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The application of T1 in the rotating frame (T1ρ) to functional MRI in humans was studied at 3 T. Increases in neural activity increased parenchymal T1ρ. Modeling suggested that cerebral blood volume mediated this increase. A pulse sequence named spin-locked echo planar imaging (SLEPI) that produces both T1ρ and T2* contrast was developed and used in a visual functional MRI (fMRI)experiment. Spin-locked contrast significantly augments the T2* blood oxygen level-dependent (BOLD) contrast in this sequence. The total functional contrast generated by the SLEPI sequence (1.31%) was 54% larger than the contrast (0.85%) obtained from a conventional gradient-echo EPI sequence using echo times of 30 ms. Analysis of image SNR revealed that the spin-locked preparation period of the sequence produced negligible signal loss from static dephasing effects. The SLEPI sequence appears to be an attractive alternative to conventional BOLD fMRI, particularly when long echo times are undesirable, such as when studying prefrontal cortex or ventral regions, where static susceptibility gradients often degrade T2*-weighted images.
The most frequently used sequence in functional MRI (fMRI) studies, gradient-echo (GE) echo-planar imaging (EPI), is particularly sensitive to changes in the oxygenation state of hemoglobin, commonly referred to as blood oxygenation level-dependent (BOLD) contrast (1). BOLD contrast results from a complex interaction of physiologic changes, including changes in cerebral blood flow, cerebral blood volume (CBV), and the cerebral metabolic rate of oxygen consumption (2). Recently introduced functional sequences detect neural activity by directly or indirectly measuring these or other physiologic changes (3–6). Generally, these sequences allow for quantitation of physiologic parameters or more accurate localization of neuronal activity, but are less sensitive in detecting activation than conventional BOLD fMRI.
An alternative method for detecting neural activity using MRI is via changes in T1ρ, the spin–lattice relaxation time in the rotating frame. T1ρ is sensitive to physical processes with correlation times (τc) close to the reciprocal of the applied spin-locking frequency (typically on the order of several milliseconds). Processes with relevant τc include dipolar fields created by slowly tumbling molecules (7), the diffusion of spins through susceptibility gradients (8), and chemical exchange processes (9). The potential of spin-locking to reveal functional changes in the brain lies in the fact that the T1ρ of blood increases with increasing hemoglobin oxygen saturation and is longer than the T1ρ of brain tissue, which is reported to be insensitive to changes in blood-induced susceptibility gradients (10).
T1ρ preweighting can be prepended to a GE EPI sequence with the resultant signal (assuming a 90° flip angle) taking the form
where x is the volume fraction of the ith compartment (e.g., gray matter, blood, CSF), TR is the repetition time, TSL is the duration of the spin-lock pulse, TE is the echo time of the gradient echo, and T1ρ is the relaxation in the rotating frame for a given spin-locking frequency. In the brain, blood and tissue compose the largest fractions of activated voxels. At 3 T, T1 is longer, and T2* is shorter, in blood compared to brain tissue. Consequently, local increases in blood volume fraction due to neural activation (reported to be on the order of 20–40% (11–13)) generate negative contrast in conventionally weighted MRI, according to the T1 and T2* terms of Eq. . This contrast subtracts from any positive BOLD contrast caused by blood saturation effects on T2*. Like T1, T1ρ is longer in blood than it is brain tissue (~110 ms versus ~75 at 3 T). However, because T1ρ decay acts like T2* in Eq. , increases in CBV will cause overall parenchymal (perfused tissue: tissue + blood) T1ρ to increase and thus create positive contrast to neural activation. Because the positive T1ρ (spin-lock) contrast due to increased CBV adds to positive T2* BOLD (EPI) contrast, a sequence that derives functional contrast from both T1ρ and T2* contrast may have certain advantages over a traditional EPI sequence.
A major distinction between T1ρ and T2* contrast is a markedly different sensitivity to static susceptibility gradients. The lengthy echo times used in the traditional EPI sequences, required to produce adequate T2* BOLD contrast, result in significant signal loss in areas with large static susceptibility gradients due to intravoxel dephasing (14). A variety of methods have been implemented to reduce susceptibility-induced signal losses in EPI data, including static gradient shimming (15), modified pulse sequences (16–18), and susceptibility matching (19). In practice, however, appreciable signal losses remain in EPI data, and fMRI sensitivity in brain regions near tissue–air boundaries is adversely affected. In contrast, T1ρ is not very sensitive to static susceptibility gradients. Spin-locking acts on the transverse magnetization in a manner analogous to a Carr–Purcell echo train (20) with a very short interecho interval, equal to the reciprocal of the spin-locking frequency. Therefore, static susceptibility effects are efficiently refocused, or “spin-locked”, and T1ρ contrast can be obtained with little static susceptibility-induced signal loss.
This work presents fMRI results at 3 T using a T1ρ-modified EPI sequence to measure activation in the visual cortex. This new sequence produces a positive CBV-based contrast, augments BOLD contrast, and reduces static susceptibility-induced signal loss while maintaining high detection sensitivity. A model is presented that estimates the CBV and blood oxygenation contributions to the T1ρ contrast, and the model predictions are compared to the experimental results from the visual fMRI study.
Experiments were conducted on a Siemens Trio 3 T whole-body scanner (Siemens, Erlangen, Germany) using a birdcage head coil for RF transmission and reception. Four healthy volunteers participated in the study, approved by the Institutional Review Board of the University of Pennsylvania, after providing informed consent.
In the spin-lock EPI (SLEPI) pulse sequence (Fig. 1), a nonselective π/2 pulse excites spins that are then spin-locked in the transverse plane by the application of two phase-alternating (±90° phase-shifted from the phase of the first π/2 pulse) spin-lock (SL) pulses. The alternately phased SL pulses implement the self-compensating method of Charagundla et al. (21). The duration of the SL pulses is denoted TSL. A second nonselective π/2 pulse restores the spin-locked magnetization to the longitudinal axis. A large amplitude dephasing gradient (indicated by the filled square) is subsequently applied to destroy any residual transverse magnetization. After the SL portion of the SLEPI sequence, the “T1ρ-prepared” longitudinal magnetization, described by
is subsequently imaged using a GE-EPI (22) sequence with rectilinear k-space sampling.
fMRI sessions with photic stimulation were performed twice on each of four subjects. Each session consisted of three GE and three SL functional trials, acquired with the following parameters: TR = 2 s, flip angle = 90°, FOV = 240 mm, voxel size = 3.75 × 3.75 × 5.00 mm, slices = 1, repetitions = 144, axial slice orientation. The SL trials had echo times (TE) of 20, 30, and 50 ms, and the GE trials had TE = 30, 50, and 90 ms. The acquisition order of sequence type and TE was randomized across subjects and sessions. The SL pulse amplitude was 500 Hz, with TSL = 50 ms. The image location was prescribed from a coronal localizer to position the slice in the center of the visual cortex, aligned with the calcarine fissure.
Photic stimulation was accomplished using a high-contrast reversing black and white checkerboard alternating at 8 Hz, with a central fixation cross presented in the rest condition. The stimulus was pseudo-randomly presented 32 times within each functional trial, with each presentation lasting 2 s. The total presentation period lasted 288 s, and the mean interval between events was 6.6 s. The stimulus timing sequence was derived using the Optseq2 software package (http://surfer.nmr.mgh.harvard.edu/opt-seq/). While blocked designs with longer stimulus durations yield larger signal changes, event-related paradigms allow for a larger number of stimulus events, resulting in increased precision in the estimation of the hemodynamic response function (hrf). Consequently, the timing paradigm was designed to maximize the statistical efficiency of the experiment, at the expense of the power of activation detection (23).
In order to quantify T1ρ in blood and brain parenchyma, single repetition SLEPI images were obtained in the neck and occipital cortex of two subjects using multiple TSL times of 10, 20, 30, 40, 50, 70, and 100 ms. The spin-lock frequency was 500 Hz. The other imaging parameters were TR = 2 s, TE = 12 ms, FOV = 240 mm, voxel size = 3.75 × 3.75 × 5 mm, averages = 6. Following the SLEPI acquisitions, a 3D-MPRAGE image of the whole head was acquired with the following parameters: TR = 1630 ms, TE = 4 ms, TI = 1100 ms, FOV = 256 mm, voxel size = 1 × 1 × 1 mm, slices = 160. The SLEPI images were coregistered to the MPRAGE, and regions of interest (ROI) were identified for voxels within the internal carotid and vertebral arteries and the internal jugular veins. Blood T1ρ was determined by fitting the mean signal intensity within the ROIs to an exponential decay versus TSL, as described by Eq. . T1ρ in brain parenchyma was derived in a similar fashion, using an ROI containing voxels activated by the visual stimulus. These ROIs were also used to mask tissue segmentation maps in order to determine the fractional composition of the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the parenchymal voxels. The segmentation maps were derived from the MPRAGE images using the automated segmentation routine in SPM99.
The image data from each functional trial were processed using standard techniques in SPM2 (Wellcome Department of Cognitive Neurology, London, UK) in the following order: (1) Spatial smoothing was applied to each EPI slice using a Gaussian kernel with a full width at half maximum of 6 mm; (2) Correlation of voxel time course data to the stimulus paradigm was carried out using the general linear framework (24). The design matrix was constructed using a canonical hemodynamic response function plus its first derivative (25). The regressions were examined for statistical significance, and only voxels exceeding a threshold of z = 3.09 (P < 0.001), corrected for multiple comparisons, were selected for further analysis. For each activated voxel, the peak activation contrast was derived from the fitted response of the regression. The contrast was converted to percentage from baseline (i.e., ΔS/S × 100) by scaling to the regression parameter used to model the DC signal component. Peak contrast was taken as the maximum amplitude of the fitted response, where the canonical basis functions (hrf plus derivative), were defined to 10 ms temporal resolution. This peak contrast represents the maximum signal change from the average response to a single stimulus event (i.e., a 2-s duration photic stimulation).
Robust activation was observed in the primary visual cortex for both sequences, in all subjects and at all TEs. Figure 2 depicts images of the SLEPI-fMRI and EPI-fMRI obtained functional contrast for the activated voxels from trials involving three of the subjects. The EPI contrast maps from each depicted session are in the top row, with the corresponding SLEPI results below. The contrast maps are overlaid on the first image from the time series. The barscale identifies the contrast, in percentages, assigned to each map color. In each image pair, the maximum SLEPI contrast was greater than the maximum EPI contrast, and the regions of activation were similar in extent, but they were not identical.
For each trial, the mean functional contrast was computed from all the significantly activated voxels. The average response of the mean activated contrast, categorized by sequence type and TE, is reported in Fig. 3, with error bars expressed as the SE across trials. It should be noted that the EPI contrast reported here is lower than some reported literature values for similar visual stimuli. As expected, both SLEPI and EPI contrast increased with longer echo time. The SLEPI contrast was significantly (P < 0.001) larger than the EPI contrast at equivalent TEs. Specifically, the SLEPI contrast was 55% (1.31 versus 0.85) and 24% (1.66 versus 1.33) larger than the EPI contrast at echo times of 30 and 50 ms, respectively.
BOLD contrast has been shown to increase linearly with echo time (26,27). The contrasts generated from the SLEPI and EPI trials in this study were linearly regressed against TE, with the results plotted in Fig. 4. For both sequence types, the linear fits were extremely good, with r = 0.996 and 0.999 for the SLEPI and EPI results, respectively. The y-intercepts of these regressions indicate the residual contrast at TE = 0 and correspond to the predicted contrast in the absence of T2* BOLD effects. Similar to the observations of others (27), the gradient echo functional contrast had a near-zero intercept (0.12 ± 0.08%). The intercept of the SLEPI data, 0.69 ± 0.06%, was significantly (P < 0.001) greater than zero and provides an estimate of the T1ρ contrast in the visual cortex for the given spin-lock parameters (amplitude = 500 Hz; TSL = 50 ms) and stimulation paradigm. This additional contrast due to the spin-locked preparation period is additive to the BOLD contrast, which accrues during the echo time.
Spin-locked sequences can provide superior sensitivity in regions of large static susceptibility gradients. For example, the prefrontal cortex has large static susceptibility gradients produced by the air–tissue interfaces around the frontal sinuses. Upon analyzing ROIs in the prefrontal and occipital cortices across multiple scans, it was found that the EPI sequence had significant signal loss in the prefrontal cortex compared to the occipital cortex. Specifically, at TE = 50 ms, the SNR in the prefrontal ROI (28.8) was only 50% of the SNR in the occipital cortex (53.7). In contrast, the SLEPI sequence with TSL = 50 ms and TE = 20 ms had comparable SNR to the T2*-weighted EPI sequence with TE = 50 ms in both the occipital cortex (52.3) and the prefrontal cortex (50.2). The preserved SNR in the prefrontal cortex provides increased detection sensitivity in the SLEPI compared to the EPI.
Working with blood phantoms at 4.7 T, Kettunen et al. demonstrated that the T1ρ relaxation time in blood is linearly related to oxygen saturation (Y) (10). In order to estimate the saturation dependence at 3 T, T1ρ was calculated from ROIs identified in the veins (internal jugular) and arteries (internal carotid and vertebral) of two subjects. The results of these calculations from one subject are shown in Fig. 5a. The arterial ROIs are depicted in red and the venous ROIs in blue. For both ROIs, the signal dependence on TSL was well approximated by a single exponential decay. In the first subject, the fitted T1ρ values were 115.3 and 98.7 ms for arterial and venous blood, respectively. The second subject produced similar values of 115.0 and 100.3 ms. Extending on the work of Kettunen and colleagues and preserving the assertion that a linear relationship exists between T1ρ and blood saturation in vivo, linear regressions of oxygen saturation versus T1ρ are shown in Fig. 5b. For these curves, oxygen saturation values of 70 ± 5% in the jugular vein (28) and 97 ± 1% in the vertebral and internal carotid arteries (29) were assumed. The solid line of Fig. 5b represents the best-fit line through the average T1ρ values from our two subjects, with the dashed lines showing the range of values given the uncertainty in the oxygen saturation. The data from the Kettunen group (from Fig. 1a in (10)) are included (red dash–dot line) for comparison. Also included is an approximation of the saturation dependence of blood T2* (lower curve). The T2* relationship was computed using typical literature values assuming
where Y is saturation, A and C are constants, and B was set to 0 based on literature reports (30,31). As is evident from the Figure 3, T2* increases more rapidly than T1ρ given equal increases in the oxygen saturation of hemoglobin, especially in the physiologic range (>55% saturation), and explains the exquisite sensitivity of GE-EPI to changes in blood oxygenation.
The measurement of T1ρ in blood as a function of oxygen saturation allows the effects of activation-induced changes in blood volume and saturation to be estimated. A two-compartment model was used to describe the signal contrast expected from the T1ρ-weighted preparation period, namely
where xblood and xtissue are the blood and tissue volume fractions, respectively. The blood volume fraction can be further divided into an arterial and a venous pool, each comprising 50% of the total blood volume. For simplicity, the capillary contribution is assumed to be included within these two pools. The T1ρ of brain tissue (gray and white matter) was calculated from the measured resting parenchymal T1ρ and subtracting the blood contribution assuming an 8% blood volume fraction. The tissue segmentation analysis revealed that the voxels used to determine the parenchymal T1ρ were predominately composed of GM, although a substantial fraction of WM contribution existed. Specifically, the average fractional composition of all activated voxels across all subjects was 62.0, 28.9, and 7.2% for GM, WM, and CSF, respectively. The contribution of oxygen saturation changes to T1ρ signal contrast during activation was calculated by allowing the T1ρ to change as a function of hemoglobin oxygenation, using the relationship established in Fig. 5b, while keeping the volume fractions (xtissue and xblood) constant. The contribution to T1ρ signal contrast due solely to CBV changes was estimated by allowing the blood and tissue volume fractions to change, while keeping saturation (and hence T1ρ) constant. Combined effects allowed both T1ρ and the volume fractions to change from the resting to the activated state. A summary of the model parameters used in the simulation is presented in Table 1. The simulation results indicate that both the blood volume and the blood oxygenation induced contributions to T1ρ contrast were positive. The majority (93%) of the modeled T1ρ contrast was due to the increase in CBV during activation. The total signal change predicted using the two-compartment model (0.68%) was in excellent agreement with the isolated SLEPI contrast (0.69%) obtained from Fig. 4.
The contrast estimate in the model was highly dependent on the resting blood volume fraction and the percentage change in the CBV. To illustrate this dependence, the modeled SLEPI contrast (ΔST1ρ/S) for a range of physiologically relevant blood volume fractions (1–10%) and activation-induced CBV changes (20–50%) is presented in Fig. 6. The isolated SLEPI contrast estimate obtained from the fMRI trials is included for comparison (dash–dot line), along with its error bounds (dotted lines). In order to agree with our experimental results, any combination of resting blood volume fraction and ΔCBV that intersect with the dash–dot line can be used in the two-compartment model. However, over the range of these values that are consistent with the data, the relative contribution of saturation and CBV changes to the total contrast is only moderately affected. For example, the CBV-derived component of the SLEPI contrast accounted for 87, 91, 93, and 95% of the total modeled contrast when using ΔCBV values of 20, 30, 40, and 50%, respectively. Similarly, choosing a resting blood fraction anywhere in the range of 4–8%results in an estimate of the ΔCBV-based component of the total SLEPI contrast, which is always in excess of 90%. Consequently, the two-compartment model suggests that the SLEPI-erived functional contrast in the visual cortex is primarily due to increases in CBV with activation.
SLEPI fMRI produced a positive contrast in response to functional activation of the visual cortex that was additive with (GE) EPI BOLD contrast. Figure 3 suggests that the magnitude of the isolated spin-lock contrast is about 0.7% using the current parameters (amplitude = 500 Hz; TSL = 50 ms). This contrast is about 80% of the conventional BOLD contrast elicited by the visual stimulus at TE = 30 ms, a commonly used echo time in fMRI experiments at 3 T (32). As is evident in Fig. 1, the spin-lock and gradient echo EPI are independent components of the pulse sequence, and thus the TSL and TE can be independently adjusted to allow the ratio of T1ρ and T2* contrast to vary over a wide range. T2* sensitivity provides BOLD based contrast that is sensitive to static susceptibility effects, while T1ρ sensitivity provides a CBV-based contrast that is relatively insensitive to static susceptibility effects.
The SLEPI sequence allows for the relative proportion of the T2* and T1ρ contrasts to be tailored to suit the specific criteria of an experiment. For example, when investigating areas with minimal static susceptibility gradients, such as the occipital or motor cortex, a long echo time and a long TSL can be used to maximize the combined contrast from saturation and CBV changes. In ventral and prefrontal regions where signal loss from static susceptibility effects are problematic for BOLD studies, a shorter echo time and long TSL will generate less total contrast but with reduced signal dropout. An optimized combination of TSL and TE may well produce a larger contrast-to-noise ratio (CNR) than possible with standard GE BOLD in such areas. This possibility is supported by the current study, where the SLEPI data at TE = 30 ms produced equivalent contrast to the EPI sequence with TE = 50 ms (~1.3%), but the SLEPI had significantly higher SNR (38.5) in the orbitofrontal cortex than did the EPI (28.8) at these same echo times. Assuming that the ratio of T1ρ to T2* functional contrast is similar with activation of the frontal cortex, this would lead to CNRs of 0.51 and 0.37 from the SLEPI and EPI sequences, respectively. Future work will attempt to verify this assumption through application of the SLEPI sequence to activation studies of the frontal cortex.
In the current model, changes in blood volume and intravascular T1ρ effects from blood oxygenation were used to account for the observed functional spin-locked contrast. Extravascular oxygenation-dependent T1ρ changes were not included. This is in contrast to current GE EPI models, which include both intravascular and extravascular (EV) sources of BOLD contrast. At 3 T, approximately 40% of the GE BOLD signal has been reported to arrive from EV space (33). This EV contrast is due to static dephasing (T2′ effects) around blood vessels. Spin echo (SE) BOLD contrast is less sensitive to these EV changes, because spin echoes refocus much of this static dephasing (34). Because spin-locking acts on the transverse magnetization in a manner analogous to a Carr–Purcell (SE) echo train with short TE, minimal EV T1ρ contrast was anticipated, and thus this potential source of contrast was excluded from the model.
An intravascular component of oxygenation level dependent T1ρ contrast was included in the model. The T1ρ of blood shows a mild dependence on oxygenation, but it is much less than the T2* dependence (see Fig. 5b). The correlation time of water diffusion across and around red blood cells has been reported to be between 0.5 and 5 ms. This is close to the 2 ms τc to which a 50-Hz spin-lock amplitude would be sensitive, and it is proposed that these diffusion processes are responsible for the observed oxygenation dependence of blood T1ρ. Work by Kettunen et al. (10) in the rat brain found minimal parenchymal T1ρ changes after the administration of paramagnetic susceptibility agents. This finding is in agreement with the predictions of the model that show that oxygenation-dependent changes in T1ρ are not large enough to explain the observed spin-lock contrast.
Consequently, the model presented here attributes functional increases in CBV as the primary source of the observed spin-lock contrast. The magnitude of this CBV-based contrast within a voxel is critically dependent on its blood volume fraction and the percentage increase in CBV. The current model was able to match the contrast found in the experimental data using physiologically plausible values (e.g., 8% blood volume; 40% CBV increase). This blood volume fraction is somewhat larger than the reported range of 4–6% (35,36), while the CBV increase is within the 30–40% range that has been previously reported (11–13). However, it should be noted that while the CBV values reported in the literature represent average blood volume fractions across the whole brain, it is likely that the local blood volume fraction varies significantly across tissue types and brain regions. BOLD activation is well known to be preferentially sensitive to the intra- and extravascular venous compartments. Lee et al. found that venous CBV in rats comprises 70 to 80% of total CBV (37). Therefore, the local, venous compartment CBV (as opposed to the lower, global value of CBV) may provide a more appropriate parameter for the model.
An alternative rationale for our slightly elevated estimate of CBV is the selection of only significantly activated voxels for analysis. Because the blood volume fraction generates the functional activation in the SLEPI sequence, highly activated voxels are expected to contain either a large resting CBV or to undergo large changes in CBV during activation. It is therefore not surprising that the blood volume fraction in these activated voxels is higher than the average blood volume fraction across all parenchyma. However, the possibility that a portion of our observed T1ρ contrast may be due to a source other than blood volume changes cannot at present be ruled out. In such a case the model, which does not account for additional contrast sources, would underestimate the observed contrast. For example, using a lower range of CBV values (5% blood volume and 40% ΔCBV), the model only accounts for about 65% of the observed SLEPI contrast. In order to more rigorously verify the accuracy of the model, an analysis of local blood volume fraction and activation-specific ΔCBV changes is required.
The combination of SLEPI and EPI contrast, possibly with higher spatial resolution than obtained here, offers the potential to separate CBV and saturation changes during neural activity. The current data have a significant GE BOLD component, due to the relatively long echo times used, and prevent a more precise characterization of spin-lock contrast. Future work, wherein the echo time is minimized to decrease GE BOLD effects, will provide a better opportunity to analyze the spatial and temporal properties of the isolated spin-lock contrast and to verify that CBV indeed mediates the T1ρ contrast, as predicted by the current two-compartment model.
Both the duration and the amplitude of the spin-lock preparation can be varied to adjust the functional T1ρ contrast. Higher spin-lock amplitudes will further increase the magnitude of the T1ρ difference between blood and tissue, thus increasing functional spin-lock contrast. The current sequence, using a spin-lock pulse amplitude of 500 Hz, approaches specific absorption rate (SAR) limitations at TSL longer than 65 ms. Sequence development is currently underway that is less SAR intensive and will allow for higher spin-lock amplitudes. The relationship between spin-locking amplitude and duration, as well as the resultant functional contrast, will be another focus of future work. Finally, the current implementation is limited to a single-slice acquisition, due to the spatially nonselective spin-locking pulse. Recent work has demonstrated the successful in vivo implementation of spin-locking in a multislice spin-echo imaging sequence (38). However, a serious impediment to the development of a multislice EPI sequence is the additional increase in SAR that would be incurred.
This work demonstrated that neural activity increases parenchymal T1ρ. Modeling suggests that cerebral blood volume mediates this increase. However, the two-compartment model only considered changes in oxygenation and blood volume, and it is possible that other mechanisms contribute to the observed contrast. A T1ρ modified GE-EPI sequence, SLEPI, that produces T1ρ-weighted contrast in addition to T2* contrast was introduced for use in fMRI studies. Experiments showed that SLEPI contrast is additive to traditional BOLD-based contrast and is particularly useful in brain regions with large static susceptibility gradients.
Grant Sponsor: National Institute of Health; Grant Numbers: 5-T32-HL07614, RR02305, and MH64045.