Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI

NeuroImage 55 (2011) 1024–1033

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Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI J. Cohen-Adad a,b,⁎, M-M. El Mendili a, S. Lehéricy c, P-F. Pradat d, S. Blancho e, S. Rossignol f, H. Benali a a

UMR-678, INSERM-UPMC, Pitié-Salpêtrière Hospital, Paris, France A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA Centre for Neuroimaging Research (CENIR), Centre de Recherche de l'Institut du Cerveau et de la Moelle epiniere, UPMC, UMR-S975, INSERM U975, CNRS UMR 7225, Groupe Hospitalier Pitie-Salpetriere, Paris, France d Fédération des Maladies du Système Nerveux, AP-HP, Pitié-Salpêtrière Hospital, Paris, France e Institut pour la Recherche sur la Moelle Epinière et l'Encéphale, France f GRSNC, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada b c

a r t i c l e

i n f o

Article history: Received 9 September 2010 Revised 15 November 2010 Accepted 17 November 2010 Available online 11 January 2011 Keywords: Spinal cord injury Diffusion-weighted MRI Magnetization transfer Atrophy ASIA

a b s t r a c t Characterizing demyelination/degeneration of spinal pathways in traumatic spinal cord injured (SCI) patients is crucial for assessing the prognosis of functional rehabilitation. Novel techniques based on diffusionweighted (DW) magnetic resonance imaging (MRI) and magnetization transfer (MT) imaging provide sensitive and specific markers of white matter pathology. In this paper we combined for the first time high angular resolution diffusion-weighted imaging (HARDI), MT imaging and atrophy measurements to evaluate the cervical spinal cord of fourteen SCI patients and age-matched controls. We used high in-plane resolution to delineate dorsal and ventrolateral pathways. Significant differences were detected between patients and controls in the normal-appearing white matter for fractional anisotropy (FA, p b 0.0001), axial diffusivity (p b 0.05), radial diffusivity (p b 0.05), generalized fractional anisotropy (GFA, p b 0.0001), magnetization transfer ratio (MTR, p b 0.0001) and cord area (p b 0.05). No significant difference was detected in mean diffusivity (p = 0.41), T1-weighted (p = 0.76) and T2-weighted (p = 0.09) signals. MRI metrics were remarkably well correlated with clinical disability (Pearson's correlations, FA: p b 0.01, GFA: p b 0.01, radial diffusivity: p = 0.01, MTR: p = 0.04 and atrophy: p b 0.01). Stepwise linear regressions showed that measures of MTR in the dorsal spinal cord predicted the sensory disability whereas measures of MTR in the ventrolateral spinal cord predicted the motor disability (ASIA score). However, diffusion metrics were not specific to the sensorimotor scores. Due to the specificity of axial and radial diffusivity and MT measurements, results suggest the detection of demyelination and degeneration in SCI patients. Combining HARDI with MT imaging is a promising approach to gain specificity in characterizing spinal cord pathways in traumatic injury. © 2011 Elsevier Inc. All rights reserved.

Introduction Sensorimotor impairments after spinal cord injuries (SCI) largely depend on the damage to ascending and descending myelinated tracts in the white matter that are distributed throughout the various quadrants of the spinal cord. The dorsal columns are clearly delineated and contain mainly ascending sensory pathways important for proprioception. Other tracts ascend more laterally and carry sensory information to the cerebellum (ventral and dorsal spinocerebellar tracts) or the thalamus

Abbreviations: SCI, spinal cord injury; DW, Diffusion-Weighted; MT, Magnetization Transfer; HARDI, High Angular Resolution Diffusion Imaging; FA, Fractional Anisotropy; GFA, Generalized Fractional Anisotropy; MD, Mean Diffusivity; DTI, Diffusion Tensor Imaging; QBI, Q-Ball Imaging; FOV, Field Of View. ⁎ Corresponding author. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteen St, Charlestown, MA 02129, USA. Fax: + 1 617 726 1383. E-mail address: [email protected] (J. Cohen-Adad). 1053-8119/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.11.089

(lateral and spinothalamic tracts). Major descending tracts are located laterally (corticospinal and rubrospinal) and ventrally and carry information mainly from the vestibular system, the reticular system and some direct ipsilateral corticospinal projections. Traumatic lesions (including primary and secondary lesions) not only can induce a physical discontinuity of the tracts but also anterograde wallerian demyelination as well as some retrograde degeneration. After SCI, some pathways may be preserved and contribute to recovery of function. This could be achieved by regeneration of pathways or sprouting of undamaged pathways (Bareyre et al., 2004; Maier and Schwab, 2006; Rossignol et al., 2007). Whereas in the first case, pathways are replaced by regenerated fibers, in the second case, new connections are either made or strengthened through existing structures. Thus, damage to the corticospinal tract can be in part offset by sprouting new connections through propriospinal or reticulospinal pathways, which then act more or less as a new (or enhanced relay) between the cortex and the

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spinal cord. It is thus important to develop prognostic imaging tools that will allow the characterization of the damaged tracts and the state of residual tracts. Diffusion-weighted (DW) magnetic resonance imaging (MRI) exploits signal attenuation of water molecules that diffuse preferentially along white matter axons. It is possible to model this diffusion profile using diffusion tensor imaging (DTI) (Basser et al., 1994) and derive metrics of fractional anisotropy (FA), mean diffusivity (MD), axial and radial diffusivities that provide sensitive biomarkers for characterizing abnormality in the white matter. Animal studies notably showed that axial and radial diffusivities are good predictors of axonal loss and demyelination, respectively (Budde et al., 2007). DTI has been applied to assess the severity of the spinal cord injury (Agosta et al., 2007; Budde et al., 2007; Cohen-Adad et al., 2008a; DeBoy et al., 2007; Deo et al., 2006; Ducreux et al., 2007; Ellingson et al., 2008; Fujiyoshi et al., 2007; Kim et al., 2007; Lammertse et al., 2007; Nevo et al., 2001; Ohgiya et al., 2007; Plank et al., 2007; Ries et al., 2000; Schwartz et al., 2005; Shen et al., 2007; Thurnher and Bammer, 2006; Valsasina et al., 2005; Vargas et al., 2007). As an extension to DTI, high angular resolution diffusion imaging (HARDI) and Q-Ball imaging (QBI) can represent more than one diffusion direction, thereby alleviating limitations of the diffusion tensor in presence of crossing fibers (Tuch, 2004). HARDI has proven efficient in the detection of subtle axonal connections in the spinal cord (CohenAdad et al., 2008b; Lundell et al., 2009) and HARDI-based metrics such as the generalized fractional anisotropy (GFA) might be a good surrogate of white matter pathology, as suggested in previous work (Barmpoutis et al., 2009; Cohen-Adad et al., 2009b). One limitation of DTI/HARDI in the characterization of white matter integrity however, is the lack of specificity for determining demyelination and axonal loss. Several physical parameters can influence diffusion metrics including myelination, axonal density, axonal diameter, or orientation of fiber bundles (Beaulieu, 2002; Sen and Basser, 2005). Therefore combining DW-MRI with an independent measure that is sensitive to demyelination would increase the reliability of diagnosis. Magnetization transfer (MT) contrast is based on the interaction between hydrogen protons bounded to macromolecules (e.g. lipid constituted of axons myelin sheet), thereby providing an indirect surrogate for myelin content (Kucharczyk et al., 1994; Pike et al., 2000). One advantage of MT is its specificity to demyelination and degeneration, as assessed by histopathology (Mottershead et al., 2003; Schmierer et al., 2004). Using high resolution MT measurements in the spinal cord, it is possible to assess demyelination of specific spinal pathways, as shown in MS patients (Zackowski et al., 2009). It should however be stressed that MTR is a semi-quantitative measure that not only depends on the size of the macromolecular pool but also on the exchange rate between the bound and mobile proton pools, decreasing its specificity for myelin imaging (McCreary et al., 2009). Hence, combining measures of MT and DW-MRI is a means to become more specific to white matter pathology (Reich et al., 2007). Moreover, the high reproducibility of MT and DW-MRI in the human cervical cord at 3T suggests that these measures would provide robust assessment of white matter pathology (Smith et al., 2009). The goal of this study was to assess the state of spinal tracts in patients with chronic SCI by combining HARDI, magnetization transfer imaging and measures of cord atrophy. We performed correlations and stepwise regressions between MRI metrics and clinical parameters. Our hypotheses were: 1. HARDI metrics and MTR (measured in the normal appearing tissue as assessed using conventional T2 contrast) and cord area differ between SCI patients and age-matched controls. 2. HARDI metrics and MTR correlate with clinical disability score and are specific to the tracts involved, i.e., dorsal region for sensory scores and ventrolateral regions for the motor scores.

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Materials and methods Subjects Patients with chronic cervical SCI were recruited (N = 14, age = 45± 14 years, three women, delay after injury = 25 ± 35 years) (see Table 1). Exclusion criteria were: significant acute and chronic medical conditions, significant psychiatric or neurological history (other than SCI for patients), use of psychoactive drugs, osteosynthesis material in the spine and standard contraindications to MRI. Most patients presented spasticity and were treated with baclofen (Lioresal, 30 mg/day). Neuropathic pains were treated in twelve patients using pregabalin (Lyrica). All patients were clinically assessed and scored on the motor and sensory ASIA score (ASIA, 2002) within the week of MRI acquisition. The ASIA motor (ASIAm) ranges from 0 to 50 for each limb (maximum score of 100). The ASIA sensory score (ASIAs) only involved the “light touch” test to assess superficial sensitivity, and ranges from 0 to 56 for each limb (maximum score of 112). Patients were compared to age-matched controls (N = 14, age = 45 ± 17 years, five women). The local ethics committee of our institution approved all experimental procedures of the study, and written informed consent was obtained from each participant. Spinal lesions were identified by an experienced neuroradiologist (S.L.) using high-resolution T2-weighted images (see MRI acquisition). The presence of signal hyperintensity and/or cord compression in the spinal cord region was evaluated (see Table 1). Regions of normal appearing spinal cord white matter were identified for further analyses with HARDI and MT data.

MRI acquisition Subjects were positioned head-first supine, with a 2 cm thick pillow to lift the head and no pillow below the neck. This strategy was used to limit the natural cervical cord lordosis at around C3–C4, i.e., excessive cord curvature in the antero-posterior (A-P) direction. Straightening the spinal cord during positioning ensured limited partial volume effects when imaging in axial orientation. A typical example of subject positioning is illustrated in Fig. 1. Following positioning, pulse oxymeter probe was put on a finger. Before each scan the subject was asked not to swallow (or minimally). Acquisitions were conducted on a 3T MRI system (TIM Trio, Siemens Healthcare, Erlangen, Germany). RF excitation was performed using the body coil and detection was achieved using a combination of 12-channel head-coil, 4-channel neck coil and 24Table 1 Demography of SCI patients with T2 findings. R: Right, L: Left. In case the injury is present both dorsally and ventrally (or left and right), the preferential localization of hypersignal is indicated by the “N” sign. Gender Age Hypersignal T2 1 2 3

M M M

62 27 62

4 5 6 7 8 9 10 11 12

M F M M M M M F F

46 65 35 55 63 65 42 36 24

13 M 14 M

23 21

C3–C4. Bilateral; R N L C6–C7. Total C3–C4. Dorsolateral bilateral C5–C6. Dorsal, L in C6 C6–C7 C6–C7. Central C3–C5. Lateral. C3–C4. Lateral bilateral C3–C4. Dorsolateral, R N L C4–C6. Heterogeneous C6–C7. Dorsal N ventral C6–C7. Dorsal N Ventral, LNR C5–C6. No signal change C4–C5. L N R

ASIA ASIAm total/ 100

ASIAs total/ 112

B B C

60 54 36

64 72 47

C D A C B C B A B

41 84 28 38 49 74 26 30 26

76 96 29 104 66 64 37 28 46

D D

98 74

106 106

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Fig. 1. Localizer image demonstrating typical patient positioning with minimum lordosis. A: Slice prescription for diffusion-weighted acquisition, covering C2 to T2 vertebral levels. Saturation bands were set ventrally and dorsally to the spinal cord to limit aliasing and ghosting artifacts. B: Box prescription for manual shimming, ensuring good B0 homogeneity within the imaged portion of the spinal cord.

channel spine matrix (only the 3 most rostral elements of the spine matrix were used). A rapid localizer image was first acquired in the three orthogonal plans to ensure proper slice orientation for DW and MT imaging. Total imaging time was approximately 40 min. Conventional imaging Anatomical scans were conducted to evaluate the anatomical integrity of the spinal cord. We used a T2-weighted SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolution) sequence. This sequence is a 3D turbo spin echo with slab selective excitation pulses. It provides high SNR due to the 3D acquisition, high resolution due to the isotropic acquisition, short acquisition times by combining parallel acquisition with high turbo factors and low specific absorption rate due to low flip angles. Parameters were: sagittal orientation, one slab of 52 slices, field of view (FOV) = 280 × 280 mm2, TR = 1500 ms, TE = 120 ms, voxel size = 0.9 × 0.9 × 0.9 mm3, flip angle = 140°, parallel acquisition with R = 3 acceleration factor and generalized autocalibrating partially parallel acquisitions reconstruction (GRAPPA) (Griswold et al., 2002), phase encoding direction head-foot, phase oversampling 80%, slice oversampling 7.7%, bandwidth = 744 Hz/Pixel, turbo factor = 69 and acquisition time ~6 min. Diffusion-weighted imaging HARDI data were acquired using a single shot EPI sequence with monopolar DW scheme to achieve low TE (Callot et al., 2009; Stejskal and Tanner, 1965). Eight axial slices were prescribed to cover C2 to T2 vertebral levels (see Fig. 1A). Slices were centred in the middle of each vertebral body to minimize B0 inhomogeneities (Cohen-Adad et al., 2010a; Cooke et al., 2004). The acquisition was cardiac-gated with slices acquired during the quiescent phase of cardiac-related motion of the spinal cord (Summers et al., 2006). Parameters were: FOV = 128 mm, TR = ~ 700 ms (depending on heart beat), TE = 96 ms, voxel size = 1 × 1 × 5 mm3, parallel acquisition: R = 2 with 24 reference lines and GRAPPA reconstruction, phase encoding direction A-P, number of diffusion-weighting directions = 64, bvalue = 1000 s/mm 2 , bandwidth = 1086 Hz/Pixel, echo spacing = 1.04 ms and number of repetitions = 4. Two vertical saturation bands were used. One was positioned ventrally over the trachea to limit flow effects and motion due to swallowing. The other saturation band was set dorsally and aimed at suppressing signal from the non-

spinal cord tissue close to the surface coils and producing high intensity signal. Manual shimming was estimated within a parallelepiped closely fitting the cervical spinal cord (see Fig. 1B). Magnetization transfer imaging T1-weighted 3D gradient echo images with slab-selective excitation were acquired with and without magnetization transfer saturation pulse (Gaussian envelop, duration = 9984 μs, frequency offset = 1200 Hz). Parameters were: axial orientation, 52 slices (spaced with 20% gap), FOV = 230 × 230 mm 2 , TR = 28 ms, TE = 3.2 ms, voxel size = 0.9 × 0.9 × 2 mm3, flip angle = 23°, phase encoding direction right-left, phase partial Fourier 6/8, bandwidth = 400 Hz/Pix, acquisition time ~ 5 min for each volume (~10 min for both volumes with and without MT pulse). Data processing Atrophy measurement The cord area was measured from the T2-SPACE images at the level C1–C2. The plane perpendicular to the spinal cord was resampled to maximize the accuracy of area measurements, as done in Lundell et al. (2011). The cord area was then measured using the semi-automatic method described in Losseff et al. (1996). HARDI Motion correction was done using FSL FLIRT (Jenkinson et al., 2002). HARDI data were first split along the Z direction and motion correction was applied slice-by-slice to account for the non-rigid motion of structures across slices, notably induced by the B0 fluctuations close to the lungs (Van de Moortele et al., 2002). The motion correction algorithm minimized the correlation coefficient ratio between each image (including the b = 0 image) and the mean DW image using three degrees of freedom (Tx, Ty and Rz), as suggested elsewhere to be optimal for axial spinal cord EPI (Cohen-Adad et al., 2010a). The data were then averaged across repetitions. Diffusion tensor and its related metrics were estimated voxel-wise using FSL (Smith et al., 2004). Of all metrics computed, the fractional anisotropy (FA), the first eigenvalue (axial diffusivity, λ//) and the average of the 2nd and 3rd eigenvalues (radial diffusivity, λ⊥) were further considered for analysis. Q-Ball diffusion orientation distribution functions (ODF) were estimated using the method described in

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(Descoteaux et al., 2007). The HARDI signal was expressed by means of the spherical harmonic basis, which allows an analytical solution for the Funk–Radon transform to obtain the diffusion ODF. We used spherical harmonic decomposition of order 4 and a regularization parameter of 0.006 (Descoteaux et al., 2006). From the reconstructed ODF, we computed the generalized fractional anisotropy (GFA) (Tuch, 2004). As an extension of the FA, the GFA is defined as the standard deviation divided by the root mean square of the ODF. Hence, it is a measurement of anisotropy generalized throughout more than three eigenvalues. Magnetization transfer T1-weighted volumes with and without MT pulse were coregistered using the non-linear method available in FSL FNIRT (Smith et al., 2004). Magnetization transfer ratio (MTR) was computed voxel-wise following the equation [(S0 − SMT)/S0] × 100, where S0 and SMT are the T1-weighted image without and with the MT pre-saturation pulse, respectively. Two patients were discarded from this analysis due to important motion within either the saturated or the non-saturated T1-weighted acquisition. To compare T1-weighted signal between the two populations, images without MT pulse were normalized by the signal in the cerebrospinal fluid to account for B1 inhomogeneities. ROI-based analysis Due to the large gap between slices, we did not quantify diffusion metrics using tractography. Moreover, with coexistent pathology, tract-specific ROI definition based on tractography (Ciccarelli et al., 2007; Van Hecke et al., 2008) or fuzzy-logic (Ellingson et al., 2007) is potentially biased. As suggested in (Xu et al., 2010), we used geometry-based ROI definition to isolate the dorsal and ventrolateral quadrants of the cord. Fig. 2 illustrates the definition of ROI for both DWI and MT analyses. For each modality (HARDI and MT), ROIs were created by selecting voxels in the dorsal, ventral, left and right aspects of the spinal cord, as done in (Ciccarelli et al., 2007; Cohen-Adad et al., 2008a; Onu et al., 2010). ROIs were selected at vertebral levels presenting no abnormality on the T2-weighted SPACE image. All vertebral levels were used to define ROIs in healthy controls, according to a previous study showing that DTI and MT metrics are similar across vertebral levels in the cervical spinal cord (Smith et al., 2009). To avoid any bias in the definition of the ROI, i.e., circularity induced by defining ROIs based on HARDI or MT metrics, ROIs were defined on the mean diffusion-weighted images (for HARDI analysis) and on the T1-weighted image (for MT analysis). Although T1weighted images offered better white/grey matter contrast with higher in-plane resolution than diffusion-weighted images, we chose not to apply T1-based ROIs on diffusion-weighted images, because of patient motion and susceptibility-related distortions in EPI.

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Statistical analysis Statistical analysis was performed using Matlab (The Mathworks, MA, USA) and the Statistical Package for the Social Sciences (SPSS Inc, Chicago, IL, USA). Group differences To detect differences in HARDI metrics and MTR between patients and controls in regions of normal appearing white matter, we ran a 2tailed Student T-test for several MRI metrics: FA, GFA, axial and radial diffusivities, MD, MTR, T1- and T2-weighted signals (normalized by CSF) and cord area. Due to the heterogeneity of lesion location across patients, metrics were averaged between all sub-ROIs (dorsal and ventrolateral). Correlations between MRI measurements and clinical disability scores To establish correlations between MRI metrics and clinical disability scores in patients, we first conducted Pearson's correlation measures between the total MRI metrics in the normal appearing white matter (dorsal and ventrolateral regions) and the global clinical scores (ASIA motor + sensory). Then, tract-specific MRI metrics were tested with regards to the motor or sensory ASIA score. Namely, we hypothesized the severity of the sensory or motor disability to be predicted by more severe white matter pathology in the dorsal and ventrolateral aspect of the cord, respectively. Regression analyses were performed for each MRI metric where column-specific information was available (FA, axial and radial diffusivity, MD, GFA, MTR). A forward stepwise model was used, with the test score as the dependent variable (ASIAm or ASIAs) and MRI parameters and age as independent variables. The probability for a predictor to enter the stepwise model was based on a Fisher test, with a p-value set to 0.05. Relationship between lesion location, MRI metrics and disability We further tested the relationship between the actual location of the lesion in each individual (based on the anatomical T2-weighted image) and the ASIA score and MRI metrics. To test whether patients presenting a more dorsal lesion have lower sensory score coupled with low FA or MTR, we categorized the location of the lesion in each patient as being more dorsal (1) or ventral (0). This binary vector was then fitted to a logistic regression model using both sensory and motor ASIA scores, as well as ventrolateral and dorsal MRI metrics as predictors. Results Group differences Significant differences were detected between SCI patients and controls for metrics measured in the normal-appearing spinal cord

Fig. 2. ROIs were anatomically defined on the mean diffusion-weighted data (for HARDI metrics) and on the T1-weighted image (for MTR). ROIs were selected in the ventro-lateral (red) and dorsal (green) aspect of the spinal cord, to include most descending and ascending tracts, respectively.

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(see Fig. 3). Cord area was 77.5 ± 3.2 mm2 in controls and 68.8 ± 12.1 mm2 in patients. Student's T-test showed differences in FA (p b 0.0001), axial diffusivity (p = 0.0138), radial diffusivity (p = 0.0135), GFA (p b 0.0001), MTR (p b 0.0001) and atrophy index (p = 0.0201). No significant difference was detected in MD (p = 0.41), T1-weighted (p = 0.76) and T2-weighted (p = 0.09) signals.

measured in the dorsal and ventrolateral aspects of the spinal cord. Results show that dorsal measures of FA (p b 0.05), GFA (p b 0.01), radial diffusivity (p b 0.05) and MTR (p b 0.05) predicted sensory disability whereas ventrolateral measures of MTR predicted motor disability (p b 0.05). However, ventrolateral measures of FA, GFA and radial diffusivity explained sensory disability, which suggest a somewhat lower specificity of diffusion measures.

Correlation with clinical scores Fig. 4 shows HARDI, MT and atrophy measures plotted against total ASIA score in SCI patients. Significant correlations were detected for FA (p b 0.01), GFA (p b 0.01), radial diffusivity (p = 0.01), MTR (p = 0.04) and atrophy (p b 0.01). The sign of correlations was consistent with previous hypotheses, i.e., decrease in FA, GFA, MTR and cord area and increase in radial diffusivity. Table 2 shows the full correlation table between motor and sensory scores, atrophy index, age, disease duration and MRI metrics measured in the ventrolateral and dorsal aspects (FA, GFA, Radial Diffusivity and MTR). Stepwise regressions were conducted to evaluate the tract-based specificity to motor or sensory deficits (see Table 3). MRI metrics were

Relationship between lesion location, MRI metrics and disability We performed a logistic regression to test the relationship between the location of the lesion in each individual, the ASIA score and the MRI metrics. As a result, no predictor was significantly correlated with the localization of the lesion (using p-threshold of 0.05). We think this reflects the somewhat difficult task in categorizing patients based on the presentation of their lesions on the T2 image, given the presence of white matter degeneration and possibly local cytotoxic events in the normal appearing white matter.

Fig. 3. Boxplots of MRI metrics averaged in the normal appearing spinal cord white matter in the control and the patient groups. Group differences were assessed using Student Ttest. In each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually.

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Fig. 4. Pearson's correlations between total clinical score and MRI metrics.

Discussion This study shows that HARDI and MT measurements at 3T in the cervical spinal cord of chronic SCI patients detected spinal cord damage in regions where conventional imaging was negative. Moreover, some metrics (FA, GFA, radial diffusivity, MTR and atrophy) were remarkably well correlated with clinical disability. Stepwise linear regressions showed that measures of MTR in the dorsal spinal cord explained sensory disability (ASIA score) whereas measures of MTR in the ventro-lateral spinal cord explained motor disability. However, diffusion metrics were not specific to the sensorimotor scores. To our knowledge, this is the first study that combines DTI/QBI metrics, MTR and atrophy measurements in the injured spinal cord. DTI and MTR findings in the normal-appearing white matter Significant differences were observed between controls and patients for most diffusion metrics (FA, MD, GFA and radial diffusivity), MTR and atrophy. No significant change was detected for the T1- and

T2-weighted signal measured in the same spinal cord mask. This finding validates the first hypothesis, which suggested that diffusion metrics and MTR could detect signal changes in the normal appearing tissue. It also confirms previous studies where DTI (Cohen-Adad et al., 2008a; DeBoy et al., 2007; Kim et al., 2007; Zhang et al., 2009) and MTR (McCreary et al., 2009) showed changes in animal models of spinal cord injury distal to the site of the lesion. These changes may be associated with global demyelination and degeneration in the spinal cord of SCI patients, rostral and caudal to the injury, notably due anterograde and retrograde Wallerian degeneration (Beirowski et al., 2005). Secondary pathological processes including ischemia, inflammation and excitotoxic events may also have occurred (Park et al., 2004; Tator and Fehlings, 1991). We also investigated the influence of including normal appearing white matter both rostrally and caudally to the lesion site, when determining the average MT and diffusion metrics. We did not observe difference between the three conditions (rostrally, caudally, or both). Therefore, we decided to include both the rostral and caudal portion of the cord for two reasons: 1) to increase the statistical power

Table 2 Pearson's correlations table. DD: Disease Duration; λ⊥: Radial diffusivity; vl: ventrolateral, d: dorsal. Levels of significance are indicated as *: P b 0.05; **: P b 0.005.

ASIAm ASIAs Age DD Atrophy FAvl FAd GFAvl GFAd λ⊥vl λ⊥ d MTRvl MTRd

ASIAm

ASIAs

Age

DD

Atrophy

FAvl

FAd

GFAvl

GFAd

λ⊥vl

λ⊥d

MTRvl

MTRd

1 0.65* 0.04 − 0.13 0.69* 0.62* 0.84** 0.71** 0.73** − 0.52 − 0.69* 0.61* 0.45

1 − 0.13 − 0.28 0.70* 0.51 0.66* 0.59* 0.70* − 0.48 − 0.61* 0.56 0.64*

1 0.07 − 0.33 − 0.04 0.26 − 0.20 0.08 − 0.28 − 0.39 − 0.20 0.38

1 − 0.53 − 0.34 − 0.14 − 0.06 0.10 0.59* 0.41 − 0.08 − 0.20

1 0.46 0.50 0.55* 0.46 − 0.55* − 0.58* 0.30 0.18

1 0.75** 0.66* 0.48 − 0.79** − 0.60* 0.87** 0.21

1 0.63* 0.80** − 0.68* − 0.80** 0.58* 0.51

1 0.82** − 0.28 − 0.26 0.67* 0.04

1 − 0.25 − 0.41 0.44 0.30

1 0.87** − 0.61* − 0.58*

1 − 0.37 − 0.76**

1 0.45

1

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Table 3 Results of stepwise linear regressions that assessed the specificity of MRI metrics with motor and sensory disability. Dependent variables were clinical scores (ASIAm or ASIAs). Independent variables were the age and MRI metrics (FA, radial diffusivity, axial diffusivity, MD, GFA, and MTR) measured in the ventrolateral (VL) or dorsal (D) aspect of the spinal cord. Significant predictors are reported along with their p-value. “–” indicates that no variable entered the stepwise regression. Motor disability (ASIAm) was predicted by the ventrolateral MTR and the dorsal FA, GFA and radial diffusivity, whereas sensory disability (ASIAs) was predicted by the dorsal FA, GFA, radial diffusivity and MTR.

FA GFA Axial diffusivity Radial diffusivity MD MTR

ASIAm

ASIAs

D (p = 0.000) D (p = 0.003) – D (p = 0.006) – VL (p = 0.034)

D D – D – D

(p = 0.011) (p = 0.006) (p = 0.02) (p = 0.025)

of the analysis by including more voxels and 2) degeneration of tract were expected both rostrally and caudally to the lesion, as been observed in animal models (Zhang et al., 2009). Specificity to white matter demyelination and degeneration Here we detected significant changes in DTI axial and radial diffusivity between controls and patients. Previous studies conducted in animal models of de/dysmyelination and axonal damage showed that axial and radial diffusivity could respectively predict axonal degeneration and demyelination (Budde et al., 2007, 2008, 2009; Hofling et al., 2009; Mac Donald et al., 2007; Song et al., 2002; Xie et al., 2010; Zhang et al., 2009). Other studies however reported possible dependences between axial and radial diffusivities, thereby limiting the specificity of these measures (Herrera et al., 2008; Sun et al., 2006; Wheeler-Kingshott and Cercignani, 2009). One argument refers to the pathophysiology of axon degeneration, as this process is known to be associated with demyelination in several pathologies such as in MS (Schmierer et al., 2007) or in SCI (Zhang et al., 2009). Another argument is related to the biophysical properties of DW-MRI: diffusion metrics depend on several physical parameters including myelination, axonal density, axonal diameter and orientation of fiber bundles (Beaulieu, 2002; Sen and Basser, 2005; Wheeler-Kingshott and Cercignani, 2009). A study combining DTI with multiecho T2 measurements in MS patients did not find significant correlation between myelin water content and any of the DTI metrics (Kolind et al., 2008). Here we combined DW and MT imaging to gain confidence in the identification of pathological processes affecting the white matter. Significant change in MTR was detected between controls and patients in the spinal cord white matter. Previous studies showed that MTR has greater pathological specificity for changes in myelin content than conventional MRI does. It was notably shown that MTR decreases with acute demyelination (Chen et al., 2007; DeloireGrassin et al., 2000; Inglese et al., 2002; McCreary et al., 2009; Merkler et al., 2005; Schmierer et al., 2004). However, MTR is a semiquantitative measure that not only depends on the size of the macromolecular pool but also on the exchange rate between the bound and mobile proton pools (McCreary et al., 2009). To overcome the multi-parametric dependence of MTR, quantitative MT (qMT) may provide a more direct surrogate of myelin content (Davies et al., 2003; Schmierer et al., 2007). However, estimation of qMT requires assumptions in the number of proton pools in the sample and necessitates multiple MT measurements as well as an independent measurement of T1 (Levesque and Pike, 2009). In the present study we quantified MTR rather than qMT due to constraints in imaging time for SCI patients. One benefit of combining DTI and MTR was that it provided two sets of markers sensitive to white matter pathology based on different biophysical properties, thereby increasing the reliability of diagnosis.

However, as above-mentioned, these markers may still be influenced by multiple biophysical parameters. Complementary techniques may be added to the imaging protocol to further improve the specificity to various sub-types of white matter pathology. Notably, myelin water fraction estimated from the short T2 relaxation of water trapped within myelin sheets is another measure that correlates with myelin content (Kozlowski et al., 2008; Laule et al., 2006). Despite several difficulties related to spinal cord motion and low SNR, this technique has been successfully applied in the human spinal cord at 1.5T (Minty et al., 2009). Correlation with clinical disability For this study we recruited patients with chronic SCI to avoid additional source of signal variation caused by hemorrhage or edema (mean time between lesion and imaging was 20 ± 24 months). Also, clinical parameters are more stable in chronic patients, which is required for establishing accurate correlations with MRI parameters. Most MRI parameters measured in the normal appearing spinal cord correlated with the total clinical disability score (ASIA motor + sensory), which partly validated the second hypothesis. Highest correlations were observed for the FA (p b 0.01), GFA (p b 0.01) and atrophy (p b 0.01). The sign of correlations was consistent with previous studies, i.e., decrease in FA, GFA, MTR and cord area and increase in radial diffusivity. We also tested whether the degeneration of descending pathways detected in the ventrolateral spinal cord would mostly correlate with motor disability score (ASIAm) whereas degeneration of ascending pathways detected in the dorsal spinal cord would mostly correlate with sensory disability score (ASIAs). Results of stepwise regression analysis indeed showed that dorsal measures of FA, GFA, radial diffusivity and MTR explained sensory disability whereas ventrolateral measures of MTR explained motor disability. The hypothesis is therefore only partially confirmed, due to the somewhat lower specificity of ventrolateral HARDI measures in regards to the motor disability, as these measures were rather correlated to sensory deficits. Several arguments could explain these discrepancies. First, although relatively high spatial resolution was used for HARDI and MTR measurements, partial volume effect was still present, adding a systematic error to the correlations. Second, ASIA motor and sensory scores, although providing quantitative measures of clinical disability, only describe global trends in each individual — partly influenced by the rater and patient condition, and are therefore subject to some inaccuracies. The use of sensorimotor evoked potential may provide higher correlations (Lindberg et al., 2007). Moreover, ASIA scores are not specific to the spinal cord and could also be affected by lesions in the brain, which sometimes occur in traumatic injuries. Third, the simplistic sub-division of the spinal cord to distinguish ascending and descending pathways was somewhat inaccurate. For instance, we chose to consider lateral voxels for correlation with motor disability. However, tracts located laterally also transmit ascending information (e.g., spinocerebellar and spinothalamic tracts). Although we managed to minimize the overlap by excluding most lateral voxels (where some ascending fibers are located), it is possible that changes in diffusion and MT metrics observed in patients experiencing pain might have been partly caused by modifications in the spinothalamic tracts. Future developments seeking to improve the spatial resolution will help in that matter. High angular resolution diffusion imaging One original aspect of this study was the use of another reconstruction method than DTI to estimate anisotropy measures in the spinal cord white matter and to assess its usefulness in pathological condition. We reconstructed the Q-Ball diffusion ODF from single-shell measurements using spherical harmonic

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decomposition (Anderson, 2005; Descoteaux, 2007; Hess et al., 2006) and estimated the generalized fractional anisotropy (GFA), which is a measure similar to FA (Tuch, 2004). Results showed that like the FA, the GFA measured in the normal appearing white matter showed significant differences between controls and patients (p b 0.0001). More interestingly, it showed stronger correlations with clinical score (total ASIA score). Similar results were observed in animal models of SCI, where the GFA along with DTI metrics showed significant changes distal to the site of the lesion (Cohen-Adad et al., 2009a,b,c). However, drawing definite conclusions about the usefulness of the GFA for detecting white matter abnormalities is still limited by the interpretation of the biophysical processes underlying the diffusion ODF, since contrarily to the tensor model there is no direct relationship between the diffusivity and the value on the diffusion ODF. The use of nonGaussian metrics such as the diffusion Kurtosis (Hui et al., 2008; Jensen et al., 2005) or metrics derived from multi-tensor modeling (Hosey et al., 2005; Kreher et al., 2005) or higher order tensor (Barmpoutis et al., 2009; Ghosh et al., 2008) may be useful in the course of finding biomarkers for characterizing white matter pathways in the central nervous system. Notably, metrics based on the directionality of diffusion may provide new phenotypes of pathological processes in the white matter and could potentially help following the course of anatomical reorganization in the central nervous system, as suggested in recent work (Barmpoutis et al., 2009; Cohen-Adad et al., 2009c). Limitations and future developments Spatial resolution Although relatively high spatial resolution was employed here (1 × 1 mm2 in-plane for HARDI and 0.9 × 0.9 mm2 for MT imaging), partial volume effect was still present between the white and the grey matter and the cerebrospinal fluid. We do not think this was critical for addressing the first hypothesis of this study, which compared HARDI metrics and MTR between two populations having the same spatial resolution. However, partial volume effect was more critical for the delineation of white matter sub-quadrants to isolate ascending and descending tracts, which may partly explain the inconsistencies observed in the regression analysis between HARDI metrics and the ASIAm score. The slightly higher spatial resolution of MT measurements and the absence of susceptibility-related distortions may partly explain that both sensory and motor ASIA scores were respectively predicted by the dorsal and ventrolateral MTR. The significant atrophy measured in the spinal cord of SCI patients might have also biased the measurement of MRI metrics in the spinal cord, due to an increased partial volume effect. This limitation highlights the need for higher spatial resolution. The ongoing research that could help increasing the spatial resolution includes the development of new receive coils for higher sensitivity (Cohen-Adad et al., 2010b), pulse sequences for minimizing distortions in DW-MRI (Saritas et al., 2008; Wilm et al., 2008) and ultra-high field MRI. Thoraco-lumbar imaging For this study we recruited patients with cervical SCI. The goal was to demonstrate the proof-of-principle for in vivo detection of demyelination/degeneration in the normal appearing spinal cord white matter using HARDI and MT measurements. Although theoretically feasible in the thoraco-lumbar region, the proposed method necessitates further optimization for best results. Challenges in imaging the thoraco-lumbar spinal cord include: (i) the limited access for surrounding the imaged region with small coil elements as could be done in the neck. Typically, an arrangement of posterior surface coils would be used for the thoraco-lumbar spinal cord, limiting parallel imaging capabilities when phase encoding is set to antero-posterior direction (as in most studies); (ii) respiratoryinduced fluctuations create ghosting patterns lowering the quality

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of images in this region. Moreover, the movements of the chest induce local fluctuations in the B0 field creating phase errors in EPI-based diffusion images (Van de Moortele et al., 2002; van Gelderen et al., 2007); (iii) the natural curvature of the cord at the thoraco-lumbar level renders more difficult imaging planes perpendicular to the cord, therefore the use of a sequence allowing to tilt slices within one slab would be valuable to keep each slice perpendicular to the cord (Xu et al., 2010). Conclusion High angular resolution diffusion-weighted imaging (HARDI), magnetization transfer ratio and cord atrophy are sensitive markers of spinal cord pathology and clinical disability in patients with spinal cord injury. Moreover, tract-specific information could be derived to predict sensory and motor disability in the normal appearing white matter. Multi-parametric MRI provides sensitive markers of demyelination and degeneration, opening the door to longitudinal studies for testing therapeutic strategies in spinal cord injury. Acknowledgments We thank Dr. Maxime Descoteaux for providing the code to compute the Q-Ball ODF and Dr. Henrik Lundell for providing the code to measure the cord area. We also thank Drs. Stéphane Ouary, Olivier Freund, Kevin Nigaud, Alexandre Vignaud and Eric Bardinet for helping with the project. We thank Drs. Thierry Albert, Bertrand Baussart, Caroline Hugeron, Hugues Pascal Moussellard, Frédéric Petit and MarcAntoine Rousseau for helping with patient recruitment and we thank all subjects. We also thank the reviewers for their helpful comments that greatly improved the quality of the manuscript. This study was supported by the Association Française contre les Myopathies (AFM) and by the Institut pour la Recherche sur la Moelle épinière et l'Encéphale (IRME). S.R. received a special fellowship from the IRME to participate in these studies during a sabbatical leave in Paris. References Agosta, F., Absinta, M., Sormani, M.P., Ghezzi, A., Bertolotto, A., Montanari, E., Comi, G., Filippi, M., 2007. In vivo assessment of cervical cord damage in MS patients: a longitudinal diffusion tensor MRI study. Brain 130, 2211–2219. Anderson, A.W., 2005. Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1194–1206. ASIA, 2002. American Spinal Injury Association. International standards for neurological classification of spinal cord injury. revised 2000, reprinted 2002 ASIA, Chicago. Bareyre, F.M., Kerschensteiner, M., Raineteau, O., Mettenleiter, T.C., Weinmann, O., Schwab, M.E., 2004. The injured spinal cord spontaneously forms a new intraspinal circuit in adult rats. Nat. Neurosci. 7, 269–277. Barmpoutis, A., Hwang, M.S., Howland, D., Forder, J.R., Vemuri, B.C., 2009. Regularized positive-definite fourth order tensor field estimation from DW-MRI. Neuroimage 45, S153–S162. Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. 103, 247–254. Beaulieu, C., 2002. The basis of anisotropic water diffusion in the nervous system — a technical review. NMR Biomed. 15, 435–455. Beirowski, B., Adalbert, R., Wagner, D., Grumme, D.S., Addicks, K., Ribchester, R.R., Coleman, M.P., 2005. The progressive nature of Wallerian degeneration in wildtype and slow Wallerian degeneration (WldS) nerves. BMC Neurosci. 6, 6. Budde, M.D., Kim, J.H., Liang, H.F., Schmidt, R.E., Russell, J.H., Cross, A.H., Song, S.K., 2007. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn. Reson. Med. 57, 688–695. 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. Budde, M.D., Xie, M., Cross, A.H., Song, S.-K., 2009. Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J. Neurosci. 29, 2805–2813. Callot, V., Duhamel, G., Vignaud, A., Cozzone, P., 2009. Toward a better description of the gray matter spinal cord by using highly resolved diffusion-weighted and morphologic T2*-weighted MRI. Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Honolulu, p. 1302. Chen, J.T., Kuhlmann, T., Jansen, G.H., Collins, D.L., Atkins, H.L., Freedman, M.S., O'Connor, P.W., Arnold, D.L., 2007. Voxel-based analysis of the evolution of magnetization transfer ratio to quantify remyelination and demyelination with

1032

J. Cohen-Adad et al. / NeuroImage 55 (2011) 1024–1033

histopathological validation in a multiple sclerosis lesion. Neuroimage 36, 1152–1158. Ciccarelli, O., Wheeler-Kingshott, C.A., McLean, M.A., Cercignani, M., Wimpey, K., Miller, D.H., Thompson, A.J., 2007. Spinal cord spectroscopy and diffusion-based tractography to assess acute disability in multiple sclerosis. Brain 130, 2220–2231. Cohen-Adad, J., Benali, H., Hoge, R.D., Rossignol, S., 2008a. In vivo DTI of the healthy and injured cat spinal cord at high spatial and angular resolution. Neuroimage 40, 685–697. Cohen-Adad, J., Descoteaux, M., Rossignol, S., Hoge, R.D., Deriche, R., Benali, H., 2008b. Detection of multiple pathways in the spinal cord using q-ball imaging. Neuroimage 42, 739–749. Cohen-Adad, J., Ghosh, A., Leblond, H., Descoteaux, M., Deriche, R., Benali, H., Rossignol, S., 2009a. Comparison of DTI and Q-Ball imaging metrics in a cat model of spinal cord injury. 14th Annual Meeting of the Organization for Human Brain Mapping (HBM), San Francisco, USA. p. 51. Cohen-Adad, J., Leblond, H., Delivet-Mongrain, H., Ghosh, A., Descoteaux, M., Deriche, R., Benali, H., Rossignol, S., 2009b. High angular resolution diffusion MRI of spinal cord injured cats. 39th Annual meeting of the Society for Neuroscience, Chicago, USA. p. 858.7. Cohen-Adad, J., Leblond, H., Ghosh, A., Descoteaux, M., Deriche, R., Benali, H., Rossignol, S., 2009c. Evaluation of q-ball metrics for assessing the integrity of the injured spinal cord. Proceedings of the 17th Annual Meeting of ISMRM, Honolulu, USA, vol. 3582. Cohen-Adad, J., Gauthier, C.J., Brooks, J.C.W., Slessarev, M., Han, J., Fisher, J.A., Rossignol, S., Hoge, R.D., 2010a. BOLD signal responses to controlled hypercapnia in human spinal cord. Neuroimage 50, 1074–1084. Cohen-Adad, J., Mareyam, A., Polimeni, J.R., Wald, L.L., 2010b. Improving diffusion and functional MRI of the brain and spinal cord using a new 32ch coil. Proceedings of the 16th Annual Meeting of OHBM, Barcelona, Spain. p. 3694. Cooke, F.J., Blamire, A.M., Manners, D.N., Styles, P., Rajagopalan, B., 2004. Quantitative proton magnetic resonance spectroscopy of the cervical spinal cord. Magn. Reson. Med. 51, 1122–1128. Davies, G.R., Ramani, A., Dalton, C.M., Tozer, D.J., Wheeler-Kingshott, C.A., Barker, G.J., Thompson, A.J., Miller, D.H., Tofts, P.S., 2003. Preliminary magnetic resonance study of the macromolecular proton fraction in white matter: a potential marker of myelin? Mult. Scler. 9, 246–249. DeBoy, C.A., Zhang, J., Dike, S., Shats, I., Jones, M., Reich, D.S., Mori, S., Nguyen, T., Rothstein, B., Miller, R.H., Griffin, J.T., Kerr, D.A., Calabresi, P.A., 2007. High resolution diffusion tensor imaging of axonal damage in focal inflammatory and demyelinating lesions in rat spinal cord. Brain 130, 2199–2210. Deloire-Grassin, M.S., Brochet, B., Quesson, B., Delalande, C., Dousset, V., Canioni, P., Petry, K.G., 2000. In vivo evaluation of remyelination in rat brain by magnetization transfer imaging. J. Neurol. Sci. 178, 10–16. Deo, A.A., Grill, R.J., Hasan, K.M., Narayana, P.A., 2006. In vivo serial diffusion tensor imaging of experimental spinal cord injury. J. Neurosci. Res. 83, 801–810. Descoteaux, M., 2007. A Linear and Regularized ODF estimation algorithm to recover multiple fibers in Q-Ball Imaging. INRIA Research Report, pp. 1–48. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., 2006. Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn. Reson. Med. 56, 395–410. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., 2007. Regularized, fast, and robust analytical Q-ball imaging. Magn. Reson. Med. 58, 497–510. Ducreux, D., Fillard, P., Facon, D., Ozanne, A., Lepeintre, J.F., Renoux, J., Tadie, M., Lasjaunias, P., 2007. Diffusion tensor magnetic resonance imaging and fiber tracking in spinal cord lesions: current and future indications. Neuroimaging Clin. N. Am. 17, 137–147. Ellingson, B.M., Ulmer, J.L., Schmit, B.D., 2007. Gray and white matter delineation in the human spinal cord using diffusion tensor imaging and fuzzy logic. Acad. Radiol. 14, 847–858. Ellingson, B.M., Ulmer, J.L., Kurpad, S.N., Schmit, B.D., 2008. Diffusion tensor MR imaging in chronic spinal cord injury. AJNR Am. J. Neuroradiol. 29, 1976–1982. 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. Ghosh, A., Descoteaux, M., Deriche, R., 2008. Riemannian framework for estimating symmetric positive definite 4th order diffusion tensors. Med. Image Comput. Comput. Assist. Interv. 11, 858–865. Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A., 2002. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47, 1202–1210. Herrera, J.J., Chacko, T., Narayana, P.A., 2008. Histological correlation of diffusion tensor imaging metrics in experimental spinal cord injury. J. Neurosci. Res. 86, 443–447. Hess, C.P., Mukherjee, P., Han, E.T., Xu, D., Vigneron, D.B., 2006. Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn. Reson. Med. 56, 104–117. Hofling, A.A., Kim, J.H., Fantz, C.R., Sands, M.S., Song, S.-K., 2009. Diffusion tensor imaging detects axonal injury and demyelination in the spinal cord and cranial nerves of a murine model of globoid cell leukodystrophy. NMR Biomed. 22, 1100–1106. Hosey, T., Williams, G., Ansorge, R., 2005. Inference of multiple fiber orientations in high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1480–1489. Hui, E.S., Cheung, M.M., Qi, L., Wu, E.X., 2008. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 42, 122–134.

Inglese, M., Salvi, F., Iannucci, G., Mancardi, G.L., Mascalchi, M., Filippi, M., 2002. Magnetization transfer and diffusion tensor MR imaging of acute disseminated encephalomyelitis. AJNR Am. J. Neuroradiol. 23, 267–272. Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841. Jensen, J.H., Helpern, J.A., Ramani, A., Lu, H., Kaczynski, K., 2005. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53, 1432–1440. Kim, J.H., Loy, D.N., Liang, H.F., Trinkaus, K., Schmidt, R.E., Song, S.K., 2007. Noninvasive diffusion tensor imaging of evolving white matter pathology in a mouse model of acute spinal cord injury. Magn. Reson. Med. 58, 253–260. Kolind, S.H., Laule, C., Vavasour, I.M., Li, D.K.B., Traboulsee, A.L., Mädler, B., Moore, G.R. W., MacKay, A.L., 2008. Complementary information from multi-exponential T2 relaxation and diffusion tensor imaging reveals differences between multiple sclerosis lesions. Neuroimage 40, 77–85. Kozlowski, P., Liu, J., Yung, A., Tetzlaff, W., 2008. High-resolution myelin water measurements in rat spinal cord. Magn. Reson. Med. 59, 796–802. Kreher, B.W., Schneider, J.F., Mader, I., Martin, E., Hennig, J., Il'yasov, K.A., 2005. Multitensor approach for analysis and tracking of complex fiber configurations. Magn. Reson. Med. 54, 1216–1225. Kucharczyk, W., Macdonald, P.M., Stanisz, G.J., Henkelman, R.M., 1994. Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH. Radiology 192, 521–529. Lammertse, D., Dungan, D., Dreisbach, J., Falci, S., Flanders, A., Marino, R., Schwartz, E., 2007. Neuroimaging in traumatic spinal cord injury: an evidence-based review for clinical practice and research. J. Spinal Cord Med. 30, 205–214. Laule, C., Leung, E., Lis, D.K., Traboulsee, A.L., Paty, D.W., MacKay, A.L., Moore, G.R., 2006. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult. Scler. 12, 747–753. Levesque, I.R., Pike, G.B., 2009. Characterizing healthy and diseased white matter using quantitative magnetization transfer and multicomponent T(2) relaxometry: A unified view via a four-pool model. Magn. Reson. Med. 62, 1487–1496. Lindberg, P.G., Skejo, P.H., Rounis, E., Nagy, Z., Schmitz, C., Wernegren, H., Bring, A., Engardt, M., Forssberg, H., Borg, J., 2007. Wallerian degeneration of the corticofugal tracts in chronic stroke: a pilot study relating diffusion tensor imaging, transcranial magnetic stimulation, and hand function. Neurorehabil. Neural Repair 21, 551–560. Losseff, N.A., Wang, L., Lai, H.M., Yoo, D.S., Gawne-Cain, M.L., McDonald, W.I., Miller, D.H., Thompson, A.J., 1996. Progressive cerebral atrophy in multiple sclerosis. A serial MRI study. Brain 119 (Pt 6), 2009–2019. Lundell, H., Dyrby, T., Ptito, M., Nielsen, J.B., 2009. Crossing fibers in lateral white matter of the cervical spinal cord detected with diffusion MRI in monkey postmortem. Proceedings of the 17th Annual Meeting of ISMRM, Honolulu, USA, vol. 1497. Lundell, H., Barthelemy, D., Skimminge, A., Dyrby, T.B., Biering-Sørensen, F., Nielsen, J.B., 2011. Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury. Spinal Cord 49, 70–75. Mac Donald, C.L., Dikranian, K., Bayly, P., Holtzman, D., Brody, D., 2007. Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J. Neurosci. 27, 11869–11876. Maier, I.C., Schwab, M.E., 2006. Sprouting, regeneration and circuit formation in the injured spinal cord: factors and activity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 1611–1634. McCreary, C.R., Bjarnason, T.A., Skihar, V., Mitchell, J.R., Yong, V.W., Dunn, J.F., 2009. Multiexponential T2 and magnetization transfer MRI of demyelination and remyelination in murine spinal cord. Neuroimage 45, 1173–1182. Merkler, D., Boretius, S., Stadelmann, C., Ernsting, T., Michaelis, T., Frahm, J., Brück, W., 2005. Multicontrast MRI of remyelination in the central nervous system. NMR Biomed. 18, 395–403. Minty, E.P., Bjarnason, T.A., Laule, C., Mackay, A.L., 2009. Myelin water measurement in the spinal cord. Magn. Reson. Med. 61, 883–892. Mottershead, J.P., Schmierer, K., Clemence, M., Thornton, J.S., Scaravilli, F., Barker, G.J., Tofts, P.S., Newcombe, J., Cuzner, M.L., Ordidge, R.J., McDonald, W.I., Miller, D.H., 2003. High field MRI correlates of myelin content and axonal density in multiple sclerosis — a post-mortem study of the spinal cord. J. Neurol. 250, 1293–1301. Nevo, U., Hauben, E., Yoles, E., Agranov, E., Akselrod, S., Schwartz, M., Neeman, M., 2001. Diffusion anisotropy MRI for quantitative assessment of recovery in injured rat spinal cord. Magn. Reson. Med. 45, 1–9. Ohgiya, Y., Oka, M., Hiwatashi, A., Liu, X., Kakimoto, N., Westesson, P.L., Ekholm, S.E., 2007. Diffusion tensor MR imaging of the cervical spinal cord in patients with multiple sclerosis. Eur. Radiol. 17, 2499–2504. Onu, M., Gervai, P., Cohen-Adad, J., Lawrence, J., Kornelsen, J., Tomanek, B., SbotoFrankenstein, U.N., 2010. Human cervical spinal cord funiculi: investigation with magnetic resonance diffusion tensor imaging. J. Magn. Reson. Imaging 31, 829–837. Park, E., Velumian, A.A., Fehlings, M.G., 2004. The role of excitotoxicity in secondary mechanisms of spinal cord injury: a review with an emphasis on the implications for white matter degeneration. J. Neurotrauma 21, 754–774. Pike, G.B., De Stefano, N., Narayanan, S., Worsley, K.J., Pelletier, D., Francis, G.S., Antel, J.P., Arnold, D.L., 2000. Multiple sclerosis: magnetization transfer MR imaging of white matter before lesion appearance on T2-weighted images. Radiology 215, 824–830. Plank, C., Koller, A., Mueller-Mang, C., Bammer, R., Thurnher, M.M., 2007. Diffusionweighted MR imaging (DWI) in the evaluation of epidural spinal lesions. Neuroradiology 49, 977–985. Reich, D.S., Smith, S.A., Zackowski, K.M., Gordon-Lipkin, E.M., Jones, C.K., Farrell, J.A., Mori, S., van Zijl, P.C., Calabresi, P.A., 2007. Multiparametric magnetic resonance

J. Cohen-Adad et al. / NeuroImage 55 (2011) 1024–1033 imaging analysis of the corticospinal tract in multiple sclerosis. Neuroimage 38, 271–279. Ries, M., Jones, R.A., Dousset, V., Moonen, C.T., 2000. Diffusion tensor MRI of the spinal cord. Magn. Reson. Med. 44, 884–892. Rossignol, S., Schwab, M., Schwartz, M., Fehlings, M.G., 2007. Spinal cord injury: time to move? J. Neurosci. 27, 11782–11792. Saritas, E.U., Cunningham, C.H., Lee, J.H., Han, E.T., Nishimura, D.G., 2008. DWI of the spinal cord with reduced FOV single-shot EPI. Magn. Reson. Med. 60, 468–473. Schmierer, K., Scaravilli, F., Altmann, D.R., Barker, G.J., Miller, D.H., 2004. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415. Schmierer, K., Tozer, D.J., Scaravilli, F., Altmann, D.R., Barker, G.J., Tofts, P.S., Miller, D.H., 2007. Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J. Magn. Reson. Imaging 26, 41–51. Schwartz, E.D., Duda, J., Shumsky, J.S., Cooper, E.T., Gee, J., 2005. Spinal cord diffusion tensor imaging and fiber tracking can identify white matter tract disruption and glial scar orientation following lateral funiculotomy. J. Neurotrauma 22, 1388–1398. Sen, P.N., Basser, P.J., 2005. A model for diffusion in white matter in the brain. Biophys. J. 89, 2927–2938. Shen, H., Tang, Y., Huang, L., Yang, R., Wu, Y., Wang, P., Shi, Y., He, X., Liu, H., Ye, J., 2007. Applications of diffusion-weighted MRI in thoracic spinal cord injury without radiographic abnormality. Int. Orthop. 31, 375–383. 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.A., Jones, C.K., Gifford, A., Belegu, V., Chodkowski, B., Farrell, J.A.D., Landman, B.A., Reich, D.S., Calabresi, P.A., McDonald, J.W., Van Zijl, P.C.M., 2010. Reproducibility of tract-specific magnetization transfer and diffusion tensor imaging in the cervical spinal cord at 3 tesla. NMR Biomed. 23, 207–217. Song, S.K., Sun, S.W., Ramsbottom, M.J., Chang, C., Russell, J., Cross, A.H., 2002. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 17, 1429–1436. Stejskal, E.O., Tanner, J.E., 1965. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42, 288–292. Summers, P., Staempfli, P., Jaermann, T., Kwiecinski, S., Kollias, S., 2006. A preliminary study of the effects of trigger timing on diffusion tensor imaging of the human spinal cord. AJNR Am. J. Neuroradiol. 27, 1952–1961. Sun, S.-W., Liang, H.-F., Trinkaus, K., Cross, A.H., Armstrong, R.C., Song, S.-K., 2006. Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn. Reson. Med. 55, 302–308.

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Tator, C.H., Fehlings, M.G., 1991. Review of the secondary injury theory of acute spinal cord trauma with emphasis on vascular mechanisms. J. Neurosurg. 75, 15–26. Thurnher, M.M., Bammer, R., 2006. Diffusion-weighted magnetic resonance imaging of the spine and spinal cord. Semin. Roentgenol. 41, 294–311. Tuch, D.S., 2004. Q-ball imaging. Magn. Reson. Med. 52, 1358–1372. Valsasina, P., Rocca, M.A., Agosta, F., Benedetti, B., Horsfield, M.A., Gallo, A., Rovaris, M., Comi, G., Filippi, M., 2005. Mean diffusivity and fractional anisotropy histogram analysis of the cervical cord in MS patients. Neuroimage 26, 822–828. Van de Moortele, P.F., Pfeuffer, J., Glover, G.H., Ugurbil, K., Hu, X., 2002. Respirationinduced B0 fluctuations and their spatial distribution in the human brain at 7 Tesla. Magn. Reson. Med. 47, 888–895. van Gelderen, P., de Zwart, J.A., Starewicz, P., Hinks, R.S., Duyn, J.H., 2007. Real-time shimming to compensate for respiration-induced B0 fluctuations. Magn. Reson. Med. 57, 362–368. Van Hecke, W., Leemans, A., Sijbers, J., Vandervliet, E., Van Goethem, J., Parizel, P.M., 2008. A tracking-based diffusion tensor imaging segmentation method for the detection of diffusion-related changes of the cervical spinal cord with aging. J. Magn. Reson. Imaging 27, 978–991. Vargas, M.I., Delavelle, J., Jlassi, H., Rilliet, B., Viallon, M., Becker, C.D., Lövblad, K.-O., 2008. Clinical applications of diffusion tensor tractography of the spinal cord. Neuroradiology 50, 25–29. Wheeler-Kingshott, C., Cercignani, M., 2009. About “axial” and “radial” diffusivities. Magn. Reson. Med. 61, 1255–1260. Wilm, B.J., Gamper, U., Henning, A., Pruessmann, K.P., Kollias, S.S., Boesiger, P., 2009. Diffusion-weighted imaging of the entire spinal cord. NMR Biomed. 22, 174–181. Xie, M., Tobin, J.E., Budde, M.D., Chen, C.-I., Trinkaus, K., Cross, A.H., Mcdaniel, D.P., Song, S.-K., Armstrong, R.C., 2010. Rostrocaudal analysis of corpus callosum demyelination and axon damage across disease stages refines diffusion tensor imaging correlations with pathological features. J. Neuropathol. Exp. Neurol. 69, 704–716. Xu, J., Klawiter, E., Shimony, J.S., Snyder, A.Z., Naismith, R., Priatna, A., Benzinger, T., Cross, A., Song, S.-K., 2010. Toward reproducible tract-specific in vivo diffusion quantification in human cervical spinal cord. Proceedings of the 18th Annual Meeting of ISMRM, Stockholm, Sweden, vol. 2458. Zackowski, K.M., Smith, S.A., Reich, D.S., Gordon-Lipkin, E., Chodkowski, B.A., Sambandan, D.R., Shteyman, M., Bastian, A.J., van Zijl, P.C., Calabresi, P.A., 2009. Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord. Brain 132, 1200–1209. Zhang, J., Jones, M., DeBoy, C.A., Reich, D.S., Farrell, J.A., Hoffman, P.N., Griffin, J.W., Sheikh, K.A., Miller, M.I., Mori, S., Calabresi, P.A., 2009. Diffusion tensor magnetic resonance imaging of Wallerian degeneration in rat spinal cord after dorsal root axotomy. J. Neurosci. 29, 3160–3171.