Functional magnetic resonance imaging

Functional MRI or functional Magnetic Resonance Imaging (fMRI) is a type of specialized MRI scan. It measures the hemodynamic response (change in blood flow) related to neural activity in the brain or spinal cord of humans or other animals. It is one of the most recently developed forms of neuroimaging. Since the early 1990s, fMRI has come to dominate the brain mapping field due to its relatively low invasiveness, absence of radiation exposure, and relatively wide availability.

fMRI statistics (yellow) overlaid on an average of the brain anatomies of several humans (gray)

Contents

Background

Since the 1890s it has been known that changes in blood flow and blood oxygenation in the brain (collectively known as hemodynamics) are closely linked to neural activity.[1] When neural cells are active they increase their consumption of energy from glucose and switching to less energetically effective, but more rapid aerobic glycolysis.[2][3] The local response to this energy utilization is to increase blood flow to regions of increased neural activity, which occurs after a delay of approximately 1–5 seconds. This hemodynamic response rises to a peak over 4–5 seconds, before falling back to baseline (and typically undershooting slightly). This leads to local changes in the relative concentration of oxyhemoglobin and deoxyhemoglobin and changes in local cerebral blood volume and in local cerebral blood flow.

History

Blood-oxygen-level dependence (BOLD) is the MRI contrast of blood deoxyhemoglobin, first discovered in 1990 by Seiji Ogawa at AT&T Bell labs.[4] Ogawa and colleagues had recognized the potential importance of BOLD for functional brain imaging with MRI, but the first successful fMRI study was reported by John W. Belliveau and colleagues in 1991.[5] Using a visual stimulus paradigm, localized increases in blood volume (32 +/- 10 percent, n = 7 subjects) were detected in the primary visual cortex. In 1992, three papers were published using endogenous BOLD contrast MRI. One was submitted by Peter Bandettini at the Medical College of Wisconsin on February 5, revised March 31, accepted March 31 and published in the June 1992 issue of Magnetic Resonance in Medicine (MRM). The second by Kenneth Kwong also applied BOLD to image human brain activities with MRI and submitted his findings on March 26 which were published in the June issue of PNAS in 1992.[6] In the same year, Dr. Ogawa submitted their result on March 31 and published in July issue of PNAS.[7] In the following year, Dr. Ogawa published the biophysics model of BOLD contrast in Biophysical Journal.[8] Dr. Bandettini also published a further paper in 1993 demonstrating quantitative determination of functional activation maps.[9]

Physiology

As neurons do not have internal reserves for glucose and oxygen, more neuronal activity requires more glucose and oxygen to be delivered through blood stream rapidly. Through a process called the hemodynamic response, blood releases glucose to neurons and astrocyte at a greater rate than in the area of inactive neurons. It results in a surplus of oxyhemoglobin in the veins of the area and distinguishable change of the local ratio of oxyhemoglobin to deoxyhemoglobin, the "marker" of BOLD for MRI.[3]

Hemoglobin is diamagnetic when oxygenated (oxyhemoglobin) but paramagnetic when deoxygenated (deoxyhemoglobin).[10] The magnetic resonance (MR) signal of blood is therefore slightly different depending on the level of oxygenation. Higher BOLD signal intensities arise from increases in the concentration of oxygenated hemoglobin since the blood magnetic susceptibility now more closely matches the tissue magnetic susceptibility. By collecting data in an MRI scanner with sequence parameters sensitive to changes in magnetic susceptibility one can assess changes in BOLD contrast. These changes can be either positive or negative depending upon the relative changes in both cerebral blood flow (CBF) and oxygen consumption. Increases in CBF that outstrip changes in oxygen consumption will lead to increased BOLD signal, conversely decreases in CBF that outstrip changes in oxygen consumption will cause decreased BOLD signal intensity. The signal difference is very small, but given many repetitions of a thought, action or experience, statistical methods can be used to determine the areas of the brain which reliably show more of this difference as a result, and therefore which areas of the brain are active during that thought, action or experience.

Almost all current fMRI research uses BOLD as the method for determining where activity occurs in the brain as the result of various experiences, but because the signals are relative and not individually quantitative, some question its rigor.[11] Other methods which propose to measure neural activity more directly have been attempted (for example measurement of the Oxygen Extraction Fraction (OEF) in regions of the brain, which measures how much of the oxyhemoglobin in the blood has been converted to deoxyhemoglobin[12] or direct detection of magnetic fields generated by neuronal currents[13]), but because the electromagnetic fields created by an active or firing neuron are so weak, the signal-to-noise ratio is extremely low and statistical methods used to extract quantitative data have been largely unsuccessful as of yet.

Neural correlates of BOLD

The precise relationship between neural signals and BOLD is under active research. In general, changes in BOLD signal are well correlated with changes in blood flow. Numerous studies during the past several decades have identified a coupling between blood flow and metabolic rate [14]; that is, the blood supply is tightly regulated in space and time to provide the nutrients for brain metabolism. However, neuroscientists have been seeking a more direct relationship between the blood supply and the neural inputs/outputs that can be related to observable electrical activity and circuit models of brain function.

While current data indicate that local field potentials, an index of integrated electrical activity, form a marginally better correlation with blood flow than the spiking action potentials that are most directly associated with neural communication [15], no simple measure of electrical activity to date has provided an adequate correlation with metabolism and the blood supply across a wide dynamic range. Presumably, this reflects the complex nature of metabolic processes, which form a superset with regards to electrical activity. Some recent results have suggested that the increase in cerebral blood flow (CBF) following neural activity is not causally related to the metabolic demands of the brain region, but rather is driven by the presence of neurotransmitters, like glutamate [16], serotonin, nitric oxide[17], acetylcholin, dopamine and noradrenalin.

Some other recent results suggest that an initial small, negative dip before the main positive BOLD signal is more highly localized and also correlates with measured local decreases in tissue oxygen concentration (perhaps reflecting increased local metabolism during neuron activation)[18][19]. Use of this more localized negative BOLD signal has enabled imaging of human ocular dominance columns in primary visual cortex, with resolution of about 0.5 mm[20]. One problem with this technique is that the early negative BOLD signal is small and can only be seen using larger scanners with magnetic fields of at least 3 Tesla. Further, the signal is much smaller than the normal BOLD signal, making extraction of the signal from noise more difficult. Also, this initial dip occurs within 1–2 seconds of stimulus initiation, which may not be captured when signals are recorded at long repetition (TR). If the TR is sufficiently low, increased speed of the cerebral blood flow response due to consumption of vasoactive drugs (such as caffeine[21]) or natural differences in vascular responsivnesses may further obscure observation of the initial dip.

The BOLD signal is composed of CBF contributions from larger arteries and veins, smaller arterioles and venules, and capillaries. Experimental results indicate that the BOLD signal can be weighted to the smaller vessels, and hence closer to the active neurons, by using larger magnetic fields. For example, whereas about 70% of the BOLD signal arises from larger vessels in a 1.5 tesla scanner, about 70% arises from smaller vessels in a 7 tesla scanner[22]. Furthermore, the size of the BOLD signal increases roughly as the square of the magnetic field strength[23]. Hence there has been a push for larger field scanners to both improve localization and increase the signal. A few 7 tesla commercial scanners have become operational, and experimental 8 and 9 tesla scanners are under development.

Technique

BOLD effects are measured using rapid volumetric acquisition of images with contrast weighed by T1 or T2*. Such images can be acquired with moderately good spatial and temporal resolution; images are usually taken every 1–4 seconds, and the voxels in the resulting image typically represent cubes of tissue about 2–4 millimeters on each side in humans. Recent technical advancements, such as the use of high magnetic fields[24] and multichannel RF reception[25][26][27], have advanced spatial resolution to the millimeter scale. Although responses to stimuli presented as close together as one or two seconds can be distinguished from one another, using a method known as event-related fMRI, the full time course of a BOLD response to a briefly presented stimulus lasts about 15 seconds for the robust positive response.

fMRI studies draw from many disciplines

fMRI is a highly interdisciplinary research area and many studies draw on knowledge in several fields:

Advantages and Disadvantages of fMRI

Like any technique, fMRI has advantages and disadvantages, and in order to be useful, the experiments that employ it must be carefully designed and conducted to maximize its strengths and minimize its weaknesses.

Advantages of fMRI

Disadvantages of fMRI

For these reasons, Functional imaging provides insights into neural processing that are complementary to insights of other studies in neurophysiology.

Scanning in practice

Berkeley's 4T fMRI scanner.

Subjects participating in a fMRI experiment are asked to lie still and are usually restrained with soft pads to prevent movement from disturbing measurements. Some labs also employ bite bars to reduce motion, although these are unpopular as they can be uncomfortable. Small head movements can be corrected for in post-processing of the data, but large transient motion cannot be corrected. Motion in excess of around 3 millimeters results in unusable data. Motion is an issue for all populations, but most especially problematic for subjects with certain medical conditions (e.g. Alzheimer's Disease or schizophrenia) or with young children. Participants can be habituated to the scanning environment and trained to remain still in an MRI simulator.

An fMRI experiment usually lasts between 15 minutes and an hour. Depending on the purpose of study, subjects may view movies, hear sounds, smell odors, perform cognitive tasks such as n-back, memorization or imagination, press a few buttons, or perform other tasks. Researchers are required to give detailed instructions and descriptions of the experiment plan to each subject, who must sign a consent form before the experiment.

Safety is an important issue in all experiments involving MRI. Potential subjects must ensure that they are able to enter the MRI environment. The MRI scanner is built around an extremely strong magnet (1.5 teslas or more), so potential subjects must be thoroughly examined for any ferromagnetic objects (e.g. watches, glasses, hair pins, pacemakers, bone plates and screws, etc.) before entering the scanning environment.

Related techniques

Aside from BOLD fMRI, there are other related ways to probe brain activity using magnetic resonance properties:

Diffusion based functional MRI

Neuronal activity produces some immediate physical changes in cell shape that can be detected because they affect the compartment shape and size for water diffusion. A much improved spatial and temporal resolution for fMRI data collection has now been achieved by using diffusion MRI methodology that can detect these changes in neurons.[30].The abrupt onset of increased neuron cell size occurs before the metabolic response commences, is shorter in duration and does not extend significantly beyond the area of the actual cell population involved.[31] This technique is a diffusion weighted technique (DWI). There is some evidence that similar changes in axonal volume in white matter may accompany activity and this has been observed using a DTI (diffusion tensor imaging) technique.[32] The future importance of diffusion-based functional techniques relative to BOLD techniques is not yet clear.

Contrast MR

An injected contrast agent such as an iron oxide that has been coated by a sugar or starch (to hide from the body's defense system), causes a local disturbance in the magnetic field that is measurable by the MRI scanner. The signals associated with these kinds of contrast agents are proportional to the cerebral blood volume. While this semi-invasive method presents a considerable disadvantage in terms of studying brain function in normal subjects, it enables far greater detection sensitivity than BOLD signal, which may increase the viability of fMRI in clinical populations. Other methods of investigating blood volume that do not require an injection are a subject of current research, although no alternative technique in theory can match the high sensitivity provided by injection of contrast agent.

Arterial spin labeling

By magnetic labeling the proximal blood supply using "arterial spin labeling" (ASL), the associated signal is proportional to the cerebral blood flow, or perfusion. This method provides more quantitative physiological information than BOLD signal, and has the same sensitivity for detecting task-induced changes in local brain function.

Magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRS) is another, NMR-based process for assessing function within the living brain. MRS takes advantage of the fact that protons (hydrogen atoms) residing in differing chemical environments depending upon the molecule they inhabit (H2O vs. protein, for example) possess slightly different resonant properties (chemical shift). For a given volume of brain (typically > 1 cubic cm), the distribution of these H resonances can be displayed as a spectrum.

The area under the peak for each resonance provides a quantitative measure of the relative abundance of that compound. The largest peak is composed of H2O. However, there are also discernible peaks for choline, creatine, N-acetylaspartate (NAA) and lactate. Fortuitously, NAA is mostly inactive within the neuron, serving as a precursor to glutamate and as storage for acetyl groups (to be used in fatty acid synthesis) — but its relative levels are a reasonable approximation of neuronal integrity and functional status. Brain diseases (schizophrenia, stroke, certain tumors, multiple sclerosis) can be characterized by the regional alteration in NAA levels when compared to healthy subjects. Creatine is used as a relative control value since its levels remain fairly constant, while choline and lactate levels have been used to evaluate brain tumors.

Diffusion tensor imaging

Diffusion tensor imaging (DTI) is a related use of MR to measure anatomical connectivity between areas. Although it is not strictly a functional imaging technique because it does not measure dynamic changes in brain function, the measures of inter-area connectivity it provides are complementary to images of cortical function provided by BOLD fMRI. White matter bundles carry functional information between brain regions. The diffusion of water molecules is hindered across the axes of these bundles, such that measurements of water diffusion can reveal information about the location of large white matter pathways. Illnesses that disrupt the normal organization or integrity of cerebral white matter (such as multiple sclerosis) have a quantitative impact on DTI measures.

fMRI and EEG

Functional MRI has high spatial resolution but relatively poor temporal resolution (of the order of several seconds). Electroencephalography (EEG) directly measures the brain's electrical activity, giving high temporal resolution (~milliseconds) but low spatial resolution. The two techniques are therefore complementary and may be used simultaneously to record brain activity.

Recording an EEG signal inside an MRI system is technically challenging. The MRI system introduces artifacts into the EEG recording by inducing currents in the EEG leads via Faraday induction. This can happen through several different mechanisms. An imaging sequence applies a series of short radiofrequency pulses which induce a signal in the EEG system. The pulses are short and relatively infrequent, so interference may be avoided by blanking (switching off) the EEG system during their transmission. Magnetic field gradients used during imaging also induce a signal, which is harder to remove as it is in a similar frequency range to the EEG signal. Current is also induced when EEG leads move inside the magnet bore (i.e. when the patient moves during the exam). Finally, pulsed blood flow in the patient in the static magnetic field also induces a signal (called a ballistocardiographic artifact), which is also within the frequency range of interest. The EEG system also affects the MRI scan. Metal in the EEG leads and electrodes can introduce susceptibility artifacts into MR images. Care must also be taken to limit currents induced in the EEG leads via the MRI RF system, which could heat the leads sufficiently to burn the subject.

Having simultaneously recorded EEG and fMRI data, the final hurdle is to co-register the two datasets, as each is reconstructed using a different algorithm, subject to different distortions.

Nuclear neuroimaging

Before the advent of fMRI functional neuroimaging was typically performed with positron emission tomography (PET) scanners or more rarely with SPECT scanners. Niels A. Lassen and his coworkers lead the earliest efforts of functional neuroimaging, using radioactive gasses to construct images of the working brain.

These nuclear imaging techniques do not use the nuclear magnetic resonance property and employ entirely different scanners.

Approaches to fMRI data analysis

The ultimate goal of fMRI data analysis is to detect correlations between brain activation and the task the subject performs during the scan. The BOLD signature of activation is relatively weak, however, so other sources of noise in the acquired data must be carefully controlled. This means that a series of processing steps must be performed on the acquired images before the actual statistical search for task-related activation can begin.

For a typical fMRI scan, the 3D volume of the subject's head is imaged every one or two seconds, producing a few hundred to a few thousand complete images per scanning session. The nature of MRI is such that these images are acquired in Fourier transform space, so they must be transformed back to image space to be useful. Because of practical limitations of the scanner the Fourier samples are not acquired on a grid, and scanner imperfections like thermal drift and spike noise introduce additional distortions. Small motions on the part of the subject and the subject's pulse and respiration will also affect the images.

The most common situation is that the researcher uses a pulse sequence supplied by the scanner vendor, such as an echo-planar imaging (EPI) sequence that allows for relatively rapid acquisition of many images. Software in the scanner platform itself then performs the reconstruction of images from Fourier transform space. During this stage some information is lost (specifically the complex phase of the reconstructed signal). Some types of artifacts, for example spike noise, become more difficult to remove after reconstruction, but if the scanner is working well these artifacts are thought to be relatively unimportant. For pulse sequences not provided by the vendor, for example spiral EPI, reconstruction may have to be done by software running on a separate platform.

After reconstruction the output of the scanning session consists of a series of 3D images of the brain. The most common corrections performed on these images are motion correction and correction for physiological effects. Outlier correction and spatial and/or temporal filtering may also be performed. If the task performed by the subject is thought to produce bursts of activation which are short compared to the BOLD response time (on the order of 6 seconds), temporal filtering may be performed at this stage to attempt to deconvolve out the BOLD response and recover the temporal pattern of activation.

At this point the data provides a time series of samples for each voxel in the scanned volume. A variety of methods are used to correlate these voxel time series with the task in order to produce maps of task-dependent activation.

There are many software packages available for analysing fMRI data.

Cost of fMRI

The major cost of an fMRI experiment is the MR scanner itself. New 1.5 tesla scanners often cost between $1,000,000 USD and $1,500,000 USD. New 3.0 tesla scanners often cost between $2,000,000 and $2,300,000 USD. Construction of MRI suites can cost $500,000 USD.

MRI procedures themselves can vary considerably in cost but generally fall somewhere between $400 and $3,500, depending on the facility and which region of the body is being scanned. Extremity scans (feet, hands, etc) tend to be lower in price while body scans (including the brain) tend to be higher.[33]

Commercial use

Most fMRI scans are for research or clinical use. Commercial use is limited. However, a few companies have been set up that attempt to sell fMRI specific hardware or services for research or clinical use, e.g.,

At least two companies have been set up to use fMRI in lie detection (No Lie MRI, Inc[37] and Cephos Corporation[38]).

In using fMRI techniques for use in lie detection, activated areas of the brain are observed while the subject is making a statement. Depending on what regions are the most active, the technician might determine whether a subject is telling the truth or not. Since a specific combination of brain functions are needed in order to tell a lie, the simultaneous activation of these regions often indicates deception. This technology is in its early stages of development, and many of its proponents hope to replace older lie detection techniques.[39]

In clinical trials, the usage of fMRI as a method of lie detection has appeared reliable, with studies from 2005 by Kozel et al indicating a 90% to 93% success rate.[40]

However, there is still a fair amount of controversy over whether these techniques are reliable enough to be used in a legal setting. Some studies indicate that while there is an overall positive correlation, there is a great deal of variation between findings and in some cases considerable difficulty in replicating the findings.[41]

See also

References

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  2. ↑ Raichle, M. E., Mintun, M. A. (2006). "Brain Work and Brain Imaging". The Annual Review of Neuroscience. 
  3. ↑ 3.0 3.1 Laureys, S., Boly, M., Tononi, G.: "Functional Neuroimaging" in "The Neurology of Consciousness: Cognitive Neuroscience and Neuropathology" (Laureys, S., Tononi, G. eds.), Academic Press-Elsevier, 2009, ISBN 978-0-12-374168-4, pp.31-42
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  16. ↑ Yang, G. and Iadecola, C. (1996). "Glutamate microinjections in cerebellar cortex reproduce cerebrovascular effects of parallel fiber stimulation". Am J Physiol Regul Integr Comp Physiol 271 (6): R1568–1575. PMID 8997354. http://ajpregu.physiology.org/cgi/content/abstract/271/6/R1568. 
  17. ↑ doi:10.1016/S0168-0102(00)00109-7
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  18. ↑ doi:10.1038/72109
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  19. ↑ doi:10.1126/science.272.5261.551
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  20. ↑ doi:10.1016/S0959-4388(00)00198-7
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  21. ↑ Behzadi, Y. et al. (2006). "Caffeine reduces the initial dip in the visual bold response at 3 t.". Neuroimage 32 (1): 9–15. doi:10.1016/j.neuroimage.2006.03.005. PMID 16635577. 
  22. ↑ http://www.ru.nl/fcdonders/facilities/fmri_scanners/
  23. ↑ doi:10.1016/j.ejrad.2003.08.010
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  24. ↑ van der Zwaag W, Francis S, Head K, Peters A, Gowland P, Morris P, Bowtell R. (May 2009). "fMRI at 1.5, 3 and 7 T: Characterising BOLD signal changes.". Neuroimage 47 (4): 1425–34. doi:10.1016/j.neuroimage.2009.05.015. PMID 19446641. 
  25. ↑ Roemer PB, Edelstein WA, Hayes CE, Souza SP, Mueller OM. (November 1990). "The NMR phased array.". Magn Reson Med. 16 (2): 192–225. doi:10.1002/mrm.1910160203. PMID 2266841. 
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  27. ↑ Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. (June 2002). "Generalized autocalibrating partially parallel acquisitions (GRAPPA).". Magn Reson Med. 47 (6): 1202–10. doi:10.1002/mrm.10171. PMID 12111967. 
  28. ↑ http://www.wired.com/wiredscience/2009/09/fmrisalmon/
  29. ↑ Logothetis, N.K.; Pauls, J; Augath, M; Trinath, T; Oeltermann, A (2001). "Neurophysiological investigation of the basis of the fMRI signal.". Nature 412 (6843): 150. doi:10.1038/35084005. PMID 11449264. http://www.ssc.uwo.ca/psychology/culhamlab/fmri/pdfs/Logothetis.pdf. 
  30. ↑ Le Bihan D, et al (2006). "Direct and fast detection of neuronal activation in the human brain with diffusion MRI". PNAS 103 (21): 8263–8268. doi:10.1073/pnas.0600644103. PMID 16702549. 
  31. ↑ Kohno S et al. (2009). "Water diffusion slowdown in the human visual cortex on visual stimulation precedes vascular responses". Journal of Cerebral Blood Flow & Metabolism 29 (6): 1197–1207. doi:10.1038/jcbfm.2009.45. PMID 19384332. 
  32. ↑ Mandl RCW et al. (2008). "Functional diffusion tensor imaging: Measuring task-related fractional anisotropy changes in the human brain along white matter tracts". PLoS ONE 3 (11): 1–10. doi:10.371/journal.pone.0003631. 
  33. ↑ http://www.comparemricost.com/
  34. ↑ Omneuron
  35. ↑ Applied fMRI
  36. ↑ Imagilys
  37. ↑ No Lie MRI Inc
  38. ↑ Cephos Corporation
  39. ↑ Kozel FA et al. "A Replication Study of the Neural Correlates of Deception." Behavioral Neuroscience, Vol. 118, No. 4, 2004 http://teaching.ust.hk/~econ695/Neural%20Correlates%20of%20Different%20Types%20of%20Deception.pdf
  40. ↑ Kozel FA et al. "Detecting Deception Using Functional Magnetic Resonance Imaging." Biol Psychiatry. 2005 Sep 23 http://www.musc.edu/pr/fmri.pdf
  41. ↑ Spence, Sean A. "Playing Devil's advocate: The case against fMRI lie detection." Legal and Criminological Psychology, Volume 13, Number 1, February 2008 , pp. 11-25(15) http://www.ingentaconnect.com/content/bpsoc/lcp/2008/00000013/00000001/art00002

Textbooks

Journal articles

Weiller C et al. (2006). "Role of functional imaging in neurological disorders". Journal of Magnetic Resonance Imaging 23 (6): 840–850. doi:10.1002/jmri.20591. PMID 16649207. 

Lin, Lyons, and Berkowitz (2007). "Somatotopic Identification of Language-SMA in Language Processing via fMRI". Journal of Scientific and Practical Computing 1 (2): 3–8.  [1]

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