outcomes at 2-years corrected age in very preterm infants Early brain morphometrics from neonatal MRI predict motor and cognitive NeuroImage

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ContentslistsavailableatScienceDirect

NeuroImage

journalhomepage:www.elsevier.com/locate/neuroimage

Early brain morphometrics from neonatal MRI predict motor and cognitive outcomes at 2-years corrected age in very preterm infants

Alex M. Pagnozzi

^a^,^∗

, Liza van Eijk

^a^,^b

, Kerstin Pannek

^a

, Roslyn N. Boyd

^c

, Susmita Saha

^a

, Joanne George

^c^,^d

, Samudragupta Bora

^e

, DanaKai Bradford

^a

, Michael Fahey

^f

,

Michael Ditchﬁeld

^g^,^h

, Atul Malhotra

^f^,ⁱ

, Helen Liley

^e

, Paul B. Colditz

^j

, Stephen Rose

^a

, Jurgen Fripp

^a

aCSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, Herston, Brisbane, QLD 4029, Australia

bDepartment of Psychology, James Cook University, Townsville, Queensland, Australia

cChild Health Research Centre, Queensland Cerebral Palsy and Rehabilitation Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia

dPhysiotherapy Department, Queensland Children’s Hospital, Children’s Health Queensland Hospital and Health Service, Brisbane, Australia

eMothers, Babies and Women’s Health Program, Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia

fMonash Health Paediatric Neurology Unit and Department of Paediatrics, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia

gMonash Imaging, Monash Health, Melbourne, Victoria, Australia

hDepartment of Medicine, Monash University, Melbourne, Victoria, Australia

iMonash Newborn, Monash Children’s Hospital, Melbourne, Victoria, Australia

jPerinatal Research Centre, Faculty of Medicine, The University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, Queensland, Australia

a r t i c le i n f o

Keywords:

Preterm birth Structural MRI Biomarkers Neurodevelopment Motor

Cognition Language Neonates

a b s t r a ct

Infantsbornverypretermfacearangeofneurodevelopmentalchallengesincognitive,language,behavioural and/ormotordomains.Earlyaccurateidentificationofthoseatriskofadverseneurodevelopmentaloutcomes, throughclinicalassessmentandMagneticResonanceImaging(MRI),enablesprognosticationofoutcomesand theinitiationoftargetedearlyinterventions.Thisstudyutilisesaprospectivecohortof181infantsborn<31 weeksgestation,whohad3TMRIsacquiredat29-35weekspostmenstrualageandacomprehensiveneurodevel- opmentalevaluationat2yearscorrectedage(CA).Cognitive,languageandmotoroutcomeswereassessedusing theBayleyScalesofInfantandToddlerDevelopment– ThirdEditionandfunctionalmotoroutcomesusingthe Neuro-sensoryMotorDevelopmentalAssessment.ByleveragingadvancedstructuralMRIpre-processingstepsto standardisethedata,andthestate-of-the-artdevelopingHumanConnectomePipeline,earlyMRIbiomarkersof neurodevelopmentaloutcomeswereidentified.UsingLeastAbsoluteShrinkageandSelectionOperator(LASSO) regression,significantassociationsbetweenbrainstructureonearlyMRIswith2-yearoutcomeswereobtained (r=0.51and0.48formotorandcognitiveoutcomesrespectively)onanindependent25%ofthedata.Addi- tionally,importantbrainbiomarkersfromearlyMRIswereidentified,includingcorticalgreymattervolumes, aswellascorticalthicknessandsulcaldepthacrosstheentirecortex.AdverseoutcomeontheBayley-IIImotor andcognitivecompositescoreswereaccuratelypredicted,withanAreaUndertheCurveof0.86forbothscores.

Theseassociationsbetween2-yearoutcomesandpatientprognosisandearlyneonatalMRImeasuresdemonstrate theutilityofimagingpriortotermequivalentageforprovidingearliercommencementoftargetedinterventions forinfantsbornpreterm.

Abbreviations:AUC,AreaUndertheCurve;CA,CorrectedAge;CP,CerebralPalsy;CPAP,ContinuousPositiveAirwayPressure;CSF,CerebrospinalFluid;CT, CorticalThickness;dHCP,DevelopingHumanConnectomeProject;ETT,EndotrachealTube;GA,GestationalAge;GI,GyriﬁcationIndex;GM,GreyMatter;GMA, GeneralMovementsAssessment;LASSO,LeastAbsoluteShrinkageandSelectionOperator;MAE,MeanAbsoluteError;MRI,MagneticResonanceImaging;NSMDA, Neuro-SensoryMotorDevelopmentalAssessment;PMA,PostmenstrualAge;PPREMO,PredictionofPREtermMotorOutcomes;PREBO,PredictionofPretermBrain Outcomes;SA,SurfaceArea;SD,SulcalDepth;SES,SocioeconomicStatus;TEA,TermEquivalentAge;TPN,TotalParenteralNutrition;WM,WhiteMatter.

∗Correspondingauthor.

E-mailaddress:[email protected](A.M.Pagnozzi).

https://doi.org/10.1016/j.neuroimage.2022.119815.

Received6July2022;Receivedinrevisedform5December2022;Accepted13December2022 Availableonline15December2022.

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1. Introduction

Thecurrentglobalprevalenceofpretermbirthisestimatedataround 10%(Chawanpaiboonetal.,2019).Verypreterminfants(gestational age[GA]atbirth<32weeks)areatgreaterriskofcognitive,language, behaviouralandmotorproblems.Fromtheendofthesecondandbegin- ningofthethirdtrimesters,corticalneurogenesis,migrationandfolding arerapidlyoccurringupuntilTermEquivalentAge(TEA)(Duboisetal., 2008).Thisdevelopmentmaybedisruptedbythetypesofinjurythat canensuefromprematurebirth,potentiallyresultinginalteredwhite andgreymattervolumes,destructivelesionsinthewhitematter,neu- ronalandaxonalinjuryinvolvingthecerebralcortexanddeepgreymat- ter,aswellasaxonaldegeneration,hypomyelination,abnormalapop- tosisanddiminutionoflatemigratingneurons(Volpe,2009).

Brainmagneticresonanceimaging(MRI)isimprovingtheabilityto detectevidenceofbraininjury,withMRIatTEAplayinganimportant roleintheidentiﬁcationofinfantsatriskofadverseneurodevelopmen- taloutcomes(Georgeetal.,2018;van’tHooftetal.,2015).Increasingly, brainMRIistechnicallyfeasibleatearliertimepoints,suchas30-32 weekspostmenstrualage(PMA)(Counselletal.,2003;Georgeetal., 2015;Ibrahimetal.,2018)andfrom26weeksPMA(Makropoulosetal., 2018),potentially allowingan evenearlier prognostication,opening anewwindow fortherapeuticinterventionsatatimeofrapidbrain developmentwhiletheinfantis stillreceivingcare inaNeonatalIn- tensiveCare Unit(NICU).Manyverypreterm infantsaredischarged priortoTEA,soMRIbeforeTEAmaybeamorepracticaltime-point atwhichimagingbiomarkerscanbemeasuredtoassistclinicalassess- ment(Malhotra etal., 2017).Earlierimagingcan helptoensurein- fantswhoareatgreatestriskofadverseoutcomesareidentiﬁedand receiveappropriatesurveillanceandearlyinterventions(Morganetal., 2021).

Volumetricinformation(fromstructuralMRI)andmicrostructural information(fromdiffusionMRI)provideinsightsintobraindevelop- mentandmaturation,andinturnareassociatedwithneurodevelopmen- taloutcomesat2yearsandbeyond(Andersonetal.,2015;DeBruïne etal., 2013; Thompson etal., 2014),however this haslargely been basedon MRIacquiredatTEA.Usingthestate-of-the-art developing HumanConnectome Pipeline(dHCP) pipeline(Duboiset al., 2021), Klineetal.(2020)foundcorticalcurvatureandsurfaceareatobeasso- ciatedwithcognitiveandlanguagescoresat2years,andthevolumeof thethalamusinadditiontosurfaceareaandcorticalgyrificationtobe associatedwithmotoroutcomesat2years(Klineetal.,2020)inavery pretermcohortscannedatTEA.QualitativefindingsfromearlierMRI beforeTEAhavebeenshowntobeassociatedwithabnormalneurode- velopmentaloutcome(Milleretal.,2005).Quantitativefindingsinclud- ingtissuevolumesandcorticalmorphologyhavebeenassociatedwith motorandcognitiveoutcomesat2-3years(Moeskopsetal.,2017).As yet,quantitativebiomarkersfromEarlyMRI(29-35weeksPMA)have beenrelativelyunder-investigatedcomparedtoimagingatTEA,despite potentiallyprovidingearlierdetectionforinfantsatriskofimpairments andreducingburdenonfamiliesasimagingcanbeperformedwhilethe infantisstillintheNICU.

OurteamhaspreviouslyusedourlargeuniqueprospectivePredic- tionofPREtermMotorOutcomes(PPREMO,n=121)(Georgeetal., 2015)andPredictionofPretermBrainOutcomes(PREBO,n=150)co- hortstodemonstrateidentiﬁcationofinfantsatriskofadversemotor outcomesandCPat2yearscorrectedage(CA)usingsemi-quantitative EarlyandTEAMRImeasures(Georgeetal.,2021),basedontheexist- ingmanualscoringsystem(Kidokoroetal.,2013)modiﬁedforinfants bornverypreterm. Thesestudiesindicate thatthebrainchanges re- sponsibleforthese2-yearoutcomesmayalreadybepresentasearlyas 29-35weeksPMA(Earlytimepoint).Inthisstudyweaimtoinvestigate whetherheterogeneityinquantitativeneonatalbrainmeasuressuchas anatomicalvolumesandcorticalmorphometricsfrom3TMRIacquired atthisearlytimepointareassociatedwithcognitive,language,andmo- toroutcomesat2yearsCA.

2. Methods 2.1. Participants

Thisstudyanalyzeddatafromtwolongitudinalprospectivecohort studiesofverypreterminfants:thePredictionofPREtermMotorOut- comes(PPREMO)study(Georgeetal.,2015)andthePretermBrainOut- comes(PREBO)study.BoththePPREMOandPREBOcohortsrecruited infantsbornat<31weeks’gestationwithnocongenitalorchromosomal abnormalitywhoseparents/caregiverswereEnglish-speakingandlived withina200kmradiusoftherecruitinghospital.Infantswererecruited attheRoyalBrisbaneandWomen’sHospital(RBWH)andMonashChil- dren’sHospital (MCH),Mater MothersHospital (MMH)andQueens- landChildren’sHospital(QCH);PPREMObetweenFebruary2013and February2016(RBWHonly),andPREBObetweenFebruary2016and December2019(RBWH,MCH,MMH,QCH).

For PPREMO and PREBO cohorts, ethics approval was obtained from the Royal Brisbane and Women’s Hospital Human Re- searchEthicsCommittee(HREC/12/QRBW/245),theQueenslandChil- dren’s Hospital (HREC/15/QRCH/7), The University of Queensland (2012001060,2015000290)andMonashUniversity/MonashChildren’s Hospital(SSA/15/MonH/41).Bothcohortstudieswereregisteredwith the AustralianNew Zealand Clinical Trials Registry (PPREMO: AC- TRN12613000280707;PREBO:ACTRN12615000591550).Forbothco- horts, informed written parental/caregiverconsent was obtained for eachinfant.

2.2. Imageacquisition

Infantswerescannedat29-35weeksPMAduringnaturalsleepona 3TMRIscanner(SiemensTimTrio[PPREMO],SiemensSkyra[PREBO RBWHandQLDChildren’sHospitalforMMHinfants],Erlangen,Ger- many;PhilipsIngenia[PREBOMCH])(totaln=271)usinganMRcom- patibleincubatorwithadedicated8-channelneonatalheadcoil(Lam- mersLMT,Lübeck,Germany)[PPREMO,PREBORBWHandQCH],or paediatric 32-channelheadcoil withno incubator(Phillips, Amster- dam, Netherlands) [PREBOMonash]. Infantswere placed onan im- mobilisationpillowtominimisemovement.NoisefromtheMRIscan- nerwasattenuatedusingminimuﬀs(NatusMedicalInc.,SanCarlos, CA).Allinfantsweremonitoredwithpulseoximetryandelectrocardio- graphicmonitoring.Nosedationoranaesthesiawasused.ThePPREMO studyusedmulti-echoT2-weightedturbospin-echo(TSE)volumesac- quired in the axial plane with the following parameters at RBWH:

TR/TE1/TE2/TE310,580/27/122/189ms;flipangle150°,fieldofview 144 ×180mm,matrix204×256,in-planeresolution 0.7×0.7mm, slicethickness2mm,scantime5:40min.InthelaterPREBOstudyat RBWH andQCH,we acquiredthree orthogonal T2-weighted images (inaxial,coronal,andsagittalplane)toimproveimagequalityinre- lationtomotion,withthefollowingparameters:atRBWHT2HASTE TR/TE2,280/117ms;flipangle120°,fieldofview160×206mm,ma- trix252×198mm,voxelsize0.8×0.8mm,slicethickness1.8mm,scan time3:11mineach,andatMonash:T2TSETR/TE2,280/117ms;flip angle120°,fieldof view160×200mm,matrix256×256,in-plane resolution0.80×0.80mm,slicethickness1.8mm,scantime2:05min each.

2.3. Imageprocessing

2.3.1. Super-resolutionreconstruction

Toenableaccuratecorticalreconstructionandvolumetry,thethree T2HASTEorTSEimages(PREBOcohortonly)werereconstructedinto onesuper-resolution3Dimagewithresolutionmatchingthein-plane resolution(0.8mm³ isotropic[PREBORBWH, QCHandMonash]),as illustratedinFig.1.ForeachindividualHASTEimage,thebrainwas extractedusingFSL’s‘bet’tool(Smith,2002).Approximately50%of theautomaticallygeneratedbrainmasksweremanuallycorrected to

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Fig.1. OriginalT2MRI(ﬁrstcolumn)foroneparticipant(age33weeksPMA)fromthePREBOstudy,illustratingthethick-sliceacquisitionsintheaxial,coronal andsagittalplanesintherespectiverows.Eachacquisitionunderwentbrainmasking(showninredinthesecondcolumn),withslicescontainingartefactremoved.

Theseslicesarereplacedinthethirdcolumn,withinterpolateddatausedtoreconstructtheﬁnalhigh-resolutionT2MRI.

preventthemaskextendingintotheorbitalcavityandbelowthecere- bellum,requiringabout15minpermask tocorrect.Slicesimpacted bysubjectmotionweredetectedandreplacedusinganin-houseauto- matedstructuralMRIPythonworkﬂow.Inthisworkﬂow,aslicewas detectedasanoutlierifitsmeansignalintensitywaslessthan92%of themeanintensityoftheadjacenttwoslices(previousandnext).This thresholdwasdeterminedempiricallyfromasubset(20%)ofthedata.

Incaseswhereoneoftheadjacentsliceshadasubstantiallylowerin- tensity(80%oftheotheradjacentslice),themeansliceintensitywas onlycompared totheslice withthehigherintensity.Incaseswhere detectedoutliersliceswereadjacenttotwonormalslices(unimpacted bymotionartefact),theywerethenreplacedbyinterpolatedanatomy derivedasthemid-pointbetweentwoadjacentslicesusing‘ANTsun- biasedpairwiseregistration’(Yushkevichetal.,2012).Adjacentoutlier sliceswerenot correctedbythisapproachduetoalackofanorma- tiveslice comparison.Asaresult, darkslices willthenbe visible in thereconstructedoutput,andapoorerimagequalityrating.Afterslice interpolation,imagesweredenoisedin theBaby BrainToolkit(BTK) version2(Rousseauetal.,2013)usingthenon-localmeansalgorithm toimprovethequalityofsubsequentsuper-resolutionreconstruction.

Thesedenoisedimageswereusedasinputsforthesuper-resolutionre- constructionalsoperformedusingBTK.Super-resolutionreconstructed imageswerevisuallyinspectedandrated as‘good’(noor negligible artefacts),‘fair’(minorartefacts)or‘unusable’(reconstructionfailedor containedmajorartefacts)inaccordancewithpreviousrecommenda- tions(Backhausenetal.,2016).Ofthetotal271participantswithMRI, 192wereratesas‘good’(71%),67wereratedas‘fair’(25%)andspe- ciﬁcattentionwasdrawntowhetherthesubtleartefactsimpactedthe subsequentsegmentation,and12 hadsigniﬁcantartefactsprecluding furtheranalysis(4%).

2.3.2. Imagesegmentation

Reconstructedimagesthenunderwentseveralpre-processingoper- ations,includingsliceinterpolationup-samplingto0.7×0.7×1mm³ (forPPREMOonly)andN4biasﬁeldcorrection(Tustisonetal.,2010).

NeckslicesweremanuallyremovedforthePREBOimagescoveringa

largeportionoftheneck,usingFSL’s‘robustfov’tool.Imageswerereori- entedtoRAS(Right,Anterior,Superior)spacetoreducethefailurerate forthesubsequentsegmentationstep.Resultingimageswereprocessed withthestate-of-the-artdHCPparcellationpipeline(Makropoulosetal., 2018). The dHCP pipeline automatically performs segmentation on several cortical and subcortical areas using the DrawEM algorithm (Makropoulosetal.,2014),segmentingthebraininto87regionsusing the20ALBERTatlas(Gousiasetal.,2013, 2012).This segmentation wasfacilitatedbyalignmentoftheMRIstoanage-appropriatepreterm template(Schuhetal.,2018).ThedHCPpipelineadditionallymeasures corticalthickness(CT),sulcaldepth(SD),surfacearea(SA)andgyrifi- cationindex(GI)ofeachregionofthecerebralcortex,asdefinedbythe Gousiasneonatalatlas(Gousiasetal.,2013).AsthedHCPpipelinegen- eratesmanyvariables,particularlyforsuchasmallbrain,toreducethe riskofinflationoffalsepositive(type1)errorscorticalmeasureswere groupedintobilateralfrontal,parietal,temporalandoccipitallobes.In addition,8tissuevolumesweremeasured(CSF,corticalGM,WM,ventricles,cerebellum,DGM,brainstem,hippocampi/amygdala),resulting inatotalof24morphometricvariablesofinterest.Allsegmentations werevisuallycheckedfortransformationorsegmentationaccuracyer- rors,withinstancesofsubstantialerrorsinthesegmentationbeingre- moved.FourexamplesegmentationprovidedbythedHCPpipelineis illustratedinFig.2.Intheillustratedcase4,thecombinationofmild butvisiblesuper-resolutionartefactsaswellasventriculomegalyledto thesegmentationfailure.

2.4. Neurodevelopmentaloutcomes

Experiencedpaediatricphysiotherapists,blindedtoallearlierclin- icalandMRIﬁndings,assessedparticipantsat2years’CA.Neurode- velopmentaloutcomeswereassessedwiththeBayleyScalesofInfant andToddlerDevelopment– ThirdEdition(Bayley-III)(Bayley,2006), withthemotor, cognitiveandlanguagecompositescoresbeingused inthisstudy.TheBayley-IIIisanorm-referenced,discriminativemea- sureusedtodescribethedevelopmentalfunctioningofthechildwith goodtostrongvalidityandreliability(AlbersandGrieve,2007).Subtest

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Fig.2.IllustrationoftissueparcellationperformedbythedHCPprocessing pipeline,illustratingthreesuccessfulsegmentations(agesatscan;case132 weeks+6daysPMA,case234weeks+2daysPMA,case331weeks+5 daysPMA)andonefailure(case4,ageatscan35weeks+2daysPMA).

compositescoresrangefrom40to160,withameanof100(andstan- darddeviation±15)basedonnormativeUSdata(Piñon,2010),with higherscoresindicatingbetteroutcomes.

MotoroutcomeswerealsoassessedusingthestandardizedNeuro- Sensory Motor Developmental Assessment (NSMDA) (Burns et al., 1989).TheNSMDAiscriterion-referencedandhasnonormativedata,

ratherinfantsaregivenacategoricalscoreofmotorperformancewith higherscoresindicatingincreaseddisability(totalscore6-8normal,9- 11minimaldisability,12-14milddisability,15-19moderatedisability, 20-25severedisability,>25profounddisability).TheNSMDAhasbeen foundtobeassociatedwithqualityoflife(Boswelletal.,2017),and more severemotorimpairmentinchildrenwithoutCP(Danksetal., 2012).

Infants wereassessedat theEarlytimepoint(29-35weeks PMA, concurrent withEarlyMRI)withtheGeneralMovementsAssessment (GMA)(EinspielerandPrechtl,2004)thathaspreviouslybeenshown tobepredictiveofalaterCPdiagnosis(sensitivity75-100%,speciﬁcity 40-48%)(Bosanquetetal.,2013;Darsaklisetal.,2011)andneuromo- tordeﬁcits(Einspieleretal.,2016).Spontaneousmovementswerecat- egorisedaseitherNormal,PoorRepertoireorCrampedSynchronised during theWrithingperiodby twoadvancedGMsraters, withcases ofnon-agreementrevieweduntilconsensusreachedandadvicesought fromathirdblindedrater.Inaddition,ameasureofsocioeconomicsta- tus(SES)wasdeterminedatTEAusingacombinationoffactorsinclud- ingfamilylivingsituation,parentalrelationship,languages spokenat homeparentaleducationandworkstatus,whichwerecollectedthrough aquestionnairecompletedbyinfants’primarycaregiver(Hacketal., 1991;Robertsetal.,2008).Summingeachquestion(0norisk,1poten- tialrisk)providesatotalrawscorefrom0to12,withscoresof2and abovebeingconsideredhighsocialriskinlinewithotherresearchin thispopulation(Caesaretal.,2016;Spittleetal.,2009).

2.5. Statisticalanalyses

To examine the relationship between structural measures from MRIs takenat theEarlytimepointwith2-yearoutcomes,Least Ab- soluteShrinkageandSelectionOperator(LASSO)regressionwasused (Tibshirani,1996).Covariates wereincludedin allmodels,whichin- cludesex, gestationalage(GA) atbirth, PMA atMRI scan,SESraw score,andageatNSMDAassessment,studycohort,site,andGMA,as wellasmeasuresofclinicalcareincludingdaysoftotalparenteralnu- trition(TPN),hoursofphototherapy,daysofendotrachealtubeventila- tion,daysofcontinuouspositiveairwaypressure(CPAP)andhoursof oxygentherapy.AgeatBayley-IIIassessmentwasnotincludedinany modelasthemeasureinherentlyaccountsforthis.Giventhelargenum- berofstructuralmeasures(24regionalmeasures)relativetothenumber ofparticipants(n=181includedinfinalanalysis),modelsimplifica- tion(i.e.variablereduction)isimportanttopreventoverfittingandto identifythemostpredictivemeasures.Variablereductionisperformed implicitlyinLASSOwithapenaltyterm(alpha),withhighervaluesen- forcingsparser(fewernon-zero)modelcoefficients.Inaddition,models withonlythecovariatesincluded(i.e.noMRImeasures)werealsocon- structedtodeterminethepotentialbenefitofaddingMRIinformation.

TrainedmodelswerecomparedusingthepseudoR2,whichisequiva- lenttothemultipleR2forLASSOregressionandprovidethepercentof varianceexplainedbythemodel.

Datawassplitintotraining(75%)andtest(25%)setsthatwerecom- parableinGAatbirth,PMAatMRI,andsex.Modelsweregeneratedon thetrainingset,withtheoptimalsparsityterm(alpha)determinedus- ingmean-squareerrorand5-foldcross-validation.Thealphaofthebest performingmodelwasthenappliedtotheentiretrainingset,yieldingan optimaltrainingmodel(whichincludesonlytheretainedfeaturesfrom LASSOaswellasallcovariates).Thisoptimaltrainingmodelwasap- pliedtothetestset,withthecorrespondencebetweentheactualscores andthepredicted2-yearoutcomemeasuredwithbothPearson’srcorre- lationaswellasameanabsoluteerror(MAE).Thiswasrepeatedforeach outcome score,(Bayley-IIImotor, cognitive,language, andNSMDA), witheachusingthesametrainandtestsplits.Additionally,outcome measuresonthetestsetweredichotomisedas‘normal’or‘poorout- come’,whichforBayley-IIIwasdeﬁnedas1standarddeviationbelow themean(scores<85)andforNSMDAwasdichotomisedtheNSMDA normal/minimal(6-11)vsmild-profound(12orabove).Fromthisthe

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areaunderthecurve(AUC)wasquantiﬁedusingthe‘glmnet’libraryin R,whichmeasurestheoverallclassiﬁcationagreementbetweenthepre- dictedmodelandthedichotomisedoutcomeofthepredictivemodels.

3. Results 3.1. Participants

Atotalnumberof271recruitedparticipantsinthePPREMO/PREBO studieshadEarlyMRIsavailable.Ofthese,181participantswereavail- ablefortheanalysesinthisstudy,withthebreakdownofdatalossil- lustratedinFig.3.

The overalldemographics andclinical assessments of the cohort aredetailedinTable1.Thecohortincludeds(n=181)andexcluded (n=90)fromtheanalysisdidnotdiffersignificantlyonbaselinechar- acteristicsoroutcomeassessmentexceptonthesocioeconomicstatus riskrawscore,andMRimagequality,asexpectedasMRIqualitywas oneofthefactorsforbeingexcludedfromtheanalysis.Wenotethat inthistable,notall2-yearassessmentscoresfromthen=90excluded groupwereavailableforcomparison.Investigatingthecovariatesusing alogisticregressionmodelonthewholen=271cohort,SESriskscore wastheonlysignificantpredictorofparticipantsreturningfor2-year assessments(SupplementaryTable 1)withthoseathighersocialrisk beinglesslikelytoreturnforfollowup.

3.2. Predictionof2-yearfunctionaloutcomes

ForeachofthethreeBayley-IIImodelsandtheNSMDAfunctional grademodel,thepredictivefeaturesretainedfromLASSOregressionare providedinTable2.AshigherNSMDAscoresindicatemoreimpaired function,unlikeBayley-IIIcompositescoreswherehigherscoresindi- cateimprovedfunction,NSMDAscoreswereinvertedforregressionco- eﬃcientsofallfourmodelstoindicateassociationswithbetteroutcome.

Theregressioncoeﬃcientsforall4modelsareprovidedinTable2,how- everconﬁdenceintervalsarenotprovidedbypenalisedregressionsuch asLASSO.

OftheretainedMRIfeatures,corticalgreymattervolumewasre- tainedinthreemodelswithcorticalgreymattervolumebeingpositively

Fig.3. Diagramillustratingdataavailableforstatisticalanalysis,includingrea- sonsforexclusion.

associated withoutcomescores. ConverselyCSFvolumesandventri- clevolumes(presentinfourandtwomodels,respectively)werenega- tivelyassociatedwithoutcomescores.Ofthecorticalfeaturesretained byLASSO,corticalthicknessofthefrontal,occipitalandparietallobes wasacommonlyretainedfeatureinallfourmodels,withathickercor- texbeingassociatedwithincreasedassessmentscoresforallretainedCT features.Sulcaldepthofthefrontal,temporalandoccipitallobeswere alsofrequentlyretained,withlargersulcaldepthsbeingpredominantly positivelyassociatedwithBayley’sscore(in4of5retainedSDfeatures).

Table1

Baselinecharacteristicsoftheverypretermcohortwith2-yearfollow-upassessments,dividedintothose includedintheanalysis(n=181),andthoseexcluded(n=90)duetoMRIbeingofpoorquality(n=12), segmentationwasinaccurate(n=17),or2-yearassessmentswerenotavailable(n=64).Therewasn=3 excludedparticipantswhohadnoMRIor2-yearassessmentsavailable.

Included in analysis Excluded from analysis P-value N = 181 N = 90

Postmenstrual age at MRI, weeks ⁺^days, median (range) 32 ⁺⁴(29 ⁺¹- 35 ⁺²) 32 ⁺²(30 ⁺²- 35 ⁺⁰) 0.972 Gestational age at birth, weeks ⁺^days, median (range) 28 ⁺³(23 ⁺¹- 30 ⁺⁶) 27 ⁺³(23 ⁺⁴- 30 ⁺⁶) 0.599

Male, n (%) 98 (54%) 27 (30%) 0.680

SES risk score, mean (SD) 1.79 (1.67) 2.58 (1.58) < 0.001

High social risk (risk score > 2), n(%) 52 (29%) 25 (21%) 0.253 GMAs classiﬁcation at Early time point

Normal (%) 70 (39%) 37 (41%) 0.885

Poor Repertoire (%) 98 (54%) 44 (49%) 0.571

Cramped Synchronised (%) 13 (7%) 9 (10%) 0.612

Bayley-III motor composite, mean (SD) 97.37 (16.33) 95.15 (17.49) 0.488 Bayley-III cognitive composite, mean (SD) 95.51 (16.02) 94.04 (17.49) 0.499 Bayley-III language composite, mean (SD) 92.87 (17.01) 93.07 (16.02) 0.907 NSMDA functional grade, mean (SD) 8.61 (2.99) 9.05 (3.82) 0.429 MR image quality

No MRI (%) 0 (0%) 0 (0%) 1.0

Unusable (%) 0 (0%) 12 (13%) 0.001

Fair (%) 34 (19%) 33 (37%) 0.007

Good (%) 147 (81%) 45 (50%) < 0.001

Bayley-III,BayleyScalesofInfant andToddlerDevelopment3^rd Edition;GMA,GeneralMovementsAs- sessment;NSMDA,Neuro-SensoryMotorDevelopmentalAssessment;SD,Standarddeviation;SES,Socio- economicstatus;TEA,TermEquivalentAge.

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Table2

Theretainedmorphometricbiomarkersandcovariatesforeachofthe2-year Bayley-IIIoutcomes(motor,cognitiveandlanguagecompositescores)andNS- MDAfunctionalgrade,andtheircorrespondingregressioncoeﬃcient.

Model 1: Bayley-III motor composite

Feature Coeﬃcient

CSF -0.00036

Cortical grey matter 0.00001

Ventricles -0.00010

Occipital lobe CT 1.64

Frontal lobe SD 0.17818

Occipital lobe SD -0.05580

Temporal lobe SD 0.04080

GA at birth 0.16967

Sex (male reference) 0.24845

PMA at MRI -1.0701

SES risk score -2.39768

GMAs classiﬁcation -0.79964

Cohort (PREBO reference) -4.31271

Site < 0.00001

Days of ETT ventilation -0.29369

Days of CPAP -0.37486

Hours of oxygen therapy -0.00358

Days of TPN 0.28418

Hours of phototherapy -0.02337

Model 2: Bayley-III cognitive composite

Feature Coeﬃcient

CSF -0.00030

Frontal lobe CT 1.64

Parietal lobe CT 1.06

Frontal lobe GI 0.95901

GA at birth 0.05028

PMA at MRI -4.00087

Site < 0.00001

Days of ETT ventilation 0.21381

Days of TPN 0.11960

Hours of phototherapy 0.06188

Model 3: Bayley-III language composite

Feature Coeﬃcient

CSF -0.00036

Frontal lobe SA -0.01542

Frontal lobe SD 0.33345

Occipital lobe SD 0.24407

GA at birth 0.25547

PMA at MRI -1.4561

Cohort (PREBO reference) 0.38724

Site < 0.00001

Days of CPAP 0.10227

Days of TPN 0.38442

Hours of phototherapy 0.01127

Model 4: NSMDA functional grade

Feature Coeﬃcient

CSF -0.00002

Ventricles -0.00017

GA at birth 0.01977

( continued on next column )

Table2(continued)

PMA at MRI -0.60005

SES risk score 0.45359

Site < 0.00001

Age at NSMDA assessment -0.56597

Hours of oxygen therapy 0.00029

Days of TPN 0.04703

Hours of phototherapy -0.00530

CPAP,ContinuousPositiveAirwayPressure;CT,CorticalThickness;ETT,En- dotrachealTube;GI,GyriﬁcationIndex;GMA,GeneralMovementsAssessment;

PMA,PostmenstrualAge;SA,SurfaceArea;SD,SulcalDepth;GA,GestationAge;

SES,Socio-economicstatus;TPN,TotalParenteralNutrition.

Table3

Pseudor²ofthefouroptimalLASSOmodelsindicatingvarianceinclinical outcomeexplainedbyMRImeasuresandcovariates,aswellasthecovariates only.

Model Pseudo r ²with MRI Pseudo r ²no MRI M1: Bayley-III motor composite 0.757 0.465

M2: Bayley-III cognitive composite 0.546 0.431 M3: Bayley-III language composite 0.773 0.482 M4: NSMDA functional grade 0.660 0.492

Bayley-III,BayleyScalesofInfantandToddlerDevelopment3^rd Edition;

LASSO,Least AbsoluteShrinkageandSelectionOperator;MRI,Magnetic ResonanceImages; NSMDA,Neuro-sensory MotorDevelopmentalAssess- ment.

Covariates weremanuallyretainedinallfourmodelsinlinewith previous studies (Kline et al., 2020). Higher socio-economic status riskscoreswerenegativelyassociatedwithalldevelopmentaloutcome scores(averagereduction-2.66perpointinSESriskrawscore),sug- gestingthatthisisoneofthebiggestdriversof2-yearoutcomes.Sim- ilarly, abnormalwrithingmovements classifiedbytheGMAsclassifi- cationwerenegativelyassociatedwithalloutcomescores(averagereduction-1.01perGMAsriskcategory).HigherGAatbirthwasassoci- atedwithbetteroutcomes(scoreincrease0.12perweek),whilePMA atMRIwasnegativelyassociatedwithoutcomes(scoredecrease1.78 perweek).Girlsperformedbetterthanboysinallmodels(averagein- crease1.07).BabiesinthePPREMOcohortperformedworseonaverage thatthePREBOcohortforthreeofthefour2-yearoutcomes(average Bayley-IIIcompositescorereduction-5.01),howeversiteplayedaneg- ligibleroleinallmodels(effectsize<1e-10).Measuresofcarewerepre- dominantlynegativelyassociatedwith2-yearoutcomes,includingdays ofventilation(averagereduction-0.04perday),daysofCPAP(average reduction-0.09perday),hoursofoxygentherapy(averagereduction -0.002perhour),andhoursofphototherapy(averagereduction-0.02 perhour),withtheexceptionofdaysoftotalparenteralnutritionwhich waspositivelyassociatedwith2-yearoutcomes(averageincrease0.21 perday).

3.3. MRIscoresexplainmuchofthevariancein2-yearassessments

ThemodelﬁtforeachoftheLASSOmodelsisprovidedbythePseudo r²inTable3.WhenthefourmodelswererunwithoutMRIcorrelates, thefactorsaccountedforlessofthevarianceinBayley-IIIandNSMDA scores,asindicatedbythePseudor².

3.4. Modelvalidationonthetestset

ThetrainedLASSOmodelswereusedtopredictassessmentsscores fortheindependenttestdata.Pearson’srcorrelationsbetweenactual andpredictedassessmentscoresareprovidedinTable4.Moderatecor- relationswereobservedforallassessments,howeveronlythreeofthe

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Fig.4. TestsetcorrelationsbetweenthebestperformingLASSOmodelandtheactualBayley-IIIassessmentscores(motorandcognitivecompositescore,toprow;

languagecompositivescore,bottomleft)andNSMDAfunctionalgrade(bottomright)usingmeasuresfromtheEarlyMRIacquiredbetween29and35weeksPMA.

Table4

CorrelationsbetweenthebestperformingmodelswithandwithoutMRImea- sures,andthetestsetassessmentscoresindependentbythemodel.Signiﬁcance levelwascorrectedusingBonferronicorrection(𝛼=0.05/4models=0.0125).

Model Pearson’s r MAE AUC (Range)

M1: Bayley-III motor composite 0.508 ^∗(p = 0.008) 9.25 0.864 (0.856-0.874) M2: Bayley-III cognitive composite 0.477 ^∗(p = 0.011) 11.33 0.863 (0.846-0.880) M3: Bayley-III language composite 0.367 (p = 0.059) 12.77 0.677 (0.664-0.690) M4: NSMDA functional grade 0.624 ^∗(p = 0.001) 1.34 0.852 (0.846-0.858)

∗p<0.00125.AUC,AreaUndertheCurve;Bayley-III,BayleyScalesofIn- fantandToddlerDevelopment3^rd Edition;MAE,MeanAbsoluteError;MRI, MagneticResonanceImages;NSMDA,Neuro-sensoryMotorDevelopmentalAs- sessment.

fourmodelswerestatisticallysigniﬁcantinthetestset(p<0.0125Bon- ferronicorrected),includingtheBayley-IIImotorandcognitivecom- positescoresandNSMDAfunctionalgradewithEarlyMRIfeatures,but nottheBayley-IIIlanguagecompositescore.Thescatterplotillustrating thesetestsetcorrelationsareprovidedinFig.4.Inaddition,theAUC metricsusingthedichotomisedBayley-IIIcompositescores(<1SDbe- lowmean)andtheNSMDAfunctionalgrade(scoresabove8indicating disability)arealsoshowninTable4.

4. Discussion

Inthisstudy,wedemonstratethatmeasuresderivedfromEarlystruc- turalMRItakenbetween29-35weeksPMAareassociatedwithmotor, cognitiveandlanguageabilityattwo-yearsCAinaverypretermborn cohort.BycombiningEarlybrainmorphometricswithkeycovariatesin aLASSOpredictionmodel,estimatesof2-yearBayley-IIImotorandcog- nitivecompositescores,andNSMDAfunctionalgradewereobtained(in anindependenttestset),demonstratingthepredictiveabilityofthese earlyMRImeasures.Generaltrendsacrossthemodelsrevealedhigher GMvolumeandlowerCSFandventriclevolumestobeassociatedwith better2-yearoutcomesacrossmultiplefunctionaldomains,supporting previousstudiesthatreportedtheseearlybrainmeasuresinparticular maybeanimportantearlybiomarkersofabnormalbraindevelopment (Murphyetal.,2020).InallfourmodelswithMRImeasures,increased sulcaldepthandthickercortexacrossalllobes(frontal,parietal,occip- italandtemporal)werefrequentlyretainedamongmodelsaspredic- torsofbetteroutcome,albeitwithdiﬀerentregionsimpactingdiﬀerent functionaloutcomesInterestinglycerebellarvolumeswerenotretained in any model,despiteevidencein theliteraturethat theyarecorre- lated withmotorandcognitivedevelopment (Matthewsetal.,2018; Shahetal.,2006).Wesuspectthatthismaybebecausebrainvolume reductionoccursglobally,leadingtovolumesofthecorticalgreymatter

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andthecerebellumtocovary,andasaresultLASSOretainsonlyoneof thesemeasuresforsparsity.Additionallythecovariateswereimportant driversof2-yearoutcomes,includingsocioeconomicstatuswhichhas beendemonstratedpreviously(Klineetal.,2020),GMAclassiﬁcation whichhasbeenshowntobepredictiveofneurodevelopmentatboth2 and4yearsCA(Spittleetal.,2013),andsex,withthemalesexbeing animportantriskfactorbothforpoorneonataloutcomeaswellascog- nitionandMotoroutcomesat2-years(Peacocketal.,2012).Measures ofcarewerepredominantlyassociatedwithadverse2-yearoutcomes, whichmaybe aresultthatbabieswithmorecomplicatedbirthsdue toprematurityrequiremorecare,andalsoaremorelikelytohavead- verseneurodevelopmentaloutcomes(Kiechl-Kohlendorferetal.,2009).

Incontrast,thenumberofdaysofTPNwaspositivelyassociatedwith allfour2-yearoutcomes,supportingpreviousstudiesthatfoundgreater nutritionalintaketobepositivelyassociatedwithgrowthandneurode- velopment(Christmannetal.,2017).Infact,patientdemographicsand measuresofclinicalcarecombinedexplainedmorevariancein2-year outcomesthanwasexplainedbytoMRImeasures(Table3).Thismaybe duetocovariatesbeingretainedbyLASSOwithonlyMRImeasurespo- tentiallybeingremoved,andinfactthenumberofcovariatesexceeded thenumberofretainedMRIbiomarkersinallfourmodels.

Predictionscoresderivedfromourmodel(whichincludestheMRI biomarkersandcovariates)werecorrelatedwithactualoutcomemea- suresat2-yearfollow-up(r =0.36-0.62, Table4).This isconsistent withpreviousfindings followingup pretermcohortsimagedatTEA, with a previous study showing that cortical features (such as surfacearea,gyrificationindex,sulcaldepthandcurvature)werecorre- latedwithbothBayley’scognitiveandlanguagescores(r=0.51-0.53, n=110),respectively(Klineetal.,2020).Wenotethatinthestudyby Klineetal.(2020),modelswerevalidatedacrossthewholedatasetusing leave-one-outcrossvalidation.Inourpresentstudymodelperformance wasestimatedonthetrainingsubsetonly(with5-foldcross-validation), andthenoptimalmodelswerevalidatedonthetestsubsetofdatacom- pletelyunseenbythemodel,which shouldprovideamore unbiased estimateofthemodelerror.Inanotherstudyof86infantsscannedat theEarlytimepoint(30-32weeksPMA),volumetricsandglobalcortical morphologywerefoundtobepredictiveofbothlowBayley-IIImotor andcognitiveoutcomesat2years(AUCof0.80and0.78respectively, n=86)(Moeskopsetal.,2017).Ourfindingsareinlinewiththis,show- ingthatbrainmorphometricsincludingregionalcorticalmorphometrics arepredictiveofmotoroutcomes(AUC=0.86)andcognitiveoutcomes (AUC=0.86).Expandingonpreviouslyreportedfindings,wealsofound associationswithlanguageability(AUC=0.67)andNSMDAfunctional grade(AUC=0.85)(Table4).WhilemeanBayley-IIIscoresforhealthy AustralianinfantsarestatisticallysignificantlyhigherthantheUSstan- dardisedmeansforallsubtests(Chintaetal.,2014),thechosen cut- off of<85was foundtoprovideanaccuratedefinitionofmoderate- severeneurodevelopmentaldelay(Johnsonetal.,2014).Thesemodels demonstratethatboththeNSMDAandBayley-IIIclinicaloutcomescan bepredictedatEarly(30-32weeksPMA)timepoints,withtheseclinical assessmentsinturnbeingstronglypredictiveoflaterimpairmentat4 yearsandbeyond(Griffithsetal.,2018).

Alimitationofthisstudyisthat,forthePPREMOstudy(n=121), weonlyusedmorphologymeasuredfromthemulti-echoT2-TSEMRIs topredict2-yearoutcomes,whichhasathickslice(2mm)toproduce afastersequence.Thissequenceisalsomoresusceptibleto“ring” arte- factswhenthere ismotionwhencomparedtoHASTEsequences, resulting in greater data loss due to image quality. Ideally a 3D T1- weightedsequencewouldbeadded,whichcouldprovideanadditional MRcontrasttoassistthesegmentationprovidedbythedHCPpipeline (Makropoulosetal.,2018).Inourcohorthowever,giventhelongdura- tionofthisT1-weightedscan(∼5min),manywereaffectedbymotion artefact,andweresubsequentlyremovedfromtheanalysis.Similarly, diffusionMRIwasnot includedinthepresentanalysis,whichwould probethemicrostructureofthewhitematternotdetectablebystruc- turalimaging.Severalstudieshavefoundassociationsbetweendiffu-

siontensorimaging(DTI)orotherdiffusionmodels,and2-yearBayley- IIIoutcomes(Vassaretal.,2020),includingstudiesusingthiscohort, whichfoundstrongerassociationswith1-yearBayley-IIIoutcomesover 2-yearoutcomes,andgreaterassociationsusingfixel-basedratherthan DTImetrics(Panneketal.,2020).InfutureworkweplantoincludeMRI derivedmeasuresfromimagesacquiredatEarlyandTEAtime-points,as wellastocombinemetricsofbrainstructureandmicrostructuretoseeif thereisanenhancedpredictionoflateroutcomes.Infuture,wealsoplan toreplacetheFSL‘bet’brainmaskingwithartificialneural-networkap- proaches(Isenseeetal.,2019)tominimizetheamountofmanualmask correction.AfurtherlimitationofthisstudyisthatthedHCPtissueat- lasgroupsallanatomiesbilaterally,groupsthefunctionallydistincthip- pocampusandamygdalaasonelabel,andadditionallygroupscortical measuresbylobe.Asinpreviousstudies(Moeskopsetal.,2017),this was donetominimize modelcomplexityandavoid overfittinggiven thecurrentsamplesize(n=181),howeverasaresultitignoredfunc- tionaldifferencesbetweenthehippocampusandamygdala,andstruc- tures bilaterallyingeneral.Anotherpotential limitationofthiswork wasthatthesamplemayhavebeenskewedtowardsinfantsatlower riskofadverseneurodevelopmentaloutcomes,becauseinfantswiththe highestseverityofillnessduringthe29-35weekPMAintervalwereless likelytoberecruitedforreasonsoffeasibilityandsafetyofperforming theMRI.Furthermore,forthoseparticipantswhoseadverseoutcomes wereassociatedwithsevere brainabnormality,theseMRIswerealso morelikelytofailatthedHCPsegmentation.Additionally,thePMAat MRIrangeofthiscohortatthis‘Early’timepointisquitewide(median age=32weeks,range=29-35weeks),andalthoughaccountedforin themodel,thereisaknownbiasinourcohortwhereinfantsscanned lateratthistimepointhadpooreroutcomesastheywerenotclinically stableenoughtohavetheMRIearlier.ThismayexplainwhyPMAat MRIwasfoundtobeconsistentlynegativelyassociatedwithBayley’s compositeoutcomes,andpositivelyassociatedwithNSMDAfunctional grade(Table2).OnefinallimitationwiththepredictionLASSOmod- elsisthattheytendtounderestimatehighassessmentscoresandover- estimatelowassessmentscores,makingpredictionsmuchclosertothe mean(Fig.4).WhatthismeansisthatpredictionofthenumericBayley- IIIcompositescoreisnotaccurateintermsofMAE(Table3),however clinicallythesemodelscouldaccuratelypredictthosewithadverseout- comes(<1SDfromthemean)basedontheAUC(Table3),potentially allowingearlierfollow-upandinterventionsforthoseinfants.

There isa needtolookbeyond2-yearoutcomes andseeifEarly MRI imagingcan reveal developmental outcomes at childhood age.

This is importantasintellectual, learningandbehaviouraloutcomes cannotbe fullydetermined at1-3 yearsCA.Laterfollow up beyond 3yearsis requiredtoevaluatethetrue predictiveaccuracyof Early MRI.WhiletherelationshipbetweenMRIatTEAandchildhoodout- comeshasbeendemonstratedatpreschoolage(Vanesetal.,2021)and at7years(Andersonetal.,2017;Thompsonetal.,2014) inpreterm infants, thesestudieshowever havebeenlimitedbysamplesize,MR fieldstrength,limitingimageresolutionandquality(Andersonetal., 2017;Lohetal., 2017),andonlyinvestigatedneonatalMRItakenat TEA (Anderson etal.,2015; van’tHooftet al.,2015).Integrationof diffusionMRI-basedwhitemattermeasures,includingfixel-basedmea- sures,willbeimportantinadditiontothemacromeasuresusedinthis studytoexplaintheimpactofpretermbirthonbraindevelopmentand lateroutcomes(Panneketal.,2018).Wearecurrentlyfollowingupthis cohortat6yearsaspartofthePREBO-6study(Georgeetal.,2020), whenadiagnosisofCP,ASDandschoolreadinessoutcomescanbere- liablyobtained.Thislongitudinal informationdataiskey,increasing thevalueofthePPREMO/PREBOcohortandpotentiallythepredictive potentialoftheearlyMRI.

5. Conclusions

Inthisstudyweperformstate-of-the-artstructuralimageanalysis ononeofthelargestcohortsofinfantsbornpretermwhounderwent

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Early3TMRI(29-35weeksPMA).UsingLASSOregression,weiden- tifiedseveralbiomarkerspredictiveofmotor,cognitiveandlanguage functionat2-yearsofage,includingGMvolume,CSFandventricular volume,corticalthicknessandsulcaldepthacrossthecortex,andkey covariatesincludingsocio-economicstatus,EarlyGMAsclassification, sexandGAatbirth,whichhasbeenidentifiedpreviouslyasimportant factorsdrivingoutcomes.Furthermore,usingthesemodelsrevealedac- curatepredictionofoutcomesonanindependenttestset,indicatingthat Bayley-IIImotorandcognitivecompositescoresandNSMDAfunctional gradescouldbeestimatedasearlyas29weeksPMA.Accuratepredic- tionofoutcomesderivedfromearlyMRIwouldfacilitateinterventions tobeprovidedtoat-riskinfantsmuchearlierandmayassistwithmore effectiveinterventionstrategiesifdeliveredduringthisoptimalearly periodofneuroplasticity.Ultimately,wehopetousethisinformation toimproveclinicaloutcomesforinfantsbornpreterm.

Dataavailabililtystatement

The studyteam areavailable to collaborate with otherresearch teamsonreceiptofareasonablerequesttoaccessstudydata.Expres- sionsofinteresttoaccessstudydata, madeouttothecorresponding author,willbeconsideredandthengrouplevelorindividualleveldei- dentiﬁeddatacouldbesharedasappropriate.

DataAvailability

Datawillbemadeavailableonrequest.

Creditauthorshipcontributionstatement

AlexM.Pagnozzi:Conceptualization,Methodology,Software,Val- idation,Formal analysis,Visualization, Writing– original draft.Liza vanEijk:Datacuration, Methodology,Software,Investigation,Writ- ing– review&editing.KerstinPannek:Datacuration,Methodology, Supervision,Writing– review&editing.RoslynN.Boyd:Projectad- ministration,Writing– review&editing.SusmitaSaha:Datacuration, Methodology,Software,Writing– review&editing.JoanneGeorge:

Conceptualization,Projectadministration,Fundingacquisition,Datacu- ration,Writing– review&editing.SamudraguptaBora:Projectad- ministration,Fundingacquisition,Writing– review&editing.DanaKai Bradford:Supervision, Writing – review & editing.Michael Fahey:

Datacuration, Writing– review& editing.MichaelDitchﬁeld:Data curation, Writing– review& editing.AtulMalhotra: Datacuration, Writing– review&editing.HelenLiley:Writing– review&editing.

PaulB.Colditz:Projectadministration,Fundingacquisition,Datacu- ration,Writing– review&editing.StephenRose:Writing– review&

editing.JurgenFripp:Supervision,Projectadministration,Writing– review&editing.

Funding

ThefollowingsupporthasbeenprovidedbytheAustralianNational HealthandMedicalResearchCouncil(NHMRC):Projectgrant(PREBO NHMRC1084032)andInvestigatorFellowship(RB:NHMRC1195602) andanNHMRCClinicalCRE(NHMRC1116442CREAustralasianCP ClinicalTrialsNetwork).PPREMOwasfundedthroughgrantsfromthe CerebralPalsyAllianceResearchFoundation(IRG1413),theFinancial MarketsFoundationforChildren(2014–074),theQueenslandGovern- ment(SmartState;HealthPractitionerStimulusGrant)andtheRoyal BrisbaneandWomen’sHospital.Wewouldliketoacknowledgethefam- iliesofpreterminfantswhoparticipatedinthisstudy,themedicaland nursingstaﬀ oftheneonatalunit,radiologyandadministrativestaﬀ in thedepartmentofmedicalimagingandthePhysiotherapistsandPsy- chologistswhoassessed24-monthoutcomes,andJulieTrinderforher

managementoftheimaging,demographic,andclinicaldataforthisco- hort.

The study team are availableto collaborate with other research teamsonreceiptofareasonablerequesttoaccessstudydata.Expres- sions ofinteresttoaccessstudydata,madeouttothecorresponding author,willbeconsideredandthengrouplevelorindividualleveldei- dentiﬁeddatacouldbesharedasappropriate.

Supplementarymaterials

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