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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Neuroimage. Author manuscript; available in PMC 2010 October 25.
Published in final edited form as:
PMCID: PMC2962953

Myelination and long diffusion times alter diffusion-tensor-imaging contrast in myelin-deficient shiverer mice


Diffusion tensor imaging (DTI) using variable diffusion times (tdiff) was performed to investigate wild-type (wt) mice, myelin-deficient shiverer (shi) mutant mice and shi mice transplanted with wt neural precursor cells that differentiate and function as oligodendrocytes. At tdiff = 30 ms, the diffusion anisotropy “volume ratio” (VR), diffusion perpendicular to the fibers (λ[perpendicular]), and mean apparent diffusion coefficient (left angle bracketDright angle bracket) of the corpus callosum of shi mice were significantly higher than those of wt mice by 12 ± 2%, 13 ± 2%, and 10 ± 1%, respectively; fractional anisotropy (FA) and relative anisotropy (RA) were lower by 10 ± 1% and 11 ± 3%, respectively. Diffusion parallel to the fibers (λ//) was not statistically different between shi and wt mice. Normalized T2-weighted signal intensities showed obvious differences (27 ± 4%) between wt and shi mice in the corpus callosum but surprisingly did not detect transplant-derived myelination. In contrast, diffusion anisotropy maps detected transplant-derived myelination in the corpus callosum and its spatial distribution was consistent with the donor-derived myelination determined by immunohistochemical staining. Anisotropy indices (except λ//) in the corpus callosum showed strong tdiff dependence (30–280 ms), and the differences in λ[perpendicular] and VR between wt and shi mice became significantly larger at longer tdiff, indicative of improved DTI sensitivity at long tdiff. In contrast, anisotropy indices in the hippocampus showed very weak tdiff dependence and were not significantly different between wt and shi mice across different tdiff. This study provides insights into the biological signal sources and measurement parameters influencing DTI contrast, which could lead to developing more sensitive techniques for detection of demyelinating diseases.

Keywords: DTI, ADC, Diffusion anisotropy, MRI, High fields, Stem cell transplantation, Neural precursor cells, Myelin basic protein, Demyelination, STEAM sequence


Diffusion tensor imaging (DTI) (Basser et al., 1994a,b) has gained wide acceptance as a tool for non-invasive imaging of anatomical connectivity (Mori, 1995; Nakada and Matsuzawa, 1995; Makris et al., 1997; Xue et al., 1999) and brain microstructural morphology (Mori et al., 2001a; Mori et al., 2001b; Zhang et al., 2002). DTI has been used for detecting changes in myelination in the developing brain (Wimberger et al., 1995; Prayer et al., 1997; Neil et al., 1998) and in demyelinating diseases (Guo et al., 2001; Larsson et al., 2004), although its underlying contrast mechanism remains incompletely understood. There is some evidence that DTI is more sensitive for detecting demyelinating lesions relative to conventional T1- and T2-weighted imaging (Hajnal et al., 1991; Sukama et al., 1991; Wimberger et al., 1995; Prayer et al., 1997; Guo et al., 2001; Larsson et al., 2004). DTI contrast arises from barriers (such as cell membranes of axons and oligodendrocytes) that hinder water diffusion in some orientations more than others, giving rise to anisotropic diffusion. In white matter of the central nervous system (CNS), for example, water diffusion perpendicular to fiber tracts is more restricted than that parallel to the fiber tracts (Le Bihan et al., 1993; Beaulieu and Allen, 1994b).

In principle, DTI contrast could arise from myelin and/or axons. Diffusion measurements on myelin-deficient and demyelinated fibers (Le Bihan et al., 1993; Beaulieu and Allen, 1994b; Ono et al., 1995; Seo et al., 1999; Gulani et al., 2001) showed that diffusion anisotropy was only marginally reduced relative to normal myelinated axonal fibers, suggesting that DTI contrast likely arises from axonal density and anisotropy in axonal structure rather than from myelin content. More recently, in vivo DTI study on dysmyelinated axons in myelin-deficient shiverer (shi) mutant mice (Song et al., 2002) reported small but significant reduction in diffusion anisotropy relative to wild-type (wt) mice, again suggesting that most of the DTI contrast arises from axons but myelin could contribute to DTI contrast. Although CNS axons in shi mice remain apparently intact (Dupouey et al., 1979; Privat et al., 1979; Inoue et al., 1981; Shen et al., 1985), increased axonal protein content and abnormalities of the axonal cytoskeleton have been reported (Brady et al., 1999; Kirkpatrick et al., 2001). These and other potential pleiotropic effects associated with the shi mutation, which might modulate the apparent diffusion coefficient (ADC) and DTI contrast, remain to be addressed. Furthermore, the sensitivity of DTI contrast to detect changes in myelin per se has not been unequivocally demonstrated.

Most DTI studies use relatively short diffusion times (tdiff), typically ranging from 30 to 60 ms in animal model, in order to minimize signal loss due to T2 decay. ADC has been shown to be tdiff-dependent (Segebarth et al., 1994; Helmer et al., 1995; Pfeuffer et al., 1998). ADC in the whole brain parenchyma had been reported to decrease rapidly from ~2 to ~15 ms and gradually decreases for tdiff > 20 ms (Segebarth et al., 1994; Helmer et al., 1995; Pfeuffer et al., 1998). The root-mean-squared (rms) displacement for tdiff ~20 ms approximates the ensemble-average distant between cell membranes (i.e., on the order of cell size). The effect of tdiff on DTI contrast at longer diffusion times (few hundreds of ms), however, remains relatively unexplored. Since diffusion displacement parallel to the fiber tracts in principle is less restricted, while that perpendicular to the fiber is more restricted, we predicted that DTI contrast should improve at longer tdiff. Knowledge of the tdiff-dependent effects on DTI contrast is important for future experiments aimed at improving sensitivity of fiber tracking.

The general aim of this study was to investigate the potential contribution of myelination and diffusion times to DTI contrast. First, we extended Song et al.'s (2002) study by systematically evaluating the sensitivity of diffusion perpendicular (λ[perpendicular]) and parallel (λ//) to the fiber tracts, mean apparent diffusion coefficient (left angle bracketDright angle bracket), fractional anisotropy (FA), relative anisotropy (RA), and volume ratio of the diffusion ellipsoid (VR) to detect anisotropy differences between wt and shi mice. Further, we performed the transplantation experiments to ask if and how MR parameters might be altered by the addition of some normal myelin to shi mice. We have previously shown that intracerebroventricular transplantation of neural precursor cells in shi mice leads to engraftment and differentiation of transplanted cells, including some that function as oligodendrocytes, producing wt myelin basic protein (MBP) and morphologically normal internodal myelin sheaths (Mitome et al., 2001). Such transplantation could potentially reverse the MRI abnormality observed in shi mice. DTI of wt, shi, and transplanted shi mice thus offered a unique opportunity to evaluate the effects of myelination per se on MR parameters. Second, we analyzed the sensitivity of DTI contrast as a function of tdiff (30 to 280 ms) in wt and shi mice. We modified the Stimulated-Echo-Acquisition-Mode (STEAM) sequence to include diffusion gradients, making it possible to use very long tdiff without substantial signal loss due to T2 decay.

Materials and methods

Phantom experiments

Diffusion-weighted imaging (DWI) was performed using a modified STEAM sequence (Merboldt et al., 1991) with a pair of unipolar diffusion gradients placed during the TE/2 periods. The cross-term interactions of diffusion gradients with each other and with the imaging gradients might change the b values for different diffusion-sensitive orientations (Brockstedt et al., 1998; Gullmar et al., 2002), possibly leading to bias in diffusion anisotropy within a single tdiff as well as across different tdiff. Although these gradient cross-term interactions could be calculated (Brockstedt et al., 1998; Gullmar et al., 2002), these calculations become tedious and inaccurate for STEAM sequence with very long diffusion time because the approximation used in the calculation may be invalid when the cross-terms are large. In this study, an experimental approach was used instead. This was done by experimentally adjusting the diffusion gradients (effective b values) such that the diffusion-weighted 1-D signal intensity profiles, and thus the diffusion coefficients, on a uniform water phantom along different diffusion-sensitizing directions were the same using identical imaging parameters as the in vivo part of this study. Phase-encoding gradients were switched off, and six profiles were obtained by sequentially switching on diffusion gradients in each of the six directions (3 axes, +1 and −1 direction for each axis). Since there were no first-order cross-terms in the phase-encoding direction, the profile intensities obtained with diffusion gradients in the phase-encoding directions were equal and these profiles were used as references. The magnitudes of the diffusion gradients in the slice-selection and readout directions were adjusted such that their profile intensities were equal to the reference profile intensities. Cross-terms from higher k-space lines were ignored because the positive and negative lobes of the phase-encoding gradients result in cancellation of the cross-term effects and the high k-space lines have relatively small “signal power”. This correction was done for the larger (~1200 mm2/s) of the two b values; the cross-term effects on the low b value (~5 mm2/s) images were ignored. The advantage of this approach is that it can be readily validated experimentally across a wide range of conditions.

To test the validity of this approach, DTI measurements at different tdiff were made on a water phantom and a phantom of N-acetyl-aspartate (NAA) dissolved in dimethylsulfoxide (DMSO) at room temperature (20°C); the latter was chosen because it yielded a diffusion coefficient comparable to the ADC in the in vivo brain (i.e., gray matter) at 37°C.

Transplantation methods

Shi mice were used as transplantation hosts. The shi mutation is a large deletion in the MBP gene. Homozygous mutant mice fail to produce MBP, which is a major structural component of the myelin sheath, leading to extensive CNS dysmyelination with morphologically abnormal myelin sheaths. The shi mutation in our colony is maintained on a B6C3F1 hybrid-based stock, which is >99.9% congenic at other loci. Wide-type mice were obtained from the same hybrid stock.

Donor neural precursor cells were derived from the striatum/subventricular zone microdissected from embryonic transgenic mice on day 16 after overnight mating. These mice express an enhanced form of the jellyfish green fluorescent protein (GFP) under the control of the mouse prion promoter (Borchelt et al., 1996; van den Pol and Ghosh, 1998) and a chicken β-actin-cytomegalovirus immediate early enhancer (Niwa et al., 1991; Ikawa et al., 1995). Cells were isolated and propagated as previously described (Mitome et al., 2001). After 3–8 days in culture, these cells were harvested and transplanted into the brains of shi mice.

Neonatal shi host mice ranging in age from postnatal days 1 to 4 were cryoanesthetized and injected with 60,000 cells/1 μl into each lateral cerebral ventricle, and 120,000 cells/2 μl into the cisterna magna. The cellular suspension was expelled gently via a glass micropipette that was inserted transcutaneously into the desired location with the aid of a stereotaxic apparatus.

Imaging was performed at 8–10 weeks old. After imaging, mice were deeply anesthetized with a solution of ketamine/xylazine and perfused with ice-cold heparinized phosphate-buffered saline, followed by ice-cold 4% buffered paraformaldehyde fixative. Brains were removed, tissues were fixed for 2–5 h at 4°C, and 50 μm thick coronal sections were cut on a vibratome and stored at −20°C in cryoprotectant. Free floating sections were processed for immunohistochemistry using a primary antibody directed against MBP (mouse, 1:1000 = 1 μg/ml; Sternberger Monoclonals, Lutherville, MD) and an anti-mouse Alexa Fluor 594 secondary antibody (Molecular Probes, Eugene, OR), as previously described (Mitome et al., 2001). Slides were examined using fluorescence microscopy, with excitation wavelengths for GFP and Alexa Fluor 594 of 488 and 568 nm, respectively.

MR imaging

Five groups of age-matched mice (8–10 weeks) were studied. T2-weighted imaging and DWI at short tdiff (= 30 ms) were performed on: (i) wt mice (n = 7), (ii) homozygous shi mice (n = 8), (iii) homozygous shi mice previously transplanted with neural precursor cells (transplant, n = 6). Of the 6 shi mice transplanted with precursor cells, 3 mice did not show significant GFP fluorescence post-mortem (failed transplant) and their MRI data were not further analyzed. Group iv consisted of homozygous shi mice previously transplanted with dead neural precursor cells which were killed by repeated freezing and thawing (Renfranz et al., 1991) (transplant control, n = 3). In Group v, multiple diffusion-time DTI experiments were performed on 5 wt mice and 5 shi mice from Groups i and ii.

Imaging was performed on spontaneously breathing mice under 1% isoflurane in air (~1 1/min). A custom-designed stereotaxic headset, consisting of ear bar, tooth bar, and shoulder bar, was used to immobilize the mouse head (Nair and Duong, 2004). Support was given to the legs and lower body with the mouse curved up slightly. Respiration rate, monitored with a force transducer and a differential amplifier and recorded onto an oscilloscope, was maintained within normal physiological ranges (120–150 bpm). Rectal temperature was maintained at 37.5 ± 0.5°C by circulating warm-water through a tube running underneath the mouse's body. Saline (0.3 ml, i.p.) was remotely administered via an extended PE-50 tubing every 2 h to prevent dehydration. Each mouse took ~5 h to image.

MRI was performed on a 9.4 T, 89 mm vertical magnet (Oxford Instruments, Oxford, UK) equipped with a VarianINOVA console (Palo Alto, CA), a 100 Gauss/cm gradient (45 mm inner diameter and 100 μs rise time, Resonance Research Inc., Billerica, MA), and a custom-made surface coil (inner diameter =1.5 cm). Six DWIs were acquired with b = 1200 s/mm2 along 6 different oblique directions {(X,Y,0), (−X,Y,0), (X,0,Z), (−X,0,Z), (0,Y,Z), and (0,Y,−Z)}, and a seventh DWI was acquired with a low b value (5 s/mm2) in the (X,Y,Z) direction (Basser and Pierpaoli, 1998). For the single short tdiff study, tdiff of 30 ms was used with mixing time TM = 24 ms, duration between diffusion gradient applications Δ = 31 ms, and diffusion gradient duration δ = 3 ms. For the multiple tdiff study, tdiff of 30, 80, 180, and 280 ms (acquisition order randomized) were achieved by modulating TM (and thus Δ). The shortest tdiff possible was 30 ms for the conditions used herein. The other imaging parameters were repetition TR = 2.5 s, echo time TE = 14 ms, 4 averages, field of view (FOV) = 1.5 cm × 1.5 cm, acquisition matrix = 64 × 64, and seven 0.9-mm coronal slices with interslice spacing of 0.1 mm. T2-weighted images were also acquired using STEAM sequence with similar parameters as the low b value images at tdiff of 30 ms, but with a longer TE of 45 ms.

Data analysis

All data processing codes were written in Matlab® (Mathworks, Natick, MA) and displayed using STIMULATE software (University of Minnesota, MN). Acquisition matrix of 64 × 64 was zero-filled to 128 × 128 during reconstruction. ADC maps in 6 different directions were calculated. A 3 × 3 diffusion tensor matrix (D) was constructed and Eigenvalue decomposition was performed on the D matrix to derive Eigenvalues λ1, λ2, and λ3 and the corresponding Eigenvectors at each tdiff (Basser and Pierpaoli, 1998). left angle bracketDright angle bracket, λ//, λ[perpendicular], and anisotropy indices, namely: FA (range 0–1 with 0 being isotropic), RA (range 0–√2 with 0 being isotropic), and VR (range 0–1 with 1 being isotropic), were calculated using (Basser, 1995; Basser and Pierpaoli, 1998; Xue et al., 1999; Le Bihan et al., 2001):


FA color map was obtained by multiplying the FA index with the largest Eigenvector, and assigning three primary colors (red, green, and blue) to the three principal axes.

DTI parameters were analyzed using (1) region-of-interest (ROI) analysis and (2) spatial profile analysis. ROIs of the corpus callosum and hippocampus were drawn on the FA images (Fig. 4a) with reference to T2-weighted anatomical images. The corpus callosum was chosen as representative white matter because of its relatively large size and uniformity. Hippocampus was used as a control because it is largely composed of gray matter structures although there are some axons and white matter. The hippocampus was chosen retrospectively as it showed weak tdiff dependence. Cortex was not used because it has clear radially oriented structures. For demonstration, signal intensity spatial profile plots were projected along a line (4-pixel thick) crossing the corpus callosum in the ventral–dorsal direction at the level of the anterior commissure. For demonstration purpose, a single image slice that was similar across all animals was displayed for more accurate comparisons between different groups.

Fig. 4
(a) Representative ROIs of the corpus callosum and the hippocampus overlaid on fractional anisotropy (FA) maps. Group-average (b) FA, (c) volume ratio (VR), and (d) diffusion perpendicular to the first Eigenvector (λ[perpendicular]) of the corpus callosum ...

T2-weighted signal intensities in the corpus callosum were normalized with respect to the cortical gray matter within each animal for cross-subject comparison. The cortex was chosen over other possible structures because of its relatively uniform T2 contrast and high signal-to-noise ratio. Comparison of T2 maps would be ideal; unfortunately, the T2 maps determined with two echo times and limited signal averaging herein were quite variable across animals and could not be used. Normalizing the T2-weighted signal intensities with respect to the cortical gray matter within each animal made cross-subject comparison possible, although not ideal.

Statistical analysis used one-tail unpaired t test for comparing groups and mixed mode analysis for analyzing trends across tdiff. A P value <0.05 was considered to be statistically significant. Data in text are expressed as means ± standard deviations.


Phantom experiments

The adjustment factors for the diffusion gradients to minimize bias due to cross-term interactions were up to 7–14% for tdiff of 30 ms and 23–44% for 280 ms, with longer tdiff requiring larger corrections as expected. The surprisingly large correction factor was likely due to the very long diffusion time and the large diffusion gradients (~22 G/cm). Using the modified STEAM sequence with the experimental correction scheme, the measured water self-diffusion coefficients in a uniform phantom at 20°C ranged from 1.95 × 10−3 mm2/s to 2.0 × 10−3 mm2/s across different tdiff, consistent with those reported previously (Duong et al., 1998) albeit under slightly different temperatures. The measured diffusion coefficient of NAA in DMSO from a uniform spherical phantom at 20°C ranged from 0.50 × 10−3 to 0.52 × 10−3 mm2/s across different diffusion directions and tdiff. No published literature data were found for comparison. Diffusion coefficients and DTI parameters (λ[perpendicular], λ//, left angle bracketDright angle bracket, FA, RA, and VR) in the uniform phantoms were not statistically different across different diffusion directions or across different tdiff.

DTI of wt and shi mice

Fig. 1 shows representative T2-weighted images, FA, VR, and λ[perpendicular] maps from a wt and a shi mouse. T2-weighted images showed dramatic differences in the pixel intensity of the corpus callosum (arrowheads) between wt and shi mice whereas FA, VR, and λ[perpendicular] maps showed only subtle differences. The group-average FA, VR, λ[perpendicular], λ //, and left angle bracketDright angle bracket from corpus callosum (Fig. 4a) were quantified and are summarized in Table 1. The group-average normalized T2-weighted signal intensities, VR, λ[perpendicular], and left angle bracketDright angle bracket from the corpus callosum of shi mice were significantly higher than those of wt mice (P < 0.05); FA and RA of shi mice were significantly lower than those of wt mice (P < 0.05). λ// was, however, not statistically different in wt and shi mice (P < 0.05). The magnitude differences and statistical significances between wt and shi groups were highest in VR followed by FA and smallest in RA, although the percentage changes were similar.

Fig. 1
Coronal T2-weighted images, fractional anisotropy (FA), volume ratio (VR), diffusion perpendicular (λ[perpendicular]) to the first Eigenvector obtained from a representative wild-type (wt), and a shiverer (shi) mouse brain at short (30 ms) diffusion ...
Table 1
Group-average normalized T2-weighted signal intensities, fractional anisotropy (FA), volume ratio (VR), water diffusion perpendicular to (λ[perpendicular]) and parallel to (λ//) the direction of the axonal fibers, relative anisotropy (RA), and ...

To graphically demonstrate the differences between wt and shi mice, spatial profiles of the normalized T2-weighted signal intensities of wt and “shi + transplant control” groups were plotted starting at the caudate putamen, crossing the corpus callosum, to the cortical gray matter (Fig. 2a). It should be noted that the spatial profiles of the shi and the transplant control groups were not statistically different from each other and were therefore grouped together as “shi + transplant control” as shown. Normalized T2-weighted signal intensities in wt and “shi + transplant control” groups were similar in the gray matter but were markedly different in the corpus callosum. Likewise, spatial profiles of FA maps (Fig. 2b) from wt and “shi + transplant control” groups were similar in the gray matter but differed significantly in the corpus callosum.

Fig. 2
Group-average spatial profiles of (a) T2-weighted signal intensity normalized to cortical gray matter and (b) fractional anisotropy (FA) obtained crossing the corpus callosum of the wt (mean ± SEM, n = 7) and “shi + transplant control” ...

Effects of precursor cell transplantation on DTI contrast

To investigate the signal sources leading to different DTI contrast between wt and shi mice, DTI was performed on shi mice transplanted with myelin-producing wt neural precursor cells. The group-average FA value of the corpus callosum in the transplanted mice was found to be intermediate between those of wt and “shi + transplant control” mice (Fig. 3a) but was highly variable. On the contrary and surprisingly, the group-average normalized T2-weighted intensity spatial profile of the transplanted mice was similar to that of the “shi + transplant control” mice (data not shown).

Fig. 3
(a) Spatial profile plot of fractional anisotropy (FA) obtained crossing the corpus callosum of the transplanted mice (n = 3, mean ± SEM). Group-average data of wt and “shi + transplant control” groups are re-plotted without error ...

For detailed investigation, results from two individual animals are discussed below. Transplanted mouse #2 showed essentially normal values of FA in the corpus callosum, consistent with the highest density of donor-derived wt MBP immunoreactivity and GFP cellular distribution observed among the transplanted animals studied (Fig. 3b). However, normalized T2-weighted signal intensities in the corpus callosum did not show recovery; rather, they appeared similar to that of the “shi + transplant control” group (Fig. 3b).

Another transplanted mouse (#5) showed grossly asymmetric hemispheric GFP cellular distribution and wt MBP immunoreactivity in the corpus callosum, although neural precursor cells had been injected in both cerebral lateral ventricles and cisterna magna. FA image in grayscale appeared brighter in the corpus callosum of the hemisphere with relatively more successful transplantation (Fig. 3c). FA color tensor map did not show any apparent abnormality in the directionality of the white matter fibers of the donor-derived myelin.

DTI contrast at variable tdiff

Sensitivity of DTI parameters to different tdiff was evaluated for the corpus callosum and the hippocampal gray matter in wt and shi mice. Representative ROIs used in the analysis are shown in Fig. 4a. There was a strong trend toward increasing diffusion anisotropy with increasing tdiff (P < 0.05). The group-average FA (Fig. 4b), VR (Fig. 4c), and λ[perpendicular] (Fig. 4d) show increasing significant differences in λ[perpendicular] and VR between wt and shi mice at longer tdiff (P < 0.05). λ//, was, however, not statistically different in wt and shi mice (P > 0.05, data not shown).

By contrast, the dependence of anisotropy indices on tdiff in the hippocampus was significantly less pronounced compared to that in the corpus callosum. The group-average FA (Fig. 4b), VR (Fig. 4c), λ[perpendicular] (Fig. 4d), and λ// in the hippocampus were not statistically different between wt and shi mice at all tdiff (P > 0.05).


The major findings of this study are: (1) T2-weighted images detected obvious differences between wt and shi mice in the corpus callosum but surprisingly did not detect donor-derived myelination in shi mice transplanted with neural precursor cells. By contrast, FA showed comparatively smaller differences between wt and shi mice in the corpus callosum but detected changes due to donor-derived myelination in the transplant group. Despite the presence of partial-volume effect from limited spatial resolution, various analysis approaches yielded consistent results and interpretation across different DTI parameters. (2) A modified STEAM sequence with an experimental cross-term correction scheme was implemented. In contrast to those in the hippocampus, DTI parameters in the corpus callosum showed markedly stronger tdiff dependence, and the differences in these parameters between wt and shi mice grew larger at longer tdiff, indicative of improved DTI sensitivity at longer tdiff.

DTI of wt, shi, and transplanted mice at short tdiff

Differences in λ[perpendicular] and RA between shi and wt mice in vivo have been previously reported, with λ[perpendicular] being more sensitive and yielded more consistent changes than RA (Song et al., 2002). Our findings are in agreement with those of Song et al. (2002). Furthermore, we compared the sensitivity of RA, FA, and VR to detect differences in myelination between wt and shi mice and found that RA was the least sensitive of the three anisotropy indices, likely due to the small dynamic range for the typical RA values in vivo (Ulug and van Zijl, 1999; Partridge et al., 2004). This notion was consistent with our simulation study in which different RA, FA, and VR were calculated over the biologically relevant ranges based on experimentally measured λ[perpendicular] and λ// (data not shown). VR shows the largest dynamic range likely because the actual Eigenvalues, rather than variances of the Eigenvalues (as in FA or RA), were used in the definition. The sensitivity of RA was further reduced because left angle bracketDright angle bracket was used as the normalization factor (as opposed to the actual Eigenvalues used in the FA calculation).

In addition, we also performed transplantation of wt neural precursor cells to address the potential pleiotropic effects associated with the shi mutation. The contribution of myelin per se on DTI parameters could thus be directly evaluated. DTI parameters of animals with successful transplantation of neural precursor cell clearly demonstrated that the measured DTI parameters became closer to that of wt, although the sample size was small due to the challenging transplantation experiments. Further, regions with increased diffusion anisotropy showed good correspondence to the spatial distributions of donor-derived myelination in individual mouse brains as indicated by MBP immunohistochemistry and GFP cellular distribution.

One surprising finding is that although T2-weighted images yielded dramatic differences between wt and shi mice in the corpus callosum, T2-weighted images and semi-quantitative normalized T2-weighted signals did not appear to be sensitive to transplant-derived myelination. Although the sensitivity of T2 to detect changes in myelin has been an issue of debate, myelin content had been reported to correlate with T2 (MacKay et al., 1994; Stanisz et al., 2004). We have no definitive explanation for this apparent discrepancy. One potential explanation for the lack of observable T2 changes in transplanted shi mice is that normal and densely packed myelin might be necessary for T2 contrast (Fig. 1), whereas a partial recovery might be sufficient to induce an observable DTI contrast in white matter. While further validation is needed, our results are consistent with those reported by Guo et al. (2001), who found RA to be more sensitive than T2-weighted images in detecting dysmyelination in human Krabbe's disease and stem-cell transplantation. Similarly, Larsson et al. (2004) found that DTI is relatively more sensitive than T1- or T2-weighted images in delineating myelin-related lesions.

As mentioned above, although CNS axons in shi mice remain apparently intact (Dupouey et al., 1979; Privat et al., 1979; Inoue et al., 1981; Shen et al., 1985), increased axonal protein content and abnormalities of the axonal cytoskeleton have been reported (Brady et al., 1999; Kirkpatrick et al., 2001). Transplant control experiments argue against the possibility that MR effects might be due to the surgical procedure per se. The combined “transplant” and “transplant control” experiments thus allowed us to cautiously conclude that the observed changes in DTI parameters indeed indicate changes due to myelin. Nonetheless, the transplanted myelin could, in principle, prevent some axonal loss in shi mice, and thus the notion that “axonal loss” resulted in the observed changes in DTI parameters could not be completely excluded. Relating particular changes in the MR parameters to the specific tissue pathology is evidently challenging because diseases are complex and many tissue pathologies result in similar changes of MR parameters.

Technical considerations for the variable tdiff experiments

Diffusion measurements at long tdiff using spin-echo sequences are not common because of the short T2 of brain tissue water (i.e., gray matter T2 ~40 ms at 9.4 T). The modified STEAM sequence allows diffusion measurements to be performed at very long tdiff (Merboldt et al., 1991; Horsfield et al., 1994) because the tdiff is placed during the TM period where the signal loss due to T1 recovery, as opposed to T2 decay, is relatively small (gray matter T1 ~1.9 s at 9.4 T). The drawback of the STEAM sequence is that the stimulated-echo acquisition retains only half of the magnetization and thus the signal-to-noise ratio (SNR) is reduced by half. Reduction in SNR was, however, partially compensated by using a relatively short echo time (14 ms), high magnetic field, and a small surface coil.

Varying tdiff by changing TM in the STEAM sequence could result in reduced SNR at longer tdiff and preferential weighting toward water molecules with long T1, which could confound the interpretation of the tdiff-dependent effect. Although not negligible, SNR reduction with increasing tdiff was relatively small due to the long T1 and high SNR at high field. Reduced SNR could be compensated by increasing signal averaging at long tdiff. Increased anisotropy observed with increasing tdiff could in principle be due to T1 weighting of white matter at the expense of gray matter within a voxel (partial volume effect). However, white matter, which has shorter T1 than gray matter, is expected to be weighted less in the STEAM sequence. Thus, the T1 effect could not explain the observed tdiff-dependent effects, and the reported tdiff dependence was likely a conservative estimate.

Finally, SNR could potentially affect diffusion anisotropy. As the SNR of the DWI decreases, the apparent diffusion anisotropy calculated from the tensors tends to increase due to a statistical biasing effect (Pierpaoli and Basser, 1996; Basser and Pierpaoli, 1998). Thus, it is possible that the trend of increasing diffusion anisotropy with increasing tdiff is artificial. Against this possibility is our observation that control hippocampus data did not show such trend. Thus, the increasing diffusion anisotropy of the corpus callosum with increasing tdiff could not be artificial.

Improved DTI sensitivity at long tdiff

In the presence of restricted and anisotropic diffusion, a longer tdiff (Segebarth et al., 1994; Helmer et al., 1995; Pfeuffer et al., 1998) could in principle improve DTI contrast. However, such effect has not been systematically investigated. Although the biological system is considerably more complex, restricted diffusion in vivo could be hypothetically categorized into two regimes, one where the root-mean-squared (rms) displacement is on the orders of the average cell size and the other where the rms displacement is significantly larger than the average cell size. ADC measurements on ex vitro large squid axons showed that at very short tdiff of ~2 ms, the ADC perpendicular to the axonal fibers was high and close to the ADC parallel to the axonal fibers. When tdiff was lengthened to 28 ms, the ADC perpendicular to the axonal fibers was reduced by half, whereas ADC parallel to the axonal fibers was largely unchanged (Beaulieu and Allen, 1994a). Similarly, significant reduction in λ[perpendicular] for tdiff ranging from 5 to 50 ms had been reported using multiple quantum experiments (Seo et al., 1999).

At the other regime where the rms displacement is significantly larger than the average cell size, restricted diffusion arises predominantly with extracellular diffusion and the tdiff is on the orders of tens to hundreds of milliseconds. Such restricted diffusion has been previously reported. White matter ADC (not DTI) in the human brain showed a tdiff dependence for tdiff ranging from 40 to 800 ms (Horsfield et al., 1994) although these data were obtained with variable b values which yielded differential weighting to different spin populations and confounded interpretation of the tdiff dependent effects. In our study, we investigated DTI contrast at long tdiff (30 to 280 ms) and found strong tdiff dependence in the corpus callosum. DTI at all tdiff was measured in each animal and pair-wise comparison within the same animals made the trend relatively more apparent. In contrast, Le Bihan reported that non-significant changes in ADC perpendicular or parallel to the fiber tracts in humans were observed for tdiff ranging from 16 to 79 ms (Le Bihan et al., 1993). Indeed, our data showed small and non-significant differences in DTI parameters between tdiff of 30 ms and 80 ms for both wt and shi mice. In short, these results suggest improvement in DTI contrast at long tdiff. The biophysical mechanism(s) underlying the tdiff-dependent DTI contrast and diffusion restriction across these long diffusion times, however, remains to be elucidated.


This study provided a better understanding of the signal sources and measurement parameters underlying DTI contrasts. This could lead to developing more sensitive techniques for detection and monitoring of progression and therapeutic intervention of demyelinating diseases.


The authors would like to acknowledge Drs. Susumu Mori and Hangyi Jiang of Johns Hopkins University for their help during the development of our DTI processing codes, Dr. Karl G. Helmer of Worcester Polytechnic Institute and Dr. Qin Liu of University of Massachusetts Medical School for technical advice and assistance. This work was supported in part by the Whitaker Foundation (RG-02-0005), the American Heart Association (SDG-0430020N) to TQD, NASA grant NAG9-1356 to WJS, and the NIH/NCRR base grant to the “Yerkes National Primate Research Center, Emory University,” (RR-00165).


  • Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 1995;8:333–344. [PubMed]
  • Basser JP, Pierpaoli C. A simplified method to measure the diffusion tensor from seven MR images. Magn Reson Med. 1998;39:928–934. [PubMed]
  • Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson, B. 1994a;103:247–254. [PubMed]
  • Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J. 1994b;66:259–267. [PubMed]
  • Beaulieu C, Allen PS. Water diffusion in the giant axon of the squid: implications for diffusion-weighted MRI of the nervous system. Magn Reson Med. 1994a;32:579–583. [PubMed]
  • Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994b;31:394–400. [PubMed]
  • Borchelt DR, Davis J, Fischer M, Lee MK, Slunt HH, Ratovitsky T, Regard J, Copeland NG, Jenkins NA, Sisodia SS, Price DL. A vector for expressing foreign genes in the brains and hearts of transgenic mice. Genet Anal. 1996;13:159–163. [PubMed]
  • Brady ST, Witt AS, Kirkpatrick LL, de Waegh SM, Readhead C, Tu PH, Lee VM. Formation of compact myelin is required for maturation of the axonal cytoskeleton. J Neurosci. 1999;19:7278–7288. [PubMed]
  • Brockstedt S, Thomsen C, Wirestam R, Holtas S, Stahlberg F. Quantitative diffusion coefficient maps using fast spin-echo MRI. Magn Reson Imaging. 1998;16:877–886. [PubMed]
  • Duong TQ, Ackerman JJH, Ying HS, Neil JJ. Evaluation of extra- and intracellular apparent diffusion in normal and globally ischemic rat brain via 19 F NMR. Magn Reson Med. 1998;40:1–13. [PubMed]
  • Dupouey P, Jacque C, Bourre JM, Cesselin F, Privat A, Baumann N. Immunochemical studies of myelin basic protein in shiverer mouse devoid of major dense line of myelin. Neurosci Lett. 1979;12:113–118. [PubMed]
  • Gulani V, Webb AG, Duncan ID, Lauterbur PC. Apparent diffusion tensor measurements in myelin-deficient rat spinal cords. Magn Reson Med. 2001;45:191–195. [PubMed]
  • Gullmar D, Jaap T, Bellemann ME, Haueisen J, Reichenbach JR. DTI measurements of isotropic and anisotropic media. Biomed Tech (Berl) 2002;47:420–422. [PubMed]
  • Guo AC, Petrella JR, Kurtzberg J, Provenzale JM. Evaluation of white matter anisotropy in Krabbe disease with diffusion tensor MR imaging: initial experience. Radiology. 2001;218:809–815. [PubMed]
  • Hajnal JV, Doran M, Hall AS, Collins AG, Oatridge A, Pennock JM, Young IR, Bydder GM. MR imaging of anisotropically restricted diffusion of water in the nervous system: technical, anatomic, and pathologic considerations. J Comput Assist Tomogr. 1991;15:1–18. [PubMed]
  • Helmer KG, Dardzinski BJ, Sotak CH. The application of porous-media theory to the investigation of time-dependent diffusion in in vivo systems. NMR Biomed. 1995;8:297–306. [PubMed]
  • Horsfield MA, Barker GJ, McDonald WI. Self-diffusion in CNS tissue by volume-selective proton NMR. Magn Reson Med. 1994;31:637–644. [PubMed]
  • Ikawa M, Kominami K, Yoshimura Y, Tanaka K, Nishimune Y, Okabe M. A rapid and non-invasive selection of transgenic embryos before implantation using green fluorescent protein (GFP) FEBS Lett. 1995;375:125–128. [PubMed]
  • Inoue Y, Nakamura R, Mikoshiba K, Tsukada Y. Fine structure of the central myelin sheath in the myelin deficient mutant shiverer mouse, with special reference to the pattern of myelin formation by oligodendroglia. Brain Res. 1981;219:85–94. [PubMed]
  • Kirkpatrick LL, Witt AS, Payne HR, Shine HD, Brady ST. Changes in microtubule stability and density in myelin-deficient shiverer mouse CNS axons. J Neurosci. 2001;21:2288–2297. [PubMed]
  • Larsson EM, Englund E, Sjobeck M, Latt J, Brockstedt S. MRI with diffusion tensor imaging post-mortem at 3.0 T in a patient with frontotemporal dementia. Dementia Geriatr Cognit Disord. 2004;17:316–319. [PubMed]
  • Le Bihan D, Turner R, Douek P. Is water diffusion restricted in human brain white matter? An echo-planar NMR imaging study. NeuroReport. 1993;4:887–890. [PubMed]
  • Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;13:534–546. [PubMed]
  • MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med. 1994;31:673–677. [PubMed]
  • Makris N, Worth AJ, Sorensen AG, Papadimitriou GM, Wu O, Reese TG, Wedeen VJ, Davis TL, Stakes JW, Caviness VS, Kaplan E, Rosen BR, Pandya DN, Kennedy DN. Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann Neurol. 1997;42:951–962. [PubMed]
  • Merboldt KD, Hanicke W, Frahm J. Diffusion imaging using stimulated echoes. Magn Reson Med. 1991;19:233–239. [PubMed]
  • Mitome M, Low HP, van den Pol A, Nunnari JJ, Wolf MK, Billings-Gagliardi S, Schwartz WJ. Towards the reconstruction of central nervous system white matter using neural precursor cells. Brain. 2001;124:2147–2161. [PubMed]
  • Mori K. Relation of chemical structure to specificity of response in olfactory glomeruli. Curr Opin Neurobiol. 1995;5:467–474. [PubMed]
  • Mori S, Itoh R, Kaufmann WE, van Zijl PCM, Solaiyappan M, Yarowsky P. Diffusion tensor imaging of the developing mouse brain. Magn Reson Med. 2001a;46:18–23. [PubMed]
  • Mori S, Itoh R, Zhang J, Kaufmann WE, van Zijl PC, Solaiyappan M, Yarowsky P. Diffusion tensor imaging of the developing mouse brain. Magn Reson Med. 2001b;46:18–23. [PubMed]
  • Nair G, Duong TQ. Echo-planar BOLD fMRI of mice on a narrow-bore 9.4 T magnet. Magn Reson Med. 2004;52:430–434. [PMC free article] [PubMed]
  • Nakada T, Matsuzawa H. Three-dimensional anisotropy contrast magnetic resonance imaging of the rat nervous system: MR axonography. Neurosci Res. 1995;22:389–398. [PubMed]
  • Neil JJ, Shiran SI, McKinstry RC, Schefft GL, Snyder AZ, Almli CR, Akbudak E, Aronovitz JA, Miller JP, Lee BC, Conturo TE. Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology. 1998;209:57–66. [PubMed]
  • Niwa H, Yamamura K, Miyazaki J. Efficient selection for high-expression transfectants with a novel eukaryotic vector. Gene. 1991;108:193–199. [PubMed]
  • Ono J, Harada K, Takahashi M, Maeda M, Ikenaka K, Sakurai K, Sakai N, Kagawa T, Fritz-Zieroth B, Nagai T, et al. Differentiation between dysmyelination and demyelination using magnetic resonance diffusional anisotropy. Brain Res. 1995;671:141–148. [PubMed]
  • Partridge SC, Mukherjee P, Henry RG, Miller SP, Berman JI, Jin H, Lu Y, Glenn OA, Ferriero DM, Barkovich AJ, Vigneron DB. Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns. Neuroimage. 2004;22:1302–1314. [PubMed]
  • Pfeuffer J, Flogel U, Dreher W, Leibfritz D. Restricted diffusion and exchange of intracellular water: theoretical modelling and diffusion time dependence of 1 H NMR measurements on perfused glial cells. NMR Biomed. 1998;11:19–31. [PubMed]
  • Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906. [PubMed]
  • Prayer D, Roberts T, Barkovich AJ, Prayer L, Kucharczyk J, Moseley M, Arieff A. Diffusion-weighted MRI of myelination in the rat brain following treatment with gonadal hormones. Neuroradiology. 1997;39:320–325. [PubMed]
  • Privat A, Jacque C, Bourre JM, Dupouey P, Baumann N. Absence of the major dense line in myelin of the mutant mouse “shiverer” Neurosci Lett. 1979;12:107–112. [PubMed]
  • Renfranz PJ, Cunningham MG, McKay RD. Region-specific differentiation of the hippocampal stem cell line HiB5 upon implantation into the developing mammalian brain. Cell. 1991;66:713–729. [PubMed]
  • Segebarth C, Belle V, Delon C, Massarelli R, Decety J, Le Bas JF, Decorpts M, Benabid AL. Functional MRI of the human brain: predominance of signals from extracerebral veins. NeuroReport. 1994;5:813–816. [PubMed]
  • Seo Y, Shinar H, Morita Y, Navon G. Anisotropic and restricted diffusion of water in the sciatic nerve: a (2)H double-quantum-filtered NMR study. Magn Reson Med. 1999;42:461–466. [PubMed]
  • Shen XY, Billings-Gagliardi S, Sidman RL, Wolf MK. Myelin deficient (shimld) mutant allele: morphological comparison with shiverer (shi) allele on a B6C3 mouse stock. Brain Res. 1985;360:235–247. [PubMed]
  • Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17:1429–1436. [PubMed]
  • Stanisz GJ, Webb S, Munro CA, Pun T, Midha R. MR properties of excised neural tissue following experimentally induced inflammation. Magn Reson Med. 2004;51:473–479. [PubMed]
  • Sukama H, Nomura Y, Takeda K, Nakagawa T, Tamagawa Y, Ishii Y, Tsukamoto T. Adult and neonatal human brain: diffusional anisotropy and myelination with diffusion weighted MR imaging. Radiology. 1991;180:229–233. [PubMed]
  • Ulug AM, van Zijl PC. Orientation-independent diffusion imaging without tensor diagonalization: anisotropy definitions based on physical attributes of the diffusion ellipsoid. J Magn Reson Imaging. 1999;9:804–813. [PubMed]
  • van den Pol AN, Ghosh PK. Selective neuronal expression of green fluorescent protein with cytomegalovirus promoter reveals entire neuronal arbor in transgenic mice. J Neurosci. 1998;18:10640–10651. [PubMed]
  • Wimberger DM, Roberts TP, Barkovich AJ, Prayer LM, Moseley ME, Kucharczyk J. Identification of “premyelination” by diffusion-weighted MRI. J Comput Assist Tomogr. 1995;19:28–33. [PubMed]
  • Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med. 1999;42:1123–1127. [PubMed]
  • Zhang J, van Zijl PC, Mori S. Three-dimensional diffusion tensor magnetic resonance microimaging of adult mouse brain and hippocampus. Neuroimage. 2002;15:892–901. [PubMed]