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FUNCTIONAL MAGNETIC RESONANCE IMAGING A Brief Introduction to Functional MRI History and Today’s Developments © DIGITAL STOCK BY JAMES J. PEKAR n this issue, IEEE Engineering in Medicine and Biology Magazine focuses on modern methods for the analysis of data from functional magnetic resonance imaging (fMRI) studies. Accordingly, the guest editors have seen fit to begin with a brief article on the mechanisms and methods behind fMRI. In this context, it is worth noting that magnetic resonance imaging (MRI), in the first place, rests on a series of unlikely accomplishments: Who might have thought, at the turn of the last century, that the spin of subatomic particles could be detected in bulk matter using radio frequency (RF) energy, or that RF energy, with wavelengths of tens of centimeters, could be used to form exquisite images with submillimeter spatial resolution? In 1924, Wolfgang Pauli suggested, on theoretical grounds, that particles could have intrinsic angular momentum, or spin. In the 1920s, Otto Stern and I.I. Rabi showed that beams of such particles, traveling in a vacuum through strong magnetic fields, would absorb RF energy in a narrow range of frequencies. Those beam experiments were the first measurements of spin resonance. In 1946 Felix Bloch and Edward Mills Purcell showed that such spin resonance could be detected among nuclear spins in bulk matter; for this they shared the Nobel Prize in Physics in 1952. This phenomenon, dubbed nuclear magnetic resonance (NMR), turned out to be immensely valuable for chemistry because nuclei resonate with exquisite sensitivity to local electromagnetic fields and consequently serve as lucid reporters on their microenvironments. Applications of NMR to chemistry grew rapidly, as did applications to living systems. In the late 1950s, scientists began applying NMR to isolated cells and excised tissues; by the late 1960s, NMR data were being acquired from intact animals [1]. In the early 1970s, scientists noted that such tissue NMR signals from water’s hydrogen nuclei change in disease. A seminal contribution was made in 1973 when Paul Lauterbur [2] showed how NMR signals could be used to form an image, using a spatial gradient of the static magnetic field to yoke frequency to location, by causing the resonance frequency of nuclei to vary linearly with their spatial location. It is worth noting that these images are not diffraction limited in that, unlike, for example, optical or electron microscopy, we can use MRI to I 24 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE resolve features much smaller than the wavelength of the RF energy used. In 2004, Lauterbur shared the Nobel Prize in Physiology or Medicine with Peter Mansfield [3], who laid the foundation for snapshot MRI (image formation from data acquired in a fraction of a second). Richard Ernst introduced another central innovation [4] when, in 1975, he first demonstrated a Fourier rather than projection-based approach to magnetic resonance (MR) image encoding; for this (and other) work, he was awarded the Nobel Prize in Chemistry in 1991. Development of commercial MR scanners took off in the 1980s, and by 1990, clinical MRI was of primary importance for brain tumors, stroke, and multiple sclerosis. In 1990, Ogawa [5] showed that these MRI water signals can be sensitized to cerebral oxygenation, using deoxyhemoglobin as an endogenous susceptibility contrast agent. Using gradient-echo imaging, a form of MRI image encoding sensitive to local inhomogeneity of the static magnetic field, Ogawa demonstrated (in an animal model) that the appearance of the brain’s blood vessels changed with blood oxygenation. Within two years, his and two other groups had published papers using this blood-oxygenation-level-dependent (BOLD) contrast MRI to detect brain activation in humans [6]–[8], and, today, an explosion of studies use this so-called fMRI technique to map human brain function. Of course, BOLD fMRI does not measure brain function directly. Rather, BOLD fMRI brain activation studies are the latest in a line of approaches, dating from the 19th century, which use brain perfusion as a proxy for brain function. Without making direct measurements of brain function (i.e., without measuring computations performed in neuronal cell bodies, action potentials traveling along axons, or neurotransmitter trafficking at synaptic junctions), these approaches take advantage of the phenomenon that increases in neuronal activity are accompanied by local increases in perfusion. Generally, these approaches map changes in perfusion (or its concomitants) to shed light on regional changes in brain activity. Specifically, BOLD fMRI sensitizes MRI acquisitions to the local decreases in deoxyhemoglobin due to reactive hyperemia [9] accompanying neuronal activation: Following an increase in 0739-5175/06/$20.00©2006IEEE MARCH/APRIL 2006 neuronal activity, local blood flow increases (the mechanisms responsible for this neurovascular coupling are still being explored). The increase in perfusion, in excess of that needed to support the increased oxygen consumption due to neuronal activation, results in a local decrease in the concentration of deoxyhemoglobin. As deoxyhemoglobin is paramagnetic, a reduction in its concentration results in an increase in the homogeneity of the static magnetic field, which yields an increase in the gradient-echo MRI signal. Given that BOLD fMRI does not measure brain activity directly but, rather, relies on neurovascular coupling to encode information about brain function into detectible hemodynamic signals, it may be useful to look at how most fMRI studies are designed and performed. For BOLD fMRI, image data are typically acquired slicewise using single-shot echo planar imaging (EPI), the snapshot imaging method proposed by Mansfield. A frequency-selective RF pulse is applied in the presence of a static magnetic field gradient to selectively excite nuclear spins in a virtual slice; the slice-select gradient is then turned X [A] AFNI 2.56b: ./20050603human X Colr Swap off, and the signals from these spins are encoded along the dimensions of the slice using rapidly switched magnetic field gradients. Within approximately 50 ms, a dataset is acquired, which, when Fourier transformed, will yield an image of the slice in question. This is rapidly repeated for all the slices (typically around 30–40) in the brain, such that a complete multislice volume is built up within a time of repetition (TR), after which the process is repeated. For a typical TR of 2 s, if 200 volumes are acquired, then we have a volume movie consisting of 200 volumes of the brain (each consisting of 30–40 slices) acquired over 400 s. During these 400 s, a neurobehavioral paradigm is played out in which the research participant is exposed to sensory stimuli or asked to perform some set of mental and motor tasks or some combination of them. So we have a situation where 400 s of temporally structured brain activity (e.g., watching flashing lights every other 30 s, tapping one’s fingers every other 20 s, reading words, or solving math problems) are accompanied by the acquisition of a brain volume movie with 2 s temporal resolution (see Figure 1). [A] AFNI 2.56b: ./20050603human2_4_1.hdr+orig and 20050603human2_4_ 557 [+102] Norm c b r g i 9 z pan crop 30 Disp Sav1.ppm X [A] Mont Done Rec 2.50 AFNI 2.56b: ./20050603human 475 42 Disp Sav1.ppm Mont Done Rec 897 ent = 3.65 X: 42 Y: 38 Z: 30 Grid: 20 # 0:119 Index=0 Value=505 at 0 Scale: 1 pix/datum Mean: 506.1583 Base: Separate Sigma: 15.29321 FIM Op Fig. 1. Sample fMRI data (from an alternating hand-squeezing paradigm). Over a period of 240 s, a participant engaged in a paradigm consisting of alternating 6 s of rest, 6 s of left hand squeezing, 6 s of rest, and 6 s of right hand squeezing. Instructions were provided visually. Data were acquired at 3.0 T using single-shot echo planar imaging of axial slices. Note the relatively poor spatial resolution and contrast (compared to anatomical MRI). The right-hand figure graphs single-voxel raw data from a 5×5 grid of voxels in one axial slice. Note how the paradigm-related signal changes can be easily seen by the naked eye, even though no preprocessing has been performed on these data. The red curve shows the ideal time course (convolution of paradigm timing with assumed hemodynamic impulse response function) used to calculate the activation map shown overlaid in color. Data acquired at the F.M. Kirby Research Center for Functional Brain Imaging (http://mri.kennedykrieger.org); the figure is a screen shot from the AFNI program (http://afni.nimh.nih.gov). IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE MARCH/APRIL 2006 25 The future appears to promise a more integrative approach to functional brain imaging. This brain movie has the interesting property that any single image from it contains no information about brain function. Instead, information about brain function is encoded in the variance of image intensity over time. How then are such data typically analyzed to yield the now-familiar brain activation maps? The conventional approach to fMRI data analysis consists of three steps: preprocessing, regression, and inference [10]. In preprocessing, the raw images are subjected to spatial registration to correct for small head motions, temporally interpolated to compensate for the fact that different slices are acquired at different times, spatially smoothed to enhance signal to noise, and often spatially normalized or transformed into a common stereotactic space to facilitate group analyses and neuroanatomical labeling. In regression, the paradigm timing is convolved with an estimate of the hemodynamic impulse response function. The resultant hemodynamically lagged and blurred version of the paradigm timing is used as a regressor of interest, forming a general linear model, which is then fit to the data, allocating temporal variance in the (preprocessed) data among such regressors. In inference, the spatial map of regression coefficients resulting from the regression step is thresholded for significance, allowing formation of a map showing the suprathreshold regions as hot spots, often overlaid in color on a (higher resolution) anatomical MR image. This standard inferential univariate fMRI data analysis approach can be seen as an excellent machine for testing prior temporal hypotheses. However, it cannot discover unanticipated structure in the data, i.e., it cannot detect brain activations with timings that were unanticipated by the investigator. In the last decade, investigators have developed a variety of data-driven or exploratory techniques that can discover brain activity not anticipated in advance. It may be useful to view such approaches in the context of the finding [11] that in the resting state, synchronous fluctuations (or covariance) of voxel time courses were found throughout the bilateral motor cortex. Recent years have seen significant progress in paradigm design for fMRI as well as the development of other methods for assessing the functional anatomy of the human brain, such as diffusion tensor imaging [12] for mapping white matter fiber tracts. The future appears to promise a more integrative approach to functional brain imaging, in which data from multiple modalities are entered into comprehensive analyses of brain function and connectivity. James J. Pekar is an associate professor of radiology at the Johns Hopkins University School of Medicine and serves as manager and research coordinator of the F.M. Kirby 26 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE Research Center for Functional Brain Imaging, at Kennedy Krieger Institute. He holds a B.S. in physics from the Massachusetts Institute of Technology and a Ph.D. in Biophysics from the University of Pennsylvania. After completing a postdoctoral fellowship in neuroimaging at the National Institutes of Health (NIH), he was a senior staff fellow in the Laboratory of Diagnostic Radiology Research at the NIH. He subsequently became an assistant professor in the Department of Neurology and a member of the Institute for Cognitive and Computational Sciences at Georgetown University, before joining Johns Hopkins. His research focuses on the development of advanced methods for the acquisition and analysis of magnetic resonance data reporting on brain function; he has authored over 40 articles on brain imaging technology. Address for Correspondence: James J. Pekar, Ph.D., F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute and The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, 707 North Broadway, Baltimore, MD 21205 USA. Phone: +1 443 923 9510. Fax: +1 443 923 9505. E-mail: [email protected]. References [1] J.A. Jackson and W.H. Langham. “Whole-body NMR spectrometer,” Rev. Sci. Instrum., vol. 39, no. 4, pp. 510–513, 1968. [2] P.C. Lauterbur, “Image formation employing induced local interactions: Examples employing nuclear magnetic resonance,” Nature, vol. 242, no. 5394, pp. 190–191, 1973. [3] P. Mansfield and P.K. Grannell, “Diffraction and microscopy in solids and liquids by NMR,” Phys. Rev. B, vol. 12, no. 9, pp. 3618–3634, 1975. [4] A. Kumar, A.D. Welti, and R.R. Ernst, “NMR Fourier zeugmatography,” J. Magn. Reson., vol. 18, pp. 69–83, 1975. [5] S. Ogawa, T.M. Lee, A.R. Kay, and D.W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Nat. Acad. Sci. U.S.A., vol. 87, no. 24, pp. 9868–9872, 1990. [6] S. Ogawa, D.W. Tank, R. Menon, J.M. Ellermann, S.G. Kim, H. Merkle, and K. Ugurbil, “Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging,” Proc. Nat. Acad. Sci. U.S.A., vol. 89, no. 13, pp. 5951–5955, 1992. [7] K.K. Kwong, J.W. Belliveau, D.A. Chesler, I.E. Goldberg, R.M. Weisskoff, B.P. Poncelet, D.N. Kennedy, B.E. Hoppel, M.S. Cohen, R. Turner, H.-M. Cheng, T.J. Brady, B.R. Rosen, “Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation,” Proc. Nat. Acad. Sci. U.S.A., vol. 89, no. 12, pp. 5675–5679, 1992. [8] P.A. Bandettini, E.C. Wong, R.S. Tikofsky, R.S. Hinks, and J.S. Hyde, “Time course EPI of human brain function during task activation,” Magn. Reson. Med., vol. 25, no. 2, pp. 390–397, 1992. [9] C.S. Roy and C.S. Sherrington, “On the regulation of the blood-supply of the brain,” J. Physiol., vol. 11, pp. 85–108, 1890. [10] S.A. Huettel, A.W. Song, and G. McCarthy, Functional Magnetic Resonance Imaging. Sunderland, MA: Sinauer Associates, 2004. [11] B. Biswal, F.Z. Yetkin, V.M. Haughton, and J.S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med., vol. 34, no. 2, pp. 537–41, 1995. [12] S. Wakana, J. Jiang, L.M. Nagae-Poetscher, PCM van Zijl, and S. Mori, “Fiber tract-based atlas of human white matter anatomy,” Radiol., vol. 230, no. 1, pp. 77–87, 2004. MARCH/APRIL 2006