Functional magnetic resonance imaging of the human brain: data acquisition and analysis. R. Turner (â) ´ A. Howseman ´ G.E. Rees ´ O. Josephs ´ K. Friston.
Springer-Verlag 1998
Exp Brain Res (1998) 123:5±12
Robert Turner ´ Alastair Howseman ´ Geraint E. Rees Oliver Josephs ´ Karl Friston
Functional magnetic resonance imaging of the human brain: data acquisition and analysis
Abstract It is now feasible to create spatial maps of activity in the human brain completely non-invasively using magnetic resonance imaging. Magnetic resonance imaging (MRI) images in which the spin magnetization is refocussed by gradient switching are sensitive to local changes in magnetic susceptibility, which can occur when the oxygenation state of blood changes. Cortical neural activity causes increases in blood flow, which usually result in changes in blood oxygenation. Hence changes of image intensity can be observed, given rise to the socalled Blood Oxygenation Level Dependent (BOLD) contrast technique. Use of echo-planar imaging methods (EPI) allows the monitoring over the entire brain of such changes in real time. A temporal resolution of 1±3 s, and a spatial resolution of 2 mm in-plane, can thus be obtained. Generally in a brain mapping experiment hundred of brain image volumes are acquired at repeat times of 1±6 s, while brain tasks are performed. The data are transformed into statistical maps of image difference, using the technique known as statistical parametric mapping (SPM). This method, based on robust multilinear regression techniques, has become the method of reference for analysis of positron emission tomography (PET) image data. The special characteristics of functional MRI data require some modification of SPM algorithms and strategies, and the MRI data must be gaussianized in time and space to conform to the assumptions of the statistics of Gaussian random fields. The steps of analysis comprise: removal of head movement effects, spatial smoothing, and statistical interference, which includes temporal smoothing and removal by fitting of temporal variations slower than the experimental paradigm. By these means, activation maps can be generated with great flexibility and statistical power, giving probability estimates for activated brain regions based on intensity or spatial extent, or both combined. Recent studies have shown that patterns of activation obtained in human brain for a given stimulus are indepen-
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R. Turner ( ) ´ A. Howseman ´ G.E. Rees ´ O. Josephs ´ K. Friston The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London WC1N 3BG, UK
dent of the order and spatial orientation with which MRI images are acquired, and hence that inflow effects are not important for EPI data with a TR much longer than T1. Key words Magnetic resonance imaging ´ Brain mapping ´ Statistical parametric mapping ´ Echo-planar imaging methods
Introduction Magnetic resonance imaging (MRI) techniques have been adapted in recent years to allow formation of brain images that are sensitive to local changes in blood flow. In identification of cortical areas active for a particular brain task, the techniques permit a spatial resolution of 2 mm, a temporal resolution of 1 s, and the opportunity to rescan a single subject as often as desired. Whilst the underyling phenomenon is simple, in the sense that many MRI scanners were found to have the capability of performing functional MRI (fMRI) studies once the technique had been discovered, quantification of the effect in relation to cortical electrical activity has been elusive, and the origins of the functional signal require some explanation. The decay of signal associated with local magnetic field inhomogeneities, called T2* relaxation, was previously considered a nuisance and represented a limitation upon MRI. To mitigate this, either the so-called spin-echo technique was used, in which a second `refocussing' radiofrequency pulse following the initial excitation pulse removed the effects of dephasing, or the time between the radiofrequency excitation pulse and the acquisition of signal was reduced a much as possible, as in FLASH (Fast Low-Angle SHot imaging (Haase et al. 1986). It was only when it was realized that the presence of a paramagnetic substance in the bloodstream could act as a vascular marker, giving useful contrast, that sequences without a refocussing pulse, with a relatively long time between the excitation pulse and data acquisition (20± 80 ms), began to be used. Initially the paramagnetic con-
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trast agent was exogenous, a non-toxic compound of gadolinium introduced into the bloodstream via a leg vein. A fraction of a millimole of contrast agent per kilogram of body weight is sufficient to give a loss of signal from tissue surrounding cerebral blood vessels of perhaps 40% as a bolus of contrast agent passes through. The time integral of the signal attenuation during the first pass of the contrast agent through the brain gives a quite accurate estimate of the cerebral blood volume (CBV) and the Central Volume Theorem (Stewart 1894) can be used to obtain local cerebral blood flow as the ratio of CBV to mean transit time. Early studies (Villringer et al. 1986; Belliveau et al. 1988) examined the passage of contrast agent through the brains of rats and later dogs, making increasing use of echo-planar imaging (EPI) (Mansfield 1977), which acquires a complete image in less than 100 ms and thus allows ªsnapshotsº of the contrast agent distribution as it passes rapidly through the brain. The technique was applied eventually to functional activation studies in humans (Belliveau et al. 1991). The subjects were given visual (ªphoticº) stimulation while a bolus of contrast agent was injected into a leg vein, and single-slice images of the brain in the plane of the calcarine fissure were obtained at 0.75-s intervals to monitor the bolus passage. By integrating the time course of image intensity, estimates of relative blood volume were obtained, and compared (by image subtraction), with those obtained when the subjects were at rest in darkness. Consistent increases (up to 30%) of blood volume in primary V1 visual cortex were observed. Subsequent developments rapidly overtook this pioneering work. Working independently, Ogawa et al. 1990) and Turner et al. (1991) showed in laboratory animals that similar changes in MRI image contrast extending around the blood vessels could be obtained simply by changing the oxygenation state of the blood. This comes about because deoxyhaemoglobin is more paramagnetic than oxyhaemoglobin (observed by Pauling and Coryell 1936; which itself has almost exactly the same magnetic susceptibility as tissue. Thus deoxyhaemoglobin can be seen as nature's own contrast agent. Interventions to the state of the brain that create an imbalance between oxygen uptake and blood flow will thus inevitably cause a change in MRI signal around the cortical vessels, if MRI sequences are used that are sensitive to magnetic field inhomogeneity. This development culminated in the work of Kwong et al. (1992) and Ogawa et al. (1992) who succeeded in showing that the change in deoxyhaemoglobin in human visual cortex, while the subject viewed a bright light, was sufficient to cause measurable changes in gradientecho MRI images of a slice passing through the calcarine fissure. The technique was dubbed ªBlood Oxygenation Level Dependent (BOLD) contrastº. Thus the way was opened to functional mapping studies of the human brain without use of contrast agent, no radiation dose, and with the high spatial resolution of MRI.
Imaging techniques For human brain activation studies it is highly desirable to obtain image data quickly. There are several reasons for this, apart from the obvious need to avoid experiments lasting many hours. The first is that many perceptual and cognitive tasks of interest can be continued only for a few minutes without habituation, fatigue or boredom. Secondly, the spatial resolution is generally on the order of 1±3 mm, which means that effective head immobilization is essential. The longer a subject is lying in an uncomfortable position inside the MRI magnet, the greater the chance of large movement, for which it is difficult to correct. Thirdly, it is important to sample the activation state of the whole brain as synchronously as possible. Since MRI usually obtains image data one slice at a time, and 20±30 slices are necessary to cover the entire brain, this implies acquisition of a slice in a time very short compared with the haemodynamic response time of the cerebral vasculature of 6±8 s. The only successful MRI technique capable of this speed with reasonable spatial resolution and good signal-to-noise ratio is EPI (Stehling et al. 1991) as previously mentioned. Compared with slower MRI techniques, the spatial resolution of EPI is somewhat impaired (to about 2 mm), while the temporal resolution is improved to about 100 ms. Furthermore, the rate of information capture, that is, the signal-to-noise per unit time, is highest for EPI and its variants [such as single-shot GRASE (Feinberg and Oshio 1991) and single-shot spiral EPI (Ahn et al. 1986)]. However, EPI images (especially the gradientecho version of EPI) suffer more than the other techniques from distortion and signal loss arising from magnetic field inhomogeneities in the brain, which often derive from the natural susceptibility differences between brain and air and are thus not amenable to correction by improved shimming (adjustment of the static field homogeneity with a set of correction coils). The specification of commercially available fMRI scanners can constrain experimental design. The Siemens Vision system can acquire up to 25000 images during a single run before pausing for data processing and disc storage. An image volume can comprise any number of axial slices (64 slices representing whole-brain acquisition) at variable repeat times. Not many experimental paradigms will require continuation of scanning longer than the 40±50 min this allows, although this may be significant for paradigms concerned with sustained physiological or psychological phenomena.
Characteristics of BOLD contrast The difference in susceptibility between fully oxygenated and deoxygenated blood is small (about 0.02106 cgs units), and thus image intensity changes in BOLD contrast studies are generally small-less than 15% at a magnetic field of 2 T even when the haemoglobin oxygen sat-
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uration is reduced to 20% in acute hypoxia (Jezzard et al. 1994). In brain activation studies at 1.5 T (Kwong et al. 1992) the signal change is usually no more than 2±4%. Experiments (Turner et al. 1993) and model calculations (Ogawa et al. 1993; Weiskoff et al. 1994; have shown that Kennan et al. 1994) the change in T2* relaxation rate associated with this signal loss increases with the static magnetic field of the MRI scanner, so that the change in signal is typically about 3 times larger at 4 T than at the more commonly used field strength of 1.5 T, for the same echo time and sequence type. Given the improved signal-to-noise ratio at higher field (roughly proportional to field strength) there is a strong case for the use of high magnetic fields for this type of functional MRI. At 4T, signal changes of up to 30% have been observed for visual stimulation, with typical single-shot background noise of 0.5% or less, giving z-values of 60 or more for some pixels. Of course, for more subtle brain activations in association cortex, intensity changes are generally much smaller ± from 2% to 8% at this field strength ± and proportionately lower at the more commonly used field of 1.5 T. At the lower field strength it is often necessary to perform averaging over images to obtain significant results, although single-subject remain eminently feasible.
Data analysis principles In every quantitative science, when experimental data are collected the first and frequently the most successful analytic strategy is to attempt to fit them to a linear combination of the experimentally controllable variables, or to simple transforms of seen. Fast and efficient algorithms have been developed over the last several decades that use the methods of matrix algebra to handle multiple independent and dependent variables. If the data can be assumed, or better still demonstrated, to have normally distributed error (NDE), statistical significance can be assigned to the results in a fully validated way. Image intensity data are no different in principle from any other kind of data, though they are often more copious. In hypothesis-led experimental design and analysis, one or more independent variables are controlled or at least monitored, and the effects on intensity at any voxel in the image are recorded. The experimental design and the model used to test for specific responses to the controlled or monitored variables are embodied in what is known as the ªdesign matrixº. In principle, any non-random and measurable factor that can affect image intensity can be incorporated into the design matrix, and its contribution to intensity variations estimated within the accuracy of the assumption that the effect is linear. Both the magnitude of intensity variation, and the spatial extent of co-activated pixels, can be taken into account straightforwardly in making statistical interferences when the data be regarded as a Gaussian Random Field (GRF) (Adler 1981). The data of fMRI are no exception, and provide a suitable arena for the application of these
methods, which are commonly described as the use of multilinear regression, or the General Linear Model. Research groups that have correctly used t-tests, z-score maps or correlational analyses to interrogate their fMRI data have been implementing, wittingly or unwittingly, special cases of this technique, which encompasses all statistically valid linear analyses of NDE data. Thus the fundamental question relating to fMRI data analysis is how ªwell-behavedº, in a statistical sense, the raw data of MRI brain images are. To quality as forming a GRF, data must have the following properties: (a) the autocorrelation function most be twice differentiable, and (b) the spatial correlations must be stationary. Raw MRI data do not have these properties, because (a) the point spread function is narrow (usually not more than two pixels wide, full width at half maximum) ± thus the image is not smooth, sharp boundaries being represented by abrupt image intensity changes; and (b) head motion and physiological pulsations result in highly spatially correlated time-varying changes in image intensity. The strategy of statistical parametric mapping (SPM) when applied to fMRI data is to use a series of image transformations, which have known statistical implications, to gaussianize the data, and thereby to allow robust statistical interference. Intrinsic features of fMRI data, such as the spectral distribution of noise, favour specific types of experimental design. These reflect the temporal resolution of fMRI and the large number of measurements that are typically made. At the same time the design needs to take account of the fact that unlike positron emission tomography (PET), fMRI does not measure absolute blood flow changes but relative changes in the oxygenation of venous blood. Design issues also have implications for data processing; thus the normalization and statistical methods used to find significant activations need to address the particular characteristics of fMRI data.
Sources of statistical confounds in fMRI data In time series images of phantoms, apart from slow drifts of image intensity caused for instance by temperature changes in imaging hardware, instrument noise is primarily thermal in origin, and is thus ªwhiteº within the receiver bandwidth, appearing uniformly across the image. In fMRI time courses of human brain, additional low-frequency random and non-random noise components are evident. These arise from head motion, slow global variations in blood oxygenation, or physiological cardiac and respiratory pulsations. The spectrum of this noise typically has a ª1/fº characteristic, so called because the spectral power density falls linearly with increasing frequency, with additional peaks corresponding to periodic motions. These two generic types of noise combine to generate the form of spectrum seen in Fig. 1. In summary the noise spectrum of the fMRI signal comprises a low-frequency and wideband component. Periodic physiological noise with a short image TR appears as focal peaks in the spec-
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Fig. 1 Sketch of the noise spectrum found using echo-planar imaging (EPI) acquisition with short TR in studies of human brain
trum, or when a long image TR is used can be aliased to give an increase in wideband noise. Bulk head motion has been recognized since the earliest days of fMRI (Kwong et al. 1992; Ogawa et al. 1992) as potentially the most severe and misleading confound in fMRI studies. Fortunately a typical time series of fMRI volume data contains adequate information to characterize such motion with an accuracy of perhaps 30 m, when an appropriate algorithm is used (Friston et al. 1994a). Motion effects are removed by realignment of subsequent multislice images in a time series to the first multislice image, using sinc interpolation for greatest precision. If the repeat time is of the order of T1, out-of-plane motion creates a different spin excitation history for different groups of spins within the slice, and thus contaminates the data with motion-correlated image intensity changes (Friston et al. 1996). An ARMA-based algorithm removing any image intensity variations correlated with out-ofplane motion removes most of this source of variance, leaving functionally related activations intact. Physiological noise in brain images (Jezzard et al. 1993) arises almost entirely from T1 effects associated with the pulsatile motions of heartbeat and respiration (Weisskoff et al. 1993), with a small very slow residual component coming from spatially correlated endogenous fluctuations of blood oxygenation (Moskalenko et al. 1977; Biswal et al. 1995). The heart cycle causes both periodic blood flow and pulsatile bulk motion due to induced pressure variation. The blood flow effects are confined mainly to vessels, but the pulsatile motion, while most pronounced in the brain stem, can extend throughout the entire brain. Respiration causes a generalized variation in blood oxygenation, bulk displacement of the head in reaction to breathing, and changes in pressure in the venous sinuses that may be communicated to cerebrospinal fluid spaces. The expected time course of measured physiological noise depends not only on the sources but also on the imaging TR (repetition time). If the TR is short compared with both cardiac and respiratory cycles (i.e. TR
