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Towardsa“TreadmillTest”forCognition:ReliablePredictionofIntelligenceFromWhole-BrainTaskActivationPatterns

ChandraSripada1*,MikeAngstadt1,SaigeRutherford11DepartmentofPsychiatry,UniversityofMichigan,AnnArbor,MI*Correspondence:sripada@umich.edu

AbstractIdentifyingbrain-basedmarkersofgeneralcognitiveability,i.e.,“intelligence”,hasbeenalongstandinggoalofcognitiveandclinicalneuroscience.Previousstudiesfocusedonrelativelystatic,enduringfeaturessuchasgraymattervolumeandwhitematterstructure.Inthisreport,weinvestigatepredictionofintelligencebasedontaskactivationpatternsduringtheN-backworkingmemorytaskaswellassixothertasksintheHumanConnectomeProjectdataset,encompassing19taskcontrasts.Wefindthatwholebraintaskactivationpatternsareahighlyeffectivebasisforpredictionofintelligence,achievinga0.68correlationwithintelligencescoresinanindependentsample,whichexceedsresultsreportedfromothermodalities.Additionally,weshowthattasksthattapexecutiveprocessingandthataremorecognitivelydemandingareparticularlyeffectiveforintelligenceprediction.Theseresultssuggestapictureanalogoustotreadmilltestingforcardiacfunction:Placingthebraininanactivatedtaskstateimprovesbrain-basedpredictionofintelligence.1 IntroductionInadditiontoparticularabilitiesassociatedwithindividualcognitivetasks,thereissubstantialevidenceforanoverarchinggeneralabilityinvolvedinperformanceacrossadiverserangeoftasks.1–5Intelligencetestscomposedofmultiplesubtestscanyieldaccurateestimatesofthisgeneralability,usuallydenotedg,andwhichwehererefertoas“intelligence”.6,7Intelligenceisafundamentaldimensionofindividualdifferencesandisakeycontributortoanumberofimportantacademic,occupational,health,andwell-being-relatedoutcomes.8–13Thereisthussubstantialinterestinunderstandingtheneuralbasisofintelligenceandindeveloping“neuromarkers”ofintelligence,objectivebrain-basedmeansofmeasuringindividualdifferences.Onepotentiallypromisingrouteistoconstructneuromarkersofintelligencefromneuroimagingmaps,includingstructuralandfunctionalimaging.Existingstudieshavemainlyinvestigatedbrainsize14,corticalthickness/graymattervolume15,16,whitematterstructure17,andrestingstatefunctionalconnectivity18–20(forreviews,see21–24).Anotablefeatureofthesestudiesistheymainlyexaminestable,enduringfeaturesofthebrain,featuresthatarelargelyindependentoftheperson’scurrent

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cognitivestate,andinparticulartheiractualexerciseofthecognitiveabilitiesthatarerelevanttointelligenttaskperformance.Analternativeapproachforconstructingneuromarkersofintelligence,whoserationaleresemblesthatforcardiactreadmilltesting,attemptstofirstplacethebraininanactivatedstatethatengagesthesecognitiveabilities,renderingbrainfeaturesassociatedwiththeseabilitiesmore“visible”tofunctionalimaging(see25forasuggestionalongtheselines).Interestingly,whiletask-basedneuroimagingstudieshavebeenextensivelyusedtoinvestigatethebrain-basisofintelligence,theyhavethusfarbeenusednearlyexclusivelyforthepurposesoflocalization:Activationpatternsinhigherversuslowerintelligenceindividualsarecomparedtoidentifytheplacesinthebrainwheretherearestatisticallysignificantdifferences.26–30Task-basedstudieshavethusfarnotbeenusedwidelyinapredictionframeworkinwhichwhole-brainactivationpatternsareharnessedformakingpredictionsofeachsubject’sintelligence.Inthecurrentstudy,weaddressthisnotablegap.UtilizingtheHumanConnectomeProject’s1200release,weconstructahighlyreliablemeasureofintelligencefrom10measuresfromtheNIHToolboxandPennNeurocognitiveBattery.Wethenexaminebrain-basedpredictionofintelligencefromcontrastmapsderivedfromthe2-backworkingmemorytaskaswellassixotherfMRItasks(19taskcontrastsintotal),andweestablishtwothings.First,task-basedactivationpatternsallowhighlyreliablepredictionofintelligence,withperformanceappreciablyhigherthanthatreportedinotherneuroimagingmodalities.Second,tasksthattapexecutiveprocessingandthataremorecognitivelydemandingyieldmoreaccuratepredictionsofintelligence.2 Methods2.1 SubjectsandDataAcquisitionAllsubjectsanddatawerefromtheHCP-1200release31,32andallresearchwasperformedinaccordancewithrelevantguidelinesandregulations.Subjectsprovidedinformedconsent,andrecruitmentproceduresandinformedconsentforms,includingconsenttosharede-identifieddata,wereapprovedbytheWashingtonUniversityinstitutionalreviewboard.SubjectscompletedtworunseachofsevenscannertasksacrosstwofMRIsessions,usinga32-channelheadcoilona3TSiemensSkyrascanner(TR=720ms,TE=33.1ms,72slices,2mmisotropicvoxels,multibandaccelerationfactor=8)withright-to-leftandleft-to-rightphaseencodingdirections.ComprehensivedetailsareavailableelsewhereonHCP’soverallneuroimagingapproach31,33andHCP’staskfMRIdataset34.SubjectswereeligibletobeincludediftheyhadavailableMSMAllregisteredtaskdataforbothrunsofallseventasks,hadfullbehavioraldata,andnomorethan25%oftheirvolumesineachrunexceededaframewisedisplacementthresholdof

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0.5mm.Theseexclusionsresultedin958subjects.Thiswasfurtherreducedto944subjectsingeneratingtheTrain/Testsetsplit(seebelow).2.2 DataPreparationDatawaspreprocessedthroughtheHCPminimallypreprocessedpipeline,whichispresentedindetailbyGlasseretal.35Briefly,thepipelineincludesgradientunwarping,motioncorrection,fieldmapdistortioncorrection,brain-boundarybasedlinearregistrationoffunctionaltostructuralimages,non-linearregistrationtoMNI152space,andgrand-meanintensitynormalization.Datathenenteredasurfaced-basedpreprocessingstream,followedbygrayordinate-basedprocessing,whichinvolvesdatafromthecorticalribbonbeingprojectedtosurfacespaceandcombinedwithsubcorticalvolumetricdata.2.3 FMRITasksWeusedcontrastsfromsevenHCPtasks,describedinbriefinTable1(detaileddescriptionsareavailableelsewhere32,34).N-backtask Participantsrespondwhenthepictureshownonthescreenisthe

sameastheonetwotrialsback(=2-backcondition)orthesameasoneshownatthestartoftheblock(=0-backcondition).

IncentiveProcessing

Participantsguesswhetherthenumberonamysterycardwillbemoreorlessthan5andwinorlosemoney(rewardcondition=mostlywins;losscondition=mostlylosses)

Motor Participantsmovefingers,toes,andtongueLanguageTask ParticipantsanswerquestionsaboutAesop’sfables(=story

condition)ormathproblems(=mathcondition).SocialCognitionTask

Participantswatchvideoclipsofobjectsinteractinginanagentiveway(=theoryofmindcondition)orrandomway(=randomcondition).

RelationalTask Participantsidentifythedimensionalongwhichacuepairofobjectsdiffersanddetermineifatargetpairdiffersalongsamedimension(=relationalcondition).Ortheydetermineifacueobjectmatchesamemberofatargetpairalongagivendimension(=matchcondition).

EmotionTask Participantsdecidewhetheroneoftwopresentedfacesmatchoneatthetopofthescreen(=facecondition)orelsetheyperformthesametaskwithshapes(=shapecondition)

Table1:SevenHCPfMRITasks.Atthesinglesubject-level,fixed-effectsanalyseswereconductedusingFSL’sFEATtoestimatetheaverageeffectsacrossrunswithin-participants,using2mmsurfacesmootheddata.Sometaskspermittedmultiplecontrastsbeyondthestandardexperimentalversuscontrolcondition(e.g.,N-backallowsadditionalcontrasts

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basedonallfourstimulustypes).Toreducethecomplexityoftheanalysisandavoidlossofpowerfromsmallernumberoftrials,wefocusedonthestandardcontrastsyieldingatotalof19.AfulllistoffilenamesofthecontrastmapsusedcanbefoundintheSupplementalTableS1.2.4 ConstructingaGeneralIntelligenceFactorWeconductedanexploratoryfactoranalysisutilizingthestrategyandassociatedcodesuppliedbyDuboisandcolleagues(https://github.com/adolphslab/HCP_MRI-behavior),whorecentlyinvestigatedpredictionofintelligencefromrestingstatefMRIintheHCPdataset36.Unadjustedscoresfromtencognitivetasksfor1181HCPsubjectswereincludedintheanalysis(subjectswithmissingdataorMMSE<26wereexcluded),includingseventasksfromtheNIHToolbox(DimensionalChangeCartSort,FlankerTask,ListSortTest,PictureSequenceTest,PictureVocabularyTest,PatternCompletionTest,OralReadingRecognitionTest)andthreetasksfromthePennNeurocognitiveBattery(PennProgressiveMatrices,PennWordMemoryTest,VariableShortPennLineOrientationTest),withadditionaldetailssuppliedin36.WeappliedDuboisandcolleagues’codetothisdata,whichinturnusestheomegafunctioninthepsych(v1.8.4)package37inR(v3.4.4).Inparticular,thecodeperformsmaximumlikelihood-estimatedexploratoryfactoranalysis(specifyingabifactormodel),obliminfactorrotation,followedbyaSchmid-Leimantransformation38tofindgeneralfactorloadings.Toassessreliability,inaseparateanalysis,were-ranthefactoranalysisexcluding46subjectsthathadTest/Retestsessionsavailable.Wethenestimatedfactorscoresforbothsessionsforthesesubjectsandcalculatedtest/retestreliabilityviaintraclasscorrelation(weusedICC(2,1)intheShroutandFleissscheme39).2.5 Train/TestSplitThe958subjectsafterexclusionsweredividedintotwogroups.Togenerateatestdatasetof100unrelatedsubjectswefirstselectedthe86subjectswithnosiblingswhopassedexclusioncriteria.Then,werandomlyselected14familiesfromthosefamilieswith2siblingsincludedandrandomlydroppedonesiblingofeachpair.Thisresultedin944totalsubjectsforouranalyses,with844inthetrainingdataand100unrelatedsubjectsinthetestset.2.6 DimensionalityEstimationandPrincipalComponentAnalysisInthetrainingdataset,eachsubject’sunthresholdedtaskdatafrom19taskcontrastswasvectorizedandconcatenatedyielding19matrices,eachwithdimensionsof844subjectsby91282grayordinates.Wenextestimatedthenumberofintrinsicdimensionsassociatedwitheachtaskcontrast.Thiswasaccomplishedbysubmittingeachofthe19subjectsxgrayordinatescontrastmatricestothedimensionalityestimationprocedureofLevinaandBickel40.Thisisamaximumlikelihoodestimationmethodbasedondistancebetweencloseneighbors,whichwe

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previouslysuccessfullyappliedtoHCPrestingstatedata41.

Dimensionalityestimationfoundameanof72dimensionsacrossthe19taskcontrasts.Becausepriorstudiesbyourgroup41showedsmalldifferencesinthenumberofcomponentsmakelittledifferenceinclassifierperformance,wechosetheroundnumberof75componentsforeachtask.Wealsoverifiedthatusingtheexacttask-specificnumberofcomponentsmadenodifferencetotheoutcome(seeSupplementalTableS2).

Next,eachofthe19subjectsxgrayordinatescontrastmatriceswassubmittedtoprincipalcomponentsanalysisusingthepcafunctioninMATLAB(2015b),yielding,foreachcontrast,843componentsorderedbydescendingeigenvalues.

2.7 BrainBasisSetModelingOuraimwastopredicteachsubject’sintelligencefromtheirexpressionscoresforncomponents(wherenwastypicallysettobe75;seeabove).Toaccomplishthis,weusedBrainBasisSet(BBS)modeling,previouslydescribedindetail41.Inatrainingdataset,wecalculatetheexpressionscoresforeachofthencomponentsforeachsubjectbyprojectingtheirdataontothe75principalcomponents.Wethenfitalinearregressionmodelwiththeseexpressionscoresaspredictorsandthephenotypeofinterest(i.e.,intelligence)astheoutcome,savingB,thenx1vectoroffittedcoefficients,forlateruse.Inatestdataset,weagaincalculatetheexpressionscoresforeachofthencomponentsforeachsubject.OurpredictedphenotypeforeachtestsubjectisthedotproductofBlearnedfromthetrainingdatasetwiththevectorofcomponentexpressionscoresforthatsubject.WeassessedperformanceofBBS-basedpredictionofintelligencebycalculatingthecorrelationbetweenpredictedversusactualintelligenceinthetestsample.2.8 AddressingAdditionalPotentialConfoundsInanadditionalanalysis,weusedmultipleregressiontoremoveanumberofpotentialconfoundsfromtheintelligencevariable(i.e.,g).SimilartoDuboisetal.36,variablesregressedwere:age,handedness,gender,brainsize,multibandreconstructionalgorithmversionnumber(HCPvariables:Age_In_Yrs,Handedness,Gender,FS_BrainSeg_Vol,fMRI_3T_ReconVrs),andmeanframewisedisplacement(task-specificvalueswereused).Analysesinvolvingintelligencepredictionwerethenrepeatedwiththeconfound-cleansedvariable.ResultswerebroadlysimilartotheoriginalanalysesandarepresentedinSupplementalTableS2.2.9 ConsensusComponentMapsforVisualizationWeusedBBSwith75whole-braincomponentstomakepredictionsaboutintelligence.TohelpconveyoverallpatternsacrosstheentireBBSpredictivemodel,weconstructed“consensus”componentmaps.WefirstmultipliedeachcomponentmapwithitsassociatedbetafromthefittedBBSmodel.Next,wesummedacrossall75componentsyieldingasinglemap,andzscoredtheentries.

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3 Results3.1.ConstructingagFactorFromTenHCPBehavioralTasksAsreportedbyDubiosandcolleagues36,abifactormodelwithageneralfactorgandfourgroupfactors,fitthedataverywell(CFI=0.990;RMSEA=0.0311;SRMR=0.0201;BIC=-0.519).ThesolutionisdepictedinFigure1.FollowingDuboisandcolleagues,weinterpretthefourgroupfactorsas:1)CrystallizedAbility;2)ProcessingSpeed;3)VisuospatialAbility;and4)Memory.Thegeneralfactorg,whichwerefertothroughoutas“intelligence”andwhichisthefocusofthisreport,accountsfor58.5%ofthevariance(coefficientomegahierarchicalω42),whilegroupfactorsaccountfor18.2%ofthevariance.Basedonthe46subjectsintheretestdatasetforHCP,test-retestreliabilityforgwasfoundtobe,0.78,whichisconventionallyclassifiedasverygood(weusedICC(2,1)intheShroutandFleissscheme39).

Figure1:BifactorModelBasedonTenBehavioralTasksfromtheHCPDatasetwithGeneralFactor(“g”)andFourGroupFactors.C=CrystallizedIntelligence,S=ProcessingSpeed,V=VisuospatialAbility,M=Memory.3.2 ContrastsassociatedwiththeN-Backtaskarehighlyeffectiveatpredictingintelligenceinout-of-samplesubjects.

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Becauseworkingmemoryhasbeenstronglyandconsistentlylinkedwithintelligence35,43,44,wefirstinvestigatedpredictionofintelligencebasedontheN-backworkingmemorytask.ABBSmodelutilizing75componentswasfitinthetrainingdatasetandthenappliedtothetestdataset.Thecorrelationinthetestdatasetbetweenpredictedintelligenceandactualintelligencewas0.68(p=8.5x10-15).Figure2showsthetopthreecomponentsbasedonstatisticalsignificancedisplayedsothatgreaterexpressionofthesecomponentspredictsgreaterintelligence.Thesecomponentsincludelargeactivationsinsupplementarymotorarea(SMA),precuneus,anddlPFC,aswellasdeactivationsinanteriordefaultmodenetwork(DMN).Toconvey“average”patternsacrossall75components,weconstructedconsensuscomponentmaps(seeMethods)andtheyaredisplayedinFigure3.Theseshowadditionalpatternspredictiveofintelligence,includingdeactivationofposteriorcingulatecortexandfronto-polarcortex.

Figure2:VisualizationoftheThreeMostIntelligence-PredictiveComponentsFromthe2-backvs.0-backTaskContrast.Thethreemoststatisticallysignificantcomponentsareshownfroma75-componentbrainbasissetmodeltrainedtopredictintelligencescores.WenexttrainedadditionalBBSmodelsonthe2-backvs.baselineand0-backvs.baselinecontrasts.Thecorrelationinthetestdatasetbetweenpredictedintelligenceandactualintelligencewas0.58(p=2.0x10-10)and0.43(p=6.2x10-6),respectively.TheconsensuscomponentmodelsinFigure3revealedaninterestingchangeindirectionalityacrossthesecontrasts.Forexample,pre-SMAstronglypredictsgreaterintelligenceinthe2-backcontrastvs.baselinebutthereverseistrueinthe0-backvs.baselinecontrast.Additionally,lessactivation(i.e.,deactivation)oftheanteriorDMNpredictshigherintelligenceinthe2-backvs.0-backcontrast,butthereverseistrueinthe0-backvs.baselinecontrast.

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Figure3:ConsensusComponentMapsforSevenTaskContrastsHighlyPredictiveofIntelligence.Eachconsensuscomponentmapcapturesaggregatepatternsacrossa75-componentbrainbasissetmodelforpredictionofintelligence.(toprow)N-BackTask;(middlerow)RelationalTask;(bottomrow)Math–StoryContrast.3.3 Lookingacrossall19taskcontrasts,tasksinvolvingexecutiveprocessingandhighercognitivedemandaremoreeffectiveinpredictingintelligenceWenextcomparedintelligencepredictionbasedonthethreeN-backtaskcontrastswiththeremaining16contrastsfromtheothersixHCPtasks.AswiththeN-backtask,wetrainedBBSmodelsforeachtaskcontrastinthetraindatasetandappliedthemodelstotheheldouttestdataset.

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Figure4:IntelligencePredictionAcross19TaskContrasts.A.PredictionofIntelligenceUsingBrainBasisSetModelingFor19HCPTaskContrasts.SmallBlackDots=resultswithadditionalregressionofanumberofpotentialconfounds(see§2.8).B.FPN/DMNActivationPatternsDuringaTaskStronglyPredictEffectivenessofThatTaskinPredictingIntelligence.ResultsareshowninFigure3.Tasksinvolvingexecutiveprocessingweretopperformers,includingcontrastsfromtheN-backtaskandrelationalreasoningtask,aswellasthemathvs.storycontrastofthelanguageprocessingtask.Therewardvs.baselineandpunishmentvs.baselinecontrastsofthegamblingtask,bothofwhichinvolvemakingnumericaljudgments,alsoperformedwell.3.4 FPN/DMNactivation,ameasureoftaskdemandingness,predictswhichtasksareeffectiveforintelligencepredictionAnumberofstudieshaveobservedthattasksthatarecognitivelydemandingproduceactivationinregionsoffrontoparietalnetwork(FPN)45–48anddeactivationofregionsofdefaultmodenetwork(DMN)49–52.Buildingontheseobservations,wehypothesizedthemorecognitivelydemandingtasks(operationalizedintermsofactivationlevelsofFPNandDMN)shouldbemoreeffectiveinpredictingintelligence.Toensurecomparabilityacrosstaskcontrasts,wefocusedonthe12taskcontraststhatcomparedataskconditionversusrestingbaseline.WeperformedaregressionanalysiswithaccuracyofintelligencepredictionastheoutcomevariableandFPNandDMNactivationaspredictors.Results,showninFigure4,supportourhypothesis:Thecorrelationacrosstaskcontrastsbetweenpredictedaccuracy,basedonFPNandDMNactivation,andactualaccuracyinpredictingintelligenceis0.67(FPNstandardizedβ =0.63,DMNstandardizedβ=-0.58,p=0.0482).3.5. Acrossthe19taskcontrasts,activationsignaturesofintelligencearespatiallydistributedandtask-specific

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Wenextcomparedtheconsensuscomponentmapsassociatedwiththe19contrasts(sevenmapsareshowninFigure3,andtheremainingmapsareshowninSupplementalFigureS1).Signaturesofintelligencepredictionassociatedwitheachtaskwerehighlydistributed,withremarkablevariationinthesesignaturesacrosstasks.Prominentlyrepresentedregionsinclude:superiorparietalcortex(rewardvs.baseline,punishmentvs.baseline),dlPFC(mathvs.story),anteriorinsula(relationalv.match),fronto-polarcortex(mathvs.story),pre-SMA(relationalvs.match),andvisualcortex(relationalvs.match,rewardvs.baseline,punishmentvs.baseline).All75componentsaswellasconsensuscomponentmapsforeachofthe19taskcontrastshavebeensharedonBALSA,theHumanConnectomeProjects’websiteforsharingandhostingneuroimagingdatasetsandcanbeaccessedhere:https://balsa.wustl.edu/study/show/MZPv.4 DiscussionThisstudyisthefirsttosystematicallyassessneuroimaging-basedpredictionofintelligenceacrossmultiplefMRItaskconditions.Wefindthatwhole-braintaskactivationpatternsareahighlyeffectivebasisforpredictionofintelligence,withamodeltrainedonactivationduringtheN-backworkingmemorytaskachievinga0.68correlationwithintelligencescoresinanindependentsample.Additionally,wedemonstratethatmorecognitivelydemandingtasksareparticularlyeffectiveforintelligenceprediction.Theseresultshighlighttheimportanceofplacingthebraininanactivatedtaskstateforaccuratebrain-basedpredictionofintelligence.RoleofexecutiveregionsinpredictionofintelligenceTheimportanceoffronto-parietalnetwork,aswellasrelatedexecutiveregions(e.g.,dorsalanteriorcingulate),forintelligencehasbeenhighlightedinpreviouswork,especiallyinJungandHaier’sinfluentialfronto-parietalintegrationtheory21.Inasimilarvein,Duncanandcolleaguesproposedthat“multipledemand”cortex—regionsofthebrainthatactivateacrossabroadrangeofcognitivelydemandingtasks45—areaprimarysubstrateofintelligence27.Thepresentstudyextendsthesefindingsbydemonstratingakeyroleforfronto-parietalregionsasamajorsourceofdiscriminativeinformationformakingsubject-levelpredictionsofintelligence.Thisadditionalroleofexecutiveregionswassupportedinthreecomplementaryways.First,inlookingacrossthesetof19contrastsderivedfromsevenHCPtasks,wefoundthattasksthattapexecutiveprocessesweremorepredictiveofintelligence(e.g.,N-backtaskcontrasts,relationalreasoningtaskcontrasts,andmathvs.storycontrast).Second,wefoundthatFPNactivationandDMNdeactivation,highlyassociatedwiththecognitivedemandingnessoftaskconditions45–52,predictwhichtaskcontrastswillbeeffectiveforintelligenceprediction.Third,withinhighlypredictivecontrasts,suchthe2-backvs.0-backcontrastandmathvs.storycontrast,activationpatternsinexecutiveregionswereprominentamongregionspredictiveofintelligence.

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Interestingly,forcertainregions,thedirectionalityofpredictionofintelligenceexhibitedsomevariabilityacrosstaskcontrastsinawaysuggestiveofmoderationbytaskdifficulty(forexample,seepre-SMAin0-backcomparedto2-backandinmatchcomparedtorelational).TheseobservationsareconsistentwithaneuralefficiencymodelofintelligenceproposedbyNeubauerandFink53.Theyproposethathigherintelligenceisassociatedwithgreaterprocessingefficiencyinelementarycognitivetasks(leadingtolessactivationinmoreintelligentindividuals)butgreaterprocessingcapacityindemandingcognitivetasks(leadingtogreateractivationinmoreintelligentpeople),thuspotentiallyexplainingtheflippeddirectionsofactivationobservedacrosstheeasyandhardconditionsoftheN-backandothertasks.Whileactivationpatternsinexecutiveregionsclearlyplayanimportantroleinexplainingthesuccessofourtask-basedapproachtointelligenceprediction,thereisstillclearevidenceforsubstantialdiscriminativeinformationaboutintelligencelocatedoutsideexecutiveregions.ThisisapparentinlookingattheconsensuscomponentimagesinFigure3aswellFigureS1intheSupplement.Activationsareobservedindistributedregionsofcortex,includingnon-executiveregionssuchasvisualcortex,lateraltemporalcortex,andtemporalpole,amongotherregions.Comparisonoftask-basedpredictionwithothermodalitiesPreviousstudiesusingneuroimagingforpredictionofintelligencehaveprimarilyusedstructuralmeasures,forexamplecorticalthickness15,16orwhitematterstructure17,forreviewssee21–23.IntermsoffunctionalfMRI,recentstudieshaveexaminedrestingstateconnectivitypatterns.18–20InanimportantstudybyDuboisandcolleagues36thatexaminedthesameHCP1200datasetusedinthepresentstudy,theyfoundrestingstateconnectivitypatternspredictedintelligencewithacorrelationof0.44incross-validatedtesting(seealsoarelatedfindingfromourgroup41).Whileimpressive,thisisstillsubstantiallylessthanthepeakcorrelationof0.68achievedinthepresentstudyinout-of-sampletesting.Therearetwointerrelatedreasonswhytask-basedfMRImightpotentiallyoffermorereliablepredictionofintelligencethanothermodalities.Thefirstappealstothe“treadmilltesting”ideaalreadymentioned:actively34engagingincognitivetaskshasthepotentialtounmaskcriticalintelligence-relevantfeaturesofthebrainthatareotherwiseinvisibleinothermodalitiessuchasstructuralbrainimaging.Asecondpotentialadvantageoftask-basedmethodsisspecificity.Tasksareconstructedbytheirdesignerstotargetspecificpsychologicalprocesses,oftenwithcontrolconditionsthatsubtractawaycontributionsfromauxiliaryprocessesofnointerest.Thiswilltendtomakeclassificationmoreaccurateasthefeaturesetisculledofasizablenumberofuninformativefeatures.FutureDirectionsResultsfromthisstudyopenthedoortopromisingadditionallinesofresearch.ItisnotablethatweusedthesetofimagingtasksthatwereincludedintheHCPdataset.Theseimagingtasks,inturn,wereselectedbasedondiverseconsiderations(see34),

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butmaximizingpredictionofintelligencewasnotamongthem.Thus,itisplausiblethatonecandostillbetter:Itshouldbepossibletointentionallydesignandoptimizeanimagingtaskbattery—basedonfindingsfromtheliteratureaswellastrial-and-errorexperimentation—toyieldevenmoreaccuratetask-basedpredictionofintelligence,andfutureworkshouldexplorethispossibility.Insum,thisstudyfirmlyestablishestheeffectivenessoftask-basedfMRIforpredictionofintelligenceanddemonstratesthattasksthattapexecutiveprocessingandthataremorecognitivelydemandingareassociatedwithbetterpredictionaccuracy.

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