Functional magnetic resonance imaging
| Seiji Ogawa and Yul-Wan Sung (2007), Scholarpedia, 2(10):3105. | doi:10.4249/scholarpedia.3105 | revision #151126 [link to/cite this article] |
Functional magnetic resonance imaging (fMRI) of the brain is a non-invasive way to assess brain function using MRI signal changes associated with the functional activity. The most widely used method is based on BOLD (Blood Oxygenation Level Dependent) signal change that is due to the hemodynamic and metabolic responses which are well coupled to the neuronal activation.
One of the most important points for fMRI in investigating human brain function lies at the likely fact that brain function is spatially segmented, i.e localized, at various sites. The functional specialization can be identified to some degree and those sites are mapped at high spatial resolution. BOLD-fMRI has been widely used in various fields of brain science to identify areas as the neural basis of their relevant brain activities. These fields are now extended to social science and also to the understanding of the awake conscious state in human in the neuroimaging terms.
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BOLD based fMRI method
BOLD effect in MR images
Hemoglobin without bound oxygen molecules, deoxyhemoglobin, is known to be paramagnetic because of the high spin state (S = 2) of the heme iron in contrast to O2 bound hemoglobin, oxyhemoglobin, which has low spin (S = 0) and is diamagnetic (Pauling &Coryl 1936). The presence of deoxyhemoglobin in the red cell makes the magnetic susceptibility of the red cell different from the diamagnetic plasma in the blood and similarly the difference in magnetic susceptibility is generated between the blood and the surrounding tissue. In the large homogenous magnetic field used in MRI, the compartmentalized susceptibility differences induce small magnetic field distortions in the blood as well as in the surrounding extra-vascular area. Water protons in these areas sense such field distortions and show the effect in the signal decay process characterized by T2 (spin echo) or T2*(gradient echo) relaxation. When the content of deoxhemoglobin changes in the blood, the relaxation process of water proton is modified and one can see the phenomenon in MRI. The image intensity that varies with the deoxyhemoglobin content has been termed Blood Oxygenation Level Dependent (BOLD) and was predicted for the potential use in functional study of the brain (Ogawa et al 1990). It should be mentioned that Thulborn et al showed in their ex-vivo experiments the blood water T2 varies with deoxyhemoglonin content (Thulborn et al 1982).
The era of this type of BOLD based functional MRI (by the endogenous contrast agent, deoxyhemoglobin) was initiated by three papers appeared in 1992 ( Bandittini et al 1992, Kwong et al 1992, Ogawa et al, 1992)(see Raichle 2000 for the historical aspect of fMRI development)
Prior to these reports, another fMRI method for detection of a functional response in the human brain was published in 1991 (Belliveau et al 1991), using exogenous contrast agent injected into the blood stream. The measurement was only possible during the time window when the agent passed through the area of interests in the brain.
BOLD MR image signal and its relation to physiology
BOLD effect is related to changes in physiological conditions (Ogawa et al, 1998) and appears as a part of the relaxation rate \(1/T_2^*\).
\[ \frac{1}{T_2^*}=\frac{1}{T_{20}^*}+\frac{1}{T_{2B}^*} \]
\(1/T_{2B}^*\) is the part due to BOLD effect and \(1/T_{20}^*\) is the sum of other terms of the transverse relaxation rate. MRI signal (S) can be expressed by
\[ S=S_0e^{\frac{-TE}{T_2^*}} \]
where \(S_0\) is the signal at the echo time \(TE = 0 \) (spin density or \(T_1\) dependent term). For spin echo signal, \(T_2^*\) is replaced by \(T_2\). The fractional signal change from the resting state to the activated state or by changes in the physiology is (\(\Delta \left(1/T_{20}^*\right) = 0\))
- <math mylabel1>
\frac{\Delta S}{S}=\frac{\Delta S0}{S0}-TE\Delta\left(\frac{1}{T_{2B}^*}\right) </math>
The 1st term that is TE independent is the so-called spin density change, if any, and also the signal contributed by inflow effect (\(T_1^*\)). When \(TE\) is not short, the second term (BOLD effect) is dominating.
The \(1/T_{2B}^*\) term varies with the content of deoxyhemoglobin in the imaging voxel (Ogawa et al 1998). When the susceptibility field does not change much in the diffusion distance of water molecule (about 10\(\mu m\) in TE period), the intra-voxel signal average is a simple and static average. This is the case for the signal in the extra-vascular space around a large vessel. The \(1/T_{2B}^*\) is linear to the deoxyhemoglobin content \[ \frac{1}{T_{2B}^*}=A\left(CBVv\right)C_{Hb}\left(1-Y\right) \] where A is a constant related to the susceptibility parameters, \(C_{Hb}\)is the total hemoglobin concentration in the blood, \(Y\) is the hemoglobin oxygenation level and \(CBVv\) is the cerebral blood volume fraction that contains deoxyhemoglobin.
When the susceptibility field varies within the water diffusion distance, the averaging of the intra-voxel signals is dynamic and the cases are for the blood water signal and for the signal around capillary. The \(1/T_{2B}^*\) is nearly quadratic in the deoxyhemoglobin content in the blood. \[ \frac{1}{T_{2B}^*}=B\left(CBVv\right)\left\{C_{Hb}\left(1-Y\right)\right\}^2 \]
The change \(\Delta\)(1/\(T_{2B}^*\)) upon neuronal activation for both types of the averaging is
- <math mylabel2>
\Delta\left(\frac{1}{T_{2B}^*}\right)=\frac{1}{T_{2B}^*}\left\{\frac{\Delta CBVv}{CBVv}-\alpha\frac{\Delta Y}{\left(1-Y\right)}\right\} </math>
where, \(\alpha=1\) in case of static average, \(\alpha=2\) in case of fast dynamic average.
From (1) and (2), MRI signal change is related to the physiological parameters. It is interesting to note that in anesthetized small animals the CBV change induced by neuronal activation has been reported to come mainly from arterial side where the blood does not carry any deoxyhemoglobin. If this is applicable to the conscious human brain, then \(\Delta CBVv = 0\) in (2).
In high field MRI, the extra-vascular space contribution is important and at 1.5T the blood water contribution is the major part.
The regional oxygen balance between the supply and the demand can be described by the Fick’s principle. \(OE=CBF\cdot C_{Hb}\cdot\left(1-Y\right)\) where CBF is the cerebral blood flow and OE is (oxygen extraction). Then the oxygen extraction fraction (OEF) at steady state is \[ OEF=\frac{OE}{CBF\cdot C_{Hb}}=\left(1-Y\right) \]
The change in OEF with variation of the oxygen balance is
- <math mylabel3>
\Delta OEF= -\Delta Y </math> or
- <math mylabel4>
\frac{\Delta\left(OE\right)}{OE}-\frac{\Delta CBF}{CBF}= \frac{\left( -\Delta Y\right)}{\left(1-Y\right)} </math>
Therefore, OEF decrease (more oxygen supply than the consumption) makes\(\Delta Y\) positive. The situation corresponds to the case that \(\Delta CBF/CBF\), is larger than the fractional oxygen extraction change. The corresponding BOLD signal does increase. The energy metabolism of the brain is known to be highly oxidative at the resting state, using glucose as the carbon source. The relation of the consumption changes with the neural activation,\(\Delta CMRglu/ CMRglu = \Delta CMRO2/ CMRO2\), will be held if the metabolic process is equally oxidative as in the resting state. \(CMRx\) is the cerebral metabolic rate of \(x\). The change in the glucose consumption and CBF has been reported to be \(\Delta CMRglu/CMRglu =\Delta CBF/CBF\). Then the positive BOLD signal (\(\Delta Y > 0\)) needs to have \(\Delta CMRglu/CMRglu > \Delta CMRO2/CMRO2\) and the energy metabolism during neural activation has to be less oxidative.
The neural basis and neuro-vascular coupling
The source of neuronal process with which BOLD signal change is associated is the change in the field potential or synaptic activity at the site of activation, not the neuronal firing activity itself (Logothetis et al 2001). This means that fMRI can catch the activity of some multi-neuronal assembly, but cannot tell anything about the information content of the activity that may be carried in neuronal firing patterns.
The coupling to such system activity is very tight. It is obvious that the metabolic load change is directly coupled to the neuronal system activation. Furthermore, the vascular response is also coupled very tightly. One of those coupling mechanisms has been reported. The excess glutamate released for synaptic activation induces the change in Ca+2 in the neighboring astrocyte and the Ca+2 change there does result in releasing blood vessel dilator at the contact point of astrocyte to arterioles and therefore the increase in CBF.
Such tight coupling of BOLD signal to neuronal system activity allows us to use the fMRI signal to probe functional activity in the brain, even though the response time of BOLD signal or any other signals based on vascular changes is in seconds and much slower than neuronal processes.
Positive and negative BOLD change
The positive BOLD signal change has been shown to correspond to excitatory activation, and the increase in field potential or the appearance of evoked potential has been observed at the site where BOLD positive change is marked. For the positive BOLD (\(\Delta Y > 0\), hyper-oxygenation), local neuronal activities at the site of activation are responsible.
In addition to the positive and localized BOLD signal, there are areas that show negative BOLD although the signal change is in general relatively small. There are a few possibilities that can be considered for the case of negative BOLD. The one that has been postulated and measured is for the decrease or the suppression of the local neuro-activity from the control state. There the CBF and CMRO2 both decrease so as to yield \(\Delta Y < 0\). There are some areas that often show negative BOLD to various stimuli such as BA 31 in parietal area. The neurological reasons for the phenomena are still not well understood. There seems to be no case of definite observation where some active inhibition process causes positive BOLD.
With physiological reasons to vary cerebral circulation, it is possible for BOLD signal to change without having local neural activity change. Such signal change is likely diffused in large areas and not well synchronized to the stimulation.
Advantage and disadvantage of BOLD fMRI
In fMRI application to functional mapping of the brain, BOLD signal acquisition with gradient echo is most popular because of the relatively high sensitivity of 0.5-3% signal change by neural activation and the simplicity of measurement that makes the tracking of signal variation in time series relatively easy. BOLD signal acquired by gradient echo measurement, however, has a limit in the spatial resolution because the signal includes the contribution from veins at the sites of activation. It has been reported that the orientation column structure in the primary visual area in the cat brain can be resolved in CBV based fMRI but cannot be with the gradient echo BOLD signal. A further disadvantage of this measurement is the often observed contamination with large surface vessel signals. Activation-induced changes of these can reach very large value of 10-20%, especially when many parts of nearby activated areas supply their more oxygenated venous blood into the draining vessel.
In intra-vascular signals, BOLD effect is present regardless of the size of the vessel in both gradient and spin echo acquisitions and the signals contribute to the voxel signal unless they decay out at a long value of echo-time. The intra-vascular signal is the major component at 1.5 T MRI. The BOLD effect with spin echo at the extra-vasucular space around capillaries is relatively small but becomes important term at super-high field MRI (Kamil). Since the capillary area signal has better functional specificity, it is advantageous to measure at such high field such as above 7T where BOLD-fMRI at higher spatial resolution can be performed.
Another main disadvantage, which is common to all measurements based on vascular changes, is the slow response time of seconds. If the neural events to be measured are slowly happening, they can be tracked along with fMRI since the measurement is in real time. When the events are occurring in short time relative to the fMRI response time, the overlaps of induced fMRI signals make it difficult to resolve the events even they have different neural activity patterns. With the slow response of fMRI, it is difficult to learn the fast dynamics of neural processing which proceeds in tens to hundreds of milliseconds. For the measurement of the latency of an event of activation at a site, one needs to have helps from other non-invasive methods capable of detecting fast dynamics such as MEG and EEG. Some temporal relation between functional systems may be probed by BOLD when there is some inter-system interaction. One needs to look for event related signal changes with a well defined simple paradigm which contains only a few stimulating inputs (Sung)
Non-BOLD measurements
To overcome the disadvantage of BOLD signal, various ways of non-invasive measurements have been developed. CBF and CBV measurements, both of which are based on the vascular response have better specificity for the localization of functional activity by blood flow in capillaries or functional activity linked blood volume change. Both show the response time to be shorter than BOLD signal by about a second which corresponds to the transit time of the blood from arterial side to venous side through capillaries.
Recently, it has been shown that MRI signal with an application of strong field gradients as in water diffusion measurement responds to neuronal activation and the signal change has good spatial specificity (Le Bihan 2006). This response that appears to be a change in the apparent diffusion constant of water in the tissue could be interpreted in the way that in the presence of evoked neural activity there is significant extent of cellular swelling enough to be detected in the diffusion MRI. This phenomenon is very interesting and could turn out to be very important. It requires more study to establish the mechanism of the signal change and its characteristics. So far the dynamic response time is not far from that of CBV change. In optical measurements of functional responses, the evoked light scattering increase was reported to have much faster response.
It has been a dream in the mind of many people to detect electro-magnetic events of neural activity by MRI. Such direct detection will give the means to reach those problems which require high spatial functional resolution and fast dynamics. In spite of active efforts with many guiding experiments of in vitro or model systems, the in vivo detection of electrical events is still slipping away from the hands of MRI spectroscopists. It may require a new thinking in physics for detecting dynamic near field electric phenomena and also much higher MRI sensitivity or reduced non-thermal noise..
Low frequency oscillation of fMRI signal and spontaneous neuro-activity at rest
When a long time series MRI data are analyzed in terms of frequency distribution, one can see the oscillation power be largely in the low frequency region far below respiration rate. There are some peaks at 0.1
Even when the brain is at rest, there are spontaneous neural activities running around, especially in the awake condition. The functional sites connected through the correlation of these low frequency oscillations in MRI time series at some resting state overlap with the sites of the functional network observed when the brain performs some task (Fox & Raichle 2006). In simultaneous measurements of EEG and fMRI, some of known brain wave oscillations sporadically observed in the brain at rest are found to have networks of connected functional sites corresponding to the networks for higher order cognitive functions. Characterization of the brain activities at rest in terms of functional networks may indeed help the understanding of “consciousness” in the human brain.
Activation map and functional network
The use of non-invasive neuro-imaging methods including BOLD-fMRI has been accepted as an approach to elucidate the neural bases of many kinds of sensory/motor and mental processes known in brain science. Functional specificities of sites for visual recognition of objects with what/where information (Unggelider & Pasternak 2003) have been studied as one of neuroscience topics. Similarly language perception and recognition (Physiolog Rev) and music perception have been topics of intensive studies. Application of fMRI covers many other higher order functions in the human brain, elucidating the neural attributes of mental processes involving psychological phenomena such as emotion and feelings. The application has been extended to even wider areas into humanities and social sciences where the neural bases of human moral judgment (Princeton), of economic decision making, and further of “human mind” or “self” have been sought after.
The major part of such success in spreading the application should be attributed to the development of statistical data processing (SPM), which has given easy access to the methodology in spite that the evoked fMRI signal is fairly weak relative to various noises and some variation of signal response among subjects is inevitable. The functional map is then an ensemble average among examined subjects out of the human population. Specific functional roles of activated sits are often examined by some comparison of fMRI responses between carefully chosen two paradigms that are nearly identical but with targeted distinction. In such a comparison, many low-level activation sites as well as common functional sites are eliminated by the criteria of statistical significance.
For the understanding of how the brain works, we need to know the functional network of participating sites for a functional task. The constellation of those observed sites is the major part of the network. Some of higher order cognitive tasks generate brain wave oscillation. Simultaneous recording of fMRI and EEG data can show the presence of the functional network bound by the neural oscillation. The question there is how and in which order the sites in the network interact each other for information transfer. We need to have some new ways to get answers to the question.
It is of course necessary to know the functional specificity of each activated site in detail for the understanding of the network. Many of functional sites are activated with multiple functional inputs although these inputs may have common features. Without the knowledge of the functional specificity and local functional architecture, the working of the network can only be understood in general vague terms.
With non-invasive neuroimaging, the functional role or specificity of a site is only indirectly estimated. This is because we cannot measure the actual input to a site for activation nor the output from the site. Only means we have for controlling the site activation is through the external or internal stimulus we give to the brain. It is unknown which aspects of the original stimulus are delivered to the site in what form for the site-specific processing. Until the time we get to know the information contents of the local input and output, we can only try hard in finding response variation by modifying the stimuli we give to the brain.
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See Also
Electroencephalogram, Event Related Brain Dynamics, Magnetoencephalogram, MRI, Functional Imaging
