FIGURE 7: Whole brain EPI images at 7 Tesla: 0.75 mm isotropic Echo spacing 0.67 ms, 256 x 256 ,parallel imaging with reduction factor 4. Partial Fourier = 6/8 TE = 20 msec, 128 slices TR = 6 s, single image. Data generated in CMRR, University of Minnesota by Yacoub et al.

However, I regard these accomplishments as a prelude to a new era where whole brain high-resolution fMRI, at or approaching columnar and layer resolution is feasible at 7T. This capability has been used recently to perform 1 mm isotropic resolution whole brain resting state fMRI (De Martino et al. 2011). Figure 7 illustrates 0.75 mm isotropic whole brain single-shot EPI images acquired at 7T; these are excellent quality EPI images, better than even what most people use at much lower field strengths where EPI is easier to execute. They also have sufficient SNR to serve in an fMRI study. I believe 0.5 mm isotropic will be attainable when 7 Tesla systems with more than 32 receive channels will become available. Numerous technological and engineering solutions had to be realized at 7 Tesla before such imaging capability became feasible. These include parallel imaging to accelerate along the phase encoding direction (Pruessmann et al. 1999; Sodickson et al. 1999; Griswold et al. 2002) (probably the most important development for ultrahigh field imaging using single-shot EPI, which is the workhorse of fMRI), slice accelerated whole brain functional imaging (Moeller et al. 2008; Feinberg et al. 2010; Moeller et al. 2010), and parallel multichannel transmission (e.g. Adriany et al. 2005; Van de Moortele et al. 2005; Vaughan et al. 2006; Metzger et al. 2008; Setsompop et al. 2008; Setsompop et al. 2008; Setsompop et al. 2008; Setsompop et al. 2009); in fMRI, accompanying anatomical images are also of great importance. Yet at 7 Tesla, obtaining good anatomical images without large intensity variations that obscure tissue contrast and/or preclude automated gray-white matter segmentation was initially difficult. This problem was solved with the introduction of biased field correction methods in anatomical imaging (e.g. Duyn et al. 2007; Van de Moortele et al. 2009). Coupled with improved anatomical contrast (e.g. Duyn et al. 2007; Rooney et al. 2007; Budde et al. 2011; Henry et al. 2011) now available, ultrahigh fields provide superb anatomical images that can accompany functional images at these field strengths.

Specificity of Perfusion Changes coupled to neuronal activity

In fMRI, the degradation of spatial specificity might arise from imprecise spatial-coupling between neuronal activity and the physiological and metabolic events that ultimately yield the functional maps. The dominant physiological change induced by alteration in the neuronal activity relevant for fMRI is CBF change. There is no a priori requirement that control of CBF spatially match functional organizations as long as CBF change includes the functional territory responsible for invoking the altered CBF state. In fact, at the time of the introduction of fMRI, prevailing concepts suggested that CBF control far exceeds the territorial boundaries of columnar organizations; in other words, the brain watered the entire garden for the sake of a single thirsty flower (Malonek and Grinvald 1996). We examined this question again using newly developed MR methods (Duong et al. 2001) and showed that in fact, the brain waters the thirsty flower while it sprinkles an extensive region around it.

Images of blood flow and blood flow changes associated with increased neuronal activity can be obtained using arterial spin labeling (ASL) techniques that utilize the water protons in the blood as an endogenous transient tag. These methods rely on either continuous (e.g. Detre et al. 1992; Zhang et al. 1993; Detre et al. 1994) or dynamic, i.e. modulated versions of continuous (Barbier et al. 1999; Barbier et al. 2001) or pulsed (e.g. Edelman et al. 1994; Kim 1995; Wong et al. 1998) tagging approaches. Efficiency of some of these methods was recently evaluated quantitatively (Pohmann et al. 2010). These ASL techniques can be tuned to be selectively sensitive to capillary/tissue level flow, and the detection of blood flow or blood flow changes in large blood vessels can be minimized. Such maps report on water delivery to the capillary bed and, by exchange across the capillary wall, to surrounding tissue, i.e. perfusion.

In these MR techniques, a perturbation (the label) is induced in the population of hydrogen nuclei (spins) of water in blood, outside the region of interest; this label is monitored as it shows up in the tissue in the region of interest after a delay (the tagging time) that is long enough to permit arrival into the capillaries and the surrounding tissue but not long enough to reach thermal equilibrium. In this case, the amount of label detected in the tissue in the region of interest is proportional to blood flow, and increases with elevated neuronal activity. Since the arterial side is permeated with fresh blood significantly faster relative to the tissue, the time to reach the equilibrium state in the large arteries versus the tissue is different. Thus, long tagging times (e.g. ~1.5 to~2 s) (Tsekos et al. 1998) eliminate the arterial component from perfusion images. The long tagging times, however, may lead to the tagged spins appearing in the venous side, thus leading to false activation in the draining veins. At high magnetic fields, the T₂ and T₂* of venous blood is very short (Thulborn et al. 1982; Lee et al. 1999) so that this effect can be selectively eliminated by a brief delay after excitations of spins but before image acquisition. Accordingly, perfusion-based fMRI maps have been shown to yield accurate images that co-localize with Mn⁺² uptake (Duong et al. 2000), a marker of calcium dependent synaptic activity (Lin and Koretsky 1997).

When potential large vessel contributions are suppressed with appropriate choice of imaging parameters, perfusion-based fMRI can be used to examine the critical physiological question related to specificity of blood flow increases. A specific question that can be asked is if perfusion changes are confined accurately to the region of increased neuronal activity in the spatial scale of columnar organizations?

FIGURE 8: Activation maps of two orthogonal iso-orientation domains in the cat visual cortex obtained separately with perfusion based functional imaging mapping. Panel a and b, show perfusion based images obtained with two orthogonal orientations (45 and 135 degree gratings), demonstrating their complementarity. Each map was acquired as a single condition map where the grating of one orientation was either moving back and forth (activation condition) or static (control condition). Bottom trace (Panel c) shows the cerebral blood flow (CBF) response at the tissue level (i.e. perfusion) in all the voxels identified as activated either by the 45 degree or 135 degree grating for the two orthogonal stimulations. All voxels identified as activated for 45 degree orientation display a large perfusion increase during stimulation by this orientation gratings and show a smaller but detectable perfusion increase in response to the 135 degree gratings. The converse is observed for the voxels identified as activated for 135° gratings. A marked perfusion increase (~55-60 %) following 45° or 135° stimulus was observed in the regions tuned to these orientations, while the stimulation with the orthogonal orientation lead to a 3.3± 0.6 fold smaller perfusion increase in the same region. Adapted from reference Duong et al. 2001.

Such a study was performed using the iso-orientation columns in the cat visual system (Duong et al. 2001) using single condition mapping (stimulus vs. a non-stimulus control state); the results demonstrated that while perfusion increases that follow neuronal activation were not perfectly localized at the iso-orientation column level, the difference between active and neighboring inactive columns was large and permitted single condition mapping (Figure 8). In other words, as previously stated, the brain was found to literally water the thirsty flower while sprinkling an extensive region around it. To accomplish this, there must exist regulation of blood flow control at the submillimeter (~300 to 400 µm) scale. These data were also used to calculate a point-spread function for the CBF control in the cat cortex; this PSF, at full width at half-maximum (FWHM), was 470 µm under single-stimulus condition without differential subtraction.

It is important to emphasize that the perfusion based images of the orientation columns in the cat cortex (Duong et al. 2001) described above was obtained under single condition as opposed to differential mapping. Differential mapping refers to functional images obtained by effectively subtracting two analogous but orthogonal activation states; this approach is assumed to generate functional maps with high fidelity to the territory of altered neuronal activity because spatially non-specific mapping signals are assumed to be common to the orthogonal activation conditions. This assumption appears to be incorrect at the level of cortical columns (Shmuel et al. 2010). Nevertheless, differential mapping is expected to have better spatial accuracy than signal condition maps, which do not employ any strategy to suppress spatially inaccurate mapping signals. Mapping ocular dominance columns by stimulating one eye vs. the other in an alternating fashion is differential mapping. Stimulating one eye and using a dark state as the control would correspond to single condition mapping. Phase encoded mapping using an activation paradigm that cycles through several different possible stimulations, as employed in the visual system (e.g. Sereno et al. 1995; DeYoe et al. 1996; Engel et al. 1997), is also a kind of differential mapping because it would suppress common, non-modulating components.

Thus, the ability to obtain single-condition maps of orientation domains by perfusion imaging (Duong et al. 2001) was a fundamentally important result for brain physiology and functional mapping with magnetic resonance because it demonstrated for the first time that CBF changes are regulated even at the level of capillaries in addition to being subject to upstream control at the level or arterioles and arteries. This conclusion was initially considered to be in conflict with results from optical imaging techniques that report on CBV, which is assumed to be correlated with CBF in space and magnitude. However, there is an important difference between optical imaging and MR perfusion imaging data. Namely, the MR methods can be set to eliminate, in the image acquisition phase, the confounding problem coming from large vessels. When the origin of the functional mapping signals is restricted to the spatially accurate capillary/tissue level by the physics of the MR acquisition method, accurate columnar activation is detected. However, the optical methods do not have this selective tuning capability and report on all CBV changes; thus, they can in fact be dominated by large vessel effects. Recent optical imaging studies conducted subsequent to the perfusion mapping of iso-orientation columns by perfusion fMRI, have confirmed this fundamental difference and demonstrated that when large vessel effects are taken out, CBV based optical imaging techniques also yield columnar level mapping signals (e.g. Harrison et al. 2002; Sheth et al. 2003; Vanzetta et al. 2004). Subsequently, CBV based fMRI studies, conducted using the extravascular contrast agent MION, have also demonstrated single condition columnar resolution (Zhao et al. 2005; Harel et al. 2006); these MION based fMRI studies, with their superior sensitivity, further revealed that laminar activity can be spatially resolved and that the larger intralaminar signal changes correspond to cortical layer 4 (Harel et al. 2006). These observations further confirm that capillary level control of blood flow and volume in the brain exists and modulates activation-induced alterations in these physiological parameters at the columnar and laminar level.

Spatial Accuracy of Magnetic Resonance Signals in Functional Mapping

Although the spatial fidelity of coupling between neuronal activity and the physiological and metabolic events that ultimately yield the functional images contain sufficient precision to map columnar structures, there is another potential source of degradation of spatial accuracy, which has been already alluded to; namely, the coupling between the MRI signals and the physiologic changes that occur. This coupling in turn depends strongly on magnetic field and the pulse sequence used. Many of the issues that exist with respect to this question were already mentioned in discussing the BOLD mechanism. They are revisited and summarized here, organized in a more concise form.

All vessel types containing dHb, ranging from capillaries to large veins contributed to the T₂* GRE fMRI, the overwhelmingly dominant techniques used in fMRI. However, as previously mentioned, the large vein contribution dominates GRE fMRI at all magnetic field strengths. Thus, GRE fMRI suffers from inaccuracies in functional mapping because of large vessel contributions. Often one is concerned with the large blood vessels on the pial surface of the brain. Deoxyhemoglobin changes clearly must occur first at the location where stimulus or task induced neuronal activity changes take place; however, due to blood flow, these dHb changes must propagate down the vascular tree to draining veins on the pial surface. At some point in the vascular tree, the functionally induced changes will be diluted by blood that is pooled from other unaffected regions by the task or the stimulus, and hence will disappear from the functional map. But, before this dilution occurs, functional signals associated with draining veins will be detected; that this represents a major confound was realized and documented as early as in 1993 (Menon et al. 1993; Kim et al. 1994) and was recently further reemphasized (Polimeni et al. 2010). GRE fMRI studies would also have a strong contribution from ~50 to ~100 µ intracortical veins that run perpendicular to the cortical surface and drain the different layers. These are the veins that were visualized in the original BOLD imaging work conducted in rats (Ogawa and Lee 1990); they can also be seen in very high-resolution human brain images with high T₂* or phase contrast (e.g. Vaughan et al. 2006; Budde et al. 2011).

The GRE BOLD effect comes from both extravascular and intravascular (blood) sources. The blood contribution to fMRI signals can be the dominant source of functional maps at lower fields. This blood effect occurs at all levels of the vasculature. Its presence in capillaries and small venules is not a problem for spatial specificity; however, the functional signals associated with blood in large veins degrade the fidelity between regions of altered neuronal activity and the fMRI maps. This confound is diminished at ultrahigh fields as blood T₂* decreases (Duong et al. 2003; Uludag et al. 2009) and becomes much shorter than tissue values of this parameter. Consequently, in a typical fMRI acquisition where echo time TE is set approximately equal to tissue T₂*, blood contribution to the image can be significantly reduced or even absent with increasing magnetic field strength beyond 3 Tesla (Duong et al. 2003). Hence, at ultrahigh fields such as 7 Tesla or higher, the intravascular blood effect, one of the major sources of inaccurate functional signals, is suppressed or even eliminated.

The extravascular BOLD effect for large vessels persists at all field strengths and even increases with increasing field magnitude (Uludag et al. 2009); hence, it continues to be a source of inaccurate functional mapping signals in GRE fMRI. However, the microvascular contributions also get significantly larger at the ultrahigh fields and become comparable to the large vessel effects (Yacoub et al. 2001; Yacoub et al. 2005), enabling accurate mapping that would be difficult to achieve at lower magnetic fields. This relative microvascular gain was demonstrated in a 4T-7T comparison (Yacoub et al. 2001) and was utilized early in the history of 7T fMRI studies to reveal the tonotopic organization of the human auditory cortex for the first time (Formisano et al. 2003), at a time when attempts at studying this organization at lower fields had failed to produce convincing data. Similarly, fine-scale single-digit activations in subdivisions of the human primary somatosensory cortex (SI) in individual subjects was accomplished recently using GRE fMRI at 7T (Stringer et al. 2011). Recently, the presence of this microvascular contribution at 7T was relied upon when it was demonstrated that in high resolution 7T fMRI studies, stripping away the outer cortical layers where the pial vein contribution is dominant and looking into deeper layers of the cortex (Polimeni et al. 2010) yielded more accurate functional maps; without the presence of the microvascular contribution independent of and in addition to the large vessel contribution on the surface, this strategy would not have worked with GE fMRI.

The spatial inaccuracies introduced in functional mapping due to the large vessel confound present in GRE fMRI have been quantified experimentally in several ways. It was rigorously demonstrated that the GRE fMRI method fails to generate single-condition functional images of iso-orientation domains in the cat cortex (Duong et al. 2000; Kim et al. 2000) at 4.7 Tesla; the images of two orthogonal stimulation conditions were not complementary, as would be expected; rather, the highest activity was associated with a large draining vein, the sagittal sinus for the two orthogonal conditions. Simultaneous multiple site single unit recordings and fMRI studies on the same animal indicated that the limit of spatial specificity of GRE fMRI was in the 2 to 3 mm range for single-condition maps (Toth et al. 2001; Ugurbil et al. 2003). At 1.5 and 3 Tesla human brain studies, the full width at half maximum (FWHM) of the point spread function (PSF) for GRE fMRI was estimated to be 3.5 mm (Engel et al. 1997) and 3.9 mm (Parkes et al. 2005), respectively. In contrast, at 7 Tesla, the PSF at FWHM was measured to be ~2 mm or less in the human brain in regions devoid of the large veins visible in the image (Shmuel et al. 2007), demonstrating advantages of the ultrahigh field strength.

The large vessel effects that persist in GRE fMRI even at ultrahigh fields can be suppressed with spin echo based fMRI at ultrahigh but not at low magnetic fields. As previously discussed, SE fMRI responds to apparent T₂ changes both in the extravascular space around microvasculature (capillaries and small post-capillary venules) and in blood itself (van Zijl et al. 1998; Ugurbil et al. 1999; Ugurbil et al. 2000; Uludag et al. 2009). The spatially inaccurate intravascular effects in veins are suppressed at ultrahigh fields because, the apparent T₂ of venous blood decreases precipitously with increasing magnetic field magnitude (see Duong et al. 2003; Uludag et al. 2009 and references therein); it is diminished from ~180 ms at 1.5 Tesla (Barth and Moser 1997) to ~6 ms at 9.4 Tesla (Lee et al. 1999), significantly smaller than brain tissue T₂; thus, at TE values that correspond approximately to gray matter T₂, which would be optimal for detection of functional mapping signals associated with parenchyma at such field strengths, the blood signal would be significantly diminished, and even undetectable. At 3T, ~50% of the SE fMRI signals were shown to arise from blood (Norris et al. 2002). But at 7T, the blood contribution was estimated to be less than 10% at echo times matching gray matter T₂ (Duong et al. 2003). Consequently, at very high fields such as 7 Tesla or higher, the functionally non-specific blood component as well as the extravascular BOLD effect associated with veins are suppressed in SE studies (Duong et al. 2003; Yacoub et al. 2003; Uludag et al. 2009). Residual large vessel effects are still expected because the extravascular BOLD effect with large blood vessels is not exactly zero in SE fMRI; rather it is small and significantly less than effects associated with microvasculature (Uludag et al. 2009). This is the reason why SE fMRI techniques at ultrahigh fields yield robust columnar level mapping, providing maps that are uninterrupted by large vessel contributions (Yacoub et al. 2008).

HIGH RESOLUTION WHOLE BRAIN fMRI

The advantages of high and ultrahigh field fMRI have largely been exploited in applications where a subsection of the brain, such as the primary visual cortex (e.g. Yacoub et al. 2007; Yacoub et al. 2008; Olman et al. 2010) or the auditory cortex (e.g. Formisano et al. 2003) was studied using field of view restriction. Ultimately, however, we need to cover the entire brain in high resolution. Only recently, significant progress has been made in tackling the difficulties inherent in obtaining rapid whole brain coverage at 7 Tesla using techniques like EPI, the predominant method employed in fMRI. EPI is challenged by the shorter T₂* and increased magnetic field inhomogeneities at high fields. This difficulty is amplified with higher resolution as the EPI echo train length is elongated commensurately with resolution. Segmentation has been used in EPI to reduce the echo train length (McKinnon 1993; Feinberg and Oshio 1994; Wielopolski et al. 1995); however, in fMRI, k-space coverage with multishot techniques suffer from significantly increased sensitivity to temporal fluctuations induced by physiological processes and motion (Hu and Kim 1994; Hu et al. 1995; Moeller et al. 2006; van der Zwaag et al. 2011). These temporal instabilities can be minimized by post-processing strategies in multishot as well as in single shot techniques (e.g. Hu and Kim 1994; Hu et al. 1995; Mitra et al. 1995; Biswal et al. 1996; Ogawa et al. 1996; Mitra et al. 1997; Mitra et al. 1997; Mitra and Pesaran 1999; Glover et al. 2000; Pfeuffer et al. 2002; Van De Moortele et al. 2002). However, such strategies require intrinsically good SNR and, in some cases, rapid image acquisition so as to capture these temporal fluctuations accurately.

Of course, high resolution whole brain coverage necessarily increase the volume acquisition times (TR); using segmented, multi-shot approaches for a single slice increases this time further, leading to prolonged times for collecting sufficient data in an fMRI time series. With the advent of parallel imaging methods, which were discussed previously, the use of partial Fourier sampling (Feinberg et al. 1986), and improved gradient performance, many of the aforementioned technical limitations at high fields have been significantly alleviated. Using these advances, an example of what is feasible at 7 Tesla with GRE EPI is shown in Figure 7; these were images obtained with 0.75 mm isotropic resolution using a reduction factor, R, of 4 (i.e. 4 fold undersampling of the k space, or equivalently, 4 fold reduction in FOV, leading, in this case, to 4 fold maximum aliasing). Despite this, to date only a few whole-brain applications have been presented at ultrahigh fields and have mostly employed large slices, inter-slice gaps or have sacrificed temporal resolution (e.g. Bianciardi et al. 2009; Bianciardi et al. 2009; Bianciardi et al. 2009; Poser and Norris 2009; van der Zwaag et al. 2009; van der Zwaag et al. 2009; Poser et al. 2010), thus not fully exploiting the full benefit of ultrahigh field fMRI.

Although parallel imaging and partial Fourier sampling reduce the number of phase encoding steps for spatial encoding, and, consequently the echo train length after a single RF pulse, they do not necessarily reduce whole brain image acquisition times significantly in many applications. This is because a physiological contrast preparation period must precede the spatial encoding period for each slice and this contrast preparation period can equal or exceed the time employed for the spatial encoding in the EPI echo train. 3D echo volume (EVI) (Mansfield et al. 1994) avoids the repetition of the contrast encoding time by following a single contrast preparation period with subsequent 3D volume coverage in a single echo train. However, this approach has limitations in spatial resolution and image quality due to longer echo trains needed to fully encode the volumetric spatial information in the relatively short acquisition period dictated by T₂*, especially at high fields; the consequence is distortions and blurring on two of the 3D image axes, as well as a loss in SNR. Multi-shot (segmented with multiple excitation) 3D EPI approaches that have produced high quality images (e.g. Wielopolski et al. 1995; Poser et al. 2010) overcome this limitation; however, in order to achieve very rapid coverage of the whole brain, high parallel imaging reduction factors must be employed in the two orthogonal phase encoding dimensions in the 3D acquisition, leading to significant SNR losses due to the omitted phase encoding steps; these approaches also suffer from increased temporal fluctuations inherent in multishot techniques as discussed above. Echo shifting approaches, PRESTO (Liu et al. 1993; Golay et al. 2000), increase volume coverage efficiency in fMRI by taking advantage of TE delays to apply additional RF pulses, but run into restrictions at higher magnetic fields when T₂ and T₂* become inherently short.

Motivated by the prospect of acquiring high resolution, single shot whole brain images at 7 Tesla, but confronted with the afore-listed limitations in whole brain coverage as a major impediment, we recently introduced multiband (MB) EPI imaging for fMRI based on acceleration in two dimensions, slice acceleration and in-plane phase-encode acceleration simultaneously, in order to achieve significant reductions in whole brain coverage time without substantial or even noticeable degradation in image quality or SNR (Moeller et al. 2008; Moeller et al. 2010). In this technique, several slices are simultaneously excited using multiband RF pulses and subsequently acquired in a single EPI echo train using additional acceleration along the phase encode direction; the simultaneously acquired slices are then unaliased using parallel imaging principles. The acceleration using multiple slice excitation dates back to 2001. Simultaneous acquisition of multiple slices using GRE and subsequently unfolding them using parallel imaging principles was described for spine imaging (Larkman et al. 2001) it 2001. This GRE approach was further pursued in the CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results In Higher Acceleration) (Breuer et al. 2005; Breuer et al. 2006) technique where unaliasing of slices were improved by manipulating the phase of the RF excitation pulses progressively for each k-space line in the GRE sequence, so as to effectively shift the simultaneously acquired slices relative to each other in the phase encoding direction. The fMRI community, including us, had probably ignored these earlier efforts because they were based on gradient recalled echoes with application of one RF pulse per k-space line coverage, an approach that is simply inadequate for fMRI. A method employing gradients blips to achieve a shift for controlled aliasing as in CAIPIRINHA in multiband EPI imaging was also described in an abstract (Nunes et al. 2006). However, the approach led to voxel tilting that was in general not desirable. In our EPI implementation such controlled aliasing was not employed. Yet, we were able to attain 4 fold slice acceleration simultaneously with 4 fold acceleration in the phase encode direction, resulting in 16 fold two dimensional acceleration with 16 fold maximum aliasing.

The multiband/multislice approach has been applied rarely, either in its original form or in the CAIPIRINHA versions; subsequent to our work reviving the approach and demonstrating its potential utility (Moeller et al. 2008; Moeller et al. 2010), its use has been rapidly increasing: It has been incorporated into the Steady State Free Precession (SSFP) sequence (Stab et al. 2011) and radial acquisitions (Yutzy et al. 2011). CAIPIRINHA (Breuer et al. 2005; Breuer et al. 2006) like shifting of slices in Multiband EPI acquisition to improve g-factor penalties in unaliasing, using an gradient blip strategy similar to the strategy described previously (Nunes et al. 2006), but avoiding the voxel tilt, was introduced (Setsompop et al. 2011). We have described a modification of the Multiband EPI method that combines it with the simultaneous image refocused (SIR) scheme (Feinberg et al. 2002) which temporally interleaves signals from several slices within an EPI echo train; in this new approach, referred to as Multiplexed-EPI (M-EPI), if multiband pulses with m bands are used for each of the s interleaved RF pulses in the SIR technique (where m and s are positive non-zero integers), the result is simultaneous acquisition of m times s (i.e. m ∙ s) slices in a single echo train. The Multiplexed-EPI sequence and some images obtained with the different acceleration factors are shown in Figure 9. We have further optimized acquisition strategies to improve the ability to unalias multiple slices, achieving 6 to 8 fold slice acceleration at 3 Tesla with a 32 channel RF coil (Xu et al. 2012).

Although these slice-accelerated sequences have been introduced for fMRI initially at 7 Tesla (Moeller et al. 2008; Moeller et al. 2010), so far their use to study human brain function has only been reported at 3T (Smith et al. 2012); in this work, taking advantage of the most recent improvements achieved in our lab (Xu et al. 2012), and exploiting the improved temporal resolution and the large number of data points that can be acquired in a given period of time, time series data obtained with Multiband EPI was employed to identify functionally distinct networks, referred to as temporal functional modes (TFM), by virtue of their temporal independence; these functionally-distinct modes of spontaneous brain activity were, in general, quite different from resting-state networks (RSN) previously reported, and may have greater biological interpretability. Some of these TFMs were shown to subdivide the default-mode network identified as a RSN.

FIGURE 9: The pulse sequence for regular EPI and Multiplexed EPI (M-EPI) and images obtained with Multiplexed EPI images. EPI pulse sequence generates a single slice image during each readout train, which is repeated by the number of slices to scan the whole brain; replacing the single slice pulse with multiband (MB) pulse in this sequence to excite several slices simultaneously, and then unaliasing them using array coil sensitivity profiles yields the Multiband technique (79] which requires fewer repeats to scan the whole brain. Multiplexed-EPI (M-EPI) pulse sequence combines the SIR approach with MB technique: SIR consecutively excites s slices (s = 3) is shown in the pulse sequence diagram with pulses in red, blue and green) and acquires them in a single echo train. Using MB pulses that simultaneously excite m slices for each of the single slice pulses in the SIR approach produces the M-EPI sequence, with a slice acceleration of (s ∙ m) leading to (s ∙ m) number of slices collected in a single echo train. Four slices from 2 mm isotropic resolution, 60 slice, whole brain 3 Tesla data set obtained with the M-EPI technique are shown, together with the (s ∙ m) acceleration factors employed. Adapted from Feinberg et al. 2010; Moeller et al. 2010.

DECODING AND ENCODING WITH BOLD fMRI

Two relatively recent developments with fMRI are worth mentioning briefly with some references given for the reader to pursue further should they decide to do so. These developments involve the use of multivariate machine learning algorithms to decode brain activity patterns and a strategy that can be termed as encoding (Naselaris et al. 2011). Both of these methods offer new possibilities, especially when coupled with the information rich, high resolution, and high specificity functional mapping available at ultrahigh magnetic fields.

Multivariate machine learning algorithms applied to GE fMRI data obtained at 3 Tesla were shown to decode visual stimuli (Haxby et al. 2001; Haynes and Rees 2005; Kamitani and Tong 2005). These algorithms were able to reveal the presence of information that is known to be functionally segregated in cortical columns, e.g. ocular dominance, orientation and direction of motion. This was considered a particularly surprising result since the data were obtained with a large voxel size, 3×3×3 mm³, while these columns have dimensions of ~1 mm; in addition, the data were acquired at 3 T with GE fMRI where functional specificity is not expected to be sufficient to image at the columnar level. The functional images generated in this process have no resemblance to the actual columnar architecture. Nevertheless, voxels were found that demonstrated a preferential activation corresponding to a unique column, such as an orientation specific column.

The mechanism by which such low-resolution imaging decodes information represented at a much finer scale relative to the voxel size has been a source of debate and experimentation. Biased sampling of cortical columns by the large voxels has been hypothesized (Kamitani and Tong 2005; Haynes and Rees 2006; Kamitani and Tong 2006; Bianciardi et al. 2011). Biased sampling occurs due to the specific position that a large voxel takes within a fine columnar organization with local variations, even if the overall preferences represented by the columns are distributed equally across the investigated cortical region. Alternatively, draining macroscopic blood vessels can have bias with respect to cortical columns and thus could display selective responses (Kamitani and Tong 2005; Kamitani and Tong 2006; Chaimow et al. 2010; Shmuel et al. 2010; Bianciardi et al. 2011). The presence of bias in draining vessels with respect to ocular dominance columns was directly demonstrated in high-resolution columnar level mapping at 7 Tesla (Yacoub et al. 2007). Further analysis of these data with decoding algorithms revealed that discriminative power was conveyed by both macroscopic blood vessels and coarse-scale organization of the columnar organization, in other words irregularities in the distribution of different columns, in the gray matter areas (Shmuel et al. 2010). A detailed modeling study was performed to examine the latter, considering the pattern of cortical columns, the hemodynamic point spread function at 3 Tesla, the voxel size employed in the 3 Tesla decoding studies, and noise (Chaimow et al. 2010). These model studies demonstrated that sub-voxel supra-Nyquist spatial frequencies, including frequencies near the main frequency of the ocular dominance organization (spatial frequency of 0.5 cycles per mm) cannot contribute to the differential contrast in GRE fMRI and that the differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern (Chaimow et al. 2010). However, decoding due to such irregularities predicted decoding performances much lower than those that have been experimentally observed at 3 Tesla, suggesting the presence of additional mechanisms, such as the presence of biased draining veins implicated in Shmuel et al. (2010).

The decoding approaches described above rely predominantly on classification of image voxels where a pattern of activity across multiple voxels is used to construct a discrete class of stimulus response. Recent studies have moved beyond classification and demonstrated stimulus reconstruction to produce a literal picture of the image that was presented. Reconstruction of geometric stimuli composed of flickering checkerboard patterns was achieved by analyzing the responses of voxels in early visual areas using GRE fMRI (Thirion et al. 2006; Miyawaki et al. 2008). The more complex task of reconstruction of natural images was undertaken with significant success using forward encoding models of each voxel (Kay et al. 2008; Naselaris et al. 2009; Naselaris et al. 2011). These results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of functional imaging. In this quest, ultrahigh field fMRI is bound to play a critical role since it will provide many more informative and independent voxels with finer, more specific, information content so as to enable the expansion of encoding models and provide substantial increases in discriminative power.

CONCLUSIONS

Our understanding of MR detectable functional signals in the brain has improved significantly over the years since the development of the methodology. At the same time, we have seen major advances and refinements in instrumentation, such as the introduction of ultrahigh field instruments of ever increasing sophistication and refinement, and data acquisition and image analysis methodologies that have significantly impacted data quality. These fantastic improvements today enable mapping of cortical columns, layer specific activation or brain reading. These advances are sure to continue unabated for years to come as new instruments like the 10.5 Tesla system expected at the University of Minnesota and the 11.7 Tesla systems planned for the Intramural Research division of NIH and NeuroSpin Laboratory in France will come on line. With these instruments and the ever improving data analysis methods, MR based functional imaging may be performed in the not so distant future with totally different approaches and provide substantially better information than the already available rich techniques at the cutting edge today.

APPENDIX 1: Extravascular BOLD EFFECT

If one considers an infinite cylinder as an approximation for a blood vessel with magnetic susceptibility difference ∆χ, then the magnetic field at any point in space, will be perturbed from the applied magnetic field (Springer and Xu 1991). Inside the cylinder, the perturbation, Δν, in Hz will be given by the equation:

     (1)

At any point outside the cylinder, designated by the vector , the magnetic field will vary depending on the distance and orientation relative to the blood vessel and the external magnetic field direction, according to the equation:

   (2)

In these equations, the parameter γB₀ is the Larmour frequency of water protons expressed as an angular frequency in rad/s, B₀ is the magnetic field and γ is the gyromagnetic ratio, which is 2.6751965 x 10⁸ rad s^-1 Tesla^-1 or equivalently 42.577 in units of MHz/Tesla in a spherical water sample. The angles are defined in Figure 10; θ is the angle the cylinder makes the magnetic field B₀, generated by the magnet; for any given point in space, φ is the angle of the vector from that point running perpendicularly to the cylinder and the plane defined by the cylinder and the magnetic field B₀. Δχ₀ is the maximum susceptibility difference, expressed as parts per million (ppm) expected in the presence of fully deoxygenated blood (and depends on the hematocrit); Y₀ is the fraction of oxygenated blood at which the susceptibility difference between the blood and the surrounding tissue is zero, and Y is the fraction of oxygenated blood present in the vessel under consideration. Y₀ is usually assumed to be 1, as in (Ogawa et al. 1993); recent data assigns it a value of ~0.95 (Spees et al. 2001). Also varying values exist for Δχ₀; 0.264 ppm at unit hematocrit was recently determined experimentally (Spees et al. 2001) and differs from the value of 0.18 and 0.31 ppm used in some of the other earlier studies. At a hematocrit value of 0.4, Δχ₀ is, therefore, (0.264•0.4), which is 0.106; a value of 0.1 was used in (Ogawa et al. 1993). The parameter r_b designates the cylinder radius, r is the distance from the point of interest to the center of the cylinder in the plane normal to the cylinder. Note that outside the cylinder, the magnetic field changes rapidly over a distance comparable to two or three times the cylinder radius; at a distance equal to the diameter of the cylinder from the cylinder center, Δν^out is already down to 25% of its value at the cylinder boundary.

Since there are often different units and nomenclatures used, we can present a sample calculation: Using Δχ₀ of 0.264 ppm at unit hematocrit, a blood hematocrit of 0.4, Y₀ of 0.95, the parameter is calculated for 1 Tesla and at Y of 0.6, typical of brain venous blood, as (0.264x10^-6•0.4)•(0.95-0.6)(2.6751965x10⁸) = 9.89 Hz; thus, ~10 Hz for 1 Tesla, an easy round number to remember. Note that this applies for the veins. Capillaries, which also contain deoxyhemoglobin, do not have Y =0.6. In these vessels Y changes from arteriolar level (~1) to the venous level (~0.6) along the length of the blood vessel. Thus, using the average along the length of the capillary, is ~5 Hz for 1 Tesla, half of the value for veins.

FIGURE 10: The angles and distances that come into play in the equation describing the magnetic field outside a cylinder oriented relative to the main external magnetic field B₀ at an angle θ.

APPENDIX 2: Brief introduction into Data Acquisition in fMRI

In BOLD contrast, a signal is acquired simply after a delay, TE, following excitation. A refocusing pulse may or may not be applied during this delay. When refocusing pulses are not applied, one uses field gradient reversal to form an echo in imaging; consequently, such echoes are called gradient-recalled or gradient echoes, abbreviated simply as GRE. This is the first MR sequence used for functional mapping with the BOLD approach (Bandettini et al. 1992; Kwong et al. 1992; Ogawa et al. 1992). In the presence of a refocusing pulse, the spin-echo (SE) signal (i.e. echo-amplitude) detected after the echo time TE decreases according to exp(-TE/T₂) where T₂ is the spin-lattice relaxation time associated with the loss of magnetization with time in the transverse plane, the plane perpendicular to the main static magnetic field B₀. Spin echoes can also yield functional information in the brain leading to SE based fMRI. SE fMRI is sensitive only to a subset of the processes that lead to BOLD contrast in the GRE experiment, while everything that provides functional signals in SE fMRI also contribute to GRE fMRI.

In a gradient echo, signal loss occurs through T₂ relaxation as well as by signal cancellation that arises due to dephasing of the magnetization in the presence of magnetic field inhomogeneities. The latter is recoverable with a refocusing pulse and does not contribute to spin-echoes. In a gradient echo image, the appropriate relaxation constant of magnetization in the transverse plane is T₂*. T₂ of pure water is dominated by the magnetic interactions between the two hydrogen atoms, through the dipole-dipole coupling. In the human brain, this is still the case but the water is not in a pure form; rather, it exists in many different environments, such as tightly or weakly bound to macromolecules and metal ions, or free of such binding. The presence of these different states and the exchange between them affect the T₂ of the water resonance. The hydrogen atom of the water molecule itself can exchange between water and exchangeable –NH or –OH hydrogens on macromolecules. All of these processes affect the water proton T₂ and impart the contrast that allows the differentiation of one type of tissue from another in MRI. Another mechanism that contributes to T₂ is diffusion of water molecules around magnetic field inhomogeneities; normally in the brain this is not a very large affect though it is present and detectable especially at high magnetic fields (Bartha et al. 2002; Michaeli et al. 2002).

T₂*, on the other hand, has contributions from all of the process that affect T₂ plus the dephasing effect in the presence of magnetic field inhomogeneities. In a container of pure water placed in a fully homogenous external magnetic field, T₂ equals T₂*, except near the boundaries of the container where the magnetic field can be non-uniform. This is not the case in the brain because of magnetic field inhomogeneities that span scales ranging from microns to many millimeters and even centimeters as encountered around the air filled cavities adjacent to the brain. On the micron to millimeter scale, the inhomogeneities exist around metal containing enzymes, intracellular structures, myelin, and deoxyhemoglobin containing blood vessels ranging from capillaries to large veins; it is this last effect that provides functional mapping signals. Oxygenated blood is diamagnetic and has similar properties to tissue. However, the deoxyhemoglobin molecule is paramagnetic, and its presence leads to a large susceptibility difference between compartments that contain this molecule and others that are devoid of it. Thus, against a backdrop of numerous contributions to the inherent T₂ and T₂* of brain water, BOLD contrast refers to a T₂* or T₂ mechanism arising from magnetic field inhomogeneities generated by magnetic susceptibility differences across the luminal boundaries of blood vessels. During increased neuronal activity, deoxyhemoglobin content is altered in the brain; this change is accompanied by small but measurable decreases in T₂ and T₂*, which was experimentally documented in the early days of fMRI (Menon et al. 1993).

Image acquisition can be accomplished using slices where a frequency selective RF pulse is employed to restrict the signal origin to a single slice, typically ~1 to 3 mm in thickness at the present time, though as large as 10 mm slices were not uncommon in the early days of fMRI.

In MR imaging terminology, k-space refers to the two or three-dimensional matrix of data points collected during image acquisition. When the image acquired is from a slice only, the k-space data reside in two dimensions, representing the encoding along two orthogonal directions that define a plane perpendicular to the slice selection direction. A 2-dimensional Fourier transform then converts this into an image of the slice. In techniques like FLASH (Haase et al. 1986) and see reviews Haacke and Tkach 1990; Chien and Edelman 1991; Haacke et al. 1991), one line along a single dimension of k-space is collected after each RF pulse and multiple RF pulses are employed to cover the entire k-space in seconds. In approaches like single-shot Echo Planar imaging (EPI) (Mansfield 1977) or SPIRAL imaging (e.g. Yang et al. 1998), the entire k-space is covered after a single RF pulse using a train of gradient echoes in ~20 to 100 ms depending on the available hardware and the field magnitude at which the data are collected. Segmented EPI schemes (McKinnon 1993; Feinberg and Oshio 1994; Wielopolski et al. 1995) cover more than 1 line but less than the entire k-space in a single echo train after one RF pulse. They have been particularly useful in high-resolution functional imaging studies (Yacoub et al. 2007; Yacoub et al. 2008) because the higher resolution dictates the acquisition of many more k-space lines and points along each line.

Single shot techniques provide distinct advantages to the others for fMRI. These include the ability to cover the whole brain rapidly, and to partially suppress temporal signal fluctuations in an fMRI time series. The latter touches on a very interesting subject: Namely, in consecutively acquired images from the brain, temporal signal variations contain contributions from and can even be dominated by physiologically induced processes such as blood vessel pulsation (vasomotion), respiration, and spontaneous neuronal activity (see discussion further on). Of course, the intrinsic signal-to-noise ratio (SNR) of a single image has to be sufficiently good to detect these physiologically induced variations. These temporal instabilities come into single shot versus multiple shot acquisitions in a different way and are much more deleterious for the latter (Hu and Kim 1994; Hu et al. 1995). They can be minimized by post-processing strategies in multiple shot as well as in single shot techniques (e.g. Hu and Kim 1994; Hu et al. 1995; Mitra et al. 1995; Biswal et al. 1996; Ogawa et al. 1996; Mitra et al. 1997; Mitra and Pesaran 1999; Glover et al. 2000; Pfeuffer et al. 2002; Van De Moortele et al. 2002). However, such strategies require intrinsically good SNR and, and in some cases, rapid image acquisition so as to capture these temporal fluctuations accurately.

At higher magnetic fields, it is necessary to acquire single shot images faster because the MR signal disappears faster subsequent to excitation (i.e. the T₂* is shorter) due to increased magnetic field inhomogeneities. A critical development in this respect has been parallel imaging (PI) techniques which utilize the spatial information inherent in multiple surface coils distributed over the sample so as to omit some of the k-space lines as if the image was being acquired with a reduced field of view (FOV). Consequently, the time spent acquiring the signal after the RF pulse is shorter, which for EPI translates into shorter echo trains. When processed without the use of the spatial information from array coil sensitivity profiles, images acquired in this fashion display sections that are folded on top of each other, in other words sections that are aliased. However, they can be unfolded using parallel imaging techniques. The utility of parallel imaging has been examined for functional imaging early on after their introduction (de Zwart et al. 2006; Moeller et al. 2006); today, they are typically part of the normal fMRI acquisition, especially at high magnetic fields.

Parallel imaging works better at high magnetic fields (Ohliger et al. 2003; Wiesinger et al. 2004; Wiesinger et al. 2006) and high magnetic fields need the use of parallel imaging relatively more because of the short window available for data acquisition due to the short T₂* (e.g. see (Yacoub et al. 2001; Yacoub et al. 2003; Uludag et al. 2009) and references therein). Thus, parallel imaging and high fields are synergistic. Partial Fourier (Feinberg et al. 1986) sampling, or sparse data sampling approaches (Liang et al. 2003) are also approaches that reduce the image encoding times. With the use of these techniques, it is feasible today to acquire single shot high-resolution EPI images at high fields such as 7 Tesla, as illustrated in Figure 6.

Parallel Imaging, and Partial Fourier sampling reduce the number of phase encoding steps for spatial encoding, and consequently, the echo train length after a single RF pulse; however, they do not necessarily reduce whole brain image acquisition times significantly in many applications. Significantly faster acquisitions can be obtained using the Multiband technique as discussed in the main body of the article.

endnotes

ACKNOWLEDGEMENT

The work reported in this review that was carried out at the Center for Magnetic Resonance Research (CMRR), University of Minnesota, was supported by a Biomedical Technology Research Center grant (Grant Number P41 RR008079 from NCRR/P41 EB015894 from NIBIB) and Neuroscience Center Core grant P30 NS057091 from NINDS of the National Institutes of Health, and the Keck Foundation.

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