ContentslistsavailableatScienceDirect
NeuroImage
journalhomepage:www.elsevier.com/locate/neuroimage
Multi-band
MEG
signatures
of
BOLD
connectivity
reorganization
during
visuospatial
attention
Chiara
Favaretto
a,h,∗,
Sara
Spadone
b,
Carlo
Sestieri
b,
Viviana
Betti
c,d,
Angelo
Cenedese
e,
Stefania
Della
Penna
b,1,
Maurizio
Corbetta
a,f,g,h,1,∗a Department of Neuroscience and Padova Neuroscience Center, University of Padova, 35128 Padova, Italy
b Department of Neuroscience, Imaging and Clinical Sciences – and ITAB, Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
c Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy d IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
e Department of Information Engineering, University of Padova, 35131 Padova, Italy
f Department of Neurology, Radiology, Neuroscience, and Biomedical Engineering Washington University Saint Louis, MO 63110, USA g Venetian Institute of Molecular Medicine, VIMM, 35128 Padova, Italy
h Padova Neuroscience Center, PNC, 35131 Padova, Italy
a
r
t
i
c
l
e
i
n
f
o
Keywords: Functional connectivity MEG fMRI Band-Limited Power (BLP) Visuospatial attention, Principal components analysisa
b
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Thefunctionalarchitectureoftherestingbrain,asmeasuredwiththebloodoxygenationlevel-dependent func-tionalconnectivity(BOLD-FC),isslightlymodifiedduringtaskperformance.Inpreviouswork,wereported be-haviorallyrelevantBOLD-FCmodulationsbetweenvisualanddorsalattentionregionswhensubjectsperformed avisuospatialattentiontaskascomparedtocentralfixation(Spadoneetal.,2015).
Hereweusemagnetoencephalography(MEG)inthesamegroupofsubjectstoidentifythe electrophysiolog-icalcorrelatesoftheBOLD-FCmodulationfoundinourpreviouswork.WhileBOLD-FCtopography,separately atrestandduringvisualattention,correspondedtoneuromagneticBand-LimitedPower(BLP)correlationinthe alphaandbetabands(8–30Hz),BOLD-FCmodulationsevokedbyperformingthevisualattentiontask(Spadone etal.2015)didnotmatchanyspecificoscillatorybandBLPmodulation.Conversely,followingthe applica-tionofanorthogonalspatialdecompositionthatidentifiescommoninter-subjectco-variations,wefoundthat attention–restBOLD-FCmodulationswererecapitulatedbymulti-spectralBLP-FCcomponents.Notably, individ-ualvariabilityofalphaconnectivitybetweenFrontalEyeFieldsandvisualoccipitalregions,jointlywithdecreased interactionintheVisualnetwork,correlatedwithvisualdiscriminationaccuracy.Insummary,task-restBOLD connectivitymodulationsmatchmulti-spectralMEGBLPconnectivity.
1. Introduction
Intherestingstate,severalstudieshaveshownthatthetopography offMRInetworksisrecapitulatedbythetopographyofslow(<0.1Hz) coherentfluctuationsofband-limitedpower(BLP)(dePasqualeetal., 2010;dePasqualeetal.,2012) especiallyinthealphaandbeta fre-quencybands(Brookesetal.,2011b,2011a;dePasqualeetal.,2010; dePasqualeetal.,2012; Hippetal.,2012;Liuetal., 2010) or SNR-correctedmulti-bandpatterns(HippandSiegel,2015).Incontrast,the
Abbreviations:BLP,BandLimitedPower;FC,FunctionalConnectivity;RSN,Resting-stateNetwork;ROI,RegionofInterest;VIS,VisualNetwork;DAN,Dorsal AttentionNetwork;SPL,Superiorintraparietallobule;dFEF,dorsalaspectofthehumanfrontaleyefield;pIPS,Posteriorintraparietalsulcus;MT,Middletemporal visualarea;LFP,LocalFieldPotential;GLM,GeneralizedLinearModel;MNI,MontrealNeurologicalInstitute;ICA,IndependentComponentsAnalysis;PCA,Principal ComponentsAnalysis;PC,PrincipalComponent.
∗Correspondingauthors.
E-mailaddresses:chiara.favaretto.2@unipd.it(C.Favaretto),maurizio.corbetta@unipd.it(M.Corbetta).
1 Theseseniorauthorscontributedequallytothiswork.
literatureonthecomparisonofBOLDvs.MEGconnectivityduringtask conditionsisscant.Thisispossiblyduetothehighsimilarityofthetask andrest globalinteractionpatterns,asobserved,separately, infMRI (Coleetal.,2014;Smithetal.,2009;Spadoneetal.,2015;Tavoretal., 2016)andMEG(Bettietal.,2018,2013).
ThefewBOLD-MEGconnectivitycomparisonstudiesshowed sim-ilarfunctionalconnectivitytopographyforslowoscillations(<30Hz, see(Bettietal.,2013;Liljeströmetal.,2015)).Bettietal.(Bettietal., 2013)comparedfMRIandBLPconnectivityduringvisualfixation(rest)
https://doi.org/10.1016/j.neuroimage.2021.117781
Received30April2020;Receivedinrevisedform12January2021;Accepted15January2021 Availableonline23January2021
C. Favaretto, S. Spadone, C. Sestieri et al. NeuroImage 230 (2021) 117781 andtheobservationofamovie(task).Whiletheoveralltopographywas
maintained,thetask-inducedBOLDconnectivitydecreasesinmultiple networks,whichcorrespondedtoadecreaseofalphaandbetaBLP con-nectivity(<30 Hz).Incontrast,BLPconnectivityinotherfrequencies (theta,beta,andgamma)increased,correspondingtobothincreasesor decreasesofBOLDconnectivity.
Liljeströmetal.(Liljeströmetal.,2015)comparedfMRIandMEG networkswhensubjectsnamedeitheractionsorobjectspresentedona screenvs.whentheysimplysawthesamestimuli.AsBettietal.,they foundthegreatestsimilarityin MEGandfMRIderivednetworks for neuralfrequenciesbelow30Hz.However,BOLDnetworktaskvsrest modulationscouldnotbeattributedtoasinglefrequencyband.Instead, theentirespectralprofilewasthebestdescriptorofthecorrespondence betweenMEGandfMRInetworks.
In this study, we revisit the question of rest-task fMRI vs. MEG connectivity modulations using a well-controlled visuospatial atten-tiontask.Thisexperimentintroducesseveralinnovationsascompared toprevious studies. First, thetask is highlycontrolled psychophysi-cally,ascompared topreviousstudiesthatusednaturalistic stimula-tionwithnobehavioralcontrol(Bettietal.,2013)orcovertresponses (Liljeströmetal.,2015).Thisattentionparadigmhasbeenemployed inmultipleexperiments(Capotostoetal.,2015,2013;Shulmanetal., 2010,2009;Spadoneetal.,2015).Subjectseithermaintainvisual fixa-tion(rest)orperformavisuospatialattentiontaskinwhichthefocusof attentioniscovertlydirectedtoaleftorrightstimulusstreambasedon iso-luminantcolorcuesembeddedinthevisualstream.Thedetection oftargets,bykeypress,occurseitheronthesameoroppositesideof attention.Therelativeadvantageinaccuracyandreactiontimesfor at-tendedvs.unattendedtargetsisaprobeoftheallocationofvisuospatial attention.
Secondly,thefunctionalanatomyisknownascomparedtoprevious workinwhichtheregions ofinterestwerepredefinedbasedonprior parcellations(Bettietal.,2013)orsourcelocalizedMEGsignalsonthe sametask(Liljeströmetal.,2015).BasedonfMRIexperimentsranon thesamesubjects(Spadoneetal.2015),weknewthatarelativelysmall setofcorticalregionsweremodulatedbyattention.Theseincluded oc-cipitalvisualregions(VIS),sensitivetothelocation(left,right)of at-tention,andregionsof theso-calleddorsalattentionnetwork(DAN), sensitivetoshiftsofattention.Therefore,weusedsourcelocalizedMEG signalsfromthisspecificsetoffunctionalregions.
Thirdly,wehaveahypothesisonthedirectionofBOLDfunctional connectivity(BOLD-FC)modulationsinducedwhengoingfromvisual fixation(rest) tovisuospatialattention (task)(Spadoneet al.2015). BOLD-FCconnectivitynicelysegregatesDANandVISregionsboth dur-ingfixationandtask,butthetwonetworksincreasetheircorrelation duringthetask inparallel witharelativedecrementof connectivity withintheVISnetwork.
Theaimsofthestudyweretwofold:tocomparethepatternsof task-inducedmodulationoffMRIandMEGconnectivity,specifically within-vs.between-networkinteractions;and,toexaminewhetherthe corre-spondencebetweenBOLDandBLPconnectivitytopographyand rela-tivetask-modulation,is betteraccountedforbysinglevs. multi-band spectralpatterns.
2. MaterialsandMethods
2.1. Subjects
Twentyhealthysubjects(agerange=19–28yold;14females), right-handed(EdinburghInventory),participatedinboththefMRIandMEG scans.Theoriginalsamplesizeof(Spadoneetal.,2015)consistedof21 subjects,but1subjectwasexcludedduetotheincompatibilityofthe headsizewiththeMEGhelmet.Thesubjects,withnoprevious psychi-atricorneurologicalhistory,providedtheirinformedwrittenconsent accordingtotheCodeofEthicsoftheWorldMedicalAssociationand theInstitutionalReviewBoardandEthicsCommitteeattheUniversityof
Chieti.Thisgroupofsubjectswasenrolledthroughapreliminary behav-ioralsession,duringwhichtheyperformedacontinuousvisuospatial at-tentiontasksimilartothetaskoftheneuroimagingscans(seesubsection StimulationParadigms)toevaluateperformanceandeyepositionwith anIReye-trackingsystem(Iscanetl-400;RK-826PCI)(Spadoneetal., 2015).
2.2. StimulationParadigms
Pseudorandom sequences of continuous reorienting/maintenance stimuli(Capotostoetal.,2015, 2013),allgenerated usingthe MAT-LABPsychtoolbox-3(Brainard,1997;Kleineretal.,2007;Pelli,1997), weredeliveredtothesubjects.Stimuliconsistedoftwocontinuous drift-ingGaborpatches,3° diameter,2cycle/° spatialfrequency,0.7°/sdrift rate.Thetwopatcheswerepresentedontoascreen,overalightgrey background,atbothsidesofacentralfixationcrossataneccentricity of5.5°.DuringthefMRIstudy,stimuliwereprojected(usinganEIKI LCXG-250LProjectorSystem)onascreensituatedbehindthesubject’s headandviewedthroughamirrorlocatedabovethesubject’shead.For theMEGstudy,weusedanLCDprojector(NECMT830G+)placed out-sidetheshieldedroom,projectingimagesonapairof45° mirrorsand atranslucentscreen.Forbothprojectors:thehorizontalSyncwas au-tomaticallysetintherange15–100kHz;theverticalSyncintherange 50–100Hz.
Participants were instructed to maintain central fixation while covertlyattendingoneofthetwogratingstodetectbrieflypresented targets.Thesidetobeattendedwasindicatedbyaperipheralcue con-sistingof a300msisoluminantchangein thecolor(pinkandcyan) simultaneouslyappliedtothetwopatches.Onlyonecolor,indicated atthebeginningofeachblockandcounterbalancedacrossblocks,was relevantforcueing.Thecuecouldappearinthesamelocationasthe previous trial(staycue)ortheoppositelocation(shiftcue), indicat-ingthattheattentionhadtobeshifted.Thetargetsconsistedofbrief (150ms)changesofthepatchorientationineitherclockwiseor anti-clockwisedirection,whichshouldbesignaledbythesubjectsby press-ingtheright/leftbuttonwiththeirrightmiddleorindexfingerona re-sponsepad.Thecuecorrectlypredictedthelocationofthetargetwith 80%probability(validtrials),whereasthetargetappearedatthe un-cuedlocation(invalidtrials)in20%ofthetrials.Cuesappearedat ran-domintervalsbetween4and6s,intheMEGsession,andevery2,3,4 repetitiontimes(TR)withinatemporalwindowof+/-400mscentered ontheTR,inthefMRIsession.Aftereachcue,eitherzero,one,ortwo targetscouldbepresented.Cuesdidnotpredictwhenthetargetwould appear,buttheywerelinkedbythreetemporalconstraints:(i)targets couldnotoccurearlierthan1safteracue(ii)cuescouldnotoccur ear-lierthan2safteratarget(iii)targetsoccurred,onaverage,every11s intheMEGsessionandevery9sinfMRI(seeFig.1Aforanexampleof thedisplaysequence).Finally,wegeneratedtwopseudorandom stimu-lussequencesof~16minfortheMEGsession,eachconsistingofthree blocksofattentiontasklasting300sandinterleavedby15speriodsof fixation,usedtominimizefatigueanddropofattention.ForthefMRI session,wegenerated12pseudorandomsequences,eachlasting210s. 2.3. fMRIrecordings
ThefMRIhardwarespecificationsandacquisitionprotocolswere al-readyextensively described in(Spadoneetal., 2015).Briefly,BOLD contrastwasobtainedfromT2∗-weightedimagescollectedonaPhilips Achieva 3TScanner using a gradient echo-planar imagingsequence [TR =1.869 ms; Timeof Echo(TE) = 25 ms;39 slices acquiredin ascending interleaved order; voxel size= 3.59×3.59× 3.59mm3; 64 ×64 matrix;andflipangle=80°].Structuralimages,tobeused alsoforMEGprocessing,werecollectedusingasagittalM-PRAGE T1-weightedsequence(TR=8.14ms;TE=3.7ms;flipangle=8°;voxel size=1×1×1mm3).
Fig.1. ParadigmandinvolvedROIs.A)Exampleofthestimulusstreaminthevisuospatialattentiontask.B)RepresentationoftheROIsinvolvedduringthe performedtask.TheseROIswereselectedin(Spadoneetal.,2015),onthesamesubjectsscannedbyMEG,basedontheirfMRIresponseprofileduringtheattention task.Differentnetworksarecolor-coded(red:DorsalAttentionNetwork(DAN);yellow:VisualNetwork(VIS)).
ThefMRIsessionincluded3resting-stateruns(eachlasting5min) and12runsoftask.AnMRI-compatibleIReye-trackingsystem(Iscan etl-400;RK-826PCI)wasusedtocontroleyemovements.Subjects’ re-sponseswerecollectedthroughaLUMINALU400ResponsePad(Cedrus Corporation).
2.4. MEGRecordings
MEGscanswereobtainedthroughacustom-madewholehead sys-temdeveloped,installed,andoperatingattheUniversityofChietisince about2000(DellaPenna etal.,2000; Pizzellaetal.,2001).The sys-temwasimplemented incollaborationwiththecurrentATB Biomag UG.TheMEGsystemconsistsof153dcSQUIDmagnetometersplaced onahelmet-shapedsurfacecoveringthewholescalpandspacedabout 3.2cm.Thesensorsarehousedinsidealow-noisedewarwitha helmet-shapedbottom,atadistanceof1.8cmfromroomtemperature.MEG signals,togetherwithtwoECGandtwoEOGchannelstobeusedfor offlineartifactrejectionandformonitoringhorizontaleyemovements, wereband-passedat0.1–250Hzandsampledat1025Hz.Stimuliwere projectedontoascreensituatedinsidethemagneticallyshieldedroom. SubjectsrespondedusingaLUMINALU400ResponsePad(Cedrus Cor-poration).AMEGsessionconsistedof3runsoffixationlasting5min each,followedby2runsofthevisuospatialattentiontask.Aftereach run,thesubject’sheadpositionrelativetotheMEGsensorswas esti-matedfromthefieldproducedbyfivecoilsplacedonthescalp,whose positionsweredigitizedtogetherwith5anatomicallandmarks. 2.5. PreprocessingandFCEvaluation
2.5.1. fMRIDataAnalysis
Thepreprocessingprocedures, theROI identification, andtheFC evaluationareextensivelydescribedin(Spadoneetal.,2015)andin SupplementaryMaterialS1.1.Briefly,inourpreviousstudy,weaimedto identifyregionsshowingactivitymodulationduetothecuetype(shift, stay)orthecuelocation(left,right).Followingstandardpreprocessing, weemployedageneralizedlinearmodel(GLM)thatmadenoapriori assumptionofthehemodynamicresponseshape,bygeneratingseparate Δ-functionregressorsforeachofsevenMRframesstartingattheonset ofcuesandtargets.TheGLMused12typesofregressorsincludingfirst cues(left,right),standardcues(shiftleft,shiftright,stayleft,andstay right),targets(validleft,validright,invalidleft,andinvalidright),and additionalregressorscodingforbaselineandlineartrendineachscan.
SignificantROIswereidentifiedthroughavoxel-wiseANOVAwithcue type(stayandshift),cuelocation(leftandright),andtimeasfactorson thetimecoursesoftheevokedresponsestocuestimuli.Thevoxel-wise ANOVAswerecorrectedfornon-independenceoftimepointsby adjust-ingthedegreesoffreedomandcorrectedformultiplecomparisonsusing ajointz-score/clustersizethreshold(Formanetal.,1995) correspond-ingtoz=3.0andaclustersizeof13facecontiguousvoxels.A peak-searchalgorithmwasappliedtothecuetypebytime,andcuelocation bytimestatisticalmapsresultingintheselectionof6ROIsbelongingto theDorsalAttentionNetwork(DAN– thesuperiorparietallobule(SPL), thedorsalaspectofthehumanfrontaleyefields(dFEF),andthe pos-teriorintraparietalsulcus(pIPS),allbilateral),showingstronger shift-relatedthanstay-relatedactivity,and6ROIsoftheVisualNetwork(VIS – theventralV4-V8,thedorsalV3a-V7intheoccipitalcortexandthe lateralmiddletemporal(MT)visualarea,bilaterally),showingspatially selectiveeffects(contralateralstrongerthanipsilateralactivity).These ROIsareshownonanMNIbrainsurfaceinFig.1B.Followingremoval oftheBOLDresponseevokedbythecuesandtargets,andofthe sig-nal averagedoverthewholebrain,alow passfilterwithacutoff at 0.167Hzwasapplied.Staticconnectivitymatricesattaskandduring fixationwereestimatedthroughthez-Fishertransformofthepairwise Pearson’scorrelationcoefficientsaveragedacrossruns(ascomputedin (Spadoneetal.,2015)).
2.5.2. MEGDataAnalysis
Following thedata quality check of thechannel-level MEGdata (Larson-prioretal., 2013),2subjectswereexcludedduetothe pres-ence ofabnormal artifacts.Forsakeof datacomparison,thesame2 participantswereexcludedfromthefMRIanalysis,aswell.MEGdata wereanalyzedthroughthefollowingsteps(seeFigure2A):(i)apipeline basedonIndependentComponentAnalysis(ICA)followedbyan auto-maticclassificationprocedureaimedtoidentifythebrainandartifactual IndependentComponents(ICs)(dePasqualeetal.,2012;Mantinietal., 2011;Sebastianietal.,2014;Spadoneetal.,2012)wasappliedtothe channeldata(seeSupplementaryMaterialS1.2.1formoredetails).Then, (ii)thesensormapsofthebrainICwereprojectedinthesourcespace us-ingaweightedminimum-normleastsquare(wMNLS)estimator imple-mentedinCurry6.0(Neuroscan),usingarealisticmodelofthesource spaceandvolumeconductorobtainedfromthesubject’sanatomical im-ageandsampledbyaCartesian3Dgridwith4mmvoxelside(see Sup-plementaryMaterialS1.2.2formoredetails).Theindividualsourcemaps werethentransformedintoMNI152spacetoenablesubsequentspatial
C. Favaretto, S. Spadone, C. Sestieri et al. NeuroImage 230 (2021) 117781
Fig.2. Methods.A)ProcessingstrategystepstoobtaintheBLP-FCmatricesfromtheMEGrawdata.Afterpreprocessing,aimedatremovingartifactsandprojecting channel-levelsignalsintothesourcespace,MEGsourceactivityisfilteredinsixfrequencybandstoestimatetheBLPsignal.IndividualBLP-FCisestimatedthrough Pearson’scorrelationcoefficient(z-Fishertransformed).Finally,theindividualmatricesareaveragedacrosssubjectstogeneratethegroupBLP-FCforeachfrequency band.B)Flowchartofthemulti-bandcomparisonbetweentheBLP-andBOLD-FCmatrices.Individual,vectorizedFCmatrices(rest,taskortask–rest)areconcatenated (acrossfrequenciesandsubjectstogether,incaseofBLP-FC)andusedasinputforthePCA,whichdecomposestheoriginalmatricesintotheproductofthePCsand theweightsofeachPCacrosssubjects(andbands,incaseofBLP).Finally,theBLP-andBOLD-derivedPCsarecomparedusingMantel’sTests.
averagingacrosssubjects.Wedecidedtouseavolumegridbecausethe BOLD-FCdatafromSpadoneetal.,2015wereobtainedfroma3Dgrid intothe711-2Catlas,which wasthenco-registered toMNI, andwe wantedtouse thesametypeof sourcevolumetofacilitatethe com-parisonamongFCmatrices(e.g.wedidnotwanttointroduceany con-foundduetohandlingseedsintodifferentsourcespacesgridandcortical mantle).Moreover,coregistrationoftheindividualsourcespacetothe MNIspaceismorestraightforward,facilitatinggroupaverage.Atthis point,(iii)foreachvoxelcorrespondingtothepeakactivityofthe12 ROIsidentifiedfromfMRI(namelyseedfromnowon)wecorrectedthe leakageeffectsonthesourcespacemapsaccordingtotheGeometrical CorrectionScheme(Wensetal.,2015)appliedtothelinearinverse op-erator𝑊 =∑ic𝑨ic𝒖icasin(Bettietal.,2018;DellaPennaetal.,2019), where𝒖icistherowoftheunmixingmatrixassociatedwiththesource map𝑨ic foragenericindependentcomponentIC (seeSupplementary MaterialS1.2.3formoredetails).Foreachseed,(iv)thevoxelvector ac-tivitiesoftheotherseedswereestimatedasalinearcombinationofthe ICtimecourses,eachweightedbytherelatedleakage-correctedsource mapvalue.Foreachseedandvectorsignaldirectionthecontribution oftheevokedsignalwasadaptivelyremovedfrom eachcueand tar-getasin(DellaPennaetal.,2004)(seeSupplementaryMaterialS1.2.4 formoredetails).Then,(v)alltheleakage-correctedsignalstogether
withtheseeditselfwerefilteredindelta(𝛿)[1–4]Hz,theta(𝜃)[4–8] Hz,alpha (𝛼)[8–14] Hz,beta(𝛽)[14–30]Hz,low-gamma(𝛾l) [30– 50]Hzandhigh-gamma(𝛾h)[50–120]Hzbands.(vi)Foreachseed, asetofMEGBandLimitedPower(BLP)timeseriesatvoxelwere esti-matedbyaveraginginstantaneoussource-spacepoweroversliding win-dowsofduration=150msandstepof20ms.Finally,(vi)the individ-ual,leakage-correctedBLPFunctionalConnectivitymapswereobtained usingpair-wise Pearson’scorrelationcoefficient,followedbyz-Fisher transformation.Connectivityvaluesbetweenregionscloserthan3.5cm weremaskedtoaccountforpossiblemiscorrectioneffectsduetoseed mislocalization(Wensetal.,2015).
Eventually,thegroupFCateachfrequencybandwasobtainedby averagingindividualFCmatricesacrosssubjects.
2.6. ComparisonbetweenBOLD-andSingle-BandBLP-FCMatrices ThefirststepwastocomparetheBOLDandBLP-FCmatricesinthe singlerestortaskconditions.WefirsttestedRSNsegregationforeach modalityusingat-testcomparingwithinvs.betweenaverage correla-tion,averagingtheFCvaluesovereachsub-matrix(DAN,VIS,and DAN-VIS).ForBOLDFC,thistestwasalreadycarriedoutin(Spadoneetal., 2015),thuswerefertothemethodsandresultsreportedinthatpaper.
Thenwecomparedthepatternsobtainedbythetwomodalitiesthrough aregressionanalysisonallthepairwisecorrelationvaluesforeach sub-ject,wheretheBOLD- andsingle-bandBLP-FC datawereconsidered astheindependent,andthedependentvariables,respectivelyandwe testedthesignificanceoftheregressioncoefficientvs.0throughat-test oversubjects.
Secondly,wesearchedforBLPcorrelatesoftask-inducedBOLD-FC modulationsatthelevelofthewholematrixandsub-matrices,based onthecorrelationstrengthandcorrelationpattern.(i)Thefirsttypeof comparisonconsideredthesignpolarityofthetaskvs.restcorrelation differences.Giventhat(Spadoneetal.,2015)reportedin task-minus-restFCnoFCchangebetweenDANregions,anFCdecreasebetween VISregions,andanFCincreasebetweenVISandDANregions, respec-tively,welookedfortheirspectralsignatures.Henceforbothmodalities (andseparatelyforeachfrequencyband),themeanpairwise connectiv-ityovertask–restmatricesorsubmatriceswascomparedvs.0through a2-tailedt-testoversubjects(p<0.05,Bonferronicorrectedacross6 bands).Thesubmatricesincludedonlypairwisecorrelationsbetween re-gionsthatbelongedrespectivelytotheVISonly,DANonly,orVIS-DAN. (ii)Thesecondanalysislookedforthesimilarityoftask–restcorrelation patternsonthewholematrixandsub-matricesforeachband.Asinthe singlecondition,wefirstcomparedthewholematricesapplyingalinear regressionanalysisoverthenodepairsforeachsubject.Theregression coefficientsweretestedvs.0throughat-test,Bonferronicorrectedfor6 (thenumberoffrequency)multiplecomparisons.Thesamestrategywas appliedtosub-matrices,togetherwithadditionalanalysesdescribedin theSupplementaryMaterial(S1.3andS2.1).
2.7. ComparisonBetweenBOLD-andMulti-BandBLP-FCPrincipal Components
Inadditiontothestandardanalysesdescribedintheprevious subsec-tion,weinvestigatedwhethertheFCmatricescouldbedecomposedinto connectivitypatterns(components)thatwerecommonacrosssubjects. Thisstrategywasmotivatedbythehypothesisthatwhileinsingle con-ditionstheconnectivitypatternsweredominatedbythecontributionof alargerwithin-thanbetween-networkconnectivityasalreadyrevealed in(Spadoneetal.,2015),inthetaskvs.restdifferencealarger inter-subjectvariabilitymightemerge.Inthiscase,separationtechniquesare usefulforthefollowingreasons:(i)toextractinformationfromthedata; (ii)toselectcommon(acrosssubjects)spatialpatternsoftask-induced connectivitychangeswithouta-prioriassumptions;(iii)toreducethe numberofvariablestobecompared.Thus,weappliedaspatial sep-aration,through(not-centered)PrincipalComponentsAnalysis(PCA), toBOLD-andmultivariateBLP-FC,asshownschematicallyinFig.2B, forsinglecondition(restandtask),separately,andthedifferencetask vs.rest.WealsoappliedPCAtosinglebandBLP-FC,andadescription ofmethodsandresultsarereportedintheSupplementaryMaterial(S1.4 andS2.3).First,foreachcondition,individualBOLD-FC masked ma-triceswerevectorized(unrollingtheuppertriangularFCmatrix)and normalizedtothemaximumabsoluteFCvalue,toobtainaBOLD-FC vectorofdimension57foreachsubject(thisdimensionaccountsfor theexclusionofthesamepairswhichweremaskedintheBLPFC ma-trix).TheseindividualvectorswerearrangedinamatrixofFCs,with dimension57×18,where18wasthenumberofsubjects.Thespatial PCAdecomposedthismatrixintotheproductoftheBOLD-PCs(matrix dimensions57×NPC-BOLD)andtherelatedweightsofeachBOLD-PC acrosssubjects(matrixdimensionsNPC-BOLD×18).
Similarly, for each frequency band separately, the vectorized, masked,andnormalizedBLP-FC wasorganized intoaninputmatrix withdimension57×18.Then,allfrequencybandswerearrangedinto asingleinputmatrixwithdimension57×(18×6),wherethe band-specificvectorizedFCmatriceswereconcatenatedacrosssubjectsfor eachband.NotethatforBLP-FC,themaximumFCvaluehasbeen eval-uatedamongallthefrequencybands,topreservetheinter-bands rela-tionships.ThespatialPCAappliedtothismatrixproducedaBLP-PCs
matrix(matrixdimensions57×NPC-BLP)andaweightmatrixacross frequencybandsandsubjects(matrixdimensionsNPC-BLP×(18×6)). Foreachmodality,thenumberofPCstobeconsideredforthesimilarity analysiswasidentifiedapplyinganautomaticselectionmethod,defined astheelbowmethod,whichconsistsintheidentificationoftheelbow pointalongthescreeplotoftheexplainedvarianceofeachcomponent (Cattell,1966;Lattinetal.,2003).Variancevaluesbelowtheelboware consideredtoosmall,andtherelatedcomponentsarediscarded.
BOLD-PCsandmulti-bandBLP-PCswerecomparedthroughMantel’s test(MantelandHaenszel,1959),whichisusedtocalculatethe cor-relationsbetweencorrespondingpositionsoftwosimilaritysymmetric matricesderivedfrommultivariatedata.Specifically,thetestusesthe unfoldeduppertriangularpartofeachmatrixandevaluatesthe correla-tioncoefficientbetweenthesevectors.Mantelstatisticsweretestedfor significanceby15,000permutations,duringeachofwhichtherowsand columnsofeitheroneofthetwomatriceswerepermutedandthe Man-telstatisticwasrecomputedtodeterminetheexpecteddistributionof thestatisticunderthenullhypothesis.Finally,thevalueobtainedfrom theobserveddatawascomparedtothenulldistribution,toassessthe statisticalsignificance.
BeforecomparingtheobtainedPCsacrossmethods,weruna proce-duretoidentifythePCpolarity,whichisundeterminedinPCA. Specif-ically,eachBOLD-PCpolaritywasselectedtobepositivelycorrelated withthegroup-levelBOLDFCmatrix.ThepolarityoftheBLP-PCswas insteadsettoproduce,fortheBLP-PC,apositivemaximumcorrelation withtheBOLD-PCs.
TherobustnessofthePCtestwasanalyzedthroughaleave-one-out testdescribedinSupplementaryMaterials(S1.5andS2.4).
2.8. Task–RestMulti-BandBLP-FCComponentsandTargetDiscrimination Accuracy
Toexploretherelationshipbetweentask-inducedconnectivityBLP modulationandthebehavioralperformance,foreachtask–rest multi-bandBLP-PC,weevaluatedthesignificanceoftheassociatedoscillatory bands.First,weevaluatedthesubject-wiserelativeparticipationweight (inabsolutevalue)ofeachfrequencyband,normalizedtothe individ-ualweightsum.Inthismanner,foreachsubject,andeachBLP-PCwe obtained avectorof6components,indicatingthepercentage contri-bution(inabsolutevalue)ofeachfrequencyband.Theindividual tar-getdiscriminationaccuracywasbinarizedinhighandlowperformance levels concerning themeanaccuracyacross subjects. Foreach task– restBLP-PC,wetestedpossibledifferencesoftherelativeparticipation weightsacrossbandsusingatwo-waymultivariateANOVAacross sub-jects,withbandsasthewithin-subjectsfactorandbinaryperformance astheacross-subjectsfactor.
All BOLD andBLP averaged functionalconnectivity matrices, as well as BOLD and BLP-PCs (rest, task, and task–rest) with related participationweights,areavailablein“MendeleyData” (https://data. mendeley.com/).
3. Results
3.1. Single-bandComparisonbetweenfMRIandMEGConnectivity Matrices
ThegroupaverageBOLD-andBLP-FCmatricesatrestandtask,and theirdifference(task–rest)areshowninFig.3.Eachrowrepresentsan ROIoftheDAN,andthentheVISnetworkandthecolorscalerepresents thepairwisecorrelation(z-Fishertransformed).Blackenedcellsindicate regionpairscloserthan3.5cmandmaskedforfurtheranalysesbased ontheleakagecorrectionalgorithm(Wensetal.,2015)(seeMaterials andMethods-MEGdataanalysisandSupplementaryMaterialS1.2.3).
First,let’sfocusonseparaterestandtaskmatrices.BOLD-FCmatrices includebothpositiveandnegativeweights(i.e.,positiveandnegative correlation)whileBLP-matricescontainonlypositiveweights.Wethink
C. Favaretto, S. Spadone, C. Sestieri et al. NeuroImage 230 (2021) 117781
Fig.3.FunctionalConnectivityvalues.Top)AveragedBOLD-FCmatrices(gray-shadedarea)andaveragedBLP-FCmatricesforeachfrequencyband(fromdelta tohighgamma)forrest,task,andtask–restdifference.BlackareasindicatefunctionalconnectionsthatweremaskedoutfromtheMEGdataanalysisduetothe spatialclosenessofthecorrespondingROIpair.ThesamemaskisappliedtoBOLD-FCforcomparison.Whiletheaveragewasusedtodefinethesegroupconnectivity matrices,thefollowinganalysesarebasedonindividualdata.Bottom)ScatterplotoftheBLPfunctionalconnections(y-axis),separateforeachband,versusthe BOLDfunctionalconnections(x-axis)forrest(green),task(red),andtask–restdifference(yellow).EachcrossstandsforasinglecelloftheFCmatrix,centeredat theaverageacrosssubjects,andwiththearms’lengthequaltothestandarderroracrosssubjects.Ablackdottedlineindicatesthebisector.Greenandredcircles indicatesignificantregressionsinrestandtask,respectively.Intheseplots,itnotpossibletofindanyclearevidenceofalinearrelationshipbetweenBLP-FCand BOLD-FCvaluesfortask–restmatrices.
thisisduetothewhole-brainsignalregression,asteptakeninthefMRI preprocessingthatcentersthecorrelationvaluesaround0.Thisstep isnoteasilyimplementedintheMEGpreprocessing(seeDiscussion), butitwillbeessentiallydealtwithinthePCAanalysisdescribedinthe nextsection.Secondly,DANandVISnetworks areclearlysegregated inBOLD-FCmatrices,andthecorrelationbetweenregionsoftheDAN andtheVISnetworksignificantlyincreasesduringthetask(p<10−4; t-test),asshownin(Spadoneetal.,2015),whereallthestatistical anal-yseswerereported.IntheBLP-FCmatrices,wealsoobservenetwork segregation.In fact,within-networkBLPcorrelationis strongerthan between-networkBLPcorrelationatrestandtaskinseveralbands(rest: delta,theta, alpha,beta,lowgamma,allp<0.05;task:delta,theta, alpha,beta,low-gamma,high-gamma,allp<0.05;allpsBonferroni
corrected),asassessedthrought-tests.TheincreaseinDAN-VIS correla-tionisobservedonlyforhigh-gammaconnectivity,butthep-valuewas notsignificantanymoreafterBonferronicorrection(p=0.011,before correction).
ToanalyzetherelationshipbetweenFCpatternsatrestandduring task,atthegrouplevel,wearrangedtheFCvaluesintoscatterplotsand estimatedtheregressioncoefficients.Theseplotsareshownatthe bot-tomofFig.3,inwhichwedisplaythemeanandstandarderror(across subjects)oftheBOLD(x-axis)andBLP(y-axis)FCvaluesofeachnode pair,foreachbandandcondition(i.e.,restingreen,taskinred,and task–restinyellow).Focusingonthetaskandrestconditions,wefound significantregressioncoefficientsonlyinthealphaandbetabandsin bothtask (𝑏𝛼=0.17,p-alpha=0.038;𝑏𝛽=0.14,p-beta=0.0012, all
Fig.4. Comparisonbetweenmulti-bandBLP-andBOLD-PrincipalComponentsatrest(Top)andtask(Bottom).Left)BOLDPrincipalComponents(PC1andPC2). Middle)Multi-bandBLP-PCs.Right)Leftcolumn:scatterplotofBOLD-PC1(reddots)andBOLD-PC2(graydots)vs.BLP-PC1pairwiseregionalconnectivityvalues (p-valuesassessedthroughMantel’stest,Bonferronicorrected).NotethesignificantcorrelationbetweenBOLD-PC1andBLP-PC1.Rightcolumn:participationweights ofeachfrequencybandtotheBLP-PCs,evaluatedasthesumoftheabsoluteweightvaluesacrosssubjects.
Bonferronicorrected)andrest(𝑏𝛼=0.17,p-alpha=0.0024;𝑏𝛽=0.08,
p-beta=0.025,allBonferronicorrected).Therefore,theseresultsshow asignificantspatialsimilarityofgroupaverageBOLD-FCandBLP-FC to-pographyatrestandduringtasks,eventhoughtaskcorrelationvalues arelower.
Next,let’s consider thedifference BOLD-FC matrix(task–rest). A significantdecreaseofwithin-VIScorrelationwasparalleledbya cor-relationincrease between VISandDAN regions,asalready reported in(Spadoneetal.2015)).ThepictureforBLP-FCwasmorecomplex. Therewasanoveralldecreaseofconnectivityintwobands,assessed throught-test across subjectsof average couplingstrength vs. 0: al-pha(t-val =-3.90,p-val= 0.0016)andbetabands(t-val=-4.25, p-val=0.0005).Thereisalsoincreasedcorrelationinthehigh-gamma band(t-val =2.5536,p-val =0.02).Indelta,theta,andlow-gamma bands,bothincreasesanddecreaseswerefounddependingonthe spe-cificpairofregions.
However,atthelevelofasingleband,noneofthesespatial pat-ternswaslinearlyrelatedtothepatternoftask-evokedFCmodulation measuredinfMRI.ThisisconfirmedinthescatterplotsofFig.3bottom (yellowcrosses),wherenoregressioncoefficientwassignificantly differ-entfrom0(𝑏𝛿= −0.04;𝑏𝜃= −0.02;𝑏𝛼= 0.001;𝑏𝛽= 0.02; 𝑏𝛾𝑙= −0.04; 𝑏𝛾ℎ=−0.03;allps=n.s.).However,qualitatively,acrossbands,some
ofthesalientBOLD-FCmodulationsareevident.Forinstance, BOLD-FCdecrementsintheVISnetworkcorrespondtobothalpha-beta decre-mentsandtheta-gammaBLP-FCincreases.Similarly,DAN-VISBOLD-FC incrementscorrespondtobothalpha-betadecreases,andhighgamma BLP-FCincreases. Thissuggeststhatmulti-band modulationsmaybe importanttodescribetask-dependentchangesinconnectivity. 3.2. ComparisonbetweenBOLD-andMulti-BandBLP-FCComponents
GiventhelackofaclearBOLD-BLPFCcorrespondenceatthelevel ofgroup-averageddataandsingleBLPbandsandthepresenceofalarge commonsignalintheMEGBLPmatrices,weperformedananalysisto seeksimilarspatialcomponentpatternsacrosssubjectsandmodalities. Therefore,weranaseparatePCAonBOLDandsingle-bandBLP corre-lationmatricesandcomparedtheobtainedprincipalcomponents(PCs)
across modalities.Weappliedthisdata-drivenspatialpattern separa-tionbothinsingleconditions(restandtask)separately,andintask–rest data.ForbothfMRIandMEGdataandeverycondition,weselectedthe componentstoberetainedfortheanalysisthroughtheautomaticelbow method,toavoidanybias.
3.2.1. SingleConditionsComponents
Insingleconditions,theBLPconnectivitywasalmostcompletely de-scribedbythefirstBLP-PC,whichexplained95%and93.3%ofthetotal variance,atrestandtask,respectively.Thiscomponentmainlyloaded onalphaandbetabandsinbothconditions(Fig.4).Thisisconsistent, atleastforrest,withtheliterature(e.g.(Brookesetal.,2011b,2011a; dePasqualeetal.,2010),andourpreviousunivariateanalysis.
ForBOLDconnectivity,weidentified2componentsbothatrestand task,thefirstofwhichexplains68%(rest)and76%(task)of the to-talvariance,anditis aclearrepresentationoftheaverageBOLD-FC acrosssubjects.WhileBOLD-PC1reflectsthesegregationofRSN,with strongerwithin-thanbetween-networkcorrelation,BOLD-PC2reflects theenhancedintegrationbetweenVIS-DAN,andstronger communica-tionwithinVIS,whichisstrongerduringthevisuospatialattentiontask (seeFig.4).
ThespatialtopographyofBOLDandBLPcomponentswassimilar atrestandduringtasksconsistentlywiththepreviousanalysis. Specifi-cally,therewasasignificantcorrelationbetweenBOLD-PC1atrestand task(corr=0.95,p<10−7,Mantel’stest),andbetweenBLP-PC1atrest andtask(corr=0.93,p<10−7,Mantel’stest),suggestingthatthePC1 accountsforspatialpatternsthatdonotmodulateduringtheattentional task.Inaddition,wefoundthatBLP-PC1wassignificantlycorrelated withBOLD-PC1bothatrestandduringtask(rest:corr=0.40,p=0.016; task:corr=0.42,p=0.0011;allBonferronicorrected)(Figure4middle panel,scatterplot).BLP-PC1loadedonallbands,especiallyalphaand betabands(Fig.4right).However,thesimilaritybetweenBOLD-and BLP-PC1donotreflecttask-relatedchanges,butonlystronger within-thanbetween-networkinteractionsthatarepresentbothduringrestand taskconditions.
Insummary,thesefindingsshowthatbothBOLDandBLP connec-tivitypatternsarespatiallylowdimensional,andinthecaseoftheBLP
C. Favaretto, S. Spadone, C. Sestieri et al. NeuroImage 230 (2021) 117781
Fig.5. Comparisonbetweenmulti-bandBLP-andBOLD-task–restPrincipalComponents.Top)BOLDPrincipalComponents,sortedconcerningtheexplained vari-ance.PCssignificantlysimilar(p<0.05,Bonferronicorrected)tomulti-bandBLP-PCsareembeddedinblackandgraycontours.Middle)Multi-bandPCssorted concerningtheexplainedvariance.BLP-PCswithapatternsignificantlysimilartotheBOLD-PCsareembeddedinblackandgraycontours.Bottom-left)Pattern similaritybetweeneachpairofBLP-PCsandBOLD-PCs.Colorsrepresentthestrengthofthecorrelation,whilewhitefilleddotsindicatevaluesthataresignificantly differentfromzero(Mantel’sTest,p<0.05,Bonferronicorrected)andblackdotsmarksignificant,uncorrectedcomparisons.Bottom-right)Upper:Participation weightsofeachfrequencyband.Thesevaluesrepresentthesumoftheabsoluteweightvaluesacrosssubjects.Lower:Participationweightsofeachfrequencyband forthegroupsofsubjectswithlowandhighaccuracy,togetherwithstatisticalresults(theaveragealphaweightofsubjectswithhighaccuracyislargerthanallthe otherbandsinbothgroups,∗standsforp<0.0008).
involvemainlyalphaandbetabands.Thesepatternsaresimilarbetween restandtaskandcorrelatebetweenmodalities.Giventhepresenceof suchstrong correlatedspatial componentsacross multiplefrequency bandsisnotsurprisingthattask–restsignalsdonotcorrelatebetween BOLD-andBLP-FC,especiallyforsinglefrequencies (Fig.3bottom). Thisledustoexaminemulti-bandrelationshipsbetweenBOLDandBLP connectivity.
3.2.2. Task–RestComponents
Whenthedifferencebetweentaskandrestisconsidered,theelbow methodselected3PCsforfMRIand5PCsforBLP(seeFig.5,forPC
patterns; FigureS2,screeplotsofall separations).Resultsof control analysestotesttherobustnessoftheobtainedseparationarereported inSupplementaryMaterial(SectionS2.4).
ThefirstBOLD-PC(42% ofthetotalvariance) iscomposedof an increaseofDAN-VISinteractionsalongwithaparalleldecreaseof inter-actionsbetweenthevisualregions.BOLD-PC2(15%ofvariance)shows increasedconnectivitybetween theleftFEF,therightSPL,and,toa smallerextent,therightFEFregions(thelatteratasmallerextent)with everyotherregion,inparallelwithdecreasedconnectivitywithinthe vi-sualnetwork.BOLD-PC3(7%ofvariance)describesageneralincrease forallDAN-VISconnectivitypairs,exceptleftFEF,andaweakFC decre-mentinthevisualregions.
ThefirstBLP-PC,whichaccountsfor43.78%ofthetotalvariance, re-flectsthenon-spatiallyspecificmodulation,commontoeveryfrequency band,consistentwiththeglobalsignal.
The BLP multi-band patterns were compared to the BOLD-PC throughPearson’scorrelationcoefficient,whosesignificancewas eval-uatedbyaMantel’sTestwith15,000permutations.Thepolarityofthe BLP-PCswassettoproduce,foreachrowinthesimilaritymatrix (BLP-PC),apositive maximumcorrelationwiththeBOLD-PCs(seeFig.5, bottomleft).
Thestrongestpatternsimilarity(correlation=0.69,p<10−4, Bonfer-ronicorrected)occursbetweenBOLD-PC2andBLP-PC2(12.29% vari-ance)andcapturestheincreasedinteractionoftheleftFEFandright FEF-SPL(thelatteratalowerextent)withothernodesoftheDANand VISnetworks,aswellasthedecrementsofconnectivityintheVIS sys-tem.TheweightsofBLP-PC2loadsmainlyonthealphaband,as con-firmedbythemaineffectband(p<0.00001)inthemultivariateANOVA andsignificantpost-hoctests(p<0.0004).Aftersplittingsubjectsinto lowandhighperformersbasedonthemeantargetdiscrimination accu-racy,asignificantinteractionbandxperformance(p<0.003)showed thatalphaweightsweresignificantlylargerinhighperformersas com-paredtoallotherbands, aswell asalphaweightsin lowperformers (p<0.0008,Fig.5bottomright).
ThesecondstrongestpatternsimilarityregardsBOLD-PC3and BLP-PC5(correlation=0.52,p=0.003,Bonferronicorrected),withasimilar distributionofweightsacrossbands.
Inasupplementaryanalysis(seeSupplementaryMaterialS2.3.1),we alsocomparedsingle-vs.multi-bandBLPPCstoexplainBOLD-PC de-compositionusingtheBayesianInformationCriterion(BIC).Ingeneral, wefoundmulti-bandBLPcomponentstobeabetterdescriptorof task-relatedfMRIconnectivitychanges.Fig.6showsasummaryofBOLD-PC toBLP-PCcorrespondencewiththeirregionaltopographyandrelative weightsoftheconnectivity.BLP-PC2istheonerelatedtotheaccuracy duringthetaskforweightsonthealphaband.
4. Discussion
Thegoalofthisstudywastoidentifytheneurophysiological corre-latesoffunctionalconnectivitychanges,measuredwithfMRI (BOLD-FC) as observers go from a rest state (visualfixation) toa specific taskstateinvolvingtheallocationandshiftsofvisuospatialattention (Spadoneetal.,2015).Wefocusedonthestatictemporalcorrelationof amplitudefluctuationsofband-limitedpower(BLP-FC)measuredwith MEG,whichisconsideredthemostconsistentelectrophysiological cor-relateofBOLDRSNs.Weaskedifthesetask–restmodulationsoccurin specificfrequencybandsorinvolvejointparticipationofmultiplebands. Atthegrouplevel,wefoundanoveralldecreaseofBLP-FCacross twobands(alphaandbeta)andanincreaseinthegammaband(Fig.3, top).Whenseparatelyconsideringrestandtaskstates,wefounda sig-nificantcorrelationbetweenthetopographyoftheBOLD-FCandthat ofBLP-FCinthealphaandbetabands(Fig.3,bottom).Instead,no sig-nificanttopographycorrelationoccurredfortask–restFCmodulationin anyofthebands.Thisnegativeresultisprobablyduetothelow di-mensionalityoftheelectrophysiologicalpatternsobservedatrestand taskconditions, focusingonasinglecomponent,which loadsmostly inalphaandbetabands,andthatdoesnotchangemuchbetweenrest andtaskconditions(Fig.4).Thisdominantpatternaccounts(i)forthe largerwithin-thanbetween-networkconnectivity,incommonwiththe firstBOLD-FCpattern,and(ii)forthecommonsignalinMEG,spread acrossallfrequencybands(eventhoughmostlyinalphaandbeta).The subtractionoperationbetweentaskandrestconnectivityremovesthis commonelectrophysiologicalfactorandallowsfordifferentandhigher dimensionalpatternstoemergethatcannotberesolvedwitha single-bandapproach.
Inaccordancewiththat,wefoundasignificantsimilaritybetween task–restBOLDand multi-band BLPfunctionalconnectivity patterns afterorthogonalizationthroughaPCAprocedure.Twomainpatterns
Fig.6. SummaryoftheBOLD-BLP-PCscomparison.Spatialpatternsof connec-tivitymodulationsrepresentingtheBLP-PCscorrespondingtoBOLD-PC2and BOLD-PC3,allprojectedontotheMNIbrainrepresentation.Positiveand neg-ativevaluesarecodedinredandblue,respectively.Linethicknessrepresents thestrengthofthemodulation.
emerged:first,theinteractionofleftFEFandrightSPLwithother re-gions of theDAN andVIS along witha decrementof inter-regional VISconnectivity;second,theenhancedcorrelationbetweenDANand VISregions.Notably,thealphaweightofthefirstpatterndiffered be-tweensubjectswithlowandhighbehavioralperformance.These pat-ternsresemblethemainpatternsidentifiedinourpreviousfMRIstudy (Spadoneetal.,2015),andcorrespondtoabehaviorallyrelevant top-downinfluencefromtheDANtotheVISnetwork.
4.1. AssociationBetweenBLP-FCandBOLD-FCTask-InducedModulations Since theseminal workof Logothetis andcolleaguesin primates (Logothetisetal.,2001),BLPcorrelationshavebeenproposedasthe electrophysiologicalcorrelateoftheresting-statenetworks(RSNs) mea-suredthroughfMRI(Brookesetal.,2011b,2011a;dePasqualeetal., 2010; dePasqualeetal.,2012; Hackeretal., 2017;Heetal.,2008; Hippetal.,2012;Kelleretal.,2013;Leopoldetal.,2003;Mantinietal., 2007).MoststudieshaveemphasizedthatthesimilaritybetweenBOLD RSNsandBLPRSNsismostreliableinthealphaandbetabands.
Ourworkisconsistentwiththeseobservations.Wefindevenatthe levelofsinglesubjectsasignificantrelationshipbetweenthemagnitude oftheBOLDandBLPcorrelationinalphaandbetabands,atrest,and duringtheattentiontask(Fig.3).
However,evenearlystudiesshowedthateachfMRIRSNsis charac-terizedbyaspecificelectrophysiologicalsignaturethatinvolves
mul-C. Favaretto, S. Spadone, mul-C. Sestieri et al. NeuroImage 230 (2021) 117781 tiple brain rhythms (de Pasquale et al., 2010; Hipp et al., 2012;
Mantinietal.,2007).Moreover,alphaandbetarhythmshavethe high-estsignal-to-noise(SNR)in theMEGsignal,whichmayexplaintheir moreconsistentrelationshiptofMRIRSNs.WhenSNRdifferencesacross bandsarecorrected,thenBLPcorrelatesof fMRIRSNsextends from deltatogammaband(HippandSiegel,2015)(seealso(Garcésetal., 2016)).Accordingly,wefoundmulti-bandBLPmodulationswhen go-ingfrom resttoattention.Twoprincipalmodulationsoccurred:first, anoverallnot-spatially-specificmodulationofcorrelation across sev-eralbands, andsecond,coupledmodulationsof BLPacrossdifferent rhythmscorrespondingtospecificspatialpatternsofBOLD-FC modula-tions.
Theoverallchange ofBLPcorrelationacrossnodesandnetworks reflectsthemodulatedamplitudeoftheglobalsignalthatunderlies in-creasedvigilanceduringtheattentiontask(Liuetal.,2018;Taletal., 2013;Wongetal.,2013).Thiseffectisquitestrong,accountingformore than40%ofthevariabilityacrosssubjects, nodes,andbands. While therearedifferentstrategiestoremovetheglobalsignalfromfMRIdata (MurphyandFox,2017),awhole-brainsignalregressiononBLPdata isparticularlychallengingduetothedifficultytoaccountforpossible band-specificdifferencesinthespectralcontributiontotheglobalsignal. However,themulti-bandPCAapproacheffectivelyisolatescomponents representingmodulationoftheglobalsignalfromresttotask:although theparticipationweightofthealphabandtotheBLP-PC1providesthe largestcontributiontothisoverallmodulation,theweightsoftheother bandsarenotnegligible(Fig.5).Thus,multi-bandBLP-PC1might re-flectabroadbandelectrophysiologicalsignalrelatedtotheBOLD ongo-ingglobalsignal(WenandLiu,2016),whichwasinsteadremovedfrom fMRIdatabeforeestimatingtheFC.ChangesinMEGglobalsignalasa functionoftaskconditionsisstillarelativelyunexploredtopicworth futureinvestigationgiventherobustnessofthiseffect.
Thesecondeffectwasaspatiallyspecificmulti-bandmodulationof BLPcorrelationinvolvingnodesofthedorsalattentionnetwork(FEF, IPS,SPL)andnodesoftheVISnetwork.Therearethreemainelementsto thisinteractioninthefMRIdata:(1)anincreaseofcorrelationbetween leftFEFandrightSPL withallothernodesoftheDANandVIS;(2) adecreaseofcorrelationwithintheVIS;(3)anincreasedcorrelation betweenDANandVISnodes(Fig.4,5,6).Theseelementsarecaptured bytheBOLDPCA:PC1captures(2)(3);BOLDPC2captures(1)(2),and BOLDPC3captures(3).
BOLD-PC2(15%ofthetotalvariance)capturestheincreased interac-tionbetweenleftFEFandrightFEF-SPLwithothernodesoftheDANand VISnetworks,aswellasthedecrementsofconnectivityintheVIS sys-tem.BOLD-PC2showsthelargestpatternsimilaritywithBLP-PC2 load-ingmainlyinthealphaband.TheincreasedcorrelationbetweenFEF andotherregionsshowninBOLD-PC2andthecorrespondingBLP-PC2is consistentwiththeknownFEFroleinestablishingcue-related prepara-torysignals(CorbettaandShulman,2002)andexertingtop-down influ-enceonsensoryprocessing(Bressleretal.,2008;Heinenetal.,2017; Moore,1999),modulatingactivityandconnectivitywiththevisual oc-cipitalcortex.
Theweightsof BLP-PC2,especially in thealpha band,were pre-dictiveof accuracyacrosssubjects. Thisresultis consistentwiththe previouslyobservedpositive relationshipbetween accuracyand FEF-VISconnectivity changesduringtheattentiontask in Spadoneetal. (Spadoneetal.,2015).ThelinkbetweenalphabandBLPand perfor-manceisconsistentwithalargeliteratureonalphapowermodulationin attentionparadigms.Modulationofalphaactivityhastraditionallybeen associatedwithinhibitionofthenot-to-be-attendedside(Jensenetal., 2012)andanticipationofavisualtarget(BonnefondandJensen,2012; Jensenetal.,2012;Sausengetal.,2008;Thutetal.,2006).Whilethese paradigmshavereportedattention-relatedmodulationintheorderof hundredsofmillisecondstoseconds,time-lockedtospecificevents(cue ortarget),ourdataindicatemoresustainedmodulationsofalphaband connectivity(tensofseconds)relatedtoattentionperformance.Similar longertimesscalebehaviorallyrelevantmodulationscapturing
individ-ual variabilityhavebeenreportedinmanyfMRIconnectivitystudies (Rosenbergetal.,2020;Sestierietal.,2013;Spadoneetal.,2015).
Thesecond association involvedBOLD-PC3(7%of variance) and BLP-PC5.BOLD-PC3describesageneralincreaseofallDAN-VIS con-nectivitypairs,exceptforleftFEF,whichwasinsteadclearlyinvolved inPC2.Previousstudieshavealsoshownbroadbandmodulationsin at-tentiontasks(Siegeletal.,2008).
Howdoweaccountforthesemulti-bandpatternsoftask–rest mod-ulation?
Ononehand,thehigherSNRofthealphabandcomparedwiththe otherbands(HippandSiegel,2015)maybethereasonwhythe modu-lationinotherbandsdoesnotappeartobesignificantlyassociatedwith behavioralperformance.
Ontheotherhand,asecondexplanationisthatthemodulationof differentfrequenciescarriesdifferentkindsofinformation.Forinstance, (Magrietal.,2012)foundthatfluctuationsoftheBOLDsignalinthe vi-sualcortexco-variedwiththepowerofalpha,beta,andgammaLFP bands. However,alpha/gamma relationshipswereinformativeabout theamplitudeoftheBOLDsignal,whilebeta/gammainformedthe la-tencyofBOLD.
Ingeneral, consistentlywiththeseresults,we findthatdecreases in alpha/betabands were associated with increases in high gamma (Figure 3). It is also consistent with electrophysiological evidence that LFP power <20 Hz is important for distant BOLDconnectivity (Wangetal.,2012),andthatlowfrequenciesplayaroleinlarge-scale coordination(Bressleretal.,2008;SchroederandLakatos,2009). 4.2. MethodologicalConsiderationsontheSpatialSeparationofCommon Patterns
OurchoiceofapplyingthePrincipalComponentsAnalysistoseek fortheelectrophysiologicalcorrelatesoftheBOLDconnectivitychanges observedwhengoingfromfixationtoataskconditionwasbasedon sev-eralconsiderations.(i)Atfirst,wewantedtoextractinformationfrom thedata, takingintoaccount theacross-subjectsvariabilityobserved intask–restconnectivitymatrices.Weobservedthatthecoefficientof variation(CV)oftask–restconnectivityvaluesacrosssubjectswas sig-nificantlylargerthanchance,anddifferentthansingleconditions(rest, task)infMRI(seeSupplementaryMaterialS2.2).Thisobservation indi-catesthattheaveragetask–restBOLDFCmatrixrepresentsonlypartof theinformationcontainedinthedata.(ii)Moreover,wewantedtoselect common(acrosssubjects)spatialpatternsoftask-inducedconnectivity changesthroughadata-drivenapproach,wherenoinitialassumptions areconsidered. Thisdifferentiates, forinstance,fromtheapplication ofthegeneralizedlinearmodel(GLM),ahypothesis-drivenapproach, wherethesetofregressorsshouldbedesigneda-prioriandvalidatedby theresults.Inabsenceofanyinitialhypotheses,wethuspreferredto implementamoreflexibledata-driventechnique.(iii)Another impor-tantconsiderationregardsthevariablestobeconsideredforBOLD-FC andBLP-FCcomparison.Indeed,thePCAapplicationisasuitabletoolto reducethenumberofvariablestobecomparedbyconsideringonlythe patternsbyPCAratherthanallthepairwisecorrelationvalues.Finally, (iv)withthisspatialseparation,weaccountedforthehighpositive cor-relationsobservedinBLP-FCacrossregionsandbands.
Notably,thefirstthreemotivationsdonotexclusivelyapplyto BLP-FCdata,butalsoBOLD-FCdata.Importantly,spatialseparation tech-niques have been already applied to BOLD-FC in recent studies ex-ploitingtheinter-subjectsvariabilityofconnectivitythrougha relation-shipwithbehavioralmeasures(AmicoandGoñi,2018),ormultimodal (EEG-fMRI)topologicalsimilarity(Wirsichetal.,2020),eventhough thecommonBOLDsignalwas removedasinourstudy.Notably,we also appliedmultivariate PCAtosingle-condition connectivitymaps, which wasusedtoextractconnectivitypatternscommonacross sub-jects.Weindeedobtainedadominantpatterninthetwoconditionsand modalities(PC1).EventhoughBOLDandBLPPC1werecorrelated,this doesnotprovideinformationaboutBOLD-BLPassociationduring
task-relatedchanges,andthesingleconditionanalysisexplainswhystandard single-frequencycross-modalitycomparisonsoftaskvs.restcorrelation changesdidnotproducesignificantassociations(Fig.3).
ThemainlimitationofPCAis theabsenceofsuitablestatisticsto verifytheresults.Thisisthereasonwhywetestedtherobustnessofthe PCsthroughaleave-one-outtestoversubjects,describedinthe Supple-mentaryMaterial(SectionsS1.5andS2.4).
5. Conclusions
WefoundthatthespatialpatternsofBOLDconnectivitymodulation observedwhengoingfromresttoavisuospatialattentiontaskcannot bedirectlyassociatedwithsingle-bandBLPmodulations.Rather,BOLD componentsrepresentingcommonpatternsacrosssubjectscanbebest recapitulatedbymulti-bandcomponentsofMEGconnectivity. Specifi-cally,wefoundthatcross-networkincreasesbetweendorsalattention andvisualnetworks,andtheconcomitantdecreaseswithinthevisual networkareassociatedwithconcordantmodulationsinallthebands, withaprimaryroleofthealphaband.
DeclarationofCompetingInterest None
Creditauthorshipcontributionstatement
ChiaraFavaretto:Writing-originaldraft,Software,Formal analy-sis,Writing-review&editing.SaraSpadone:Datacuration, Investiga-tion.CarloSestieri:Investigation,Writing-review&editing.Viviana Betti:Writing-review&editing.AngeloCenedese:Writing-review &editing.StefaniaDellaPenna:Conceptualization,Writing-review &editing,Supervision.MaurizioCorbetta:Conceptualization,Writing -review&editing,Supervision.
Acknowledgements
CFissupportedbyFLAG-ERAJTC 2017andCARIPARO Founda-tion2019Padova: DarkEnergy; MCis supportedbyFLAG-ERAJTC 2017;DepartmentsofExcellenceItalinaMinistryofResearch(MIUR): NeuroDIP;CARIPAROFoundation2019Padova:DarkEnergy;Ministry ofHealthItaly:Brain-Connnetworkabnormalitiesinstroke;CELEGHIN FoundationPadova:Neuro-OncoPlan.CS,SDP andSS aresupported bythe“DepartmentsofExcellence2018-2022” initiativeoftheItalian MinistryofEducation,UniversityandResearchfortheDepartmentof Neuroscience,ImagingandClinicalSciences(DNISC)oftheUniversity ofChieti-Pescara.
Supplementarymaterials
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