Probing the neural signature of mind wandering with simultaneous fMRI-EEG and pupillometry

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ContentslistsavailableatScienceDirect

NeuroImage

journalhomepage:www.elsevier.com/locate/neuroimage

Probing the neural signature of mind wandering with simultaneous fMRI-EEG and pupillometry

Josephine M Groot

^a^,^b

, Nya M Boayue

^a

, Gábor Csifcsák

^a

, Wouter Boekel

^c

, René Huster

^d

, Birte U Forstmann

^b

, Matthias Mittner

^a^,^∗

aDepartment of Psychology, UiT – The Arctic University of Norway, 9037 Tromsø, Norway

bDepartment of Psychology, University of Amsterdam, 1001 NK Amsterdam, The Netherlands

cInstitute of Psychology, Leiden University, 2333 AK Leiden, The Netherlands

dDepartment of Psychology, University of Oslo, 0317 Oslo, Norway

a r t i c le i n f o

Keywords:

Mind wandering Default mode network Simultaneous fMRI-EEG Dynamic functional connectivity Pupillometry

Support vector machine

a b s t r a ct

Mindwanderingreﬂectstheshiftinattentionalfocusfromtask-relatedcognitiondrivenbyexternalstimulito- wardself-generatedandinternally-orientedthoughtprocesses.Althoughsuchtask-unrelatedthoughts(TUTs)are pervasiveanddetrimentaltotaskperformance,theirunderlyingneuralmechanismsareonlymodestlyunder- stood.ToinvestigateTUTswithhighspatialandtemporalprecision,wesimultaneouslymeasuredfMRI,EEG, andpupillometryinhealthyadultswhiletheyperformedasustainedattentiontaskwithexperiencesampling probes.Featuresofinterestwereextractedfromeachmodalityatthesingle-triallevelandfedtoasupportvector machinethatwastrainedontheproberesponses.Comparedtotask-focusedattention,theneuralsignatureof TUTswascharacterizedbyweakeractivityinthedefaultmodenetworkbutelevatedactivityinitsanticorrelated network,strongerfunctionalcouplingbetweenthesenetworks,widespreadincreaseinalpha,theta,delta,but notbeta,frequencypower,predominantlyreducedamplitudesoflate,butnotearly,event-relatedpotentials, andlargerbaselinepupilsize.Particularly,informationcontainedindynamicinteractionsbetweenlarge-scale corticalnetworkswaspredictiveoftransientchangesinattentionalfocusaboveothermodalities.Together,our resultsprovideinsightintothespatiotemporaldynamicsofTUTsandtheneuralmarkersthatmayfacilitatetheir detection.

1. Introduction

Humanspervasively engagein shiftingattentionalfocusfromde- mandsintheenvironmenttowardself-generated,task-unrelatedtrains ofthought(TUTs),leadingtoperformanceerrorsduringtasksthatre- quiresustainedvigilance(SmallwoodandSchooler, 2015).Although thisphenomenon,alsotermedmindwandering,hasbeenofincreasing interestinthepastdecades,itsunderlyingneuralsignatureremainsa questionofinterest.

Convergingevidencefromfunctionalmagneticresonanceimaging (fMRI)studiesindicatesanassociationbetweenactivityinareasinthe defaultmodenetwork(DMN)andmindwandering(Masonetal.,2007; Christoﬀ etal.,2009).Theseareasbehaveantagonisticallywithatask- positive,oranticorrelatednetwork(ACN)thatgenerallyconstitutesre- gionsoffrontoparietalcontrol(FPCN)anddorsalattention(DAN)networks(Fox etal., 2005; Mittneret al., 2014).Although these ﬁnd- ings support a majorrolefor the DMN in internal mentation, more

∗Correspondingauthor.

E-mailaddress:matthias.mittner@uit.no(M.Mittner).

recentaccountsarguethatits transmodalarchitectureallowsflexible coupling withother networks in order tosupport avariety of task- relevant cognitive functions(Eltonand Gao, 2015; Margulieset al., 2016; Sormaz et al.,2018). Furthermore,observations ofcoinciden- talactivityinFPCN/DANregionssuggestrecruitmentofnetworksbe- yondtheDMNduringmindwandering(Christoff etal.,2009;Foxetal., 2015).Inarecentstudy,Turnbulletal.(2019a)demonstratedthein- volvementofDANandventralattentionnetwork(VAN)systemsinreg- ulatingTUTs,whereasactivityintheposteriorcingulatecortex(PCC),a centralnodeoftheDMN,wasrelatedtodetailedongoingthoughtdur- ingworkingmemoryperformance.Together,thesefindingshighlight thecomplexityofneuralpatternsduringmindwanderingandnegate thenotionofasingletask-negativesystemrepresentedbytheDMN.

Accordingly,recentﬁndingsemphasizetheimportanceofdynamic changes in cortical functionalconnectivity (FC)to support transient cognitive processes (Kucyi, 2018; Kucyi et al., 2018; Maillet et al., 2019).AlthoughtheDMNandACNareintrinsicconnectivitynetworks

https://doi.org/10.1016/j.neuroimage.2020.117412

Received15June2020;Receivedinrevisedform28August2020;Accepted27September2020 Availableonline1October2020

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(ICNs)thatdemonstrateastablefunctionalorganizationacrossindivid- ualsandmentalstateswhenmeasuredstatically(Grattonetal.,2018), studiesinvestigatingthedynamicFC(atatemporalresolutionofsec- onds)betweenthemhavedescribedoppositeassociationswithbehav- ior, withgreater DMN/ACN anticorrelation duringvigilant attention (Thompsonetal.,2013)aswellasduringperiodsofmindwandering (Mittneretal.,2014).

Corticaldynamicsduringinternalstateshavealsobeenexamined withmoretemporallyprecisemeasuresincludingelectroencephalogra- phy(EEG).Arobustﬁndingfromthesestudiesconcernsthedecreasein amplitudeofevent-relatedpotentials(ERPs)priortoperformanceerrors andself-reportedTUTs(Smallwoodetal.,2008;Kametal.,2011),sup- portingtheideathatattentionisperceptuallydetachedfromexternal inputduringmindwanderingepisodes(Schooleretal.,2011).Sincethe attenuationofsensoryprocessingmayarisefromconcurrentincreases inalphapowerthathavebeenobservedoverwidespreadcorticalareas, alpha-bandactivitymayserveasareliableelectrophysiologicalcorre- lateofmindwandering(O’Connelletal.,2009;Comptonetal.,2019; Jinetal.,2019).

Newlinesofresearchsuggestthatfluctuationsinattentionaremod- ulated through the locus coeruleus/norepinephrinergic (LC/NE) system(Aston-JonesandCohen,2005;Mittneretal.,2016).Specifically, changesintonicandphasicNElevelsareproposedtofacilitatetransi- tionsbetweenexploratoryandexploitativestatesinordertooptimize behavior.Thesedynamicshavebeenderivedfromchangesinpupilsize atbaselineandinresponsetostimuli(Gilzenratetal.,2010).Whereas (phasic)pupilresponsesseemconsistentlysmallerduringTUTs,changes in(tonic)baselinepupilsizehaveyieldeddifferentresultsacrossexperi- ments(Smallwoodetal.,2012a;Grandchampetal.,2014;Mittneretal., 2014:Konishietal.,2017).Thissuggeststhattherearedistinctforms ofmindwanderingcharacterizedbyvaryinglevelsoftonicarousaland neuralgain(Mittneretal.,2016;UnsworthandRobison,2016,2018).

Thepossibilitytodetecttheoccurrenceofmindwanderingepisodes has been examined with machine learning techniques using neural markersfromdifferentimagingmodalities.Forexample,non-linearsup- portvectormachines(SVM)builtforEEGdataweretrainedonmind wanderingprobesduringSARTandvisualsearchtasks(Jinetal.,2019, 2020)andlivelectures(Dhindsaetal.,2019).These studiesdemon- stratethatEEGmarkerscanbeusedtopredictTUTs,andthatthispre- dictiveabilitycanbegeneralizedacrosstasksandsettings.Inanother classificationstudy,Mittneret al.(2014) successfully predictedself- reportedTUTsacrosssubjectswithanon-linearSVMbasedonsingle- trialfMRIactivity,functionalconnectivity,aswellaspupillometricmea- sures.Ratherthanexcludingallmeasuresthatcannotbedirectlyrelated toaself-reportedattentionalstate,machinelearningallowsexamination ofdatathatisnotinterruptedbythoughtprobingandoffersapower- fultoolforsingle-trialdetectionoflatentcognitiveprocesses.However, thepredictivepowerofclassifiersbasedonmultimodalimagingdatasets remainsunexplored.

Theinterplaybetweentemporallywell-definedneuralresponsesand spatially-localizedfunctionalnetworkscanbeassessedbymultimodal neuroimaging.AlthoughstudieshavebeenconductedcombiningEEG withresting-stateMRItodeterminetheelectrophysiologicalcorrelates oftheDMN(Neuneretal.,2014;Bowmanetal.,2017;Marinoetal., 2019),toourknowledgenoneexistthatinvestigatetheneuralsubstrate ofTUTsduringacognitivetask.Weexpectedthatthecomplementary contributionsofneuralmodalitiesoffersuniquespatialandtemporal informationfordetectingTUTepisodes.Therefore,wepresentthefirst studyofmindwanderingthatutilizessimultaneousfMRI-EEGandpupil- lometrymeasuresduringtaskperformance.Bycombiningmultimodal neuralinformation withmachine learning,we aimed toexplore the markerssensitivetothefluctuationsin attentionthatunderlie mind wanderingtoultimatelygainabetterunderstandingofitsneuralmech- anisms.Specifically,weaimedtoreplicatethemethodspreviouslyem- ployedbyMittneretal.(2014)withadditionofexploringmoretempo- rallyrefinedfeaturesfromEEG.

2. Materialsandmethods 2.1. Overview

SimultaneousfMRI-EEG,andpupillometrydatawerecollecteddur- ingperformance of asustained attentiontask withprobe-caught experience sampling.Featuresof interestwereselected basedon prior ﬁndings and extracted from each modality after preprocessing. We aimed to extend the single-trial analysis approach introduced by Mittneretal.(2014)byexploringactivityandsynchronicitywithinand betweenICNsaswellaschangesinEEGmarkersandpupilsizeinre- lationtoﬂuctuationsinattentionalfocus.Tothisend,weemployeda supervisedlearningalgorithmtrainedtoclassifysingletrialsaseither

‘on-task’or‘off-task’states.Wethenanalyzedandcomparedthespa- tiotemporalsignaturesofrespectivestates.Additionally,weperformed recursivefeatureeliminationproceduresacrossdifferentcombinations ofmodalitiestoassesstherelativeimportanceofindividualfeaturesin distinguishingbetweenonandoff-taskstates.Dataandcodearepub- liclyavailableandcanbefoundathttps://osf.io/43dp5.

2.2. Participants

Ethicalapprovalwasobtainedfromtheethicsreviewboardofthe UniversityofAmsterdam.Thirtyhealthyadultvolunteers(25female, aged21±2.51years)wererecruitedandscreenedforMRIcompati- bilitywithastandardsafetyquestionnaire.Participantswereeligible whennoneofthefollowingexclusioncriteriaweremet:havinga(record of)neurologicalorpsychiatricdisease,impairedvision,oranycontra- indicationforMRIsuchascertainmedicalimplantsorprostheses.Writ- teninformedconsentwasobtainedpriortotheexperiment.Participa- tionwascompensatedwitha€20,-rewardforatotaldurationofap- proximately1.5h.Twoparticipantswereexcludedduetoendingthe experimentprematurely.Therefore,weperformeddataanalysison28 datasetsofwhichtwowereincomplete(onewithoutEEGandanother withouteye-tracking)duetotechnicalissues.

2.3. Sustainedattentiontoresponsetask

Participantsperformedafast-pacedsustainedattentiontoresponse task(SART)thatconsistedofaseriesofnon-targetandtargetdigitsat anaverage9:1ratio.Stimuliwerepresentedona32inchBOLDscreen usingthePresentationsoftware(NeurobehavioralSystems,Inc.,Berke- ley,CA).TheSARTwasdividedintotworunsof700trialseach,with a1.4strialduration.Atthestartofeachtrial,acenteredfixationcross waspresentedonagraybackgroundfor400msbeforeitwasreplaced byarandomstimulus(digits1to9)for400ms.Participantswerein- structedtorespondtoeverydigitwithabuttonpressusingtheirright indexfingerunlessthetargetstimulusappeared(digit3).Thetrainof stimuliwasoccasionallyinterruptedbyathoughtprobetotrackongoing fluctuationsinattentionalfocus,whichwasformulatedasthefollowing question: “Wherewas yourattentionduringtheprevioustrials?”.To respondtotheprobe,participantshadtouseleftandright response buttonstomoveanarrowabovea4-pointslidebarrangingfrom1(off- task)to4(on-task).Afterafixeddurationof6s,thelocationatwhich thearrowwaspositionedwasregisteredastheresponsetotheprobe andthetaskcontinued.Participantswereinstructedtorespondwith

‘oﬀ-task’whentheirattentionwasnotprimarilyfocusedonthetaskor environmentaldistractionsbutoninternalprocessessuchasmemories orpersonallyrelevantthoughts.

Anonlineiterativealgorithmwasimplementedtooptimizetheon- setsofthoughtprobesinordertomaximizetheprobabilityofcapturing off-taskthoughtepisodesthroughoutthetask.Toachievethis,thereac- tiontimecoefficientofvariability(RT_CV)wastrackedasacontinuous indexofattentionalfocusbasedonpreviousfindingsrelatingmindwan- deringtoincreasesinRT_CV(BastianandSackur,2013).Foreverytrial thatreturnedanRT,theRT_CVwascomputedoverthepreviouseight

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trials(RT_SD/RT_mean).Whenathresholdwascrossedofeitherabove 80%orbelow20%oftheentireRT_CVhistory,thealgorithmsearched forapeakortrough,respectively,inthepreviousRT_CVvalues.Specif- ically,apeakwasidentiﬁediftheRT_CVofthesecondlasttrial(T–2) washigherthanthatofthethirdlasttrial(T–3)andthelasttrial(T–1), andtheRT_CVofT–1wasalsohigherthanthatofthecurrenttrial(T).

Similarly,atroughwasidentiﬁediftheRT_CVofT–2waslowerthanthat ofT–3andT–1,andtheRT_CVofT–1wasalsolowerthanthatofT.If suchapatternwasdetected,aprobeonsetwastriggered.Thealgorithm wasnotactivatedwhenthecurrenttrialdidnotreturnanRTorwhen theRT_CVdidnotcrosstheinitialthreshold.Thoughtprobeonsetswere constrainedtohavenolessthan15trials(21s)andnomorethan45 trials(63s)betweenthem.Thus,aprobeonsetwasomittedifonehad occurredwithinthepast15trialsbutforcedifonehadnotoccurredfor 45trials,regardlessofwhetherthecurrenttrial’sRT_CVreachedthresh- old.Onaverage,22thoughtprobes(min=19,max=25)werepresented perSARTrun.Ashortpracticerunofthetaskwascompletedpriorto theexperimenttoensureparticipantsunderstoodalltaskinstructions.

2.4. Behavioralanalysis

Thoughtproberesponsesweredichotomizedbycollapsingresponse options1and2into‘off-task’andresponseoptions3and4into‘on- task’.Behavioralindicesofmindwanderingwerecalculatedforwin- dows spanning10 pre-probe trials(14 s)separately foroff-task and on-taskthought probesandincluded:(i)RTcoefficient ofvariability (RT_CV=RT_SD/RT_mean);(ii)omissionerrorrate(failuretorespondto non-targets);and(iii)commissionerrorrate(failuretowithholdare- sponsetotargets).Weselecteda10-trialwindowaprioribasedonthe assumptionthatmindwanderingoccursinslowlyfluctuatingepisodes spanningmultiplesecondsandtoincludesufficientdatafordetecting differencesinerrorrates,whicharerelativelylowinthisexperimental paradigm.

2.5. Functionalneuroimaging

2.5.1. Acquisition

Participantswerescannedwitha3TeslaPhilipsAchievaMRIsystem witha32-channelheadcoil.T₁-weighted(T₁w)imageswereacquired withaturboﬁeld-echo (TFE)sequence in220 transversesliceswith 1mmslicethickness(FOV= 240×220×188mm,TR=8192ms, TE=3760ms,voxelsize=1mmisotropic).Whole-brainfunctional imageswereacquiredwithafast-echo(FE)echo-planarimaging(EPI) sequencein38transversesliceswith2.5mmthicknessanda0.25mm slicegap(FOV=200×104×200mm,TR=2250ms,TE=29.94ms, ﬂip-angle=80⁰,voxelsize=2.5mmisotropic).

2.5.2. Preprocessing

Standard image preprocessing was performed in FSL (v6.0;

Jenkinson et al., 2012) with custom Python scripts (v2.7.15;

VanRossumandDrake,2011) using theNipypeframework(v1.1.8;

Gorgolewskietal.,2011).EachofthetwofunctionalBOLDrunswasspa- tiallysmoothedwitha6mmfull-widthhalf-maximumGaussiankernel usingSUSAN(SmithandBrady,1997),motion-correctedwithMCFLIRT (Jenkinsonetal.,2002)andslice-timecorrectedwithslicetimer.Thesig- nalwasthenhigh-passfilteredat1/44Hztoremoveslowfluctuating noisesuchasscannerdrift.ThebrainwasextractedfromT₁wimages withBET(Smith,2002)andsegmentedintograymatter(GM),white matter(WM),andcerebrospinalfluid(CSF)withFAST(Zhangetal., 2001).Toinvestigatetask-unrelatedpatternsofbrainactivity,agen- erallinearmodel(GLM)wasconstructedusingFEAT(Woolrichetal., 2001)andincluded:(i)taskregressorsthatwerepreparedbyconvolving stimulus,thoughtprobe,andresponseonsetswithastandardhemody- namicresponsefunctiontomodeltask-dependentBOLDsignal;and(ii) nuisanceregressorsincludingsixmotion(directionandamplitude)pa- rametersaswellasmeantimecoursesinWMandCSFmasks.Themod-

eleddatawereobtainedviaordinaryleast-squareslinearregressionand subtractedfromthepreprocessedsignal.Theresidualtime-serieswere thenmergedacrossthetworunsforeachsubject,normalized,andused forfurtheranalyses.

2.5.3. Featureextraction

WefollowedtheproceduredescribedbyMittneretal.(2014)tode- termineregionsofinterest(ROIs)byperformingaseed-basedcorrela- tionanalysiswithapriormaskoftheposteriorcingulatecortex(PCC;

Van Maanenet al.,2011) as seed-region.First,the mask wastrans- formedtonativespaceusingFLIRT(JenkinsonandSmith,2001)andthe meantimecourseofvoxelswithinthemaskwascorrelatedwithallother voxelsinthebrain,yieldingaconnectivitymapforeachsubject.Next, individualconnectivitymapswereregisteredwithFLIRTtoMNIspace, Fisherz-transformed,andaveragedtocreateagroup-levelconnectiv- itymap.Thegroup-levelmapwasthenthresholdedtolocatethevoxels withthe5%strongestpositiveand5%strongestnegativecorrelations withthePCCtodeterminetheDMNandACN,respectively(Fig.1).Au- tomatedsegmentationofthegroup-levelthresholdedmapsintospatial clustersresultedinsevennodesfortheDMN(posteriorcingulatecortex/precuneus[PCC/PCUN],medialprefrontalcortex[mPFC],bilateral angulargyri[AG],bilateralsuperiorfrontalgyri[SFG],andleftmid- dletemporalgyrus[MTG])andsixnodesfortheACN(supplementary motorarea[SMA],rightsupramarginalgyrus[SMG],bilateralinsular cortex[INS],andbilateraldorsolateralprefrontalcortex[DLPFC]).The thresholdedROImapswereprojectedbacktonativespaceinorderto extractthemeantime-seriesfroma3×3×3cubecenteredaroundthe peak-correlationvoxelofeachROIforeachsubject.Theseindividual time-serieswerelinearlyinterpolatedtoﬁndthesignalatstimuluson- setateverytrial,resultingin13single-trialnode-activityfeaturesper subject.Additionally,themeantime-seriesofeveryROIwascorrelated withthatofeveryotherROIusingsliding-windowcorrelationsof45s, resultinginanother78single-trialnode-connectivityfeaturespersub- ject(i.e.,21pairsforintra-DMNconnectivity, 15pairsforintra-ACN connectivity,and42pairsforinter-networkconnectivity).

2.6. Electroencephalography

Continuous EEG data were concurrently acquired with an MRI- compatible,64-channelHydroCelGeodesicSensorsystemandNetAmps 300 amplifier(ElectricalGeodesics,Inc., Eugene,OR,USA)andpro- cessedwithNetStation(v4.5.2;Eugene,OR,USA).Thecap wasfit- tedwithcarbon-wireloopssensitivetomovement-inducedvariations inthemagneticfield,servingasareferenceforcardioballisticartifacts (VanderMeeretal.,2016).Thesignalwascollectedatasamplingrate of1000Hz,onlinehigh-passfilteredat0.1Hz,andreferencedtotheCz electrode.Electrooculography(EOG)wasrecordedfromfourelectrodes positionedaboveandbelowandoutercanthioftheeyes.

DatawereanalyzedinEEGLAB(v14.1.2;DelormeandMakeig,2004) using MATLAB(R2018b;Mathworks,Natick,MA,UnitedStates)and BrainVision Analyzer (v2.1.2; Brain Products GmbH, Gilching, Ger- many). First, datawere filtered witha fourth-order zerophase-shift Butterworthfilter(24dB/oct)withalowcut-off of0.33Hzfollowed byahighcut-off of125Hz.Next,averageartifactsubtraction(AAS;

Allenetal.,2000)withaslidingwindowof21artifactswasusedtocor- rectforMRgradientartifacts.Inaddition,cardioballisticartifactswere removedwiththeregression-basedmethoddescribedbyVanderMeer etal.(2016)andartifactsrelatedtoeye-movementwereremovedwith independentcomponentanalysis(ICA).BadEEGchannelswereinterpo- latedbeforere-referencingdatatotheaveragereference.Subsequently, datawerehigh-passandlow-passﬁlteredat1Hzand30Hz,respectively,segmentedintoepochsfrom−1000msto600mspost-stimulus, andDCtrendswereremoved.

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Fig.1. Seed-basednetworkparcellationofresidualtime-series.(Nvox=numberofvoxels;PCC/PCUN=posteriorcingulatecortex/precuneus;mPFC=medialprefrontal cortex;AG=angulargyrus;SFG=superiorfrontalgyrus;MTG=middletemporalgyrus;SMA=supplementarymotorarea;SMG=supramarginalgyrus;INS=insular cortex;DLPFC=dorsolateralprefrontalcortex).Note:thecolorindexdoesnotrefertospeciﬁclabelsbutservestoaidthevisualdistinctionofregionborders.

Basedonpreviousfindings,wewereinterestedinlocalchangesin prestimulusoscillatorypoweracrossmultiplefrequencybands.Toex- tractprestimulusfrequencypower, datawerefirstbaselinecorrected (1000msprestimulus)andpooledintofourchannelclusterscentered abovefrontal,bilateralparietal,andoccipitalscalplocations(Supple- mentaryFigureA.1A).Theseclusterswereselectedtoprovidebothsuf- ficientcoverageofwidespreadcorticalareasandallowinferenceson localchangeswithrespecttotheunderlying functionalanatomyand comparisontootherstudies.Datawerethenre-epochedfrom−1000ms to0mspost-stimulusandprocessedwithFastFourierTransform(FFT) withaHanningwindowof10%,resultinginafrequencyresolutionof 0.977Hz.Thesumofthepowervalueswasthenextractedforfourfre- quencybands(delta[1–4Hz],theta[4–8Hz],alpha[8–12Hz],andbeta [12–30Hz])ateachchannelcluster,yielding16single-trialprestimulus frequencypowerfeaturespersubject.

Furthermore, we were interested in differences in amplitudes of event-related EEG signals across midline occipital (MidOcc), occipitotemporal(OccTem), midlineparietal(MidPar),andmidlinefrontal (MidFro)channelclusters,roughlycorrespondingtothescalpdistribu- tionsofP1,N1,P300,andassociatedfrontalERPs,respectively(Supple- mentaryFigureA.1B).WheretheposteriorP1andN1arebelievedto signalearlyperceptualprocessesinthevisualdomain,thelaterP300 component is thought toindex working memoryand relatedcogni- tiveprocesses (ShendanandLucia,2010).Weusedanoffsetof8ms tocorrectforthedelayfromtheanti-aliasing filterof theNetAmps 300amplifier.Datawerebaselinecorrected(100msprestimulus)and pooledintoaforementionedERPclusters.Semi-automaticartifactcor- rectionwasperformed(gradientthreshold50𝜇V/ms,amplitudecriteria

±100𝜇V,andlowactivitycriterion0.5𝜇V/100ms)andappliedtothe fullepochaftervisualveriﬁcation.The0to600mspost-stimulustime windowwasthensubdividedinto24binsof25msandthemeanof rawamplitudeswasextractedforeachbinateachERPchannelcluster, whichgenerated96single-trialERPfeaturespersubject.

2.7. Pupillometry

Pupildiameter(PD)ofthelefteyewascontinuouslyrecordedwith EyeLink1000andEyeLink1000Plustrackingsystems(SRResearch, Ottawa,Canada)atasamplingrateof1000Hz.

Blinks wereidentifiedusingEyeLink’sbuilt-inonlinesaccadeand blink detection algorithm and linearly interpolated using the start- saccadeandend-saccademarkersasstartandendpointsofeachblink, respectively.Visualinspectionshowedthatblinkoffsetwasregistered prematurelyacrossthemajorityofblinksandacorrectionalbufferof 70mswasaddedtotheend-saccademarkers.Ifblinkdurationexceeded 1500ms,databetweenthestart-saccadeandend-saccademarkerswere removed. Remainingartifactswereidentified bythresholdingsingle- trialPDranges (−400msto1000mspost-stimulus)atthe95thper- centile.MostoftheseextremePDrangeswerecausedbylargeeyemove- mentsortechnicalissueswithpupiltrackingratherthanphysiological changesinpupilsize.Trialscontainingsuchartifactsorwithmorethan 40%missingdatawereexcludedfromfurtheranalysis(12.7%oftrials acrossallsubjects).

Duetothetempoatwhichstimuliwerepresented,wefoundthat baselinepupilfluctuationswerecontaminatedbyevokeddilationsfrom precedingtrials,preventingselectionofsingle-trialtimewindowsfor determiningbaselinePD.Wethereforedevelopedanovelmethodfor modelingpupillometricchanges forfast-pacedtaskdesigns,which is documentedindetailintherecentlydevelopedpackagePypillometry (Mittner,2020).First,thepreprocessedsignalwaslow-passfilteredwith azero-phaseshiftsecond-orderButterworthfilter,preservingsignalfluc- tuationsslowerthan2Hz.Thelowerpeaksinthesignalweretheniden- tifiedbasedontheirprominenceandconnectedthroughcubicsplinein- terpolation.Thisresultedinalower-peakenvelopethatwasusedasan estimationofthetonic,baselinefluctuationsonwhichthephasic,pupil responsesaresuperimposed. Consequently,single-trialbaselinepupil diameter(BPD)wasfeaturedasthevalueofthelowerpeak-envelope at stimulusonsetforeach trial.Todetermineevokedpupildiameter (EPD),single-trial regressorswitha delta-peakat each stimulusand responseonset(ifany)wereprepared andconvolvedwithanErlang gammafunction:h=s×tⁿ×e^–ⁿ^/^t^max,wheres=1/10²⁴equalsascaling constantandn=10andt_max=930areempiricallydeterminedconstants (HoeksandLevelt,1993).Aftersubtractionofthebaselinesignal,the datawerefittedwithalinearregressionmodel.Sincepupildiameter cannotphysiologicallyreachavaluebelowzero,thebetacoefficients ofthemodelwereconstrainedtobepositivewithanon-negativeleast- squaressolverasimplementedinscipy.optimize.nnls()byusingthefor-

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mula:argmin_b||Xb-y||₂forb≥0(LawsonandHanson,1987).Single- trialestimatorsforEPDwerethendeﬁnedastheestimatedbcoeﬃcients ateachtrial.

2.8. Supervisedmachinelearning

Followingprevious machine learningstudies of mind wandering (e.g.,Mittneretal.,2014;Jinetal.,2019),weusedanon-linearsup- portvectormachine(SVM)withradialbasisfunctions(RBF)askernelto classifysingletrialsintoon-taskoroﬀ-taskattentionalstateswithscikit- learn.svm(Pedregosaetal.,2011).SVMclassiﬁersattempttoseparate classeswithahyperplanethatisoptimizedbymaximizingitsmargin.

Besidesgenerallybeingwellunderstoodandeffectiveinhighdimen- sionality,SVM’sdonotrequirealinearrelationshipbetweentargetla- belsandpredictorvariablesandwereshowntooutperform(linear)lo- gisticregressionanalysiswhenpredictingmindwandering withEEG (Jinetal.,2019).TheSVM-RBFwastrainedonadatasetcontainingthe threetrials(4.2s)precedingeachthoughtprobe,resultinginn=3655 trialsthatwereassignedthedichotomizedprobe responsesastarget labels.Trainingwas basedonatotalof205single-trialfeaturesthat couldbegroupedinfivemodalities:(i)activationinsevenDMNand sixACNnodes;(ii)intra-networkandinter-networkdynamicfunctional connectivity([DMN×DMN],[ACN×ACN],[DMN×ACN]);(iii)pres- timulusfrequencypowerinfourbands[delta,theta,alpha,beta]atfour channelclusters[frontal,bilateralparietal,occipital];(iv)ERPampli- tudesatfourchannelclusters[MidFro,MidPar,OccTemp,MidOcc]in 24timewindows;and(v)baselineandevokedPD.FeaturesinthefMRI andpupilmodalitieswerestandardized(z-scored)withineachsubject, whereasthefrequencypowerfeatureswerestandardizedwithinsubjects andchannelclusters.TheERPfeatureswerefirstbaselinecorrectedby subtractingthemeanatstimulusonsetateachtrialforeachERPwithin subjectsandthenstandardizedbydividingbythestandarddeviation acrosstrialsforeachsubject.

First,tuningparametersfortheSVM-RBFwereoptimizedthrough grid-searchoveralargerangeofvalues(2⁻¹to2¹⁵forsoft-marginCand 2⁻²⁰to2⁰forkernel-width𝛾)andleave-one-subject-outcross-validation (LOSOCV),usingtheF1metricasobjectivefunction.Inthisprocedure, theclassifier wastrainedon allpossible combinationsof datasetsof sizen– 1inordertopredicttheonedatasetthatwasleftout.Classi- ficationperformancewasmeasuredastheaccuracy,recall,andpreci- sionaveragedacrossallfolds,whererecall(sensitivity)reflectstheabil- itytodetectpositivecasesandprecision(positivepredictivevalue)is theproportionofpositivecasesthatwerecorrectlyidentified.Second, themostoptimalsetoffeatureswasevaluatedwithrecursivefeature elimination(RFE),inwhichallpossiblecombinationsoffeaturesetsof sizen– 1wereevaluatedwithLOSOCV.Thefeaturesetwiththehigh- estcross-validated(CV)meanF1scorewasthenselected,resultingin theeliminationofonefeatureateveryiteration.Thisprocesswasre- peateduntilthesizeofthefeaturesetwasn=1.Thefeaturesetthat producedthehighestmeanCVaccuracyacrossalliterationswasthen selectedasthefinalsetandusedtoclassifytheremaining,unlabeled data.

Additionally,weperformedacross-modalityRFEprocedureforeach ofthefivemodalitiesseparately(nodeactivity,functionalconnectivity, frequencypower,ERPamplitudes,andpupildiameter),foreachcombi- nationofmodalities(alldoubles,triples,andquadruples),aswellasfor thefullfive-modalityclassifierdecribedabove.Thisresultedinatotal of31independentclassifiersthatallowedassessmentofthepatternof featureeliminationacrossdifferentcombinationsofmodalities.Thepro- portionoftimesafeaturesurvivedeliminationinaclassifierrelativeto thenumberoftimesthemodalitywasrepresentedwasusedtoindicate afeature’simportance(0beingalwayseliminatedand1beingnever eliminated),ortheamount ofpredictiveinformationasperceivedby theclassifierwithrespecttodistinguishingoff-taskfromon-tasktrials.

Fig.2.Theeffectofdroppingmodalitiesfromsupportvectormachinesoncross- validated(CV)classificationperformance.Averagesanderrorbars(SE)arecal- culatedacrossall31fitsfromthecross-modalityRFEprocedure.Classification performanceincreasesasafunctionofthenumberofmodalitiesaddedtothe classifier.Notethatexclusionofdynamicfunctionalconnectivityfeatures(red) resultsinthelowestaccuracyscores,suggestingthatclassificationofattentional statewasmostlydrivenbyinformationcontainedinthismodality.

3. Results

3.1. Behavioralperformanceisimpairedduringmindwandering

DuringtheSART,participantsindicatedon42.6%oftotalthought probes thattheirattentionwasfocusedon internaltrainsof thought rather thanonthetask orexternaldistractions.Inlinewithour expectations,behavioralperformancewassignificantlyworsepreceding off-task reports, with higher RT_CV (ms) (M_OFF = 236, M_ON = 184, t(27)=4.00,p<0.001),proportionofomissionerrors(M_OFF=0.046, M_ON=0.014,t(27)=2.91,p<0.01),andproportionofcommission errors(M_OFF=0.064,M_ON=0.025,t(27)=5.48,p<0.0001).Mean RT(ms)wasslightlybutsignificantlyshorterprecedingoff-taskreports (M_OFF=373,M_ON=386,t(27)=−2.36,p<0.05).

3.2. Modalitiescontributetothepredictionofmindwanderingepisodes

TheoptimizedSVM-RBFperformedsingle-trialclassificationwitha meanaccuracyof65%(F1=0.51,57%recalland54%precision)based onasetof74features(36.1%oftotal),indicatinganabovechance-level abilitytopredicttheincidenceofTUTepisodes.Thecross-modalityRFE procedurefurthermorerevealedalinearincreaseinaccuracywithin- creasingnumberofmodalitiesaddedtotheclassifier,suggestingthat featuresfromeachmodalitycontributeuniquespatialandtemporalin- formationthatimprovesthepredictionofTUTs(Fig.2).Collectively, intra-networkandinter-network functionalconnectivityfeaturescar- ried most of this predictive information, asall classifiers performed worsewhenthismodalitywasexcluded.Individualfeatureimportance scoresfromthecross-modalityRFEprocedurearepresentedinSupple- mentaryFigureA.2.

3.3. Themultimodalneuralsignaturesofmindwandering

Aftersupervisedclassificationlearning,allfeatureswerestandard- izedandaveragedseparatelyforalltrialsclassifiedaseitheroff-taskor on-task(Fig.3).Whetherafeaturesurvivedtheeliminationprocedure oftheoptimizedfive-modalitySVM-RBFwasinterpretedasanindica- tionofthatfeature’ssignificanceinpredictingTUTepisodes.Contraryto expectations,allnodesoftheDMNshowedastrongermeansignalinon- tasktrialscomparedtooff-tasktrials.Incontrast,allnodesoftheACN weremoreactiveduringoff-task,withtheexceptionoftheright-SMG (Fig.3A).Whereasmostnodeswereselectedintheoptimizedclassifier, thePCCandright-SFG(DMN)andright-DLPFC(ACN)didnotsurvive

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Fig.3.Standardizedfeatureactivationacrossalltrialsaftersupervisedclassificationlearning.(A)Averagedifference(off-taskminuson-task)inDMN(left)and ACN(right)nodeactivity,wherepositivevaluesindicatestrongeractivityduringoff-taskandnegativevaluesstrongeractivityduringon-task.(B)Averagedifference inintra-DMNandintra-ACNnode-pairconnectivity,wherepositivevaluesindicatestrongerpositivecorrelation(lessanticorrelation)duringoff-taskandnegative valuesstrongerpositivecorrelationduringon-task.(C)Averagedifferenceininter-networknode-pairconnectivity.(D)AveragebaselineandevokedPDseparately foroff-task(red)andon-task(blue).(E)Averageamplitudesinbinsof25ms(0–600mspost-stimulus)perERPchannelclusterseparatelyforoff-taskandon- task.(F)Averageprestimulusbeta(𝛽),alpha(𝛼),theta(𝜃),anddelta(𝛿)frequencypowerperchannelclusterseparatelyforoff-taskandon-task,wheremore positive(lessnegative)valuesindicatemorepower.Featuresthatsurvivedeliminationareindicatedbylarger(vs.smaller)nodes,thicker(vs.thinner)edges,or lightbluecoloredbackgrounds(DMN=defaultmodenetwork;ACN=anticorrelatednetwork;PD=pupildiameter;MidFro=midlinefrontal;MidPar=midlineparietal;

OccTem=occipitotemporal;MidOcc=midlineoccipital).

featureelimination,suggestingthatsignalﬂuctuationswithinthesere- gionswerenotpredictiveofTUTepisodes.

Forbothnetworks,nodesweremoreoftenpositivelycorrelatedwith eachotherduringon-tasktrialscomparedtooff-task trials(28of36 node-pairs;Fig.3B).Interestingly,fouroffiveintra-DMNconnections thatwerepositivelycorrelatedduringoff-taskwereconnectedtothe PCC,including:left-MTG,right-AG,andbilateralSFG.Fromthese,the PCCtoleft-MTGconnectionwasthestrongest,whereastheconnections withtheSFGandtheremainingconnection(right-SFGtoleft-MTG)were weakestanddidnotsurvivefeatureelimination.FortheACN,allthree node-pairsthatwerepositivelycorrelatedinoff-tasktrialswereselected bytheoptimizedSVM-RBF(fromstrongesttoweakest:right-SMGto right-INS,left-INStoSMA,andright-SMGtoleft-DLPFC[visibleinthe coronalviewoftheACNxACNplotinFig.3B]).

Whereasmostoftheintra-networkconnectionswerepositivelycor- relatedduringon-task,themajorityofinter-networknode-pairswere positively correlated during oﬀ-task (38 of 42 node-pairs; Fig. 3C).

Thepositiveconnectionsthatwerenoteliminatedoftenincludedthe SMA(fromstrongesttoweakest:left-AG,PCC,right-SFG),PCC(SMA, left-INS,right-DLPFC),left-INS(right-AG,right-SFG,PCC),left-DLPFC

(left-AG,right-SFG,mPFC),andright-SFG(SMA,left-INS,right-SMG).

Thus, whereasinformationin thePCCandright-SFG themselvesdid notdistinguishbetweenon-taskandoﬀ-taskstates,theirfunctionalin- terregional connectionsseemimportantfor predictingTUTepisodes.

Similar roles for the SMA andleft-INS are unsurprising given their highanatomicalandfunctionalheterogeneityandtheirinvolvementin domain-generalcognitiveprocesses(Uddinetal.,2017;ConaandSe- menza,2017;Ruanetal.,2018).

Withrespect tothepupil features, BPD wasselected in theopti- mizedSVM-RBFandindicatedmoredilationin off-taskcompared to on-tasktrials,indicatinghigherlevelsoftonicNE(Fig.3D).Pupillary response,however,didnotseemtodifferentiatebetweenthetwostates andwaseliminated.Similarly,weobservedthatearlypositiveandneg- ativepeaksreflectingP1andN1components,respectively,weremore pronouncedinoff-taskstates,indicatingtheabsenceofattenuatedearly perceptualprocessing(Fig.3E).However,decreasedamplitudesates- peciallythemidlinefrontalandparietalclustersfrom250to300ms onwardimplicatereducedinformationprocessingduringoff-taskstates atlaterlatencies.Althoughseveralearlybinsdidsurvivefeatureelimi- nation,themajorityofretainedfeaturesoccurredafterthe200mspost-

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stimulusmark(8of13bins),suggestingthatlateratherthanearlyevent- relatedsignalswerepredictiveofmindwandering.

Thefrequency poweranalysis revealeda globalincreasein pres- timulusalpha,theta, anddelta power duringmind wandering,with theexceptionofdeltapower overtherightparietalcortex(Fig.3F).

Incontrast,betapowerwasconsistentlyreducedinoff-taskcompared toon-tasktrialsacrossthescalp.Althoughbilateralparietalalphaand betafeaturesalsosurvivedelimination,thegreatestchangesinpower wereobserved over the occipital cortex. Noneof the theta features wereselectedintheoptimizedSVM-RBF,suggestingthatthetapower itselfdidnotcontributetoclassificationandthatthepredictiveinfor- mation contained in thetafluctuations was insteadcarried by other features.

4. Discussion

The detection of ongoing covert cognitive processes in humans hasbeenaproblemfacingsignificantmethodologicalchallenges.The presentstudyprovidesnewinsightsintotheneuralmarkersthat re- flecttheattentionalshiftfromexternally-orientedcognitiontowardself- generatedtrainsofthought.Byintegratingsingle-trialfeaturesacross multiple neuralmodalities in aclassification learningalgorithm, we showedthatspecificpatternsof fMRIactivityandconnectivity, EEG markers,andbaselinepupilsizewerepredictiveofTUTs.Althougheach neuralmodalityprovideduniqueinformationthatimprovedclassifica- tionperformance,thegreatestpredictivepowerencompasseddynamic interactionswithinandbetweenintrinsicconnectivitynetworks(ICNs), includingtheDMNandACN.

Ourresults indicaterecruitment ofACNnodesduringTUTs.This finding is not surprising given the growing body of evidence ad- vocating a role for these regions in spontaneous thought processes (Christoff etal.,2009;Foxetal.,2015;Dixonetal.,2018).Specifically, theirrecruitmenthasbeensuggestedtoreflectamechanisminwhich top-downcontrolsystemsexertdeliberateconstraintsonthestreamof internally-orientedthoughtsinordertoguidethemtowardmotivation- allyrelevantorrewardinggoals(Christoff etal.,2016;Shepard,2019).

Accordingtothisview,mindwanderingmaybecharacterizedbythe redistributionofexecutiveandattentionalresourcestowardtheinter- nal environmentdriven bytheprioritization of relevantinformation (Turnbulletal.,2019b).

Inlinewiththis,ithasbeenarguedthatattentionaldecouplingin theformofsuppressionofsensoryinputsmayserveadaptivefunctions byinsulatingthestreamofthoughtfromexternalinterference(Kamand Handy,2013;Smallwood,2013).Althoughwedidnotfindevidencefor deficitsinearlysensoryprocessing,ourresultsmaybe interpretedas cognitivedisengagementfromtask-relevantinformationasreflectedin reducedamplitudesofP300andmidfrontalERPspriortoself-reported TUTs.Correspondingly,taskperformancewassignificantlyaffectedas indexedbyincreasedRTvariabilityanderrorrates.Thiscorroboratesan earlierfinding(O’Connelletal.,2009)andmayimplythattheshallow processingofvisualinformationremainsrelativelyunimpairedduring mindwandering,whereaslatercognitiveanddecision-makingprocesses involvedinassimilatingthedeepermeaningofstimulineededtoaccu- ratelyperformthetaskaredisrupted.

Contrarytoexpectations,wedidnotobserveanyincreaseinDMN activityduringmindwandering.Althoughthisﬁndingseemscounter- intuitive,previousstudieshavereportedasimilarassociationbetween therecruitmentofDMNregionsandoptimizedbehavior(“in-the-zone”), whereassuboptimalbehavioral performance(“out-the-zone”)was in- steadassociatedwithDANactivation(Estermanetal.,2014;Kucyietal., 2017;Yamashitaetal.,2020).Althoughspeculative,togethertheseﬁnd- ingsmaypointtoDMNactivityduringtask-focusedattentionasrepre- sentingaweakerengagementingoal-directedbehaviororattentional stabilityneededtoaccuratelyperformthetask.Indeed,itisgenerally assumedthathabitualresponsetendenciesaredevelopedearlyduring repetitivetaskssuchastheSARTandthusstableperformancemayrely

more heavilyon automaticprocesses(Hawkinsetal., 2019).Aspre- viousworkhassuggestedarolefortheDMNinautomatedcognition asopposed tomindful, focusedattention(ShamlooandHelie,2016; Vatansever etal., 2017;Scheibneretal.,2017),ourﬁndings maybe tentativelyinterpretedasalesser engagementoftop-down resources duringthe(moreautomated)task-focusedstatecomparedtothe(more goal-directed)mindwanderingstate(Christoﬀ etal.,2016;Selietal., 2016).

Analternativeexplanationmaybethatpartsof theDMN,speciﬁ- callyitscorenodes(PCCandmPFC),arenotdirectlyinvolvedinmind wanderingbutratherfunctionasa“globalworkspace” bytailoringtheir activitytothetemporaldynamicsofotherICNs(Mittneretal.,2016).

Thus,whenattentionisfocusedeitherexternally(orientedtothetask) orinternally(mindwandering),functionallyspeciﬁcnetworksarere- cruitedtosupportgoal-directedbehaviorwhereasconvergingnetwork activityislowered,resultingindeactivationofthePCCandmPFC.While wedidnotobservethatsingle-trialactivityinthePCCitselfwaspre- dictiveofTUTs,ourresultsindicate highimportanceofthedynamic couplingbetweenthePCCandothernodesoftheDMNandACNdur- ingbothtask-relatedandtask-unrelatedthought.Togetherwithprevi- ouswork(Leechetal.,2012;KucyiandDavis,2014;Linetal.,2016; Zhouetal.,2019),thisﬁndingsupportstheintriguingpossibilitythat thePCCisinvolvedinthecoordinationofnetworkinteractionstoreg- ulateshiftsinattentionalfocusbymaintainingorsuppressingongoing trainsofthought.

Importantly, previous work has demonstrated the significance of contextfortherolethatdifferentnetworksplayinongoingthought.Ac- tivityinboththeDMNandACNhasbeenassociatedwithtask-relatedas wellastask-unrelatedcognitiveoperations,dependingontaskdifficulty (Turnbulletal.,2019a,2019b;Konuetal.,2020).Thesefindingsalign withthecontext-regulationhypothesis,whichstatesthatmindwander- inginstancesareadaptivelyregulateddependingonenvironmentalde- mandsinordertominimizethenegativeimpactonmaintainingtask performance(SmallwoodandAndrews-Hanna,2013).Thus,tobetter understandhowcomplexlarge-scalenetworkactivitygivesrisetomind wandering,specifictaskeffectsneedtobeconsidered.Onesuchtask characteristicthatvariesamongstudiesispacingoftrials.Comparedto previousstudiesshowingalinkbetweentheDMNandmindwander- ing,theSARTdesigninthecurrentstudywasfasterpaced(stop-signal paradigm;Mittneretal.,2014) andcontainedalowerproportionof targettrialsandwasoverallshorterinduration(SART;Christoff etal., 2009).Therefore,therolethattheDMNplaysinmindwanderingduring asustainedtaskmaydependheavilyonsucheffects.

Previous workindicatesthattheinteractions withinandbetween ICNsdynamicallyreconfiguretotransientchangesinongoingcognitive processessuchasmindwandering(Thompsonetal.,2013;Mittneretal., 2014).Accordingly,weobservedhighimportanceofinformationcon- tainedinfunctionalconnectivitycomparedtoothermodalities.Specifi- cally,ourresultsindicatethatmindwanderingisassociatedwithoverall decreasedconnectivitywithinandincreasedconnectivitybetweenthe DMNandACN.Thus,whereasthesenetworksareintrinsicallyanticor- relatedatrest(Foxetal.,2005),thedynamiccouplingbetweenthem duringsustainedattentionaldemandsmaysupportspontaneousfluctu- ationsinongoinginternaltrainsofthought(Smallwoodetal.,2012b; Dixonetal.,2018).

Theelectrophysiologicaloriginofthiscouplingmayconcerntheta- bandoscillations(Kametal.,2019),whichisinlinewithourobserva- tionofawidespreadincreaseinthetapowerduringTUTs,eventhough thetapoweritselfwasnotfoundtobepredictiveofmindwandering.

Wealsoreplicatedincreasesinalphapowerandreducedbetapower acrossthecortex(Jinetal.,2019;Comptonetal.,2019;VanSonetal., 2019).Althoughthefunctionalsigniﬁcanceofalphaoscillationsremains ambiguous,ourdataimplyaroleinactivemindwanderingthatmayin- volveinhibitionofirrelevantrepresentationsandtop-downinterference (PalvaandPalva,2011;Benedeketal.,2011).Inaddition,theincrease insynchronizeddelta-bandactivityoverfrontal,leftparietal,andoccip-

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italareasmayhavebeeninvolvedinthemaintenanceofongoingtrains ofthoughtbyinhibitinginterferinginformation(Harmony,2013).

Similarly,ourfindingsindicateincreasesinbaselinepupilsizedur- ingmindwanderingcomparedtotask-focusedattention,whichmayre- flecthigherlevelsoftonicNEandhasbeenproposedtounderliethe reducedsensitivitytoexternalinterferencefavoringmentalexploration (Murphyetal.,2011;Smallwoodetal.,2012a).Consequently,asex- ploitationoftask-relevantinformationisnolongerprioritized,thecog- nitivecapacity forpursuing alternativegoalsthat aremotivationally salientisenhanced(BouretandRichmond,2015).Possibly,thelowin- centiveoftheSARTmaywarranttheadaptiveredistributionofintrin- sicmotivation,regardlessofitsdetrimentaleffectonperformance.To- getherwithourobservationsinothermodalities,thisimpliesthatTUTs inourstudywerecharacterizedbyeffortfulandguidedcognitionrather thanastateoflowalertnessorarousal.Althoughpreviousworkalso suggestsalinearrelationshipbetweenphasicNEandtaskperformance, wedidnotobserveanycontributionsfromevokedpupilresponsesin differentiatingattentionalstate.

One continuing challenge concerns the differences in measur- ingmindwandering,complicatingthecomparisonof findingsacross studies. Research hasshown that mind wandering is a non-uniform construct that varies along dimensionsof intentionality (Seli et al., 2016), meta-awareness (Christoff et al., 2009), temporal locus (Liefgreen etal.,2020),emotionalvalence(Banksetal.,2016),self- relevance(Bocharov etal., 2019),andarousal (UnsworthandRobi- son,2018),whichlikelycontributestothedivergentpatternsofneu- ralactivation.Thecurrentstudyislikewiselimitedbytheuseofuni- dimensional experiencesamplingfollowedby acoarse dichotomyof attentional state.Therefore,ourattempttocapturethespatiotempo- raldynamicsofTUTswithinonesignaturebasedonasingletaskmay compromisethegeneralizabilityofourresults.AlthoughtheSARTis anattractiveandwidelyusedparadigmtostudymindwandering,more complexdesignsarenecessarytodisentangletheeffectofTUTsonother cognitiveprocessesandbehavior(Boayueetal.,2020).

Thelowcomplexityoftheparadigmcombinedwithindividualbi- asesinself-reportduetovariationinmeta-awarenessorthoughtcontent mayhavenegativelyinﬂuenced classiﬁcationperformance. Although weachievedabovechance-leveldetectionofattentionalstatewith65%

accuracyacrosssubjects,apreviousstudyreported79%accuracybased onfMRIandpupilmeasuresalone(Mittneretal.,2014).However,other EEGclassifiersshowedsimilardetectionlevelsofTUTs(Dhindsaetal., 2019;Jinetal.,2019)whichsubstantiallyimprovedwhenmodelswere fittedtoindividualdatasets,suggestingthathighinter-individualvari- abilityinEEGmarkerscanaffectcross-subjectclassification.

5. Conclusion

Althoughproventobedetrimentaltomaintainingattentiontotask- relevantevents,thecapabilitytoengageininternaltrainsofthoughtis integraltohumanneurocognitivefunctioning.Moreaccuratedetection ofmindwanderingepisodeswillleadtoamoreprofoundunderstand- ingofitseffectonothercognitiveprocesses.However,suchdetection iscomplicatedascognitionevolvesdynamicallyincomplexspatiotem- poralpatterns.Multimodalclassificationenablingsingle-trialanalyses mayprovideeffectivemeanstogainmechanisticinsightsintotheneu- ralbasisofattentionalfluctuations.Wehopethatourfindingswillmo- tivatefuturestudiestoconsideranagnostic,whole-brainapproachto better entanglethe respectivecontributions of dynamic interactions.

Furthermore,employingparadigmsthatallowcontinuoustrackingof attentionalintensitycombinedwithneuroimagingarebettersuitedto investigatetheevolutionoftask-unrelatedtrainsofthoughtwithhigher temporalprecision.

DeclarationofCompetingInterest None.

Creditauthorshipcontributionstatement

JosephineMGroot:Formalanalysis,Methodology,Datacuration, Visualization,Writing-originaldraft.NyaMBoayue:Formalanalysis, Writing-review&editing.GáborCsifcsák:Formalanalysis,Writing- review&editing.WouterBoekel:Methodology,Investigation,Writing -review&editing.René Huster:Formalanalysis,Writing-review&

editing.BirteUForstmann:Conceptualization,Methodology,Writing- review&editing,Supervision,Fundingacquisition.MatthiasMittner:

Conceptualization,Methodology,Software,Formal analysis,Writing- review&editing,Supervision,Fundingacquisition.

Funding

ThisworkwassupportedbytheNetherlandsOrganisationofScien- tiﬁcResearch(NWO;Grant016.Vici.185.052,BUF).

Supplementarymaterials

Supplementarymaterialassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.neuroimage.2020.117412.

References

Allen, P.J. , Josephs, O. , Turner, R. , 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12, 230–239 . Aston-Jones, G. , Cohen, J.D. , 2005. An integrative theory of locus coeruleus-nore-

pinephrine function: adaptive gain and optimal performance. Ann. Rev. Neurosci. 28, 403–450 .

Banks, J.B. , Welhaf, M.S. , Hood, A.V.B. , Boals, A. , Tartar, J.L. , 2016. Examining the role of emotional valence of mind wandering: all mind wandering is not equal. Conscious.

Cogn. 43, 167–176 .

Bastian, M. , Sackur, J. , 2013. Mind wandering at the ﬁngertips: automatic parsing of sub- jective states based on response time variability. Front. Psychol. 4, 573 .

Benedek, M. , Bergner, S. , Könen, T. , Fink, A. , Neubauer, A.C. , 2011. EEG alpha synchro- nization is related to top-down processing in convergent and divergent thinking. Neu- ropsychologia 49, 3505–3511 .

Boayue, N.M., Csifcsák, G., Kreis, I.V., Schmidt, C., Finn, I., Vollsund, A.E.H., Mittner, M., 2020. The interplay between cognitive control, behavioral variability and mind wandering: insights from a HD-tDCS study. PsyArXiv doi: 10.31234/osf.io/d9ngb . Bocharov, A.V. , Knyazev, G.G. , Savostyanov, A.N. , Astakhova, T.N. , Tamozhnikov, S.S. ,

2019. EEG dynamics of spontaneous stimulus-independent thoughts. Cogn. Neurosci.

10, 77–87 .

Bouret, S. , Richmond, B.J. , 2015. Sensitivity of locus ceruleus neurons to reward value for goal-directed actions. J. Neurosci. 35, 4005–4014 .

Bowman, A.D. , Griﬃs, J.C. , Visscher, K.M. , Dobbins, A.C. , Gawne, T.J. , DiFrancesco, M.W. , Szaﬂarski, J.P. , 2017. Relationship between alpha rhythm and the default mode network: an EEG-fMRI study. J. Clin. Neurophysiol. 34, 527–533 .

Christoﬀ, K. , Gordon, A.M. , Smallwood, J. , Smith, R. , Schooler, J.W. , 2009. Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc. Natl Acad. Sci. USA 106, 8719–8724 .

Christoﬀ, K. , Irving, Z.C. , Fox, K.C.R. , Spreng, N. , Andrews-Hanna, J.R. , 2016. Mind-wandering as spontaneous thought: a dynamic framework. Nat. Rev. Neurosci. 17, 718–731 .

Compton, R.J. , Gearinger, D. , Wild, H. , 2019. The wandering mind oscillates: EEG alpha power is enhanced during moments of mind-wandering. Cogn. Aﬀect. Behav. Neu- rosci. 19, 1184–1191 .

Cona, G. , Semenza, C. , 2017. Supplementary motor area as key structure for domain-gen- eral sequence processing: a uniﬁed account. Neurosci. Biobehav. Rev. 72, 28–42 . Delorme, A. , Makeig, S. , 2004. EEGLAB: an open source toolbox for analysis of single-trial

EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 .

Dhindsa, K. , Acai, A. , Wagner, N. , Bosynak, D. , Kelly, S. , Bhandari, M. , Petrisor, B. , Son- nadara, R.R. , 2019. Individualized pattern recognition for detecting mind wandering from EEG during live lectures. PLoS One 14, 0222276 .

Dixon, M.L. , De La Vega, A. , Mills, C. , Andrews-Hanna, J. , Spreng, R.N. , Cole, M.W. , Christoﬀ, K. , 2018. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc. Natl Acad. Sci. USA 115, 1598–1607 .

Elton, A. , Gao, W. , 2015. Task-positive functional connectivity of the default mode network transcends task domain. J. Cogn. Neurosci. 27, 2369–2391 .

Esterman, M. , Rosenberg, M.D. , Noonan, S.K. , 2014. Intrinsic ﬂuctuations in sustained attention and distractor processing. J. Neurosci. 34, 1724–1730 .

Fox, K.C.R. , Spreng, R.N. , Ellamil, M. , Andrews-Hanna, J.R. , Christoﬀ, K. , 2015. The wandering brain: meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. Neuroimage 111, 611–621 .