Understanding the Brain

Functional magnetic resonance imaging

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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 switch to less energetically effective, but more rapid anaerobic 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.


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 using an intraveneously administered paramagnetic contrast agent (Gadolinium).[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 and colleagues also applied BOLD to image human brain activities with MRI and was submitted on March 26 and 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]


As neurons do not have internal reserves for glucose and oxygen, more neuronal activity requires more glucose and oxygen to be delivered rapidly through the blood stream. Through a process called the hemodynamic response, blood releases glucose to neurons and astrocytes 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]


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


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] acetylcholine, dopamine and noradrenaline.

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 responsiveness 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.


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



Advantages and Disadvantages of fMRI


Advantages of fMRI

  • It can noninvasively record brain signals without risks of radiation inherent in other scanning methods, such as CT or PET scans.
  • It has high spatial resolution. 2–3 mm is typical but resolution can be as good as 1mm.
  • It can record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface.
  • fMRI is widely used and standard data-analysis approaches have been developed which allow researchers to compare results across labs.
  • fMRI produces compelling images of brain "activation".

Disadvantages of fMRI

  • The images produced must be interpreted carefully, since correlation does not imply causality, and brain processes are complex and often non-localized.
  • Statistical methods must be used carefully because they can produce false positives. One team of researchers studying reactions to pictures of human emotional expressions reported a few activated voxels in the brain of a dead salmon when no correction for multiple comparisons was applied, illustrating the need for rigorous statistical analyses.[28]
  • The BOLD signal is only an indirect measure of neural activity, and is therefore susceptible to influence by non-neural changes in the body. This also means that it is difficult to interpret positive and negative BOLD responses[29]
  • fMRI has poor temporal resolution. The BOLD response peaks approximately 5 seconds after neuronal firing begins in an area. This means that it is hard to distinguish BOLD responses to different events which occur within a short time window. Careful experimental design can reduce this problem. Also, some research groups are attempting to combine fMRI signals that have relatively high spatial resolution with signals recorded with other techniques, electroencephalography (EEG) or magnetoencephalography (MEG), which have higher temporal resolution but worse spatial resolution.
  • The BOLD response can be affected by a variety of factors, including: drugs/substances;[31] age, brain pathology;[32] local differences in neurovascular coupling;[33] attention;[34] amount of carbon dioxide in the blood;[35] etc.

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.


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


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.[36] 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.[37] 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.[38] The future importance of diffusion-based functional techniques relative to BOLD techniques is not yet clear.

Contrast MR


Arterial spin labeling

Arterial Spin Labelling (ASL), also known as arterial spin tagging, is an MRI technique capable of measuring cerebral blood flow (CBF) in vivo. ASL is capable of providing cerebral perfusion maps, without requiring the administration of a contrast agent or the use of ionising radiation, as it uses magnetically-labelled endogenous blood water as a freely-diffusible tracer. It was first proposed in 1992 [39][40] and has since benefited from a number of modifications aimed at improving its robustness. ASL can monitor changes in CBF with activation and fMRI studies can therefore be conducted using ASL instead of relying on the BOLD effect. ASL fMRI is less popular than BOLD, as it suffers from a lower signal to noise ratio, can be less sensitive to weak stimuli and its temporal resolution is poorer than in BOLD studies.[41] On the plus side, it can provide quantitative measures of a single well-defined parameter, CBF, whose baseline value can also be determined in the same experiment. It has also been found to outperform BOLD in terms of stability to slow signal drifts and localization of the activation area.[41] The ASL activation signal is believed to be dominated by changes in the capillary bed of the activated area of the cortex, wheareas the BOLD signal is likely to be dominated by changes in the oxygenation of nearby veins.

Magnetic resonance spectroscopic imaging



Diffusion tensor imaging


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.



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 gases to construct images of the working brain.


Approaches to fMRI data analysis






There are many software packages available for analysing fMRI data.

Reconstruction of MRI data needs to be tested, calibrated and confirmed. MRI can suffer from numerous artifacts that include, geometric distortions, Nyquist ghosting, and signal dropout. Medical Imaging Phantoms are used to provide a consistent geometrical source for calibration and testing purposes. Minute tumor changes can require recalibration by use of a phantom to quantify the change.

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.[42]

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.

At least two companies have been set up to use fMRI in lie detection (No Lie MRI, Inc[43] and Cephos Corporation[44]).

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.[45]

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.[46]

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.[47]

See also


  1. Roy CS, Sherrington CS (January 1890). "On the Regulation of the Blood-supply of the Brain". Journal of Physiology 11 (1–2): 85–158.17. PMC 1514242. PMID 16991945. 
  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
  4. Ogawa, S., Lee, T.M., Nayak, A.S., and Glynn, P. (1990). "Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields". Magnetic Resonance in Medicine 14 (1): 68–78. doi:10.1002/mrm.1910140108. PMID 2161986. 
  5. Belliveau JW, Kennedy DN, McKinstry RC, Buchbinder BR, Weisskoff RM, Cohen MS, Vevea JM, Brady TJ, and Rosen BR (1991). "Functional mapping of the human visual cortex by magnetic resonance imaging". Science 254 (5032): 716–719. doi:10.1126/science.1948051. PMID 1948051. 
  6. KK Kwong, JW Belliveau, DA Chesler, IE Goldberg, RM Weisskoff, BP Poncelet, DN Kennedy, BE Hoppel, MS Cohen, R Turner, H Cheng, TJ Brady, and BR Rosen (1992). "Dynamic Magnetic Resonance Imaging of Human Brain Activity During Primary Sensory Stimulation". PNAS 89 (12): 5675–79. doi:10.1073/pnas.89.12.5675. 
  7. OGAWA S, TANK DW, MENON R, ELLERMANN JM, KIM SG, MERKLE H, UGURBIL K (1992). "Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging". PNAS 89 (13): 5951–55. doi:10.1073/pnas.89.13.5951. PMC 402116. PMID 1631079. 
  8. Ogawa S, Menon RS, Tank DW, Kim SG, Merkle H, Ellermann JM, Ugurbil K. (1993). "Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model". Biophysical Journal 64 (3): 803–12. Bibcode 1993BpJ....64..803O. doi:10.1016/S0006-3495(93)81441-3. 
  9. Bandettini, P.A.; Jesmanowicz, A.; Wong, E.C.; Hyde, J.S. (1993). "Processing strategies for time-course data sets in functional MRI of the human brain". Magnetic Resonance in Medicine 30 (2): 161–173. doi:10.1002/mrm.1910300204. PMID 8366797. 
  10. L Pauling and CD Coryell (1936). "The Magnetic Properties and Structure of Hemoglobin, Oxyhemoglobin and Carbonmonoxyhemoglobin". PNAS 22 (4): 210–6. doi:10.1073/pnas.22.4.210. PMC 1076743. PMID 16577697. 
  11. Gusnard DA, Raichle ME (2001). "Searching for a baseline: Functional imaging and the resting human brain". Nature Reviews Neuroscience 2 (10): 685–694. doi:10.1038/35094500. PMID 11584306. 
  12. Yablonskiy DA, Haacke EM (1994). "Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime". Magnetic Resonance in Medicine 32 (6): 749–63. doi:10.1002/mrm.1910320610. PMID 7869897. 
  13. Konn D, Gowland P, Bowtell R (2003). "MRI detection of weak magnetic fields due to an extended current dipole in a conducting sphere: a model for direct detection of neuronal currents in the brain". Magnetic Resonance in Medicine 50 (1): 40–49. doi:10.1002/mrm.10494. PMID 12815677. 
  14. Magistretti PJ, Pellerin L. (July 1999). "Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging". Philosophical Transactions of the Royal Cociety of London 29 (354(1387)): 1155–63. PMC 1692634. PMID 10466143. 
  15. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. (July 2001). "Neurophysiological investigation of the basis of the fMRI signal". Nature 412 (6843): 150–7. doi:10.1038/35084005. PMID 11449264. 
  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. Bonvento, Giles; Cholet, Nathalie and Seylaz, Jacques (2000). "Sustained attenuation of the cerebrovascular response to a 10 min whisker stimulation following neuronal nitric oxide synthase inhibition". Neurosci Res 37 (2): 163–166. PMID 10867178. http://www.sciencedirect.com/science/article/pii/S0168010200001097. 
  18. doi:10.1038/72109
  19. doi:10.1126/science.272.5261.551
  20. doi:10.1016/S0959-4388(00)00198-7
  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
  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. 
  26. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. (Nov 1999). "SENSE: Sensitivity encoding for fast MRI". Magn Reson Med. 42 (5): 952–62. doi:10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S. PMID 10542355. 
  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. Shmuel et al. Negative fMRI response correlates with decreases in neuronal activity in monkey visual area V1. Nature Neurosci. 9(4):569-577 (2006).
  30. 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. 
  31. Magalhaes, A. (2005). Functional magnetic resonance and spectroscopy in drug and substance abuse. Top Magnetic Resonance Imaging. 3, 247-251.
  32. Chen, C., Hou, B., Holodny, A. (2008). Effect of age and tumor grade on BOLD functional MR imaging in pre-operative assessment of patients with glioma. Radiology. 3, 971-978.
  33. Aguirre, G., Zarahn, E., and D’esposito, M. (1998). The variability of human BOLD hemodynamic responses. Neuroimage. 8 (4), 360-369.
  34. Corbetta, M., Miezin, F., Dobmeyer, S., Shulman, G., Petersen, S. (1990). Attentional modulation of neural processing of shape, color, and velocity in humans. Science. 4962, 1556-1559.
  35. Haller, S., Bartsch, A. (2009). Pitfalls in fMRI. European Radiology. 19, 2689-2706.
  36. 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. PMC 1472461. PMID 16702549. 
  37. 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. 
  38. Mandl RCW et al. (2008). Warrant, Eric. ed. "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:10.1371/journal.pone.0003631. 
  39. Detre, J.A., Leigh, J.S., Williams, D.S., and Koretsky, A.P. (1992). Perfusion imaging. Magn Reson Med 23, 37-45.
  40. Williams, D.S., Detre, J.A., Leigh, J.S., and Koretsky, A.P. (1992). Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci U S A 89, 212-216.
  41. 41.0 41.1 Buxton, R.B. Introduction to Functional Magnetic Resonance Imaging, Principles and Techniques, 2009, Cambridge University Press, Cambridge, UK
  42. http://www.comparemricost.com/
  43. No Lie MRI Inc
  44. Cephos Corporation
  45. 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
  46. Kozel FA et al. "Detecting Deception Using Functional Magnetic Resonance Imaging." Biol Psychiatry. 2005 Sep 23 http://www.musc.edu/pr/fmri.pdf
  47. 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


  • Richard B. Buxton, An Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques, Cambridge Univ Press, 2002, ISBN 0-521-58113-3
  • Roberto Cabeza and Alan Kingstone, Editors, Handbook of Functional Neuroimaging of Cognition, Second Edition, MIT Press, 2006, ISBN 0-262-03344-5

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. 

External links