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Magnetic resonance imaging correlates of clinical outcomes in early multiple sclerosis

Multiple Sclerosis and Related Disorders

Abstract

Objectives

To study the association between changes in brain magnetic resonance imaging (MRI) and clinical outcomes in early MS.

Methods

MS patients within 12 months of onset were enrolled and followed up to 3 years. Clinical measures included Symbol Digit Modalities Test (SDMT), MS Functional Composite (MSFC) and low contrast letter acuity (LCLA). MRI outcomes included brain volume changes measured by SIENA and SIENAX normalized measurements [brain parenchymal volume (BPV), normal-appearing white and gray matter volume (NAWMV and GMV) and T2 lesion volume (T2LV)]. Mixed model regression measured time trends and associations between imaging and clinical outcome.

Results

Forty-three patients were enrolled within 7.5±4.9 months of onset. Baseline T2 lesion volume predicted subsequent changes in Paced Auditory Serial Addition Test (PASAT) (p=0.004), whereas baseline measures of atrophy including BPV, GMV, and NAWMV predicted longitudinal changes in MSFC (p=0.016,p=0.040,p=0.021, respectively) and Timed-25 Foot Walk (p<0.05). Each 1% decrease in SIENA was associated with 1.14 point decrease in SDMT score (p=0.03). Each 1% decrease in brain volume SIENA was associated with almost 1.5 letters decrease on LCLA (p=0.02).

Conclusion

Measures of lesion volume and overall brain volume were associated with different long-term clinical outcome measures in early MS.

Highlights

 

  • There was a longitudinal association between changes in brain volume (SIENA) and SDMT but not with PASAT.
  • One percent decrease in brain volume was associated with 1.5 letters decrease on low contrast visual acuity
  • Baseline nBPV, nGMV and nNAWMV predicted subsequent changes in disability as measured by MSFC but not EDSS.

Keywords: Multiple sclerosis, Magnetic resonance imaging, Outcomes, Neuroprotection, Brain atrophy, Cognition.

1. Introduction

Most outcome measures utilized to monitor multiple sclerosis (MS) course capture the rate of inflammatory activity (relapse rate, number of new T2 or gadolinium-enhancing lesions), while only a few (brain atrophy, and progression on various disability scores) quantify tissue injury associated with long-term disability.( Maghzi et al., 2013 ) The lack of surrogate outcomes of disability progression has limited the development of neuroprotection proof-of-concept trials.(Maghzi, Minagar, 2013)

While the Expanded Disability Status Scale (EDSS) is the gold standard measure of MS disability accepted by the Food and Drug Administration (FDA), the MS Functional Composite (MSFC) is also widely used to measure disease worsening in trials.(Cutter et al, 1999 and Kurtzke, 1983) Low contrast letter acuity (LCLA) and Symbol Digit Modalities Test (SDMT) have been reported as promising reproducible and sensitive clinical outcomes to capture MS visual and cognitive worsening.(Balcer and Frohman, 2010 and Glanz et al, 2012)

Brain atrophy measures including normalized brain volume and brain parenchymal fraction (BPF), are reproducible and reasonably sensitive to change, correlating modestly with worsening disability as measured by EDSS and MSFC.(Barkhof et al, 2009 and Simon, 2006) Moreover, improved post-processing methods and higher field MRI scans have enabled measurement of gray and white matter volumes (GMV and WMV), which represent promising markers of tissue loss. Identifying sensitive and reliable outcome measures of tissue loss could help reduce length and sample size for future neuroprotection trials.( Anderson et al., 2007 )

We aimed to study the association of imaging measures with clinical outcomes of disability for up to 3 years in patients with early MS enrolled in a phase II trial of riluzole versus placebo as an add-on to weekly interferon beta-1a (IFNB-1a)(clinicaltrials.gov, NCT00501943).

2. Methods

2.1. Study Design

A randomized (1:1), double-blind, placebo-controlled trial assessing the possible neuroprotective effects of riluzole in combination with intramuscular IFNB-1a was conducted at two centers [University of California, San Francisco (UCSF) and Oregon Health and Sciences University (OHSU)]. Patients with a history of a clinically isolated syndrome or relapsing-remitting (RR) MS with an onset within the previous 12 months, with at least two silent T2-bright areas in the deep white matter on a clinical brain or cervical MRI scan and no prior exposure to disease-modifying therapies were offered participation in the study.(McDonald and Compston, 2001 and Polman and Reingold, 2005) Other inclusion criteria included age between 18 and 55 years, and no MS exacerbation or use of glucocorticoids within the four weeks preceding randomization and first MRI scan. Patients were randomized to receive riluzole 50 mg or placebo twice daily. After three months on study drug subjects also initiated IFNB-1a. The core study was 24 months, and the first half of the subjects completing this two year period were offered continuation for an additional 12 months. An overview of the study and schedule of assessments and measurements is shown in Fig. 1 .

gr1

Fig. 1 Overview of the trial design and various time points. Numbers denote number of available measures for each visit. (ND=not done).

2.2. Clinical measurements

EDSS and MSFC [9-hole peg test (9HPT), timed 25-foot walk (T25FW) and Paced Auditory Serial Addition Test (PASAT3′)] were used to quantify neurological changes. Patients had screening and baseline MSFC to allow for practice effects. ( Kurtzke, 1983 ),(Cutter, Baier, 1999) The screening MSFC was not used for the analyses.

SDMT is a measure of cognitive speed and processing and is included as part of the Minimal Assessment of Cognitive Function in MS (MACFIMS) battery.(Benedict et al, 2006, Benedict et al, 2002, Fischer, 2001, and Rao et al, 1991) It is also it is considered as an alternative to PASAT in the MSFC.( Brochet et al., 2008 )

Monocular and binocular LCLA was measured at 2.5% and 1.25% contrast levels.( Balcer et al., 2003 ) We used the mean value of the two eyes in the analysis instead of the binocular value, since it has been shown that there is a binocular effect (binocular summation and inhibition) on LCLA.( Pineles et al., 2011 )

2.3. Brain imaging measurements

Details of the MRI protocol have been described previously. ( Mowry et al., 2009 ) Scans were performed on 3.0 T MRI scanners equipped with an 8-channel phased array coil (General Electric, Milwaukee, WI). A three-dimension volumetric inversion recovery spoiled gradient-echo T1-weighted isotropic sequence (3D-IRSPGR, 1×1×1 mm3, 180 slices) was acquired for all brain volume measurements.

Changes in brain volume between two time-points were obtained from 3D-IRSPGR images using Structural Image Evaluation, using Normalization of Atrophy (SIENA) version 2.6, a fully automated method of longitudinal brain change analysis (fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA).22Output is converted into percentage brain volume change (PBVC) between pairs of scans. Brain volume metrics for each time-point included normalized normal-appearing white matter volume (nNAWMV), normalized grey matter volume (nGMV), and normalized brain parenchymal volume (nBPV), which were obtained utilizing SIENAX.(Smith et al, 2002 and Zhang et al, 2001) Number of T2 and contrast-enhancing lesions were counted using simultaneous visualization of T2 and T1 pre- and post-enhancement images. T1 and T2 lesion masks were derived from manual segmentation of T1-visible white matter lesions on the 3D-IRSPGR using methods described previously.(Mowry, Beheshtian, 2009) T1 lesion masks were incorporated into the SIENAX program to prevent voxel misclassification errors.

2.4. Standard Protocol Approvals, Registrations, and Patient Consents

The study protocol was approved by the UCSF and OHSU Committees on Human Research. All patients provided written informed consent prior to enrollment. This clinical trial is registered with clinicaltrials.gov (NCT00501943).

2.5. Statistical analysis

Descriptive statistics were used for patients′ characteristics. A mixed model regression with the changes over baseline in the imaging variables as the predictor and the clinical variables as the outcome was used. This test allows isolating the association of clinical results with the within-person change in the imaging variable. The mixed model included random intercepts and slopes to accommodate the repeated measures nature of the data and allowed for possible time trends during the course of the study. Spearman correlations were used to assess the cross-sectional association of clinical and imaging outcomes at baseline. All analyses were conducted in SAS Version 9.2 (SAS Institute, Cary, NC). As the analyses of treatment effect showed no effect of riluzole on primary and secondary outcome measures compared with placebo, we combined both treatment groups in the analyses.( Waubant et al., 2013 ) We have not corrected our results for multiple comparisons because of the exploratory nature of our study.

3. Results

Forty three patients were randomized. Baseline characteristics are shown in Table 1 . Thirty eight patients completed the core 24 months of the study of whom 22 completed the 36 month visit. Five patients did not complete the core 24 months (discontinued at months 3, 9, 15, 18, and 21), but contributed to the analyses.

Table 1 Baseline characteristics.

Demographic
  % Female 72%
  Mean age in years±SD 36±9.32
  Mean disease duration in months±SD 7.52±4.93
  % White 98%
  % Hispanic 88.4%
 
Clinical Characteristics
  Median EDSS (range) 2.0 (0.0–5.5)
  Mean PASAT 3’±SD 50.4±10.5
  Mean T25FW (seconds)±SD 4.65±1.23
  Mean 9-HPT (dominant hand; seconds)±SD 19.3±3.27
  Mean MSFC score±SD −0.153±1.14
  Mean LCLA 100% 56.8±4.26
2.5% 36.1±8.83
1.25% 28.8±9.23
  Mean SDMT score±SD 57.9±9.42
 
Brain Imaging
  Mean nBPV (cm3)±SD 1640±118
  Mean nNAWMV (cm3)±SD 730±61.9
  Mean nGMV (cm3)±SD 908±70.7
  % of patients with enhancing lesions 30.2%
  Mean T2 lesion volume (cm3)±SD 5.78±7.52

3.1. Cross-sectional association of baseline clinical and MRI outcomes

Estimated correlations were small and not significant (p>0.05 in all cases) between MRI metrics and clinical outcomes at baseline, except for the correlation of T2LV with visual acuity (p=0.03) and low contrast visual acuities at 2.5% (p=0.001) and 1.25% (p=0.003), and 9HPT (p=0.009) ( Table 2 ).

Table 2 Cross sectional association between MRI metrics and clinical outcomes.

  EDSS PASAT3’ 9-HPT T25FW MSFC SDMT LCLA
100% 2.5% 1.25%
nBPV (SIENAX) 0.03 (−0.30, 0.34) P=0.87 −0.23 (−0.52, 0.12) P=0.20 −0.23 (−0.53, 0.12) P=0.19 0.04 (−0.30, 0.37) P=0.81 −0.01 (−0.35, 0.33) P=0.96 0.04 (−0.30, 0.37) P=0.83 0.08 (−0.25, 0.39) P=0.96 0.11 (−0.21, 0.42) P=0.49 0.07 (−0.26, 0.38) P=0.68
nGMV −0.10 (−0.41, 0.23) P=0.54 −0.16 (−0.47, 0.19) P=0.37 −0.18 (−0.49, 0.16) P=0.30 0.03 (−0.31, 0.36) P=0.88 0.02 (−0.32, 0.36) P=0.90 0.22 (−0.13, 0.51) P=0.21 0.08 (−0.24, 0.39) P=0.62 0.23 (−0.10, 0.51) P=0.16 0.18 (−0.15, 0.47) P=0.29
nNAWMV 0.07 (−0.25, 0.38) P=0.67 −0.29 (−0.58, 0.05) P=0.09 −0.24 (−0.54, 0.10) P=0.16 0.02 (−0.31, 0.35) P=0.91 −0.02 (−0.36, 0.32) P=0.90 −0.09 (−0.41, 0.25) P=0.59 0.14 (−0.19, 0.44) P=0.41 0.07 (−0.26, 0.38) P=0.69 0.01 (−0.31, 0.33) P=0.93
T2LV 0.25 (−0.07, 0.53) P=0.13 0.20 (−0.14, 0.51) P=0.24 0.44 (0.12, 0.68) P=0.009 0.04 (−0.29, 0.37) P=0.80 −0.21 (−0.52, 0.14) P=0.23 −0.14 (−0.46, 0.20) P=0.41 0.35 (0.61,0.04) P=0.03 0.51 (0.71,0.23) P=0.001 0.46 (0.68,0.17) P=0.003

3.2. Baseline MRI outcomes as predictors of longitudinal changes of clinical outcomes

Table 3 shows the association between baseline MRI metrics and longitudinal changes in clinical outcomes. Baseline nBPV, nGMV, and nNAWMV all predicted longitudinal changes in MSFC score. Considering the components of the MSFC score, there were associations with T25FW, but not with PASAT or 9HPT. Baseline T2 lesion volume predicted changes in both PASAT (p=0.004) and to a lesser extent changes in SDMT (p=0.08). No other clinical outcome changes were predicted by baseline MRI metrics.

Table 3 Baseline MRI metrics as predictors of longitudinal changes in clinical outcomes.

  Δ EDSS Δ PASAT3 Δ 9-HPT Δ T25FW Δ MSFC Δ SDMT Δ LCLA
100% 2.5% 1.25%
Baseline BPV (SIENAX)lowast −0.002 (−0.011, 0.008) P=0.73 0.002 (−0.005, 0.010) P=0.90 −0.003 (−0.012, 0.006) P=0.53 0.010 (0.02,0.002) P=0.015 0.005 (0.001, 0.009) P=0.016 0.008 (−0.08, 0.10) P=0.85 −0.000 (−0.05, 0.05) P=1.00 −0.44 (−0.13, 0.04) P=0.31 −0.03 (−0.12, 0.07) P=0.59
Baseline nGMVlowast −0.00 (−0.017, 0.017) P=0.99 0.006 (−0.007, 0.019) P=0.40 −0.004 (−0.020, 0.012) P=0.63 0.014 (0.03, 0.00) P=0.052 0.008 (0.000, 0.016) P=0.040 0.026 (−0.13, 0.19) P=0.74 −0.24 (−0.01, 0.06) P=0.58 −0.11 (−0.25, 0.03) P=0.13 −0.08 (−0.24, 0.08) P=0.33
Baseline nNAWMVlowast −0.004 (−0.023, 0.015) P=0.68 0.003 (−0.012, 0.018) P=0.79 −0.006 (−0.024, 0.012) P=0.51 0.019 (0.035,0.003) P=0.019 0.01 (0.002, 0.018) P=0.021 0.02 (−0.14, 0.18) P=0.80 0.03 (−0.066, 0.13) P=0.51 −0.02 (−0.20, 0.16) P=0.83 0.02 (−0.18, 0.21) P=0.86
Baseline T2LV −0.002 (−0.005, 0.001) P=0.29 0.003 (0.005,0.001) P=0.004 −0.001 (−0.004, 0.002) P=0.48 −0.002 (−0.004, 0.001) P=0.26 −0.000 (−0.002, 0.001) P=0.82 0.024 (0.05, 0.003) P=0.08 −0.00 (−0.013, 0.012) P=0.95 −0.009 (−0.038, 0.020) P=0.53 −0.015 (−0.046, 0.015) P=0.31

3.3. Longitudinal changes of MRI metrics as predictors of clinical worsening

Table 4 shows the association between longitudinal changes in MRI and clinical outcomes. Changes in brain volume (SIENA) were associated with SDMT changes (each 1% decrease in brain volume was associated with 1.14 decrease in SDMT score,p=0.03), but not with PASAT changes (p=0.29). In addition, each 1% decrease in brain volume was associated with decrease in almost 1.5 letters on LCLA chart at both 2.5% and 1.25% saturation (p=0.02). There was a trend for an association between changes in both brain volume (SIENA) (each 10% decrease in brain volume was associated with increase in 1 EDSS point,p=0.08) and nNAWM (each 100 cm3decrease in nNAWM was associated with increase in 0.2 point EDSS,p=0.066), and EDSS. There was a trend for association between changes in T2LV and changes in PASAT (each 10 cm3increase in T2LV was associated with a decrease in one point PASAT;p=0.09), but not changes in SDMT (p=0.79). There were no other associations between brain MRI metrics and available clinical parameters.

Table 4 Longitudinal regressions between MRI metrics and clinical outcomes.

  Δ EDSS Δ PASAT3 Δ 9-HPT Δ T25FW Δ MSFC Δ SDMT Δ LCLA
100% 2.5% 1.25%
Δ Brain volume (SIENA) 0.11 (0.24, 0.01) P=0.08 −0.05 (−0.14, 0.34) P=0.23 −0.081 (−0.20, 0.035) P=0.17 −0.011 (−0.10, 0.08) P=0.81 0.005 (−0.06, 0.07) P=0.88 1.14 (0.12, 2.17) P=0.03 0.13 (−0.44, 0.71) P=0.65 1.43 (0.22, 2.63) P=0.02 1.57 (0.24, 2.89) P=0.02
Δ BPV (SIENAX)lowast −0.082 (−0.20,0.03) P=0.16 0.009 (−0.069, 0.087) P=0.82 0.065 (−0.055, 0.184) P=0.29 0.037 (−0.060,0.133) P=0.45 0.003 (−0.05, 0.06) P=0.91 −0.09 (−1.31, 1.13) P=0.88 0.1 (−0.41, 0.62) P=0.69 0.18 (−0.95,1.31) P=0.76 0.32 (−0.90,1.55) P=0.60
Δ nGMVlowast −0.10 (−0.32,0.11) P=0.34 −0.010 (−0.157, 0.137) P=0.90 0.086 (−0.144, 0.316) P=0.46 0.055 (−0.128,0.238) P=0.55 0.004 (−0.11, 0.11) P=0.95 −0.09 (−2.49, 2.30) P=0.94 0.26 (−0.74, 1.25) P=0.61 0.65 (−1.48, 2.78) P=0.55 1.05 (−1.27, 3.38) P=0.37
ΔnNAWMVlowast 0.2 (0.4,0.01) P=0.066 0.055 (−0.096, 0.207) P=0.47 0.174 (−0.059, 0.408) P=0.14 0.102 (−0.084,0.288) P=0.28 −0.003 (−0.12, 0.11) P=0.96 −0.15 (−2.39, 2.09) P=0.89 0.1 (−0.89, 1.1) P=0.84 0.05 (−2.14, 2.24) P=0.96 0.22 (−2.14, 2.59) P=0.85
Δ T2LV 0.08 (−0.076,0.25) P=0.30 0.1 (0.21, 0.02) P=0.09 0.006 (−0.17, 0.18) P=0.95 −0.036 (−0.18,0.11) P=0.62 −0.016 (−0.01, 0.07) P=0.72 0.24 (−1.6, 2.08) P=0.79 0.38 (−0.35, 1.1) P=0.30 0.21 (−1.38,1.80) P=0.80 0.58 (−1.14,2.30) P=0.50

4. Discussion

We report cross-sectional and longitudinal associations between various imaging and clinical markers in a group of early relapsing-remitting MS patients followed for up to 3 years. We identified a longitudinal association between changes in brain volume (SIENA) and SDMT but not with PASAT. This association was substantial (1% decrease in brain volume was associated with 1.14 point decrease in SDMT score,p=0.03). While further studies are required to determine the clinical relevance of one point change on SDMT, a 4 to 5 points decline is associated with job loss in MS patients.( Morrow et al., 2010 ) Based on our data, a 4 to 5 point SDMT decline would be associated with a 4–5% decline in brain volume. On the other hand there was a trend of an association between changes in T2LV and PASAT but not SDMT. This observation raises the possibility that PASAT is mostly affected by white matter disease burden while SDMT is mostly affected by tissue loss. The lack of association of other MRI outcomes with these two cognitive measures could be due to a lesser specificity of these imaging metrics or to a floor effect of these cognitive measures in patients with very short disease duration. SDMT and PASAT are two widely used tests of cognitive function in MS which assess working memory, attention and processing speed.(Brochet, Deloire, 2008, Drake et al., 2010 ) PASAT is considered stressful and frustrating for many patients making it less likely to be completed during study visits. It relies on the level of mathematical ability of the subjects, while SDMT is more patient friendly and may capture MS cognitive deficit more reliably than PASAT.(Brochet, Deloire, 2008, Drake, Weinstock-Guttman, 2010,Gronwall, 1977, Huijbregts et al, 2004, and Tombaugh, 2006) Moreover, SDMT has a better predictive validity, correlates well with EDSS changes, and baseline BPF correlates with changes of SDMT over 5 years.(Brochet, Deloire, 2008, Drake, Weinstock-Guttman, 2010) Thus, SDMT is considered for replacement of PASAT in the MSFC. (Brochet, Deloire, 2008, Drake, Weinstock-Guttman, 2010) Our data add to the value of SDMT as a key outcome measure of MS progression.

We show that one percent decrease in brain volume is associated with 1.5 letters decrease on LCLA at both 2.5% and 1.25% saturation in early MS stages. A reduction in 7 letters on LCLA has previously been shown to be clinically meaningful;( Balcer et al., 2000 ) hence, based on our data, 4–5% brain volume loss would be expected to result in clinically meaningful visual impairment. LCLA has been proposed as a surrogate for disability in MS since it correlates with disability, MRI abnormalities (T2 lesion volume and brain parenchymal fraction), and reduced retinal nerve fiber layer (RNFL) thickness and has also been shown to improve with disease-modifying treatment.( Balcer and Frohman, 2010 ) Our early MS study confirms that monocular LCLA is cross-sectionally correlated with T2LV. This is in line with a previous RRMS study that also reported a cross-sectional correlation between T2LV and LCLA but not with EDSS and MSFC.( Wu et al., 2007 ) Our findings add to prior observations and provide additional evidence of the usefulness of LCLA measurements in clinical trials.( Balcer and Frohman, 2010 )

We observed that baseline nBPV, nGMV and nNAWMV predicted subsequent changes in disability as measured by MSFC but not EDSS. In line with our findings, a previous study on patients with various subtypes of MS showed GM and whole brain (but not WM) atrophy correlated with MSFC changes while imaging outcomes did not correlate with disease progression as measured by EDSS.( Rudick et al., 2009 ) Moreover, it has also been shown that baseline GMV predicts subsequent disability progression as measured by EDSS. (Bergsland et al, 2012, Fisher et al, 2008, and Lavorgna et al, 2013, Rudick, Lee, 2009) The discrepancy between MSFC and EDSS associations with imaging might be related to higher sensitivity to change of MSFC including at the extremes of the disability spectrum.(Goldman et al, 2010 and Schwid et al, 1997)

In contrast with our findings of associations between brain volume changes (SIENA) and changes in EDSS, LCLA and SDMT, nBPV changes (SIENAX) were not associated with clinical changes. This may be due in part to the better reproducibility of SIENA compared to SIENAX.( Cover et al., 2011 ) SIENA may thus be a more robust MRI measure for future neuroprotection trials.

The strengths of our study include the frequent longitudinal measurements of a variety of outcomes in a homogeneous cohort of subjects with very early stages of MS. The comprehensive set of outcomes we collected enabled us to compare the strength of the associations between various clinical and imaging outcomes both cross-sectionally and longitudinally up to 3 years. In addition, the statistical analyses we used took into account all the available data rather than just the first and last observations for longitudinal analyses which contributes to the robustness of the findings. We acknowledge a few limitations with our study, including the limited sample size and some missing data, which may have led to a lack of ability to detect true associations. However, as we have used all available data in analyses, this might have increased our power to detect associations. We have not corrected our results for multiple comparisons and hence might have generated some false positive results.

5. Conclusions

We have identified several interesting putative biomarkers that are strongly associated with clinical worsening in early MS. The different associations observed with T2LV and SIENA may suggest that these measures capture different disease processes. The longitudinal associations between percent brain volume change and important clinical measures of function suggest that this metric may be an appropriate marker to use in proof-of-concept neuroprotection studies in early MS.

Role of funding source

The funding sources had no role in conduct of the research and/or preparation of the article, study design; data collection, analysis and interpretation of data, writing of the report, and in the decision to submit the article for publication.

Conflict of interest Statement

Dr. Maghzi, Dr. Revirajan, Dr. Julian and Dr. Spain report no disclosures.

Dr. Mowry is receiving free medication from Teva for an ongoing trial.

Dr. Liu, Dr. Jin report no disclosures.

Dr. Green has the following disclosures: Biogen/IDEC and Applied Clinical Intelligence - End point adjudication committee service, Novartis - ADONIS study chair and OCTIMS steering committee, BAF Advisory committee Mylan - Expert Counsel Prana Pharmaceuticals- Advisor, Roche- Advisor Opera and Oratorio, Accorda- Advisor, Bionure- Scientific Advisory Board, NMSS, HHMI and NIH for research.

Dr. McCulloch reports no disclosures

Dr. Pelletier has received consulting fees from CNS Imaging Consultant, LLC, and research grants to his academic institution from Hoffmann-LaRoche, Biogen Idec, and Genzyme.

Dr. Waubant has received honorarium from Teva, Sanofi Aventis and Genentech for three educational lectures, and is on the advisory board for a trial of Novartis. Dr. Waubant has received free medication from Biogen Idec and Sanofi-Aventis for the trial from which these data were generated.

Acknowledgments

The authors thank all the patients who participated in the study. Dr. Amir-Hadi Maghzi is funded by the Multiple Sclerosis International Federation ( www.msif.org ) through a McDonald Fellowship; the study was funded by the National MS Society (PI Waubant, RG3932-A-2).

References

  • Anderson et al., 2007 V.M. Anderson, J.W. Bartlett, N.C. Fox, L. Fisniku, D.H. Miller. Detecting treatment effects on brain atrophy in relapsing remitting multiple sclerosis: sample size estimates. Journal of neurology. 2007;254:1588-1594 Crossref
  • Balcer et al., 2003 L.J. Balcer, M.L. Baier, J.A. Cohen, M.F. Kooijmans, A.W. Sandrock, M.L. Nano-Schiavi, et al. Contrast letter acuity as a visual component for the Multiple Sclerosis Functional Composite. Neurology. 2003;61:1367-1373 Crossref
  • Balcer et al., 2000 L.J. Balcer, M.L. Baier, V.S. Pelak, R.J. Fox, S. Shuwairi, S.L. Galetta, et al. New low-contrast vision charts: reliability and test characteristics in patients with multiple sclerosis. Mult Scler. 2000;6:163-171
  • Balcer and Frohman, 2010 L.J. Balcer, E.M. Frohman. Evaluating loss of visual function in multiple sclerosis as measured by low-contrast letter acuity. Neurology. 2010;74(Suppl 3):S16-S23
  • Barkhof et al., 2009 F. Barkhof, P.A. Calabresi, D.H. Miller, S.C. Reingold. Imaging outcomes for neuroprotection and repair in multiple sclerosis trials. Nature reviews Neurology. 2009;5:256-266 Crossref
  • Benedict et al., 2006 R.H. Benedict, D. Cookfair, R. Gavett, M. Gunther, F. Munschauer, N. Garg, et al. Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc. 2006;12:549-558
  • Benedict et al., 2002 R.H. Benedict, J.S. Fischer, C.J. Archibald, P.A. Arnett, W.W. Beatty, J. Bobholz, et al. Minimal neuropsychological assessment of MS patients: a consensus approach. Clin Neuropsychol. 2002;16:381-397 Crossref
  • Bergsland et al., 2012 N. Bergsland, D. Horakova, M.G. Dwyer, O. Dolezal, Z.K. Seidl, M. Vaneckova, et al. Subcortical and cortical gray matter atrophy in a large sample of patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis. AJNR American journal of neuroradiology. 2012;33:1573-1578 Crossref
  • Brochet et al., 2008 B. Brochet, M.S. Deloire, M. Bonnet, E. Salort-Campana, J.C. Ouallet, K.G. Petry, et al. Should SDMT substitute for PASAT in MSFC? A 5-year longitudinal study. Mult Scler. 2008;14:1242-1249 Crossref
  • Cover et al., 2011 K.S. Cover, R.A. van Schijndel, B.W. van Dijk, A. Redolfi, D.L. Knol, G.B. Frisoni, et al. Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans. Psychiatry research. 2011;193:182-190 Crossref
  • Cutter et al., 1999 G.R. Cutter, M.L. Baier, R.A. Rudick, D.L. Cookfair, J.S. Fischer, J. Petkau, et al. Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain: a journal of neurology. 1999;122(Pt 5):871-882 Crossref
  • Drake et al., 2010 A.S. Drake, B. Weinstock-Guttman, S.A. Morrow, D. Hojnacki, F.E. Munschauer, R.H. Benedict. Psychometrics and normative data for the Multiple Sclerosis Functional Composite: replacing the PASAT with the Symbol Digit Modalities Test. Mult Scler. 2010;16:228-237 Crossref
  • Fischer, 2001 J.S. Fischer. Cognitive impairment in multiple sclerosis. S.D. Cook (Ed.) Handbook of multiple sclerosis (Marcel Dekker, New York, 2001) 233-255
  • Fisher et al., 2008 E. Fisher, J.C. Lee, K. Nakamura, R.A. Rudick. Gray matter atrophy in multiple sclerosis: a longitudinal study. Annals of neurology. 2008;64:255-265 Crossref
  • Glanz et al., 2012 B.I. Glanz, B.C. Healy, L.E. Hviid, T. Chitnis, H.L. Weiner. Cognitive deterioration in patients with early multiple sclerosis: a 5-year study. Journal of neurology, neurosurgery, and psychiatry. 2012;83:38-43
  • Goldman et al., 2010 M.D. Goldman, R.W. Motl, R.A. Rudick. Possible clinical outcome measures for clinical trials in patients with multiple sclerosis. Therapeutic advances in neurological disorders. 2010;3:229-239 Crossref
  • Gronwall, 1977 D.M. Gronwall. Paced auditory serial-addition task: a measure of recovery from concussion. Perceptual and motor skills. 1977;44:367-373 Crossref
  • Huijbregts et al., 2004 S.C. Huijbregts, N.F. Kalkers, L.M. de Sonneville, V. de Groot, I.E. Reuling, C.H. Polman. Differences in cognitive impairment of relapsing remitting, secondary, and primary progressive MS. Neurology. 2004;63:335-339 Crossref
  • Kurtzke, 1983 J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444-1452
  • Lavorgna et al., 2013 L. Lavorgna, S. Bonavita, D. Ippolito, R. Lanzillo, G. Salemi, F. Patti, et al. Clinical and magnetic resonance imaging predictors of disease progression in multiple sclerosis: a nine-year follow-up study. Mult Scler. 2013;
  • Maghzi et al., 2013 A.H. Maghzi, A. Minagar, E. Waubant. Neuroprotection in multiple sclerosis: a therapeutic approach. CNS drugs. 2013;27:799-815 Crossref
  • McDonald and Compston, 2001 W.I. McDonald, A. Compston, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol. 2001;50:121-127 Crossref
  • Morrow et al., 2010 S.A. Morrow, A. Drake, R. Zivadinov, F. Munschauer, B. Weinstock-Guttman, R.H. Benedict. Predicting loss of employment over three years in multiple sclerosis: clinically meaningful cognitive decline. The Clinical neuropsychologist. 2010;24:1131-1145 Crossref
  • Mowry et al., 2009 E.M. Mowry, A. Beheshtian, E. Waubant, D.S. Goodin, B.A. Cree, P. Qualley, et al. Quality of life in multiple sclerosis is associated with lesion burden and brain volume measures. Neurology. 2009;72:1760-1765 Crossref
  • Pineles et al., 2011 S.L. Pineles, E.E. Birch, L.S. Talman, D.J. Sackel, E.M. Frohman, P.A. Calabresi, et al. One eye or two: a comparison of binocular and monocular low-contrast acuity testing in multiple sclerosis. American journal of ophthalmology. 2011;152:133-140 Crossref
  • Polman and Reingold, 2005 C. Polman, S. Reingold, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol.. 2005;58:840-846 Crossref
  • Rao et al., 1991 S.M. Rao, G.J. Leo, L. Bernardin, F. Unverzagt. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology. 1991;41:685-691 Crossref
  • Rudick et al., 2009 R.A. Rudick, J.C. Lee, K. Nakamura, E. Fisher. Gray matter atrophy correlates with MS disability progression measured with MSFC but not EDSS. Journal of the neurological sciences. 2009;282:106-111 Crossref
  • Schwid et al., 1997 S.R. Schwid, A.D. Goodman, D.H. Mattson, C. Mihai, K.M. Donohoe, M.D. Petrie, et al. The measurement of ambulatory impairment in multiple sclerosis. Neurology. 1997;49:1419-1424 Crossref
  • Simon, 2006 J.H. Simon. Brain atrophy in multiple sclerosis: what we know and would like to know. Mult Scler. 2006;12:679-687 Crossref
  • Smith et al., 2002 S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage. 2002;17:479-489 Crossref
  • Tombaugh, 2006 T.N. Tombaugh. A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of clinical neuropsychology: the official journal of the National Academy of Neuropsychologists. 2006;21:53-76 Crossref
  • Waubant et al., 2013 E. Waubant, A.H. Maghzi, N. Revirajan, R. Spain, L. Julian, E. Mowry, et al. A phase II trial of neuroprotection with riluzole in early relapsing-remitting MS. Mult Scler.. 2013;19(S1):560
  • Wu et al., 2007 G.F. Wu, E.D. Schwartz, T. Lei, A. Souza, S. Mishra, D.A. Jacobs, et al. Relation of vision to global and regional brain MRI in multiple sclerosis. Neurology. 2007;69:2128-2135 Crossref
  • Zhang et al., 2001 Y. Zhang, M. Brady, S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging. 2001;20:45-57 Crossref

Footnotes

a Departments of Neurology, University of California San Francisco, San Francisco, CA

b Departments of Pediatrics, University of California San Francisco, San Francisco, CA

c Departments of Internal Medicine, University of California San Francisco, San Francisco, CA

d Departments of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA

e Department of Neurology, Oregon Health and Science University, OR

f Department of Neurology, Johns Hopkins University, Baltimore, MD

g Department of Neurology, Yale school of medicine, New Haven, CT

lowast Corresponding author. Tel.: (+1) 415 502 7224; fax: (+1) 415 514 2470.