The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T.

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The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T.

WIRSICH, Jonathan, et al.

Abstract

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total [...]

WIRSICH, Jonathan, et al . The relationship between EEG and fMRI connectomes is

reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T. NeuroImage , 2021, vol.

231, p. 117864

DOI : 10.1016/j.neuroimage.2021.117864 PMID : 33592241

Available at:

http://archive-ouverte.unige.ch/unige:150331

Disclaimer: layout of this document may differ from the published version.

ContentslistsavailableatScienceDirect

NeuroImage

journalhomepage:www.elsevier.com/locate/neuroimage

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Jonathan Wirsich

, João Jorge

, Giannina Rita Iannotti

, Elhum A Shamshiri

,

Frédéric Grouiller

, Rodolfo Abreu

, François Lazeyras

, Anne-Lise Giraud

, Rolf Gruetter

, Sepideh Sadaghiani

, Serge Vulliémoz

aEEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland

bLaboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

cSystems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland

dSwiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland

eISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal

fCoimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal

gDepartment of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland

hDepartment of Neuroscience, University of Geneva, Geneva, Switzerland

iDepartment of Radiology, University of Lausanne, Lausanne, Switzerland

jBeckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States

kPsychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, United States

a b s t r a c t

Bothelectroencephalography(EEG)andfunctionalMagneticResonanceImaging(fMRI)arenon-invasivemethodsthatshowcomplementaryaspectsofhumanbrain activity.Despitemeasuringdifferentproxiesofbrainactivity,boththemeasuredblood-oxygenation(fMRI)andneurophysiologicalrecordings(EEG)areindirectly coupled.TheelectrophysiologicalandBOLDsignalcanmaptheunderlyingfunctionalconnectivitystructureatthewholebrainscaleatdifferenttimescales.Previous workdemonstratedamoderatebutsignificantcorrelationbetweenresting-statefunctionalconnectivityofbothmodalities,howeverthereisawiderangeoftechnical setupstomeasuresimultaneousEEG-fMRIandthereliabilityofthosemeasuresbetweendifferentsetupsremainsunknown.Thisistruenotablywithrespectto differentmagneticfieldstrengths(lowandhighfield)anddifferentspatialsamplingofEEG(mediumtohigh-densityelectrodecoverage).Here,weinvestigatedthe reproducibilityofthebimodalEEG-fMRIfunctionalconnectomeinthemostcomprehensiveresting-statesimultaneousEEG-fMRIdatasetcompiledtodateincluding atotalof72subjectsfromfourdifferentimagingcenters.Datawasacquiredfrom1.5T,3Tand7TscannerswithsimultaneouslyrecordedEEGusing64or256 electrodes.Wedemonstratethatthewhole-brainmonomodalconnectivityreproduciblycorrelatesacrossdifferentdatasetsandthatamoderatecrossmodalcorrelation betweenEEGandfMRIconnectivityofr≈0.3canbereproduciblyextractedinlow-andhigh-fieldscanners.ThecrossmodalcorrelationwasstrongestintheEEG-𝛽 frequencybandbutexistsacrossallfrequencybands.Bothhomotopicandwithinintrinsicconnectivitynetwork(ICN)connectionscontributedthemosttothe crossmodalrelationship.Thisstudyconfirms,usingaconsiderablydiverserangeofrecordingsetups,thatsimultaneousEEG-fMRIoffersaconsistentestimateof multimodalfunctionalconnectomesinhealthysubjectsthataredominantlylinkedthroughafunctionalcoreofICNsacrossspanningacrossthedifferenttimescales measuredbyEEGandfMRI.ThisopensnewavenuesforestimatingthedynamicsofbrainfunctionandprovidesabetterunderstandingofinteractionsbetweenEEG andfMRImeasures.Thisobservedlevelofreproducibilityalsodefinesabaselineforthestudyofalterationsofthiscouplinginpathologicalconditionsandtheirrole aspotentialclinicalmarkers.

Abbreviations

AEC Amplitudeenvelopecorrelation dMRI DiffusionMagnetResonanceImaging ECG Electrocardiogram

FC Functionalconnectivity ICA Independentcomponentanalysis ICN Intrinsicconnectivitynetwork iCoh Imaginarypartofthecoherency M/EEG Magneto/Electroencephalography SC Structuralconnectivity

∗Correspondingauthor.

E-mailaddress:jonathan.wirsich@unige.ch(J.Wirsich).

1. Introduction

Thebrainisacomplexsystemofinteractingneuronscontinuously communicating with each other. This intrinsicbrainfunctional con- nectivity (FC) has been shown tobe organized in macro-scale pat- ternsofinterconnectedregions,theso-calledintrinsicconnectivitynet- works (ICNs) (Biswal et al., 1995; Fox et al., 2005; Greicius et al., 2003). Though originally described for fMRI (FC_fMRI) data, those ICNs have also been found using Magnetoencephalography (MEG, FC_MEG)(Brookesetal.,2011)andElectroencephalography(EEG,FC_EEG)

https://doi.org/10.1016/j.neuroimage.2021.117864

Received8July2020;Receivedinrevisedform21January2021;Accepted9February2021 Availableonline13February2021

1053-8119/© 2021TheAuthors.PublishedbyElsevierInc.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/)

(Abreu et al., 2020; de Pasquale et al., 2010; de Pasquale et al., 2012; Fingeret al., 2016; Wirsich etal., 2017). Expandingthis ini- tiallynetwork-specificview,cross-modalstudiesshowedthatFCinboth electrophysiological(M/EEG)andBOLDsignalsarerelatedwhenus- ingstaticmeasures(averagedoverperiods>5min)acrossthewhole brainscaleofresting-staterecordings(Deligiannietal.,2014;Hippand Siegel,2015;Wirsichetal.,2017).Understandingthefunctionallinks between EEG- and fMRI-derived FC measures is a key question in current neuroimaging research, as it could help clarifying the neu- ronalsubstrates ofbothmodalities,andofresting-stateactivityitself (SadaghianiandWirsich,2020).

TherelationbetweenFC_fMRIandFC_M/EEG,hasalsobeenshownto bemediatedbytheunderlyingwhitematterstructuralconnectivityde- rivedfromdiffusionMRI(SC_dMRI)(Chuetal.,2015;Deligiannietal., 2019, 2016; Honey et al., 2009; Meier et al., 2016; Wirsich et al., 2017).Beyond the staticrelationship of EEGand fMRItostructure, wealsoobservedcrossmodallinkeddynamics(1minslidingwindow) (Wirsichetal.,2020b).Whilemostworkfocusedoncrossmodalagree- mentofFC,wehaveequallyshownthatthecrossmodalconnectivity canbesplitupintoacommonandcomplimentaryconnectivityprofile acrossdifferenttimescales(Wirsichetal.,2020a).Acrucialopenques- tioniswhethercrossmodaldissimilaritiesarisemainlyfromdifferences indataqualityandacquisitionsetup,orrepresentdifferencesarising frommeasuringcomplementarymultimodalaspectsofbrainfunctional connectivity(SadaghianiandWirsich,2020).Inordertoaddressthis issue,datafromindependentresearchsitesareneeded.Thiswoulden- ableustocharacterizethebaselinerelationshipbetweenconnectivity derivedfrombothmodalities.

Fromamonomodalpointofview,thetest-retestreliabilityofFC_fMRI measures is well characterized (Noble et al., 2019) with a low in- traclasscorrelation(ICC=0.29)forindividualconnectionsandrepro- ducible ICN estimations across sites (Badhwar et al., 2020). While someofthevariancestemsfromdifferentscannersystems(Hanetal., 2006)orinter-individualdifferences(AmicoandGoñi,2018;Finnetal., 2015),thechosenpost-processingofdataalsointroducesvariabilityof theresults(Botvinik-Nezeretal.,2020;Carp,2012).Duetothiscon- nectivityvariability,itisimportanttoestimatethereproducibilityof thismeasure,asabaselineforsubsequentmeasuresofalterationsin different normalandpathological conditions(DeVico Fallaniet al., 2014).

Thereproducibilityof FC_M/EEG measureshasbeenanalyzed from differentangles.DifferentmeasuresofFC(Colcloughetal.,2016)have shownhighlycorrelated(topographicallysimilar)connectomeswhile Coqueletetal.(2020)haveshownthatthecrossmodalrelationshipof MEGandEEGconnectivityisreproducibleacrossdifferentEEGforward models.Marquetandetal.(2019)observedgoodintersessiontest-retest reliability(ICC>0.67)forbothmodalities.Fromafrequencypointof view,alpha hasbeenshown tobe themost reliable estimate across subjects(Colcloughetal.,2016;Marquetandetal.,2019).Whilethe majorityofFC_fMRIworkderivesconnectivityfromcorrelatingregional timecoursesof theBOLD signal, noconsensushas beenreached yet for FC_M/EEG, especially whethertouse phase or amplitudecoupling (Colcloughetal.,2016;SadaghianiandWirsich,2020).Bothphasecou- plingusingtheimaginarypartof thecoherency(iCoh)(Nolteetal., 2004; Wirsichetal., 2017) andamplitudecouplingusingamplitude envelopecorrelation (AEC)(Brookesetal.,2011; dePasqualeetal., 2010;Deligiannietal.,2014;HippandSiegel,2015)havebeenlinked toFC_fMRI.Ithasbeenarguedthatphasecouplingmightbeclosertothe underlyingSC_dMRIandamplitudecouplingisproposedtobemorere- latedtoFC_fMRI(Engeletal.,2013).Nevertheless,mostoftheliterature suggestsarathersimilarconnectivitypatternofintrinsicbrainactivity onthewholebrainscalewhenaveragingoverresting-staterecordings lastingseveralminutes(Colcloughetal.,2016;MostameandSadaghi- ani,2020;SadaghianiandWirsich,2020).SiemsandSiegel(2020)ob- servedhighlycorrelated,butnotidenticalconnectivitybetweenboth typesofmeasures,andtheneurobiologicalinterpretationofthosediffer-

enceshaveyettobeexplored.Recentlythosechangeshavebeenlinked totask-specificconnectivity(MostameandSadaghiani,2020),butul- timatelytherelevanceofdifferentconnectivitymeasuresforbrainbe- haviorandpathologyisstillanopenquestion.

Electrophysiological measures suffer from an ill-posed problem, whenreconstructingsparselysampledsensorsignalsonthescalpinto the three-dimensional brain space, resulting in source-activity leak- ing into different regions which can distort connectivity measures (Palvaetal.,2018).Theselectionofanoptimalbrainparcellationcan reducethevariabilityarisingfromregionalcrosstalkduetosourceleak- ageoftheinversesolution(Farahibozorgetal.,2018).Giventhoselimi- tationsofelectrophysiologicalmeasures,lessworkhasbeendonetoex- tractelectrophysiologicalconnectomes(SadaghianiandWirsich,2020).

However,nostudyhascomparediftheEEGorthefMRIconnectomecan bemeasuredmorereliablyacrosssubjects,especiallyinasimultaneous recording.

InthecaseofsimultaneousEEG-fMRI,signalqualityisdiminished by1) artifactsonfMRIdatainducedbytheEEGelectrodes interact- ingwiththestaticmagneticfieldandtheMR-pulses(Mullingeretal., 2008b)2)gradient-andpulse-relatedEEGartifactsinducedbythemag- neticgradientsontheEEGelectrodesandcables(Abreuetal.,2018; Allenetal.,2000).At7T,EEG-fMRIcanbeaffectedbystrongerrecord- ingartifacts(Abreuetal.,2016;Jorgeetal.,2015b;Mullingeretal., 2008a;Neuneretal.,2013).Overcomingthoselimitationswoulden- courageanalysisofthedynamicelectrophysiologicalcorrelatesofBOLD signalatmuchhighertemporalandspatialresolutions(Meyeretal., 2019;ScheeringaandFries,2017).Thoughitiswelldocumentedthat thedata qualityof EEGandfMRIdepends onscanner fieldstrength (Debeneretal.,2008;Mullingeretal.,2008b),nodataexistsassessing theimpactonthedatareliabilityattheleveloffunctionalconnectiv- ity.Thepossibilitytocompensateforthelargerartifactsathigherfield (Jorgeetal.,2015a)mightbesufficienttoachievereliableEEG-fMRI connectivitymeasures.

ToclosethegapofunknownreproducibilityofEEG-fMRIconnec- tomesacrossexperimentalsetupsandtoevaluatethesuitabilityofa7T EEG-fMRIsetupformultimodalconnectomicsinthisstudyweaimto:

1) comparethemonomodaltopographicalsimilarityofFC_fMRItoFC_EEG derivedfromsimultaneousEEG-fMRIacrossdifferentimagingcen- ters

2) characterize the reproducibility of crossmodal FC_fMRI-FC_EEG rela- tionshipacrossheterogeneoussetupsand,forthefirsttime,at7T 3) characterize the robustness of the crossmodal relationship to

methodologicalchoicesregardingthechosenbrainparcellationand EEGconnectivitymeasure

4) characterizethestabilityofthecrossmodalrelationshipwithrespect toacquisitiondurationandnumberofsubjectsusedforgroupaver- ages

5) characterizethespatialcontributionsofindividualconnectionsto this crossmodal relationship and the topographical similarity of thesecontributionacrossdifferentdatasets

Wewill considerthat thecrossmodalrelationship isreproducible ifthemonomodalmeasuresarecorrelatedacrossdatasets andifthe crossmodalrelationshipremainssignificantacrossalldatasets.Wefur- therexpectthatthecrossmodalrelationshipisrobusttomethodological choices.Forexample,weexpectarobustcrossmodalrelationshiptobe significantforarangeofmethodologicalchoiceswhilethemagnitude oftherelationshipmightchange.Ourpreviousworksuggestthiscor- relation tobe moderatearoundr~0.3 (Wirsich etal.,2020a, 2017).

Thereproducibilityofmonomodalmeasures,thecrossmodalrelation- shipandtherobustnessofthelattertomethodologicalchoiceswould stronglysupportthegeneralizabilityofconcurrentlyrecordedEEG-fMRI connectomes.

Todoso,wecombinedsimultaneousEEG-fMRIrestingstateacqui- sitions from 4different centerstotaling 72 subjects, andcomprising

recordingsusinga1.5T,3Tand7TMRscanner incombinationwith a64-or256-electrodeEEGsystem.

2. Methods

2.1. Subjectsandacquisitionsetup

Weanalyzedatotalof72subjectsdividedupinto4datasets:16sub- jectsusinga64-channelEEGsetupina1.5TMR-scanner(64Ch-1.5T), 26subjectsusinga64-channelEEGsetupina3TMR-scanner(64Ch- 3T),21subjectsusing a256-channelEEGsetup ina3TMR-scanner (256Ch-3T)and9subjectsusinga64channelEEGsetupina7TMR- scanner(64Ch-7T).Maindifferencesbetweenstudyparadigm,hardware andsoftwaresetuparesummarizedinSITable1.

2.2. Dataset1(64Ch-1.5T)

16subjects(6females,meanage:32.87,range22–53)withnohis- toryof neurologicalorpsychiatricillness wererecorded.Ethicalap- provalwasgivenbylocal ResearchEthicsCommittee(UCLResearch ethics committee, project ID: 4290/001)and informed consent was obtained from all subjects (Deligianni et al., 2016, 2014). In each subjectone runof 10 min48 sresting-statesimultaneousEEG-fMRI was acquired. Subjects were asked not to move, to remain awake andfixate on a white cross presented on a black background. MRI was acquired using a 1.5 Tesla MR-scanner (Siemens Avanto). The fMRIscancomprisedofthefollowingparameters:GRE-EPIsequence, TR=2160,TE=30ms,30slices,210×210mmFieldofView,voxelsize 3.3×3.3×4.0mm³(1mmgap),flipangle75°,totalof300vol.The subjects’headwasimmobilizedusingavacuumcushionduringscan- ning.Additionally,ananatomicalT1-weightedimagewasacquired(176 sagittalslices,1.0×1.0×1.0mm,TA=11min).EEGwasacquiredus- ingtwo32-channelMR-compatibleamplifiers(BrainAmpMR,sampling rate1kHz),63 electrodes (BrainCapMR,Gilching,Germany),refer- encedtoFCz,1ECGelectrode.Thescannerclockwastime-lockedwith theamplifierclock(Mandelkowetal.,2006).TheMR-compatibleam- plifierwaspositionedbehindoutsidetheborebehindtheheadofthe subject.

2.3. Dataset2(64Ch-3T)

26healthysubjects(8females,meanage24.39,agerange18–31) withno historyof neurologicalor psychiatricillness wererecorded.

EthicalapprovalwasgivenbylocalResearch EthicsCommittee(CPP Ile-de France III) andinformed consent was obtained from all sub- jects(Sadaghianietal.,2010).Ineachsubject,3runsof10min(to- tal30mins)resting-statesimultaneousEEG-fMRIwereacquired.Sub- jectswereaskednottomoveandtoremainawakeandkeeptheireyes closedduringtheresting-statescan.Forthreesubjects,oneoutofthe threerestsessionswasexcludeddue toinsufficient EEGquality.The resting-statesessionswerepartofa studywithtwoadditionalnatu- ralisticfilmstimuliof10minnotanalyzedinthecurrent study,and acquiredafterresting runs1and2of the restingstateasdescribed inMorillonetal.(Morillonetal.,2010).MRIwasacquiredusinga3 TeslaMR-scanner(SiemensTim-Trio).ThefMRIscancomprisedofthe followingparameters:GRE-EPIsequence,TR=2000ms,TE=50ms,40 slices,192×192mmFieldofView,voxelsize3×3×3mm³,flipan- gle78°,totalof150vol(totalallsessions450vol).AnanatomicalT1- weightedimagewasacquired(176sagittalslices,1.0×1.0×1.0mm, TA=7min).EEGwasacquiredusingtwo32-channelMR-compatibleam- plifiers(BrainAmpMR,samplingrate5kHz),62electrodes(Easycap, Herrsching,Germany),referencedtoFCz,1ECGelectrode,and1EOG electrode.Thescannerclockwastime-lockedwiththeamplifierclock (Mandelkowetal.,2006).TheMR-compatibleamplifierwaspositioned behindoutsidetheborebehindtheheadofthesubject.

2.4. Dataset3(256Ch-3T)

21 healthysubjects(7 females,32.13,agerange24–47) withno historyofneurologicalorpsychiatricillnesswererecorded.Ethicalap- provalwasgivenbylocalResearchEthicsCommittee(Ethicscommittee ofGeneva)andinformedconsentwasobtainedfromallsubjects.Asub- groupofthiscohorthasbeenalreadyanalyzedby(Iannottietal.,2015).

Ineachsubjectonerunof4min58.5sresting-statesimultaneousEEG- fMRIwereacquired.Fivesubjectshadalongerrecordingof19min52s andonesubjecthadarecordingof9min56s,inallthosecasesthe totalrunwasanalyzed.Subjectswereaskednottomoveandtoremain awakeandkeeptheireyesclosedduringtheresting-satescan.MRIwas acquiredusinga3TeslaMR-scanner(SiemensMagnetomTrio,Siemens Prismafor19min52ssessions).ThefMRIscancomprisedthefollow- ingparameters:GRE-EPIsequence,TR=1990ms,TE=30ms,32slices, 192×192mmFieldofView,voxelsize3×3×3.75mm³,flipangle 90°,totalof150vol.Additionally,ananatomicalT1-weightedimage wasacquired(176sagittalslices,1.0×1.0×1.0mm,TA=7min).EEG wasacquiredusinga258-channelMR-compatibleamplifier(Electrical GeodesicInc.,Eugene,OR,USA,samplingrate1kHz),256electrodes (GeodesicSensorNet256),referencedtoCz,2ECGelectrodes.Thescan- nerclockwastime-lockedwiththeamplifierclock(Mandelkowetal., 2006).Anelasticbandagewaspulledoverthesubjects’headandEEG captoassurethecontactofelectrodesonthescalp.TheMR-compatible amplifier waspositionedtotheleft ofthesubjectandEEGandECG cableswerepassedthroughthefrontendofthebore.

2.5. Dataset4(64Ch-7T)

9healthysubjects(4females,meanage23.56,agerange22–26)with nohistoryofneurologicalorpsychiatricillnesswererecorded.Ethical approvalwasgivenbythelocalResearchEthicsCommittee(CER-VD) andinformedconsentwasobtainedfromallsubjects(Jorgeetal.,2019).

Ineachsubject1runof8minresting-statesimultaneousEEG-fMRIwas acquired.SubjectswereaskednottomoveintheMRscanner andto keeptheireyesopenduringtheresting-statescan,fixatingonasmall redcrosspresentedonagraybackground,tominimizeheadandeye movements.Paddingwasalsousedtofurtherrestrictmotion.MRIwas acquiredusinga7Tesla headMR-scanner(SiemensMagnetom).The fMRIscanwasperformedusingasimultaneousmulti-slice(SMS)GRE- EPIsequence(3×SMSand2×in-planeGRAPPAaccelerations),with TR=1000ms,TE=25ms,69slices,220×220mmFieldofView,voxel size2.2×2.2×2.2mm,flipangle54°,andatotalof480vol.Ashort EPIacquisition(5vol)withreversedphaseencodingdirectionwasalso performed,forimagedistortioncorrection.Additionally,ananatomical T1-weightedimagewasacquired(160sagittalslices,1.0×1.0×1.0mm, TA=10min).EEGwasacquiredusingtwo32-channelMR-compatible amplifiers (BrainAmpMR,samplingrate 5kHz),anda63-electrode (EasyCap,Herrsching,Germany),referencedtoFCz,1ECGelectrode, connected via12-cm bundledcables toreduce artifact contributions (Jorgeetal., 2015b).Fourof the64 electrodes(T7,T8, F5andF6) werecustomizedtoserveasmotionartifactsensors(Jorgeetal.,2015a).

Atotalof59electrodes thereforeremaineddedicatedtoEEGrecord- ing.Thescannerclockwastime-synchronizedwiththeamplifierclock (Mandelkowetal.,2006)).TheMR-compatibleamplifierwaspositioned insidetheborebehindtheheadofthesubject.

2.6. Analysis

Asdataacquisitionalreadyhasaconsiderablenumberofvarying parametersthatmightaffectthefinalFCestimation,westressherethat alsofortheanalysiswedidnotstrictlycontroleveryprocessingstep.

Therationale ofthiswas thatindependentof setupandpostprocess- ing,thedifferentdatasetsshouldgeneralizeacrossafamilyofanalyses (Botvinik-Nezeretal.,2020)andresultinreproduciblemonomodaland crossmodalmeasures.Appliedtothisstudy,thecrossmodalrelationship

canbeconsideredreproducibleifitisrobusttodifferenthardwarese- tupsandpreprocessingapproaches.Thedifferentstartingpointsacross datasetsgivenbythedifferentEEGandfMRIequipmentdescribedabove makeithardtoacquireperfectlyunbiasedrecordingsacrossdatasets.

Inconsequence,ourapproachherewastooptimizetheanalysisofeach datasetto obtainthe best possible signal quality (E.g. varying with fieldstrength,several fMRIparameters areaffected:e.g. optimalTE andtherebythetimeavailabletoexecutetheEPIreadout;techniques likeGRAPPAandSMS-EPI-likeusedinthe7Tdata-becomemoreor lesseffective,thespatialresolutionvs.physiologicalnoiserelationship changesetc.).RegardingEEGside,aspointedoutlater,thechannelge- ometry(Iannottietal.,2015)orpresenceofadditionalartifactsensors (Jorgeetal.,2015a)presentopportunitiesfordenoisingthatshouldbe takenadvantageofwhereveravailable.

2.7. Brainparcellation

We used the Freesurfer toolbox to process the T1-weighted im- ages (recon-all, v6.0.0 http://surfer.nmr.mgh.harvard.edu/) in order to perform non-uniformity and intensity correction, skull stripping andgray/whitemattersegmentation.Thecortexwasparcellatedinto 148regions accordingtotheDestrieux atlas(Destrieuxetal., 2010;

Fischl et al., 2004) andinto 68 regions according to the Desikan(- Killiany) atlas (Desikan et al., 2006). According to the results of Farahibozorgetal.(2018),showingthattheoptimalsizeofparcella- tiontocaptureindependentEEGsignalscontainsaround70regionswe decidedtousetheDesikanatlasasreference.

Specificstrategyfor64Ch-7T:DuetolocalsignaldropsintheT1- weightedimagesneartheEEGleadconvergencepoints(Jorgeetal., 2015b)wewerenotabletoruntheFreesurferindividualsegmentation (recon-all)forallsubjects.Inordertonotloseanyofthesubjectsandto keepconsistencywithinthedatasetwecoregisteredallfMRIimagesto theMNItemplate.WeusedFreesurfertoextractthesurfacesoftheMNI template.Inordertoaccountforsubjectspecificvariances,wedilated theatlas imagesby 3voxels (usinghttps://github.com/mattcieslak/

easy_lausanne).ThetransformationofthesegmentedMNItemplateto fMRIwascalculatedbycoregisteringthefMRIimagestotheT1image usingFSL-FLIRT(6.0.2)(Jenkinsonetal.,2002)withboundary-based registrationusingwhitemattermasksobtainedbysegmentationwith ANTS(version2.2.0)(Avantsetal.,2011).TheT1wascoregisteredto theMNItemplateusingFSL-FNIRT(6.0.2)(JenkinsonandSmith,2001).

2.7.1. fMRIprocessing

Slice timing correction was applied to the fMRI timeseries (for the Ch64–3T and Ch256–3T datasets only). This was fol- lowed by spatial realignment using the SPM12 toolbox (Ch64–

1.5T/Ch64–3T: revision 6906; Ch256–3T/Ch64–7T revision 7475;

http://www.fil.ion.ucl.ac.uk/spm/software/spm12).TheT1imagesof eachsubjectandtheDesikan/Destrieuxatlas(alreadyinsubjectspace, asdescribedabove)werecoregisteredtothefMRIimages(FSL-FLIRT 6.0.2). We extracted signalsof no interest such as theaverage sig- nalsofcerebrospinalfluid(CSF)andwhitematterfrommanuallyde- finedregions of interest(ROI, 5mm sphere, Marsbar Toolbox0.44, http://marsbar.sourceforge.net) andregressedout of theBOLDtime- seriesalongwith6rotation,translationmotionparametersandglobal graymattersignal(Wirsichetal.,2017).Thenwebandpass-filteredthe timeseriesat0.009–0.08Hz(Poweretal.,2014).LikeinWirsichetal.

(2020a, 2017),wescrubbedthedatausing framewisedisplacement (threshold0.5mm,byexcludingthesuper-thresholdtimeframes)asde- finedbyPoweretal.(2012).

Specificstrategyfor64Ch-7T:DatawasalsoB0-unwarpedbefore spatialalignment,usingFSL-TOPUP (6.0.2)(Anderssonetal.,2003), based on thereverse-encoding referenceacquisition, to mitigatethe moreaccentuatedimagedistortionspresentat7T(Jorgeetal.,2018).

GiventheshortTRof1snoslice timingcorrectionwascarried out (Smithetal.,2013).

Specificstrategyfor64Ch-1.5T:Inordertosticktotheoriginalpro- cessingof(Deligiannietal.,2014)noslicetimingcorrectionwascarried out.WenotethatasshownbyWuetal.(2011)andShireretal.(2015), slicetimingcorrectionhasminimaltonoeffectonbrainconnectivity (intermsoftest-retestreliability,signaltonoiseratioandgroupsepa- rability).

2.8. fMRIconnectivitymeasures

AveragetimeseriesofeachregionwasthenusedtocalculateFC_fMRI bytakingthepairwisePearsoncorrelationofeachregions’cleanedtime- course(seeschemaFig1).Thefinalconnectivitymatrixwasconstructed bytheunthresholdedvaluesofthePearsoncorrelation.

2.9. EEGprocessing

EEGdatawaspreprocessedindividuallyforthedifferentsetups:

64Ch-1.5T:EEGwascorrectedforthescannergradientartifactus- ingtemplatesubtraction,adaptivenoisecancelationanddownsampling to250Hz(Allenetal.,2000)followedbypulse-relatedartifacttemplate subtraction(Allenetal.,1998).ThenICA-baseddenoising(forremoval ofgradientandpulseartifactresiduals,eye-blinks,muscleartifacts)us- ingtheBrainVisionAnalyzer2software(BrainProducts,Gilching,Ger- many)wascarriedout.

64Ch-3T: EEGwascorrected forthescanner gradientartifactus- ingtemplatesubtraction,adaptivenoisecancelationfollowedbylow- passfilteringat75Hz,downsamplingto250Hz(Allenetal.,2000).

Thenpulse-relatedartifacttemplatesubtraction(Allenetal.,1998)us- ingEEGlabv.7 (http://sccn.ucsd.edu/eeglab) andtheFMRIBplug-in (https://fsl.fmrib.ox.ac.uk/eeglab/fmribplugin/)wascarriedout.

256Ch-3T:EEGwascorrectedforthescannergradientartifactus- ingtemplatesubtractionwithoptimalbasissetandadaptivenoisecan- celation (Allenet al., 2000; Niazy etal., 2005),followed by pulse- relatedartifacttemplatesubtraction(Allenetal.,1998)usingin-house codeMatlabcodeforballistocardiogrampeak detectionasdescribed in (Iannottietal.,2015).Electrodesplacedonthecheeksandin the facewereexcludedformdataanalysisresultinginfinal204usedelec- trodes.ThiswasfollowedbymanualICA-baseddenoising(forremoval ofgradientandpulseartifactresiduals,eye-blinks,muscleartifacts,info- Max,runICA-functionEEGLabrevision1.29(BellandSejnowski,1995; DelormeandMakeig,2004))

64Ch-7T:EEGdatapre-processingincludedthefollowingsteps:gra- dient artifactcorrectionusing template substraction(as described in (Jorge et al., 2015a)), bad channel interpolation (1–4 channels per subject), temporal band-pass filtering (1–70 Hz), pulse-related arti- factcorrection(usingak-meansclustering-basedapproachvalidatedin (Jorgeetal.,2019)inlinewith(Gonçalvesetal.,2007)),downsampling to500Hz,motionartifactcorrection(offlinemulti-channelrecursive least-squaresregression,usingthemotionsensorsignals,asdescribed in(Jorgeetal.,2015a)),andmanualICA-baseddenoising(forremoval ofe.g.gradientandpulseartifactresiduals,eye-blinks,muscleartifacts, in-houseICAextendedInfomaxalgorithm).

All datasets: Cleaned EEG data was analyzed with Brain- storm software (Tadel et al., 2011), which is documented and freely available under the GNU general public license (http://neuroimage.usc.edu/brainstorm, 64Ch-1.5T and 64Ch-3T data set: version 10th August 2017 asaccording to (Wirsich et al., 2020b),256Ch-3Tand64Ch-3Tdataset:version15thJanuary2019).

Data was bandpass-filtered at 0.3–70 Hz (64Ch-1.5T at 0.5–70 Hz, 64Ch-7Tat1–70Hz).DatawassegmentedaccordingtooneTRoras amultipleTRsofthefMRIacquisition(64Ch-1.5:2160ms,64Ch-3T:

2000ms, 256Ch-3T:1990ms,64Ch-7T: slidingwindow of4000ms with1000ms(1TR)steps).

InordertominimizeeffectofheadmotionEEGepochscontaining motionweresemi-automaticallydetectedifthesignalinanychannel exceededthemeanchanneltimecourseby4standarddeviations.Then

Fig1. OverviewontheconstructionofEEGandfMRIconnectomes.EEGandfMRIdatawereparcellatedintothe148corticalregionsoftheDestrieuxatlas(and 68regionsoftheDesikanatlas,coregisteredtoeachsubject’sindividualT1)asfollows:ForfMRI,theBOLDsignaltimecoursewasaveragedoverthevoxelsineach regionforeachsubject.ThePearsoncorrelationoftheregionaveragedfMRI-BOLDtimecoursewascalculatedtobuildafunctionconnectivitymatrix/connectome (FC_fMRI).FortheEEG,thesignalofeachsensorwassourcereconstructedtothecorticalsurface(15,000solutionpoints)usingtheTikhonov-regularizedminimum norm.Then,thetimecoursesofthesolutionpointswereaveragedpercorticalregion.Theimaginarypartofthecoherency(iCoh)orenvelopeamplitudescorrelation (AEC,orthogonalizedandnon-orthogonalized)ofaveragedEEGsourcesignalswereusedtocalculateFC_EEGforeachsubject(Figureadaptedfrom(Wirsichetal., 2020a)).Pleaserefertothemethodsforadetaileddescriptionofeachstep.

thewholetimecoursewasalsovisuallyinspectedtoexcludeallmotion segmentsfromfurtheranalysis(Wirsichetal.,2020a,2017).Electrode positionsandT1werecoregisteredbymanuallyaligningtheelectrode positionsontotheelectrodeartifactsvisibleintheT1image.Aforward modeloftheskullwascalculatedbasedontheindividualT1imageof eachsubjectusingtheOpenMEEGBEMmodel,(Gramfortetal.,2010; Kybicetal., 2005).The EEGsignal wasprojected intosource space (15,000solutionpoints on thecorticalsurface) using theTikhonov- regularizedminimumnorm(Bailletetal.,2001)withtheTikhonovpa- rametersetto10%(Ch64–1.5T/Ch64–3Tbrainstorm2016implemen- tationandCh256–3T/Ch64–7Tbrainstorm2018implementation,with defaultparameters:assumedSNRratio3.0,usingcurrentdensitymaps, constrainedsourcesnormaltocortexwithsignsflippedintoonedirec- tion,depthweighting0.5/maxamount10).Finally,thesourceactivity ofeachsolutionpointwasaveragedineachcorticalregionoftheDe- sikanandtheDestrieuxatlas.

2.10. EEGconnectivitymeasures

For each epoch the imaginary part of the coherency (iCoh, (Nolteetal.,2004))ofthesourceactivitywascalculatedbetweeneach regionpair (cortical regions only:Desikanatlas- 68 regions or De- strieuxatlas -148 regions) using binsof 2 Hz frequency resolution (Wirsichetal.,2020a,2017)(Brainstormimplementation,version27–

01–2019;imaginarypartwascorrectedbytherealpartofthecoherence coh:𝑖𝐶𝑜ℎ= ^{𝐼𝑚(𝑐𝑜ℎ)2}

1−𝑅𝑒(𝑐𝑜ℎ)² (Ewaldetal.,2012),significanceofeachvalue wasdeterminedaccordingto(Schelteretal.,2006),connectionswith p<0.05weresetto0).The2Hzbinswereaveragedfor5canonical frequencybands:delta(𝛿0.5–4Hz,64Ch-7T:at1–4 Hz),theta(𝜃4–

8Hz),alpha(𝛼8–12Hz),beta(𝛽12–30Hz),gamma(𝛾30–60Hz).The segmentswerethenaveragedforeachsubjecttooneFC_EEGmatrix.

Wecalculatedtheamplitudeenvelopecorrelation(AEC)ofthesig- nalbytakingtheHilbertenvelopeoftheconcatenatedepochsofeach subject filtered intothecanonical frequency bandsboth forthe De- strieux and Desikan atlas. We calculated the correlation of the fil- tereddatabothwithout(Deligiannietal., 2014; Glombetal., 2020) andwith(Brookesetal.,2012;Hippetal.,2012) asubsequentpair- wiseorthogonalizationapproachtoattenuatecrosstalkbetweensignals (AECnon-orthogonalized/AECorthogonalized,Brainstormimplementation;ver- sion27–01–2019implementationof(Hippetal.,2012)).

2.11. Monomodalreproducibility

We assessedthe modality-specific reproducibility by determining thetopographicalsimilarityoftheaverageconnectivitymatrixthrough calculatingthemonomodalinter-datasetcorrelationbetweenaveraged FC_fMRI (ofeach datasetacross allrunsandsubjects).Thesameanal- ysis wasperformedforFC_EEG(ineachfrequencyband).Intra-dataset monomodalreproducibilitywasassessedbysplittingupeach dataset intotwohalvesandcalculatingthecorrelationoftheaverageconnec- tomebetweenbothhalves.

2.12. Statisticalanalyses

Toanalyzetheimpactof groupaverages,we usedapermutation approachthatrandomizesthelabelsofthevariableofinterestinorder todefineap-value(5000randompermutations).Inthecaseofstatistical assessmentofthe64ch-7Tdatasetwithsizeof9subjectsonlylabel512 permutationsexist,inwhich casewetestedforall512permutations todefineap-value. Wereportall rawp-valuesalongsideanexplicit Bonferronithresholdincaseofmultiplecomparisons.

2.13. ThecrossmodalcorrelationbetweenEEGandfMRI

We thenassessedthe crossmodalcorrelationbetween FC_fMRI and FC_EEG for each EEG frequency band across different configurations (brain atlas, EEG connectivity measure). Effects on the crossmodal FC_EEG-FC_fMRIcorrelationdue toEEG frequencybands(𝛿,𝜃,𝛼,𝛽, 𝛾), atlaschoice(Desikanvs.Destrieux),EEGconnectivitymeasure(iCoh, AECnon-orthogonalized,AECorthogonalized)wereassessedontheaveragecon- nectomeofeachdataset(permutationtestwith5000iterationsor,512 permutationsforthe64Ch-7Tdataset,testingtheeffectsagainstaver- age connectomes withswitched labelsat theindividual level, inor- der tobeabletocomparethe2278connectionsof theDesikanatlas tothe 11,026connections of theDestrieux atlaswerandomly drew 2278out ofthe11,026Destrieuxconnectionsforeach iteration.We testedifthisrandomsamplingintroducesabiastothemeasuredcross- modal correlation by comparing 5000(or 512 permutations for the 64Ch-7Tdataset)drawsof2278connectiontothecrossmodalcorre- lationofallconnections.Weobservedthattheabsolutedifferencewas rdiff-sampling<|0.0004|.Weconsideredthisvaluenegligiblein orderto testforsignificantatlasdifferencesoftheorderr_diff-atlas~0.01).

Toassesstheeffectofsmallsamplesizesandshortrecordinglength, wecutalldatasetstoarecordinglengthofthefirst4min58.5s(accord- ingtotherecordinglengthindataset256Ch-3T).Significancewastested byrandomlyswitchinglabelsbetween4min58.5sandfull-lengthdata (5000iterations,or512permutationsforthe64Ch-7Tdataset).Thenwe tookonlythefirstninesubjectsofeachdatasettocalculatetheconnec- tomeaverageofEEGandfMRI(accordingtothenumberofsubjectsin dataset64Ch-7T).Significancewasdefinedbycomparingtheaverage crossmodalcorrelationofallsubjectsagainsttheaverage9randomly sampledsubjectsfromthegroup(5000iterations).

2.14. Spatialcharacterizationofthecrossmodalcorrelation

FocusingonthemultimodalconnectomesintheDesikanatlasav- eragedoveralldatasets,weassessedtherelativecontributionofeach connectiontothecorrelationbetweenFC_EEGandFC_fMRIaccordingto Colclough etal.(2016). The relativecontributionc of each connec- tioniisgivenby: 𝑐_𝑖= ^𝑧

𝑥𝑖𝑧^𝑦_𝑖

∑𝑖𝑧^𝑥_𝑖𝑧^𝑦_𝑖 = ^𝑧

𝑥𝑖𝑧^𝑦_𝑖

𝑟 with𝑧^𝑥_𝑖 = √_∑^{𝑥𝑖−⟨𝑥⟩}

𝑖(𝑥_𝑖−⟨𝑥⟩)² and𝑧^𝑦_𝑖 =

𝑦_𝑖−⟨𝑦⟩

√_∑

𝑖(𝑦_𝑖−⟨𝑦⟩)² giventhePearsoncorrelationcoefficientoftwovectorsx andy:𝑟=

∑𝑖(𝑥_𝑖−⟨𝑥⟩)(𝑦_𝑖−⟨𝑦⟩)

√_∑

𝑖(𝑥𝑖−⟨𝑥⟩)²√_∑

𝑖(𝑦𝑖−⟨𝑦⟩)²

= ∑

𝑖 𝑧^𝑥_𝑖𝑧^𝑦_𝑖.Theresultingspatialcon- tributionmatrix wasthen correlatedacross thedifferent datasets to assessthereproduciblyof thisspatialcontributiontothecrossmodal relationship.Toclassifytheresultswemappedthe68regions ofthe Desikanatlasto7canonicalICNs(VIS:Visual,SM:Somatomotor,DA:

DorsalAttention,VA:VentralAttention,L:Limbic,FP:Fronto-Parietal, DMN:DefaultModeNetwork)(Amicoetal.,2017;Yeoetal.,2011)and wesubdividedtheconnectionsintohomotopic,intrahemisphericand interhemisphericconnections.Wetestedifthespatialcontributionto thecrossmodalcorrelationwasmoreprominentforeachICN,forho- motopicconnectionsandforinterhemisphericconnections(one-sided ttest).Finally,wetestedifthemagnitudeofthecrossmodalcorrelation wasincreasedforeachICN,forhomotopicconnectionsandforintra- hemisphericconnections(one-sidedttest).

2.15. Reproducibilityofthecrossmodalcorrelationfromsinglesubjectto theaverageconnectome

Besidestakingtheaveragemultimodalconnectomeofeachdataset wealsocalculatedthecorrelationbetweenEEGandfMRIconnectomes ineachsubject.Usingatwo-wayANOVAwetestedifthecrossmodal correlation differs in termsof the datasetand EEG frequency band ortheinteractionbetweendatasetandfrequencyband(Matlabfunc- tionANOVAN,p<0.05).Toidentifyanypossible effectsofaspecific datasetorfrequency band onthecrossmodal correlation, weused a TukeyPosthoctest(Matlabfunctionmultcompare,p<0.05Bonferroni corrected).Inordertoexcludealarge effectofmotiononthecross- modalrelationshipwecorrelatedtheaverageframewisedisplacement ofeachsubjecttotheindividualcrossmodalrelationship(seeSIsection impactofmovement).Additionally,tolimitingthegroupaverageto9 subjects,inordertobetterunderstandtherelationshipbetweensingle subjectanddatasetaveragedestimatesweaimedtodefinethenumber ofsubjectsneededforareliableaveragedconnectome.Todosonowand randomlyselected1,2,…,nsubjects(5000iterationseachstep)andav- eragedtheEEGandfMRIconnectomesoftheselectedsubjects.Thenwe calculatedthecorrelationbetweenFC_EEGandFC_fMRIforeachofthesub- jectsteps.Werepeatedthisapproachforthecombineddatasetsampling anaveragecorrelationbetweentheaveragedEEGandfMRIconnectome fromnrandomlydrawnsubjects.Todeterminehowmanysubjectsare neededforastableaverageconnectomewetooktheaveragecrossmodal correlationoverallsubjectsofeachfrequencybandasreferencecorre- lation.Wethencomparedthevalueof1%ofreferencecorrelationto thechangeratewhenaddingonerandomsubject(5000iterations).The crossmodalcorrelationwasconsideredstablewhenthechangeratedid notdiffermorethan1%ofthetotalcrossmodalreferencecorrelation.

Table1

Inter- and intra-dataset correlation of FC_fMRI.;The first row shows the intra-datasetcorrelation of the dataset’ssplit-half averagedfMRI connectome.Theorangecellsshowtheinter- datasetcorrelationof datasetaveragefMRI connectome(De- sikanatlas)betweenthedifferentdatasets.

64Ch-1.5T 64Ch-3T 256Ch-3T 64Ch-7T Split-half 0.82 0.95 0.88 0.78

64Ch-1.5T 0.81 0.78 0.69

64Ch-3T 0.90 0.69

256Ch-3T 0.64

Table2

Inter- and intra-dataset correlation of FC_EEG. The first row showsofeachfrequency-bandtheintra-datasetcorrelationof thedataset’ssplit-halfaveragedEEGconnectome.Theorange cellsshowtheinter-datasetcorrelationofdatasetaveragedEEG connectome(Desikanatlas,imaginarypartofthecoherency)be- tweenthedifferentdatasets.

64Ch-1.5T 64Ch-3T 256Ch-3T 64Ch-7T Delta

Split-half 0.79 0.82 0.76 0.78

64Ch-1.5T 0.81 0.68 0.66

64Ch-3T 0.75 0.71

256Ch-3T 0.68

Theta

Split-half 0.76 0.88 0.79 0.80

64Ch-1.5T 0.83 0.68 0.66

64Ch-3T 0.77 0.71

256Ch-3T 0.72

Alpha

Split-half 0.70 0.85 0.75 0.77

64Ch-1.5T 0.63 0.76 0.71

64Ch-3T 0.57 0.51

256Ch-3T 0.73

Beta

Split-half 0.85 0.90 0.81 0.79

64Ch-1.5T 0.84 0.85 0.73

64Ch-3T 0.79 0.64

256Ch-3T 0.69

Gamma

Split-half 0.77 0.82 0.47 0.64

64Ch-1.5T 0.69 0.43 0.68

64Ch-3T 0.57 0.57

256Ch-3T 0.47

3. Results

3.1. Monomodalreproducibilitybetweendatasets

We measured monomodal reproducibility (topographical similar- ity)bytakingcorrelationsofconnectivitiesofeachmodality.Between datasets,monomodalconnectivitymatriceswereallcorrelatedforboth modalities (Table 1,Table 2). 7TfMRIdatacorrelatedless withthe otherdatasets(Table1),EEGcorrelationswerelowerthancorrelations betweenfMRI(Table1,Table2).Intermsofinter-datasetconnectome correlation,delta,thetaandbetabandconnectomeswerethemostcor- relatedacrossdatasets(r>0.65,seeTable2).Unlikeotherwisestated, theresultsofthissectionareallderivedfromthegroupaveragedcon- nectomesofeachdataset.

3.2. CrossmodalcorrelationofgroupaveragedEEGandfMRIconnectomes

CorrelationsbetweenEEGandfMRIwerehighestforthebetaband, whilegammaanddeltabandwerethemostvariableacross datasets.

Agrandaverageacrossalldatasets(72subjects)resultedinthehigh- estcorrelationbetweenEEGandfMRIforallbands(Fig2a).Whilethe grandaverageFC_fMRI-FC_EEG-𝛽 correlationwassignificantlyhigherthan alltheotherbands(p<0.0002,5000permutations,see3),thegroup-

Fig2.a)Crossmodalcorrelation datasetav- eragedEEGandfMRIconnectomesusingthe Desikanatlas(FC_EEGmeasure:imaginarypart ofthecoherency);b)Crossmodalcorrelation datasetaveragedEEGandfMRIconnectomes usingtheDestrieuxatlas(FC_EEGmeasure:imag- inarypartofthecoherency).

averaged FC_fMRI-FC_EEG-𝛼 correlation was significantly increased only whencomparedtoFC_fMRI-FC_EEG-𝛾(onlyfor64Ch-3Tdataset:p=0.0002 and256–3Tdataset:p=0.0004,5000permutations,seeSITable3).

3.3. CrossmodalcorrelationofgroupaveragedEEGandfMRIconnectomes usinganalternativeatlasandalternativeEEGconnectivitymeasures

Inthis section we compared the above used atlas(Desikan)and EEGconnectivitymeasures(iCoh)toalternatives.Takinganatlaswith higherresolutionresulted ina reductionof crossmodalFC_fMRI-FC_EEG correlationgrandaverageoverall datasets(Fig2b,p<0.0002 forall frequencybands,5000permutations,seeSITable4,asubsetoftheDe- strieuxconnectionswererandomlychosenforeachiterationtomatch thenumberof connectionsof theDesikanatlas:see methods).When usingAECnon-orthogonalasFC_EEGconnectivitymeasurecrossmodalcorre- lationincreasedascomparedtotakingiCoh(Fig3b,forFC_EEG-𝛾only, p<0.0002,5000 permutations,SITable 5),theorthogonalizationap- proach (AEC_orthogonal) resulted in lower correlationcompared tothe iCoh(Fig3b,allEEGfrequencybands,p<0.0002,5000permutations, see SITable5).We didnotfind anysignificantcorrelationbetween FC_fMRIandFC_EEG-𝛾inthe256Ch-3Tdataset.

3.4. Effectofnumberofsubjectsandlengthofresting-staterecordingon thecrossmodalcorrelation

Nextconnectomeswerecutdowntothesessionlengthoftheshort- estdataset(4min58.5s,Fig4a)andsubjectaverageswerecalculated

usingthesamenumberofsubjects(9subjects,Fig4b).Thecrossmodal correlationaveragedoveralldatasetsisnotsignificantlydifferentwhen thesessionsarecut downtothefirst4min58.5s(p>0.07forallfre- quencybands,5000permutations,SITable6).Notethatthoughwedid notfindanysignificantdifferencesforgroupaveragedconnectomes,we observedsignificantdifferencesindividualcrossmodalcorrelationwhen limitingthe30 minofthe64Ch-3Tdatasetto4min58.5s(one-sided ttest,significantforFC_fMRI-FC_EEG-𝛿,FC_fMRI-FC_EEG-𝜃andFC_fMRI-FC_EEG-𝛼, p<0.0003,SITable7).Whentakingtheaverageconnectivityofonlythe first9subjectsthecrossmodalcorrelationbetweenFC_fMRIandFC_EEG-𝛾 decreasessignificantly(p=0.0006,5000permutations,SITable6).

3.5. SpatialcharacterizationofdifferencesandICNcrossmodalcorrelation

We defined the connections that contribute most to the FC_fMRI- FC_EEG correlationusing theconnectomes averagedover all datasets (Colcloughetal.,2016).Table3showsthatthetopographythisspatial contributioniscorrelatedbetweenalldatasets.Thevisualnetworkcon- tributesthemosttothecrossmodalcorrelationaswellasthehomotopic connections(Fig5).Connectionsofthevisualnetworkcontributedsig- nificantlymoretothecrossmodalFC_fMRI-FC_EEGcorrelationascompared tointer-ICNconnectionsforallfrequencybands(t-testspatialcontribu- tionVisual>interICN:FC_fMRI-FC_EEG-𝛿/FC_EEG-𝜃/FC_EEG-𝛼/FC_EEG-𝛽/FC_EEG-𝛾; p=3.2×10⁻⁴⁷/1.8×10⁻³⁴/p=5.1×10⁻⁶⁹/p=4.1×10⁻⁵³/1.5×10⁻⁸¹_, Bonferronicorrectionthresholdfor5frequenciesand7ICNsisdefined atp=0.05/35=0.0014).Connectionsofthelimbicnetworkcontributed significantlymoretotheFC_fMRI-FC_EEGcorrelationascomparedtointer-

Fig 3. Crossmodal correlation dataset aver- aged EEGandfMRI connectomesusingAm- plitudeenvelopecorrelation(AEC) toderive FC_EEG a)non-orthogonalizedAEC andb)or- thogonalizedAEC(resultsforDesikanatlas).

ICNconnectionsforallfrequencybandsexceptEEG-𝛾(t-testspatialcon- tribution Limbic>inter-ICN: FC_fMRI-FC_EEG-𝛿/FC_EEG-𝜃/FC_EEG-𝛼/FC_EEG-𝛽; p = 5.4 ×10⁻⁰⁶/4.3 ×10⁻⁰⁶/4.6× 10⁻⁰⁵/3.2 × 10⁻⁰⁵_, Bonferroni correction threshold for 5 frequencies and 7 ICNs is defined at p=0.05/35=0.0014).

Homotopic connections contributed significantly more to the FC_fMRI-FC_EEG correlation as compared to the rest of the connec- tome for all bands (t-test spatial contribution homotopic>other connections: FC_fMRI-FC_EEG-𝛿/FC_EEG-𝜃/FC_EEG-𝛼/FC_EEG-𝛽/FC_EEG-𝛾: p=2.1×10⁻⁵²/8.710×10⁻⁵⁰/4.3×10⁻⁵²/p=1.2×10⁻⁵⁵/9.6×10⁻⁵⁶_, Bonferroni correction threshold for 5 frequencies is de- fined at p = 0.05/5 = 0.01). Additionally, intrahemi- spheric connections contributed significantly more than in- terhemispheric connections to the crossmodal relationship (t- test spatial contribution intrahemispheric>interhemispheric:

FC_fMRI-FC_EEG-𝛿/FC_EEG-𝜃/FC_EEG-𝛼/FC_EEG-𝛽/FC_EEG-𝛾:

p=1.1×10⁻¹³/1.3×10⁻¹⁴/1.6×10⁻¹⁶/9.3×10⁻²³/5.9×10⁻²⁸, Bonferroni correction threshold for 5 frequencies is defined at p=0.05/5=0.01)

TofollowupweexploredatthecrossmodalcorrelationofFC_fMRI- FC_EEG while only selecting connectionsinside one ICN. When com- paringthecrossmodalcorrelationinsidethedifferentcanonicalICNs tothecrossmodalcorrelationofrandomin-betweennetworkconnec- tionsofthesamenetworksize(randomlysampled,5000iterations),we observea highercorrelationin theVisualnetwork(r_fMRI,_EEG-𝛼 =0.61 (r>r_random:p=0.028),r_fMR,EEG-𝛾=0.63(r>r_random:p=0.007),theLim- bicnetwork(r_fMRI,_EEG-𝛿=0.56, (r>r_random: p =0.03), r_{fMRI,EEG-𝛽}=0.59

(r>r_random:p=0.022),r_{fMRI,EEG-𝛾}=0.60(r>r_random:p=0.0034))andthe DMN (r_{fMRI,EEG-𝛾}=0.52(r>r_random:p<0.0002),allsignificantatuncor- rectedthresholdp<0.05,Bonferronicorrectionthresholdisdefinedat p=0.05/35=0.0014).

3.6. Reproducibilityofcrossmodalcorrelationinindividualconnectomes

WegenerallyobservedthesamedistributionofFC_EEG-FC_fMRIcorrela- tioninindividualascomparedtothedatasetaveragesuchashighcross- modalcorrelationforFC_EEG-𝛽andlowcrossmodalcorrelationforFC_EEG-𝛾 (Table4).Lowestcorrelationwasobservedinthe256Ch-3Tdataset.

A 2-way ANOVA of the individual subject crossmodal correla- tion revealed a significant main effect of datasets F(3, 71)=36.61, p=1.6×10⁻²⁰;andasignificantmainEffectofEEGFrequencybands F(4, 71)=6.85, p = 2.6 × 10⁻⁵. The interaction term dataset^∗band was notsignificant(F(12, 71)=1.32,p=0.21,significance threshold p<0.05).TukeyPosthoct-testsonthemaineffectofthedatasetsestab- lished thefollowingorder of correlationmagnitude:64Ch-3T>64Ch- 7T>64Ch-1.5T>256Ch-3T (64Ch-3T > 64Ch-1.5T(p<0.0001), 64Ch- 3T>64Ch-7T (p = 0.0031), 64Ch-3T>256Ch-3T (p<0.0001); 64Ch- 7T>256Ch-3T(p=0.003)).TukeyPosthoct-testsonthemaineffectof EEGfrequencybandsrevealedthatFC_fMRI-FC_EEG-𝛾correlationwassig- nificantly smallerthanFC_fMRI-FC_EEG-𝛽 (p<0.0001)andFC_fMRI-FC_EEG-𝜃 correlation(p=0.027).

FC_EEG-𝛽correlatesthebestwithFC_fMRIforalldatasets(Fig6,Fig7).

OnthecontraryFC_EEG-𝛾-FC_fMRIwasdependentonthedatasetshowing the2ndstrongestcorrelationforthe64Ch-1.5Tdatasetandbeingbyfar

Fig4. Crossmodalcorrelationbetweendataset averaged EEG and fMRI connectomeslimit- ingalldatasetsto:a)thefirst4min58.5sof eachsubject’ssession(accordingtothesession lengthofthe256Ch-3Tdataset)andb)thefirst 9subjectsof each dataset(accordingtothe numberofsubjectsofthe64Ch-7Tdataset).

Fig5. Relativespatialcontribution(seemethods)ofeachconnectiontothecrossmodalcorrelationbetweenFC_EEG-𝛽andFC_fMRIbasedontheaverageoverall72 subjects.(a)Spatialcontributionisorderedaccordingtothe7canonicalICNs(Yeoetal.2010).TheVisualNetworkandtheLimibicNetworkcontributedthe mosttothecrossmodalrelationship.(b)Spatialcontributionisorderedaccordingtothetwohemispheres.Off-diagonalshighlightthehomotopicconnectionsthat contributedthemosttothecrossmodalcorrelation.Abbreviations:VIS:Visual,SM:somatomotor,DA:dorsalattention,VA:ventralattention,L:Limbic,FP:Fronto parietal,DMN:defaultmodenetwork.

Table3

Inter-andintra-datasetcorrelationofspatialregion-specificcontributions totheaveragedFC_fMRI-FC_EEGcrossmodalcorrelation.Thefirstrowofeach frequency-bandshowstheintra-datasetcorrelationofthedataset’ssplit halfaveragedEEG-fMRIconnectomes.Theorangecellsshowtheinter- datasetcorrelationofthedatasetaveragedEEG-fMRIconnectome(De- sikanatlas,imaginarypartofthecoherency).

Spatial contribution 64Ch-1.5T 64Ch-3T 256Ch-3T 64Ch-7T Delta

Split-half 0.72 0.85 0.76 0.79

64Ch-1.5T 0.76 0.62 0.64

64Ch-3T 0.76 0.64

256Ch-3T 0.61

Theta

Split-half 0.69 0.87 0.81 0.80

64Ch-1.5T 0.76 0.65 0.61

64Ch-3T 0.78 0.62

256Ch-3T 0.64

Alpha

Split-half 0.72 0.84 0.80 0.79

64Ch-1.5T 0.56 0.75 0.76

64Ch-3T 0.62 0.50

256Ch-3T 0.69

Beta

Split-half 0.82 0.88 0.86 0.81

64Ch-1.5T 0.81 0.80 0.74

64Ch-3T 0.84 0.65

256Ch-3T 0.66

Gamma

Split-half 0.80 0.86 0.65 0.75

64Ch-1.5T 0.79 0.66 0.70

64Ch-3T 0.71 0.66

256Ch-3T 0.61

Fig6. Subjectswererandomlysampledfromalldatasetstaking1…nsubjects (5000iterations)thenthecrossmodalcorrelationbetweentheEEGandfMRI connectomeofeachfrequencybandwascalculated,thecrossmodalcorrelation doesnotchangemorethan1%ofthemaximumvalueafteraveragingaround 7–12subjects(seeSITable2).FormaximumcorrelationseealsoFig2(all datasets).

thelowestcorrelationforthe64Ch-3Tand256Ch-3Tdataset(Fig7).In- dependentofthemagnitudeofthecrossmodalcorrelationofeachEEG frequencyband,thecrossmodalcorrelationwasobservedtobestable foreachdatasetwhenaveraging7–12subjects(stable=adding1sub- jectdidnotchangethecorrelationmorethan1%ofthetotalcrossmodal correlationwhenaveragingconnectomesover72subjects,Error!Ref- erencesourcenotfound.).Combiningalldatasettooneaveragecon- nectomemaximizedthecrossmodalrelationshipforbetweenFC_fMRIand FC_EEG-𝛿, FC_EEG-𝜃,FC_EEG-𝛼andFC_EEG-𝛽 butnotfor FC_EEG-𝛾. The64Ch- 1.5Tdatasethasanexceptionallyhighcrossmodalcorrelationbetween FC_EEG-𝛾 andFC_fMRI. Consequentlythecorrelationis notincreasedby takingtheaverageofall72subjects(andeffectclearlyobservedforall otherbandsFig2a:64Ch-1.5Tdataset:r(FC_EEG-𝛾,FC_fMRI)=0.38vs.all datasets:r(FC_EEG-𝛾,FC_fMRI)=0.39).

4. Discussion

This study showed that thecrossmodal correlation between EEG andfMRI connectivitycanbe simultaneouslyrecordedwithhighre- producibility from avariety of experimentalsetups anddesigns,no- tablydifferent MR magnetic fields andEEGelectrodeconfigurations (monomodalconnectivitycorrelationforEEGandfMRIr~0.5–0.9be- tweenalldatasetsandcrossmodal connectivitycorrelationr~0.3–0.4 acrossalldatasets).Ofspecialnote,wedemonstratedforthefirsttime thatconcurrentEEGandfMRIconnectomesderivedfrom7Tshowthe samemonomodalandcrossmodalcorrelationsascomparedtodatafrom 1.5Tand3T.FromanEEG-frequencypointofviewthecrossmodalcorre- lationwashighestforFC_fMRI-FC_EEG-𝛽,whilefromaspatialpointofview thevisualnetworkandhomotopicconnectionscontributedthemostto thecrossmodalcorrelation.Whenaveragingsubjectsacrossalldatasets, thecorrelationreachesastablevaluefrom7–12 subjects(seeFig6, SI Table2).Froma single-subjectpointof view,crossmodal correla- tionwas weak(r~0.12–0.2). Whencomparingthecorrelationtothe moreestablishedcrossmodalrelationshipsbetweenFC_fMRIandSC_dMRI wenotethatourcorrelationsbetweenEEGandfMRIhavethesameor- derofmagnitudeforbothgroupaveragedconnectomes(e.g.r=0.36 (Honeyetal.,2009),r=0.34(Goñietal., 2014))andsingle-subject connectomes(e.g.r=0.18(Skudlarskietal.,2008),r=0.19(vanden HeuvelMPetal.,2013)).

4.1. Frequencyspecificcontributions

When combining all datasets, FC_EEG-𝛼 and FC_EEG-𝛽 correlated best withFC_fMRI (r_{fMRI-EEG-𝛼}=0.41andr_{fMRI-EEG-𝛽}=0.43, averageover all datasets, see Fig 2a). The weaker crossmodal correlation be- tween FC_EEG-𝛾/FC_EEG-𝛿 andFC_fMRI is in linewith previous findings (Tewarieetal.,2016;Wirsichetal.,2017).Theseresultssuggestthat, besidesband-specificSNRobservedinourmonomodalinter-andintra- dataset topographical similarity (which would predict the strongest crossmodal couplingbetween FC_fMRI andFC_EEG-𝛼 insteadof FC_-EEG-𝛽 (Colclough et al., 2016; Marquetand et al., 2019)), frequency spe- cificFC_fMRI-FC_EEGcorrelationcanadvancethefunctionalunderstanding oflarge-scaleconnectivity.Specifically,theparticularlystrongtiebe- tweenFC_EEG-𝛼/FC_EEG-𝛽 andFC_fMRImaysuggestthatphasesynchrony

Table4

Averagecrossmodal correlation betweenEEG andfMRI connectomesof individuals acrosseachdatasetandfrequencyband(standarddeviationinbrackets).

64Ch-1.5T 64Ch-3T 256Ch-3T 64Ch-7T All datasets FC fMRI-FC EEG-𝛿 0.13 (0.05) 0.20 (0.05) 0.12 (0.04) 0.15 (0.04) 0.15 (0.06) FC fMRI-FC EEG-𝜃 0.12 (0.04) 0.19 (0.06) 0.13 (0.05) 0.16 (0.06) 0.15 (0.06) FC fMRI-FC EEG-𝛼 0.12 (0.05) 0.18 (0.06) 0.13 (0.05) 0.16 (0.05) 0.15 (0.06) FC fMRI-FC EEG-𝛽 0.16 (0.04) 0.22 (0.06) 0.14 (0.04) 0.17 (0.04) 0.17 (0.06) FC fMRI-FC EEG-𝛾 0.14 (0.05) 0.14 (0.06) 0.08 (0.05) 0.13 (0.04) 0.12 (0.06)