Diffusion tensor imaging and tractography of the spinal cord: From experimental studies to clinical application

Experimental Neurology 242 (2013) 74–82

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Review

Diffusion tensor imaging and tractography of the spinal cord: From experimental studies to clinical application Kanehiro Fujiyoshi a, b, f, 1, Tsunehiko Konomi a, b, 1, Masayuki Yamada d, Keigo Hikishima b, e, Osahiko Tsuji a, b, Yuji Komaki b, Suketaka Momoshima c, Yoshiaki Toyama a, Masaya Nakamura a,⁎, Hideyuki Okano b,⁎⁎ a

Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160‐8582, Japan Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160‐8582, Japan Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160‐8582, Japan d Fujita Health University, School of Health Sciences, Faculty of Radiological Technology, 1‐98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi 470‐1192, Japan e Central Institute for Experimental Animals, 3-25-12 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210‐0821, Japan f Department of Orthopedic Surgery, National Hospital Organization, Murayama Medical Center, 2-37-1 Gakuen, Musashimurayama, Tokyo 208‐0011, Japan b c

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Article history: Received 14 February 2011 Revised 21 June 2012 Accepted 24 July 2012 Available online 31 July 2012 Keywords: Magnetic resonance imaging Diffusion-weighted imaging Diffusion tensor imaging Diffusion tensor tractography Spinal cord Common marmoset Spinal cord injury Pyramidal decussation Voxel based analysis Tract based spatial statistics

a b s t r a c t Diffusion-weighted magnetic resonance imaging provides detailed information about biological structures. In particular, diffusion tensor imaging and diffusion tensor tractography (DTT) are powerful tools for evaluating white matter fibers in the central nervous system. We previously established a reproducible spinal cord injury model in adult common marmosets and showed that DTT could be used to trace the neural tracts in the intact and injured spinal cord of these animals in vivo. Recently, many reports using DTT to analyze the spinal cord area have been published. Based on the findings from our experimental studies, we are now routinely performing DTT of the human spinal cord in the clinic. In this review we outline the basic principles of DTT, and describe the characteristics, limitations, and future uses of DTT to examine the spinal cord. © 2012 Published by Elsevier Inc.

Contents Introduction . . . . . . . . . . . . . . . . . . . . Diffusion anisotropy measurement and tensor analysis FA and FA maps . . . . . . . . . . . . . . . . Color-coded FA maps and RBG color . . . . . . . Construction of diffusion tensor tractography . . Diffusion tensor tractography of the spinal cord . . . Clinical need for DTT of the spinal cord . . . . . DTT of the cervical cord of a primate, the common Recent studies on DTI of the spinal cord . . . . . . . DTT limitations and future prospects . . . . . . . .

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Abbreviations: SCI, spinal cord injury; MRI, magnetic resonance imaging; DWI, diffusion-weighted magnetic resonance imaging; MPG, motion-proving gradient; T1WI, T1 weighted magnetic resonance imaging; T2WI, T2 weighted magnetic resonance imaging; DTI, diffusion tensor imaging; DTT, diffusion tensor tractography; FA, fractional anisotropy; ADC, apparent diffusion coefficient; ROI, region of interest; LFB, luxol fast blue; CST, corticospinal tract; EAE, experimental autoimmune encephalomyelitis; ALS, amyotrophic lateral sclerosis; HARDI, high angular resolution diffusion imaging; BOLD-fMRI, blood oxygenation level-dependent functional magnetic resonance imaging. ⁎ Corresponding author. Fax: +81 3 3357 6597. ⁎⁎ Corresponding author. Fax: +81 3 3357 5445. E-mail addresses: [email protected] (M. Nakamura), [email protected] (H. Okano). 1 Equally contributed to this work. 0014-4886/$ – see front matter © 2012 Published by Elsevier Inc. http://dx.doi.org/10.1016/j.expneurol.2012.07.015

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Resolution . . . . . . . . . . . . . . . . . . . . . Depiction of complicated axonal pathways . . . . . . DTI and statistical analysis: combination with functional MRI From bench to bedside . . . . . . . . . . . . . . . Author contributions . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .

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Introduction

Diffusion anisotropy measurement and tensor analysis

Magnetic resonance imaging (MRI) has become widely accepted as an excellent method for examining the spinal cord. Diffusion MRI, which exploits the diffusion of water molecules, is also a widely recognized technique in human medicine. Reports on diffusion MRI of the brain were first published in the 1980s (Le Bihan et al., 1986, 1988), In the 1990s, its use was extended to the examination of human stroke (Burdette et al., 1998, 1999), and it has now become a well-known technique worldwide. It is expected to become even more useful due to the recent development of diffusion-weighted whole-body imaging with background body signal suppression, which can be used to evaluate the metastases of malignant tumors in three dimensions (Yamashita et al., 2009). In these techniques, “diffusion” refers to the Brownian motion of water molecules, which is strictly different from their “flow.” Thus, diffusion-weighted MRI (DWI) 2 can be used to detect and visualize water molecule diffusion in tissues by adding a bipolar gradient pulse called a motionproving gradient (MPG). Diffusion-weighted MRI differs from conventional T1 or T2 weighted MRI (T1WI or T2WI) in that it provides high-contrast resolution based on diffusion, which allows new information on lesions to be obtained. The direction of water molecule diffusion in living tissues is always limited to some degree. The property by which the rate of diffusion varies with direction is called diffusion anisotropy or anisotropic diffusion (Le Bihan et al., 2001). To detect lesions by DWI, the anisotropy is deliberately reduced using imaging techniques and processing to avoid detecting the signals from normal white matter. The diffusion-weighted images from which the effect of anisotropic diffusion has been eliminated are sometimes called isotropic diffusionweighted images (isotropic DWI). Anisotropic diffusion is represented as an ellipsoid, and isotropic diffusion is represented as a sphere (Fig. 1). The white matter of the spinal cord is composed of nerve fiber bundles that course in a craniocaudal direction in a relatively ordered manner, and the water within them diffuses along the course of the axons. Thus, white matter has high anisotropy, and the nerve fibers can be tracked and visualized based on their anisotropy information by diffusion tensor tractography (DTT). To conduct DTT, parameters such as the apparent diffusion coefficient (ADC), which is an expression of the magnitude of the diffusion, and the fractional anisotropy (FA), which is an expression of the anisotropy of the diffusion, must be calculated. Tensor analysis is used in calculating these parameters (Basser and Jones, 2002; Le Bihan and van Zijl, 2002; Mori and Zhang, 2006), which has given rise to the name, “tensor diffusion.” 3

FA and FA maps

2 According to textbooks, DWI “includes all MRI images weighted with proton diffusion motion by some method,” but in practice it is more narrowly defined as meaning the original images acquired by adding a diffusion-weighted gradient magnetic field (Aoki et al., 2005). 3 Tensor is a function of a vector variable that possesses multilinearity, and when multiplied by the vector (on the left), it forms a matrix that yields vectors, with each component of the matrix being closely related to the coordinate system (Le Bihan et al., 2001). In fact, a 3 × 3, 2-step matrix operation is necessary.

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Information about diffusion in various directions is needed to measure anisotropy; therefore, it is necessary to acquire MR images to which MPG has been applied in at least six axes and to acquire T2-weighted images to which MPG has not been applied. Eigen values (λ1, λ2, λ3) that define the magnitude of the diffusion, and Eigen vectors (v1, v2, v3) that define the direction, are calculated by tensor analysis of the data obtained in this way. The collected anisotropy information in each voxel is approximated as a “diffusion ellipsoid” (Fig. 1). This information is depicted in two dimensions as an FA map (Fig. 2). Fractional anisotropy is a commonly used anisotropy index that takes on values between zero (perfectly isotropic diffusion) and one (the hypothetical case of an infinite cylinder), and is thus directly comparable between subjects. In contrast to FA, ADC is an index that expresses the magnitude of the diffusion irrespective of its direction. These indices are calculated by the following formulae: λ1 þ λ2 þ λ3 ADC ¼ ¼ hDi 3 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi rffiffiffi 2 2 2 3 ðλ1−bD >Þ þ ðλ2−bD >Þ þ ðλ3−bD >Þ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi FA ¼ 2 λ12 þ λ22 þ λ32 In isotropy, λ1, λ2, and λ3 = bD>, and FA becomes zero. As anisotropy increases, λ1 >> λ2 = λ3, and FA approaches one. The strength of the signal in FA maps represents the magnitude of FA, and, unlike conventional DWI, it is quantitative. Fractional anisotropy decreases at lesion sites, such as those associated with cerebral infarction and hemorrhage, and we have reported that FA is also decreased in Wallerian degeneration (Takagi et al., 2009). Diffusion tensor analysis has the novel feature of not only quantifying anisotropy by FA and other parameters, but also of analyzing directionality. Color-coded FA maps and RBG color Fractional anisotropy maps that show anisotropy in different colors according to the direction of the major axis are called color-coded FA maps (or “color maps”). Color maps make it possible to differentiate fibers based on the direction in which they run. The colors are arbitrary, but red (Red) is often assigned to the left-right, blue (Blue) to the superior–inferior, and green (Green) to the anterior-posterior orientation, and this system is referred to as RBG color (Pajevic and Pierpaoli, 2000). Fig. 2 shows a color map of an axial section of the common marmoset spinal cord in which the white matter fibers can be seen as blue tracts in the craniocaudal direction. Construction of diffusion tensor tractography Magnetic resonance images that make use of tensor analysis, such as FA maps and color maps, are collectively called diffusion tensor imaging (DTI). In the broadest sense, DTT can be considered a subtype of DTI, but it is often deliberately differentiated from DTI. Diffusion tensor tractography reveals the course of fibers, such as those in white

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Fig. 1. Diffusion of water molecules. A: Isotropic diffusion is represented as a sphere. B: Anisotropic diffusion is approximated by a diffusion ellipsoid. The diffusion ellipsoid is calculated from an Eigen value and an Eigen vector.

matter, by tracking the direction of the major axis of the ellipsoid of every voxel, i.e., by tracking the predominant directions of anisotropy and reconstructing it in three dimensions. Numerous algorithms for tracking anisotropy exist, but the Fiber Assignment by Continuous Tracking (FACT) algorithm (Mori and van Zijl, 2002) is the best known. Simply stated, the fiber tracts are reconstructed by tracking the major axis of the largest diffusion ellipsoid for every neighboring pixel (voxel). Diffusion tensor tractography can clearly depict white matter fibers, such as the corticospinal tract and corpus callosum in the brain, which have been difficult to distinguish with conventional MRI, and there is even a report of it being used for neurosurgical navigation (Kamada et al., 2005). An advantage of DTT is that the researcher or clinician can selectively choose the information to be depicted any number of times, by setting a region of interest (ROI). Several software programs have been developed for DTT analysis, but we use the free software dTV for DTI analysis, which was developed by the Image Computing and Analysis Laboratory, Department of Radiology, University of Tokyo Hospital, Japan. The dTV software is available at http://www.ut-radiology.umin.jp/people/masutani/dTV.htm (Masutani et al., 2003). The steps for performing DTI and DTT are shown in Fig. 3.

Diffusion tensor tractography of the spinal cord Clinical need for DTT of the spinal cord The spinal cord white matter is composed of many nerve fiber bundles, but tissue specimens appear uniform and it is impossible to observe the individual projections unless a special dye is used. Moreover, because proton relaxation times are very similar for different white-matter tracts, it is impossible to tell them apart by DTI, even using the monochrome contrast provided by T1WI or T2WI. Thus, although information about fiber tracts and the integrity of axons in the spinal cord is extremely important, it has been difficult to obtain in vivo. Even when the latest artifact-reducing imaging sequences and high-magnetic-field MRI scanners are used, the fiber bundles comprising individual tracts in the spinal-cord white matter cannot be distinguished. To make a prognosis or select the appropriate treatments for the spinal cord injury (SCI) that we have discussed here, it is extremely important to know where axons have been destroyed or demyelinated, and to what extent axons have been spared. However, until now, even using the best imaging technology available, it has

Fig. 2. Conventional magnetic resonance imaging and diffusion tensor imaging in the brain and spinal cord of a living common marmoset. A: T2-weighted magnetic resonance images (T2WI). B: Fractional anisotropy (FA) maps. C: Color-coded FA maps.

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Fig. 3. Flow charts for constructing diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) images.

only been possible to detect the location of the injury and the degree of compression. In the last decade or so, many reports have described the use of DTT to depict neuronal fibers in the white matter of the brain. In contrast, high-resolution data for the spinal cord has been difficult to obtain, because the spinal cord is smaller than the brain, it is located deep in the body, and imaging it requires discriminating among tissues and materials with different magnetic properties, such as the spinal cord white matter, cerebrospinal fluid, vertebrae, muscle, and air, which are intermingled within a small space (Basser and Jones, 2002; Maier and Mamata, 2005). Thus, although occasional reports on the use of DTT to depict the spinal cord in humans have been published, their precision has been low, and few have included histological studies (Ducreux et al., 2006; Facon et al., 2005; Tsuchiya et al., 2005). To apply DTT to the spinal cord in a clinical setting, this method must be verified by conducting a detailed comparison between DTT and histological findings. We therefore performed DTT imaging of the spinal cord in a primate, the common marmoset, and examined its accuracy by conducting comparative tissue studies.

DTT of the cervical cord of a primate, the common marmoset We created a reproducible model of SCI in the common marmoset, which is a primate and therefore more closely related to humans than to rodents, and found that the transplantation of human neural stem cells contributed to functional recovery in this model (Iwanami et al., 2005a, 2005b). Unfortunately, there are still no reports about curing complete SCI in humans, although the results of animal experiments in rats, cats and monkeys indicate that some functional recovery can be anticipated if approximately 5–25% of the axons are spared from injury (Eidelberg et al., 1981; Fehlings and Tator, 1995; Raineteau and Schwab, 2001; Windle et al., 1958). In experiments with animals, injured axons can be evaluated histologically by injecting a tracer, such as biotinylated dextran amine, into the primary motor cortex of the cerebrum, but such an analysis obviously cannot be used in the clinic. To examine whether DTT is applicable to injured axons in humans, we first used it to examine common marmosets with SCI. In these animal experiments, a 7.0-Tesla MRI device 4 was used to obtain images 4

The Tesla is a unit for the strength of a magnetic field, where 1.0 Tesla=10,000 Gauss.

at high resolution and a spin echo sequence was employed to minimize magnetic susceptibility. In this way, we obtained the first clear DTT depiction of the primate spinal cord (Fujiyoshi et al., 2007). To determine whether the DTT results accurately reflected the condition of the axons, we employed a simpler cervical cord (C5/6) hemisection injury model. Using this model, we performed DTT on animals that had been sacrificed two weeks after injury (postmortem model). In a reconstruction of the nerve fiber interruption, DTT clearly depicted the hemisection injury, which was detectable by conventional MRI only in the form of T1WI and T2WI contrast (Fig. 4). Moreover, the DTI and DTT results reflected the histological results obtained with Luxol Fast Blue (LFB) and other staining methods. Next, by optimizing the depth of anesthesia to minimize artifacts from respiratory movements and cerebrospinal fluid pulsation, and by making various modifications based on the findings in the postmortem model, we were successful in constructing DTT images that reproducibly depicted the different fiber tracts of the spinal cord in living common marmosets (Fig. 5). Moreover, it was possible to depict the pyramidal decussation with DTT, which had previously been difficult to perform by this method (Fig. 6). Although there are some limitations, pathway-specific DTT conducted in live animals yielded results similar to those observed in postmortem animals, especially with respect to major tract morphology. This approach demonstrated that DTT could be used instead of conventional tracers for fiber tracking. All interventions and animal care procedures were performed in accordance with the Laboratory Animal Welfare Act, the Guide for the Care and Use of Laboratory Animals (National Institutes of Health), and the Guidelines and Policies for Animal Surgery provided by the Animal Study Committee of the Central Institute for Experimental Animals. The protocols were approved by the ethics committee of Keio University. Recent studies on DTI of the spinal cord As the performance of MRI devices improves, the number of DTI studies of the spinal cord should increase. There are two categories of such studies. One involves visualizing the spinal cord disorders by tractography, including the visualization of inflammatory and degenerative disorders (Cruz et al., 2009; Renoux et al., 2006), the localization and characterization of spinal cord tumors (Ducreux et al., 2006; Setzer et al., 2010), and characterization of the deformation and

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Fig. 4. The hemisected spinal cord in a postmortem common marmoset at two weeks post injury. A, B: Coronal and sagittal T2-weighted magnetic resonance images depict the hemisection injury as low- and high-intensity areas with no change in the cord caudal to the injury. C: Diffusion tensor tractography (DTT) of the hemisected spinal cord. The region of interest was placed in the upper cervical spinal cord and DTT was traced in the caudal direction revealing the disruption of white matter fibers on the hemisected side. The tracts became untraceable at the injury site, while those on the contralateral side continued caudally. D, E: Sagittal fractional anisotropy (FA) maps. F: Three-dimensional FA map. G: Axial FA maps. H: Luxol Fast Blue staining of the spinal cord from top to bottom, 8 mm cranial to the injury site, at the hemisection site, and 8 mm caudal to the hemisection site . The FA findings of a normal spinal cord cranial to the hemisection site, and the significant decrease in the signal from white matter at the hemisection site were confirmed by the histological findings. Scale bars: 1 mm. I: By performing a voxel statistical analysis of the DTT data, we determined that the tensor and tract-line numbers were valid. Arrowheads indicate the hemisection site. These data suggest that DTT is a more straightforward tool for visualizing axonal disruption than any other magnetic resonance imaging technique. (Partial modification of Fujiyoshi K and Yamada M, et al: J Neurosci (2007) 27: 11991–11998).

interruption of local fibers caused by arteriovenous malformations (Ozanne et al., 2007) and Brown Sequard syndrome (Rajasekaran et al., 2010). The other category involves the quantitative analysis and examination of correlations between diffusion and both functional and histological conditions. It has been reported that axial diffusion in the ventrolateral white matter in the experimental autoimmune encephalomyelitis (EAE) mouse model is significantly negatively correlated with the EAE clinical score, and is significantly lower in mice with severe EAE than in mice with moderate EAE (Budde et al., 2008). In the ventral white matter tracts of the lumbar spinal cord of amyotrophic lateral sclerosis (ALS)-affected SOD1 transgenic mice (G93A-SOD1 mice), FA values decrease with disease progression (Underwood et al., 2010). Another study revealed statistically significant decreases in axial diffusivity and ADC in the ventrolateral white matter in both the cervical and lumbar spinal cord of another model of ALS, G93A-SOD1 mice (Kim et al., 2010a). In an SCI study, Kim et al. identified the spared, normal ventrolateral white matter by hyperacute axial diffusion 3 h after injury, and a histological analysis performed 14 days later showed a good correlation with the spinal contusion injury (Kim et al., 2010b). Loy et al. demonstrated that axial diffusivity in ventral white matter differentiated mild, moderate, and severe contusive SCI, with good histological correlation (Loy et al., 2007). In summary, these recent studies indicate that quantitative DTI parameters are sensitive and specific biomarkers for spinal cord white matter integrity. Several clinical studies suggest that the increased sensitivity of DTI makes it appropriate for the early detection of myelopathic changes

in patients with cervical myelopathy. A significant positive correlation between FA at the compressed level and the clinical assessment has been demonstrated (Budzik et al., 2010; Xiangshui et al., 2010). This correlation was also seen in patients with SCI (Facon et al., 2005; Shanmuganathan et al., 2008). Chang et al. (2010) revealed that quantitative fiber tract results correlate to some extent with the severity of cervical SCI in patients. In summary, diffusion imaging is more sensitive than conventional MRI for precisely determining the extent of spinal disorders via non-invasive, longitudinal examinations, in both human patients and animal models. Moreover, FA analysis may prove more useful than other diffusional indices because of its simplicity, accuracy, and ability to reveal diverse spinal cord disorders, especially in the clinical situation.

DTT limitations and future prospects Resolution Diffusion tensor tractography does not depict the axons themselves, although this is frequently misunderstood. The voxel size required for analyzing anisotropy is far greater than the diameter of an axon, so it is actually the diffusion anisotropy of fiber bundles composed of 102 axons or more that is depicted. The size of the voxels (pixels) used to create the image shown in Figs. 2B and C was 0.215 mm; the average axon diameter is about 5 μm.

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Fig. 5. In vivo pathway-specific diffusion tensor tractography (DTT) of intact and injured spinal cords in live common marmosets. A–D: T2-weighted magnetic resonance images and tract-specific DTT of the intact spinal cord. E–H: Hemisected spinal cord two weeks after injury. DTT of the corticospinal tract (CST) (B, F), spinothalamic tract (C, G), and dorsal column-medial lemniscus pathway (D, H) in both groups revealed tract disruption at the hemisection site (C5/6 level) in all pathways (Reprinted from Fujiyoshi K and Yamada M, et al: J Neurosci (2007) 27: 11991–11998).

Fig. 6. Pathway-specific diffusion tensor tractography (DTT) in a postmortem common marmoset revealing the course of the corticospinal tract (CST) with pyramidal decussation. A: DTT of the CST conducted with two regions of interest (the pyramid of the medulla and the contralateral lateral funiculus in the upper cervical cord). B: DTT of the pyramidal decussation was superimposed on three-dimensional magnetic resonance images to macroscopically confirm that the pyramidal decussation was depicted with the proper height in the medulla and upper cervical cord. The cerebellum was used as a reference point. The position and course of the CST depicted by DTT was verified by histology, as previously reported. (Partially reprinted from Fujiyoshi K and Yamada M, et al: J Neurosci (2007) 27: 11991–11998).

Depiction of complicated axonal pathways Closely related to the resolution problem, the anisotropies of fibers oriented in different directions within the same voxel sometimes cancel each other out. This is the result of a partial volume effect that stops the tracking. Conversely, if the anisotropy is not cancelled out, the sum of the conflicting information allows tracking, but sometimes leads to the depiction of a structure that cannot actually exist. Therefore, DTT images do not always reflect actual anatomical structures. We have addressed this issue by setting a threshold value for FA that reduces the likelihood of depicting an erroneous tract, and setting a threshold value for anisotropy tracking that prevents sharp changes in direction.

At present, we consider it necessary to interpret DTT results cautiously by drawing on all of the anatomical and histological information available, which is the usual procedure when reading ordinary MRI scans. Complicated, multiaxial nerve tracts can be analyzed by using high angular resolution diffusion imaging (HARDI) with a high b value 5 and many directional MPGs (Schmahmann et al., 2007; Tuch et al., 2003). We also have succeeded in clearly depicting the optic chiasm 5 The “b value” is related to the diffusion setting in DWI. It is calculated by the following formula, in which γ (rad/s/Tesla) is the gyromagnetic ratio, G (mT/m) is the amplitude of the MPG, δ (ms) is the MPG application time, and Δ (ms) is the separated time of each pair of gradient pulse.

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and cranial nerves using HARDI (Fig. 7), but because these analyses require an enormous amount of time they are still not feasible in a clinical setting (Yamada et al., 2008). DTI and statistical analysis: combination with functional MRI Although manual or semi-automated guided ROI measurements are needed to assess diffusional changes in the spinal cord, questions arise about the validity of the investigator-determined ROI, even though the ROI can be defined so that the measurement is highly reliable. Moreover, it is difficult to decide on the ROI in cases where the location and/or type of abnormality is unknown (Abe et al., 2010; Ciccarelli et al., 2008). To resolve these problems, a voxel-based analysis can be used not only for structural MRI (e.g., voxel-based morphometry) but also for diffusion MRI (Hikishima et al., 2010; Wright et al., 1995). Voxel-based analyses of FA data are also carried out using tract-based spatial statistics (Smith et al., 2006), which are part of the Functional MRI of the Brain Software Library (Functional MRI of the Brain Analysis Group, Oxford, UK; Smith et al., 2004). Tract-based spatial statistics project all of the FA data from one subject onto a mean FA tract skeleton, before applying voxel wise cross-subject statistics. Some authors have suggested that voxel- and ROI-based methods provide different types of information, and that a voxel-based analysis can be used as an exploratory whole-brain approach to identify abnormal brain regions that should then be validated using ROI-based analyses. These methods may be complementary to each other in terms of DTI analysis (Abe et al., 2010; Giuliani et al., 2005). Furthermore, brain templates are currently available for use in applying voxel-based analysis across a variety of animal species to detect subtle anatomical differences (Black et al., 2001a, 2001b; Hikishima et al., 2010; McLaren et al., 2009; Quallo et al., 2010; Rilling et al., 2007). Endo et al. recently measured the blood oxygenation level-dependent functional MRI (BOLD-fMRI) signal in rats with thoracic transection injury and found that hindlimb stimulation results in significant lumbar dorsal horn (spinothalamic) activation in the dorsal horn caudal to the injury (Endo et al., 2008; Harel and Strittmatter, 2008; Lilja, 2006). However, it is difficult to distinguish the BOLD signal of white matter from that of the cell bodies, or grey matter. Therefore, the structural tract anatomy revealed by DTI needs to be further improved, and the data obtained from BOLD and DTI need to be better integrated, to address this question. A combination of BOLD-fMRI and DTI would help to track the regrowth of damaged tracts connecting functional neurons (Harel and Strittmatter, 2008). From bench to bedside By exploiting our DTT findings from the spinal cord of marmosets, we have already applied this method clinically to a fairly large

number of cases. We are currently assessing the usefulness of DTT for examining cervical spondylotic myelopathy, ossification of the posterior longitudinal ligament (Nakamura et al., 2012), and spinal cord tumors, as well as SCI (Fig. 8). Beyond the utility of this technique to monitor the extent of spinal cord lesions, this technique may prove instrumental in chronically evaluating the plastic changes associated with spontaneous functional recovery, as well as the effectiveness of therapeutic interventions such as cell transplantation (Tsuji et al., 2010; Nori et al., 2011) and growth factor-infusion (Kitamura et al., 2011) to enhance this remodeling process. The application of DTT is restricted to diseases of the cervical region, because it is difficult to obtain high-resolution images in the trunk. We hope that future advances in the development of MRI hardware and software will enable clinicians and researchers to overcome the problems of long image acquisition time, image resolution, and artifacts. Moreover, we believe that by revealing more anatomical structural information about the tracts, it will become possible to detect and evaluate the lesions of even more spinal cord diseases. The study of the human spinal cord was performed using a 1.5-Tesla MRI according to the guidelines of the ethical committee of our institute (Keio University School of Medicine) and was done with the informed consent of the patient. 2

2 2

b ¼ γ Gx δ ðΔ−δ=3Þ For the DTT images described in this study, we used b = 1000 (s/ mm2). By contrast, a “high” b value is 2500 (s/mm2) or more, which makes it possible to reflect strongly restricted diffusion such as intracellular diffusion and the diffusion of more complicated nerve pathways that cannot be perceived by conventional DWI. Author contributions K.F., M.Y., K.H., Y.T., M.N., and H.O. designed the research; K.F., M.Y., K.H., O.T., and M.N. performed the research; K.F., Y.K., T.K., M.Y., K.H., O.T., S.M., M.N., and H.O. analyzed the data; and K.F. T.K., M.N., and H.O. wrote the paper. Acknowledgments This work was supported by grants from Grants-in-Aid for Scientific Research from JSPS and the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT), the project for realization of regenerative medicine and support for the core institutes for iPS cell research from MEXT, the General Insurance Association of Japan, the Funding Program for World-leading Innovative R&D on Science and

Fig. 7. Diffusion tensor tractography (DTT) and high angular resolution diffusion imaging (HARDI) of the optic chiasma in the common marmoset. A: DTT. B: HARDI. When the region of interest was set on the optic nerve on one side and DTT was performed, it showed the wrong tract heading toward the opposite side. HARDI was found to depict the path of the optic nerve in a more anatomically and histologically correct manner.

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Fig. 8. Diffusion tensor tractography (DTT) images from a human patient. T2-weighted magnetic resonance images (sagittal; A), and DTT (B) images of a complete spinal transection in a human patient. This patient suffered from complete paraplegia due to fracture-dislocation at the C6 level. By setting the region of interest at the C1 level, DTT was traced in the caudal direction revealing the disruption of white matter fibers at the lesion epicenter. Arrows indicate the lesion epicenter.

Technology, and a grant-in-aid from Global COE Programs of MEXT to Keio University. References Abe, O., Takao, H., Gonoi, W., Sasaki, H., Murakami, M., Kabasawa, H., Kawaguchi, H., Goto, M., Yamada, H., Yamasue, H., Kasai, K., Aoki, S., Ohtomo, K., 2010. Voxelbased analysis of the diffusion tensor. Neuroradiology 52, 699–710. Aoki, S., Abe, O., Masutani, Y., 2005. New Edition: Making Sense of Diffusion MRI (Japanese). Shujunsha. Basser, P.J., Jones, D.K., 2002. Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed. 15, 456–467. Black, K.J., Koller, J.M., Snyder, A.Z., Perlmutter, J.S., 2001a. Template images for nonhuman primate neuroimaging: 2. Macaque. Neuroimage 14, 744–748. Black, K.J., Snyder, A.Z., Koller, J.M., Gado, M.H., Perlmutter, J.S., 2001b. Template images for nonhuman primate neuroimaging: 1. Baboon. Neuroimage 14, 736–743. Budde, M.D., Kim, J.H., Liang, H.F., Russell, J.H., Cross, A.H., Song, S.K., 2008. Axonal injury detected by in vivo diffusion tensor imaging correlates with neurological disability in a mouse model of multiple sclerosis. NMR Biomed. 21, 589–597. Budzik, J.-F., Balbi, V., Thuc, V., Duhamel, A., Assaker, R., Cotten, A., 2010. Diffusion tensor imaging and fibre tracking in cervical spondylotic myelopathy. Eur. Radiol. 1–8. Burdette, J.H., Ricci, P.E., Petitti, N., Elster, A.D., 1998. Cerebral infarction: time course of signal intensity changes on diffusion-weighted MR images. AJR Am. J. Roentgenol. 171, 791–795. Burdette, J.H., Elster, A.D., Ricci, P.E., 1999. Acute cerebral infarction: quantification of spin-density and T2 shine-through phenomena on diffusion-weighted MR images. Radiology 212, 333–339. Chang, Y., Jung, T.-D., Yoo, D.S., Hyun, J.K., 2010. Diffusion tensor imaging and fiber tractography of patients with cervical spinal cord injury. J. Neurotrauma 27, 2033–2040. Ciccarelli, O., Catani, M., Johansen-Berg, H., Clark, C., Thompson, A., 2008. Diffusionbased tractography in neurological disorders: concepts, applications, and future developments. Lancet Neurol. 7, 715–727. Cruz Jr., L.C., Domingues, R.C., Gasparetto, E.L., 2009. Diffusion tensor imaging of the cervical spinal cord of patients with relapsing–remising multiple sclerosis: a study of 41 cases. Arq. Neuropsiquiatr. 67, 391–395. dTV http://www.ut-radiology.umin.jp/people/masutani/dTV.htm. Ducreux, D., Lepeintre, J.F., Fillard, P., Loureiro, C., Tadie, M., Lasjaunias, P., 2006. MR diffusion tensor imaging and fiber tracking in 5 spinal cord astrocytomas. AJNR Am. J. Neuroradiol. 27, 214–216. Eidelberg, E., Walden, J.G., Nguyen, L.H., 1981. Locomotor control in macaque monkeys. Brain 104, 647–663. Endo, T., Spenger, C., Westman, E., Tominaga, T., Olson, L., 2008. Reorganization of sensory processing below the level of spinal cord injury as revealed by fMRI. Exp. Neurol. 209, 155–160. Facon, D., Ozanne, A., Fillard, P., Lepeintre, J., Tournoux-Facon, C., Ducreux, D., 2005. MR diffusion tensor imaging and fiber tracking in spinal cord compression. Am. J. Neuroradiol. 26, 1587.

Fehlings, M.G., Tator, C.H., 1995. The relationships among the severity of spinal cord injury, residual neurological function, axon counts, and counts of retrogradely labeled neurons after experimental spinal cord injury. Exp. Neurol. 132, 220–228. Fujiyoshi, K., Yamada, M., Nakamura, M., Yamane, J., Katoh, H., Kitamura, K., Kawai, K., Okada, S., Momoshima, S., Toyama, Y., Okano, H., 2007. In vivo tracing of neural tracts in the intact and injured spinal cord of marmosets by diffusion tensor tractography. J. Neurosci. 27, 11991–11998. Giuliani, N., Calhoun, V., Pearlson, G., Francis, A., Buchanan, R., 2005. Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophr. Res. 74, 135–148. Harel, N., Strittmatter, S., 2008. Functional MRI and other non-invasive imaging technologies: providing visual biomarkers for spinal cord structure and function after injury. Exp. Neurol. 211, 324–328. Hikishima, K., Quallo, M.M., Komaki, Y., Yamada, M., Kawai, K., Momoshima, S., Okano, H.J., Sasaki, E., Tamaoki, N., Lemon, R.N., Iriki, A., Okano, H., 2010. Population-averaged standard template brain atlas for the common marmoset (Callithrix jacchus). Neuroimage 1–9. Iwanami, A., Kaneko, S., Nakamura, M., Kanemura, Y., Mori, H., Kobayashi, S., Yamasaki, M., Momoshima, S., Ishii, H., Ando, K., Tanioka, Y., Tamaoki, N., Nomura, T., Toyama, Y., Okano, H., 2005a. Transplantation of human neural stem cells for spinal cord injury in primates. J. Neurosci. Res. 80, 182–190. Iwanami, A., Yamane, J., Katoh, H., Nakamura, M., Momoshima, S., Ishii, H., Tanioka, Y., Tamaoki, N., Nomura, T., Toyama, Y., Okano, H., 2005b. Establishment of graded spinal cord injury model in a nonhuman primate: the common marmoset. J. Neurosci. Res. 80, 172–181. Kamada, K., Todo, T., Masutani, Y., Aoki, S., Ino, K., Takano, T., Kirino, T., Kawahara, N., Morita, A., 2005. Combined use of tractography-integrated functional neuronavigation and direct fiber stimulation. J. Neurosurg. 102, 664–672. Kim, J.H., Loy, D.N., Wang, Q., Budde, M.D., Schmidt, R.E., Trinkaus, K., Song, S.-K., 2010a. Diffusion tensor imaging at 3 hours after traumatic spinal cord injury predicts long-term locomotor recovery. J. Neurotrauma 27, 587–598. Kim, J.H., Wu, T.-H., Budde, M.D., Lee, J.-M., Song, S.-K., 2010b. Noninvasive detection of brainstem and spinal cord axonal degeneration in an amyotrophic lateral sclerosis mouse model. NMR Biomed. (n/a-n/a). Kitamura, K., Fujiyoshi, K., Yamane, J., Toyota, F., Hikishima, K., Nomura, T., Funakoshi, H., Nakamura, T., Aoki, M., Toyama, Y., Okano, H., Nakamura, M., 2011. Human hepatocyte growth factor promotes functional recovery in primates after spinal cord injury. PLoS One 6, e27706. Le Bihan, D., van Zijl, P., 2002. From the diffusion coefficient to the diffusion tensor. NMR Biomed. 15, 431–434. Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., Laval-Jeantet, M., 1986. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407. Le Bihan, D., Breton, E., Lallemand, D., Aubin, M.L., Vignaud, J., Laval-Jeantet, M., 1988. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168, 497–505. Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabriat, H., 2001. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534–546.

82

K. Fujiyoshi et al. / Experimental Neurology 242 (2013) 74–82

Lilja, J., 2006. Blood oxygenation level-dependent visualization of synaptic relay stations of sensory pathways along the neuroaxis in response to graded sensory stimulation of a limb. J. Neurosci. 26, 6330–6336. Loy, D.N., Kim, J.H., Xie, M., Schmidt, R.E., Trinkaus, K., Song, S.-K., 2007. Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J. Neurotrauma 24, 979–990. Maier, S.E., Mamata, H., 2005. Diffusion tensor imaging of the spinal cord. Ann. N. Y. Acad. Sci. 1064, 50–60. Masutani, Y., Aoki, S., Abe, O., Hayashi, N., Otomo, K., 2003. MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. Eur. J. Radiol. 46, 53–66. McLaren, D.G., Kosmatka, K.J., Oakes, T.R., Kroenke, C.D., Kohama, S.G., Matochik, J.A., Ingram, D.K., Johnson, S.C., 2009. A population-average MRI-based atlas collection of the rhesus macaque. Neuroimage 45, 52–59. Mori, S., van Zijl, P.C.M., 2002. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 15, 468–480. Mori, S., Zhang, J., 2006. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51, 527–539. Nakamura, M., Fujiyoshi, K., Tsuji, O., Konomi, T., Hosogane, N., Watanabe, K., Tsuji, T., Ishii, K., Momoshima, S., Toyama, Y., Chiba, K., Matsumoto, M., 2012. Clinical significance of diffusion tensor tractography as a prognostic predictor of functional recovery in cervical compressive myelopathy patients after laminoplasty. J. Neurosurg. Spine 17 (2), 147–152. Nori, S., Okada, Y., Yasuda, A., Tsuji, O., Takahashi, Y., Kobayashi, Y., Fujiyoshi, K., Koike, M., Uchiyama, Y., Ikeda, E., Toyama, Y., Yamanaka, S., Nakamura, M., Okano, H., 2011. Grafted human-induced pluripotent stem cell‐derived neurospheres promote motor functional recovery after spinal cord injury in mice. Proc. Natl. Acad. Sci. U. S. A. 108, 16825–16830. Ozanne, A., Krings, T., Facon, D., Fillard, P., Dumas, J.L., Alvarez, H., Ducreux, D., Lasjaunias, P., 2007. MR diffusion tensor imaging and fiber tracking in spinal cord arteriovenous malformations: a preliminary study. AJNR Am. J. Neuroradiol. 28, 1271–1279. Pajevic, S., Pierpaoli, C., 2000. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn. Reson. Med. 43, 921. Quallo, M.M., Price, C.J., Ueno, K., Asamizuya, T., Cheng, K., Lemon, R.N., Iriki, A., 2010. Creating a population-averaged standard brain template for Japanese macaques (M. fuscata). Neuroimage 52, 1328–1333. Raineteau, O., Schwab, M.E., 2001. Plasticity of motor systems after incomplete spinal cord injury. Nat. Rev. Neurosci. 2, 263–273. Rajasekaran, S., Kanna, R.M., Karunanithi, R., Shetty, A.P., 2010. Diffusion tensor tractography demonstration of partially injured spinal cord tracts in a patient with posttraumatic brown sequard syndrome. J. Magn. Reson. Imaging 32, 978–981. Renoux, J., Facon, D., Fillard, P., Huynh, I., Lasjaunias, P., Ducreux, D., 2006. MR diffusion tensor imaging and fiber tracking in inflammatory diseases of the spinal cord. AJNR Am. J. Neuroradiol. 27, 1947–1951. Rilling, J.K., Barks, S.K., Parr, L.A., Preuss, T.M., Faber, T.L., Pagnoni, G., Bremner, J.D., Votaw, J.R., 2007. A comparison of resting-state brain activity in humans and chimpanzees. Proc. Natl. Acad. Sci. U. S. A. 104, 17146–17151.

Schmahmann, J.D., Pandya, D.N., Wang, R., Dai, G., D'Arceuil, H.E., de Crespigny, A.J., Wedeen, V.J., 2007. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130, 630–653. Setzer, M., Murtagh, R.D., Murtagh, F.R., Eleraky, M., Jain, S., Marquardt, G., Seifert, V., Vrionis, F.D., 2010. Diffusion tensor imaging tractography in patients with intramedullary tumors: comparison with intraoperative findings and value for prediction of tumor resectability. J. Neurosurg. Spine 13, 371–380. Shanmuganathan, K., Gullapalli, R.P., Zhuo, J., Mirvis, S.E., 2008. Diffusion tensor MR imaging in cervical spine trauma. AJNR Am. J. Neuroradiol. 29, 655–659. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., JohansenBerg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M., 2004. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl. 1), S208–S219. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505. Takagi, T., Nakamura, M., Yamada, M., Hikishima, K., Momoshima, S., Fujiyoshi, K., Shibata, S., Okano, H., Toyama, Y., Okano, H., 2009. Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography. Neuroimage 44, 884–892. Tsuchiya, K., Fujikawa, A., Suzuki, Y., 2005. Diffusion tractography of the cervical spinal cord by using parallel imaging. AJNR Am. J. Neuroradiol. 26, 398–400. Tsuji, O., Miura, K., Okada, Y., Fujiyoshi, K., Mukaino, M., Nagoshi, N., Kitamura, K., Kumagai, G., Nishino, M., Tomisato, S., Higashi, H., Nagai, T., Katoh, H., Kohda, K., Matsuzaki, Y., Yuzaki, M., Ikeda, E., Toyama, Y., Nakamura, M., Yamanaka, S., Okano, H., 2010. Therapeutic potential of appropriately evaluated ‘safe’ induced pluripotent stem cells for spinal cord injury. Proc. Natl. Acad. Sci. U. S. A. 107, 12704–12709. Tuch, D.S., Reese, T.G., Wiegell, M.R., Wedeen, V.J., 2003. Diffusion MRI of complex neural architecture. Neuron 40, 885–895. Underwood, C.K., Kurniawan, N.D., Butler, T.J., Cowin, G.J., Wallace, R.H., 2010. Noninvasive diffusion tensor imaging detects white matter degeneration in the spinal cord of a mouse model of amyotrophic lateral sclerosis. Neuroimage 1–28. Windle, W.F., Smart, J.O., Beers, J.J., 1958. Residual function after subtotal spinal cord transection in adult cats. Neurology 8, 518–521. Wright, I.C., McGuire, P.K., Poline, J.B., Travere, J.M., Murray, R.M., Frith, C.D., Frackowiak, R.S., Friston, K.J., 1995. A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 2, 244–252. Xiangshui, M., Xiangjun, C., Xiaoming, Z., Qingshi, Z., Yi, C., Chuanqiang, Q., Xiangxing, M., Chuanfu, L., Jinwen, H., 2010. 3 T magnetic resonance diffusion tensor imaging and fibre tracking in cervical myelopathy. Clin. Radiol. 65, 465–473. Yamada, M., Momoshima, S., Masutani, Y., Fujiyoshi, K., Abe, O., Nakamura, M., Aoki, S., Tamaoki, N., Okano, H., 2008. Diffusion-tensor neuronal fiber tractography and manganese-enhanced MR imaging of primate visual pathway in the common marmoset: preliminary results. Radiology 249, 855–864. Yamashita, T., Kwee, T.C., Takahara, T., 2009. Whole-body magnetic resonance neurography. N. Engl. J. Med. 361, 538–539.