Longitudinal network re-organization across learning and development

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ContentslistsavailableatScienceDirect

NeuroImage

journalhomepage:www.elsevier.com/locate/neuroimage

Longitudinal

network

re-organization

across

learning

and

development

Ethan

M. McCormick

a,∗

_,

_Sabine

_Peters

b,c

_,

_Eveline

_A.

_Crone

b,c,d

_,

_Eva

_H.

_Telzer

a a Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC 27599, United States b Department of Developmental and Educational Psychology, Leiden University, 2333AK Leiden, the Netherlands

c Leiden Institute for Brain and Cognition, 2333ZA Leiden, the Netherlands

d School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands

a

b

s

t

r

a

c

t

Whileitiswellunderstoodthatthebrainexperienceschangesacrossshort-termexperience/learningandlong-termdevelopment,itisunclearhowthesetwo mechanismsinteracttoproducedevelopmentaloutcomes.Herewetestaninteractivemodeloflearninganddevelopmentwherecertainlearning-relatedchanges areconstrainedbydevelopmentalchangesinthebrainagainstanalternativedevelopment-as-practicemodelwhereoutcomesaredeterminedprimarilybythe accumulationofexperienceregardlessofage.Participants(8–29years)participatedinathree-wave,acceleratedlongitudinalstudyduringwhichtheycompleteda feedbacklearningtaskduringanfMRIscan.Adoptinganovellongitudinalmodelingapproach,weprobedtheuniqueandmoderatedeﬀectsoflearning,experience, anddevelopmentsimultaneouslyonbehavioralperformanceandnetworkmodularityduringthetask.Wefoundnonlinearpatternsofdevelopmentforbothbehavior andbrain,andthatgreaterexperiencesupportedincreasedlearningandnetworkmodularityrelativetonaïvesubjects.Wealsofoundchangingbrain-behavior relationshipsacrossadolescentdevelopment,whereheightenednetworkmodularitypredictedimprovedlearning,butonlyfollowingthetransitionfromadolescence toyoungadulthood.Theseresultspresentcompellingsupportforaninteractiveviewofexperienceanddevelopment,wherechangesinthebrainimpactbehavior incontext-speciﬁcfashionbasedondevelopmentalgoals.

1. Introduction

Thebrainisadynamicsystemcapableofreshapingitselfacrosstime toadapttoitsexternalenvironment.Forsomedevelopmentalprocesses (e.g.,cognitivecontrolorrisk-taking;Casey,2015;orsocioemotional development;BlakemoreandMills,2014),thesechangesunfoldacross longtimehorizons(e.g.,monthsoryears).However,functional devel-opmentdoesnotrequireyears,orevenmonths,toshowmeasurable changes.Indeed,abroadliteraturehasdemonstratedthatbrain func-tionrapidlyadaptstotaskdemandsandfeedbacktosupportskill acqui-sitionorgoal-directedbehavior(e.g.,Dawetal.,2006;Bassettetal., 2011; McCormick and Telzer,2017a, 2017b, 2018; Telesford et al., 2017;Gerratyetal.,2018).However,itremainsunclear towhat ex-tenttheseshort-term,learning-relatedchangesinbrainactivation over-lapwiththelong-term,maturationalplasticity seenacross yearsand decadesofdevelopment(Galván,2010).Here,wetesttwopotential ex-planationsforhowexperienceanddevelopmentinteractacrosstimeto explainchangesinlearningperformanceandthefunctionalbrain sys-temsthatsupportthatperformanceacrosstime.Toprobethese interac-tions,weadoptanovelapplicationoflongitudinalmodelingthatallows ustoconsiderchangesacrossminutes,years,andthecourseof devel-opmentsimultaneously.Thisapproachoﬀersanintegratedperspective oflearninganddevelopmentasco-dependentprocessesofneuraland behaviorplasticitywhichinteractacrosstime.

∗_{Corresponding}_author.

E-mail address: emccormick@unc.edu(E.M.McCormick).

While traditionally thought of as a period of vulnerability (Steinberg et al., 2008; Casey et al., 2008; Shulman et al., 2016), adolescence isalso aperiodassociated withincreasesin flexible be-havior and thecapacity tolearn from feedback in the environment (Johnsonand Wilbrecht,2011; Crone andDahl, 2012; Casey,2015; Vigilantetal.,2015),withneuralchangesassociatedwithage support-ingincreasedlearning(VanDuijvenvoordeetal.,2008;Petersetal., 2016;McCormickandTelzer,2017a;PetersandCrone,2017).In gen-eral.theabilitytolearnandengageinothercomplexcognitivetasks (Caseyetal.,2005;Lunaetal.,2010),improveswithagethroughthe firstdecadesoflife.However,thisco-occurrencedoesnotbyitself im-plythatmaturationisnecessaryfortheage-relatedimprovements in learningseenduringdevelopment.Withincreasedagealsocomesmore experienceandpracticeatskillsneededtosupporttaskperformance. Underthisview,developmentinvolvestheaccumulationofpracticeor trainingofneuralsystems,andtheneuralmechanismsforthisprocess shouldcloselyresemblethoseinvolvedinshort-termlearning.In con-creteterms,thiswouldimplythatdevelopmentallyyoungerindividuals canbetrainedtoperformaswellasolderindividualsgivensufficient practice.

Incontrast,aninteractiveviewoflearninganddevelopmentwould suggestthatcertainkindsofneuralchangesinresponsetolearningare constrainedbydevelopmentalchangesinthebrain.Inotherwords,it shouldbepracticallyimpossibletotrainachildtoperformatadult lev-elsbecausetheyhavenotexperiencedthematurationalchangesinthe brainnecessarytosupportthatperformance.Thiswouldsuggestthat

https://doi.org/10.1016/j.neuroimage.2021.117784

Received23October2020;Receivedinrevisedform7January2021;Accepted11January2021 Availableonline24January2021

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certainkindsofneuralchangesinresponsetolearningwillberelatively uniquetoolderindividuals.Thesetwoalternativeaccountsarenot mu-tuallyexclusive,sincetrainingstudiesin youngerindividualsclearly demonstratethatthere issomecapacitytoimprovecognitive perfor-manceandshiftbrainfunctioneveninthematuringbrain(Jollesand Crone,2012).However,theﬁrstexplanationofhowlearningand devel-opmentinteractwouldpredictthatthiscapacitytotrainshouldbequite extensive,whereasthesecondexplanationwouldpredictthat biologi-calmaturationimposesstricterlimitationsontheabilitytotrainyoung individualto“adult” levelsofperformance.Itisimportanttonote, how-ever,thatthese limitationsmaynotbemaladaptive,butratherserve someotherdevelopmentalfunctionwhere“immature” brainstatesor behavioralperformanceareimportantforﬂexiblelearningand adapta-tion(e.g.,JohnsonandWilbrecht,2011;JollesandCrone,2012;Crone andSteinbeis,2017;McCormickandTelzer,2017a).

Amajorchallengeinmodelingexperienceanddevelopment simulta-neouslyisthatinrealdata,theyareoftenconfounded(Bell,1953;Jolles andCrone,2012;Telzeretal.,2018)indevelopmentalmodels.In lon-gitudinalstudieswhichuseacohort-sequential(orpanel)design,where individualsarerepeatedlyassessedatthesameages,olderparticipants arealsomore-experiencedparticipants(bothinlifeandpracticeinthe specificmeasuresofinterest).Inneuroimagingcontexts,these experi-enceeffectscanconfounddevelopmentaleffectsinanumberofways, includingreducinganxietyaboutthescanner environment,changing baselineconditions(the“taskB” problem),orreduce errorsontasks throughfamiliarityratherthanchangeinunderlyingability(Jollesand Crone,2012;Telzeretal.,2018).Fortunately,wecanleveragean alter-native,theacceleratedlongitudinaldesign,toaddressthesechallenges. Inacceleratedlongitudinalstudies,individualsvaryintheageoffirst as-sessmentandarefollowedlongitudinallythereafter.Byadoptingthis de-sign,wecande-coupleexperiencefromage(orothermeasureof devel-opmentalstage)sufficientlytosuccessfullymodeltheaccumulationof experienceanddevelopmentalmaturationsimultaneously(McCormick,

preprint).

Thecurrentstudyteststhetwocompetinghypothesesofhow expe-rienceanddevelopmentinteracttodriveneuralplasticityduring learn-ing.Participantsacrossawideagerange(8–29years)participatedina three-wave,acceleratedlongitudinalneuroimagingstudyduringwhich they completeda feedback learningtask.Byleveraging the acceler-atedlongitudinaldesignandanovelextensionofmixed-effectsmodels (McCormick,preprint),wedifferentiatebetweenthreetemporallevels ofneuralplasticity:1)short-termpractice-relatedchangeswithinascan session(within-individual)acrossblocksoffeedbacklearning;2) long-termchangeswithin individuals,acrossmeasurementoccasions (i.e., waves);and3)themixed(i.e.,within-andbetween-individual)effect ofchangesassociatedwithage.Byconsideringthesethreelevelsinthe samemodel,wecanpartitioneffectsateachlevel.(1)Within-session changesreflecthowbrainandbehavioradaptduringlearningthetask structure,(2)between-session changesreflect changesdue to experi-enceafterrepeatedexposuretothetaskandtestingenvironments,(3) whileagereflectsthedevelopmentaleffect.Importantly,including ef-fectsatthesecondlevelallowsustode-confoundageandexperience, givingamore reliableestimateof thedevelopmentaleffect. Because learningis anintegrative process,involvingtheinteractionsbetween manybrainregions (Bassettet al.,2011; Gerratyetal.,2014, 2015; McCormickandTelzer,2017a;Gerratyetal.,2018;McCormick,Gates, &Telzer, 2019),wetestthis developmentalmodelinthecontext of brainnetworks.Specifically,wemodeltheinteractionofpractice, expe-rience,anddevelopmenteffectsonnetworkmodularity.Modularityis ameasureofthedegreeofnetworksegregationintodistinctfunctional units(BullmoreandBassett,2011).Higherlevelsofmodularityinbrain networkspredictsincreasedlearning(Bassettetal.,2011;Ellefsenetal., 2015)andworkingmemory(Braunetal.,2015)performanceinadults. Ouranalyticapproachtoaddressingthesequestionsinvolvedseveral steps.First,wefitmixed-effectsmodelswithonlylinearandquadratic effectsofageonbehavioralperformanceandnetworkmodularity

dur-inglearningseparately(Petersetal., 2016;PetersandCrone,2017) forcomparisontomorecomplexmodels.Wethenincludedpredictors ofwithin-andbetween-sessionchangeasmaineffectstoconsiderthe uniqueeffectsofpractice,experience,anddevelopment,beforefitting amodelthatincludedinteractiontermsbetweenourpredictors.This thirdmodelallowedustoprobehowtheeffectsofthelower-level pre-dictors change across development in a continuous fashion. Finally, weestimatedabrain-as-predictormodelwhereweprobedhowbrain states(e.g.,highversuslowmodularity)differentiallypredicted learn-ingperformanceacrosspractice,experience,anddevelopment.This fi-nalmodeltestsacoredifferencebetweenthetwoexplanationsof devel-opmentalimprovementinlearningperformance.Inthe development-as-practiceview,networkmodularityshouldpredictlearningperformance consistentlyregardless ofwhenin thedevelopmentaltrajectory(i.e., there isnomoderationbyage).This iscontrastedbytheinteraction viewofdevelopmentandexperience,wherewewouldexpectthat mod-ularitywouldpredictperformancedifferentiallydependingonage. 2. Methods

2.1. Sample

Atotal of299participants(ages 8–29years;153female) partici-patedina3-wave,acceleratedlongitudinalMRIstudy.Participantswere scannedevery2years,spanninga5-yearperiod(Fig.1).Atwave1,28 participantswereexcludedforanumberoffactorsincludingnot com-pletingtheMRIsession(N=4),excessivemovementduringthescan session(>3mmrelativemotionin anydirection/rotation)(N=22), ADDdiagnosisdisclosure(N=1),andreportedmedicineuse(N=1), re-sultinginaﬁnalsampleof271participantsattheinitialdatacollection (140female;M_age=14.17,SD=3.63,range=8.01–25.95years).Atwave2 (2yearslater),254participantswerescanned(33couldnotbescanned duetobraces;11declinedtoreturn).Ofthescannedparticipants,an additional21wereexcluded(12formotion;2forpreprocessingerrors; 5for T2artifacts;1formedicine use;1forADDdiagnosis),leaving aﬁnalsampleof233participants(121female;M_age=16.15,SD=3.62,

range=10.02–26.61years).Duringtheﬁnalwave(2yearslater),243 participantswerescanned(11couldnotbescannedduetobraces;45 declinedtoreturn).Ofthese,11wereexcluded(3didnotcompleteMRI session;4formotion;2forprocessingerrors;1formedicineuse;1for ADDdiagnosis),foratotalﬁnalsampleof232participants(121female;

M_age=18.15,SD=3.68,range=11.94–28.72years).Acrossthedataset, 183participantshaddataatallthreewaves,78participantshaddata attwowaves,31participantshaddataatonlyonewave,and7were excludedatallthreewaves.Atotalof736scanswereincludedforﬁnal dataanalyses.Whenconsideredatthetraillevel,thesescansyielded 4799observationsformodeling.

IQscoresweremeasuredattheﬁrsttwowavesofdatacollection, us-ingtheWISC-III(forparticipants<16years;NW1=195;NW2=119)or WAIS-III(forparticipants≥16years;NW1=76;NW2=114).All partic-ipantswerewithinthenormalrangeatwave1(M=109.8,SD=10.34,

range=80–142.5)andwave2(M=108.3,SD=10.27,range=80–147.5). Further details andthedistributions of thedescriptivevariables are availableinthesupplementalmaterial.

2.2. Feedbacklearningtask

ParticipantscompletedafeedbacklearningtaskduringanfMRI ses-sion(Petersetal.,2014,2016).Oneachtrial,participantssawascreen withthreeemptyboxesandone(outofapossiblesetofthree)stimulus underneath(Fig.2).Participantsweretoldthateachstimuluswithina givensethadacorrespondingcorrectlocationamongtheemptyboxes andthattheirgoalonthetaskwastoappropriatelysorteachstimulus intoitslocation.Foreachstimulus-locationchoice,participantseither receivedpositive(a“+” sign)ornegative(a“-” sign)feedbackbased ontheirchoice.Positivefeedbackindicatedcorrectstimulusplacement,

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Fig.1. Structureofrepeatedmeasureswithin the accelerated longitudinal design. Partici-pantsareorderedinascendingorderbasedon theirageatwave1.Sexisdenotedbyseparate colors.

Fig.2. DuringtheFeedbackLearningtask,participantslearnedthecorrectplacementofeachstimuli(e.g.,theelephant)throughfeedback.Participantsreceived eitherpositiveornegativefeedbackoneachtrial.

whilenegativefeedbackindicatedincorrectplacement.Eachstimulus withinasetassociatedwithaunique,deterministicallycorrectlocation. Stimuliwithinasetwerepresentedinpseudorandomorder,constrained suchthatnostimuluswithinasetwaspresentmorethantwiceinarow. Afteramaximumof12trialsperblock,orafterallthreestimuliwithin asetwerecorrectlyplacedtwice(indicatingthatalllocationswere suc-cessfullylearned),stimulussetswereswappedoutforanewsetwith threenewstimuli.Participantssawatotalof15blocksof3-stimulisets (foramaximumpossibleof180trials)atwaves1and2,and10blocks (maximumpossible120trials)atwave3.PriortotheMRIsession, par-ticipantspracticedthreeexamplesetsofstimuli.Eachtrialconsistedof thefollowing:1)a500-msﬁxationcross,2)stimuluspresentationfor 2500mswhileparticipantsmadelocationdecisions,and3)feedback presentationfor1000ms. Trialswereseparatedbyintervalsjittered basedonOptSeq(Dale,1999),withdurationsthatvariedbetween0 and6s.

2.3. Behavioralanalyses 2.3.1. Taskmetricsofbehavior

Ourprimarymetricoftaskperformancewasthelearningrate par-ticipantsdisplayedinformingcorrectstimulus-locationassociations.To calculate learningrate,we distinguishedbetween twophasesoftask performance:thelearningandtheapplicationphase(Petersetal.,2014, 2016).Thelearningphasewasdeﬁnedastrialswherethecorrect loca-tionforagivenstimuluswasstillunknown,andparticipantsneeded torely ontrial-and-erroror hypothesistesting tocorrectlyplacethe stimulus.Trialsinthelearningphasecouldresultineitherpositive (in-dicatingafuture staystrategy)ornegative(promptingafutureshift strategy)feedback. Incontrast,theapplicationphasewas deﬁnedas trialswherethecorrectlocationforthepresentedstimulusisalready known(asestablishedbyanearlierlearningtrial)andparticipants cor-rectly placethatstimulusagain.Learning ratewascalculatedas the

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proportionoftrialsinthelearningphasewherefeedbackwascorrectly appliedinthefollowingtrialinvolvingthesamestimulus(eitheras re-peatedplacementfollowingpositivefeedbackorasalteredplacement followingnegativefeedback)outofall thetrialsduringthelearning phase.

2.3.2. Linearmixed-eﬀectsmodel

Totestourdevelopmental/experienceinteractionmodel,wefita lin-earmixed-effectsmodeltoparticipants’learningratedata.Wefollowed amodel-buildingproceduresimilartotheoneusedinpreviouswork inthissample(Petersetal.,2016;PetersandCrone,2017).This pro-cedureinvolvedabuild-upapproachwherewetestedmaineffectsand theninteractionsoftimetoestablishtheoptimaldevelopmentalform beforebringinginadditionalpredictors.WefitarandomeffectsANOVA modelwitharandominterceptwhichservedasacomparisonfor subse-quentmodels.Fordescriptivepurposesattherandomeffectslevel,we fitathreelevelmodelwhereblockswerenestedwithinwaveandthen withinperson,howeverforcomparisonwithfuturemodels,wealsofita twolevelmodelwherewaveandagewereincludedatlevel1.To com-pareacrosslevelsofchange,weconstructedamodelusingthelme4 softwarepackagethroughR(version1.1–21;Batesetal.,2015),where stimulusblocks(N=1-max15)andwave(N=1–3)werenestedwithin individual,andagewasincludedasatime-varyingcovariate.Because wave(i.e.,repeatedexposuretothetask)wasapredictorofinterest, wedidnotnestwithrespecttowavesincethatwouldresultina vari-ablethatactsasbothanestingfactorandlineareffectofinterest.We includedinteractionsbetweenwave,age,andblocks.Tocapturemore complexchangesin behaviorbetweenblocks ofthetask,weutilized piece-wiseregressionatlevel1(Flora,2008;Lietal.,2001),including predictorswhichmodelthelineareffectsacrossthefirstandsecondhalf ofthetaskseparately.Thismodelresultedinthefollowingequation:

ReducedForm: 𝐿𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑅𝑎𝑡𝑒𝑖𝑗 =𝛾00+𝛾10𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 +𝛾20𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 +𝛾30𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾40𝐴𝑔𝑒𝑖𝑗 +𝛾50𝐴𝑔𝑒2𝑗 +𝛾60𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾70𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾80𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾90𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾100𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒2𝑖𝑗 +𝛾110𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒2𝑖𝑗 +𝛾120𝑊𝑎𝑣𝑒𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾130𝑊𝑎𝑣𝑒𝑖𝑗 ∗𝐴𝑔𝑒2𝑖𝑗 +𝑢0𝑗 +𝑟𝑖𝑗

While previous work has discouraged using wave as a predic-torin longitudinalmodels(insteadusing preciseage;see Mehtaand West,2000),herewedrawameaningfuldistinctionbetweenwaveand age.Wewouldexpectchangesinbehavioraftereachsubsequent expo-surethetaskenvironment(Telzeretal.,2018),whichduetothe sam-plingmethodinacceleratedlongitudinaldesignsisdisassociatedwith agetosome degreebecausealarge agerangeis representedateach wave.

2.4. fMRIdataacquisitionandprocessing 2.4.1. MRIdataacquisition

Scans across all three waves were acquired using the same Philips 3T MRI scanner, utilizing identical scan settings. The Feed-back Learning Task includedT2∗_-weighted _echoplanar _images _(EPI; slicethickness=2.75mm;38slices; sequentialacquisition; TR=2.2s; TE=30 ms; FOV=220 × 220 × 114.68 mm). Additionally, struc-tural images were acquired, including a high-resolution 3D T1-FFE anatomical scan (TR=9.76 ms; TE=4.59 ms; 140 slices; voxel size=0.875×0.875×1.2mm;FOV=224×177×168mm;ﬂipangle=8). Priortoundergoingthescanprocedure,participantswereintroducedto thescannerenvironment(e.g.,spaceandnoises)throughamockscan session.

2.4.2. fMRIdatapreprocessingandanalysis

PreprocessingandanalysesutilizedasuiteoftoolsfromFSLFMRIBs SoftwareLibrary(FSLv6.0;https://fsl.fmrib.ox.ac.uk/fsl/),Stepstaken duringpreprocessingincludedskullstrippingofallimagesusingBET; andslice-to-slicemotioncorrectionofEPIimagesusingMCFLIRT; co-registerationinatwo-stepsequencetothehigh-resolutionT2-weighted andT1-FFEanatomicalimagesusingFLIRTinordertowarptheminto thestandardstereotacticspacedeﬁnedbytheMontrealNeurological Institute(MNI) andtheInternationalConsortium forBrainMapping; andtheapplicationofa128shigh-passtemporalﬁltertoremovelow frequencydriftwithinthetime-series.

2.4.3. Nuisanceregressors

Prior tomodelingthefMRIdatafurther,wetook severalstepsto reducetheinfluenceofmotion.Motion,asmeasuredbyframewise dis-placement(Poweretal.,2012),wasminimalacrossthesample(mean acrossparticipants=0.12mmFD;max=0.77mm;average percent-ageof volumeswith>0.3mm FD=4.95%).Wealsocontrolledfor 8nuisanceregressorsin theGLM andtime-seriesanalyses:6motion parametersgeneratedduringrealignmentandtheaveragesignalfrom boththewhitematterandcerebrospinalfluidmasks.Previouswork(see Ciricetal.,2017)hasshownthatthesestrategiesreducetheinfluence ofmotiononfunctionalconnectivityanalyses.

2.4.4. Graphconstruction

Wethenutilizedagraphtheoreticalapproach toinvestigate how networks in the brainchanged across levels of practice, experience, anddevelopment.UsingasubsetoftheBigBrainparcellationscheme (Sietzmanetal.,2020),anatlascomprisedof300,5-mmsphereparcels fromcorticalandsubcorticalregions,weextractedfunctionaltimeseries dataforeachblock(15intotal)inordertomodelchangesinnetwork structureacrosstimeduringthetask.Wechosetoexaminenetwork fea-turesbetweenregions withtheoreticalrelevancetotask performance during learning. This resulted in 147regions including those in the cingulo-opercular(14),defaultmode(55),fronto-parietal(27),salience (14),ventral(9)anddorsal(14)attention,hippocampal(6),andreward (8)sub-networks (fortherelevantROI coordinatesona whole-brain projection,seeFigureS1).Selectionofthesenetworkswereguidedby thoseregionsengagedinthefeedbacklearningtaskinpreviousresearch (Petersetal.,2016;Petersetal.,2016)orclassicallyengagedduring learninganddecision-making (DawandShohamy, 2008; Sadaghiani andD’Esposito,2014;McCormickandTelzer,2018a;McCormicketal., 2019).Thissubsetwaschosentobalanceincludingenoughregionsof interestwiththechallengesofcomputingwhole-brainnetworkson rel-ativelyshorttimeseries.Regionswereincludedorexcludedasagroup basedontheirnetworklabel(e.g.,allfronto-paretialregionswere in-cludedwhileallvisualregionswereexcluded).Asafollow-up sensitiv-ityanalysis,were-ranallanalyseswithmodularitycalculatedonthe whole-brainnetwork(whole-brainmodularityvalues areavailablein theposteddataﬁle;https://osf.io/62gwz/).Resultsremained substan-tivelyunchangedandweretainedthesubsettomaintainﬁdelitytothe originalanalysisplan.

Toextract, weconstructed a task regressormadefrom theonset anddurationofeachblockofstimuliconvolvedwithanHRFfunction. These regressorsweremultipliedwiththeentiretimeseriesextracted fromROIsinordertogiveasetoftime-seriesﬁlesforeachindividual ateachwave.Blockdurations(M=45.32s;SD=3.26;range=37.27– 60.23 s)werecomparabletoapproachesused indynamic functional connectivityanalyses(approximately30s;Shireretal.,2012; Gonzalez-Castilloetal.,2015).Correlationmatriceswereconstructedby comput-ingthezero-lagcross-correlationbetweeneachROI.Graphmetricswere calculatedacrossarangeofcosts(5–20%in5%increments;Cohenand D’Esposito,2016).Weutilizedthestandardcommunityassignmentfor distinguishingwithin-versusbetween-networkedges(Sietzmanetal., 2020).

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Fig.3.ProbingInteractionswithAge.Fourdistinctageswerechosentoprobeinteractionswithage,includingduringearlyadolescence(12,[~ −1SD];red),middle adolescence(16,[~ meanage],green),lateadolescence(20,[+1SD];blue),andyoungadulthood(25,[~ +2SD],purple).Barheightrepresentstheproportionof observationsatthatlevel.Rugplothashes(belowx-axis)representindividualobservations.

2.4.5. Graphmetric

Forourmeasureofbraindevelopment,wecalculatednetwork mod-ularity,orthedegreetowhichcommunitieswithinthebrainnetwork aresegregated.Modularityiscomputedastherelativenumberofedges betweennodesofthesamecommunitycomparedtothenumberof to-taledgeswithinthebraingraph.Wecalculatedmodularity(Q∗₎_using positiveandnegativeweightededgesas:

𝑄∗₌ 1 2𝑊+ ∑ 𝑖𝑗 ( 𝑤+ 𝑖𝑗 −𝑒+𝑖𝑗 ) 𝛿(𝑚𝑖 ,𝑚𝑗 ) − 1 2𝑊+₊₂_𝑊− ∑ 𝑖𝑗 ( 𝑤− 𝑖𝑗 −𝑒−𝑖𝑗 ) 𝛿(𝑚𝑖 ,𝑚𝑗 )

wherew+_is_the_number_of_positively_weighted_edges_and_w−_is_the num-berofnegativelyweightededges.The𝑒𝑖𝑗 termrepresentstheexpected numberofedgesbetweentwonodesiandj,andthe𝛿(𝑚𝑖 ,𝑚𝑗 )termis1 ifthenodesiandjareinthesamemoduleand0iftheyarenotinthe samemodule.Noticethatnegativelyweightededgesaregivenless inﬂu-encethanpositivelyweightededgesincomputingmodularity(Rubinov &Sporns,2011).

2.5. Developmentalmodel

Wethenutilizedthesamemulti-levelmodelingapproachusedfor thebehaviortomodelchangeinbrainnetworksacrossblocks,waves, andage: 𝑀𝑜𝑑𝑢𝑙𝑎𝑟𝑖𝑡𝑦𝑖𝑗 =𝛾00+𝛾10𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 +𝛾20𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 +𝛾30𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾40𝐴𝑔𝑒𝑖𝑗 +𝛾50𝐴𝑔𝑒2_𝑗+𝛾60𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾70𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝑊𝑎𝑣𝑒𝑖𝑗 +𝛾80𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾90𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾100𝐹𝑖𝑟𝑠𝑡𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒2𝑖𝑗 +𝛾110𝑆𝑒𝑐𝑜𝑛𝑑𝐻𝑎𝑙𝑓𝑖𝑗 ∗𝐴𝑔𝑒2𝑖𝑗 +𝛾120𝑊𝑎𝑣𝑒𝑖𝑗 ∗𝐴𝑔𝑒𝑖𝑗 +𝛾130𝑊𝑎𝑣𝑒𝑖𝑗 ∗𝐴𝑔𝑒2_𝑖𝑗+𝑢0𝑗 +𝑟𝑖𝑗 2.6. Probinginteractions

Tobetterunderstandpotentialinteractioneffectsinvolvingagein themodelsofbehaviorandbrain,weprobedtheeffectsat four dis-tinct ages (Fig. 3). Ageswere chosen tobe evenlyspaced (approxi-mately standarddeviationdistances within thesample)androughly correspondtodifferentdevelopmentalperiodsincludingearly,middle, andlateadolescences,aswellasyoungadulthood(e.g.,Shulmanetal., 2016).Interactioneffectsin themodelarecontinuousacross theage rangeandthereforeleverageinformationacrossthesample.However, duetolowercoverageofobservationsatlaterages,thesimpleslope esti-mateswhenprobingtheinteractionattheselevelshavecorrespondingly largerstandarderrors.

3. Results

3.1. Descriptivesandage-onlygrowthmodels

Beforeformallyfittingmodelstothedata,weassesseddescriptives ofbothlearningrateandnetworkmodularityasafunctionofwaveand age(takingthemeanofwithin-sessiondata).Connecteddatapoints rep-resentthesameindividualacrosstimeandwavesarelabeledwith differ-entcolors(Fig.4A&B).Asreportedearlier(PetersandCrone,2017), learningratewashighoverallwithlateadolescentsperformingnear ceiling,andeitherlevelingoff or decliningforolderparticipants. Al-thoughnotconstrainedinthesamewayaslearningperformanceat up-pervalues,neuralnetworkmodularityappearstoincreaseatearlierages anddecliningatlaterages(seeSupplementalforformalregionsof sig-nificanceanalysisforallmodels).Asourfirstmodelbuildingstep,we fitarelativelysimplemodelbyincludinglinearandquadraticeffects ofagetobothlearningrateandnetworkmodularity.Predictedvalues ofeachmeasurewereobtainedfromthemixed-effectsmodel(Fig.4C &D;seeTable1),confirmingthesetrends.However,thissimplemodel neithercaptureswithin-sessionpractice-relatedchange,nordoesit dis-aggregatewithin-personchangesduetoexperience(i.e.,acrosswaves) andbetween-personchangesduetomaturation(i.e.,acrossage).We

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Fig.4. Learning(A)andNetworkModularity(B)AcrossAgeandWave.Meansofwithin-sessiondatawereplottedagainstagetovisualizepotentialtime-related trends.Wavenumberwasindicatedbycolor,anddatafromthesameindividualwereconnectedbyasolidline.Ifindividualsonlycontributeddataatonetimepoint, datawasindicatedwithalonepoint.Linearmixed-effectsmodelsthatonlyconsiderthemixedeffectofagesuggestquadraticeffectspeakinginlateadolescencefor learningperformance(C),andfornetworkmodularity(D).Thesemodelsfailtoconsidereffectsofexperience(i.e.,wave)andwithin-sessionpracticeeffects.

Table1

Modeloutputfromage-onlymodels.

Learning Rate Network Modularity

Predictors Estimates SE P-Value df Estimates SE P-Value df

Intercept 0.937 0.004 < 0.001 359.604 0.994 0.011 < 0.001 361.688 Age std 0.277 0.023 < 0.001 1003.133 0.275 0.023 < 0.001 1992.711 Age 2 std − 0.140 0.021 < 0.001 1924.892 − 0.209 0.020 < 0.001 3523.698 Random Effects 𝜎2 _0.010 _0.055 𝜏00 0.002 ID 0.030 ID ICC 0.18 0.35 N 297 ID 297 ID Observations 4799 4799 Marginal R 2 / Conditional R 2 _{0.065 / 0.236} _{0.075 / 0.402}

Note: Alleffectsroundedtothethirddecimalplacefordisplaypurposes.std=standardizedeffectsreported.𝜎2₌_level₁ randomeffect.𝜏00=higherlevelrandomeffect(effectspecifiedbysubscript).ICC=Intraclasscorrelation.N =number ofunitsateachlevel.

nextformallytestedtheseeﬀectsusingthedevelopmentalmodel speci-ﬁedabove.

3.2. Behavioralimprovementsinlearningperformance

WebeganbyﬁttinganunconditionalrandomeﬀectsANOVAmodel (i.e.,arandominterceptateachlevel)todeterminethedistributionof varianceacrossthethreelevelsofthemodel(level1=within-session; level2=waves;level3=individual).Resultsindicatedthatthe

major-ityofvarianceinlearningratewasbetweentrialswithinthesamescan session(68.7%),anadditional19.3%ofthevariancewasbetweenscan sessionswithinthesameindividual(i.e.,changeacrosswaves),andthe remaining11.9%variancewasaccountedforbybetween-individual dif-ferencesinoveralllearningperformance(seeTable2 forfulldetails). However,toestablish abaselineforfuturemodels,wealsoﬁtatwo levelrandomeﬀectsANOVAmodelwherelevel1and2arecollapsed (seeTable2).

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Table2

ModeloutputfromrandomeﬀectsANOVAmodels.

3-Level Model Learning Rate Network Modularity

Intercept 0.927 0.003 < 0.001 256.681 0.949 0.012 < 0.001 284.150 Random Effects 𝜎2 _0.009 _0.048 𝜏00 0.002 ID:Wave 0.014 ID:Wave 0.002 ID 0.030 ID ICC 0.31 0.49 N 297 ID 297 ID 3 Wave 3 Wave Observations 4799 4799 Marginal R 2 / Conditional R 2 _{0.000 / 0.313} _{0.000 / 0.486}

2-Level Model Learning Rate Network Modularity

Intercept 0.925 0.003 < 0.001 271.214 0.946 0.012 < 0.001 289.668 Random Effects 𝜎2 _0.010 _0.056 𝜏00 0.003 ID 0.037 ID ICC 0.22 0.40 N 297 ID 297 ID Observations 4799 4799 Marginal R 2 / Conditional R 2 _{0.000 / 0.218} _{0.000 / 0.398} Note: Alleﬀectsroundedtothethirddecimalplacefordisplaypurposes.𝜎2₌_level₁_random_eﬀect._𝜏

00=higherlevel randomeffect(effectspecifiedbysubscript).ICC=Intraclasscorrelation.N =numberofunitsateachlevel.

Table3

Modeloutputfrommaineﬀects-onlymodels.

Intercept 0.927 0.004 < 0.001 717.508 0.987 0.013 < 0.001 520.937 First Half std 0.025 0.016 0.113 4473.435 0.088 0.013 < 0.001 4491.297 Second Half std 0.022 0.016 0.151 4473.468 0.121 0.013 < 0.001 4491.319 Wave std 0.023 0.018 0.198 1205.547 − 0.004 0.019 0.845 694.861 Age std 0.250 0.31 < 0.001 299.628 0.285 0.039 < 0.001 311.080 Age 2 std − 0.136 0.021 < 0.001 1941.031 − 0.209 0.020 < 0.001 3747.423 Random Effects 𝜎2 _0.010 _0.051 𝜏00 0.002 ID 0.030 ID ICC 0.18 0.37 N 297 ID 297 ID Observations 4799 4799 Marginal R 2 / Conditional R 2 _{0.060 / 0.232} _{0.112 / 0.441}

3.2.1. Separatingeﬀectsofageandwave

Next,wefita mixedeffectsmodelwithfixed predictorsof learn-ingrateateachleveltoassesstheseparableeffectsofageandwave onlearningrate.Importantly,waveandageweresufficiently decou-pledinthis model(r =0.397,varianceinflationfactor= 1.19,SE in-flation=1.08times),andallpredictorswerecentered.Inthismodel neithereffectofthewithin-sessionpracticepredictorsweresignificant. Thissuggeststhattherewasnototalsystematicchangeinlearningrate withinascansessionnettheeffectsofwaveandage,norwastherea significantwithin-personeffectofwave.Inotherwords,neither within-sessionpracticeorbetween-sessionexperiencerelatedtoincreased per-formancewhenaccountingfortheage-relatedchange.However, indi-vidualsshowedsignificant linear(𝛽 = 0.250,SE=0.031,p< .001) andquadratic(𝛽 =−0.136,SE= 0.021,p<.001)effects ofage on learningrate.Allresultsarereportedasstandardizedeffects(Table3). Alikelihoodratiotestsuggeststhatthismodeloffersanimprovement overtheunconditionalmodel(ΔAIC=−158.7;𝜒_{𝑑𝑖𝑓𝑓}2 =168.71,df=5,

p<.001).

3.2.2. Learningrateimprovementsshowinteractionsacrosslevelsoftime

Finally,wetestedtheinteractivemodeloflearninganddevelopment forparticipants’learningrates.Todoso,weaddedtwo-waycross-level interactiontermstothepreviousmodel.Three-wayinteractionswere exploredbutwerenotfoundtobesignificantandsothemodelwith two-wayinteractionswasretained.Allpredictorswerecenteredto cre-ate interactiontermswhich wereuncorrelatedwiththe maineffects andtofacilitatetheinterpretationof maineffectsinthepresenceof interactionterms(AikenandWest,1991).Therewasasignificant posi-tiveinteractionofwaveandthequadraticeffectofageonlearningrate (𝛽 =0.083,SE=0.021,p<.001)suchthatateachlaterwaves,the quadraticdecreaseslessen.Toprobethisinteraction,weplottedmean within-sessionlevelincreasesinlearningrateacrossageforeachwave (Fig.5).Resultssuggestthatwithoutrepeatedexposuretothetask(i.e., experience) therearepredicteddecreasesin learningperformanceat youngerages(redtrajectory),butthatpracticehelpscompensateand causeperformancetoleveloff instead(greenandbluetrajectories).The quadraticeffectwherewaveiscodedaszero(i.e.,wave2)reflectsthe

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Fig. 5.Increased Experience Impacts

Learn-ing Trajectories. Compared with ﬁrst

expo-sure(red),accumulatingexperience(greenand blue)betweenwavestendedtopredictbetter learningperformanceatlaterages, compensat-ingforexpecteddeclinesinperformance dur-ingyoungadulthood.

Table4

Modeloutputfrominteractionsmodels.

Intercept 0.935 0.005 < 0.001 768.901 0.997 0.014 < 0.001 513.739 First Half std − 0.007 0.020 0.751 4473.838 0.094 0.017 < 0.001 4495.715 Second Half std 0.021 0.020 0.297 4473.787 0.115 0.017 < 0.001 4495.776 Wave std 0.011 0.031 0.722 3143.488 − 0.044 0.029 0.131 1734.974 Age std 0.160 0.044 < 0.001 650.191 0.327 0.049 < 0.001 471.938 Age 2 std − 0.080 0.042 0.053 615.163 − 0.261 0.047 < 0.001 513.285 First Half ∗ Wave

std − 0.016 0.022 0.469 4484.108 − 0.036 0.019 0.058 4508.580 Second Half ∗ Wave

std − 0.044 0.022 0.048 4484.488 − 0.022 0.019 0.244 4508.591 First Half ∗ Age

std − 0.049 0.022 0.027 4466.136 0.068 0.019 < 0.001 4482.736 Second Half ∗ Age

std 0.025 0.022 0.252 4466.143 − 0.004 0.019 0.818 4482.736 Wave ∗ Age

std − 0.038 0.024 0.111 870.145 0.002 0.026 0.947 648.001 First Half ∗ Age 2

std 0.047 0.024 0.048 4464.637 − 0.027 0.020 0.182 4481.364 Second Half ∗ Age 2

std − 0.025 0.024 0.292 4464.630 − 0.002 0.020 0.906 4481.360 Wave ∗ Age 2 std 0.083 0.021 < 0.001 4746.733 0.057 0.018 0.001 4674.686 Random Effects 𝜎2 _0.010 _0.051 𝜏00 0.002 ID 0.030 ID ICC 0.19 0.37 N 297 ID 297 ID Observations 4799 4799 Marginal R 2 / Conditional R 2 _{0.066 / 0.239} _{0.126 / 0.449}

effectsseenintheage-onlymodelandisconsistentwithpriorresearch (Petersetal.,2016;PetersandCrone,2017),however,byincluding in-teractionswithexperience,weshowhowthattotaleffectisinfluenced byrepeatedexposuretothetask(seeTable4 forfulldetails).A likeli-hoodratiotestsuggeststhatthismodeloffersanimprovementoverthe main-effectsonlymodel(ΔAIC=−22;𝜒_{𝑑𝑖𝑓𝑓}2 =38.03,df=8,p<.001). Inafollow-upsensitivityanalysis,wefoundthatthispatternofeffects heldwhenincludingIQasacovariate.

3.3. Changesinnetworkmodularity

Similartothebehavioralanalysis,wefirstfitanunconditional ran-domeffectsANOVAmodeltoparticipants’neuralnetworkmodularity data.Themajorityofvarianceinnetworkmodularitywasbetweentrials withinthesamescansession(51.4%),relativelyless(15.6%)ofthe

vari-ancewasbetweenscansessionswithinthesameindividual(i.e.,change acrosswaves),withtheremainder(33.0%)accountedforby between-individualdiﬀerencesinnetworkmodularity(Table2).

3.3.1. Separatingeﬀectsofageandwave

Next,weﬁtamain-eﬀectsonlymodelwithpredictorsincludingtask block,wave,andage.Therewerelinear(𝛽 =0.285,SE=0.0.39,p<

.001)andquadratic(𝛽 =−0.136,SE=0.021,p<.001)effectsofage, suchthatnetworkmodularitytendedtoincreaseearlyinadolescence andlevel off and decrease acrosslateadolescence andyoung adult-hood. Additionally,modularityincreasedacross blockswithin waves (i.e., practice) across both halves of the task (first half:𝛽 = 0.088,

SE=0.013,p<.001;secondhalf:𝛽 =0.121,SE=0.013,p<.001). Therewasnoindependenteﬀectofwaveonnetworkmodularity.This suggeststhatwhilemodularitytendstoincreasewithin-sessionacross

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Fig.6. ExperienceandAgeImpactBrainNetworkOrganization.A)Accumulatingexperience(greenandblue)acrosswavespredictsincreasednetworkmodularity comparedwithfirstexposure(red),howeverthesedifferencesonlyemergeduringthetransitionfromadolescencetoyoungadulthood.B)Increasedagepredicts greaterpositivegainsinnetworkmodularityacrossblocksinthefirsthalfofthetask.

thetaskandwitholderindividuals,thereisnoindependenteffectof repeatedexposuretothesametaskenvironment(Table3).Asexpected, thismodelofferedimprovementsovertherandomeffectsANOVAmodel (ΔAIC=−476.3;𝜒_{𝑑𝑖𝑓𝑓}2 =486.3,df=5,p<.001).

3.3.2. Networkmodularityshowsinteractionsacrosslevelsoftime

Wenextfittheinteractivemodelof development tothenetwork modularitydata.Similartothemodelwithlearningperformance,there wasasignificantinteractionofwaveandthequadraticeffectofageon networkmodularity(𝛽 =0.057,SE=0.018,p=.001;Table4).When probed(Fig.6A),therewasasimilarcompensatorypatterntotheone seeninlearningperformance,suchthatexperienceacrosswaves(green andblue)predictedpositiveshiftsinmodularityatlateragescompared withthefirstexposuretothetask(red).Interestingly,thesedifferences appeartoonlyemergeduringthetransitionfromadolescencetoyoung adulthood,whereasincreasedexperiencedoesnotimpactmodularityat youngerages.Furthermore,therewasasignificantpositiveinteraction ofageandpracticeinthefirsthalfofthetask(𝛽 =0.068,SE=0.019,p <.001),suchthatolderindividualsshowedmorerapidgainsin modu-larityacrossthefirsthalfofthetask(Fig.6B).Thismodeloffered con-tinuedimprovementsoverthemaineffectsonlymodel(ΔAIC=−18.18;

𝜒2

𝑑𝑖𝑓𝑓 =34.18,df=8,p<.001).

3.3.3. Predictinglearningwithnetworkmodularity

Finally,wetestedwhethernetworkmodularitypredictslearning per-formanceaboveandbeyondtheeffectsoftime.Todoso,weentered net-workmodularityandinteractiontermsbetweenmodularityandthetime predictorsintothemodel.Inadditiontosimilareffectsofthetime pre-dictors,thismodelrevealedasignificantpositiveinteractionbetween networkmodularityandage(𝛽 =0.062,SE=0.023,p=.007;Table5). Probingthisinteraction(Fig.7),weplottedthemodel-implied trajec-toryoflearningrateacrossageforindividualswhoshowedrelatively low(−1SD),mean,andrelativelyhigh(+1SD)networkmodularity. Thisshowedthatindividualswhoevincedrelativelyhighnetwork mod-ularity(bluetrajectory)tendedtoshowhigherlearningrates,butonly afterthetransitionfromadolescencetoyoungadulthood(seeregion ofsignificance analysisinSupplementfordetails).Alikelihoodratio

testsuggeststhatincludingthebrainasapredictorincreasedmodelﬁt (ΔAIC=−4.1;𝜒_{𝑑𝑖𝑓𝑓}2 =20.10,df=8,p=.010).

4. Discussion

Thecontributionsofexperienceversusdevelopmentcanbedifficult toteaseapart,especiallywithinalongitudinalsample(JollesandCrone, 2012; Telzeret al.,2018) becauseage,experience, andpractice are confounded.Totestaninteractionmodelofexperienceand develop-ment,weutilizedanovellongitudinalapproachtoseparateout vari-ability in learningperformanceandbrainnetworkmodularityalong different timescales:1)within-sessionpracticeacrossblocksof learn-ing,2)within-person,across-waves,and3)acrossage.Briefly,wefound thatlearningperformancetendstoimprovethroughoutadolescenceand leveloff intoadulthood.Incontrast,networkmodularityappearstopeak aroundmiddleadolescenceandthenshowsdeclinesacrossyoung adult-hood.However,bothoftheseeffectsaremoderatedbytheamountof experienceindividualshaveaccumulatedacrosswaves,withmore ex-periencerelatingtohigherperformanceandnetworkmodularity dur-inglearning.Whenconsideringthebrain-behaviorrelationships, rela-tivelyhighermodularitypredictsbetterlearningperformancebutonly atolderages,suggestingthattheimportanceofindividualdifferences arounddevelopmentaltrendsfordeterminingbehavioraloutcomesmay dependontimingindevelopment.Wediscusseachofthesefindingsin greaterdetailbelow.

4.1. Multi-Levelchangesinlearningrateandbrainnetworks

Evenwithoutprobingcross-levelinteractions,amajoradvantageof themodelsemployedhereistheseparationofvarianceintheoutcome acrosslevelsoftime.Inthecurrentstudy,wefoundthatbehavioral im-provementsinlearningoccuracrossage,butdonotchange systemati-callyacrosswavesorblockswithinthetask.Incontrast,network mod-ularityshowedsystematicwithin-sessionincreasesaswellaspositive ageeffects,whileshowingnosystematicchangesacrosswaves.While these totaleffectsshouldbe interpretedwithcaution giventhe pres-enceofhigher-orderinteractioneffects,theyneverthelesshighlightan

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Fig.7. DiﬀerentialImpactsofNetwork Modu-larityonLearningRateByAge.Atearlierages, increasedmodularitydidnotpredictincreased learning,butthisrelationshipemerges follow-ingadolescence.

Table5

Modeloutputfrombrainaspredictoroflearningperformancemodel. Learning Rate

Predictors Estimates SE P-Value df

Intercept 0.935 0.005 < 0.001 794.679 First Half std − 0.003 0.021 0.886 4476.805 Second Half std 0.006 0.021 0.775 4482.941 Wave std − 0.056 0.067 0.401 4670.922 Age std 0.174 0.045 < 0.001 718.911 Age 2 std − 0.086 0.043 0.046 679.282

First Half ∗ Wave

std − 0.018 0.022 0.415 4481.722 Second Half ∗ Wave

std − 0.040 0.022 0.072 4485.072 First Half ∗ Age

std − 0.054 0.023 0.018 4472.145 Second Half ∗ Age

std 0.005 0.023 0.822 4502.070 Wave ∗ Age

std − 0.034 0.025 0.172 987.428

First Half ∗ Age 2std 0.044 0.025 0.073 4469.835

Second Half ∗ Age 2std − 0.013 0.025 0.604 4483.426

Wave ∗ Age 2std 0.076 0.022 _0.001 4754.347

Modularity std − 0.014 0.029 0.627 4764.879 Modularity ∗ First Half

std 0.006 0.021 0.786 4532.995 Modularity ∗ Second Half

std 0.044 0.021 0.044 4545.020 Modularity ∗ Wave std 0.061 0.061 0.317 4774.206 Modularity ∗ Age std 0.062 0.023 0.007 4222.697 Modularity ∗ Age 2 std − 0.004 0.025 0.888 4389.408 Modularity ∗ Wave ∗ Age

std 0.035 0.021 0.093 4776.952 Modularity ∗ Wave ∗ Age 2

std − 0.043 0.021 0.041 4724.872 Random Effects 𝜎2 _0.010 𝜏00ID 0.002 ICC 0.19 N ID 297 Observations 4799 Marginal R 2 / Conditional R 2 _{0.071 / 0.243}

Note: Alleffectsroundedtothethirddecimalplacefordisplaypurposes. std=standardizedeffectsreported.𝜎2₌_level₁_random_effect._𝜏

00=higher levelrandomeffect(effectspecifiedbysubscript).ICC=Intraclass correla-tion.N =numberofunitsateachlevel.

advantageofthegrowthmodelemployedhere(McCormick,preprint). Controllingfortheeffectsofrepeatedexposuretothetaskenvironment allowustobemoreconfidentthattheeffectsofagearedueto matura-tionalforcesratherthancomfortorfamiliaritywiththetaskorscanner environment(Bell,1953;JollesandCrone,2012).Eveniftheseeffects arenotsignificant,asisthecasehere,thesemodelsallowustocheck ourassumptionsabouttheprocessesunderlyinglongitudinalchange.

4.2. Cross-levelinteractionsbetweengrowthatdiﬀerentscales

Moreinterestingly,thereweresignificantcross-levelinteractions be-tweentimepredictorsinthemodelsforbothbehaviorandbrain tra-jectories.Forbothoutcomesofinterest,resultssupportedtheideaof aninteractivemodelofdevelopment,wheretheimpactsofexperience changeacrossage.Forlearningrate,themodelrecapitulatedprevious findings in thistask (Peters andCrone,2017) showingthat learning ratereachespeaklevelsaroundlateadolescence,andisconsistentwith other findingsshowing improvements inlearningacross adolescence (vanDuijvenvoordeetal.,2008; Petersetal.,2016;McCormick and Telzer,2017a).This highlightsthatthemodelwithgrowthat multi-plelevelsaccommodatesthesameinferencesmadewiththeage-only models.Inthisway,adoptingthecurrentapproachoffersadvantages withoutlimitingtheinferencesmadeaboutdevelopmentaltrajectories. Indeed,theinteractionofwaveandthequadraticeffectofageon learn-ingperformancerevealsthatchangesinbehavioraredrivenbycomplex interplays betweendevelopmentandexperience,withexperience ap-pearingtoplaydifferentrolesinsupportingoverallperformanceacross adolescenceandyoungadulthood.Specifically,probingtheinteraction showsthatincreasedexperiencecompensatesforthemaineffectofage, boostingbehavioralperformancegainsfurtherintotoyoungadulthood. Extensive experience(blue trajectoryinFig.5)bluntsotherwise pre-dicteddeclinesandstabilizesperformanceatthepeakachievedduring middletolateadolescence.However,thisexperience-related improve-mentisnotuniversal acrossthedevelopmentalperiodconsidered,as mid-adolescentperformanceisheightenedevenatfirstwave.Thismight suggestthatduringadolescence,learningisuniversallyimproved,and thenindividualdifferencesbecomemorerelevantasindividuals tran-sition out ofthis period(see Pattwellet al.,2011 forasimilar idea in theareaofcontextualfearduringadolescence).Noneof the mod-els testedshowed within-sessioneffectson learningperformance, al-thoughwemayhavebeenunder-poweredtodetectsucheffectsgiven therestraintsof thetask (i.e.,therewasamaximumof12 trialsper blockregardlessoflearning).Overall,theseresultsareapowerful val-idation ofthemodel,convergingwithpreviousfindings onthesame datawhilesimultaneouslyallowingfortheadditionofmorecomplex time-dependentrelationships.

Findings withthebrainshowedadistinct patternof eﬀectsfrom growthinlearningperformance.Previousworkinthisareahasshown developmental changes duringlearningin regional activation ofthe fronto-parietal (Peters et al., 2016) and striatal regions (Peters and

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Crone,2017;McCormickandTelzer,2017a),aswellasseed-based con-nectivitywiththeorbitofrontalcortex(McCormickandTelzer,2017a). Inthe current study, we insteadexamine changes in brainnetwork organizationduringlearning.Learningovershortperiodsoftimehas beenshowntoalterneuralnetworkorganization(Bassettetal.,2011; Telesfordetal.,2017;Gerratyetal.,2018),buttheseprocesseshavenot beencomparedwithlong-termchangesduetomaturationpreviously. Inthecurrentstudy,weshowchangesinnetworkmodularitybothat theshort-termwithin-sessionandlong-termdevelopmentallevel.Inthe shortterm,playing repeatedblocks within-sessionis associatedwith heightenedmodularitybetween brainnetworksinvolvedin learning, andthiseffectacrossthefirsthalfofthetaskincreaseswithage.This suggeststhatolderindividualsareabletosegregate(i.e.,high within-networkandlowbetween-networkconnectivity)therelevantnetworks ofregionstoagreaterextentthanyoungerparticipants.Thequadratic totaleffectofagesuggeststhatratherthanalinearincreasein modu-larityacrossexperienceacrosswavesandmaturation,network modu-larityinsteaddecreasesatlaterages(Fig.6A),withlateadolescentsand youngadultsshowingdecreasinglymodularnetworkscomparedwith middle-adolescents(alsoconsistentwiththeage-onlymodel(Fig.4D). Thispatternofnetworkconnectivityevolvessimilarlyacross develop-menttostriatalactivationduringlearningversusapplication(Petersand Crone,2017).However,theinteractionof this ageeffect withwave showsananalogouseffectasseeninlearningperformance,where ex-perienceappearstobluntexpecteddecreasesinnetworkmodularityin youngadulthood.Whilethiscompensatoryeffectdoesnotcounteract theoveralldecreasesasseeninthebehavioralresults,itdoesserveto shiftnetworkorganizationtowardamoreadolescent-typicalphenotype. Forbothbehavioralperformanceandnetworkmodularity,thepattern ofresultsseenhereclearlysupportaninteractiveviewofexperience anddevelopment,wherematurationconstrainstheeffectofexperience ratherthansimplyservingasaformofextendedpractice.

Regardlessoftheeffectsofexperience(i.e.,wave),thereappearto bemoresubstantialdecreasesinmodularitycomparedwithbehavioral performance.Inthecontextoflearninganddevelopment,thispattern oflong-termchangeinneuralnetworksmighthavetwopotential ex-planations.First,itmightbethatbrainnetworksshowthegreatest ca-pacityfor modularityduring adolescence,andthis capacitysupports theheightenedflexiblelearning(JohnsonandWilbrecht,2011;Casey, 2015)andfeedbacksensitivity(Petersetal.,2016;vanDuijvenvoorde etal.,2014;McCormickandTelzer,2017a;b;2018b)thatcharacterize thisdevelopmentalperiodregardlessofpracticeorexposureeffects. Al-ternatively,thesechangesmightreflectdifferencesinhowthebrain per-formssimilaractionsacrossdevelopment.Forinstance,highmodularity mightbenecessaryforadolescentstoachievehighperformance,while adultsmightnotrequirethistothesamedegree.Forinstance, stabiliz-inglearningperformancewithexperienceisstillassociatedwithoverall (albeitblunted)decreasesinmodularityduringyoungadulthood.This mightalsobeconsistentwithanexpansion-normalizationtheoryof de-velopmentandlearning(Wengeretal.,2017)wherebrainsshowinitial changesinstructureandfunction(e.g.,increasedsynapticformation, in-creasedactivation)thatthenreturntobaselinewithoutcompromising behavioralperformance.Thesehypothesesarenotmutuallyexclusive, andtheeffectsprobedheremightsuggestboththatadolescentbrains supportimprovedperformanceregardlessofotherinfluences(e.g., ex-perience)andthatreturnstonetworkphenotypesseeninyounger ado-lescentsdoesnotleadtoacompletecollapseofbehavioralperformance.

4.3. Changingbrain-behaviorrelationships

Whilecharacterizingtrajectoriesofbrainandbehaviorseparatelyis informative,wealsoexamined whetherindividualdifferencesin net-workorganizationmightpredictlearningperformanceaboveand be-yonddevelopmental(PetersandCrone,2017)andexperientialeffects, aswellas whetherthat relationshipchangedacross time.Consistent withtheinteractionviewofdevelopmentandexperience,significant

in-teractionsbetweennetworkmodularityandtimepredictorsinthemodel revealedasignificantmoderationofbrain-behaviorrelationshipsacross age.Thismeansthatinearlyadolescence,thereisnoeffectofincreased modularityonlearningperformance,butthatincreasedmodularity pre-dictsenhancedlearningratesforolderindividuals.Thesefindingsare consistentwithpreviousworkinyoungadultsshowingpositive associa-tionsbetweennetworkmodularityandsuccessfullearning(Bassettetal., 2011;Ellefsenetal.,2015)andhigher-ordercognitiveprocessing gener-ally(Kitzbichleretal.,2011;Braunetal.,2015).However,thesepositive associationsatlateragesareparticularlyinterestinginthecontextof thedevelopmentaltrendsdetected,whereolderindividualsonaverage showdecreasedmodularityacrosswaves.Despitenormativedecreases innetworkmodularityduringlateadolescenceandyoungadulthood, individualswithhighermodularityshowgreaterlearningperformance. Thismightlendsupporttothefirstexplanationoftheinteraction ef-fectofageandwave– thatadolescent-typicalneuralphenotypesoffer advantagesinperformance(e.g.,JohnsonandWilbrecht,2011;Jones etal.,2014;vanvanDuijvenvoordeetal.,2016).Ifyoungadultssimply didnotneedhighlymodularnetworkstoperformwhatisarelatively easytaskforthem(i.e.,theexpansion-renormalizationhypothesis),then wewouldexpectthattherelationshipbetweenmodularityandlearning performancewoulddecrease.However,thepositiveinteractioneffect (Fig.7)suggeststhatolderindividualsshowanevengreaterdependence onhighmodularityforsuccessfullearningperformancecomparedwith youngerparticipants.Thispatternofresultspresentsacompellingcase foradolescent-specificadvantagesinlearning.Forolderindividuals, re-taining“immature,” butapparentlymore-optimal,network configura-tionshelpsboostbehavioralperformance.

4.4. Conclusions

Insummary, wetook anovelmodelingapproachtodisaggregate change in learning and neuralnetworks across different timescales. Whilewefocusonlearninghere,theseresultshighlightthepotential flexibilityofmixed-effectsmodelsforprobingcomplexdevelopmental trajectories in otherdomains.Wefoundnonlinearpatternsof devel-opment for both behaviorandbrain, which were moderatedby ex-perience. Specifically,greater experience withthetask across waves supportedincreasedlearningandnetworkmodularityrelativeto com-paratively naïvesubjectsat laterages,highlightingthattheseeffects canpotentiallybiasage-relatedinferencesunlessexplicitlyincludedin longitudinalmodels.Futureresearchusingacceleratedlongitudinal de-signs(orseeMcCormick,preprint foralternatives)shouldtakecareto model practice/exposure-relatedeffectstoremove this confound. Fi-nally,weshowedchangingbrain-behaviorrelationshipsacross adoles-cence, wherehigher networkmodularitypredicts increased learning performanceonlyfollowingthetransitionintoyoung-adulthood.These results presentcompellingsupport foraninteractiveview of experi-ence anddevelopment,wherechanges in thebrainimpact behavior in context-specific fashionbased ondevelopmentalgoals (Crone and Dahl,2012;Romeretal.,2017).

Credit authorship contribution statement

Ethan M. McCormick: Conceptualization, Methodology, Formal analysis, Writing - original draft, Visualization. Sabine Peters: In-vestigation, Data curation, Writing - review & editing, Supervision. Eveline A. Crone: Investigation,Datacuration,Writing-review& edit-ing, Supervision,Project administration,Funding acquisition. Eva H. Telzer: Conceptualization,Writing-review&editing,Supervision. Acknowledgments

AuthorContributions:S.P.andE.A.C.designedresearch,and per-formedresearch;E.M.M.,analyzeddata;E.M.M.,S.P.,E.A.C.&E.H.T. wrotethepaper.

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Thisresearchwasfundedbyastartinggrantof theEuropean Re-searchCouncil(ERC-2010-StG-263234awardedtoE.A.C.)andagrant fromtheNetherlandsOrganizationforScientiﬁcResearch(NWO-VICI 453–14–001awardedtoE.A.C.).WewouldliketothankLauravander Aar,SibelAltikulaç,NeeltjeBlankenstein,BarbaraBraams,Suzannevan deGroep,JulietteCassé,DiannevanderHeide,JorienvanHoorn, Cé-dricKoolschijn,BabetteLangeveld,KyraLubbers,BatshevaMannheim, MaravanderMeulen,RosaMeuwese,SandyOvergaauw,JiskaPeper, Elisabeth Schreuders,Merel Schrijver,JochemSpaans, MarijeStolte, ErikdeWater,andBiancaWesthoﬀ fortheirhelpwithdatacollection.. Finallywewouldliketothankallparticipantsandtheirparentsfortheir collaboration.

E.M.M.wassupportedinthisresearchbygrantsfromtheNational InstitutesofHealth(R01DA039923,R01EB022904awardedtoE.H.T.) andgenerousfundsfromtheUniversityofNorthCarolinaatChapelHill.

Theauthorsdeclarenocompetingﬁnancialinterests. Data and code availability

ProcessedbehavioralandfMRIdata,aswellasallcodeusedinthe analysessupportingtheprimaryﬁndingsofthisstudyareavailableat OpenScienceFramework;projecthttps://osf.io/62gwz/.Investigators interestedin obtainingrawbehavioralandfMRIdatashouldcontact E.A.C..

Supplementary materials

Supplementarymaterialassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.neuroimage.2021.117784. References

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