently,
human functional data have been constrained by severely limited spatial resolution, as provided by electrical recording
methods, or by the need for radionuclide (e.g. Positron Emission Tomography or "PET") imaging involving complex
apparatus and radio-pharmaceuticals, even then achieving only moderate (~5-10 mm) spatial resolution. A confluence
of MRI developments, particularly those involving ultra-fast imaging, have resulted recently in techniques by which
activity in the human brain can be observed non-invasively with spatial resolution of a few millimeters and temporal
resolution of less than a second. The MRI approach is technically challenging, expensive, and less than two years old,
yet the publications on both method and results are already too extensive to summarize fully in a short review. These
new techniques, generically termed functional MRI (ŠMRI) have led already to an improved understanding of the
neural processing of higher level information; they will contribute substantially to the ability of the neuroscientist to
explore the higher level workings of the human mind.
Principles of Magnetic Resonance
To understand the ŠMRI method, investigators should be familiar with the physical principles of magnetic resonance
that determine its signal characteristics, and through which it is possible to form images. In overview the process is as
follows: 1. The subject is first placed into a strong and homogeneous magnetic field. Various atomic nuclei, particularly
the proton nucleus of the hydrogen atom (from here, we will consider only the proton), align themselves with this field
and reach a thermal equilibrium. The subject is thereby "magnetized." 2. The proton nuclei precess about the applied
field at a characteristic frequency, but at a random phase (or orientation) with respect to one another. 3. Application of
a brief radio frequency (RF) electromagnetic pulse disturbs the equilibrium and introduces a transient phase coherence
to the nuclear magnetization that can, in turn, be detected as a radio signal and formed into an image.
Signal Changes with Blood Oxygenation
The rate at which the MR signal decays: T2*, depends upon a variety of physiological and physical factors. Variations
in precessional frequency among the excited nuclei results in signal loss (from spin dephasing -- see box 1). One of the
chief mechanisms for this is the presence of local variations in magnetic field strength caused by the presence of
particles or tissues with differing magnetizability or "susceptibility." As early as 1936 [2], Pauling noted that the
magnetic susceptibility of oxyhemoglobin and deoxyhemoglobin differed slightly. Thulborn predicted and, in 1982
demonstrated[3], that the signal decay rate of deoxyhemoglobin is more rapid than its oxygenated counterpart.
MR Signal Formation (Box 1)
The proton nuclei of the hydrogen atom possess a small magnetic moment. When placed within a magnetic
field, a torque will be exerted upon them, resulting in a slight energetic advantage of one orientation (parallel to
the field) over another (the anti-parallel orientation). Over time, random atomic collisions and other
perturbations allow the complete system to reach a magnetic and thermal equilibrium with an excess of protons
aligned with the magnetic field. The combined alignment of all of these protons results in a net magnetic
moment; a subject placed within a magnetic field thus becomes "magnetized." In biological tissues, this
magnetization is exceedingly small, and generally not observable. In addition to their magnetic moment, atomic
nuclei possess angular momentum - a quantum property known as "spin." Because of this angular momentum,
rather than aligning simply with magnetic fields, the individual nuclei precess about it, much as a spinning top
or gyroscope might, when placed in the earthÌs gravitational field (figure 1, left). The precessional rate, or
frequency, is characteristic of the atomic nucleus (e.g. protons) and is proportional to the strength of the
magnetic field (figure 1, right), a property crucial to the process of image formation. With the magnetic field
strengths in use for todayÌs typical MR imagers, the precessional frequency is between 10 MHz and 100 MHz just below FM radio range.
Figure 1. Magnetic Properties of the proton nucleus of the Hydrogen Atom.
(Left) The hydrogen proton possesses the quantum property of "spin" or angular momentum, and has a
small magnetic dipole moment. When placed in a magnetic field, a torque is exerted on the particle,
causing it to precess about the applied field. (Right) The precessional frequency of the protons is
directly proportional to the magnetic field strength. Protons precess at about 43 MHz/Tesla.
Figure 2 shows that the proton magnetization can be decomposed into the sum of a stationary (longitudinal) and
a rotating (transverse) component. Each proton nucleus within a magnetic field thus yields a tiny field that
rotates about that applied field. The rotating field from individual nuclei is in generally aligned at random with
respect to other protons in the subject or sample. In macroscopic systems, the average rotating field will
effectively be zero, since that arising from any individual nucleus is canceled by another, oppositely oriented,
neighbor.
Figure 2. Vector description of proton magnetization.
The rotating magnetic moment of the proton can be decomposed into a longitudinal component, along
the applied magnetic field, and a transverse component orthogonal to it and rotating (precessing) about
it.
In nuclear magnetic resonance (NMR), a second magnetic field is applied, which is orthogonal to the static
field, and which rotates about the static field at the precessional frequency of the atomic nuclei. When the
rotating field is present, the nuclei will precess about it, forcing the magnetization away from equilibrium , and
causing the ensemble of protons to precess together, or "in-phase." The combined rotating magnetic moment
thus produced by the ensemble of protons is observable as a time varying electromagnetic (radio) signal. The
second, rotating magnetic field is applied at radio frequencies and is therefore known as an "RF" pulse. These
fundamental principles were elucidated more than forty years ago; among the seminal contributions were those
of Felix Bloch [4,5] and Erwin Hahn [6].
Signal Characteristics (T1, T2, etc...)
Two fundamental temporal parameters are used to describe the MR signal. The longitudinal relaxation rate,
"T1", is the rate at which nuclei, once placed in a magnetic field, exponentially approach thermal equilibrium,
so that the magnetization (M) is described by the formula: M = M0(1-exp(-t/T1)), where M0 is the equilibrium
magnetization. In biological tissues, the proton T1 is quite long: from tens of milliseconds to seconds.
Differences in the T1Ìs of tissues are one of the primary bases of contrast in clinical MRI. A second parameter
time constant describes the rate at which the MR signal decays. Once an MR signal is formed, i.e. after an RF
pulse, it fades quickly; small variations in the local magnetic field, for example those caused by neighboring
magnetic nuclei, cause the protons to precess at slightly different rates and therefore to become out of phase
with one another. Interactions among the magnetized protons, and motion in inhomogeneous fields, due for
example to diffusion, also results in signal dephasing. The observed signal decay rate, (T2*) generally ranges
from a few milliseconds to tens of milliseconds and, to a reasonable approximation, also follows first order
kinetics. The MR signal, S(t), signal decays according to the formula: S(t) = S0 (exp (-t/T2*) where S0 is the
signal strength immediately following the RF excitation pulse. The observed T2* decay is the net effect of all
the dephasing terms: 1/T2* = 1/T2 + 1/T2m +1/T2D + other terms÷, where T2m represents the dephasing due
to magnetic field inhomogeneities and T2D is the diffusion-related signal loss. Like T1, the T2 signal decay
rates differ among body tissues. For most current ŠMRI, T2 is the dominant contrast mechanism. As discussed
below (box 2), blood oxygen content strongly effects the observed signal decay rate. By waiting for a short
period, "TE", following the RF excitation pulse, differences in the signal decay rate will become evident as
differences in the MR signal intensity: tissues with longer T2Ìs will have stronger signals than those with short
T2Ìs, whose signals decay more rapidly (figure 3). Modifications to the pattern of RF excitation (the "pulse
sequence") can modulate the contributions of the various relaxation processes to the resulting MR signal. In
particular a "spin echo" pulse sequence can be used to nearly eliminate the T2m contribution, increasing the
relative contributions of other terms, such as proton diffusion, to the image contrast.
Figure 3. Spontaneous Decay of Transverse Magnetization (Signal).
Immediately following an RF excitation pulse, the coherent rotation of the ensemble of protons forms a
detectable signal .This signal decays spontaneously, with first order kinetics, at the characteristic rate,
T2. At the time the MR signal is sampled (TE), the signal intensity from tissues with long T2 will be
greater than that from short T2 tissues. Differences in effective T2 form the contrast basis for most ŠMRI
methods.
Image Formation
Spatial Encoding with Gradients
The suggestion that the NMR signal could be used to form images was made first by Lauterbur[7] in 1973.
Reasoning that the precessional frequency of the atomic nuclei depended upon the local magnetic field, he
proposed that by forming a spatially varying magnetic field, it would be possible to separate the signal from
different locations according to frequency. With a sample placed within a linear magnetic field gradient, for
example, the Fourier transform of the signal would show its strength at each frequency, and thus at each
position. Present day MR imaging instruments use three mutually orthogonal sets of electromagnetic "gradient
coils" to encode the three spatial coordinates of the MR signal.
Imaging Speed
Detecting small differences in frequency (which in MRI, as discussed above, are equivalent to small differences
in position) requires sampling the signal for a relatively long time; the smaller the frequency difference, the
longer the time needed. In commercial MRI, one of the challenging tasks is to switch on and off the large
magnetic field gradients needed for adequate spatial encoding in the limited time that the MR signal is available
(which, as you will recall, is limited by T2). In conventional imaging instruments, this problem is handled by
performing, in effect, only part of the spatial encoding at a time, and later re-exciting the MR signal to perform
further encoding, repeating this process as many as several hundred times to form a complete image. For this
reason, MR imaging times have traditionally been extremely long: from 3 to 15 minutes for an imaging series.
By minimizing the perturbation of the magnetization from its equilibrium, a method known as FLASH [8,9]
enables a reduction of the time between successive excitation pulses, and has recently permitted imaging times
of less than a second, with some penalty in total contrast.
A method first proposed in 1977 by Sir Peter Mansfield, known as echo-planar imaging (EPI) [10], performs all
required spatial encoding during the several tens of milliseconds that the MR signal is present, without resorting
to repeated excitation-sampling cycles. This technically challenging method, reduced to practice in 1984 [11],
and to high magnetic field whole body imaging in 1987 [12,13], makes it possible to form complete MR images
in as little as 20 msec. Various modifications to EPI have been developed over the years that followed, to bring
high resolution and controllable contrast to the technique, enabling a wide variety of novel medical and
scientific applications [14,15]. Commercial devices with EPI capability are now available from several
manufacturers. Both FLASH scanning (in about 6 seconds per image) and EPI (in about 0.1 second per image)
achieve excellent functional MR imaging results. While the FLASH method allows good control of both T1 and
T2* contrast, the EPI method is more flexible in controlling the relative contributions of T2m and T2D to the
images.
Effects of blood oxygen on T2* were first reported in MR images by Seiji Ogawa in 1990 [16] who noted that
cortical blood vessels became more visible as blood oxygen was lowered. He understood this to be due to the
creation of local magnetic field inhomogeneities, and thus signal losses, from deoxyhemoglobin and termed it
the BOLD (Blood Oxygenation Level Dependent) method. Robert Turner, at the NIH, demonstrated that with
ultra-fast echo-planar imaging, he was able to observe the time course of these oxygenation changes while an
animal breathed an oxygen-deprived nitrogen atmosphere. Shortly thereafter, Kenneth Kwong [17] reported
seeing similar changes in humans during breath-holding.
Functional Activation
Alterations in Blood O2
Activated areas of the human brain show localized increases in blood flow; these are exploited in functional
imaging with positron emission tomography [18]. Blood volume also increases during sensory stimulation [19]
and, using another MR imaging method (which measures blood volume based on signal changes following
contrast agent injections [20]), these volume changes were utilized by Belliveau to make the first functional MR
images of brain activation [21]. The increases in blood flow seem to outstrip increases in oxygen utilization
[22]. Thus, the oxygen content of venous blood increases during brain activation, resulting in increased MR
signal intensity (figure 4). Using blood as an endogenous contrast agent, Kwong [23] and Ogawa [24] each
demonstrated that with rapid MR imaging methods, they could observe the transient changes in MR signal that
accompany these hemodynamic events. It is this latter result that has revolutionized the field of functional MR
imaging, forming the basis for practical, non-invasive observation of the hemodynamic changes accompanying
neuronal activity.
Figure 4. During periods of neuronal activity, local blood flow and volume increase with little or no
change in oxygen consumption. As a consequence, the oxygen content of the venous blood is elevated,
resulting in an increase in the MR signal.
Signal Changes with Blood Flow
The prolonged(T1) rate at which the MR signal approaches equilibrium (see box 1) can also be used to detect
vascular signal changes. In KwongÌs original publication[23] , he noted that the increased inflow of blood into
the imaging volume can also result in a detectable signal change due to T1 effects. By using a relatively strong
RF excitation pulse (a "180°" pulse) the signal available from a slice of tissue can be much reduced, so that the
flow of fresh blood into that tissue slice or volume results in a signal increase. The flow-dependent signal
difference between baseline and stimulus behavioral conditions may also be used for MR functional imaging.
Through clever manipulation of the MR signal acquisition, the newly proposed EPISTAR technique [25] may
further amplify the usable MR signal change. These flow based techniques are of special interest because they
may in principle be used to quantify blood flow change. Furthermore, while the T2* effects probably reflect
signal changes primarily in the venous system, the T1 changes may be biased more to the arterial supply.
Characteristics of the ŠMRI Signal
Magnitudes
Blood makes up a very small fraction (about 6%) of gray matter, (and even less of white matter): the
hemodynamic signal changes which occur in MR during brain activation are extremely small, from 2 to 5 % at
moderate magnetic field strengths (1.5 Tesla) to about 15% at very high fields (4 Tesla). Nevertheless, with
adequate signal to noise ratio (SNR) in the basic images, they are clearly visible. Figure 5A shows signal
changes in the human primary visual cortex (V1) as signal difference images during visual (photic) stimulation.
The accompanying graph (figure 5B), from KwongÌs original publication [23], plots the signal intensity as a
function of time in a region within V1, showing the excellent signal to noise ratio available with this method.
Figure 5A. MR signal difference map during photic (visual) stimulation.
In an image aligned along the calcarine fissure the signal intensity increases visibly during presentation
of a photic stimulus, consisting of an 8 Hz patterned flash. The image at the upper left was acquired in
darkness and the four images which follow were subtracted from it. The local signal increases can be
seen along the calcarine fissure. The intensity scale represents multiples of the baseline standard
deviation (contrast to noise ratio)
Figure 5B. Signal Intensity changes within the visual cortex.
The signal in a small (Å60 mm2) area near the calcarine fissure during exposure to an 8 Hz patterned
flash. Images were acquired once every 3 seconds. Note the signal decrease following cessation of the
stimulation. Signal intensity is in arbitrary units, data are from a different subject than in figure 5A.
Reproduced by permission from K. Kwong et al. [23].
The MR signal intensity in darkness, preceding the first period of visual stimulation, fluctuates slightly. At
present, it is not known how much of this fluctuation is the result of actual variations in physiological signal and
how much is simply the consequence of instabilities in the MR instrument [26]. Furthermore, the relative
contributions of these components can be expected to vary among MR scanners. Note that in figure 5A the pixel
intensity scale is normalized by the standard deviation of the signal intensity during the initial baseline period in
order to account for local differences in baseline fluctuation; the pixel intensities are thus in units of "contrast to
noise ratio."
Response Latencies
The ŠMRI signal takes some time to reach its peak following the onset of the stimulus presentation. Kwong
fitted the response increase to a monoexponential function and showed the time constant to be about 4.4
seconds for images of this kind, while the more flow sensitive techniques seemed to have slightly longer
response latencies. The characteristic response delay differs across brain regions and stimulus regimens (see, for
example [27,28] or figure 5 of [23]). It is nevertheless substantially slower than the neural or psychophysical
response. ŠMRI would thus seem to straddle the temporal resolution of electrical recording methods such as
electroencephalography or direct cellular recordings and that of PET. Through the use of a spectroscopic nonimaging NMR method, Hennig and Ernst [29]recently reported small changes in the strength of the MR signal
with latencies of about 500 msec to the presentation of a visual stimulus which may prove useful in the future to
advance ŠMRIÌs temporal resolution. In any case, these response latencies likely represent fairly repeatable
physiological delays; the MRI imaging methods should, in principle, be able to resolve any signal changes
which occur within a few tens of milliseconds. Thus the temporal resolution of ŠMRI would seem to be limited
by the phenomenon (detection of vascular signal changes) rather than by the "camera."
Temporal Fluctuations
The ŠMRI signal intensity during activated periods may be quite variable even with constant stimulus
intensities. From figure 5B one can see that the response not only takes some time to appear, but begins to
decrease prior to the cessation of the stimuli and seems, furthermore to fluctuate during the stimulus
presentation. Compared to the signal variations in the absence of stimulation, those during activated periods are
often larger and may be more regular. We will address this observation in more detail below, when we consider
the applications of ŠMRI.
ŠMRI Results
Primary Sensory and Motor Activation
In single individuals, ŠMRI responses have been reported to visual stimuli [30-34,28,23], somatosensory/motor
activity[35-38], and acoustic stimuli [39]. Where it has been studied (e.g. [23]), the magnitude of the ŠMRI
response seems to be scaled to the stimulus intensity, but the linearity of the response scaling, and the ultimate
sensitivity to low intensity stimuli, are still unknown. The results of Blamire and Bandettini [40,41] suggest that
averaging the responses to repeated low-intensity stimuli may improve that sensitivity.
Higher Level Function
Language Tasks
An important challenge for ŠMRI (or any other functional imaging technique) is its ability to detect signal
changes during subtle cognitive tasks. In PET activation studies, blood flow changes in association areas (e.g.
during language performance) are more difficult to detect than those seen in primary visual or motor cortices
[42]. In several studies, ŠMRI has successfully demonstrated activation during covert word generation [43-46]
in the inferior frontal lobe, a probable language-association region. The task-related changes reported during
word generation replicate and extend prior PET studies [47]. Recent work on single word reading [39]has also
demonstrated activation in BrocaÌs area, as well as visual pre-striate cortex.
Pre-Motor and Imagery
Among the more intriguing results in ŠMRI is the localized signal changed observed during covert mental
activity ("mental imagery" or "ideation"). In the hands of several investigators, and in visual, somatosensory
and motor systems, the MR signal increases when the subject imagines a visual stimulus [48], or performs a
motor task [35]. Such blood flow changes were reported using non-tomographic techniques more than fifteen
years ago [49] and through the use of PET [50], but with ŠMRI it becomes possible to interrogate the locus of
such activity on single subjects with a high degree of reliability. Using this technique one can examine such
questions as whether the mental image of a visual stimulus is represented in pseudo-retinotopic form on the
primary visual cortex during a recall task - a question of abiding concern in the study of mental imagery [51].
While the temporal resolution of ŠMRI is slow in comparison to neuronal firing, it may be appropriate to the
study of a variety of physiological processes. In a study of pediatric epilepsy, Graham Jackson et al.
demonstrated the spread of seizure activity through its blood flow effects with ŠMRI (personal communication).
Such studies may become valuable both in understanding better the physiological basis of the disease, and in its
therapeutic management.
Technical Issues
Spatial Resolution
Hemodynamic response data obtained by optical methods [52,53] suggest that the cortical vascular responses
may be localized to the columnar level. Because the ŠMRI response apparently corresponds to local changes in
blood flow, it might in principle be possible to obtain ŠMRI functional maps of cortical columns. Furthermore,
the feature resolution of standard MRI can be readily brought to 100 µm or so - the appropriate size range to
assess columnar anatomy [54,55] . To date, no such ŠMRI results have been reported. In practice, a variety of
factors limit the useful spatial resolution of the ŠMRI method.
The magnetic resonance signal is intrinsic, arising from the tissues of the brain, and it is quite small. Increases
in the spatial resolution (decreases in the image feature size) result in smaller MR signal energy per pixel in the
final MR image. It is an unfortunate characteristic of MRI that reductions in voxel volume reduce the available
signal per voxel, while the noise (per voxel) remains essentially constant. Thus, the SNR scales with the third
power of the linear voxel dimensions (or feature resolution). Since the method typically exploits signal changes
of only a few percent, the SNR must be quite high for such changes to be observable. The spatial resolution in
ŠMRI must therefore be somewhat coarse compared to the theoretical resolving power of conventional MRI
techniques.
The ŠMRI technique is presumably sensitive primarily to changes in the signal from venous blood. As the voxel
volume content of blood increases, less blood oxygen change is needed to produce the same ŠMRI signal
change. Some investigators have suggested that much of the ŠMRI signal change might be seen within brain
areas having little or no neural tissue, being instead images of the venous vasculature [56]. By implication, such
signal changes would be spatially displaced from the activated neural tissue. At this time, the relationship
between vessel size and vascular territory are poorly understood. A variant of MRI, known as magnetic
resonance angiography (MRA) [57] images blood vessels of only a few hundred microns. Properly used, it is
possible to exclude pixels containing these vessels from the functional image analysis, thereby mitigating this
problem somewhat. Theoretical and experimental work by Fisel [58] and later by Weisskoff and colleagues
([59] and unpublished observations) suggests that it might be possible to "tune" the sensitivity of the MR
method to vessels of a certain size range, such that signals from the microvasculature below a few tens of
microns are more effective at modulating the MR signal than are large vessels. This feature comes about when
so-called "spin echo" as opposed to "gradient echo" MR methods are used. While the latter are sensitive to
magnetic field inhomogeneities within voxels (as described above) the former show large signal changes only
when the protons are able to diffuse a relatively large distance compared to the blood vessel size. By fortunate
coincidence, in the time scales appropriate for MR imaging, the protons can only diffuse a distance comparable
to the size of the capillaries. These microscopic vessels will therefore have a disproportionately large effect on
the MR signal.
These observations not withstanding, ŠMRI appears to have excellent spatial sensitivity as compared to other
functional neuroimaging methods. As seen in figure 5A, the activation maps conform in an obvious way to the
basic shape of the cortical surface, at least in primary visual cortex. It would be reasonable to anticipate
resolving power of a millimeter or two. Considerable work must still be done, from a theoretical and practical
level, to understand the limiting spatial resolution of ŠMRI.
Sensitivity
Among the most important advantages of ŠMRI is its ability to detect obvious and relatively large signal
changes in single individuals, with a wide variety of stimuli and cognitive tasks. Because of this, the activation
maps of multiple individuals need not be combined to achieve sufficient sensitivity, and it is therefore not
necessary to transform the coordinate system of one brain to conform to that of another. It would be difficult to
overstate the advantage of this fact alone; neither the morphological or functional topography of the brain will
be identical across individuals and therefore the combination of spatial data across subjects necessarily results
in a reduction of signal and obfuscation of individual differences. The value of single subject analysis is well
demonstrated in WatsonÌs recent PET study of area V5 in the human [60].
There are many unanswered questions about the sensitivity of ŠMRI. Only a few scattered reports exist, for
example, assessing the magnitude of the ŠMRI response as a function of stimulus intensity, be it the brightness
of a flashing light or the complexity of a cognitive task. As in most functional PET, MEG, or EEG studies,
ŠMRI responses are typically presented as the normalized difference in signal intensity between control and
activated conditions. Interpretation of such results assumes a graded response to the stimulus conditions. The
contribution of a brain region which activated "all or none" in a task may be difficult to assess, or even detect.
A more subtle question (common between ŠMRI, PET and perhaps MEG and EEG) concerns the relationship
between magnitude and extent of cortical activation. Being generally SNR-limited, these techniques are much
better at detection of large "activation" of a small region than small activation of a large region, introducing a
bias in the results, and their interpretation, to finding localized processing centers. Many questions of this sort
will challenge the field: how do we handle the effects of training; e.g. will the activation of a region be
systematically reduced (to the point of invisibility) with repeated or continued exposure?
Experimental Design and Statistical Issues
Traditional statistical analysis of PET activation images typically involves averaging across a group of subjects
[61,62]; this has been used to improve signal to noise of the blood flow response and to localize the results,
usually to some common coordinate system [63,64]. One of the great advantages of ŠMRI is its built-in
localization power, and the high SNR, allowing for detection of change within a subject. The disadvantage of a
single subject approach is that there is no obvious way to combine data across subjects to establish reliability of
the results.
Statistical approaches used in ŠMRI thus far generally include: normalization of response to baseline variance,
and performing t-tests of activation blocks vs. rest. This approach has the same statistical effect as PET in which
responses within an activation condition are averaged, effectively losing temporal resolution. Another approach
[40] models the ŠMRI response to repeated rest-activation cycles as a sinusoid (effectively fitting the first
moment - a first-order exponential); this approach appreciates the rest-activation-rest÷ protocol but otherwise
closely resembles the t-test design. Both approaches assume an a priori model of cortical activation: that blood
flow increases during activation and decreases during baseline in the relevant brain regions. There is some
evidence that a simple increase in blood flow is only one possible response type; a change in variance of the
signal intensity without a change in mean intensity would not be detected with the mean-comparison approachyet this response pattern has been observed ([36] and Stern, personal communication). An alternative statistic,
developed as an ŠMRI method by Robert Weisskoff and others at the Massachusetts General Hospital NMR
center, compares activation and rest periods using the Kolmogorov-Smirnov statistic, which is nearly as
sensitive to changes in the mean as the t-statistic, but which also detects changes in skew and variance. Such a
method (or some variant) may be better suited to exploratory brain imaging methods where the local response
change is not known a priori.
In combining data across subjects, very few studies have performed tests supporting the reliability of localized
task-related activation. One approach applies a non-parametric measure of the number of subjects showing
activation of a cortical region and number of regions activated in each hemisphere as a measure of lateral
asymmetry [44] . Reference to a common coordinate system, although possible, has been little utilized in ŠMRI
studies.
Problems
Field Strength
From the basic principles of magnetic resonance, we expect the magnitude of the MR signal to increase with
greater magnetic field strengths, offering a net SNR advantage at higher fields. Furthermore, because the
magnitude of the magnetization difference between oxy- and deoxy-hemoglobin should increase also with field
strength, researchers have suggested that the useful SNR of the ŠMRI method might scale with the square of the
magnetic field. While magnitude differences do appear to be larger at higher fields [65,23,24,3], so does the
spontaneous signal variation of the quiescent MR signal. The achievable contrast sensitivity thus does not
appear to depend as strongly on field strength as might otherwise have been hoped. Because the cost of the MR
instrument increases rapidly with increases in field strength, this is a crucial issue in the design of practical,
dedicated, ŠMRI units.
Head Motion
The high spatial resolution of MRI, coupled with its high intrinsic contrast, results in the disadvantage that,
when activation-related signal changes are very small, even slight misregistration creates significant artifacts
following baseline subtraction. Head motion not only reduces SNR in activated regions but also produces
spurious "activations", especially at borders at the edge of the brain and between large fissures. Because of need
the for stringent head motion control, the choice of subject responses available for measurement is limited.
Experiments focused on primary sensory systems can rely on the stimulus, such as auditory input or visual
flashes, to produce activation without the need for a behavioral response. In motor experiments, small hand or
foot movements need not elicit excessive head motion. But studies of higher cognitive functions such as
language and memory may be severely curtailed, as speaking aloud is difficult to accomplish without significant
head movement. One alternative is to have subjects perform a speech task covertly, as in silent word generation;
some have found this sufficient [43,44] while others have failed to show expected activation when performing
the task silently [66] Covert performance requires a high level of cooperation from subjects, and while this may
be feasible for highly motivated (e.g. paid) normal volunteers, it is less so when studying patients or children.
Further, to determine the study was successful - that the subjects performed the task-, one has to already know
the correct answer (i.e., this paradigm produces activity in inferior frontal gyrus), not an ideal requirement for
performing original research.
Ideally, a concurrent, observable and measurable behavioral response, such as yes/no bar press response,
measuring accuracy or reaction time, should verify task performance. Ideally, one might hope to register a time
series of images retrospectively based on surface features [67] on cortical landmarks [68] or on overall image
intensity [69] and such methods have met with some success thus far. These approaches are limited, though, in
that the inherent image contrast in MR images, e.g. between gray and white matter, can be quite large so that
adjacent pixels may differ in signal intensity by more than 20%. Consequently, misregistrations of a fraction of
a pixel can swamp the functional contrast (figure 6).
Figure 6.
Left. Raw "functional" image of the visual cortex of a human subject. Middle. Difference image created
from the subtraction off the image at left from the identical image offset by one pixel. The calculated
image appears as a rim of dark and light pixels , similar to a pattern of cortical activation. Right. Graph
of the signal intensity (arbitrary units) of the baseline and difference images along the line indicated on
the left, Note that a single pixel shift can easily appear as a large increase in signal. Typical activation
signals would be on the order of 3 to 15%.
Perspective
The mapping of cortical and subcortical function in the human brain will ultimately require methods having the
appropriate balance of temporal and spatial resolution, coupled with low enough risk to the subject to justify
repeated experimentation on normal volunteers. Furthermore, one must know not only the absolute locus of
activation, but its relation to anatomical structure and, ideally, the temporal relationship of its activation to that
of other areas involved in processing of the same cognitive or sensory information. Functional MRI by itself
will not accomplish these goals, but it has moved us closer to the ideal.
Figure 7
Adapted from Churchland and Sejnowski [70] and reprinted from Belliveau et al. [71] , this figure
relates the temporal and spatial resolution of methods for the study of brain function to the size scale of
neural features and to the "invasiveness" of the methods.
MEG=magneto-encephalography; ERP=evoked response potentials; ŠMRI=functional magnetic
resonance imaging; PET=positron emission tomography.
Figure 7, adapted from Churchland and Sejnowski [70] and reprinted from Belliveau et al. [71] relates the
temporal and spatial resolving power of a variety of methods for the study of brain function. When ŠMRI is
added to this framework, it would seem to provide a satisfying level of spatial resolution - near to that of
cortical columns - but a still disappointing (by neural processing standards) temporal resolving power of
seconds. In addition to the resolution axes, this adapted figure superimposes "invasiveness," i.e. the risk of harm
to the subject, for each method. Here, ŠMRI holds a special position of apparently complete safety (barring
pacemakers and certain metal implants). With ŠMRI it will be possible to perform longitudinal studies on
individual subjects substantially advancing the practical spatial resolution of functional imaging and enabling
vastly more complex experimental designs. Though few neuroscientists will be able to afford MR devices of
their own, with thousands of installed units, and tremendous and intensive creative effort, ŠMRI will have an
active and expanding role in the understanding of brain function.
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