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Dynamics of task-related electrophysiological networks:
a benchmarking study
Judie Tabbal, Aya Kabbara, Mohamad Khalil, Pascal Benquet, Mahmoud
Hassan
To cite this version:
Judie Tabbal, Aya Kabbara, Mohamad Khalil, Pascal Benquet, Mahmoud Hassan. Dynamics of
task-related electrophysiological networks: a benchmarking study. NeuroImage, Elsevier, 2021, 231,
pp.117829. �10.1016/j.neuroimage.2021.117829�. �hal-03190818�
ContentslistsavailableatScienceDirect
NeuroImage
journalhomepage:www.elsevier.com/locate/neuroimage
Dynamics
of
task-related
electrophysiological
networks:
a
benchmarking
study
Judie
Tabbal
a,b,∗,
Aya
Kabbara
a,
Mohamad
Khalil
b,c,
Pascal
Benquet
a,
Mahmoud
Hassan
da Univ Rennes, LTSI - U1099, F-35000 Rennes, France
b Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon c CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
d NeuroKyma, F-35000 Rennes, France
a
r
t
i
c
l
e
i
n
f
o
Keywords:
Magneto-encephalography (MEG) Electrophysiological brain networks Dynamic functional connectivity Dimensionality reduction Source separation
a
b
s
t
r
a
c
t
Motor,sensoryandcognitivefunctionsrelyondynamicreshapingoffunctionalbrainnetworks.Trackingthese rapidchangesiscrucialtounderstandinformationprocessinginthebrain,butchallengingduetothegreat varietyofdimensionalityreductionmethodsusedatthenetwork-levelandthelimitedevaluationstudies. Us-ingMagnetoencephalography(MEG)combinedwithSourceSeparation(SS)methods,wepresentanintegrated frameworktotrackfastdynamicsofelectrophysiologicalbrainnetworks.WeevaluatenineSSmethodsappliedto threeindependentMEGdatabases(N=95)duringmotorandmemorytasks.Wereportdifferencesbetweenthese methodsatthegroupandsubjectlevel.WeseektohelpresearchersinchoosingobjectivelytheappropriateSS methodwhentrackingfastreconfigurationoffunctionalbrainnetworks,duetoitsenormousbenefitsincognitive andclinicalneuroscience.
1. Introduction
Evolvingevidenceshowthatmotor,sensory,emotionaland cogni-tivefunctionsemergefromdynamicinteractionsbetweencorticaland subcorticalbrainstructures.Specificrhythmsofneuralnetworksallow synchronization andlong-rangecommunication between distant and distributedbrainareas.Thisphenomenawasshowncrucialduring vi-sual(BolaandSabel,2015;Hassanetal.,2015;Mheichetal.,2018), auditory(Fontolanetal., 2014),sensorimotor (Pomperet al., 2015; Wilkins andYao, 2020) andcognitive (Negrón-Oyarzo etal., 2018; Rouhinenetal.,2020)tasks. Thisbraincommunicationisvery tran-sientandthereisadynamicreorganizationoffunctionalbrainnetworks duringbehavioraltasks,evenatsub-secondtimescale(Vidaurreetal., 2018b).Therefore,theanalysisofwhole-braindynamicfunctional con-nectivity(dFC)hasbecomeaburgeoningfieldofresearchincognitive neuroscience(BassettandSporns,2017;Bullmore andSporns, 2009; Irajietal.,2020;Kabbaraetal.,2020).Inthisregard, Magneto/Electro-encephalography(MEG/EEG)provides a unique directand noninva-siveaccesstotheelectrophysiologicalactivityof thewhole brain,at the millisecond scale. Benefiting from the excellent time resolution of the MEG/EEG (~millisecond), current methods allow of estimat-ingsub-secondtime-varyingfunctionalbrainnetworks inthecortical space through sensor-level signals(Hassan et al., 2014; Hassan and
∗Correspondingauthor.
E-mailaddress:judytabal95@gmail.com(J.Tabbal).
Wendling,2018).Thekeychallengehere ishowtocharacterize and quantifytheserapidlychangingnetworks.
In this context, several frameworks have been used toexplicitly model/capture dynamics over time such as Hidden Markov Model (HMM)(Bakeretal.,2014;Vidaurreetal.,2018a,2018b,2016), Au-toregressivemodel(AR)(Casorsoetal.,2019)andGeneralLinearModel (GLM)(Friston,1994).Forexample,HMMdescribesthebrainactivity asasequenceof districtstates;each representsauniquepattern ob-tainedfromanobservationmodel,andastatetimecourseindicatingthe pointsintimeatwhichthatstateisactive.Otherapproachesanalyzethe timevaryingsignalusingdata-driventechniques,where‘brainnetwork states’arederiveddirectlyfrom thedatawithoutapriorihypothesis on thefittingmodel. Thesemethods haveshowedpromising results, despitethefactthattheselectionoftheusedalgorithmislargely em-pirical.Thesemethodsarebasedontwomainsteps:(1)slidingwindow approach,thatformsaseriesoftemporalnetworks,(2)adimensionality reductionorclusteringapproachincludingKmeans(Allenetal.,2014; Ciricetal.,2017;Duetal.,2016;Fongetal.,2019;LiuandDuyn,2013; Mheichetal.,2015;O’Neilletal.,2015),componentanalysissuchas temporalIndependentComponentAnalysistICA(O’Neilletal.,2017), PrincipalComponentAnalysis(PCA)(Leonardietal.,2013)and Non-negativeMatrixFactorization(NMF)(Chaietal.,2017).Althoughthe conceptualdifferencebetweenthesemethods(andwithineachfamily ofmethodssuchasdifferentICAalgorithms)istheoreticallyobvious(as
https://doi.org/10.1016/j.neuroimage.2021.117829
Received13November2020;Receivedinrevisedform25January2021;Accepted29January2021 Availableonline5February2021
theyarebasedondifferentassumptions),thestudiesthatinvestigatethe differencesbetweenthemremainedveryfew.Theexistingcomparative studiesaremainlylimitedtoconfirmingresultsofdifferencesbetween twoconditions(Leonardietal.,2013)ortoprovethatobtainedresults areunaffectedbythemethod’schoice(Milleretal.,2016).However,a throughoutquantitativeandqualitativecomparativestudyusingboth simulation-basedanddata-drivenapproachesisstillmissingandthere isnoclearconsensusaboutthe‘best’(ifany)sourceseparationor clus-teringmethodtobeusedtoadequatelytrackingdFC,whichisthemain objectiveofourstudy.
Here,weevaluatetheperformanceofnine dimensionality reduc-tionmethodsusedtotrackfunctionalconnectivitystatesatbothgroup andindividuallevels.This wasdoneusing simulationsandthree in-dependentMEGdatasets(N=95)recordedduringmotorandworking memorytasks(seeFig.1).Thedynamicbrainnetworks were recon-structedusingMEGsourceconnectivitymethodcombinedwitha slid-ingwindowtechnique.Thedimensionalityreductionalgorithmswere comparedintermsoftheirtemporalandspatialaccuracy.These meth-odsincludePCA,NMF,KmeansandsixvariousversionsofICA(Joint ApproximationDiagonalizationof Eigen-Matrices (JADE),INFOMAX, Second-OrderBlind Identification(SOBI), fixed-pointalgorithm (Fas-tICA),COM2andPenalizedSemi-AlgebraicUnitaryDeflation(P-SAUD). The motivationbehind using several ICA subtypes is that each one hasitsowndefinitionof statisticalindependenceandseveralstudies showedconceptualdifferencesbetweenthem(Kachenouraetal.,2008; Sahonero-AlvarezandCalderon,2017).Wealsoanalyzedtheoptimal numberofsubjectsneededforeachmethodtorevealsignificantresults. Thisstudyaimsatprovidingaframeworkforresearchersinterestedin studyingreconfigurationoffunctionalbrainnetworksduringcognitive processes.
2. Materialsandmethods 2.1. Data
2.1.1. Dataset1:‘self-pacedbuttonpresstask’
Thisdatasetincludes15healthyrighthandedparticipants(9male and6female,aged25±4years(mean±SD)).Theywereaskedtopress abuttonwiththeindexfingeroftheirnon-dominanthand,onceevery 30seconds,andshouldnotcountthetimebetweenpresses.Moredetails aboutthisdatasetcanbefoundin(Kabbaraetal.,2019;O’Neilletal., 2017).
2.1.2. Dataset2:‘HCPlefthandmovementTask’
61healthyparticipants(28maleand33female,aged22-35) com-pletedtheMEGMotortaskprovidedbytheHumanConnectomeProject (HCP) (MEG-1 release) (Van Essen et al., 2012). The correspond-ingexperimentalprotocolwas adaptedfrom Bucknerandcolleagues (Buckneretal.,2011;ThomasYeoetal.,2011).Itwasperformedin twosessionsof14mineach,withasmallbreakbetweenthem.Each sessionconsistedof42totalblocksrandomlydistributed;32ofthem werepartitionedinto16handmovementsblocks(8rightand8left), and16-footmovementsblocks(8rightand8left),andtheremaining 10blockswereinterleavedresting/fixationblocks.Eachmotoreffector blockwasprecededbya3secvisualcuethatpromptsparticipantsto eithertaptheirleftorrightindexandthumbfingersorsqueezetheir leftorrighttoes.Theblocklastedfor12secandconsistedof10 sequen-tialmovements,eachinitiatedwith150mspacingstimulifollowedby 1050msblackscreenfortaskexecution.Here,forsimplicity,wewere in-terestedinthetrialsrelatedtothelefthandmovesonly.MEGdatawas recordedatSaintLouisUniversityat508.6275Hzsamplingfrequency andco-registeredwiththeavailablesubjectspecificMRI.EMGactivity wasalsorecordedfromeachlimb.
2.1.3. Dataset3:‘sternbergworkingmemorytask’
19 healthyparticipants(10maleand9female, aged25±3years (mean±SD))performedSternbergtask,inwhichtwoexamplevisual stimuli,mainlyabstractgeometricshapes,weresuccessivelypresented onascreen;eachfor0.6secandseparatedby1sec.Then,amaintenance periodof7secwasleftbeforethepresentationofathirdprobe stimu-lus.Consequently,subjectswereaskedtopressabuttonwiththeirright indexfingeronlyiftheprobestimulusmatchedeitherofthetwo ex-amplestimuliandanimmediatefeedbackwillbegiventoshowtheir responsecorrectness.30trialswerepresentedseparatedby30secofrest. Inbothdatasets1and3,MEGdatawererecordedusinga275-channel CTFMEGsystemat600Hzsamplingfrequencyandco-registeredwith subject-specificMRI.BothdatasetswereapprovedbytheUniversityof NottinghamMedicalSchoolResearchEthicsCommittee(O’Neilletal., 2017;Vidaurreetal.,2018a).
2.2. Methodology 2.2.1. Preprocessing
Bothdatasets 1and3werereceivedalready preprocessedas de-scribed in (O’Neillet al., 2017).Briefly, bad segmentsproducedby muscles,eyeor headmovementwerealready visuallyinspectedand removed. Fordataset 2, we used the preprocessingpipeline offered bytheHCPconsortium,which includesremoving badchannels, seg-mentsandbadindependentcomponentsfromtaskdata.Segmentswere retrievedfromthedataset1intheinterval[-15;+15sec]relativeto thebuttonpressonset,andfromthedataset3intheintervalof[-16; +28sec]relativetostimuluspresentation.InHCPanalysis, wechose dataepochstime-lockedtoEMGonsetaswewereconcernedin explor-ingbrainnetworksinvolvedduringmovementexecution.Thus,trials weresegmentedin[-1.2;+1.2sec]relativetoEMGonset.Then,as func-tionalconnectivitywasprovedtobefrequency-dependent(Bakeretal., 2014;Hippetal.,2012),eachdatasetwaspreprocessedinits appro-priatefrequencybandactivelyinvolvedinthecorrespondingcognitive task.Whilebetaband[13-30Hz]wasusedforself-pacedandHCPleft handmotortask,workingmemorydatawasfilteredinabroaderband [4-30Hz]asitishasbeenshowntoinvolvemultiplefrequencybands, accordingtopreviousstudies(Brookesetal.,2012;O’Neilletal.,2017). Afterthesepreprocessingsteps,anaverageof34,150and29persubject werekeptfromdataset1,2and3,respectively.
2.2.2. Sourcereconstructionandfunctionalconnectivity
Inordertolocalizebrainsourcesandreconstructtheiractivities,we usedtheLinearlyConstrainedMinimumVarianceBeamforming(LCMV) (ROBINSON, 1999) approach on parcellated cortex using AAL atlas (N=78regionsofinterests-ROIs-(Gongetal.,2009))(Hillebrandetal., 2016).Thiswasdonebyregisteringeachsubject’sanatomicalMRIto anMNItemplate(Smithetal.,2004)followedbyaninverse registra-tiontotheanatomicalsubjectspace. Datacovariance wascomputed withinthespecificfrequencybandusedandatimewindowspanning thewholeexperiment(Brookesetal.,2008)witharegularization pa-rameter(5%)usingTikhonovmethod.Theforwardmodelwasbased uponadipoleapproximation(Sarvas,1987)andamultiplelocalsphere headmodelfittedtothesubject-specificMRIscalpsurface.Dipole ori-entationwasdetermined usinganon-linear searchforoptimum ‘sig-nal tonoiseratio’(SNR)(SekiharaandNagarajan,2008).Following this,weestimated thefunctionalconnectivity bycomputingthe am-plitude envelope correlations(usingHilberttransformation) between all ROIs(Brookeset al.,2012; Hippet al.,2012).In ordertoavoid spurious estimates of functionalconnectivity, we performed leakage correction on the reconstructed sources signals. We used the multi-variateapproach based onsymmetric orthogonalisationproposed by (Brookesetal.,2012;Colcloughetal.,2015)fordatasets1and3,while pair-wiseorthogonalization(Brookesetal.,2016;Tewarieetal.,2019b) wasappliedtodataset2duetotheshorttimeperiodofthetask.
Fig. 1. Illustration of the investigation structure for each of the three task-related paradigms. A. The fundamental processing pipeline applied on each subject data from sensor-level (using non-invasive MEG tech-nique) to cortical-level (using beamforming astheinverseproblemsolution) todynamic functional connectivity computation (S-dFC) (usingtheslidingwindowapproach).B. Con-catenationofS-dFCofallsubjectsalongtime axistoformagroupdatareferredtoasG-dFC, C.Comparativeanalysisbetweenninedifferent sourceseparation(SS)methods(sixvariantsof ICA,PCA,NMFandKmeans)appliedonboth group-level (Xgroup) and subject-level (Xsubj) datainordertoderivekdominanttask-related spatiotemporal components (the mixing ma-tricesrepresent brainspatialmapswhilethe extracted sources represent corresponding temporalweightsfluctuations).
2.2.3. Dynamicfunctionalconnectivityanalysis(dFC)
Toestimatethedynamicfunctionalbrainnetworks,weadoptedthe widelyusedapproachofslidingwindowsfordatasets1and3.Tothis end,atimewindowoflength6secwith0.5secwasusedfordatasets 1and3asappliedby(O’Neilletal.,2017).Concerningthedataset2 (HCPdataset),thefasttimescaleofthetaskimposesaverysmalltime windowwidththatmaybetoonoisytoextractmeaningful informa-tion(Liuzzietal.,2019).Thus,weavoidedtoapplytheslidingwindow approach,andusedinsteadthehightemporalresolutionversionof am-plitudeenvelopecorrelationmetric;the‘InstanteneousAmplitude Cor-relation’(IAC)alreadyvalidatedinarecentworkforthesamedataset
(Tewarieetal.,2019b).Asaresult,weobtained,foreachsubjecttrial,a ‘dynamicfunctionalconnectivity(dFC)’matrixofdimension[NxNxT], whereTreferstothenumberofwindowsfordatasets1and3,and num-beroftotaltimesamplesfordataset2(T=49,1221and75fordatasets 1,2and3respectively).Next,duetosymmetry,weunfoldedthismatrix intoa2-D[Nx(N−1)/2×T]matrixbyremovingtheredundant connec-tionsineachtimewindow.Then,themeanofeachrowofthismatrix issubtractedfromthedata.Finally,allsubjects’trialsdFCwere con-catenated alongthetemporaldimension.Wedefined thismatrixasa ‘Groupdynamicfunctionalconnectivitymatrix(G-dFC)’,denoted‘X’. Notethatfordataset2,weaveragedconnectivitymatricesofalltrials
relativetoeachsubject(Zhuetal.,2020)duetomemorylimitationin Matlabregardinghighdimensionaldataofthetemporallyconcatenated ‘sample-by-sample’dFCofallsubject’strials.
2.2.4. Task-relatedfunctionalbrainnetworks
2.2.4.1. Problem statement. The resultant G-dFC matrix representing thetime-varyingfeaturescanbeexpressedasasalinearmixtureof ele-mentarybrainnetworksthatfluctuatedynamicallyovertime.Suchissue isthemainconcernofSourceSeparation(SS)approachaimingat recov-ering‘k’hiddensourcesfromasetofobservationswithminimalpriori knowledgeaboutthesesources.Inthiscontext,theSSproblemcanbe formulatedasfollows:
X=A × S (1)
Where:
• ‘X’isthecomputedG-dFCmatrixofdimension[qxm]:
○ q=Nx(N−1)/2withN=78,representingconnectivitiesbetween allROIs.
○ m=TxNtotwithTisthenumberoftimewindowsandNtotisthe totalnumberoftrialsforallsubjects.
• ‘A’is the mixingmatrixof dimension [qxk]illustrating the con-tributionweightsofeachindividualconnectiontothecomponents sources,thusthespatialmapsofbrainnetworks(k<min(q,m)).
• ‘S’isthesourcesmatrixofdimension[kxm]representingtemporal sourcessignaturesofG-dFC,collapsedacrossallconnections. AmongexistingSSalgorithms,wechoseninepopular/well-known methods: six different variants of temporal Independent Component Analysis(tICA),PrincipalComponentAnalysis(PCA),Non-negative Ma-trix Factorization(NMF) andKmeans as a state-of-the-art clustering method.Theyalltransformthedesiredmatrixfactorizationintospatial mapsandtimeseries.However,theydifferprimarilyintheconstraints imposedondecomposed components.Below,wewillgiveasuccinct descriptionaboutthesemethods.
2.2.4.2. Independent component analysis: ‘temporal statistical indepen-dence’. ICAtendstolinearlytransformmultivariateobservationsinto asetof‘statisticallymutuallyindependent’latentvariablesunderthe hypothesisthatthesevariablesareas‘non-Gaussian’aspossible.Inour study,we examinetemporalICA (tICA)adoptedbyseveral previous studies(O’Neilletal.,2017;Yaesoubietal.,2015)inordertoobtain statesthatfluctuateindependentlyintime.Inthiscontext,decomposed signals‘S’consistofthe‘k’sourcetimecoursesandtheassociatedmixing matrix‘A’illustratesthecontributionoftemporallyindependentmaps. Thereareseveralcriteria tomeasure independencesuchas mini-mizationofmutualinformationandmaximizationofnon-Gaussianity. Hence,differentalgorithmsareproposedtoperformICAdecomposition, eachyieldingtodifferentICAmodelwithspecificcharacteristics.Here, weevaluatetICAusingsixdifferentpopularandprominentmethods: (1)JADE,(2)InfoMax,(3)SOBI,(4)FastICA,(5)CoM2and(6)PSAUD. Thesemethodsarechoseninsuchawaytocovervariousstatistical inde-pendencedefinitions,statisticalorderandcomputationalprocess tech-niques.Briefly,InfoMaxandFastICAarebasedoninformationtheory, whileallotherselectedmethodsoptimizecontrastfunctionsbasedon cumulantsofthedata.Amongthem,SOBIusesonlySecondOrder(SO) cumulantsincontrasttoothersthatexploitbothSOandFourthOrder (FO)cumulants.Inaddition,FastICAandPSAUDuseadeflationprocess fordecompositionwhileotherICAvariantsjointlyseparatesources. De-tailsaboutICAsubtypesusedcanbefoundinsupplementarymaterials. 2.2.4.3. Principalcomponentanalysis:‘variancemaximization’. PCAisa basiclineartechniquewidelyusedfordatadimensionalityreduction. Itinvolvesamathematicalprocedurethattransformsasetof observa-tionsofpossiblycorrelatedvariablesintosmallernumberoforthogonal, hencelinearly uncorrelatedvariablescalled principal componentsor ‘eigenvectors.Thisprocedureisdefinedinsuchawaythatthevariance
or‘eigenvalues’ofthedataismaximized.Then,afixednumber‘k’of eigenvectorsandtheirrespectiveeigenvaluescanbechosentoobtaina consistentrepresentationofthedata.Here,weapplytheSingularValue Decomposition(SVD)algorithmofPCA(GolubandReinsch,1970)on ourpredefinedinputmatrix‘X’.Defining‘A’and‘S’matricesfromSVD outputsismoreclarifiedinSupplementaryMaterials.
2.2.4.4. Non-negativematrixfactorization:‘positivity’. Nonnegative ma-trixfactorization(NMF)isanunsupervisedmachine-learningtechnique (LeeandSeung,1999)thatimposes‘non-negativity’constraintonthe decomposedfactorswhensolvingSSproblem.WhenappliedtoG-dFC data‘X’,NMFleadstoparts-basedrepresentationthatcapturesadditive combinationofbasissubgraphs‘A’ateachtimewindowwith tempo-ralcoefficients‘S’eliminatingnegativesignalvariations.Amongseveral existingNMFapproaches,weselectedAlternatingLeastSquares(ALS) algorithmthathaspreviouslyshowngoodperformanceinfMRIcontext (Dingetal.,2013)with100timesreplications.
2.2.4.5. Kmeansclustering:‘sparsity’. Kmeansisoneofthesimplest un-supervisedlearningalgorithmsthatsolvetheSSproblemthrough clus-teringapproach(Lloyd,1982).Thealgorithmworksiterativelytoassign eachpointtoonlyoneofthe‘k’groupsbasedonfeaturesimilarity. Math-ematicalcomputationofKmeansclustersisdefinedinSupplementary Materials.Inourframework,thesparsecodingadoptedbyKmeans re-strictsasingletimepointtohaveauniqueactivatednetworkstate.The computedclusters‘A’representsthestructureofcommonconnectivity patternsacrosssubjects.Foragiventrial,eachtimewindowisassigned withthecorrespondingclusterindex.Then,thematrix‘S’iscalculated asthefrequencyofreoccurrenceofeachclusterateachtimewindow acrossalltrialsandsubjects.Here,weadaptedthesameprocedureof KmeansusedbyAllenetal.(Allenetal.,2014):L1(Manhattan)distance isused,asitwassuggestedtobemoreeffectivethanL2(Euclidiean) dis-tanceforhigh-dimensionaldata(Aggarwaletal.,2001).Thealgorithm isreplicated100timestoincreasechancesofescapinglocalminima, andcentroidpositionswererandomlyinitialized.Then,Kmeansreturns thesolutionwiththelowest‘SUMD’(within-clusterSumsof points-to-centroidsDistances).
2.2.5. Comparativeanalysis 2.2.5.1. MEGgroup-levelanalysis.
2.2.5.1.1. Selectionofoptimalnumberofcomponents(NCopt). Inthe
contextofdimensionalityreductionmethods,thechoiceoftheoptimal numberofcomponents(NCopt)tobeextractedisstillachallengingissue.
Here,weusedthewell-knownapproach:‘Elbowcriterion’(Allenetal., 2014)forKmeansmethod(withmaximumnumberofclusters=10).For allotherSSmethods,weestimatedNCoptbasedonthegoodnessoffit
approach(TimmermanandKiers,2000;Wangetal.,2018)previously usedbymanyrecentworks(Tewarieetal.,2019b;Zhuetal.,2020). Weperformedthe‘DIFFIT’methodthatreferstothedifferenceindata fittingwitharangeofinputNCvariedfrom2(formotortasks)and4 (forworkingmemorytask)to10components,andselectedtheNCthat givesthelargestDIFFITvalueastheNCopt.Technicaldetailsaboutthese approachescanbefoundintheSupplementaryMaterials.
2.2.5.1.2. Selectionofsignificantcomponents. AmongtheNCopt
ex-tractedcomponents,identifyingthosethatreflectgenuinebrainactivity relatedtothetaskiscritical.Inthispaper,wefollowedatesting pro-cedure adoptedby(O’Neilletal., 2017)andpreviouslydescribed in (Huntetal.,2012;Winkleretal.,2014)todeterminesignificant com-ponentsmodulatedbythetasks.Thetestingreliesontheconstruction ofempiricalnulldistributionbasedona‘signflipping’permutation ap-proach.Therefore,acomponentwasconsideredsignificantif,atany timepoint,thecorrespondingtimesignal,averagedovertrials,fell out-sideathresholddefinedat0.05withcorrections.Foralldatasets, 2-taileddistributionwasallowed,andBonferronicorrectionswereapplied formultiplecomparisonsacrosstheNCoptcomponentsandacross
tem-poraldegreeoffreedom.Moredetailsabout‘sign-flipping’approachand thresholdvaluessettingcanbefoundinSupplementaryMaterials. 2.2.5.2. MEG subject-levelanalysis. Besides group-levelanalysis, itis crucialtotesttheperformanceofeachmethodwhenapplieddirectly onindividualdFC.Tothisend,insteadofconcatenatingtrialsfromall subjectsasinthefinalstepof‘G-dFC’computation,weperform,foreach subject,adFCconcatenationofalltrialsrelatedonlytothissubjectto formasubjectspecificdFC,denoted‘S-dFC’.Then,allselectedSS meth-odswereapplied on‘S-dFC’matrixtoextractsubject-specificspatial andtemporalsignatures(k=10).Inordertoquantitativelyevaluateand comparemethodsstrengthatsubject-levelcontext,wemeasure,foreach method,bothspatialandtemporalsimilaritiesbetweeneachextracted S-dFCcomponentandsignificantG-dFCcomponents.Theseparameters are:
(1)AverageDistance(AD)for‘spatialsimilarity’: 𝐴𝐷= ∑ 𝑠𝑑(𝑛𝑠,𝑛𝑔) 𝑁𝑠 𝑠∈ [ 1,𝑁𝑠];𝑔∈[1,𝑁𝐺] (2) Where𝑑(𝑛𝑠,𝑛𝑔)istheEuclidiandistancebetweenthenode𝑛𝑠ofS-dFC
networkandthenearestnode𝑛𝑔fromthesignificantG-dFCnetwork.𝑁𝑠
isthetotalnumberofnodesinS-dFCnetwork,and𝑁𝐺denotesthetotal
numberofnodesinG-dFCnetwork.Allnetworkswere70%thresholded. LowervaluesofADindicatestrongerspatialsimilaritybetweenS-dFC andG-dFCnetworks.
(2)CorrelationSignals(CS)for‘TemporalSimilarity’: 𝐶𝑆(𝑇𝑆,𝑇𝐺)= ∑ 𝑠∑𝑔(𝑇𝑆𝑠𝑔−𝑇𝑆)(𝑇𝐺𝑠𝑔−𝑇𝐺) √(∑ 𝑠∑𝑔(𝑇𝑆𝑠𝑔−𝑇𝑆) 2)(∑ 𝑠∑𝑔(𝑇𝐺𝑠𝑔−𝑇𝐺) 2) 𝑠∈[1,𝐿𝑠];𝑔∈[1,𝐿𝐺] (3)
Where𝑇𝑆 isthetemporalsignalof eachS-dFCcomponentof length 𝐿𝑠and𝑇𝐺 representstemporalsignalsofG-dFCsignificantcomponent
oflength𝐿𝐺.HighervaluesofCSrevealstrongertemporalsimilarity
betweenS-dFCandG-dFCsignals.
Weperformthisanalysisoneachsubjectamongthe15subjectsof theMEGdataset1(Motortask).Therefore,foreachmethod,wecounted thenumberofsubjectsthatshowsatisfactoryresultsperformanceinthe contextofS-dFC,basedonthepreviouslyexplainedmeasures.Then,to approximatethenumberofsubjects/trialsneededforeachSSmethodto givesignificantresults,wefollowthesameprocedureexplainedabove, butinsteadofsinglesubjectS-dFCcomputation,weincreasedthe num-berofconcatenatedsubjectsindFCcomputationfromNsubj=2to14,
progressively.Inordertohavegeneralizedandreliableresults,we con-sideredallpossiblecombinationsrelativetoeachNsubj(𝐶𝑁15𝑠𝑢𝑏𝑗),where
differentsetsofNsubjsubjectswereselectedamongthe15existingdata
subjects. 3. Results
In the following, we present our evaluation study on real MEG data, however our methodology was also tested on simulated data. Theseresultscanbefoundinthesupplementarymaterial.Briefly,the simulation-basedanalysisshowedthatallmethodsprovidesatisfactory resultsintermsofspatialandtemporalsimilaritybetweenreconstructed andsimulatedcomponentswiththebestperformanceforNMFmethod andthe worst for SOBI. All methods, except for FastICA, NMFand Kmeans,providedconsistentresults.PSAUDandPCAwerethefastest. ResultsrevealedthatSOBI,NMFandKmeansconvergemoreslowlythan otherswiththeincreasedvalueofSNR.Readercanreferto supplemen-tarymaterialtoseethedetailedquantitativeanalysisonsimulateddata. WefirstlyraneachalgorithmateachvalueofNCandcalculatedthe correspondingDIFFITvaluesinordertoselecttheoptimalnumberof components(NCopt)relativetotheseSSmethods.Resultsareshownin
Fig.2forthethree empiricaldatasets.Hereinafter,we setNCtothe computedNCoptvaluerelativetoeachmethodandtask.
ResultsofdifferentSSmethodsappliedonempiricaldataare illus-tratedinFigs.3–5.IneachFig.,wepresentedonlythecomponentsthat demonstratedsignificanttaskmodulationbasedontheappliednull dis-tributionapproach(describedin themethodssection).Thenetworks werethresholdedonlyforvisualizationpurpose(70%fordataset1and 3,85%fordataset2).Correspondingdynamicreconfigurationofeach significant networkwereplotted together.Thetemporalfluctuations representcomponenttimesignalsaveragedovertrialsandsubjects. 3.1. Self-pacedbuttonpresstask
Inthistask,participantswereaskedtopressabuttonwiththeindex oftheirnon-dominanthandevery30seconds.
Basedonliteraturefindings (seetableS1insupplementary mate-rials),wewereinterestedinquantifyingSSmethodsabilitytoextract asensorimotornetworkfromsignificantcomponents.Tothisend,we definedabrainnetworkwithactivatedAALregionsinbothmotor cor-tex(includingprecentral,paracentral,rolandicandsupplementary mo-torareas)andsomatosensorycortex(includingpostcentral,parietaland supramarginalareas)servingasamasktemplateforournetworkof in-terest(sensorimotornetwork),illustratedinFig.7.Then,weselected eachsignificantnetworkandcomputedthestrengthofeachactivated nodeinthatsignificantnetwork(definedasthesumofalledgesweights connectedtothatnode).TheratioofthestrengthofactivatedAALnodes thatbelongstosensorimotormaskiscalculatedrelativetothestrength of allactivatednodesinthatsignificantnetwork.Incasetheratiois greaterthanacertainthresholdvalue,thenetworkisconsideredasa sensorimotornetworkdenotedas‘mot’intheFig.3.Otherwise,the net-workisdenoted‘Aux’referringtoauxiliarynetwork.Aftermanytrials (threshold=0.5,0.6,0.7),thresholdvaluewassetto0.6asithasshown moreconvenientresults,whenvisuallyinspectingcomponents classifi-cation(falsepositiveandfalsenegative).Thereadercanreferto sup-plementarymaterials(Fig.S8)formoredetailsaboutthecomputation oftheratiovaluesforallcomponents.
Fig.3showsthatallSSmethodswereabletoextractatleastone sig-nificant‘Mot’network.Allsignificantcomponentsextractedfromthe fiveICAmethods(JADE,InfoMax,FastICA,CoM2andPSAUD),NMF and Kmeans methodswere categorizedas‘Mot’ networkdue to the strongparticipationofsensorimotornodesinthesenetworks (sensori-motorstrength ratio>0.6) althoughsome ofthemmayinvolve addi-tionalfewconnectionstootherregions.Ontheotherhand,SOBIand PCAmethodsshowed‘Aux’networks(sensorimotorstrengthratio<0.6) besides‘Mot’networks,withremarkableactivationsinfrontalregions. Temporalvariationwassimilarforalmostall significantcomponents overallmethodsshowingapeakvalueat0sec,thebuttonpresstime, withslightdifferencesinamplitudevalues,indicatingsignalintensities relativetoeachcomponent.Notethatnegativeconnectivity,referredto asblueconnectionsinspatialnetworksandnegativetemporalvaluesin temporalsignals,representsdesynchronizationbetweenbrainregions. Therefore,allstudiedSSmethodswereabletoextractatleastone sig-nificantcomponentthathighlightstrongconnectionsbetweensensory andmotorregionsmodulatedsignificantlybythetaskattheexact but-tonpressinstant(‘Mot’).
3.2. Left-handmovementtask
Thistaskisalsomotorbutdifferentthanthepreviousone.Herethe participantswereaskedtorapidlyandsuccessivelytaptheirleftindex andthumbfingers.Similarlytotheprevioustask,thesamesensorimotor maskwasappliedtoquantifyresultantnetworkstodiscriminate‘Mot’ from‘Aux’networks.Thereadercanrefertosupplementarymaterials (Fig.S9)formoredetailsaboutthecomputationoftheratiovaluesfor allcomponents.
Fig.2.OptimalNumberofComponents(NCopt)results.DIFFITvaluesareplottedagainstnumberofcomponent‘J’forallICAmethods,PCAandNMF.Theblueplot
correspondstotheself-pacedbuttonpresstask,theorangeplotforHCPleft-handmovementtaskandtheyellowoneforWorkingMemorytask.TheoptimalNCthat givesthehighestvalueofDIFITrelativetoeachtaskismarkedbyasmallcircleonthex-axis.ResultsofoptimalNCrelativetoKmeansusingtheelbowcriterionis alsoshown.(Forinterpretationofthereferencestocolourinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)
Fig.4showsthatnotallSSmethodswereabletoextracta sensori-motor‘Mot’network.Forexample,noneofthesignificantcomponents ofSOBI,PSAUDandKmeanshassurvivedthethresholdimposedforthe strengthofactivatedsensorimotornodes(Fig.S9)andaretherefore con-sideredas‘Aux’networksasindicatedinFig.4.Inthesemethods,‘Aux’ networksconsistofeitheravisualnetworksignificantlymodulated di-rectlyaftertheonsetofmovementinKmeansand0.35secbeforeonset inSOBIandPSAUD,oranetwork(oneinSOBIandPSAUDandsixin Kmeans)involvingstrongconnectionsbetweenalmostallbrainareas modulatedat0.2secbeforeandafteronset.Itshouldbenotedthat al-thoughthisnetworkshowsstrongactivationoftherightprecentraland postcentralnodes,itfailedtobequantifiedasasensorimotornetwork duetothehighcoverageofthebrain.
AllremainingSS methodswereabletoextract one‘Mot’network amongallsignificantcomponents.Thespatialrepresentationof‘mot’ networkinvolvessensorimotorwithsomecingulatenodesfromtheleft cortexinJADE, rightcortexinInfoMaxandbothleft andright cor-ticesinotherSSmethods.These‘Mot’networksshowsignificantdrop inconnectivityaround0.2secfollowingthemovementonset.Significant increasedmodulationwasalsoobservedat-0.2secinInfoMax,FastICA, CoM2,PCAandNMFmethods.Exacttimesofcomponentssignificance areindicatedwithstarsonFig.4.FromthisFig.,wecanseethat‘Aux’ networksshowvariousspatialpatternsbetweenSSmethods(suchasthe integrationofareasfromvisual,motor-frontal,motor-visual,temporal lobes…).
Regardingtemporalevolution,thehandmovementherearemuch morefrequentthantheprevioustask.Clearly,thefastneuralactivity duetotheshorttimebetweensuccessivebuttonpressesis expressed asanoscillatorybehaviourofbrainnetworkactivityaroundthezero timebuttonpress,asshowedalsopreviously(Vidaurreetal.,2018a).
Thisyieldstotheobtainedtemporalvariationwherethemotornetwork stateseemstohavehighconnectivitybeforebuttonpressandbeginto haveadrop-inconnectivitytoreachitssignificantpeakafter~0.2sec (referredtoasadesynchronizationinhighfrequencies(Vidaurreetal., 2018a)).
3.3. Workingmemorytask
Thistaskismuchmorecomplexcomparingtotheothertwotasks. Subjectsherewereaskedtovisualizeandmemorizetwovisualshapes andrespond toa thirdprobe stimulusbyabutton press(withtheir rightindexfinger)in caseofmatching.Theincreasedcognitiveload evoked by the Sternberg task is expected to induce variations in a greater numberofsignificantbrainnetworksincludingstimulus visu-alisation(visualnetwork),semanticprocessingandpatternrecognition (semantic,languagenetworks)andbuttonpressresponse(sensorimotor network).
Inasimilarcontextofprevioustasks,fourmasksweredefinedhere relatedtothemostrelevantworkingmemoryrelatednetworksfound in literature. These masks are also illustrated in Fig. 7. The visual mask consistsoftheactivationof primaryvisualcortex(occipital ar-eas,cuneus,calcarineandlingual)andisdenotedas‘Vis’intheFig.5. Thesemanticmaskinvolvesconnectionsbetweenbilateraltemporal (in-cludingfusiform,heschl,parahippocampal)andparietallobe (postcen-tral,supramarginal,angular,precuneus).Thelanguagemask(denoted ‘Lang’)isdefinedasaleftlateralisednetworkwithactivationofnodes fromtemporal,frontalandparietalregionsfromtheleftcortex.The sen-sorimotor mask(‘Mot’)ispreviouslydefinedinmotortasks.Detailed ratiovaluesofthefourmasksforallcomponentscanbefoundin de-tails insupplementary materials(Fig.S10).Inthistask,byapplying
Fig.3.Self-pacedmotortaskresults.SpatialandtemporaldistributionofallsignificantcomponentsderivedfromallcomparedSSmethodsappliedonG-dFCin theself-pacedmotortask(N=15subjects).Allbrainnetworkswerethresholdedforvisualization;lineswidthindicatesconnectivitystrengthbetweenregions.Red linesrepresentpositiveconnectivityvalueswhiletheblueonesrepresentnegativevalues.IntegratedAALnodesarerepresentedbyspheresofdifferentsizesthat revealconnectivityweights(strength)betweenthatregionandtherestofbrain.Correspondingtemporalevolutionisaveragedacrossalltrialsandsubjects.Time valuesonthex-axisrepresentthepositionoftheslidingwindow’scenter,relativetothebuttonpressatt=0sec(asillustratedbyaverticalline).Acolorcodeis attributedforeachcomponentinspaceandtime.ForeachSSmethod,onlysignificantcomponents(pcorrected<0.05)thatappearoutsidethe‘sign-flip’basednull
distribution(asdescribedinmethodssections)areshownhere.AllNCoptextractedcomponentswithcorrespondingnulldistributionareshowninSupplementary
Fig.S4foranexampleofICA-JADEmethod.NotethatsensorimotornetworkisclearlyactivatedatthebuttonpressinstantinallSSmethods.InthisFig.,‘Mot’ referstosensorimotornetworkand‘Aux’referstoallothers‘non-sensorimotor’networks.AninteractiveversionofICA-JADEresultscanbefoundonourgithub https://github.com/judytabbal/dynbrainSS.gitusingrotatableMATLABfigures.(Forinterpretationofthereferencestocolourinthisfigurelegend,thereaderis referredtothewebversionofthisarticle.)
simultaneouslyfourmasksonthesamenetworkcomponent,thereisa possibilitythattheratiostrengthofmorethanonemasksurvivesthe threshold(0.6).Inthiscase,thenetworkbelongstothemaskthatgives thehighestratiostrengthvalue.Incaseofequalitybetweentwomasks, weconsiderthatthenetworkbelongstobothmasks.
Fig.5illustratesthe spatialdistributionof all significant compo-nents for all SS methods, and temporalvariation for three of these methods(JADE,NMFandKmeans)forvisualisationclarity.The tem-poralevolutionofother SSmethodscan be foundinSupplementary Fig.S7.
Startingfromt=0sec,twovisualstimulus(shapes)werepresented successively,eachfor0.6sec.Duringthisperiod,allmethods,exceptfor PCA,wereabletoextractoneormoresignificant‘Vis’network.Wecan noticefewadditionalconnectionsfromoccipitaltoparietalortemporal regionsinthisnetwork.Timevariationofthisnetworkshowssignificant peakduringthefirsttwosecondsperiod.
Followingstimuluspresentation,subjectsshouldretaintheobserved shapesinworkingmemory.Duringthisperiod,knownasthe mainte-nancephase,allmethods,exceptKmeans,showsignificantdecreased modulationat[4-6sec]ofatleastone‘Sem’network.Thebreakdownin
Fig.4.HCPlefthandmovementstaskresults.SpatialandtemporaldistributionofallsignificantcomponentsderivedfromallcomparedSSmethodsappliedon G-dFCintheleft-handmotortask(N2=61subjects).Allbrainnetworksarethresholdedforvisualization.Timevaluesonthex-axisrepresentthepositionofthe slidingwindow’scenter,relativetothebuttonpressatt=0sec(asillustratedbyaverticalline).Acolorcodeisalsoattributedforeachcomponentinspaceandtime. ForeachSSmethod,onlysignificantcomponents(pcorrected<0.05)thatappearoutsidethenulldistributionareshownhere.Exacttimesofsignificancerelativeto eachcomponentareindicatedwithstars.,revealinganoscillatorytemporalactivationofmotorcomponentforallSSmethods.AllNCoptextractedcomponentswith
correspondingnulldistributionareshowninSupplementaryFig.S5foranexampleofICA-JADEmethod.InthisFig.,‘Mot’referstosensorimotornetworkand‘Aux’ referstoallothers‘non-sensorimotor’networks.AninteractiveversionofICA-JADEresultscanbefoundonourgithubhttps://github.com/judytabbal/dynbrainSS.git usingrotatableMATLABFigs..Reproducingtheseresultsisalsopossible/availableusingtheMATLABinterfaceongithub.
thisnetwork’sconnectivitywaspreviouslydemonstrated(O’Neilletal., 2017).Duringthesameperiod,wecannoticeadrop-inconnectivityin ‘Sens’networkrevealedonlybyNMFmethod.
At[10-12sec]period,manynetworksseemtobesignificantly mod-ulatedamongallmethods:(1)‘Vis’isre-activatedattheprobestimulus presentationinallmethodsexceptforPSAUD,(2)‘Sens’shows signifi-cantincreasewithFastICAmethod.Thisnetworkbecomesmoststrongly connectedaroundthetimebuttonpressresponse,(3)‘Lang’ is com-monlyderivedbyJADE,InfoMax,andPCA,andexhibitsanincreased connectivitypeakingduringprobepresentation.Wecannoticethatthis networkisalsosignificantlydecreasedinCoM2,PSAUDandKmeans around5sec.(4)Threemethods(JADE,InfoMaxandPCA)alsoshowed significantincreasedmodulationof‘Sem’network.(5)Anetworkthat belongsequallytoboth‘Sem’and‘Sens’masks(denotedas‘Sem+Sens’
network)isstronglyactivatedduringthisperiodinCoM2andPSAUD. Notethatthesetwomaskshavecommonbrainregionsmainlyinparietal loberesponsibleforsensoryprocessingwhichiscoherentwiththetask evolution.This‘Sem+Sens’networkisalsomodulatedinPCAmethod. Besidesthesecomponents,few‘Aux’networkswerealsoconsidered sig-nificantasshowninFig.5.
In summary,the three SS methods(CoM2,PSAUD andPCA) suc-ceededtoderiveallexpectedcomponentswiththeirappropriate tem-poral significant modulation. JADE and InfoMax were able to ex-tract visual, semantic and language but not the sensorimotor net-work. FastICA and NMF missed the language component. How-ever, SOBI was unable to show both sensorimotor and language networks and Kmeans failed to extract semantic and sensorimotor components.
Fig.5. Sternbergworkingmemorytaskresults.SpatialandtemporaldistributionofallsignificantcomponentsderivedfromallcomparedSSmethodsappliedon G-dFCintheworkingmemorytask(N3=19subjects).Allbrainnetworksarethresholdedforvisualization.Timevaluesonthex-axisrepresentthepositionofthe slidingwindow’scenter,relativetothefirstvisualstimuluspresentationatt=0sec(asillustratedbyaverticalline).Thefirsttwoverticallinesillustratetheinstant ofsuccessivevisualexamplespresentationatt=0sand1.6secandthethirdverticallineatt~9secseparatesbetweenthemaintenanceperiodthatlastsfor7secand theprobepresentationfollowedbyapossiblebuttonpressandfeedback.Acolorcodeisattributedforeachcomponentinspaceandtime.ForeachSSmethod, onlysignificantcomponents(pcorrected<0.05)thatappearoutsidethenulldistributionareshownhere.Exacttimesofsignificancerelativetoeachcomponentare
indicatedwithstars.TemporalvariationofonlyJADE,NMFandKmeansisillustrated,whereastherestareshowninSupplementaryFig.S7.AllNCoptextracted
componentswithcorrespondingnulldistributionareshowninSupplementaryFig.S6foranexampleofICA-JADEmethod.Notethatinthistask,muchlargervariety ofsignificantnetworksareextractedamongSSmethods,includingvisual,sensorimotor,language,semantic,andothernetworksatdifferenttemporalactivation.In thisFig.,‘Vis’referstoVisualnetwork,‘Sem’toSemantic,‘Sens’toSensorimotor,‘Lang’toLanguageand‘Aux’toothernetworks.AninteractiveversionofICA-JADE resultscanbefoundonourgithubhttps://github.com/judytabbal/dynbrainSS.gitusingrotatableMATLABfigures.
Fig.6. TypicalexampleofthespatiotemporalreconfigurationofbrainnetworksduringworkingmemorytaskusingICA-JADE.Allsignificantnetworksextracted fromJADEarecollectedandpresentedsequentiallyrelativetoeacheventandperiodtime.Thenominationandtheexacttemporalperiodofsignificantactivation ofeachnetworkisclearlyindicated.Correspondingcognitivefunctionsarealsospecified.Inthisfigure,‘Vis’referstoVisualnetwork,‘Sem’toSemantic,‘Sens’to Sensorimotor,‘Lang’toLanguageand‘Aux’toothernetworks.
Forthethreetasks,spatialandtemporaldistributionof theNCopt componentsderivedfromJADEmethod,withcorrespondingnull dis-tribution,canbefoundinSupplementaryFigs.S3,S4,S5.Forfurther clarityinvisualisationandinterpretation,weillustrated,inFig.6,the spatiotemporalreconfigurationofthefunctionalbrainnetworksas ob-tainedbyICA-JADE.
Furthermore,wediscriminateddifferentSSmethodsperformancein termsoftheactivationofrelevantbrainnetworksineachtask.For ex-ample,inmotortasks,wecalculatedthe‘Mot’networkoccupancy per-centagedefinedasthenumberof‘Mot’networks(quantitativelydefined bythemaskasexplainedabove)dividedbythetotalnumberof signif-icantcomponentsfoundinthecorrespondingSSmethod.Similarly,for workingmemorytask,theoccupancypercentageofvisual, semantic, language,sensorimotorandauxiliarynetworkswereevaluatedforeach method.ResultsareshowninFig.7withthespatialrepresentationof thecorrespondingrelevantbrainregions.Therefore,Fig.7resumesthe overallperformanceofeachSSmethodshowingvariabilityinthe meth-ods’abilitytodirectlyextracttheappropriatetask-relatedcomponents. 3.4. PerformanceofeachSSmethodsatsubject-level
Hereourobjectiveistoevaluatetheperformanceofthemethods atthesubject-level.Wetesti)thecapacityofeachmethodtoextract significantcomponentsrelatedtothetask:todoso,wecomputedthe correlationbetweenthecomponentsobtainedbyeachmethodoneach subjectwiththesignificantnetworkobtained atthegroupleveland ii)thenumberofsubjectsneededforeachmethodtodetect‘expected’ networks:herewetestedtheoverallperformanceofeachmethodby increasingthenumberofsubjects,goingfrom1to15asweperformed subject-levelanalysisontheself-paceddata.Fig.8.Asummarizesthe subject-levelanalysisscenario.Foreachmethod,wechoseoneofthe significantmotorcomponentsderivedfromthedecomposition ofthe
group-level(N=15subjects),mostlytheoneshowinglittleintervention fromregionsotherthansensorimotor(‘Mot’)andhavinghightemporal coefficientsamplitude(supposedtobethebestforeachmethod).This componentillustratesa‘group’motornetworkwithtemporal modula-tionatthebuttonpresstime.Itwill,eventually,serveasa‘mas’ compo-nentforsubject-levelanalysis,asweareconcernedinmotorcomponent extraction.
ForeachSS method,NC=10 componentswerederivedfromeach subjectdata.Then,AverageDistance(AD)andCorrelationSignals(CS) betweeneachofthesecomponentsandthe‘group’motorcomponent rel-ativetotheSSmethodwerecomputedinordertoquantifytheabilityof themethodtoextract,fromasinglesubject,atask-relatedcomponentin space(motornetwork)andtime(temporalmodulationatbuttonpress time)respectively.Followingthis,onlyoneofthese10componentsis selectedforresultscalculation.Thisselectionisbasedontwoconditions criteriaonADandCSvalues.InthecasewhereADcomponentisless thanathreshold(setastheaverageofADvaluesofallcomponentsfor allsubjectsandSSmethods),andCSishigherthan0.7(chosenasa trade-off betweenmoderateandhighcorrelation),thenthecomponent isconsideredtobeamotorcomponent.Bysettingthesethresholds,we consideredtheexistenceofinter-subjectdifferences,thus,allowing sub-jectstohavedifferentbutnearspatialdistributionofmotornetwork. Therefore,ifatleastoneoftheextractedcomponentspassthese condi-tions,correspondingADandCSvaluesaredenotedandthenumberof subjectsthatgivesimilarresultstogroup-levelisraisedby1.Otherwise, weselectedcomponent𝑖asthenearestcomponenttothegroup-level re-sult,withacompromisebetweenspatialandtemporalsimilarities.
AtypicalexampleisillustratedinFig.8.AshowingthatPCA decom-positionwasabletoextractamotorcomponentfromsubject2 (com-ponent5),whereasnomotorcomponentwasderivedfromsubject14, ADandCSvaluesofcomponent7weredenotedinthiscase.Spatialand
Fig.7. SSmethodsperformanceevaluationforrealMEGtasks.Foreachtask,brainregionsinvolvedineachrelevantnetworkareillustratedontheleftsideusing theAALatlas,whilebrainnetworksoccupancyareshownontherightside.Theoccupancypercentagerepresentsthepresencepercentageofthesedefinedbrain networksrelativetoallsignificantextractedcomponents.Formotortasks,motornetwork(‘Mot’)wasemphasizedwithauxiliary(‘Aux’)networksrelativetoall significantcomponents,whilevisual(‘Vis’),semantic(‘Sem’),language(‘Lang’),sensorimotor(‘Sens’)and‘Sem+Sens’networksarehighlightedincontrasttoother auxiliary(‘Aux’)networks.Referringtotheserepresentations,capabilitiesofdifferentSSmethodsinextractingrelevanttask-relatedcomponentscanbeevaluated.
temporaldistributionsof selectedsubjects’componentsin bothcases areshowninFig.8.Resultsoftheremainingcomponentsforthese2 subjects’examplesareshowninSupplementaryFig.S11.
Asaresult,twoparameterswerecollectedandrepresentedinFig.8.B and8.C respectively. Group-subject similaritypercentage was calcu-latedasthenumberofsubjectsthatgivesamotorcomponentsimilarto thegroup-levelresultrelativetothetotalnumberofsubjects(N=15). Fig.8.BillustratesthisparameterforallSSmethods.Wecanseethat JADEwasabletoextract atask-relatedcomponentfrom8outof15 subjects(53.33%),InfoMaxandPSAUDfrom7subjects(46.67%),SOBI andCoM2from6(40%),FastICAfrom5(33%),PCAandNMFfrom 4(26.67%)andKmeansfrom3(20%).TheFig.8.Cshowsthe distri-butionsofADandCSvaluesofselectedcomponentsfromeachsubject
overallSSmethods.Methodswithhighersubject-groupsimilarity per-centagehavelowermedianvaluesofADandhighermedianvaluesof CS.Inaddition,wecannoticefromADandCSmedianvaluesthat sim-ilarityinspacewasmucheasiertobesatisfiedthantemporalsimilarity formostSSmethods.Interquartilerangevaluesshowtheexistenceof inter-subjectvariabilityresults.However,somemethodsshowedhigher interquartilerangeofADvalues(CoM2andPCA),orCSvalues(JADE, CoM2andNMF)relativetoothermethods.
3.5. TheoptimalnumberofsubjectsofeachSSmethod
Then,thesameprocedurewasappliedwithincreasingthenumberof subjectsfromonesubject(single-subject)to14subjects.ADandCSare
Fig.8. Subject-levelanalysisandresultsrelativetotheself-pacedmotorexperiment.A.descriptionofsubjectcomponentselectionprocedurebasedonAverage Distance(AD)andCorrelationSignals(CS)valuesbetweensubject’scomponentandthegroupmotorcomponent,whenSSmethodsareappliedonS-dFCdata. GroupmotorcomponentisshownforPCAexample.ADandCSvaluescomputedforallcomponentsarepresentedfortwosubjects.BasedonADandCScondition limitshighlightedineachpolarbar,successandfailureinextractingmotorcomponentarebothillustratedbysubjects2and14respectively.Spatialandtemporal distributionwithcorrespondingADandCSvaluesforallcomponentsofbothsubjects2and14areillustratedinSupplementaryFig.S11.Asmalltableontheright illustratesresultsofsuccessandfailureforallsubjectsinPCA.B.ResultsofthenumberofsubjectsthatsuccessfullyextractedamotorcomponentineachSSmethod relativetothetotalsubject’snumber,denotedGroup-SubjectSimilarity,areshown.C.distributionsofADandCSvaluesofallselectedcomponentsforthe15subjects areillustrated.D.GeneralizationstudywithincreasingnumberofsubjectsandthecorrespondingresultsofGroup-Subjectsimilaritypercentage(whenconsidering allpossiblecombinations).E.Greyshadedvalues(5,6,10and14)representthecriticalnumberofsubjectsrequiredforeachSSmethodtohavea90%precisionin extractingthetaskrelatedcomponent.
computedforallpossiblecombinations.Thenumberofpossible com-binationsiscalculated.Forexample,7-subjectsanalysisrequires6435 combinations,hence6435valuesof ADandCS.Foreach numberof subjects,wecalculatedsubjecttogroupsimilarityastheratiobetween numberofcombinationsthatsucceededinextractingamotor compo-nentrelativetothetotalnumberofpossiblecombinations.Resultsare illustratedinFig.8.E,F.Asexpected,thepercentagesimilarityincreases withincreasingnumberofsubjectsforallSSmethods.Fluctuationsin similarityresultsareobservedinsomemethods(asFastICA,NMFand Kmeans)duetothenon-consistencycharacteristicofthesemethods(as previouslyproved).Somemethodsrequiredasmallernumberof sub-jectsfordataanalysistoprovidesatisfactoryresults(motorcomponent atbuttonpresstimeinourcase)thanothers.Forexample,thefourICA versions(JADE,InfoMax,SOBIandPSAUD)required5subjectsinorder toattainaminimumsimilaritylevelof90%betweensubjectandgroup levelresults.CoM2andPCArequired6subjects,whilemuchmore sub-jectswereneededforothers(10subjectsforFastICAandKmeansand 14forNMF)asshowedinFig.8.E.Overall,ICAmethodsandspecially thosebasedonthehighorderstatistics(suchasJADE)outperformother methodsinextractingnetworksatthesubject-level.
4. Discussion
Inthisstudy,wehaveevaluatedtherobustnessofthemost popu-larSSmethodsappliedtoextractthemainbrainnetworksfluctuating duringtimeinordertohelpresearchersmakearationalchoice(ifany) amongthemultitudeofavailablemethods.Specifically,ninealgorithms havebeencomparedusingsimulateddata(seesupplementary materi-als)andthreeindependentMEGdatasets(N=95)recordedduringmotor andmemorytasks.Thediscrepancyinthedatasetssizeandbehavioral tasksperformed allowstesting SS methodsperformance ondifferent scenarios.Astheevokedresponses(analyzedhere)last forhundreds ofmilliseconds,weconductedourcomparativeanalysisbasedonMEG datasetstobenefitfromtheexcellenttemporalresolutionofthis tech-nique.However,thesamepipelinestudycanbeappliedintask-related fMRIcontext.
Overall,ourresultsshowvariabilitybetweentheevaluatedSS meth-odsandevenbetweenICAsubtypes.Theperformanceofthesemethods dependsonthenatureofthetask(simplevscomplex,slowvsfasttime scaletasks).Inasimpleandrelativelyslowtimescaletask(asself-paced buttonpresstask),allmethodssucceededintrackingspatiallyand tem-porallythedynamicbrainactivity.However,whenitcomestomuch fastertask(HCPmotortask)ormorecomplextask(WorkingMemory), somemethods(SOBIandKmeansforinstance)showedlower perfor-manceinextractingrelevantbrainnetworks(asdefinedbyourmasks). Resultsrelativetoeachtaskwillbediscussedlaterindetails.
First,thequantitativecomparisonperformedonsimulateddynamic networksshowedthatallSSmethodshavesuccessfullyseparated func-tionalnetworksbasedontheirconnectivitytimecourses.However, spa-tialandtemporalsimilaritiesinSOBIweresignificantlylowerthanother SSmethods,especiallyforthefourthsimulatedstate(P4)andthe sec-ondstate(P2)asshowninSupplementaryFig.S3,whichinvolvesmore complexspatiotemporalactivitythanotherstates.Asexpected,FastICA, NMFandKmeansmethodswereprovedinconsistentwithmultipleruns. Thisiscausedbythenatureofthesealgorithmsthatisbasedonrandom inputinitialisationsuntilsolutionconvergence.Thenoiseeffectonthe resultsobtainedwasalsotestedandshowedanincreasedperformance forallmethodswithhigherSNRvaluewithslowerconvergencetothe optimalaccuracyforsomemethods(SOBI,NMFandKmeans)relative toothers.Regardingcomputationtime,CoM2,PCAandPSAUDwere thefastestwhereasInfoMaxandJADEweretheslowest.Still,the ex-ecutedtimeofthese algorithmsissensibletodataset’sfeatures(size, complexity,type…).Othermetricssuchasthenumberoffloating-point operations(FLOPs)requiredforthealgorithmcompletioncouldbealso tested.Wealsosuggestforfuturestudiestoexploreotherdata simula-tionapproachesthatbuildthedesiredground-truthbrainstatesbased
onmorerealisticmodeling(usingNeuralMassforinstance),however, thismayintroducetheeffectofotherparametersinthecomparison (for-wardproblem,inversesolution…).Besidessimulationapproach,some studiesattempt toconsiderfMRIdata asagroundtruth toquantify andcompareSSmethodsperformanceinthecontextofM/EEG stud-ies(Colcloughetal.,2016;Jetal.,2020).
The method’s performance were evaluated on three real MEG datasetsalreadypublishedandtestedbypreviousstudies(Casorsoetal., 2019;O’Neilletal.,2017;Tewarieetal.,2019a;Vidaurreetal.,2018a; Zhuetal.,2020).Accordingtoself-pacedmotortask,resultsshowed thatallSSmethodshavesuccessfullyextractedoneormoresignificant networkthatinvolvestrongconnectivitybetweensensorimotorregions (‘Mot’). ForHCP data, a similar sensorimotornetwork wasrevealed amongsignificantcomponentsinallSSmethodsexceptforSOBI,PSAUD andKmeans.Integratedregionsinthisnetworkmainlyincludenodes fromcentralandparietalgyrus.Thesensorimotornetworkisstrongly coherentwiththetask(Melniketal.,2017;Yousry,1997)sinceit re-quiresbothmovement(throughbuttonpressorhandmovement)and tactileresponse(asthesubjectwillfeelthebuttonorfingerstape).The effectofright-handednessofallparticipantsofself-paceddatasetisalso revealed bythepresencestrongerimplication ofsensorimotor nodes fromtherightcortexrelativetotheleftoneasrevealedbythesphere sizesandconnectionsinFig.3.Itisnoteworthytomentiontheexistence ofanetworkthathighlightedsignificantconnectionsinthevisuallobe inJADEforself-pacedbuttonpresstaskandmostSSmethodsforHCP task.ThisnetworkwaspreviouslynoticedbyOneilletal.studyingthe samebuttonpresstask(O’Neilletal.,2017).Thiscanbeinterpretedasa crossmodalsynchronizationbetweenvisualandsensorimotorcortexas previouslystudied(Baueretal.,2020).Regardingtemporalevolution, itisclearthatallnetworksmodulatesignificantlywiththeexactbutton presstimeforself-pacedtask.Differently,thetemporalvariationrelated toHCPmotortasktakesanoscillatoryshape,whichwasalsoreported byotherstudiesdealingwiththesamedataset(Vidaurreetal.,2018a; Zhuetal.,2020).Apossiblereasonforthisactivitywassuggestedby (Vidaurreetal.,2018a)consideringaleakageeffectoftemporalactivity ofpreviousbuttonpressintothenexttrialduetothefastsuccessive tri-als.Inbothtasks,thereexistsauxiliary‘Aux’networksthatsignificantly modulatedwiththetaskbutnotdirectlyrelatedtothemotorcortex ac-tivity.Theoccupancypercentageof‘Aux’networksincreasesforallSS methodsinHCPresultsmainlyinCoM2andKmeans.Thepresenceof thesenetworkscanberelatedeithertotherobustness/sensitivityofthe SSmethodrelativetospuriousnetworksortothereliabilityofthe tech-niquesusedforselectionofoptimalnumberofcomponents(DIFFIT)or significantcomponents(nulldistribution)thatwillbefurtherdiscussed later.
InordertoevaluatethespatialandtemporalaccuracyofSS meth-odsathigherlevelsofcomplexity,wetestedthemethodsonSternberg workingmemorytask.AllSSmethodsdetectedvisualnetwork,which is consistent withthe presentationof visual stimuliattwo different times.Regionsintheprimaryvisualloberelatedtostimulus visualisa-tion(Grill-Spectoretal.,1998)andlateraloccipitalcortexresponsible forobject/shaperecognition(Corbettaetal.,1991;Grill-Spectoretal., 2001; KourtziandKanwisher,2001)werepresentinthese networks. Thebuttonpressresponseisreflectedbysensorimotorconnections con-sistentlywithpreviousworkingmemorystudies(Metzaketal.,2011; Yamashitaetal.,2015)onlybyusingCoM2,PSAUDandPCAmethods. Inordertoprocessandmaintainobservedstimuliasawayto memo-rizethem,ahigherlevelofcognitionisillustratedbya‘semantic net-work’,whichmainlyencompassesbilateralparietalandtemporalareas activationinallSSmethods,exceptforKmeans.Thisiscoherentwith previousstudiesthatdemonstratetheevidentroleofparietalcortexas aworkspacefor sensoryandperceptualprocessingin working mem-oryframework(Chaietal.,2018)throughangular(Frackowiak,1992; Vandenbergheetal.,1996),precuneus(CavannaandTrimble,2006), and hippocampal (Baddeley et al., 2011) areas. Bilateral inferior temporal regions also play important role in semantic processing
(Nestoretal.,2006;Vigneauetal.,2006).Fusiformgyri,strongly mod-ulatedinourresults,hasalsoshownaparticularconcerninthis con-text(Mion etal., 2010). The detectionof the‘language’ networkby JADE,InfoMax,CoM2,PSAUD,PCAandKmeansmethods,was compat-iblewithpreviousfindings(Brookesetal.,2011b;O’Neilletal.,2017). Temporalandparietallobeswereremarkablyactivatedbythese meth-ods,mainlytheparahippocampalandsupramarginalgyrirespectively. Theseregionsarecriticalinmemoryencodingandretrievaland seman-ticcognition(Axmacheretal.,2008;Caminitietal.,2015;Dembetal., 1995; Derrfusset al.,2004; Deschamps etal., 2014; Vigneau et al., 2006).Inasimilar (abstractshapebased)workingmemorytask,the interpretation ofthis networkwas relatedtoaverbalisationnaming strategyemployedbyparticipantsasawaytoaidinmemoryencoding (Caminiti etal.,2015;O’Neilletal.,2017).Therefore,thisnetwork’s activationmaybepossiblewiththetaskasitmodulatesstronglywith theprobepresentationandresponsetime.
Itisimportanttopointhere thattheresultantnetworkswere de-noted objectivelyin this studyusing a quantification approach. For a better interpretation of the functional significance of results, we built template brain masks, referring to the literature (see table S1 insupplementarymaterials),fromAAL corticalregions.These masks areused for seedingour networks of interest: ‘Mot’ in motor tasks, ‘Vis’/’Sem’/’Lang’/’Sens’inworkingmemorytask.Networkswerethen classifiedbasedontheiractivatednodesstrengthrelativetoeachmask. Inaddition,theusedmaskshavedistinctspatialdistribution,withsome sharedregions mainlybetween ‘Sem’and‘Sens’networks.However, whenweaimtodeeplyinvestigatetask-relatedsub-networks,the‘mask’ techniqueseemstohavemuchmorecomplexityrelatedtothespecificity oftheintegratedbrainregionsandtheprecisionofanaccuratethreshold inordertoappropriatelyclassifynetworksresults.
Althoughweperformedourstudyoncognitivetasks,itisatopicof greatinteresttoapplythismethodologypipelineonresting-state exper-imentssincemanystudieshaveshownthedynamicreconfigurationof thebrainduringrestaswell(Kabbaraetal.,2017;Liégeoisetal.,2019). Regardingmethodologicalconsiderations,first,theoptimalnumber ofcomponentstobederivedwasstillachallengingquestionforallSS methodsratherthanadirectlimitationofouralgorithms.Inthisstudy, weappliedtwowell-knownalgorithms(DIFFITandelbowcriterion)for arangeofnumberofcomponentstoautomaticallyselecttheoptimal numberofcomponentsrelativetoeachSSmethod.Theevaluatedrange ofNCvalues wasupperlimitedby10 componentsin ordertoavoid spuriousnetworks.Moreover,wetriedtofixthenumberofcomponents to10forallSSmethodsintheself-pacedmotortaskatthegroup-level, asalreadysetinapreviouswork(O’Neilletal.,2017)thatusedFastICA algorithmandlittledifferencewasobservedfortheoverallresults.
Second,weshouldpointthatnotallthesecomponentsare neces-sarilyessential,especiallyinthecaseofsimpletasksasmotortasks.To thisend,wefollowedtheapproachofthenulldistributionbasedonsign flippingalgorithm(Huntetal.,2012;O’Neilletal.,2017;Winkleretal., 2014;Zhuetal.,2020)toselectonlycomponentswhosetemporal dy-namicssignificantlymodulatewiththetask.Inthisway,weensurethat braindynamicsrelativetothestudiedbehaviouraltaskscanbe summa-rizedanddescribedbytheretainedcomponentsthroughanautomatic waythatallowsustoobjectivelycomparetheSSmethodsperformance. Inaddition,thefactthatthistechniqueisapurelydata-driven proce-durethatdoesnotrequireanypriorhypothesisorconditions manip-ulationmakesitlikelyadaptedtothespecificexamineddataset.Two pointsregardingsignificantcomponentsselectionareimportantto men-tionhere.First,intheappliednulldistribution,networksweredefined tobesignificantiftheyfelloutsidethenulldistributionineither posi-tiveornegativesidesbecausetheyreflecttrial-onset-lockedthateither increasesordecreasesinconnectivityacrosssubjects(asamplitude en-velopecorrelationwasadopted).Second,itshouldbenotedthat com-ponentssignificancewasevaluatedrelativetothespecifictaskduration. Forexample,temporaldurationoftheentireanalysisforself-pacedand workingmemorytaskscanincludedynamicsthatshouldbeexcluded
fromtheanalysis.Inthiscontext,welimitedoursignificance interpreta-tion/assessmentintheintervalof[-2;+2sec]and[-0.5;+0.5sec]relative tothebuttonpressinstantinthecaseofself-pacedandHCPmotortasks respectively,and[-2;+16sec]relativetothevisualstimuluspresentation formemorytask.Inaddition,fewlimitationsaretobediscussedwhen dealingwiththisselection.First,itwasnotconvenienttorelyonthis techniquewhenthenumberoftrialsandsubjectswaseithertoosmall ortoobig.Asmallnumberwillnotallowtobuildareliablenull distri-butionwhileahugeonewillhaveitscomputationalcostregardingall possiblesubjects’combinationsforsign-flippingprocedure,asalready executedintheHCPanalysis.Moreover,thereisnoconsensusaboutthe thresholds/marginsthatdefinewellalimitlevelforcomponent’s am-plitude.Forinstance,thereexistsnetworkswhosetemporalvariation peaksatthelimitofnulldistributionenvelope.Theseareconsideredto becriticalcomponentsthatmaybeintegratedinthetaskbut consid-erednottobefollowingtheautomaticcriteriaofthisnulldistribution. Futureworksshouldthereforeinvestigatemoreaboutthis methodolog-icalapproachintheframeworkofcognitivetasks,inadditiontoresting stateexperiments.Also, itis crucialtoseekmoremethodstouseor combinewiththeappliedtechniqueinordertohavemorerobustbasis forsignificantcomponentsselection.Itisnoteworthytoreportthatnull distribution-basedtechniquewasapplieduniformlyforallSSmethods, thusourmainobjectiveofcomparisonwasbuiltonaunifiedevaluation framework.
Third,weusedthesamepipelinesupportedbytheprevious stud-iesdealingwiththesamedataset(corticalparcellation,source recon-struction,functionalconnectivitymetricandsourceleakagecorrection, frequency bandsandslidingwindow settings)(Kabbaraet al.,2019; O’Neilletal.,2017).Byapplyingalreadytestedandvalidated method-ologicalapproaches,we avoidinfluencingfactorsonthecomparison performed.However,wepointoutthatothermethodologicalsolutions couldbeexploitedbyotherresearchesusingthesamepipelineadopted inthiswork.Regardingcorticalparcellation,wechoseAALatlasbased onitssuccessfuluseinpreviousMEGinvestigations(O’Neilletal.,2017; Tewarieetal., 2016).Thisatlasalsoprovidesgoodbasisfor the or-thogonalisationprocedureadoptedsinceitsnumberofregionsis suffi-cientlylow(78ROIs)andwellseparated(Colcloughetal.,2015).The beamformerspatialfilteringwasselectedastheinverseproblem solu-tion duetoits demonstrated efficiencyin themeasurementof static (Brookeset al., 2011a) anddynamic (Bakeret al., 2014) functional connectivity. Functionalconnectivity between ROIsregions was esti-matedthroughAmplitudeEnvelopeCorrelation(AEC).Thistechnique hasbeensuccessfulinelucidatingelectrophysiologicalnetworksof func-tionalconnectivity (Colcloughet al.,2016). Othermethods, such as phasecouplingscanbeconsideredasanalternativewaytoprobe dif-ferent typeof functionalconnectivity(Lachauxetal.,1999). Sliding windowsettings(lengthandstep)wereselectedcarefullyasatrade-off betweentemporalresolutionandtheaccuracyofthederivedadjacency matrices(length=6sec, step=0.5secforself-pacedandworking mem-orytasks)(O’Neilletal., 2017).However,accordingtorecentworks (Fraschinietal.,2016;Liuzzietal.,2019),itcanbeseenthatmetrics (in-cludingamplitudeenvelopecorrelation)performpoorlyforveryshort statedurationswhencombinedwiththeslidingwindowapproachbelow fewseconds,providingnoisyresultsoflowcorrelationwithgroundtruth insimulations.Forthisreason,wefollowedtheworkof(Tewarieetal., 2019b)toestimatedynamicfunctionalconnectivitybytakingsample by sampletimeseriesratherthanwindowedaggregated samples us-ingtheInstantaneousAmplitudeCorrelation(IAC).Thishightemporal resolutionmeasureofFChasshowngreatsensitivitytogenuine fluc-tuationsinfunctionalconnectivityappliedinthesamecontextofour study.
Itwouldbeinterestingtotestdata-drivenwindowsapproachinthis context using therecurrence plots of theamplitude envelopesas in (Tewarieetal.,2019b)insteadof averagingtrialsindataset2to re-duceheavydFCmatrices.Itcouldbe alsotestedagainstfixedsliding windowapproachasfordatasets1and3.
Concerning frequency bands, it was crucial to preprocess each datasetinitsappropriatebandwidth.Forexample,brainsignalsin self-pacedandHCPmotortaskswereprovedtobemoreactiveinthebeta band,whilebroaderrangeoffrequencybandsareintegratedin com-plexcognitivetasksasworkingmemory(O’Neilletal.,2017;Zhuetal., 2020).
5. Conclusion
Decipheringofdynamicsofelectrophysiologicalbrainnetworksis oneofthemostimportantgoalsinneuroscience.Inthispaper,we eval-uatedandcomparedninepopularsource separation(SS)methodsto identifydominantnetworksofconnectionswithcorresponding tempo-raldynamicsatgroup-levelaswellassubject-level, usingsimulation andempiricalMEGdata(N=95subjects)recordedduringthreedifferent tasks:(1)simplebuttonpresstask,(2)fastfingermovementtask(HCP) and(3)Sternbergworkingmemorytask.Resultsshowcloseconsistency forall SS methodsin successfully identifyingatransientnetworkof connectionslinkingsomatosensoryandprimarymotorregions inthe relativelyslowandsimplebuttonpresstask.Variabilitybetweenthese methods’performanceisrevealedinrapidtasksofsub-secondtimescale (HCPmotortask)andin amorecomplex task(Sternberg).TheSOBI andKmeansalgorithmsshowedtheweakestperformanceamongtested methods.CoM2,PSAUDandPCAshowedpromisingresultsinworking memorytask,revealingtheformationanddissolutionofmultiple net-worksthatrelatetosemanticprocessing,patternrecognitionand lan-guageaswellasvisionandmovement.Atthesubjectlevelanalysis,ICA methodsusinghighstatisticalorder(JADE,InfoMax,CoM2andPSAUD) outperformothermethods.Ourmainmessageisthatresearchersshould beawaretoselecttheappropriateSSmethodsandotherrelated param-eters(epochlength,taskcomplexityanddatasetsize)whenanalyzing dynamicsofbehavioraltasks.
Dataavailability
Data supporting the findings of this study are available in the link(https://github.com/judytabbal/dynbrainSS.git).AllHCPdataare available on https://www.humanconnectome.org/software/hcp-meg-pipelines.Thedatasets1and3areavailableuponrequest.
Codeavailability
Codes supporting the findings of this study are available in the link(https://github.com/judytabbal/dynbrainSS.git).Allanalysiscodes necessarytoproducetheresultsherewereperformedinMATLAB soft-ware,usingFieldTripToolboxhttp://www.fieldtriptoolbox.orgfordata segmentation,filteringandsourcereconstructionsteps,EEGLAB tool-boxforsomeSSmethodsasJADE,InfoMaxandSOBIandother MAT-LABimplemented functionsasdetailedandprovidedin theprevious link.Agraphicaluserinterfaceismadefreelyavailableallowingother researcherstotestthemethodsonour(ortheir)simulatedandrealdata. Creditauthorstatement
JT,AK,MHandPBcontributedtothedesignandimplementation oftheresearch,totheanalysisoftheresultsandtothewritingofthe manuscript.MKandPBwereinvolvedinprovidingfundingsourcesfor thestudyandsupervisedthework.Allauthorsprovidedcritical feed-backandhelpedshapethestudy.
Acknowledgement
ThisworkwasfinancedbytheRennesUniversityandtheInstitute ofClinicalNeuroscienceofRennes(projectnamedEEGCog).Thestudy wasalsofundedbytheNationalCouncilforScientificResearch(CNRS) in Lebanon. The authorswould alsolike tothank thethe Lebanese
University, theLebanese Associationfor ScientificResearch (LASER) andCampusFrance,ProgrammeHubertCurienCEDRE(PROJECTNo. 42257YA),forsupportingthisstudy.
Supplementarymaterials
Supplementarymaterialassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.neuroimage.2021.117829. References
Aggarwal, C.C., Hinneburg, A., Keim, D.A., 2001. On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (Eds.), Database Theory — ICDT 2001, Lecture Notes in Computer Science. Springer, Berlin, Heidel- berg, pp. 420–434. doi: 10.1007/3-540-44503-X_27 .
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D., 2014. Track- ing whole-brain connectivity dynamics in the resting state. Cereb Cortex 24, 663–676. doi: 10.1093/cercor/bhs352 .
Axmacher, N., Schmitz, D.P., Wagner, T., Elger, C.E., Fell, J., 2008. Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial eeg and functional magnetic resonance imaging study. J. Neurosci. 28, 7304–7312. doi: 10.1523/JNEUROSCI.1778-08.2008 .
Baddeley, A., Jarrold, C., Vargha-Khadem, F., 2011. Working memory and the hippocam- pus. J. Cogn. Neurosci. 23, 3855–3861. doi: 10.1162/jocn_a_00066 .
Baker, A.P., Brookes, M.J., Rezek, I.A., Smith, S.M., Behrens, T., Probert Smith, P.J., Wool- rich, M., 2014. Fast transient networks in spontaneous human brain activity. eLife 3, e01867. doi: 10.7554/eLife.01867 .
Bassett, D.S. , Sporns, O. , 2017. Network neuroscience. Nature Neurosci. 20, 353 .
Bauer, A.-K.R., Debener, S., Nobre, A.C., 2020. Synchronisation of neural os- cillations and cross-modal Influences. Trends Cognit. Sci. 24, 481–495. doi: 10.1016/j.tics.2020.03.003 .
Bola, M. , Sabel, B.A. , 2015. Dynamic reorganization of brain functional networks during cognition. Neuroimage 114, 398–413 .
Brookes, M.J., Hale, J.R., Zumer, J.M., Stevenson, C.M., Francis, S.T., Barnes, G.R., Owen, J.P., Morris, P.G., Nagarajan, S.S., 2011a. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. Neuroimage 56, 1082–1104. doi: 10.1016/j.neuroimage.2011.02.054 .
Brookes, M.J., Tewarie, P.K., Hunt, B.A.E., Robson, S.E., Gascoyne, L.E., Liddle, E.B., Lid- dle, P.F., Morris, P.G., 2016. A multi-layer network approach to MEG connectivity analysis. NeuroImage 132, 425–438. doi: 10.1016/j.neuroimage.2016.02.045 .
Brookes, M.J., Vrba, J., Robinson, S.E., Stevenson, C.M., Peters, A.M., Barnes, G.R., Hille- brand, A., Morris, P.G., 2008. Optimising experimental design for MEG beamformer imaging. NeuroImage 39, 1788–1802. doi: 10.1016/j.neuroimage.2007.09.050 .
Brookes, M.J., Wood, J.R., Stevenson, C.M., Zumer, J.M., White, T.P., Liddle, P.F., Morris, P.G., 2011b. Changes in brain network activity during working mem- ory tasks: a magnetoencephalography study. Neuroimage 55, 1804–1815. doi: 10.1016/j.neuroimage.2010.10.074 .
Brookes, M.J., Woolrich, M.W., Barnes, G.R., 2012. Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage 63, 910–920. doi: 10.1016/j.neuroimage.2012.03.048 .
Buckner, R.L., Krienen, F.M., Castellanos, A., Diaz, J.C., Yeo, B.T.T., 2011. The organiza- tion of the human cerebellum estimated by intrinsic functional connectivity. J Neu- rophysiol 106, 2322–2345. doi: 10.1152/jn.00339.2011 .
Bullmore, E., Sporns, O., 2009. Complex brain networks: graph theoretical anal- ysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198. doi: 10.1038/nrn2575 .
Caminiti, S.P., Siri, C., Guidi, L., Antonini, A., Perani, D., 2015. The neural correlates of spatial and object working memory in elderly and Parkinson’s disease subjects. Behav. Neurol. 2015, 1–10. doi: 10.1155/2015/123636 .
Casorso, J., Kong, X., Chi, W., Van De Ville, D., Yeo, B.T.T., Liégeois, R., 2019. Dy- namic mode decomposition of resting-state and task fMRI. NeuroImage 194, 42–54. doi: 10.1016/j.neuroimage.2019.03.019 .
Cavanna, A.E., Trimble, M.R., 2006. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564–583. doi: 10.1093/brain/awl004 .
Chai, L.R., Khambhati, A.N., Ciric, R., Moore, T.M., Gur, R.C., Gur, R.E., Satterth- waite, T.D., Bassett, D.S., 2017. Evolution of brain network dynamics in neurode- velopment. Network Neurosci. 1, 14–30. doi: 10.1162/NETN_a_00001 .
Chai, W.J., Abd Hamid, A.I., Abdullah, J.M., 2018. Working memory from the psycho- logical and neurosciences perspectives: a review. Front Psychol. 9. doi: 10.3389/fp- syg.2018.00401 .
Ciric, R., Nomi, J.S., Uddin, L.Q., Satpute, A.B., 2017. Contextual connectivity: a frame- work for understanding the intrinsic dynamic architecture of large-scale functional brain networks. Sci. Reports 7, 1–16. doi: 10.1038/s41598-017-06866-w .
Colclough, G.L., Brookes, M.J., Smith, S.M., Woolrich, M.W., 2015. A symmetric mul- tivariate leakage correction for MEG connectomes. Neuroimage 117, 439–448. doi: 10.1016/j.neuroimage.2015.03.071 .
Colclough, G.L., Woolrich, M.W., Tewarie, P.K., Brookes, M.J., Quinn, A.J., Smith, S.M., 2016. How reliable are MEG resting-state connectivity metrics? Neuroimage 138, 284–293. doi: 10.1016/j.neuroimage.2016.05.070 .
Corbetta, M. , Miezin, F.M. , Dobmeyer, S. , Shulman, G.L. , Petersen, S.E. , 1991. Selective and divided attention during visual discriminations of shape, color, and speed: func- tional anatomy by positron emission tomography. J. Neurosci. 11, 2383–2402 .
Demb, J.B., Desmond, J.E., Wagner, A.D., Vaidya, C.J., Glover, G.H., Gabrieli, J.D., 1995. Semantic encoding and retrieval in the left inferior prefrontal cortex: a functional
