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April 07, 2015; 84 (14) Article

Differential patterns of spinal cord and brain atrophy in NMO and MS

Yaou Liu, Jinhui Wang, Marita Daams, Florian Weiler, Horst K. Hahn, Yunyun Duan, Jing Huang, Zhuoqiong Ren, Jing Ye, Huiqing Dong, Hugo Vrenken, Mike P. Wattjes, Fu-Dong Shi, Kuncheng Li, Frederik Barkhof
First published March 11, 2015, DOI: https://doi.org/10.1212/WNL.0000000000001441
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Abstract

Objective: To investigate spinal cord and brain atrophy in neuromyelitis optica (NMO), and its relationship with other MRI measurements and clinical disability, compared with patients with multiple sclerosis (MS) and healthy controls (HC).

Methods: We recruited 35 patients with NMO, 35 patients with MS, and 35 HC, who underwent both spinal cord and brain MRI. Mean upper cervical cord area (MUCCA), brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and spinal cord and brain lesion loads were measured and compared among groups. Multivariate associations between MUCCA and brain volume measurement and clinical variables were assessed by partial correlations and multiple linear regression.

Results: Patients with NMO showed smaller MUCCA than HC (p = 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p = 0.07), with no significant difference between the patient groups. Patients with NMO showed lower BPF than HC, and patients with MS had lower BPF and GMF than patients with NMO. In NMO, MUCCA was correlated with Expanded Disability Status Scale score (EDSS), number of relapses, and total spinal cord lesion length, while in MS, MUCCA was correlated with WMF and EDSS. MUCCA was the only independent variable for predicting clinical disability measured by EDSS in NMO (R2 = 0.55, p < 0.001) and MS (R2 = 0.17, p = 0.013).

Conclusion: NMO showed predominately spinal cord atrophy with mild brain atrophy, while MS demonstrated more brain atrophy, especially in the gray matter. MUCCA is the main MRI-derived parameter for explaining clinical disability in NMO and MS, and may serve as a potential biomarker for further clinical trials, especially in NMO.

GLOSSARY

AQP4=
aquaporin-4;
ARR=
annualized relapse rate;
BPF=
brain parenchymal fraction;
EDSS=
Expanded Disability Status Scale;
FA=
flip angle;
FLAIR=
fluid-attenuated inversion recovery;
GM=
gray matter;
GMF=
gray matter fraction;
HC=
healthy controls;
ICC=
intraclass correlation coefficient;
MPRAGE=
magnetization-prepared rapidly acquired gradient echo;
MS=
multiple sclerosis;
MUCCA=
mean upper cervical cord area;
NMO=
neuromyelitis optica;
RRMS=
relapsing-remitting multiple sclerosis;
T1BH=
T1 black hole;
T2LV=
T2 lesion volumes;
TIV=
total intracranial volume;
TE=
echo time;
TR=
repetition time;
WM=
white matter;
WMF=
white matter fraction

Distinguishing neuromyelitis optica (NMO) from multiple sclerosis (MS) is essential clinically, as the prognosis and treatment of these 2 diseases differ.1,–,6 Several treatment trials for NMO have been conducted recently with outcomes based on clinical information such as Expanded Disability Status Scale (EDSS) or relapse rates.7,8

MRI offers objective quantitative measures such as T2 lesion volumes (T2LV) and is therefore widely used in MS clinical trials.9 However, as most patients with NMO have no brain lesions especially in the early stage of the disease,1 using standard MRI outcome parameters for MS may not be appropriate for clinical trials in NMO. It is essential to assess the value of the currently available MRI parameters and develop objective quantitative MRI measures to study prognosis and treatment trials in NMO.

The major MRI feature of NMO is spinal cord pathology such as atrophy and longitudinal extensive lesions,1,3 while brain atrophy has also been reported in NMO.10,–,13 However, the relationship between brain and spinal cord pathology in NMO and which of these measurements serves as the most informative biomarker for clinical disability and for clinical trials is unknown.

This study aims to evaluate spinal cord MRI measurements, including cord lesions and cord atrophy measured by mean upper cervical cord area (MUCCA),14 together with brain structural MRI measurements, in patients with NMO compared with patients with MS and healthy controls (HC), and to relate brain and spinal cord pathology to disability score, disease duration, and relapse rate.

METHODS

Standard protocol approvals, registrations, and patient consents.

The institutional review board of Xuanwu Hospital, Capital Medical University, approved the study, and written informed consent was obtained from each participant prior to participation.

Subjects.

Thirty-five patients with relapsing NMO were recruited from the Department of Neurology, Xuanwu Hospital. The NMO diagnosis was determined according to the revised diagnostic criteria.2 All patients had optic neuritis and myelitis, and met 2 of the 3 supporting criteria, including aquaporin-4 (AQP4) antibody positive, brain MRI negative, or nondiagnostic for MS, and a spinal cord lesion involving at least 3 vertebral segments. For direct comparison with patients with NMO, we selected 35 patients with relapsing-remitting MS (RRMS) fulfilling the McDonald criteria15 by matching them for age, sex, disease duration, and EDSS score to the NMO group from an ongoing MS cohort study. To exclude the confounding effect of edema or medication on the spinal cord measurements, all of the participating patients had been relapse-free and without treatment with disease-modifying medications or steroid within 2 weeks before the MRI scans were obtained. The main demographic and clinical characteristics of the patients were assessed by 2 experienced neurologists (H.D. and J.Y.) (table). All the MRI scans were reviewed by an experienced neuroradiologist (Y.L.). Twenty of the patients with NMO had no lesions identified by cerebral MRI and 15 had nonspecific white matter (WM) lesions (small rounded WM hyperintensities on T2/fluid-attenuated inversion recovery (FLAIR), which did not fulfill the imaging characteristics or criteria for MS). Twenty-six patients with NMO (74.2%) were AQP4 antibody positive using an indirect immunofluorescence method.16 We selected 35 sex- and age-matched HC with no current or previous history of neurologic dysfunction and no MRI-visible abnormalities (table).

View this table:
Table

Demographic and clinical characteristics and brain and spinal cord volume measurement

Data acquisition.

All brain and spinal cord MRI scans were performed on a 3.0T MRI system (Siemens Magnetom Trio Tim System, Munich, Germany).

Brain MRI.

The routine axial slices were positioned to run parallel to the anterior commissure/posterior commissure line, with in-plane resolution 1 mm2, number of sections = 35, section thickness = 4 mm, which were used for lesion detection. Axial T2-weighted turbo spin echo: repetition time (TR) = 5,000 ms, echo time (TE) = 87 ms, echo train length = 15; axial FLAIR: TR = 8,500 ms, TE = 87 ms, inversion time (TI) = 2,500 ms. High-resolution anatomical images were acquired using T1-weighted 3D volumetric magnetization-prepared rapidly acquired gradient echo (MPRAGE) sequence: 176 axial slices; TR = 1,600 ms; TE = 2.13 ms; flip angle (FA) = 9°; TI = 1,000 ms; slice thickness = 1 mm; in-plane resolution 1 mm2, which was used for brain volume and MUCCA measurement.

Spinal cord MRI.

Whole spinal cord imaging included 3-mm-thick sagittal sections and 4-mm-thick axial sections using turbo spin-echo T2-weighted sequences (TR/TE: 3,000/130 ms, in-plane resolution 1 mm2). In addition, to validate the use of brain MPRAGE for MUCCA measurement, high-resolution anatomical spinal cord images were acquired in 30 subjects (10 NMO, 10 MS, and 10 HC) at the same time as the brain MPRAGE scan using T1-weighted 3D MPRAGE sequence: 96 sagittal slices; TR = 2,000 ms; TE = 3.36 ms; FA = 10°; TI = 1,100 ms; slice thickness = 1 mm; in-plane resolution 1 mm2.

Image analysis.

Marking and measurement of hyperintense (higher signal than gray matter [GM]) brain lesions (T2LV) on T2-weighted and FLAIR images and hypointense (lower signal than GM) brain lesions (T1 black hole [T1BH]) on T1-weighted images was performed by an experienced neuroradiologist (Y.L.) using MRIcro software (http://www.mccauslandcenter.sc.edu/mricro/mricro/mricro.html). As the patients with NMO had no lesions or only nonspecific WM abnormalities, T2LV and T1BH were not measured in patients with NMO. Using T2-weighted spinal cord images, the number and total length of spinal cord lesions were marked and counted in all patients. The total lesion length in each patient with NMO and patient with MS was calculated by adding the lengths of all spinal cord lesions on sagittal MRI.

Brain GM, WM, and CSF volumes were calculated using SPM8 on 3D MPRAGE images. In order to avoid classification of lesions as GM, T2 lesion masks were implemented into the MPRAGE images, using individually averaged normal-appearing WM values prior to segmentation.17 Brain parenchymal fraction (BPF), gray matter fraction (GMF), and white matter fraction (WMF) were calculated as follows: BPF = (GMV + WMV)/total intracranial volume (TIV), where TIV = GMV + WMV + CSF volume. GMF = GMV/TIV and WMF = WMV/TIV were calculated separately.

MUCCA was measured by the same neuroradiologist (Y.L.) on each available sagittal 3D T1-weighted brain dataset, on which the upper cervical cord was visible with sufficient image quality. Images were postprocessed using a semiautomated volumetry method over a predefined 30-mm section length (NeuroQLab; Fraunhofer Mevis, Bremen, Germany) (figure 1). To minimize the effect of cervical cord lesions on the segmentation, we measured MUCCA over multiple segments and used a 3-compartment Gaussian mixture model to segment the spinal cord.14 The workflow and the reliability of the software was described in previous publications.14,18,–,20 Intrarater and interrater reproducibility were evaluated in the current study (appendix e-1 on the Neurology® Web site at Neurology.org).

Figure 1
Figure 1 Examples of mean upper cervical cord area measurements in neuromyelitis optica, multiple sclerosis, and healthy controls

(A) Sagittal T1-weighted MRI of brain illustrates region of interest selection (30-mm section length) in the upper cervical cord, starting at upper borders of vertebral level C2. (B–D) Representative sagittal magnetization-prepared rapidly acquired gradient echo of brain and axial reformats with overlaid corresponding segmentation images in a healthy control subject (mean upper cervical cord area [MUCCA]: 0.83 cm2), a patient with multiple sclerosis (MUCCA: 0.76 cm2), and a patient with neuromyelitis optica (MUCCA: 0.69 cm2).

Statistical analysis.

Analyses were performed using SPSS software (version 18; SPSS, Chicago, IL). Kolmogorov-Smirnov tests were performed together with visual inspection of histograms to assess normality of the variables.

For MUCCA reproducibility measures, intrarater and interrater reproducibility were determined with intraclass correlation coefficient (ICC). Furthermore, we also calculated the ICC between MUCCA measurements from brain and spinal cord images, and created Bland-Altman plots.

Comparisons of the demographic data and MRI parameters between patients with NMO, patients with MS, and HC were conducted using analysis of variance and Tukey honestly significant difference test was used for post hoc comparisons. Partial correlations using Pearson correlation coefficient were used to explore the relationship between clinical variables and MRI outcome measures within MS and NMO, corrected for age and sex.

To test whether a linear model was appropriate for our data, we performed a residual analysis, and identified that the residuals of the linear regression models were normally distributed by both graphical approach (Q-Q plots) and mathematical approach (Kolmogorov-Smirnov test p > 0.1). Then, stepwise forward linear regression analyses were performed to statistically predict EDSS using demographic and clinical variables including age, sex, disease duration, number of relapses, annualized relapse rate (ARR), and MRI measurements including MUCCA, total spinal cord lesion length, number of spinal cord lesions, WMF, GMF, and BPF as independent candidate variables in NMO, while T2LV and T1BH were added as independent candidates in MS. In addition, a similar approach was applied to predict MUCCA using all the demographic, clinical, and MRI measurements as independent candidate variables. The independent variables such as number of spinal cord lesions and disease duration were not normally distributed (Kolmogorov-Smirnov test p < 0.05), thus we log-transformed them before adding them into the linear regression model.

RESULTS

Demographics and clinical and MRI characteristics.

As shown in the table, no significant differences were observed in age or sex among the 3 groups (NMO, MS, and HC). Patients with NMO showed a trend towards higher EDSS than patients with MS (p = 0.08), while ARR, number of relapses, and disease duration showed no significant differences between patients with MS and patients with NMO.

In patients with NMO, spinal cord lesions were present in all patients at presentation; in 2 patients, they had resolved on the current scans. The median lesion number in patients with NMO was 1 (range 0–4), and the average total lesion length was 4.4 vertebral segments (SD 2.8, range 0–11). In patients with MS, spinal cord lesions were present in 29 patients (82.9%), with a median of 2 lesions per patient (range 0–9), and an average total lesion length of 0.9 vertebral segments (SD 0.8, range 0–3). The median brain T2LV and T1BH in MS was 472 mm3 and 32 mm3, respectively.

Spinal cord and brain atrophy.

Patients with NMO showed smaller MUCCA than HC (p = 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p = 0.07). No significant difference was identified between patients with MS and patients with NMO (p = 0.561) (table and figure e-1). Excellent agreement (ICC > 0.9) was identified in a subgroup of the subjects for intrarater and interrater reproducibility (appendix e-1) and between the measurement of MUCCA on brain and spinal cord images (ICC 0.947; NMO 0.907; MS 0.935; HC 0.968). The mean difference of MUCCA from brain images compared to spinal cord images was −0.01 (−1.3%) (NMO 0.004 [0.6%]; MS −0.027 [−3.6%]; HC −0.006 [−0.7%]) as depicted in Bland-Altman plots (figure e-2).

For the brain volume measurement, BPF was significantly different among patients with NMO, patients with MS, and HC (p < 0.001). Post hoc analysis showed that patients with MS had significantly lower BPF than patients with NMO (p < 0.001) and HC (p < 0.001), while the patients with NMO had significantly lower BPF than HC (p = 0.002). When considering the WM and GM compartments separately, patients with NMO showed a trend of lower WMF compared to HC (p = 0.07), but with no significant difference in GMF (p = 0.249). Both WMF and GMF were significantly decreased in patients with MS compared to HC (p = 0.008; p < 0.001, respectively). Comparing the patient groups, patients with MS showed a significantly lower GMF than patients with NMO (p < 0.001) (table and figure e-1).

Determinants of spinal cord atrophy.

In patients with NMO, a smaller MUCCA was associated with a longer total lesion length (r = −0.552, p = 0.001) (figure 2A) and a higher number of relapses (r = −0.435, p = 0.011) (figure 2B). In patients with MS, MUCCA was associated with WMF (r = 0.450, p = 0.009) (figure 2C), but not correlated with either spinal cord lesion number (r = −0.126, p = 0.532) or length (r = −0.251, p = 0.207). Using stepwise linear regression, MUCCA was predicted by EDSS (β = −0.518, t[34] = −4.052, p < 0.001), total lesion length (β = −0.274, t[34] = −2.301, p = 0.028), and number of relapses (β = −0.250, t[34] = −2.200, p = 0.035) in NMO, explaining 66% of the variance (F2,32 = 12.924, p < 0.001, R2 = 0.66, R2Adjusted = 0.63). In MS, MUCCA was predicted by EDSS (β = 0.394, t[34] = 2.673, p = 0.012) and WMF (β = −0.350, t[34] = −2.374, p = 0.024), explaining 32% of the variance (F2,32 = 7.651, p = 0.002, R2 = 0.32, R2Adjusted = 0.28).

Figure 2
Figure 2 Determinants of spinal cord atrophy in neuromyelitis optica and multiple sclerosis

Correlation between mean upper cervical cord area (MUCCA) and total lesion length (A) and the number of relapses (B) in neuromyelitis optica (NMO) and white matter fraction (WMF) in multiple sclerosis (MS) (C).

Determinants of clinical disability.

EDSS correlated with MUCCA (r = −0.751, p < 0.001) (figure 3A), number of relapses (r = 0.380, p = 0.029) (figure 3B), and total lesion length (r = 0.449, p = 0.009) (figure 3C) in NMO. In MS, EDSS was only correlated with MUCCA (r = −0.455, p = 0.008) (figure 3D). Using the stepwise linear regression model, the only independent variable surviving in the linear regression model for predicting EDSS was MUCCA, which explained 55% of the variance of EDSS in NMO (β = −0.743, t[34] = −6.384, p < 0.001, R2 = 0.55, R2Adjusted = 0.54), and 17% of the variance of EDSS in MS (β = −0.415, t[34] = −2.622, p = 013, R2 = 0.17, R2Adjusted = 0.15).

Figure 3
Figure 3 Determinants of Expanded Disability Status Scale score in neuromyelitis optica and multiple sclerosis

Correlation between Expanded Disability Status Scale (EDSS) score and mean upper cervical cord area (MUCCA) (A), total lesion length (B), the number of relapses in neuromyelitis optica (NMO) (C), and MUCCA in multiple sclerosis (MS) (D).

DISCUSSION

In this study, we demonstrated different atrophy patterns of brain and spinal cord between NMO and MS, where NMO showed predominant spinal cord atrophy with mild brain atrophy mainly in WM, while MS demonstrated much more brain atrophy especially in GM, but certainly not more spinal cord atrophy than NMO.

Spinal cord atrophy in NMO is a common observation clinically21,22; however, it has not been measured quantitatively before. In this study, we quantitatively measured MUCCA using a robust semiautomatic spinal cord measurement method,14,18 revealing spinal cord atrophy in patients with NMO (7.6% reduction compared with HC) outside relapses, not significantly different from MS (5.1% reduction compared with HC).

Mild brain atrophy especially of the WM was observed in NMO, which is consistent with some previous studies,10,13 supporting WM atrophy as a predominant feature of NMO rather than GM atrophy. This decrease in WM volume in NMO may be explained either by degeneration due to optic neuritis or myelitis or by a direct inflammatory attack of the brain WM.13 Although global GM volume was not significantly decreased in patients with NMO in our study, atrophy of some brain areas (such as visual cortex and precentral gyrus) has been reported by voxel-based morphometry10 or cortical thickness11 analysis, suggesting mild and limited GM atrophy in NMO. The differences between WM and GM atrophy suggest there may be different injury mechanisms for these 2 brain components in NMO such as expression of different isoforms of AQP, or differential blood–brain barrier permeability between GM and WM.23,24

In contrast, patients with MS had a significant reduction of all the measured brain volumes including BPF, WMF, and GMF, but only a trend of MUCCA reduction, compared to HC. This finding is consistent with numerous previous studies revealing brain atrophy in different clinical phenotypes of MS,19,25,26 but less predominant spinal cord atrophy in RRMS.19,25 Interestingly, when comparing patients with NMO and patients with MS directly, patients with MS showed more brain atrophy compared with patients with NMO, especially in the GM. This different atrophy pattern may help understand the different underlying mechanisms and improve differential diagnosis between these 2 diseases.

As we used brain images to measure MUCCA, like in a previous study,19 a validation approach using dedicated spinal cord images was performed in a subset of the subjects. We found a high concordance between spinal cord images and brain images to measure MUCCA, suggesting that one can reliably use the brain MRI to assess the upper spinal cord atrophy instead of acquiring a separate spinal cord scan with more cost and more scanning time, although a study with large sample size in a multicenter setting is warranted.

Spinal cord atrophy in patients with NMO and patients with MS was associated with different MRI abnormalities and clinical variables. In patients with NMO, MUCCA was predicted by total spinal cord lesion length, which implies that local spinal cord damage contributes to cord atrophy. In contrast, MUCCA in MS was predicted by brain WMF, which suggests that secondary changes caused by brain abnormalities such as Wallerian degeneration of long fiber tracts, rather than local damage to these tracts, is the main factor leading to tissue loss in the cord in MS. Our finding in MS is consistent with both pathologic and MRI studies, which showed that individual lesions play a minor role in cord atrophy in MS.27,28 Furthermore, number of relapses is another important factor contributing to spinal cord atrophy in NMO, but not in MS. NMO tends to cause severe symptomatic inflammatory damage and is not clinically associated with a progressive phase.29 It is hence anticipated that spinal cord atrophy was mainly determined by the inflammatory attacks in NMO, but not by either diffuse normal-appearing WM damage or progressive changes outside relapses, which is the potential factor for atrophy in MS.30

In NMO, clinical disability in terms of EDSS was correlated with several clinical and MRI parameters including MUCCA, number of relapses, and total lesion length; however, the most predominant one was MUCCA. In the regression model, MUCCA was the only surviving variable, and explained 55% of the variance of EDSS. In MS, although the correlation between whole brain atrophy and EDSS has been reported by many previous studies,30 we did not identify a similar association, which may be due to the small sample size, short disease duration, and relatively low EDSS of the patients with MS in the current study. MUCCA was the major contributor for EDSS in MS, which is consistent with several previous studies.19,31,–,33 To account for the possible confounding by outliers with a high EDSS score, we also performed a nonparametric correlation coefficient and confirmed the associations (Spearman rho = −0.374, p = 0.027). Considering that the primary outcome measure in clinical trials is usually clinical disability, our findings of an association between spinal cord atrophy and disability in both NMO and MS suggest that this measurement could be included in future clinical trials, especially in NMO in the early phase without brain abnormalities.

However, to further improve the clinical correlation or future clinical trials in NMO and MS, other MRI measurements should be explored and integrated, such as diffusion metrics,34,35 functional MRI36 in both brain and spinal cord, and structural and diffusion measurements of the optic nerve.37

Several limitations apply to this work. First, as optimal parameters for spinal cord volume normalization remain controversial,38,39 we did not normalize the upper cervical cord atrophy measures. Second, this study is a preliminary study with relatively small sample size and multiple comparisons of brain and spinal cord measurement; therefore, further studies with larger sample sizes are warranted to confirm our results. Finally, this is a cross-sectional study using structural MRI measurements. A longitudinal study with multimodality measurements is warranted, including structural, diffusion, and functional MRI to validate the current findings and detect disease-related changes over time.

Different atrophy patterns of spinal cord and brain were identified between NMO and MS, which may suggest different underlying pathophysiologic mechanisms in these 2 diseases. Spinal cord atrophy measured by MUCCA was identified as an essential determinant of clinical disability in both NMO and MS, and could serve as a biomarker for further clinical trials especially in NMO.

AUTHOR CONTRIBUTIONS

Guarantor of integrity of the entire study: Yaou Liu, Kuncheng Li. Study concepts: Yaou Liu, Jinhui Wang, Hugo Vrenken, Mike P. Wattjes, Frederik Barkhof. Study design: Yaou Liu, Jinhui Wang, Fu-Dong Shi. Definition of intellectual content: Yaou Liu, Jinhui Wang. Literature research: Yaou Liu, Yunyun Duan, Jinhui Wang, Marita Daams. Clinical studies: Huiqing Dong, Jing Ye, Yunyun Duan. Experimental studies: Jing Ye, Yaou Liu, Yunyun Duan. Data acquisition: Yunyun Duan, Yaou Liu, Jing Huang, Zhuoqiong Ren. Data analysis: Yaou Liu, Jinhui Wang, Marita Daams, Florian Weiler, Horst K. Hahn. Statistical analysis: Yaou Liu, Jinhui Wang. Manuscript preparation: Yaou Liu, Jinhui Wang. Manuscript editing: Yaou Liu, Jinhui Wang, Marita Daams, Hugo Vrenken, Mike P. Wattjes, Frederik Barkhof. Manuscript review: Fu-Dong Shi, Hugo Vrenken, Mike P. Wattjes, Frederik Barkhof.

STUDY FUNDING

Supported by the ECTRIMS-MAGNMIS Fellowship from ECTRIMS (Y.L.), the National Science Foundation of China (nos. 81101038 and 30930029), the Beijing Natural Science fund (no. 7133244), and the Beijing Nova Programme (xx2013045).

DISCLOSURE

Y. Liu, J. Wang, M. Daams, F. Weiler, H. Hahn, Y. Duan, J. Huang, Z. Ren, J. Ye, and H. Dong report no disclosures relevant to the manuscript. H. Vrenken has received funding from Novartis, Pfizer, and Merck Serono for collaborative research projects. M. Wattjes serves as a consultant for Biogen Idec and Roche. Dr. Mike Wattjes serves as a consultant for Biogen Idec and Roche. F. Shi and K. Li report no disclosures relevant to the manuscript. F. Barkhof serves as a consultant for Bayer-Schering Pharma, Sanofi-Aventis, Biogen Idec, Teva, Merck Serono, Novartis, Roche, Synthon, and Jansen Research. Go to Neurology.org for full disclosures.

Footnotes

  • Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Supplemental data at Neurology.org

  • Received June 26, 2014.
  • Accepted in final form December 16, 2014.

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