Brain mapping center, UCLA School of Medicine, 710 Westwood Plaza, Los Angeles, CA 90024
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 (ƒMRI) 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.
To understand the ƒMRI 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.
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.
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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.
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.
The prolonged(T1) rate at which the MR signal approaches equilibrium (see box 1) can also be used to detect vascular signal changes. In Kwong’s 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.
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 Kwong’s 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.
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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."
The ƒMRI 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. ƒMRI 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 non-imaging 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 ƒMRI’s 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 ƒMRI would seem to be limited by the phenomenon (detection of vascular signal changes) rather than by the "camera."
The ƒMRI 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 ƒMRI.
In single individuals, ƒMRI 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 ƒMRI 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.
An important challenge for ƒMRI (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, ƒMRI 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 Broca’s area, as well as visual pre-striate cortex.
Among the more intriguing results in ƒMRI 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 ƒMRI 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 ƒMRI 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 ƒMRI (personal communication). Such studies may become valuable both in understanding better the physiological basis of the disease, and in its therapeutic management.
Hemodynamic response data obtained by optical methods [52,53] suggest that the cortical vascular responses may be localized to the columnar level. Because the ƒMRI response apparently corresponds to local changes in blood flow, it might in principle be possible to obtain ƒMRI 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 ƒMRI results have been reported. In practice, a variety of factors limit the useful spatial resolution of the ƒMRI 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 ƒMRI must therefore be somewhat coarse compared to the theoretical resolving power of conventional MRI techniques.
The ƒMRI 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 ƒMRI signal change. Some investigators have suggested that much of the ƒMRI 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, ƒMRI 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 ƒMRI.
Among the most important advantages of ƒMRI 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 Watson’s recent PET study of area V5 in the human [60].
There are many unanswered questions about the sensitivity of ƒMRI. Only a few scattered reports exist, for example, assessing the magnitude of the ƒMRI 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, ƒMRI 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 ƒMRI, 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?
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 ƒMRI 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 ƒMRI 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 ƒMRI 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 approach- yet this response pattern has been observed ([36] and Stern, personal communication). An alternative statistic, developed as an ƒMRI 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 ƒMRI studies.
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 ƒMRI 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, ƒMRI units.
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).
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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.
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Figure 4, 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 ƒMRI 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, ƒMRI holds a special position of apparently complete safety (barring pacemakers and certain metal implants). With ƒMRI 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, ƒMRI will have an active and expanding role in the understanding of brain function.
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