Author's Accepted Manuscript

Magnetic resonance imaging correlates of clinical outcomes in early multiple sclerosis Amir-Hadi Maghzi, Nisha Revirajan, Laura J. Julian, Rebecca Spain, Ellen M Mowry, Shuang Liu, Chengshi Jin, Ari J. Green, Charles E. McCulloch, Daniel Pelletier, Emmanuelle Waubant www.elsevier.com/msard

PII: DOI: Reference:

S2211-0348(14)00069-8 http://dx.doi.org/10.1016/j.msard.2014.07.003 MSARD201

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Multiple Sclerosis and Related Disorders

Cite this article as: Amir-Hadi Maghzi, Nisha Revirajan, Laura J. Julian, Rebecca Spain, Ellen M Mowry, Shuang Liu, Chengshi Jin, Ari J. Green, Charles E. McCulloch, Daniel Pelletier, Emmanuelle Waubant, Magnetic resonance imaging correlates of clinical outcomes in early multiple sclerosis, Multiple Sclerosis and Related Disorders, http://dx.doi.org/10.1016/j.msard.2014.07.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1

 Magnetic Resonance Imaging correlates of clinical outcomes in early multiple sclerosis

Amir-Hadi Maghzi, MD, Nisha Revirajan, MD, Laura J. Julian ,PhD, Rebecca Spain ,MD, Ellen M Mowry, MD, Shuang Liu, PhD, Chengshi Jin, PhD, Ari J. Green, MD, Charles E. McCulloch, PhD, Daniel Pelletier, MD, and Emmanuelle Waubant, MD, PhD From Departments of Neurology (A.-H.M., N.R.,A.J.G., E.W.), Pediatrics (E.W.), Internal Medicine (L.J.J.), and Epidemiology and Biostatistics (C.E.M, C.J.), University of California San Francisco, San Francisco, CA; Department of Neurology (R.S,), Oregon Health and Science University, OR; Department of Neurology (EMM), Johns Hopkins University, Baltimore, MD; Department of Neurology (D.P., S.L.), Yale school of medicine, New Haven, CT

Keywords: Multiple sclerosis, Magnetic resonance imaging, outcomes, neuroprotection, brain atrophy, cognition

Corresponding author: Amir-Hadi Maghzi, M.D., Multiple Sclerosis Center, Department of Neurology, University of California San Francisco (UCSF), 675 Nelson Rising Lane, Room 221F, San Francisco, CA, Box 3206, Zip code: 94158. Email: [email protected] Phone: (+1) 415-502-7224



Fax: (+1) 415-514-2470



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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: Fortythree 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 (p0.05 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). 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. 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



8

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 EDSSpoint, p=0.08) and nNAWM (each 100 cm3 decrease 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 cm3 increase 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. 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



9

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, 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 diseasemodifying 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)



10

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, 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, 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.



11

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. 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.

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).



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References AndersonVM,BartlettJW,FoxNC,FisnikuL,MillerDH.Detectingtreatmenteffectsonbrainatrophy inrelapsingremittingmultiplesclerosis:samplesizeestimates.Journalofneurology.2007;254:1588 94. BalcerLJ,BaierML,CohenJA,KooijmansMF,SandrockAW,NanoSchiaviML,etal.Contrastletter acuityasavisualcomponentfortheMultipleSclerosisFunctionalComposite.Neurology. 2003;61:136773. BalcerLJ,BaierML,PelakVS,FoxRJ,ShuwairiS,GalettaSL,etal.Newlowcontrastvisioncharts: reliabilityandtestcharacteristicsinpatientswithmultiplesclerosis.MultScler.2000;6:16371. BalcerLJ,FrohmanEM.Evaluatinglossofvisualfunctioninmultiplesclerosisasmeasuredbylow contrastletteracuity.Neurology.2010;74Suppl3:S1623. BarkhofF,CalabresiPA,MillerDH,ReingoldSC.Imagingoutcomesforneuroprotectionandrepairin multiplesclerosistrials.NaturereviewsNeurology.2009;5:25666. BenedictRH,CookfairD,GavettR,GuntherM,MunschauerF,GargN,etal.Validityoftheminimal assessmentofcognitivefunctioninmultiplesclerosis(MACFIMS).JIntNeuropsycholSoc. 2006;12:54958. BenedictRH,FischerJS,ArchibaldCJ,ArnettPA,BeattyWW,BobholzJ,etal.Minimal neuropsychologicalassessmentofMSpatients:aconsensusapproach.ClinNeuropsychol. 2002;16:38197. BergslandN,HorakovaD,DwyerMG,DolezalO,SeidlZK,VaneckovaM,etal.Subcorticalandcortical graymatteratrophyinalargesampleofpatientswithclinicallyisolatedsyndromeandearly relapsingremittingmultiplesclerosis.AJNRAmericanjournalofneuroradiology.2012;33:15738. BrochetB,DeloireMS,BonnetM,SalortCampanaE,OualletJC,PetryKG,etal.ShouldSDMT substituteforPASATinMSFC?A5yearlongitudinalstudy.MultScler.2008;14:12429. CoverKS,vanSchijndelRA,vanDijkBW,RedolfiA,KnolDL,FrisoniGB,etal.Assessingthe reproducibilityoftheSienaXandSienabrainatrophymeasuresusingtheADNIbacktobackMPRAGE MRIscans.Psychiatryresearch.2011;193:18290. CutterGR,BaierML,RudickRA,CookfairDL,FischerJS,PetkauJ,etal.Developmentofamultiple sclerosisfunctionalcompositeasaclinicaltrialoutcomemeasure.Brain:ajournalofneurology. 1999;122(Pt5):87182. DrakeAS,WeinstockGuttmanB,MorrowSA,HojnackiD,MunschauerFE,BenedictRH.Psychometrics andnormativedatafortheMultipleSclerosisFunctionalComposite:replacingthePASATwiththe SymbolDigitModalitiesTest.MultScler.2010;16:22837. FischerJS.Cognitiveimpairmentinmultiplesclerosis.In:CookSD,editor.Handbookofmultiple sclerosis.NewYork:MarcelDekker;2001.p.23355. FisherE,LeeJC,NakamuraK,RudickRA.Graymatteratrophyinmultiplesclerosis:alongitudinal study.Annalsofneurology.2008;64:25565. GlanzBI,HealyBC,HviidLE,ChitnisT,WeinerHL.Cognitivedeteriorationinpatientswithearly multiplesclerosis:a5yearstudy.Journalofneurology,neurosurgery,andpsychiatry.2012;83:3843. GoldmanMD,MotlRW,RudickRA.Possibleclinicaloutcomemeasuresforclinicaltrialsinpatients withmultiplesclerosis.Therapeuticadvancesinneurologicaldisorders.2010;3:22939. GronwallDM.Pacedauditoryserialadditiontask:ameasureofrecoveryfromconcussion.Perceptual andmotorskills.1977;44:36773.



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HuijbregtsSC,KalkersNF,deSonnevilleLM,deGrootV,ReulingIE,PolmanCH.Differencesin cognitiveimpairmentofrelapsingremitting,secondary,andprimaryprogressiveMS.Neurology. 2004;63:3359. KurtzkeJF.Ratingneurologicimpairmentinmultiplesclerosis:anexpandeddisabilitystatusscale (EDSS).Neurology.1983;33:144452. LavorgnaL,BonavitaS,IppolitoD,LanzilloR,SalemiG,PattiF,etal.Clinicalandmagneticresonance imagingpredictorsofdiseaseprogressioninmultiplesclerosis:anineyearfollowupstudy.Mult Scler.2013. MaghziAH,MinagarA,WaubantE.Neuroprotectioninmultiplesclerosis:atherapeuticapproach.CNS drugs.2013;27:799815. McDonaldWI,CompstonA,al.e.Recommendeddiagnosticcriteriaformultiplesclerosis:guidelines fromtheInternationalPanelonthediagnosisofmultiplesclerosis.AnnNeurol.2001;50:1217. MorrowSA,DrakeA,ZivadinovR,MunschauerF,WeinstockGuttmanB,BenedictRH.Predictingloss ofemploymentoverthreeyearsinmultiplesclerosis:clinicallymeaningfulcognitivedecline.The Clinicalneuropsychologist.2010;24:113145. MowryEM,BeheshtianA,WaubantE,GoodinDS,CreeBA,QualleyP,etal.Qualityoflifeinmultiple sclerosisisassociatedwithlesionburdenandbrainvolumemeasures.Neurology.2009;72:17605. PinelesSL,BirchEE,TalmanLS,SackelDJ,FrohmanEM,CalabresiPA,etal.Oneeyeortwo:a comparisonofbinocularandmonocularlowcontrastacuitytestinginmultiplesclerosis.American journalofophthalmology.2011;152:13340. PolmanC,ReingoldS,al.e.Diagnosticcriteriaformultiplesclerosis:2005revisionstothe"McDonald Criteria".AnnNeurol.2005;58:8406. RaoSM,LeoGJ,BernardinL,UnverzagtF.Cognitivedysfunctioninmultiplesclerosis.I.Frequency, patterns,andprediction.Neurology.1991;41:68591. RudickRA,LeeJC,NakamuraK,FisherE.GraymatteratrophycorrelateswithMSdisability progressionmeasuredwithMSFCbutnotEDSS.Journaloftheneurologicalsciences.2009;282:10611. SchwidSR,GoodmanAD,MattsonDH,MihaiC,DonohoeKM,PetrieMD,etal.Themeasurementof ambulatoryimpairmentinmultiplesclerosis.Neurology.1997;49:141924. SimonJH.Brainatrophyinmultiplesclerosis:whatweknowandwouldliketoknow.MultScler. 2006;12:67987. SmithSM,ZhangY,JenkinsonM,ChenJ,MatthewsPM,FedericoA,etal.Accurate,robust,and automatedlongitudinalandcrosssectionalbrainchangeanalysis.NeuroImage.2002;17:47989. TombaughTN.AcomprehensivereviewofthePacedAuditorySerialAdditionTest(PASAT).Archives ofclinicalneuropsychology:theofficialjournaloftheNationalAcademyofNeuropsychologists. 2006;21:5376. WaubantE,MaghziAH,RevirajanN,SpainR,JulianL,MowryE,etal.AphaseIItrialof neuroprotectionwithriluzoleinearlyrelapsingremittingMS.MultScler.2013;19(S1):560. WuGF,SchwartzED,LeiT,SouzaA,MishraS,JacobsDA,etal.Relationofvisiontoglobalandregional brainMRIinmultiplesclerosis.Neurology.2007;69:212835. ZhangY,BradyM,SmithS.SegmentationofbrainMRimagesthroughahiddenMarkovrandomfield modelandtheexpectationmaximizationalgorithm.IEEEtransactionsonmedicalimaging. 2001;20:4557. 

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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%

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

Clinical Characteristics

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

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

Brain Imaging



15



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

0.03

-0.23

-0.23

0.04

-0.01

0.04

0.08

0.11

0.07

(SIENAX)

(-0.30,

(-0.52,

(-0.53,

(-0.30,

(-0.35,

(-0.30,

(-0.25,

(-0.21,

(-0.26,

0.34)

0.12)

0.12)

0.37)

0.33)

0.37)

0.39)

0.42)

0.38)

P=0.87

P=0.20

P=0.19

P=0.81

P=0.96

P=0.83

P=0.96

P=0.49

P=0.68

-0.10

-0.16

-0.18

0.03

0.02

0.22

0.08

0.23

0.18

(-0.41,

(-0.47,

(-0.49,

(-0.31,

(-0.32,

(-0.13,

(-0.24,

(-0.10,

(-0.15,

0.23)

0.19)

0.16)

0.36)

0.36)

0.51)

0.39)

0.51)

0.47)

P=0.54

P=0.37

P=0.30

P=0.88

P=0.90

P=0.21

P=0.62

P=0.16

P=0.29

0.07

-0.29

-0.24

0.02

-0.02

-0.09

0.14

0.07

0.01

(-0.25,

(-0.58,

(-0.54,

(-0.31,

(-0.36,

(-0.41,

(-0.19,

(-0.26,

(-0.31,

0.38)

0.05)

0.10)

0.35)

0.32)

0.25)

0.44)

0.38)

0.33)

P=0.67

P=0.09

P=0.16

P=0.91

P=0.90

P=0.59

P=0.41

P=0.69

P=0.93

0.25

0.20

0.44

0.04

-0.21

-0.14

-0.35

-0.51

-0.46

(-0.07,

(-0.14,

(0.12,

(-0.29,

(-0.52,

(-0.46,

(-0.61, -

(-0.71, -

(-0.68, -

0.53)

0.51)

0.68)

0.37)

0.14)

0.20)

0.04)

0.23)

0.17)

P=0.13

P=0.24

P=0.009

P=0.80

P=0.23

P=0.41

P=0.03

P=0.001

P=0.003

nGMV

nNAWMV

T2LV

16



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

 9-HPT

 T25FW

 MSFC

 SDMT

 LCLA 100%

2.5%

1.25%

Baseline BPV

-0.002

0.002

-0.003

-0.010

0.005

0.008

-0.000

-0.44

-0.03

(SIENAX)*

(-0.011,

(-0.005,

(-0.012,

(-0.02, -

(0.001,

(-0.08,

(-0.05,

(-0.13,

(-0.12,

0.008)

0.010)

0.006)

0.002)

0.009)

0.10)

0.05)

0.04)

0.07)

P=0.73

P=0.90

P=0.53

P=0.015

P=0.016

P=0.85

P=1.00

P=0.31

P=0.59

Baseline

-0.00

0.006

-0.004

-0.014

0.008

0.026

-0.24

-0.11

-0.08

nGMV*

(-0.017,

(-0.007,

(-0.020,

(-0.03,

(0.000,

(-0.13,

(-0.01,

(-0.25,

(-0.24,

0.017)

0.019)

0.012)

0.00)

0.016)

0.19)

0.06)

0.03)

0.08)

P=0.99

P=0.40

P=0.63

P=0.052

P=0.040

P=0.74

P=0.58

P=0.13

P=0.33

Baseline

-0.004

0.003

-0.006

-0.019

0.01

0.03

-0.02

0.02

nNAWMV*

(-0.023,

(-0.012,

(-0.024,

(-0.035, -

(0.002,

(-0.14,

(-0.066,

(-0.20,

(-0.18,

0.015)

0.018)

0.012)

0.003)

0.018)

0.18)

0.13)

0.16)

0.21)

P=0.68

P=0.79

P=0.51

P=0.019

P=0.021

P=0.80

P=0.51

P=0.83

P=0.86

-0.002

-0.003

-0.001

-0.002

-0.000

-0.024

-0.00

-0.009

-0.015

(-0.005,

(-0.005, -

(-0.004,

(-0.004,

(-0.002,

(-0.05,

(-0.013,

(-0.038,

(-0.046,

0.001)

0.001)

0.002)

0.001)

0.001)

0.003)

0.012)

0.020)

0.015)

P=0.29

P=0.004

P=0.48

P=0.26

P=0.82

P=0.08

P=0.95

P=0.53

P=0.31

Baseline T2LV



 PASAT3’

0.02

Table 4: Longitudinal regressions between MRI metrics and clinical outcomes  EDSS

 PASAT3’

 9-HPT

 T25FW

 MSFC

 SDMT 100%

2.5%

1.25%

 Brain

-0.11

-0.05

-0.081

-0.011

0.005

1.14

0.13

1.43

1.57

volume

(-0.24, 0.01)

(-0.14, 0.34)

(-0.20, 0.035)

(-0.10, 0.08)

(-0.06, 0.07)

(0.12, 2.17)

(-0.44, 0.71)

(0.22, 2.63)

(0.24, 2.89)

(SIENA)

P=0.08

P=0.23

P=0.17

P=0.81

P=0.88

P=0.03

P=0.65

P=0.02

P=0.02

-0.082

0.009

0.065

0.037

0.003

-0.09

0.1

0.18

0.32

(-0.20,0.03)

(-0.069, 0.087)

(-0.055, 0.184)

(-0.060,0.133)

(-0.05, 0.06)

(-1.31, 1.13)

(-0.41, 0.62)

(-0.95,1.31)

(-0.90,1.55)

P=0.16

P=0.82

P=0.29

P=0.45

P=0.91

P=0.88

P=0.69

P=0.76

P=0.60

 BPV *

(SIENAX)

*

 nGMV

nNAWMV*

 T2LV



 LCLA

-0.10

-0.010

0.086

0.055

0.004

-0.09

0.26

0.65

1.05

(-0.32,0.11)

(-0.157, 0.137)

(-0.144, 0.316)

(-0.128,0.238)

(-0.11, 0.11)

(-2.49, 2.30)

(-0.74, 1.25)

(-1.48, 2.78)

(-1.27, 3.38)

P=0.34

P=0.90

P=0.46

P=0.55

P=0.95

P=0.94

P=0.61

P=0.55

P=0.37

-0.2

0.055

0.174

0.102

-0.003

-0.15

0.1

0.05

0.22

(-0.4,0.01)

(-0.096, 0.207)

(-0.059, 0.408)

(-0.084,0.288)

(-0.12, 0.11)

(-2.39, 2.09)

(-0.89, 1.1)

(-2.14, 2.24)

(-2.14, 2.59)

P=0.066

P=0.47

P=0.14

P=0.28

P=0.96

P=0.89

P=0.84

P=0.96

P=0.85

0.08

-0.1

0.006

-0.036

-0.016

0.24

0.38

0.21

0.58

(-0.076,0.25)

(-0.21, 0.02)

(-0.17, 0.18)

(-0.18,0.11)

(-0.01, 0.07)

(-1.6, 2.08)

(-0.35, 1.1)

(-1.38,1.80)

(-1.14,2.30)

P=0.30

P=0.09

P=0.95

P=0.62

P=0.72

P=0.79

P=0.30

P=0.80

P=0.50

17



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

Highlights x x x

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.

Figure

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.