Diffusion tensor imaging and disability progression in multiple sclerosis : a 4-year follow-up study

Brain and Behavior. 2019;9:e01194.   | 1 of 10 https://doi.org/10.1002/brb3.1194 wileyonlinelibrary.com/journal/brb3 Received:10October2018 | Revised:26November2018 | Accepted:5December2018 DOI:10.1002/brb3.1194 ORIGINAL RESEARCH Diffusion tensor imaging and disability progression in multiple sclerosis: A 4‐year follow‐up study Marcin Kolasa1,2 | Ullamari Hakulinen2,3,4 | Antti Brander2 | Sanna Hagman1 | Prasun Dastidar2 | Irina Elovaara1 | Marja‐Liisa Sumelahti1 ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium, provided the original work is properly cited. ©2018TheAuthors. Brain and BehaviorpublishedbyWileyPeriodicals,Inc. 1FacultyofMedicineandLife Sciences,TampereUniversity,Tampere, Finland 2DepartmentofRadiology,Medical ImagingCenterofPirkanmaaHospital District,TampereUniversityHospital, Tampere,Finland 3Faculty of Biomedical Sciences and Engineering,TampereUniversityof Technology,Tampere,Finland 4DepartmentofMedicalPhysics,Medical ImagingCenter,TampereUniversity Hospital,Tampere,Finland Correspondence MarcinKolasa,DepartmentofRadiology, MedicalImagingCenterofPirkanmaa HospitalDistrict,TampereUniversity Hospital,Tampere,Finland. Email: [email protected] Funding information Suomen Kulttuurirahasto; Suomen Aivosäätiö;CompetitiveResearchFundingof TampereUniversityHospital Abstract Objectives: Diffusion tensor imaging (DTI) is sensitive technique to detect wide‐ spreadchangesinwaterdiffusivityinthenormal‐appearingwhitematter(NAWM) that appears unaffected in conventional magnetic resonance imaging. We aimed to investigate theprognosticvalueandstability ofDTIindicesin theNAWMofthe brain in an assessment of disability progression in patients with a relapsing‐onset multiplesclerosis(MS). Methods:Forty‐sixMSpatientswerestudiedforDTIindices(fractionalanisotropy (FA),meandiffusivity(MD),radial(RD),andaxial(AD)diffusivity)intheNAWMofthe corpuscallosum(CC)and theinternalcapsuleat baselineand at1yearafter. DTI analysisfor10healthycontrolswasalsoperformedatbaseline.Simultaneously,focal brain lesion volume and atrophy measurements were done at baseline for MS pa‐ tients.AssociationsbetweenDTIindices,volumetricmeasurements,anddisability progression over 4 years were studied by multivariate logistic regression analysis. Results:Atbaseline,mostDTImetricsdifferedsignificantlybetweenMSpatients andhealthycontrols.TherewastendencyforassociationsbetweenbaselineDTIin‐ dices in the CC and disability progression (p<0.05). Changes in DTI indices over 1yearwereobservedonlyintheCC(p<0.008),andthosechangeswerenotfound to predict clinical worsening over 4 years. Clear‐cut association with disability pro‐ gression was not detected for baseline volumetric measurements. Conclusion:AberrantdiffusivitymeasuresintheNAWMoftheCCmayprovidead‐ ditional information for individual disability progression over 4 years in MS with the relapsing‐onsetdisease.CCmaybeagoodtargetforDTImeasurementsinmonitor‐ ingdiseaseactivityinMS,andmorestudiesareneededtoassesstherelatedprog‐ nostic potential. KEYWORDS diffusiontensorimaging,longitudinalstudy,multiplesclerosis 2 of 10 | KOLASA et AL. 1 | INTRODUCTION In multiple sclerosis (MS), demyelination and axonal injury in the central nervous system are responsible for neurological disability. Conventionalmagneticresonanceimaging(MRI)detectingT1andT2 focalbrainlesionsisnotspecifictotheunderlyingpathology,andit lacks sensitivity to the microstructural diffuse damage in the normal‐ appearingwhite matter(NAWM)(Filippi,Absinta,&Rocca,2013). Conventional MRI markers correlate only moderately with clinical disability(Tintoreetal.,2015),andtheirprognosticvalueintheas‐ sessment ofdisabilityprogressionin definite MSislimited(Filippi etal.,2013).Consequently,brainatrophythathasbeenrelatedto long‐term disability in MS (De Stefano et al., 2016) expresses the underlying pathological processes only nonspecifically. Confounding factors,suchasdisease‐modifyingtherapiesandcausesunrelatedto MS,complicateinterpretationofMRImarkersandatrophyinclinical practice(Kaunzner&Gauthier,2017;Wattjesetal.,2015). Diffusion tensor imaging (DTI) quantifies the magnitude and direction of water diffusion, and it is sensitive to diffuse micro‐ structural abnormalities in the brain that appears unaffected on con‐ ventionalMRIs(Rovarisetal.,2005).DTI‐derivedmetrics,including fractional anisotropy (FA), mean diffusivity (MD), radial (RD), and axial(AD)diffusivities,seemtoprovideabetterspecificitytodemy‐ elinationandaxonalinjurythanconventionalMRIs(Sunetal.,2006). Increased MD and decreased FA in the NAWM of different brain regions,includingthecorpuscallosum(CC),havebeentypicallyde‐ tectedinMS(Banaszek,Bladowska,Pokryszko‐Dragan,Podemski, &Sasiadek,2015;Preziosaetal.,2011;Sigal,Shmuel,Mark,Gil,& Anat,2012).However,inconsistentresultsregardingthecorrelation betweendisabilityandDTIindicesintheCCandthepyramidaltract have been reported in cross‐sectional studies using different meth‐ odsofDTIanalysisandclinicalscalesofdisability(Lin,Yu,Jiang,Li, &Chan,2007;Llufriuetal.,2012;Pokryszko‐Draganetal.,2018; Roosendaaletal.,2009;Tortorellaetal.,2014).Thecorrelationbe‐ tweenRDandsecondaryprogressioninMShasbeenobservedina 50‐yearclinicalfollow‐upstudyindicatingthepotentialroleofDTIin thepredictionofoutcomesinMS(Andersenetal.,2018). Previously,decreasedFAandincreasedRDmostlyintheCCof MS were observed in a 2‐year longitudinal study (Harrison et al., 2011).Incontrast, nochangesin diffusivitywere observedin the NAWMofMSover2–4years(Ontanedaetal.,2017;Rashidetal., 2008).Moreover,fewstudieswithashort(1–2years)follow‐uphave appliedaregionalandwhole‐brainDTIanalysistolongitudinalmea‐ surements of diffusivity aiming to evaluate the prognostic value of DTIintheassessmentofdisabilityprogressioninMS(Rashidetal., 2008;Samannetal.,2012;Schmiereretal.,2004).Inoneofthese studies,theincreaseofMDinthewhitematteroffrontallobeover 1 year was associated with clinical impairment in primary‐progres‐ sive MS (Schmierer et al., 2004), while in another study, in early relapsing‐remittingMS,nodiffusivitychangesweredetectedover 2years(Rashidetal.,2008). The investigation of the prognostic value of DTI in this cross‐ sectional and 4‐year longitudinal study aims to assess white matter diffusion change and its stability in the relapsing‐onset MS cohort considering the variable rate of disease progression. 2 | MATERIALS AND METHODS The study was approved by the local ethics committee in the Hospital District of Pirkanmaa (R05157). All subjects provided in‐ formed written consent. 2.1 | Subjects Intotal,56individuals,46patientswithrelapsing‐onsetMS,and10 healthysubjectswere enrolledinthis4‐yearfollow‐upstudy (be‐ tween2006and2012)attheTampereUniversityHospital,Finland. TheMSdiagnosiswasbasedontherevisedMcDonaldcriteriafrom 2005(Polmanetal.,2005)andthediseasecourseclassificationon LublinandReingoldcriteria(Lublinetal.,2014).Theinclusioncrite‐ riawereadiagnosisofrelapsing‐remittingMS(RRMS)orsecondary‐ progressiveMS(SPMS),nosteroidtreatmentatleast8weeksbefore clinical and radiological assessments, and an Expanded Disability StatusScale(EDSS)scorebothatthestudyentryandafter4years. Healthysubjectsconsistedoffivefemalesandfivemales,andthe meanageofthesubjectswas39.7years(range26–61).Healthysub‐ jects were recruited from the hospital staff or their relatives with no history of neurological or psychiatric illness. Duringthefollow‐up,MSpatientsunderwentaclinicalexamina‐ tionbythesameneurologistatbaselineandannuallyfor4years(in totalfiveexaminations).Clinicalprogressionwasdeterminedasthe differencebetweenthebaselineEDSSandEDSS4yearsafterthe baseline. Progression of disability during the follow‐up was defined asanEDSSscoreincrease≥1.0whenthebaselineEDSSwas<6.0or anincreaseofEDSS≥0.5whenthebaselineEDSS≥6.0,andthese subjectswereassignedtoaprogressiongroup(Rovarisetal.,2003). Alltheotherpatientswereincludedinthestablegroup. 2.2 | MR imaging acquisition MRIvolumetryincludedT1andFLAIRbrainlesionvolumeandbrain atrophy measurements, and it was carried out at baseline for 42 MSpatients.DTIin46caseswasperformedatbaselineand1year afterthebaselinevisit.HealthycontrolswereassessedwithDTIat baseline. ThepatientsunderwentMRIonthesamedayasaclinicalex‐ amination. The patients and controls underwent a whole‐brain imaging by using a 1.5‐Tesla MR scanner (Magnetom Avanto SQ, SiemensMedicalSolutions,Erlangen,Germany),andtheMRIacqui‐ sition and protocol were as follows: T1‐weighted header followed byanaxialthree‐dimensional(3D)T1‐weightedmagnetizationpre‐ pared rapid gradient echo (MPRAGE), 3D T2‐weighted turbo spin echo, fluid‐attenuated inversion recovery (FLAIR), T1‐weighted spin echo with magnetization transfer contrasts, multidirectional diffusion‐weightedecho‐planarimaging,andgadolinium‐enhanced | 3 of 10 KOLASA et AL. T1‐weightedMPRAGEwhenneeded.TheDTIprotocolconsistedof a single‐shot spin‐echo‐based echo‐planar diffusion‐weighted imag‐ ingwiththreeaveragesand12gradientencodingdirections,withb valuesof0and1,000s/mm2. The imaging parameters are presented in Table 1. 2.3 | MR imaging postprocessing The DTI datawereanalyzedwith commercialNeuro3Dsoftware (Siemens Healthcare, Malvern, USA) at an offline workstation. Multidirectional diffusion data were assessed visually for the pres‐ ence of distortions and artifacts. There were no significant eddy cur‐ rent distortions due to the diffusion gradients. Six freehand regions of interest (ROI) of approximately 26–48mm2 (depending on the anatomicalregion)werepositionedontheleftandrightposterior limbsoftheinternalcapsule(IC),CCgenu,leftandrightCCbody, andCCsplenium(Figure1).TheROIsweremanuallyplacedexactly the same way at both time points on axial images of the color‐coded FAmapsandwereautomaticallytransferredontheMD,eigenval‐ ues,andnon‐diffusion‐weightedb0maps.TheROIswerecentered on the anatomical structure in the most homogeneous area, with guidance from conventional T2 images to exclude focal lesions from theROIandpartialvolumeeffectfromborderareas.ThesizeofROI wasreducedifafocallesionwasidentifiedintheROI.Thediffer‐ enceinROIsizebetweenthebaselineand1‐yearfollow‐upwasvery small,<9%(range1.8%–8.8%)inallROIs.Thevaluesofthefollowing DTIparameterswereobtained:FA,MD,AD,andRD. ThewholebrainvolumeoftheT1hypointense,FLAIRhyperin‐ tense lesions, and brain parenchymal fraction (BPF) were assessed blindlyusingthesemi‐automaticsegmentationsoftwareAnatomatic™ 2.23(Heinonenetal.,1998)bythesamereader.BPFwasdefinedas a ratio of brain parenchymal volume to the total volume within the brainsurfacecontour(Rudick,Fisher,Lee,Simon,&Jacobs,1999). 2.4 | Statistical analysis Means and standard deviations were given for normally distributed variables and medians and ranges for skewed distributed data. For thedemographicandvolumetricdata,groupswerecomparedusing independent sample t tests for normally distributed continuous vari‐ ablesandMann–WhitneyU tests for skewed distributed continuous variables. Spearman's rank correlations were determined for correla‐ tionsbetweenclinicalandMRIparameters.TheWilcoxontestwas usedtoperformcomparisonsbetweenDTIvaluesatbaselineand 1year.ToinvestigateassociationbetweenDTImetrics,volumetric measurements,and disabilityprogression over4years, aseriesof logistic regression models were created. The presence or absence of disability progression was used as a dependent variable in all models.Inlogistic regressionModel1,theage andtimefromthe onset(firstsymptoms)tobaselineweresetascovariates.InModel 2,thecovariateswereasfollows:sex,diseaseduration(timefrom MSdiagnosistobaseline),baselineEDSS,numberofrelapsesupto 3yearsprecedingthebaseline,immunomodulatorymedicationsta‐ tus,andvolumetricmeasurements(T1/FLAIRlesionvolume,BPF). A resulting odds ratio (OR) is given with 95% confidence interval (CI),andthep‐value<0.05wasconsideredstatisticallysignificant. The Bonferroni‐corrected p‐valuesforsixcomparisons(p<0.008) werealsoinvestigatedintheanalysesconcerningDTI.Astatistical analysis was performed using SPSS Statistics for Windows version 22(IBMCorp.,Armonk,NY,USA). 3 | RESULTS 3.1 | Clinical and radiological assessment at baseline and over the follow‐up Intotal,22of46(48%)patientsshoweddisabilityprogressionover 4years.Themeanageofpatientsatbaselinewas39.6years(range 18–61). The demographic and clinical characteristics are summa‐ rizedinTable2.Sevenpatientshadonedemyelinatingplaqueinthe IC,fourpatientshadonedemyelinatingplaqueintheCC,andone patienthadseveralplaquesintheCC. Incidentalfindingsinthebrainwhitematterwerefoundinfour healthy subjects from control group; three subjects had one to two punctate white matter hyperintensities, one subject had several punctate white matter hyperintensities, and none of healthy sub‐ jects presented clinical signs of demyelinating disease. InMS,comparedtohealthysubjects,thestrongestdifferences (p<0.001)werefoundintheCCforFA,MD,andRD(Supporting InformationFigureS1). The FLAIR lesion volumes were significantly higher (p<0.05) in the disability progression group compared to the stable disabil‐ itygroup(Table2).Nosignificantcorrelationswerefoundbetween baselineDTIandage,diseaseduration,baselineEDSS,andnumber ofrelapsesbeforebaseline(datanotshown). At baseline, significant correlations (p<0.008, r>0.4) were foundbetweenMRIvolumetricmeasurementsandDTIindices.The strongest correlations were found in the CC genu between the T1 brain lesion volume and FA (p=0.001, r=−0.48), MD (p<0.001, r=0.52), RD (p<0.001, r=0.52) and between FLAIR lesion vol‐ umeandFA(p<0.001,r=−0.6),MD(p<0.001,r=0.54),andRD TABLE 1 Imagingparameters Axial T1WI Axial FLAIR Axial DTI Slice thickness (mm) 0.9 5 5 Interslicegap(mm) 0 0 1.5 Fieldofview(mm) 230 × 230 230 × 230 230 × 230 Matrix 256×256 256×256 128 × 128 Echotime(ms) 4.2 100 96 Repetition time (ms) 1,160 8,500 3,500 Inversiontime(ms) 600 2,500 Note. DTI: diffusion tensor imaging; FLAIR: fluid‐attenuated inversion recovery;T1WI:T1‐weightedimaging. 4 of 10 | KOLASA et AL. (p<0.001,r=0.6).Regardingbrainatrophy,thestrongestcorrela‐ tionswerefoundbetweenBPFandRD(p=0.002,r=−0.46)inthe right CC body, RD (p=0.007, r=−0.41) in the left CC body, MD (p=0.004,r=−0.44)andAD(p=0.001,r=−0.49)intherightIC, andMD(p<0.001,r=−0.53)andAD(p=0.002,r=−0.47)inthe leftIC(SupportingInformationTableS1). Duringthe1‐yearfollow‐up,FAsignificantly(p<0.05)increased in4/6ROIs,andRDdecreasedin4/6ROIs(CCgenu,body,andthe CCsplenium).ADshowedasignificantincreasein3/6ROIs(theCC genu,CCbody).TheresultsremainedsignificantexceptforRDin theCCgenuandADinleftCCbodyaftertheBonferronicorrections (p<0.008).IntheIC,thechangeswerenonsignificant(Table3). NogroupdifferencesexistedregardingDTIchangeover1year inanyROIsbetweendisabilityprogressionandstablegroups(data notshown). Toassesstheintra‐observerrepeatabilityofDTImeasurements, the intraclass correlations (ICCs) were calculated for 20 patients. Thesameobserver(U.H.)repeatedthemeasurementsforthesame scanswithatimeintervalofapproximately3months.InallROIs,the ICCsweregoodandexcellentandwere0.77–0.98(mean0.91)for FA,0.75–0.96(mean0.84)forMD,0.64–0.93(mean0.85)forAD, and0.85–0.95(mean0.91)forRD. 3.2 | Association between MRI markers and disability progression InlogisticregressionModel1withcovariatesofageandtimefrom theonsettobaseline(Table4),alowerbaselineFAandhigherRDin theCCgenu,rightCCbody,andtheCCspleniumwereassociated with disability progression (p˂0.05). Moreover, a higher baseline MD in the right CC body and higher MD and AD in the CC sple‐ nium were associated with disability progression. The results did not remain significant after the Bonferroni corrections. There were no significantassociationsbetweenbaselineDTIindicesintheICand disability progression over the follow‐up. The age and symptom time hadnoeffectinanyoftheanalyzedROIs. InModel2,whichcontainedbaselineEDSSandrelapsenumber beforebaseline,anassociationbetweenDTIanddisabilityprogres‐ siondisappearedintheCCgenu,body,andthespleniumregrading severaldiffusivityparameters;however,noneoftheseexplanatory variables reached statistical significance (Supporting Information Tables S2 and S3). Medication, disease duration, and sex had no effect on disability progression (data not shown). T1, FLAIR, and BPF were not explanatory for disability progression (Supporting Information Table S4). However, the association between disabil‐ ityprogressionandDTIdisappearedintheCCgenu,andstatistical power slightly decreasedin theother CC areas in the models,in‐ cludingFLAIRlesionvolumeandBPF.TheT1lesionvolumehadno effectinanyregressionmodel(datanotshown). DTIchange over1year didnotrelatetodisability progression over4yearsinanyROIs(datanotshown). 4 | DISCUSSION The prognostic assessment of clinical disability accumulation by usingconventionalMRIisstillsuboptimal(Filippietal.,2013),where additional challenges concern the individual and heterogenous dis‐ ability progression. In the present study, the relapsing‐onset MS patientcohortshowedalteredDTIindicesatbaselinecomparedto healthycontrols,especiallyintheCCandtoalesserdegreeinthe IC.Theanatomicallocationoftheobserveddifferences mayindi‐ catethatDTIissensitivetomicrostructuralabnormalitiesoccurring intheNAWMtractsresponsibleforcognitiveandlocomotorfunc‐ tions.OurfindingcorroboratesotherreportsshowingthatADisless affectedwhencomparedtoRDintheCCandthepyramidaltract, FIGURE 1 FreehandROIplacementonthecolor‐codedfractionalanisotropyaxialmaps.(1)Genuofthecorpuscallosum(sizeofROI means26mm2,range13–71),(2)posteriorlimboftheinternalcapsule(48mm2,13–81),(3)spleniumofthecorpuscallosum(32mm2, 13–81),(4)bodyofthecorpuscallosum(26mm2,19–84).Pixelsize1.8×1.8mm | 5 of 10 KOLASA et AL. includingIC(Henry,Oh,Nelson,&Pelletier,2003;Linetal.,2007; Roosendaaletal.,2009). IntheNAWMofMS,FAistypicallydecreased,whereasMDis increased,expressingthelossofwhitemattertractsdirectionality andtheincreaseinoverallwaterdiffusivity,respectively(Alexander, Lee,Lazar,&Field,2007).IncreasedRD,ameasureofperpendicu‐ lardiffusivitytothefibers,isusuallylinkedtodemyelination(Fink et al., 2010). Diffusionparalleltothefibers,that is,AD,amarker ofaxonalintegrity,istypicallydecreasedandcorrelatesclearlywith axonaldamageattheearlystagesofMS.AtthechronicstageofMS, ADmayconverselyincrease,representingtheconfoundingeffectof reparativeprocesses,suchasgliosisandcellularinfiltration(Aung, Mar,&Benzinger,2013).Inthiscontext,thenonsignificantdiffer‐ ence between healthy controls and MS patients in our study may resultfromdifferentdirectionsofchangeinADrepresentingcom‐ peting pathological processes at different progression stages in MS. Thecorrelationbetweenbaselinebrainlesionvolume,brainatrophy, and DTI measurements in our MS group suggests that diffusivity abnormalitiesmaybesecondarytoprogression,bothWalleriande‐ generation of the axons passing through remote macroscopic brain TABLE 2 Demographic,clinicalandradiologicaldataforMSpatients Whole group Stable group Progression group p‐Valuea No.ofpatients 46 24 22 Female:male 31:15 17:7 14:8 0.6 Meanageatbaseline,years,mean(range) 39.6(18–61) 39.1(20–61) 40.2(18–58) 0.3 Mediantimefromonsetsymptomtobaseline, years(range) 9(0.7–32.2) 7.6(1.4–32.2) 12.3(0.7–31.2) 0.6 Mediandiseaseduration,years(range) 4.2(0–31.2) 2.3(0–27.2) 5.9(0–31.2) 0.1 EDSS,median(range) Baseline 2(0–7) 1.5(0–6) 3.0(0–7) 0.2 Year1 2(0–7.5) 1.5(0–6) 3.5(0–7.5) Year2 2.5(0–8) 1.5(0–6) 5.5(0–8) Year3 2(0–8) 1.5(0–6) 5.5(0–8) Year4 2(0–8) 1.5(0–6) 6.0(1–8) <0.001 DifferencebetweenEDSSover4years, median(range) 0.5(–1.5to4) 0(0.5to−1.5) 1.5(0.5–4) No.ofrelapsesuptothreeyearsbeforebaseline,no.ofpatients(%) 015(33) 5(21) 10(45) 0.07 1–2 24(52) 14(58) 10(45) 3–5 7(15) 5(21) 2(10) No.ofrelapsesduringthefollow‐up,no.ofpatients(%) 024(52.2) 12(50) 12(54.5) 1.00 1–2 12(26.1) 7(29.2) 5(22.7) 3–6 10(21.7) 5(20.8) 5(22.7) Durationoftreatmentatbaseline,months, median(range) 18.5(1–122) 18.5(1–70) 15.5(1–122) 0.9 Treatmentatbaseline,no.ofpatients(%)b18(39) 12(50) 6(27) 0.2 Treatmentattheendofthefollow‐up, no.ofpatients(%)b 20(43.4) 12(50) 8(36) 0.3 T1 brain lesion load at baseline cm3, median(range)c 1.7(0.1–28.5) 1(0.1–28.5) 2.2(0.1–14.7) 0.1 FLAIRbrainlesionloadatbaselinecm3, median(range)c 5.8(1–39) 2.8(1–39) 8.2(1–33) 0.03 Brainparenchymalfractionatbaseline, median(range)c 0.72(0.6–0.81) 0.73(0.64–0.8) 0.68(0.6–0.81) 0.2 Note.EDSS,ExpandedDisabilityStatusScale;Rangewasdefinedasminimumandmaximumvalues. aComparisonbetweenstableversusprogressiongroups,Mann‐WhitneyUtestformedianvalues,ttestformeanvalues,andchi‐squaretestforde‐ scriptivedata;inbold,p<0.05.bFirst‐linetreatment(beta‐interferon,glatirameracetate).cValuescalculatedfor42MSpatients(23patientsinstable group,19patientsinprogressiongroup);therewerenosignificantdifferencesregardingclinicalanddemographicdatabetweengroupofpatientswith DTI(n=46)andthevolumetricanalysis. 6 of 10 | KOLASA et AL. lesions(Geetal.,2004;Linetal.,2007)andbrainatrophyduetothe partialvolumeeffectwithinvoxels(Roosendaaletal.,2009). The main observation in our study is the tendency for baseline DTImetrics’associationintheCCwithdisabilityprogressionover 4 years with the most consistent and stable correlation observed in theCCsplenium.WeobservedanincreasedbaselineADandRD, indirectly representing axonal integrity and demyelination, which is associated with disability progression even after correcting for focal lesion volume. This result corroborates observations in a pre‐ viousstudyanalyzingonlyFAmapswheredecreasedFAintheCC spleniuminprimary‐progressiveMS(Bodinietal.,2013)wasasso‐ ciated with EDSS progression over 5years; however, longitudinal stabilityofDTIindiceshasnotbeenanalyzedinthisstudy.Aswe investigatedlongitudinalchangesinbothADandRDindices,which aremorespecificallyrelatedtoMSpathology,wecanspeculatethat inflammatory activity and axonal degeneration are responsible for clinicalworseninginourMScohort.Similartoourresults,increased RDintheCCbodyhasbeenassociatedwithmotorimpairmentex‐ pressedbythe9‐holepegtest(NHPT)ina1‐yearfollow‐upstudy withasmallnumber(n=22)ofpatientswithRRMS(Kern,Sarcona, TABLE 3 DTIindicesatbaselineandafter1yearofthefollow‐upinMSpatients Relapsing‐onset MS patients, n = 46 DTI metrics Baseline Year 1 Annual change p‐Valuea Median Min Max Median Min Max Median Min Max Corpus callosum genu FA 0.78 0.48 0.88 0.81 0.48 0.93 0.03 −0.08 0.14 <0.001 MD 0.80 0.62 1.31 0.82 0.67 1.07 −0.01 −0.34 0.20 0.891 AD 1.73 1.47 2.25 1.84 1.40 2.23 0.10 −0.62 0.43 0.006 RD 0.34 0.17 0.84 0.32 0.12 0.66 −0.04 −0.25 0.14 0.009 Corpus callosum body right FA 0.56 0.30 0.87 0.68 0.32 0.89 0.06 −0.17 0.30 <0.001 MD 0.83 0.58 1.08 0.81 0.70 1.11 0.01 −0.17 0.24 0.797 AD 1.47 1.07 1.84 1.63 1.13 1.95 0.12 −0.37 0.65 0.003 RD 0.53 0.20 0.92 0.44 0.20 0.75 −0.08 −0.33 0.11 <0.001 Corpus callosum body left FA 0.58 0.29 0.85 0.68 0.37 0.88 0.10 −0.18 0.31 0.001 MD 0.82 0.67 1.39 0.83 0.68 1.11 0.01 −0.42 0.24 0.589 AD 1.46 1.06 2.08 1.64 1.08 2.00 0.12 −0.37 0.65 0.027 RD 0.52 0.23 1.14 0.45 0.20 0.74 −0.09 −0.40 0.15 <0.001 Corpus callosum splenium FA 0.79 0.52 0.94 0.82 0.58 0.94 0.02 −0.07 0.20 0.004 MD 0.75 0.56 1.28 0.75 0.58 1.11 −0.02 −0.20 0.21 0.215 AD 1.67 1.23 2.13 1.69 1.42 2.07 0.04 −0.27 0.30 0.157 RD 0.28 0.11 0.86 0.25 0.09 0.69 −0.04 −0.31 0.14 0.003 Internalcapsuleright FA 0.72 0.62 0.83 0.71 0.55 0.85 0.00 −0.16 0.07 0.304 MD 0.74 0.66 0.79 0.74 0.67 0.83 0.01 −0.05 0.10 0.245 AD 1.46 1.33 1.73 1.45 1.27 1.80 0.00 −0.13 0.14 0.743 RD 0.36 0.24 0.47 0.37 0.23 0.52 0.00 −0.08 0.18 0.345 Internalcapsuleleft FA 0.71 0.47 0.80 0.71 0.49 0.83 0.01 −0.18 0.32 0.814 MD 0.73 0.66 0.87 0.73 0.66 0.84 0.01 −0.06 0.07 0.092 AD 1.46 1.25 1.69 1.48 1.25 1.83 0.03 −0.20 0.45 0.068 RD 0.35 0.26 0.61 0.35 0.25 0.58 0.01 −0.27 0.15 0.566 Note.AnnualchangeisdefinedasdifferencebetweenmedianDTIvalueat1yearandmedianDTIvalueatbaseline. DTI: diffusion tensor imaging; FA: fractional anisotropy; MD: mean diffusivity (×10−3 mm2/s); axial diffusivity (×10−3 mm2/s); radial diffusivity (×10−3 mm2/s). ap‐ValueforWilcoxontest;inbold,p<0.05. | 7 of 10 KOLASA et AL. Montag, Giesser, & Sicotte, 2011). Moreover, a histogram‐based analysis revealed a correlation between whole‐brain diffusivity al‐ terations and disability progression expressed by the MS Functional CompositeScaleover1year(Samannetal.,2012).Thesignificance of our observation is strengthened by the fact that axonal degener‐ ation,representedherebyincreasedAD,ismainlyresponsiblefor sustaineddisabilityinMS(Tallantyreetal.,2010).Thereasonwhy themoststablecorrelationbetweenDTIanddisabilityprogression was observed in the CC splenium of our study cohort might be re‐ lated to thin axons that are densest in the splenium and their pref‐ erentialsusceptibilitytoinjuryinMS,asalsosuggestedbyothers (Ciccarellietal.,2003).AstheCCbodyisathinanatomicalstructure, the partial volume effect from cerebrospinal fluid may influence the resultsofDTImeasurementsintheregionweobserved.ROI‐based methodology is sensitiveto the change inDTI parameters, avoids postprocessing calculation errors, and is suitable for investigating well‐definedbrainstructuressuchasCCandIC(Snook,Plewes,& Beaulieu,2007).GoodreproducibilityofDTImeasurementsinour presentandpreviousstudies(Branderetal.,2010;Hakulinenetal., 2012;Kolasaetal.,2015),alongwithcoherentfibersinthewhite mattertractsoftheICandtheCC,suggeststhatdiffusivityabnor‐ malities,asobservedhere,mayberelatedtowhitematterpathology TABLE 4 RelationshipofbaselineDTImetricswithdisabilityprogressionmeasuredbyEDSSincreaseoverthe4‐yearfollow‐up DTI metrics Stable group n = 24 Progression group n = 22 p‐ValueaOdds ratio 95% CIMedian Min Max Median Min Max Corpus callosum genu FA 0.81 0.52 0.88 0.74 0.48 0.88 0.04 0.00 0.00 0.61 MD 0.80 0.62 1.21 0.83 0.67 1.31 0.06 1.05 1.00 1.10 AD 1.70 1.47 2.07 1.76 1.55 2.25 0.36 1.02 0.98 1.06 RD 0.28 0.17 0.80 0.37 0.17 0.84 0.04 1.05 1.00 1.09 Corpus callosum body right FA 0.67 0.40 0.87 0.52 0.30 0.77 0.01 0.00 0.00 0.24 MD 0.80 0.58 1.07 0.87 0.69 1.08 0.04 1.08 1.00 1.15 AD 1.51 1.07 1.79 1.39 1.18 1.84 0.25 0.98 0.95 1.01 RD 0.46 0.20 0.73 0.62 0.30 0.92 0.01 1.07 1.02 1.12 Corpus callosum body left FA 0.66 0.38 0.85 0.53 0.29 0.82 0.07 0.02 0.00 1.37 MD 0.81 0.67 1.32 0.84 0.70 1.39 0.12 1.03 0.99 1.08 AD 1.50 1.19 2.08 1.42 1.06 2.04 0.53 0.99 0.97 1.02 RD 0.46 0.23 0.93 0.58 0.30 1.14 0.04 1.04 1.00 1.08 Corpus callosum splenium FA 0.82 0.63 0.89 0.76 0.52 0.94 0.04 0.00 0.00 0.76 MD 0.71 0.56 0.95 0.79 0.68 1.28 0.01 1.13 1.03 1.23 AD 1.62 1.23 1.87 1.80 1.50 2.13 0.01 1.08 1.02 1.14 RD 0.25 0.16 0.50 0.35 0.11 0.86 0.02 1.07 1.01 1.14 Internalcapsuleright FA 0.72 0.62 0.78 0.72 0.62 0.83 0.89 1.01 0.89 1.14 MD 0.72 0.66 0.79 0.75 0.66 0.78 0.14 1.13 0.96 1.33 AD 1.45 1.33 1.61 1.49 1.34 1.73 0.16 1.05 0.98 1.12 RD 0.35 0.29 0.47 0.36 0.24 0.46 0.78 1.02 0.90 1.15 Internalcapsuleleft FA 0.71 0.47 0.80 0.71 0.58 0.80 0.49 1.03 0.94 1.13 MD 0.71 0.66 0.87 0.74 0.66 0.80 0.15 1.12 0.96 1.32 AD 1.42 1.25 1.63 1.48 1.30 1.69 0.05 1.07 1.00 1.15 RD 0.35 0.26 0.61 0.36 0.31 0.48 0.86 0.99 0.90 1.09 Note.DTI:diffusiontensorimaging;FA:fractionalanisotropy;MD:meandiffusivity(×10−3 mm2/s);axialdiffusivity(×10−3 mm2/s);radialdiffusivity (×10−3 mm2/s);EDSS:ExpandedDisabilityStatusScale. ap‐ValueforlogisticregressionadjustedforageanddurationofsymptomsforpredictionofEDSSprogressionoverthe4‐yearfollow‐up;inbold, p<0.05. 8 of 10 | KOLASA et AL. rather than method‐based variability or crossing fibers within a voxel(Wheeler‐Kingshott&Cercignani,2009). The1‐yearlongitudinalDTIanalysisrevealedasignificantchange ofDTImetricsintheCCbutnotintheIC.Inthiscohortwithac‐ tive MS (Lublin et al., 2014), we observed an increase instead of theexpecteddecreaseofFAintheCC.Thisincreasewasdrivenby increasedADanddecreasedRDintheCCgenuandthebodyand decreasedRDintheCCsplenium.Duetoashortradiologicalfol‐ low‐upwithonlytwoMRIexaminations,wecannotfullydetermine thesustainedchangesinDTIparameters.AlongitudinalDTIstudy withshorterintervalMRIexaminationswouldbemoreappropriate toevaluate thetemporalchangesindiffusivity(Tianet al.,2012). Moreover, without healthy controls in the longitudinal analysis, we cannot clearly assess pathophysiological processes involved in temporalDTIchangesobservedhereintheCC.Similartoourob‐ servation, serial DTI study using tractography showed significant longitudinalchangeinDTImetricsinthesupratentorialbrainandthe CCoftheMScohortwithdifferentdiseasephenotypes(Harrisonet al.,2011).However,suchtemporalDTIevolutionwasnotobserved inarecentROI‐basedMSstudyincludingnatalizumab‐treatedpa‐ tients(Ontanedaetal.,2017).InanotherstudyinearlyRRMSwitha 2‐yearfollow‐up,therateofchangeindiffusivitycharacteristicsas‐ sessed by a histogram‐based whole‐brain analysis did not correlate with disability progression expressed by an EDSS increase, which confirmsourresults(Rashidetal.,2008).Conversely,theassociation betweendiffusivityinthefrontalNAWManddisabilityasmeasured by the MS Functional Composite Scale has been found in primary‐ progressiveMS(Schmiereretal.,2004).Thus,inconsistentresults observed in previous studies may relate to technical differences, intrinsicheterogeneityofMS(Baroneetal.,2018)andMScohorts, and different clinical scales used in disability evaluation in MS. Altogether,theresultsofourlongitudinalstudysuggestthatDTI is a sensitive tool in monitoring diffuse abnormalities responsible for disabilityaccumulation,andCCmaybeagoodtargetforDTIanaly‐ sis.WebelievethatanassessmentoftheprognosticvalueofDTIin an MS cohort with variable clinical characteristics such as ours which istypicallyencounteredineverydaypracticehaspracticalvalue,as suggested by others (Harrison et al., 2011). Moreover, changes in RDobservedheremayplayanimportantroleinmonitoringimmu‐ nomodulatory treatment effects because the attenuation of inflam‐ matory demyelination is the main target of current MS therapies. This statement is supported by the results of the study by Fox et al.,whereDTIabnormalitiesindicatingremyelinationhavebeenob‐ servedafterstartingnatalizumabtreatment(Foxetal.,2011). We did not observe any associations in the IC between base‐ line DTI and disability progression. Moreover, no longitudinal changes in DTI metrics were observed in the IC, although signifi‐ cant differences related to DTI between healthy controls and the MS group were already observed. This result indicates that diffu‐ sivityabnormalitiesmayalreadyexistintheIC,buttheyprogress atdifferentrates,andimagedisabilityprogressiondistinctlythanin theCC(Geetal.,2004).Ourfindingissupportedbystudieswhere no correlation between DTI indices in the corticospinal tract and disabilityprogressionexpressedbyanEDSSincreasehasbeenob‐ served(Fritz,Keller,Calabresi,&Zackowski,2017;Linetal.,2007). Conversely,suchcorrelationbetweenDTIparametersandEDSShas been previously reported in cross‐sectional studies (Daams et al., 2015;Tovar‐Molletal.,2015). Our results corroborate the observed lack of clear‐cut asso‐ ciationbetweentheT1/T2 brainlesionload,brain atrophy,and disabilityprogressionexpressedbyEDSSchangeinotherfollow‐ upstudiesforover2yearsinrelapsingMS(Enzingeretal.,2011; Tiberioetal.,2005).Althoughvolumetricmeasurementsdidnot clearlycorrelatewithdisabilityprogressioninourstudy,theFLAIR lesion volume and BPF showed some effect and modified the cor‐ relation between DTI and disability progression. In contrast to ourresults,associationbetweenshort‐termphysicalworsening, T2brainlesionload(Gauthieretal.,2007;Moodieetal.,2012), andbrainatrophyhasbeenreportedelsewhere(Minnebooetal., 2008;Samannetal.,2012).Thesediscordantresultssuggestthat the focal brain lesion load and brain atrophy may have additional impact on disability accumulation in relapsing‐onset MS. The lack of significant correlation here may be limited by a small number ofcasesinthestudycohort,wherediseaseactivityanddisabil‐ ity progression were variable. Other limitations in our inferences mayresultfromthefairlygrossnatureoftotalEDSSinasituation where there is a need to evaluate subtle changes in motor func‐ tions during a short observation period. Inconclusion,ourresultsamongotherssuggestthatdiffusivity abnormalitiesexist inrelapsing‐onsetMS patients;however,their dynamic change over time is different with respect to anatomical location. Additionally, diffusivity metrics in the normal‐appearing CC may be associated with disability accumulation in relapsing‐onset MS and suggest the crucial role of the CC in monitoring disease pro‐ gression.Givenitshighsensitivityindetectingdiffusebrainabnor‐ malities,DTIindicesmayserveasapotentialbiomarkerofdisease progression;however,methodstandardizationisneeded.Moreover, stabilityandsensitivitytounderlyingpathologyofDTImetricshave tobeconfirmedinlongitudinalstudies(Wattjesetal.,2015).Acom‐ bination of diffusion measures with other findings from conventional MRImayprovidecomplementaryinformationondifferenttypesof pathological damage in MS. ACKNOWLEDGMENTS The authors thank Mika Helminen, MSc, for statistical assistance, Minna Raunio, MD, for neurological examination of the patients, Maija Rossi, MSc, DSc, for volumetric measurements, and Pabitra Basnyat,MSc,forhelpinpreparationofthefigures.Thisstudywas funded by Competitive Research Funding of Tampere University Hospital, the Finnish Cultural Foundation, and the Finnish Brain Foundation. CONFLICT OF INTEREST We declare that we have no conflict of interest. | 9 of 10 KOLASA et AL. ORCID Marcin Kolasa https://orcid.org/0000‐0003‐3782‐4938 Marja‐Liisa Sumelahti https://orcid.org/0000‐0001‐6581‐0483 REFERENCES Alexander, A. L., Lee, J. E., Lazar, M., & Field, A. S. (2007). Diffusion tensor imaging of the brain. Neurotherapeutics: the Journal of the American Society for Experimental NeuroTherapeutics,4(3),316–329. S1933‐7213(07)00095‐5 Andersen,O.,Hildeman,A.,Longfils,M.,Tedeholm,H.,Skoog,B.,Tian, W.,…Maier,S.E.(2018).Diffusiontensorimaginginmultiplesclero‐ sis at different final outcomes. Acta Neurologica Scandinavica,137(2), 165–173.https://doi.org/10.1111/ane.12797 Aung,W.Y.,Mar,S.,&Benzinger,T.L.(2013).DiffusiontensorMRIas a biomarker in axonal and myelin damage. Imaging in Medicine,5(5), 427–440.https://doi.org/10.2217/iim.13.49 Banaszek, A., Bladowska, J., Pokryszko‐Dragan, A., Podemski, R., & Sasiadek, M. J. (2015). Evaluation of the degradation of the se‐ lected projectile, commissural and association white matter tracts within normal appearing white matter in patients with multiple sclerosisusingdiffusiontensorMRimaging–Apreliminarystudy. Polish Journal of Radiology, 80, 457–463. https://doi.org/10.12659/ PJR.894661 Barone, S., Caligiuri, M. E., Valentino, P., Cherubini, A., Chiriaco, C., Granata,A.,…Quattrone,A.(2018).Multimodalassessmentofnor‐ mal‐appearing corpus callosum is a useful marker of disability in re‐ lapsing‐remittingmultiplesclerosis:AnMRIclusteranalysisstudy. Journal of Neurology, 265(10), 2243–2250. https://doi.org/10.1007/ s00415‐018‐8980‐y Bodini,B.,Cercignani,M.,Khaleeli,Z.,Miller,D.H.,Ron,M.,Penny,S., …Ciccarelli,O.(2013).Corpuscallosumdamagepredictsdisability progression and cognitive dysfunction in primary‐progressive MS after five years. Human Brain Mapping,34(5),1163–1172.https://doi. org/10.1002/hbm.21499 Brander, A., Kataja, A., Saastamoinen, A., Ryymin, P., Huhtala, H., Ohman, J., … Dastidar, P. (2010). Diffusion tensor imaging of the braininahealthyadultpopulation:Normativevaluesandmeasure‐ mentreproducibilityat3Tand1.5T.Acta Radiologica (Stockholm, Sweden: 1987),51(7),800–807.https://doi.org/10.3109/02841851. 2010.495351 Ciccarelli, O., Werring, D. J., Barker, G. J., Griffin, C. M., Wheeler‐ Kingshott,C.A.,Miller,D.H.,&Thompson,A.J.(2003).Astudyof the mechanisms of normal‐appearing white matter damage in mul‐ tiplesclerosisusingdiffusiontensorimaging–evidenceofwallerian degeneration. Journal of Neurology, 250(3), 287–292. https://doi. org/10.1007/s00415‐003‐0992‐5 Daams, M., Steenwijk, M. D., Wattjes, M. P., Geurts, J. J., Uitdehaag, B. M., Tewarie, P. K., … Barkhof, F. (2015). Unraveling the neuro‐ imaging predictors for motor dysfunction in long‐standing multi‐ ple sclerosis. Neurology, 85(3), 248–255. https://doi.org/10.1212/ WNL.0000000000001756 DeStefano,N.,Stromillo,M.L.,Giorgio,A.,Bartolozzi,M.L.,Battaglini, M.,Baldini,M.,…Sormani,M.P.(2016).Establishingpathologicalcut‐ offs of brain atrophy rates in multiple sclerosis. Journal of Neurology, Neurosurgery, and Psychiatry,87(1),93–99.https://doi.org/10.1136/ jnnp‐2014‐309903 Enzinger, C., Fuchs, S., Pichler, A., Wallner‐Blazek, M., Khalil, M., Langkammer, C., … Fazekas, F. (2011). Predicting the sever‐ ity of relapsing‐remitting MS: The contribution of cross‐sec‐ tional and short‐term follow‐up MRI data. Multiple Sclerosis (Houndmills, Basingstoke, England), 17(6), 695–701. https://doi. org/10.1177/1352458510394454 Filippi,M.,Absinta,M.,&Rocca,M.A.(2013).FutureMRItoolsinmul‐ tiple sclerosis. Journal of the Neurological Sciences,331(1–2),14–18. https://doi.org/10.1016/j.jns.2013.04.025 Fink,F.,Klein,J.,Lanz,M.,Mitrovics,T.,Lentschig,M.,Hahn,H.K.,& Hildebrandt,H.(2010).Comparisonofdiffusiontensor‐basedtrac‐ tographyandquantifiedbrainatrophyforanalyzingdemyelination and axonal loss in MS. Journal of Neuroimaging, 20(4), 334–344. https://doi.org/10.1111/j.1552‐6569.2009.00377.x Fox,R.J.,Cronin,T.,Lin,J.,Wang,X.,Sakaie,K.,Ontaneda,D.,…Phillips, M.D.(2011).Measuringmyelinrepairandaxonallosswithdiffusion tensor imaging. American Journal of Neuroradiology, 32(1), 85–91. https://doi.org/10.3174/ajnr.A2238 Fritz, N. E., Keller, J., Calabresi, P. A., & Zackowski, K. M. (2017). Quantitative measures of walking and strength provide insight into brain corticospinal tract pathology in multiple sclerosis. NeuroImage. Clinical, 14, 490–498. https://doi.org/10.1016/j. nicl.2017.02.006 Gauthier,S.A.,Mandel,M.,Guttmann,C.R.,Glanz,B.I.,Khoury,S.J., Betensky,R.A.,&Weiner,H.L.(2007).Predictingshort‐termdisabil‐ ity in multiple sclerosis. Neurology,68(24),2059–2065.68/24/2059 Ge, Y., Law, M., Johnson, G., Herbert, J., Babb, J. S., Mannon, L. J., & Grossman,R.I.(2004).Preferentialoccultinjuryofcorpuscallosum in multiple sclerosis measured by diffusion tensor imaging. Journal of Magnetic Resonance Imaging,20(1),1–7.https://doi.org/10.1002/ jmri.20083 Hakulinen, U., Brander, A., Ryymin, P., Ohman, J., Soimakallio, S., Helminen, M., … Eskola, H. (2012). Repeatability and variation of region‐of‐interest methods using quantitative diffusion tensor MR imaging of the brain. BMC Medical Imaging,12,30.https://doi. org/10.1186/1471‐2342‐12‐30 Harrison,D.M.,Caffo,B.S.,Shiee,N.,Farrell,J.A.,Bazin,P.L.,Farrell, S. K., … Reich, D. S. (2011). Longitudinal changes in diffusion ten‐ sor‐based quantitative MRI in multiple sclerosis. Neurology, 76(2), 179–186.https://doi.org/10.1212/WNL.0b013e318206ca61 Heinonen,T.,Dastidar,P.,Eskola,H.,Frey,H.,Ryymin,P.,&Laasonen,E. (1998).Applicabilityofsemi‐automaticsegmentationforvolumetric analysis of brain lesions. Journal of Medical Engineering & Technology, 22(4),173–178.https://doi.org/10.3109/03091909809032536 Henry,R.G.,Oh,J.,Nelson,S.J.,&Pelletier,D.(2003).Directionaldif‐ fusion in relapsing‐remitting multiple sclerosis: A possible in vivo signature of wallerian degeneration. Journal of Magnetic Resonance Imaging,18(4),420–426.https://doi.org/10.1002/jmri.10379 Kaunzner, U.W.,& Gauthier,S.A.(2017). MRI in the assessmentand monitoring of multiple sclerosis: An update on best practice. Therapeutic Advances in Neurological Disorders, 10(6), 247–261. https://doi.org/10.1177/1756285617708911 Kern, K. C., Sarcona, J., Montag, M., Giesser, B. S., & Sicotte, N. L. (2011). Corpus callosal diffusivity predicts motor impairment in relapsing‐remitting multiple sclerosis: A TBSS and tractography study. NeuroImage, 55(3), 1169–1177. https://doi.org/10.1016/j. neuroimage.2010.10.077 Kolasa,M.,Hakulinen,U.,Helminen,M.,Hagman,S.,Raunio,M.,Rossi, M., … Elovaara, I.(2015). Longitudinalassessment ofclinicallyiso‐ lated syndrome with diffusion tensor imaging and volumetric MRI. Clinical Imaging, 39(2), 207–212. https://doi.org/10.1016/j. clinimag.2014.10.014 Lin,F.,Yu,C.,Jiang,T.,Li,K.,&Chan,P.(2007).Diffusiontensortractog‐ raphy‐based group mapping of the pyramidal tract in relapsing‐remit‐ ting multiple sclerosis patients. American Journal of Neuroradiology, 28(2),278–282.28/2/278 Llufriu,S.,Blanco,Y.,Martinez‐Heras,E.,Casanova‐Molla,J.,Gabilondo, I., Sepulveda, M., … Saiz, A. (2012). Influence of corpus callosum damageoncognitionandphysicaldisabilityinmultiplesclerosis:A multimodal study. PLoS ONE,7(5),e37167.https://doi.org/10.1371/ journal.pone.0037167