ARTICLE OPEN ACCESS

Network Damage Predicts Clinical Worsening inMultiple SclerosisA 64-Year Study

Maria A Rocca MD Paola Valsasina MSc Alessandro Meani MSc Elisabetta Pagani MSc

Claudio Cordani PT Chiara Cervellin PT and Massimo Filippi MD

Neurol Neuroimmunol Neuroinflamm 20218e1006 doi101212NXI0000000000001006

Correspondence

Prof Filippi

filippimassimohsrit

AbstractObjectiveIn multiple sclerosis (MS) clinical impairment is likely due to both structural damage andabnormal brain function We assessed the added value of integrating structural and functionalnetwork MRI measures to predict 64-year MS clinical disability deterioration

MethodsBaseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233patients with MS and 77 healthy controls Patients underwent a neurologic evaluation atbaseline and at 64-year median follow-up (interquartile range = 506ndash751 years) At follow-uppatients were classified as clinically stableworsened according to disability changes Inrelapsing-remitting (RR) MS secondary progressive (SP) MS conversion was evaluatedGlobal brain volumetry was obtained Furthermore independent component analysis identifiedthe main functional connectivity (FC) and gray matter (GM) network patterns

ResultsAt follow-up 105233 (45) patients were clinically worsened 26157 (16) patients withRRMS evolved to SPMS The treatment-adjusted random forest model identified normalizedGM and brain volumes decreased FC between default-mode networks increased FC of the leftprecentral gyrus in the sensorimotor network (SMN) and GM atrophy in the fronto-parietalnetwork (false discovery rate [FDR]-corrected p = range 001ndash009) as predictors of clinicalworsening (out-of-bag [OOB] accuracy = 074) An expected contribution of baseline disabilitywas also present (FDR-p = 001) Baseline disability normalized GM volume and GM atrophyin the SMN (FDR-p = range 001ndash009) were independently associated with SPMS conversion(OOB accuracy = 084) At receiver operating characteristic analysis including network MRIvariables improved disability worsening (p = 005) and SPMS conversion (p = 002) prediction

ConclusionsIntegration of MRI network measures helped determining the relative contributions of globallocal GM damage and functional reorganization to clinical deterioration in MS

From the Neuroimaging Research Unit (MAR) Division of Neuroscience and Neurology Unit IRCCS San Raffaele Scientific Institute Vita-Salute San Raffaele University (MARMF) Neuroimaging Research Unit (PV AM EP Claudio Cordani Chiara Cervellin) Division of Neuroscience IRCCS San Raffaele Scientific Institute and Neuroimaging ResearchUnit (MF) Division of Neuroscience Neurology Unit Neurorehabilitation Unit and Neurophysiology Service IRCCS San Raffaele Scientific Institute Milan Italy

Go to NeurologyorgNN for full disclosures Funding information is provided at the end of the article

The Article Processing Charge was funded by the authors

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 40 (CC BY-NC-ND) which permits downloadingand sharing the work provided it is properly cited The work cannot be changed in any way or used commercially without permission from the journal

Copyright copy 2021 The Author(s) Published by Wolters Kluwer Health Inc on behalf of the American Academy of Neurology 1

Multiple sclerosis (MS) is the most common inflammatorydemyelinating and neurodegenerative CNS disease charac-terized by a great interindividual variability1 Now that severaldisease-modifying treatments (DMTs) are available2 there isan increasing need to identify patients with MS having a moresevere disease evolution to optimize patientsrsquo managementand increase the benefitrisk ratio

Conventional MRI revolutionized MS evaluation and was for-mally included inMS diagnostic criteria3 In treated patients withMS MRI disease activity measures are combined with clinicalevaluation to guide treatment decisions4 However the predictivevalue of conventional MRI on subsequent disease course is stilldebated with only some studies detecting a significant influenceof white matter (WM) lesion burden on disease evolution5-7

A large effort has been spent to improve predictivity of MRIin MS by assessing clinically relevant compartments (ie graymatter [GM] strategic WM tracts and spinal cord) and byapplying quantitative techniques sensitive toward differentpathologic substrates8 This helped demonstrate that in pa-tients with established MS and in progressive MS (PMS) GMdamage plays amajor role in explaining long-term disability andcognitive decline9-12

Brain function depends on not only local processing but alsoeffective global communication and integration of informationBecause impaired interaction among the main brain areas maycause disability accumulation in MS mapping structural andfunctional brain networks might be clinically relevant Studiesof structural network abnormalities which decomposed WMlesions and GM maps into distinct patterns covarying acrosssubjects revealed that a stronger network disruption in these 2compartments was correlated with severe disability13 and wasable to predict the subsequent disease course1415 Resting-state(RS) functional connectivity (FC) network studies showedcomplex abnormalities in patients with MS1617 characterizedby an early RS FC increase and RS FC decrease in moreadvanced phases Abnormal within- and between-network RSFC contributed to explain MS phenotypic variability and cog-nitive deficits1618 and was able to predict clinical deteriorationat medium term1920 whereas abnormal FC during a psycho-logical stress functional (fMRI) paradigm contributed to ex-plain future GM atrophy21 However the combined ability offunctional and structural network techniques in predictingsubsequent clinical worsening of MS was not investigated yet

In this study we hypothesized that integrating structural andfunctional network information may help to identify specificcircuits being critical for clinical deterioration in heterogeneousdiseases such as MS and may improve prediction of the sub-sequent disease course in terms of disability increase and evo-lution to a more severe clinical phenotype To test this weanalyzed volumetric and RS fMRI data from a large MS cohortand mapped abnormalities of the main data-driven functionaland structural GM networks Then we assessed the added valueof integrated structural and functional networkMRImeasures topredict clinical disability deterioration and conversion to sec-ondary progressive (SP) MS over a 64-year follow-up

MethodsStandard Protocol Approvals Registrationsand Patient ConsentsApproval was received from the local ethical standards com-mittee on human experimentation (protocol ID FISM 2008R13) written informed consent was obtained from all sub-jects before study participation

SubjectsRecruited subjects are part of a prospective cohort at HospitalSan Raffaele Patients are assessed clinically at the MS Centerat least once per year and are offered the opportunity toundergo research brain MRI Inclusion criteria are reported inthe e-Methods (linkslwwcomNXIA486) For the currentanalysis we selected patients with a clinical follow-up ge3 yearsfrom MRI acquisition The final cohort included 233 patientswithMS (90143malesfemales mean age = 423 years SD =108 years) there were 157 relapsing-remitting (RR) MS 59SPMS and 17 primary progressive (PP) MS22 Cross-sectional MRI from 77 matched healthy controls (HCs)(3542 malesfemales mean age = 410 years SD = 145years) were also analyzed Part of baseline MRIclinicalevaluations was previously published1623

Clinical AssessmentAt baseline a complete neurologic evaluation was performedin patients with MS with rating of the Expanded DisabilityStatus Scale (EDSS) score24 and DMT recording The follow-up neurologic assessment was performed after a median of 64years (interquartile range = 506ndash751 years) and includedEDSS score rating occurrence of clinical relapses and DMTchanges (binary coded) At follow-up patients were clinically

GlossaryAUC = area under the curve BGN = basal ganglia network DMN = default-mode network DMT = disease-modifyingtreatment EDSS = Expanded Disability Status Scale FC = functional connectivity FDR = false discovery rate fMRI =functional MRI FPN = fronto-parietal networkGM = gray matterHC = healthy control IC = independent component LV =lesion volume MS = multiple sclerosis OOB = out of bag PPMS = primary progressive MS RF = random forest RR =relapsing-remitting MS RS = resting state SBM = source-based morphometry SMA = supplementary motor area SMN =sensorimotor network SN = salience network SPMS = secondary progressive MS WM = white matter

2 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

Table 1 Main Demographic Clinical and Conventional MRI Measures of HCs and Patients With MS (as a Whole andAccording to the Clinical Phenotype)

HCs(n = 77)

Patients with MS(n = 233)

pValuea

Patients with RRMS(n = 157)

Patients with PMS(n = 76)

pValueb

pValuec

pValued

Men () 35 (45) 90 (39) 029e 62 (40) 28 (37) 038e 028e 070e

Women () 42 (55) 143 (61) 95 (60) 48 (63)

Mean age (SD) [y] 410 (145) 423 (108) 049f 393 (98) 484 (102) 035f lt0001f lt0001f

Median disease duration(IQR) [y]

mdash 130 (75ndash180) mdash 110 (58ndash161) 170 (125ndash240) mdash mdash lt0001g

Median EDSS score (IQR) mdash 25 (15ndash50) mdash 20 (15ndash25) 60 (53ndash65) mdash mdash lt0001g

Baseline DMT mdash mdash mdash mdash lt0001e

No DMT (n) 37 11 26

Interferons (n)glatirameracetate (n)

10644 8533 2111

Natalizumab (n) 16 13 3

Fingolimod (n) 13 12 1

Immunosuppressants

Azathioprine (n) 10 1 9

Cyclophosphamide (n) 2 0 2

Methotrexate (n) 1 1 0

Mitoxantrone (n) 4 1 3

Median follow-up duration(IQR) [y]

mdash 64 (51ndash75) mdash 65 (55ndash76) 533 (37ndash71) mdash mdash 0001g

Mean ARR at follow-up (SD) mdash 007 (014) mdash 008 (015) 004 (011) mdash mdash 002h

DMT change mdash mdash mdash mdash 083i

Yes () 111(47) 76 (48) 35 (46)

No () 122 (53) 81 (52) 41 (54)

Median T2 LV(IQR) [mL]

02(008ndash08)

62 (25ndash138) lt0001j 45 (19ndash85) 140 (54ndash243) lt0001j lt0001j lt0001j

Median T1 LV(IQR) [mL]

mdash 43 (94ndash100) mdash 28 (11ndash58) 92 (39ndash177) mdash mdash lt0001j

Mean NBV (SD) [mL] 1568 (81) 1489 (108) lt0001j 1514 (102) 1436 (102) lt0001j lt0001j lt0001j

Mean NGMV (SD) [mL] 732 (49) 672 (80) lt0001j 690 (75) 633 (75) lt0001j lt0001j 0002j

Mean NWMV (SD) [mL] 836 (43) 817 (48) lt0001j 824 (43) 803 (56) 004j lt0001j 008j

Mean NDGMV (SD) [mL] 52 (4) 47 (6) lt0001j 48 (5) 44 (7) lt0001j lt0001j lt0001j

Abbreviations ARR = annualized relapse rate DMT = disease-modifying treatment EDSS = Expanded Disability Status Scale HC = healthy control IQR =interquartile range LV = lesion volume MS = multiple sclerosis NBV = normalized brain volume NDGMV = normalized deep gray matter volume NGMV =normalized gray matter volume NWMV = normalized white matter volume PMS = progressive multiple sclerosis RRMS = relapsing-remitting multiplesclerosisa Patients with MS vs HCsb Patients with RRMS vs HCsc Patients with PMS vs HCsd Patients with PMS vs RRMSe Chi-square testf Two-sample testg Mann-Whitney U testh Negative binomial regression modeli Univariate logistic regression model adjusted for follow-up durationj Age- and sex-adjusted linear models

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worsened if they had an EDSS score increase ge10 when thebaseline EDSS score was lt60 ge05 when the baseline EDSSscore was ge609 or ge15 if the baseline score EDSS was 025

EDSS score changes were confirmed after a 3-month relapse-free period In RRMS conversion to SPMS (defined inagreement with previous studies112627 by development ofirreversible EDSS score increase over at least a 1‐year dura-tion independent of relapses) was also assessed

MRIAcquisition andConventionalMRI AnalysisAt baseline the following MRI scans were collected using a30T scanner from patients and HC (e-Methods linkslwwcomNXIA486) (1) T2-weighted single-shot echo planarimaging for RS fMRI (2) 3D T1-weighted fast field echo forT1-hypointense lesion volume (LV) and global brain

volumetry assessment and (3) dual-echo turbo spin echo forT2-hyperintense LV assessment

RS FC Within and Among NetworksThe main purpose was to use independent component (IC)analysis to produce RS FC networks This was achieved after RSfMRI preprocessing (e-Methods linkslwwcomNXIA486)using the GIFT software (mialabmrnorgsoftwaregift) andInfomax algorithm The number of group ICs was 40 accordingto the minimum description length criterion Visual inspectionand template matching28-31 allowed selection of sensory motorand high-order integrative networks The temporal associationamong selected ICs was explored using the functional networkconnectivity (FNC) toolbox (mialabmrnorg e-Methods linkslwwcomNXIA486)

Table 2 Demographic Clinical and Conventional MRI Measures of Patients With MS Divided According to ClinicalWorsening at Follow-up and of Patients With RRMS Divided According to Conversion to SPMS at Follow-up

Clinically stableMS (n = 128)

Clinicallyworsened MS (n =105)

pValuea

Patients with RRMS notconverting to SPMS (n = 131)

Patients with RRMSconverting to SPMS (n = 26)

pValuea

Men ()Women ()

44 (34)84 (66)

46 (44)59 (56)

012 46 (35)85 (65)

16 (62)10 (38)

0009

Mean baseline age (SD) [y] 395 (105) 456 (102) lt0001 382 (95) 448 (94) 0002

Median baseline diseaseduration (IQR) [y]

118 (57ndash167) 153 (100ndash200) 0003 102 (57ndash159) 139 (95ndash170) 017

Median baseline EDSSscore (IQR)

20 (15ndash35) 40 (20ndash60) lt0001 15 (15ndash20) 30 (20ndash40) lt0001

Baseline phenotype(RRMSprogressive MS)

10523 5253 lt0001 mdash mdash mdash

Baseline DMT 068 085

No () 22 (17) 15 (14) 9 (7) 2 (8)

Yes () 106 (33) 90 (86) 122 (93) 24 (92)

Median follow-upduration (IQR) [y]

63 (48ndash72) 65 (53ndash77) 022b 65 (53ndash76) 68 (58ndash76) 044b

Mean ARR at follow-up(SD)

007 (015) 007 (012) 069 009 (015) 007 (011) 053

DMT change 0006 001

Yes () 50 (39) 61 (58) 57 (44) 19 (73)

No () 78 (61) 44 (42) 74 (56) 7 (27)

Median T2 LV (IQR) [mL] 47 (20ndash93) 81 (31ndash184) 0002 42 (17ndash80) 62 (25ndash125) 022

Median T1 LV (IQR) [mL] 28 (11ndash61) 56 (21ndash133) 0001 24 (10ndash56) 44 (15ndash99) 020

Mean NBV (SD) [mL] 1518 (102) 1453 (104) lt0001 1527 (101) 1451 (80) lt0001

Mean NGMV (SD) [mL] 696 (76) 643 (74) lt0001 701 (75) 639 (55) lt0001

Mean NWMV (SD) [mL] 822 (47) 810 (50) 005 826 (43) 812 (39) 012

Mean NDGMV (SD) [mL] 48 (5) 45 (6) lt0001 48 (5) 47 (4) 009

Abbreviations ARR = annualized relapse rate DMT = disease-modifying treatment EDSS = Expanded Disability Status Scale IQR = interquartile range LV =lesion volume MS = multiple sclerosis NBV = normalized brain volume NDGMV = normalized deep gray matter volume NGMV = normalized gray mattervolume NWMV = normalized white matter volume RRMS = relapsing-remitting multiple sclerosis SPMS = secondary progressive multiple sclerosisa Univariate logistic regression model adjusted for follow-up durationb Mann-Whitney U test

4 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

Structural GM Network Analysis Source-Based MorphometryThe main aim was to use source-based morphometry (SBM)to produce GM networks that is groups of distinct GM re-gions showing common covariations among subjects Pre-processed GMmaps (e-Methods linkslwwcomNXIA486)underwent the GIFT SBM toolbox and the Infomax algo-rithm32 The SBM model order (n = 40) matched functionalanalysis After IC selection by visual inspection and spatialmatching with functional components33 GM loading coeffi-cients representing the degree to which a network is presentin individual subjects were extracted and used for statistics Ifthe main sign was negative GM IC maps and loading coef-ficients were inverted32 Only pairs of matching functional-structural networks were included

Statistical Analysis

Between-Group ComparisonsAnalyses were performed using SAS (r94) and R software(v344) Normal distribution was checked with theKolmogorov-Smirnov test Shapiro-Wilk test and Q-Q plotsLVs were log transformed Between-group comparisons of de-mographic and clinical variables were assessed using the Pearsonχ2 2-sample t or Mann-Whitney U tests as appropriate Age-and sex-adjusted linear models were used to compare conven-tional MRI between groups Patients with SPMS and those withPPMSwere grouped together leading to these comparisons (1)patients with MS vs HCs (2) patients with RRMS vs HCs (3)patients with PMS vsHCs and (4) patients with PMS vs RRMS

Voxel-wise RS FC differences between patients with MS andHCs were investigated using SPM12 and age- and sex-adjustedlinear models (p lt 0001 uncorrected and p lt 005 clusterwisefamily-wise error corrected) Models were masked with cor-rected effects of interest retaining only significant RS FC withineach network Average RS FC Z-scores of regional significantdifference were extracted using the MarsBaR toolbox

Prediction AnalysisFollow-up duration-adjusted logistic regressions were runon all demographic clinical conventional and network-

Figure 2 Between-Group Comparison of Functional andStructural Networks Between HC and MS

(A) Voxel-wise between-group comparisons of RS FC the bluendashlight bluecolor scale shows decreased RS FC inMS vsHCwhereas the red-yellow colorscale shows increased RS FC in MS vs HC (age- and sex-adjusted 2-sample ttest p lt 0001 uncorrected for illustrative purposes) (B) Boxplots showingaverage loading coefficients (means and SDs) of relevantGMnetworksMS =multiple sclerosis FC = functional connectivity GM = gray matter HC =healthy controls

Figure 1 Main Functional and Structural Networks of In-terest Related to Sensory Motor and High-OrderIntegrative Functions in Patients With MultipleSclerosis (MS) and Healthy Controls (HCs)

Spatial maps of relevant (A) resting-state (RS) functional connectivity (FC)and (B) gray matter (GM) networks RS FC networks were thresholded at p lt005 with family-wise error corrected (positive effects of interest from the 2-sample t test) GM structural networks were selected by visual inspectionand spatial matching with RS FC networks and as in previous studies23 theywere displayed using a threshold z-score gt2

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Table 3 Candidate Predictors (p lt 010) of Disability Worsening in All Patients With MS and Candidate Predictors ofConversion to SPMS in RRMS by Follow-up Adjusted Logistic Regression Models

Predictors of disability worseningOR in all MS(95 CI) SE p (unc) p (FDR)

OR in RRMS(95 CI)

OR in PMS(95 CI) p (unc)a p (FDR)

Baseline ageb 183 (139ndash241) 036 lt0001 lt0001 184 (126ndash269) 103 (062ndash17) 007 052

Baseline DDb 169 (120ndash238) 023 0003 001 143 (087ndash236) 095 (053ndash17) 030 073

Baseline EDSS score 162 (138ndash190) 054 lt0001 lt0001 178 (126ndash251) 105 (065ndash17) 008 052

Baseline T2 LV 229 (134ndash389) 024 0002 001 157 (075ndash328) 089 (03ndash264) 040 087

Baseline T1 LV 230 (140ndash380) 026 0001 0007 177 (090ndash35) 092 (034ndash25) 029 073

Baseline NBVc 053 (040ndash07) minus038 lt0001 lt0001 050 (034ndash072) 10 (060ndash168) 003 052

Baseline NGMVc 038 (026ndash056) minus042 lt0001 lt0001 037 (022ndash062) 080 (039ndash16) 008 052

Baseline NWMVc 057 (034ndash010) minus015 005 013 041 (018ndash094) 156 (059ndash414) 004 052

Baseline NDGMVd 044 (027ndash071) minus027 lt0001 0006 036 (018ndash073) 125 (056ndash276) 002 052

RS FC SMN IR SMA 072 (055ndash1) minus015 005 013 077 (053ndash113) 065 (037ndash117) 064 092

RS FC SMN II ndash L Prec 151 (100ndash225) 015 005 013 137 (085ndash221) 189 (078ndash458) 052 092

FNC DMN I-DMN III 496 (089ndash2747) 014 006 018 910 (034ndash9389) 359 (039ndash323) 064 092

FNC DMN II-DMN III 017 (003ndash081) minus017 002 009 025 (001ndash436) 015 (002ndash124) 079 095

FNC DMN III-FPN 236 (105ndash532) 015 003 012 634 (093ndash430) 166 (060ndash458) 022 071

GM SMN I 062 (047ndash083) minus026 0001 0007 058 (040ndash085) 088 (049ndash158) 025 071

GM SMN II 071 (054ndash094) minus019 002 006 070 (048ndash10) 085 (053ndash137) 051 092

GM BGN 059 (044ndash078) minus030 lt0001 0002 056 (038ndash082) 10 (062ndash164) 006 052

GM SN 057 (042ndash077) minus029 lt0001 lt0001 067 (045ndash098) 070 (037ndash133) 088 095

GM DMN I 071 (054ndash094) minus018 002 006 085 (060ndash121) 080 (045ndash142) 086 095

GM DMN II 067 (051ndash088) minus022 0004 002 082 (058ndash116) 064 (036ndash112) 045 092

GM FPN 072 (055ndash094) minus019 001 006 071 (050ndash101) 103 (060ndash175) 026 071

Predictors of conversion to SPMSOR for conversion to SPMSin RRMS (95 CI) SE p (unc) p (FDR)

Sex (male vs female) 328 (134ndash803) 032 0009 008

Baseline ageb 217 (132ndash357) 042 0002 002

Baseline EDSS score 322 (205ndash507) 065 lt0001 lt0001

NBVc 047 (030ndash074) minus042 lt0001 001

NGMVc 033 (018ndash061) minus046 lt0001 001

NDGMVd 048 (021ndash111) minus021 009 035

RS FC SMN I - R SMA 063 (039ndash104) minus023 007 035

FNC DMN II - BGN 352 (086ndash143) 023 007 035

FNC DMN II ndash DMN III 008 (0009ndash088) minus022 004 024

GM SMN I 041 (024ndash070) minus051 0001 001

GM SMN II 042 (025ndash070) minus046 lt0001 001

GM BGN 066 (042ndash104) minus021 007 035

Continued

6 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

based MRI variables assessed in this study (n = 62 listed ine-table 1 linkslwwcomNXIA485) to determine candidatepredictors (p lt 01)91034 of disability worsening and conversionto SPMS to be further considered for random forest (RF) anal-ysis The heterogeneity of between-phenotype effects was testedwith interaction terms Then RF probabilitymodels were used toidentify variables independently associated with clinical worsen-ing and SPMS conversion For each model 10000 trees werebuilt on a random subset of covariates (including only de-mographic and clinical variables adding conventional and finallynetwork MRI variables) with a follow-up duration-weightedbootstrap resampling of observations We assessed feature rele-vance with an outcome permutation test (1000 permutations)providing an unbiased measure of variable importance and sig-nificance p values for each predictor35 DMT change but notDMT at baseline was included as a covariate because it did notdiffer between stableworsened MS nor between SPMSconvertersnonconverters We reported the out-of-bag (OOB)Brier score (mean squared prediction error) of final modelsbased on selected features A receiver operating characteristicanalysis tested the significance of partial contributions of con-ventional and network MRI vs clinical variables to model dis-criminative ability (OOB area under the curve [AUC])

Considering the exploratory nature of our analysis significancein logistic regression and RF analyses was set at p lt 01 falsediscovery rate (FDR) corrected for multiple comparisons(Benjamini-Hochberg procedure) Uncorrected results (p lt005) are also reported

Data AvailabilityThe data set used and analyzed during the current study isavailable from the corresponding author on reasonable request

ResultsDemographic Clinical and Conventional MRICompared with HCs patients with MS (as a whole andaccording to phenotype) showed significantly lower brain anddeep GM volumetry Patients with PMS were older (p lt

0001) had higher EDSS score (p lt 0001) longer diseaseduration (p lt 0001) higher T2 (p lt 0001) and T1 LV (p lt0001) and lower brain volumetry (p = range lt0001ndash0002)than those with RRMS (table 1)

The median EDSS score at follow-up was = 40 (interquartilerange = 15ndash65) (median EDSS score change betweenbaseline and follow-up = 05 interquartile range = 00ndash15 pvalue vs baselinelt00001) According to EDSS score changes105 patients with MS (45) were clinically worsened atfollow-up 1 patient (of 2 50) had baseline EDSS score = 071 patients (of 184 38) had baseline EDSS score between05 and 55 and 33 patients (of 47 70) had baseline EDSSscore ge6 Moreover 26 patients with RRMS (16) convertedto SPMS

At baseline compared with clinically stable patients withclinically worsened MS were older had longer disease dura-tion higher EDSS score higher LV and more severe whole-brain and GM atrophy (table 2) Compared with patientsremaining RRMS SPMS converters had a higher proportionof males were older and had higher baseline EDSS score andlower brain volumetry (table 2)

RS FC NetworksEight functional networks (figure 1) were selected 2 senso-rimotor networks (SMN I and II)28 1 basal ganglia network(BGN)29 3 default-mode networks (DMNs I II and III)30 1salience network (SN)31 and 1 fronto-parietal network(FPN) associated with working memory and dorsal atten-tion28 (r with corresponding templates = range 040ndash065)

At the regional level decreased and increased RS FC withinspecific regions in patients with MS vs HC were found (e-table 2 linkslwwcomNXIA485 figure 2) Specificallydecreased RS FC was observed in patients with MS within theSMN I in the right paracentral lobule and supplementarymotor area (SMA) within the BGN in the right cerebellumwithin the SN in the right insula and left caudate nucleuswithin the DMN IIII in the left posterior cingulate and right

Table 3 Candidate Predictors (p lt 010) of Disability Worsening in All Patients With MS and Candidate Predictors ofConversion to SPMS in RRMS by Follow-up Adjusted Logistic Regression Models (continued)

Predictors of conversion to SPMSOR for conversion to SPMSin RRMS (95 CI) SE p (unc) p (FDR)

GM DMN I 066 (041ndash105) minus023 008 035

GM FPN 057 (036ndash092) minus032 002 012

Abbreviations BGN=basal ganglia network CI = confidence interval DD =disease duration DMN=default-modenetwork EDSS = ExpandedDisability StatusScale FDR = false discovery rate FNC = functional network connectivity FPN = fronto-parietal network GM = gray matter L = left LV = lesion volume MS =multiple sclerosis NBV = normalized brain volume NDGMV = normalized deep gray matter volume NGMV = normalized gray matter volume NWMV =normalizedwhitematter volume PMS = progressiveMS Prec = precentral gyrus R = right RRMS = relapsing-remittingmultiple sclerosis RS FC = resting-statefunctional connectivity SE = standardized estimate of the beta regression coefficient SMA = supplementary motor area SMN = sensorimotor network SN =salience network SPMS = secondary progressive multiple sclerosis unc = uncorrectedFDR-corrected p values are also reporteda Predictor times phenotype interaction assessing the heterogeneity of effects between patients with relapsing and progressive MSb For age and DD ORs associated with a 10-year increase are calculated to have a proper scalingc For NBV NGVM and NWMV ORs associated with a 100-mL increase are calculatedd For NDGMV ORs associated with a 10-mL increase are calculated

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Table 4 Informative Predictors of Disability Worsening in All Patients With MS as Well as Independent Predictors ofConversion to SPMS in RRMS Selected by Random Forest Analyses

Predictors of disability worsening Relative importancepValue

p (FDRcorrected)

OOB-Brierscore

OOB-AUC (95CI)

pValue

Clinical variables

Baseline EDSS score 1000 0001 0003 0227 068 (061ndash075) mdash

DMT change 217 003 005

Clinical and conventional MRIvariables

Baseline EDSS score 1000 0001 0004 0223 071 (064ndash077) 038a

NGMV 618 0001 0004

NBV 371 001 003

DMT change 183 001 003

Clinical conventional and network MRIvariables

Baseline EDSS score 1000 0001 001 0199 076 (069ndash082) 0009a

NGMV 735 0001 001

NBV 357 0005 003

FNC DMN II-DMN III 239 003 009

RS FC SMN II ndash L precentral gyrus 189 003 009

GM FPN 182 003 009

GM SMN II 168 004 011

GM SN 154 004 011

DMT change 79 001 008

Predictors of conversion to SPMSRelativeimportance p Value p (FDR corrected)

OOB-Brierscore OOB-AUC (95CI) p Value

Clinical variables

Baseline EDSS score 1000 0001 003 0120 075 (066ndash085) mdash

DMT change 189 004 009

Clinical and conventional MRI variables

NGMV 1000 0004 0009 0104 083 (073ndash092) 009a

Baseline EDSS score 705 0001 0006

DMT change 278 0003 0009

Clinical conventional andnetworkMRI variables

Baseline EDSS score 1000 lt0001 001 0111 084 (076ndash091) 002a

NGMV 994 0001 001

GM SMN I 477 003 009

DMT change 148 0002 001

Abbreviations AUC = area under the curve CI = confidence interval DMN = default-mode network DMT = disease-modifying treatment EDSS = ExpandedDisability Status Scale FDR = false discovery rate FNC = functional network connectivity FPN = fronto-parietal network GM = gray matter L = left MS =multiple sclerosis NBV = normalized brain volume NGMV = normalized graymatter volume OOB = out of bag RRMS = relapsing-remittingmultiple sclerosisRS FC = resting-state functional connectivity SMN = sensorimotor network SN = salience network SPMS = secondary progressive multiple sclerosisOut-of-bag (OOB) area under the curve (AUC) values and related p values highlight the performance increase associated with the inclusion of conventionaland network MRI variables compared with models including confounding covariates and clinical variablesa Compared with the model including clinical variables

8 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

caudate nucleus and within the FPN in the right anteriorcingulate Increased RS FC was detected in the bilateral

precentral and postcentral gyrus within the SMN III andfrontal regions within the DMN II

Considering FNC analysis connectivity strength was signifi-cantly decreased in patients with MS vs HCs between SMNI-BGN (p = 002) and SMN I-DMN II (p = 001) as well asbetween FPN-DMN I (p = 001) and FPN-SN (p = 0002) (e-table 2 linkslwwcomNXIA485)

GM NetworksSeven GM networks matched functional networks 2 SMNsone including precentral and postcentral middle occipital gyriand cerebellum (r with functional SMN I = 032 p lt 0001) andthe other including precentral gyri SMA and cerebellum (rwith functional SMN II = 044 p lt 0001) 1 BGN includingdeep GM and cerebellum (r with functional BGN = 080 p lt0001) 2 DMNs one including posterior cingulate precuneusand angular gyri (r with functional DMN I = 055 p lt 0001)and the other including medial frontal middle frontal andangular gyri (r with functional DMN II = 073 p lt 0001) 1 SN(r with functional SN = 056 p lt 0001) and 1 FPN (r withfunctional FPN = 043 p lt 0001)33 Patients with MS showedsignificant GM atrophy (ie lower GM loadings) vs HCs in allnetworks except for SMN II (figure 2)

Prediction AnalysisThe univariate analysis identified at corrected thresholdolder baseline age longer disease duration higher EDSSscore higher LV lower global brain volumetry lower GMloading coefficients for all networks and reduced FNC be-tween DMN II-DMN III as candidate predictors of disabilityworsening (table 3) At an uncorrected threshold the fol-lowing candidate predictors of EDSS score worsening werealso identified decreased RS FC in the SMA and increasedRS FC in the left precentral gyrus of SMNs reduced FNCbetween DMN III-FPN and increased FNC between DMNI-DMN III (table 3)

RF analysis identified as FDR-corrected predictors of clinicalworsening a higher baseline EDSS score lower normalizedbrain and GM volumes reduced FNC between DMN II-DMN III increased RS FC of the left precentral gyrus (SMNII) and GM atrophy in FPN (table 4 figure 3) An FDR-uncorrected contribution of GM atrophy in SMN II and SNwas also found When considering all predictors the receiveroperating characteristic analysis (AUC = 076 table 4 figure3) highlighted that the inclusion of network MRI variablessignificantly improved (p = 0009) prediction performancesResults did not change substantially with FDR-correctedpredictors only (AUC = 074 improvement of predictionperformance p = 005)

The univariate analysis identified as FDR-corrected predictorsfor SPMS conversion male sex older baseline age higherEDSS score lower normalized brain and GM volumes andGM loadings of SMN and FPN (table 3) At an uncorrected

Figure 3 Analysis of Prediction

Results of the ROC analysis showing the area under the curve (OOB AUC) ofrandom forest models predicting clinical disability worsening (A) and con-version to SPMS (B)Models show the increments of AUC associatedwith theinclusion of conventional MRI variables (green lines) and network MRI var-iables (orange lines) in addition to confounding covariates and clinical var-iables (purple lines) AUC = area under the curve OOB = out-of-bag ROC =receiver operating characteristic SPMS = secondary progressive multiplesclerosis

NeurologyorgNN Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 9

threshold the following candidate SPMS predictors were alsodetected lower deep GM volumes decreased RS FC in theSMA (SMN I) decreased FNC between DMN II-DMN IIIand DMN II-BGN and lower GM loadings of BGN andDMN (table 3) As shown in table 4 the RF model selectedbaseline EDSS score normalized GM volume and GM at-rophy in the SMN I as FDR-corrected predictors of SPMSconversion The receiver operating characteristic analysis(table 4 figure 3) highlighted the significant contribution (p =002) of network MRI variables to model performance

DiscussionBy analyzing a large cohort of patients with MS having a 64-year follow-up clinical evaluation we found that integratingstructural and functional network measures significantly im-proved clinical worsening prediction Besides higher baselineEDSS score and lower whole-GM volumetry abnormalbaseline RS FC within and between sensorimotor and DMNsas well as sensorimotor and cognitive GM network atrophycontributed to explain overall disability progression More-over GM atrophy in an SMN was among the determinants ofSPMS conversion Prediction models of both clinical wors-ening and SPMS conversion were produced using RF meth-odology a machine-learning technique that is less sensitive tofalse discoveries than classic multivariate logistic models36

being based on the use of different bootstrap samples of dataand different subsets of predictors to construct thousands ofdecision trees on which hypotheses are tested36

As clinical outcomes we selected the EDSS score the mostwidely validated MS disability measure which was used in allprognostic studies679-121415 and evolution to SPMS that isassociated with a poor prognosis mostly because of the lim-ited DMT effect in these patients During the 64-year follow-up 45 of patients had a worsening of disability and 16 ofpatients with RRMS evolved to SPMS In line with previousstudies older age longer disease duration and higher baselineEDSS score were associated with poorer clinical outcomes737

In particular a higher baseline EDSS score was retained bymultivariable analysis as a predictor of both worsening ofdisability and evolution to SPMS This is in line with a recentmeta-analysis indicating baseline EDSS score as the mostfrequent predictor of future disease course7 On the otherhand this result may be partially driven by autocorrelationissues

In addition to baseline disability different MRI measurescontributed to the prediction of disease evolution Concern-ing conventional MRI whole-brain atrophy was predictive ofdisease worsening at RF analysis Likewise whole-GM atro-phy was retained as a predictor of both clinical worsening andSPMS conversion The notion that whole-GM atrophy is oneof the most important MRI measures explaining disabilitydeterioration is in line with previous medium- and long-termstudies91112 Because we included both RRMS and PMS ourresults support the importance of this measure independently

from phenotype and reinforce the notion that the neurode-generative MS-related damage is more critical than in-flammation to explain a worse clinical course In line with thisLV was not retained as a predictor by any RF model

One of the main strengths of this study compared with pre-vious ones was the assessment of network-specific MRImeasures This was a rewarding strategy to ameliorate pre-diction of MS disease evolution because both functional andstructural network MRI abnormalities were found to be in-formative of subsequent clinical deterioration at medium-term and inclusion of network MRI metrics in RF modelssignificantly improved prediction performance

Considering the prediction of EDSS score worsening 2 fMRIoutcomes were selected by RF analysis namely decreasedFNC among DMNs and increased RS FC of the left precentralgyrus in the SMN II The role of fMRI for prognosis has still tobe fully investigated Although there is some preliminary evi-dence of a significant influence of baseline FC during a psy-chological stress fMRI task on subsequent development ofatrophy21 and a significant influence of baseline RS FC ab-normalities on subsequent clinical disability1920 studies in largepatientsrsquo groups including all main disease phenotypes are stillmissing Here a prevalent decrease of RS FC among net-works18 and a heterogeneous pattern of regionally increasedand decreased RS FC were confirmed182338 However onlyabnormalities of RS FC in the DMN and SMNwere associatedwith EDSS score worsening The DMN is one of the keynetworks of the brain and abnormal DMNRS FC is present inseveral brain disorders including MS2330 A good control ofDMN activity is crucial for an efficient brain function30 In linewith this we found that reduced RS FC between 2 DMNpatterns (including the posterior and the anterior nodes of thisnetwork respectively) was able to explain clinical worseningThis reinforces the notion of a close correlation between im-paired functional communication and increasing disability inMS On the other hand we also found that a selective increaseof RS FC within a specific region of the SMN (ie the leftprecentral gyrus) was predictive of clinical deterioration Aprevious 2-year graph analysis study in 38 patients with earlyRRMS20 showed that at baseline patients had an increasednetwork RS FC which tended to decrease during the studyfollow-up concomitantly with disability progression Previousfindings suggest that increased RS FC may occur at early dis-ease stages to compensate structural damage andmay stop afterreaching a maximum level192039 In later phases RS FC de-pletion is thought to contribute to disability progression2023

Our results further support such hypothesis in a larger group ofpatients and with a longer follow-up Remarkably we foundthat this behavior distinguished the SMN which is most likelyclinically related to EDSS score deterioration

Of interest besides whole-GM atrophy RF analysis alsoidentified GM atrophy in the FPN to be predictive of a worsedisease evolution This is not the first study that highlights asignificant contribution of network GM measures to MS

10 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

prognosis1415 Taken together these results suggest that theassessment of specific system involvement may convey moreclinically relevant pieces of information than global MRImeasures in these patients

In patients with RRMS RF analysis identified atrophy ofSMN I as significantly contributing to explain evolution toSPMS whereas none of the other advanced MRI measuressignificant at the univariate analysis was retained A uniquedefinition of SPMS is still lacking and is a matter of currentresearch40 To be consistent with available literature we ap-plied the definition used in integrated clinical-MRI prognosticstudies91126 The relation that we found between SMN at-rophy and evolution to SPMS is not unexpected consideringthat disability worsening in these patients is mostly driven by aprogressive impairment of ambulation In line with this pre-vious cross-sectional studies found peculiar atrophy of brainmotor areas in patients with SPMS41

Our study has the unique value of a large monocentric well-characterized patientsrsquo cohort However this study has somelimitations First clinical evaluation did not include a baselineand follow-up cognitive assessment which can have detri-mental consequences on patientsrsquo functioning Moreoverbecause we did not have a cognitive evaluation we could notassess worsening of other scores than EDSS (eg theMultipleSclerosis Functional Composite) Second because of thecomplexity of the MRI protocol we did not include a spinalcord evaluation which may be relevant for disability accu-mulation and SPMS evolution11 Third a follow-up MRI scanwas not available thus making it impossible to quantifychanges of WM LV andor other MRI disease severity pa-rameters Fourth not all data-driven structural and functionalnetworks had a significant spatial correspondence leading tothe exclusion of potentially interesting components (eg thevisual network) moreover they may be not fully replicable indifferent data sets Fifth given the exploratory nature of thisstudy we used a relatively liberal threshold for FDR correc-tion moreover the small portion of results not surviving atthis threshold should be interpreted with extreme cautionSixth the use of the whole data set for feature selection (withlogistic regressions) and for the subsequent construction ofRF models may increase the risk of model overfitting as suchresults may be biased by double-dipping issues Finally thisstudy was not planned to assess the role of treatment onclinical outcomes Therefore detailed information on treat-ment exposure was not collected and analyzed

To conclude we found that integrating structural andfunctional MRI network measures improved prediction ofclinical worsening The added value of other MRI and se-rologic biomarkers such as WM network damage assessedby diffusion-weighted MRI demyelinationremyelinationindices derived from magnetization transfer imaging orneurofilament light chain might be the topic of futureinvestigations

Study FundingThis study was partially supported by FISM (FondazioneItaliana Sclerosi Multipla) cod 2018R5 and was financed orco-financed with the ldquo5 per millerdquo public funding

DisclosureMA Rocca received speaker honoraria from Bayer BiogenBristol-Myers Squibb Celgene Genzyme Merck-SeronoNovartis Roche and Teva and receives research supportfrom the Italian Ministry of Health MS Society of Canada andFondazione Italiana Sclerosi Multipla P Valsasina A Meaniand E Pagani received speakerrsquos honoraria fromBiogen Idec CCordani and C Cervellin report no disclosures relevant to themanuscript M Filippi is Editor-in-Chief of the Journal ofNeurology and Associate Editor of Human Brain Mapping re-ceived compensation for consulting services andor speakingactivities from Almirall Alexion Bayer Biogen Celgene EliLilly Genzyme Merck-Serono Novartis Roche SanofiTakeda and Teva Pharmaceutical Industries and receives re-search support from Biogen Idec Merck-Serono NovartisRoche Teva Pharmaceutical Industries Italian Ministryof Health Fondazione Italiana Sclerosi Multipla andARiSLA (Fondazione Italiana di Ricerca per la SLA) Go toNeurologyorgNN for full disclosures

Publication HistoryReceived by Neurology Neuroimmunology amp NeuroinflammationAugust 26 2020 Accepted in final form March 5 2021

Appendix Authors

Name Location Contribution

Maria ARocca MD

IRCCS San Raffaele ScientificInstitute Milan Italy Vita-Salute San RaffaeleUniversity Milan Italy

Study concept analysis andinterpretation of the dataand draftingrevising themanuscript

PaolaValsasinaMSc

IRCCS San Raffaele ScientificInstitute Milan Italy

Analysis and interpretation ofthe data and draftingrevisingthe manuscript

AlessandroMeani MsC

IRCCS San Raffaele ScientificInstitute Milan Italy

Analysis and interpretation ofthe data and draftingrevisingthe manuscript

ElisabettaPaganiMSc

IRCCS San Raffaele ScientificInstitute Milan Italy

Analysis and interpretation ofthe data and draftingrevisingthe manuscript

ClaudioCordani PT

IRCCS San Raffaele ScientificInstitute Milan Italy

Acquisition of the data andanalysis and interpretation ofthe data

ChiaraCervellinPT

IRCCS San Raffaele ScientificInstitute Milan Italy

Acquisition of the data andanalysis and interpretation ofthe data

MassimoFilippi MD

IRCCS San Raffaele ScientificInstitute Vita-Salute SanRaffaele University MilanItaly

Draftingrevising themanuscript study conceptand analysis andinterpretation of the data Healso acted as the studysupervisor

NeurologyorgNN Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 11

References1 Filippi M Bar-Or A Piehl F et al Multiple sclerosis Nat Rev Dis Primers 20184432 Montalban X Gold R Thompson AJ et al ECTRIMSEAN Guideline on the

pharmacological treatment of people with multiple sclerosis Mult Scler 20182496ndash120

3 Thompson AJ Banwell BL Barkhof F et al Diagnosis of multiple sclerosis 2017revisions of the McDonald criteria Lancet Neurol 201817162ndash173

4 Gasperini C Prosperini L TintoreM et al Unraveling treatment response inmultiplesclerosis a clinical and MRI challenge Neurology 201992180ndash192

5 Fisniku LK Brex PA Altmann DR et al Disability and T2 MRI lesions a 20-yearfollow-up of patients with relapse onset of multiple sclerosis Brain 2008131808ndash817

6 Elliott C Belachew S Wolinsky JS et al Chronic white matter lesion activity predictsclinical progression in primary progressive multiple sclerosis Brain 20191422787ndash2799

7 Brown FS Glasmacher SA Kearns PKA et al Systematic review of prediction modelsin relapsing remitting multiple sclerosis PLoS One 202015e0233575

8 Enzinger C Barkhof F Ciccarelli O et al Nonconventional MRI and microstructuralcerebral changes in multiple sclerosis Nat Rev Neurol 201511676ndash686

9 Filippi M Preziosa P Copetti M et al Gray matter damage predicts the accumulationof disability 13 years later Neurology 2013811759ndash1767

10 Rocca MA Sormani MP Rovaris M et al Long-term disability progression in primaryprogressive multiple sclerosis a 15-year study Brain 20171402814ndash2819

11 Brownlee WJ Altmann DR Prados F et al Early imaging predictors of long-termoutcomes in relapse-onset multiple sclerosis Brain 20191422276ndash2287

12 Dekker I Eijlers AJC Popescu V et al Predicting clinical progression in multiplesclerosis after 6 and 12 years Eur J Neurol 201926893ndash902

13 He Y Dagher A Chen Z et al Impaired small-world efficiency in structural corticalnetworks in multiple sclerosis associated with white matter lesion load Brain 20091323366ndash3379

14 Muthuraman M Fleischer V Kroth J et al Covarying patterns of white matter lesionsand cortical atrophy predict progression in early MS Neurol Neuroimmunol Neuro-inflamm 20207e681

15 Tur C Kanber B Eshaghi A et al Clinical relevance of cortical network dynamics inearly primary progressive MS Mult Scler 202026442ndash456

16 Rocca MA Valsasina P Meani A Falini A Comi G Filippi M Impaired functionalintegration in multiple sclerosis a graph theory study Brain Struct Funct 2016221115ndash131

17 Liu Y Duan Y Dong H Barkhof F Li K Shu N Disrupted module efficiency ofstructural and functional brain connectomes in clinically isolated syndrome andmultiple sclerosis Front Hum Neurosci 201812138

18 Rocca MA Valsasina P Martinelli V et al Large-scale neuronal network dysfunctionin relapsing-remitting multiple sclerosis Neurology 2012791449ndash1457

19 Hidalgo de La Cruz M Valsasina P Mesaros S et al Clinical predictivity of thalamicsub-regional connectivity in clinically isolated syndrome a 7-year study Mol Psychi-atry 2020

20 Faivre A Robinet E Guye M et al Depletion of brain functional connectivity en-hancement leads to disability progression in multiple sclerosis a longitudinal resting-state fMRI study Mult Scler 2016221695ndash1708

21 Meyer-Arndt L Hetzer S Asseyer S et al Blunted neural and psychological stressprocessing predicts future grey matter atrophy in multiple sclerosis Neurobiol Stress202013100244

22 Lublin FD Reingold SC Cohen JA et al Defining the clinical course of multiplesclerosis the 2013 revisions Neurology 201483278ndash286

23 Rocca MA Valsasina P Leavitt VM et al Functional network connectivity abnor-malities in multiple sclerosis correlations with disability and cognitive impairmentMult Scler 201824459ndash471

24 Kurtzke JF Rating neurologic impairment in multiple sclerosis an expanded disabilitystatus scale (EDSS) Neurology 1983331444ndash1452

25 Healy BC Engler D Glanz B Musallam A Chitnis T Assessment of definitions ofsustained disease progression in relapsing-remitting multiple sclerosis Mult Scler Int20132013189624

26 University of California SFMSET Cree BA Gourraud PA Oksenberg JR et al Long-termevolution of multiple sclerosis disability in the treatment era Ann Neurol 201680499ndash510

27 Scalfari A Neuhaus A DaumerM Deluca GCMuraro PA Ebers GC Early relapses onsetof progression and late outcome in multiple sclerosis JAMA Neurol 201370214ndash222

28 Smith SM Fox PT Miller KL et al Correspondence of the brainrsquos functional ar-chitecture during activation and rest Proc Natl Acad Sci United States America 200910613040ndash13045

29 Patenaude B Smith SM Kennedy DN Jenkinson M A Bayesian model of shape andappearance for subcortical brain segmentation NeuroImage 201156907ndash922

30 Raichle ME Snyder AZ A default mode of brain function a brief history of anevolving idea NeuroImage 2007371083ndash1090 discussion 1097-1089

31 Seeley WW Menon V Schatzberg AF et al Dissociable intrinsic connectivity net-works for salience processing and executive control J Neurosci 2007272349ndash2356

32 Xu L Groth KM Pearlson G Schretlen DJ Calhoun VD Source-based morphom-etry the use of independent component analysis to identify gray matter differenceswith application to schizophrenia Hum Brain Mapp 200930711ndash724

33 Segall JM Allen EA Jung RE et al Correspondence between structure and functionin the human brain at rest Front neuroinformatics 2012610

34 Rocca MA Parisi L Pagani E et al Regional but not global brain damage contributesto fatigue in multiple sclerosis Radiology 2014273511ndash520

35 Altmann A Tolosi L Sander O Lengauer T Permutation importance a correctedfeature importance measure Bioinformatics 2010261340ndash1347

36 Kacar K Rocca MA Copetti M et al Overcoming the clinical-MR imaging paradox ofmultiple sclerosis MR imaging data assessed with a random forest approach AJNRAm J Neuroradiol 2011322098ndash2102

37 Fambiatos A Jokubaitis V Horakova D et al Risk of secondary progressive multiplesclerosis a longitudinal study Mult Scler 20202679ndash90

38 Faivre A Rico A Zaaraoui W et al Assessing brain connectivity at rest is clinicallyrelevant in early multiple sclerosis Mult Scler 2012181251ndash1258

39 Rocca MA Hidalgo de La Cruz M Valsasina P et al Two-year dynamic functionalnetwork connectivity in clinically isolated syndrome Mult Scler 20191352458519837704

40 Lorscheider J Buzzard K Jokubaitis V et al Defining secondary progressive multiplesclerosis Brain 20161392395ndash2405

41 Ceccarelli A RoccaMA Pagani E et al A voxel-basedmorphometry study of greymatterloss in MS patients with different clinical phenotypes NeuroImage 200842315ndash322

12 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 4 | July 2021 NeurologyorgNN

DOI 101212NXI000000000000100620218 Neurol Neuroimmunol Neuroinflamm

Maria A Rocca Paola Valsasina Alessandro Meani et al Network Damage Predicts Clinical Worsening in Multiple Sclerosis A 64-Year Study

This information is current as of May 21 2021

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