Integrated analysis of anatomical and electrophysiological ...€¦ · Integrated analysis of...

Integratedanalysisofanatomicalandelectrophysiologicalhumanintracranialdata

ArjenStolk1,SandonM.Griffin1,RoemervanderMeij2,CallumDewar1,3,IgnacioSaez1,JackJ.Lin4,GiovanniPiantoni5,Jan-MathijsSchoffelen6,RobertT.Knight1,7&RobertOostenveld6,81HelenWillsNeuroscienceInstitute,UniversityofCalifornia,Berkeley,Berkeley,CA94720,USA;2DepartmentofCognitiveScience,UniversityofCalifornia,SanDiego,LaJolla,CA92093,USA;3CollegeofMedicine,UniversityofIllinois,Chicago,IL60612,USA;4DepartmentofNeurology,UniversityofCalifornia,Irvine,Irvine,CA92697,USA;5MassachusettsGeneralHospitalandHarvardMedicalSchool,Boston,MA02114,USA;6RadboudUniversity,DondersInstituteforBrain,Cognition,andBehaviour,6500HBNijmegen,TheNetherlands;7DepartmentofPsychology,UniversityofCalifornia,Berkeley,Berkeley,CA94720,USA;8NatMEG,KarolinskaInstitutet,SE-17177Stockholm,SwedenCorrespondenceshouldbeaddressedtoA.S.([email protected]).Keywords:directbrainrecording,electrocorticography,ECoG,stereoelectroencephalography,SEEG,epilepsyAbstractTheexquisitespatiotemporalprecisionofhumanintracranialEEGrecordings(iEEG)permitscharacterizingneuralprocessingwithalevelofdetailthatisinaccessibletoscalp-EEG,MEG,orfMRI.However,thesamequalitiesthatmakeiEEGanexceptionallypowerfultoolalsopresentuniquechallenges.Untilnow,thefusionofanatomicaldata(MRIandCTimages)withtheelectrophysiologicaldataanditssubsequentanalysishasreliedontechnologicallyandconceptuallychallengingcombinationsofsoftware.Here,wedescribeacomprehensiveprotocolthataddressesthecomplexitiesassociatedwithhumaniEEG,providingcompletetransparencyandflexibilityintheevolutionofrawdataintoillustrativerepresentations.Theprotocolisdirectlyintegratedwithanopensourcetoolboxforelectrophysiologicaldataanalysis(FieldTrip).ThisallowsiEEGresearcherstobuildonacontinuouslygrowingbodyofscriptableandreproducibleanalysismethodsthat,overthepastdecade,havebeendevelopedandemployedbyalargeresearchcommunity.WedemonstratetheprotocolforanexamplecomplexiEEGdatasettoprovideanintuitiveandrapidapproachtodealingwithbothneuroanatomicalinformationandlargeelectrophysiologicaldatasets.Weexplainhowtheprotocolcanbelargelyautomated,takingunderanhourtocomplete,andreadilyadjustedtoiEEGdatasetswithothercharacteristics.

.CC-BY-NC 4.0 International licensenot certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which wasthis version posted December 8, 2017. . https://doi.org/10.1101/230912doi: bioRxiv preprint

https://doi.org/10.1101/230912

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INTRODUCTIONIntracranialEEG(iEEG)allowssimultaneousrecordingsfromtenstohundredsofelectrodesplaceddirectlyontheneocortex(electrocorticography,ECoG),orintracortically(stereoelectroencephalography,SEEG).Inhumans,themostcommonimplementationofiEEGiswhennon-invasivetechniquessuchasscalp-EEGandMRIdonotprovidesufficientinformationtoguidesurgeryinmedicationrefractoryepilepsypatients.Eachelectrodereflectstheactivityoftensofthousandsofneurons1,2,andtherecordingandstimulationoftheseneuralpopulationsallowforidentificationofepileptogeniczones,aswellasformappingoffunctionallyeloquentareasofhumancortextoguideneurosurgery.Theoutcomeoftheseprocedurescanbedirectlyobservedwhentheneuralorbehavioralresponseisstraightforwardsuchasspeecharrestormusclemovementwithdirectstimulation3.Anymorecomplexempiricalstudyrequiresaccurateknowledgeofanelectrode’slocationinrelationtothebrain’sanatomythatislinkedtothelocalelectrophysiologicalsignal.Thisintegratedinformationiskeytobasicandclinicalresearchworkaimedatunderstandinghumanneuralandcognitiveprocessing4,5. HumaniEEGanalysishastraditionallyreliedonstand-aloneandadhocworkflowsfortheseparateanalysisofanatomicalandfunctionalaspectsoftheiEEGdata,presentingresearcherswithaseriesofchallengestorealizethefullpotentialofthisexceptionallypowerfultool.Toprocesstheneuroanatomicaldataresearchlabsaretaskedwithassemblingsoftwarecombinationsfortheconversionoffileformats(e.g.,DICOMtoNIfTIusingMRIConvert),coregistrationofanatomicalscans(e.g.,CTtoMRIusingSPM6,FSL7,orAFNI8),localizationofelectrodes(e.g.,BioImageSuite9),andthesortingandlabelingofelectrodestomatchtheformatofthefunctionalrecordingfile(manually,orusingcustomsoftware).Thistechnologicalobstacleisreceivingincreasingattentionintheformofmoreefficientworkflowsforlocalizingandvisualizingelectrodes10–16,butnoprotocolexiststhatallowsresearcherstoefficientlyprocesstheanatomicaldatawithinasingleworkenvironment,andseamlesslyfusewiththeelectrophysiologicaldataanditssubsequentanalysis.Ideally,inthelightofscientificreproducibility17,suchaprotocolshouldalsoprovidecompletetransparencyintheevolutionofrawdataintoresultsandillustrativerepresentations,allowingforaconvenientandefficientexchangeofdataandworkflowsbetweenresearchers.Thesetwocomponentsareparticularlyvaluableinagrowingfieldwheretheanalysisofdataisuniquelycomplex,butwherethegoldstandardforthatanalysisisyettobedefined. Here,wedescribe–attheimplementationlevel–acomprehensiveprotocoltoaddresstheseriesofchallengesassociatedwithboththeanatomicalandfunctionalaspectsofhumaniEEGanalysis.TheprotocolisdirectlyintegratedwiththeMATLAB-basedopensourceFieldTriptoolbox(Box1),offeringtheopportunitytoreadilyandflexiblybuildonacontinuouslygrowingsetofanalysistechniquesthathavealreadybeendevelopedandemployedbyalargeresearchcommunity.TheFieldTriptoolboxsupportsthedataformatsofmostpopularelectrophysiologicaldataacquisitionsystemsandsharesanalysiscodewithothersoftwarepackagessuchasSPMandEEGLAB18.Incontrasttothehostofproprietaryprogramscurrentlyavailablefortheanalysisofelectrophysiologicaldata,thecentraltenetofFieldTripistoprovidecompletetransparencyinordertopromotea


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deeperunderstandingoftheanalysistechniquesandenhancethequalityofthescientificworkthatdependsonthesetechniques.Accordingly,allcomputercodeisfullyaccessibleandthewell-defineddatastructurescontainfullprovenancetofacilitatesharingbetweenresearchers.OuraimistoutilizetheseopensourcefeaturestoadvancethefieldofhumaniEEGbypromotinginteractionwithinandacrossmethodologicallycontiguousresearchareas(e.g.,non-invasiveelectrophysiologysuchasEEGorMEG).ApplicationoftheprotocolOurprotocolisespeciallyusefulforstudyinghumanneuralandcognitiveprocesseswithintracranialEEG.HumaniEEGanalysisisuniquelycomplexbecauseitrequiresdealingwithbothneuroanatomicalandlargeelectrophysiologicaldatasets.ThescopeofiEEGencompassesawiderangeofbasicandclinicalresearch,varyingfromstudiesofhigher-ordercognition19,20tothelocalizationandunderstandingofthesourcesandfeaturesofepileptogenicactivity21,22.ThemethodologicalchallengesthatiEEGresearchersfacecanbegroupedintoobstaclesthatarecommontomostempiricalworkandobstaclesthatarestudy-specific.Thisprotocolaimstoresolvetheformer,whileprovidingadequatesupportandflexibilityforthelatter.AdvantagesandlimitationsoftheprotocolThemainadvantagesofourprotocolarethatit(i)guidestheresearcherfromthemultitudeofrawintracranialdatafilestointegratedobservations,inafastandefficientway,(ii)isdirectlyintegratedwithacomprehensiveandopensourcehubforelectrophysiologicaldataanalysis,(iii)canbereadilyadaptedandautomated,(iv)iscompletelytransparentand(v)producesreproducibleworkflowsanddatasetsthatcanbeeasilysharedandgeneralizedtootherresearchmodalities.ThemainlimitationisthattheMATLABcommandlineinterfacerequiressomebasicprogrammingknowledge,whichmayrequiremoreinitiallearningascomparedtotheexecutionofcomputercommandsthrougha(blackbox)graphicaluserinterface.However,theuseofcomputercommandscanberelativelyeasilymasteredbyvirtueofusingthisprotocol,pavingthewayforbatchscriptinginordertoefficientlydealwithrepeatedanalyseswithinandacrosssubjectsand,ultimately,foradeeperunderstandingoftheunderlyingalgorithms. Humanintracranialdatasetsareapproachedfromvariousanglesandcomeindifferentshapesandsizes,soitiscriticalforaprotocoltostriketherightbalancebetweenefficiencyandflexibility.Thisneedisfurtheramplifiedbytherelativelyuniquenatureofintracranialdata,typicallyimposinggreaterdemandsonalternativeoptionsandstrategiesintheanalysisthannon-invasivedatarecordedwithmorestandardizedhard-andsoftwareindedicatedlaboratorysettings.Besidesprovidingaquickguidetointerpretableresults,ourprotocolallowsforeasyswitchingbetweenmethodstoaccommodatedifferentcasesandsituations.Bychangingasingleparameteratexecution,onecanforinstancereadilyapplyadifferentfusioncostfunctionorfiltersetting.Utilizingthisversatilityshouldnotnegativelyimpactcontinuationwiththeprotocol.Infact,thefullandautomaticprovenance,incombinationwiththesystematicfilenaming,encouragesadaptingto


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thecircumstancesbyalleviatingpotentialconcernsregardingoversightandreproducibility. ThespatiotemporalprecisionofintracranialEEGprovidesauniquewindowonneuralprocessing.Thesizeanddimensionsofthiswindow,however,maygrowdisproportionallylargewithcertaintypesofanalyses,complicatingtheoverallinterpretabilityofthedata.Startingfromthetwodimensionsoftherawneuralsignal(channelsandtime),atime-frequencyanalysis,forinstance,resultsin3dimensionsintheoutput(powerasfunctionofchannel,timeandfrequency),whereasbetween-channelconnectivityanalysisexpandsthecombinatorialspaceto4dimensions.Ourprotocoladdressesthisdimensionalityissueandillustrateshowtheinteractivemanipulationofanatomicallyinformedgraphicalrepresentationsoftheneuraldatafacilitatestheinspectionofthemulti-dimensionaloutcomeofaniEEGanalysis,takingmaximumadvantageofthegroundworklaidbytheintegratedprocessingoftheanatomicalandfunctionaldata.IntegrationwithFieldTripInadditiontothecompletetransparencythatcomeswithanopensourcetoolbox,theintegrationwithFieldTripprovidesuniquebenefitstoiEEGresearchersbyallowingthemtobuildonalgorithmsforreadinginrawdataofvariousformats,datapreprocessing,event-relatedpotentialanalysis,spectralanalysis,sourcemodeling,connectivityanalysis,classification,real-timedataprocessing,andstatisticalinference.AppliedtohumaniEEGdata,thesemethodspermitcharacterizationofneuralinformationflowwithalevelofdetailinaccessibletonon-invasivetechniques.Additionally,invasiveandnon-invasivehumanelectrophysiologycanbedirectlyoverlaidusingverysimilaranalysispipelinesforanintegrativeperspectiveofneuralprocessing,oracomparisonofMEG/EEGsourcereconstructionmethodswithiEEG. Theopensourcedevelopmentmodelallowsforarelativelyeasyextensionoftheprotocol.Forinstance,severaltechniquesexisttocompensateforelectrodedisplacementduetothe"brainshift"phenomenonexplainedbelow11,12,23–30.Givendifferentstrengthsandweaknesses,thesetechniquesmayneedtobeevaluatedonacase-by-casebasis.FieldTrip'smodulararchitecturefacilitatesdeveloperstoincorporatenewtechniquesanduserstosubsequentlyemploythosetechniquesbyvirtueofchangingasingleparameteratfunctioncall.Inasimilarvein,theprotocolcanbeextendedtoanumberofexcitingnewresearchareas.Theseincludesingle-andmultiunitrecordings,‘NeuroGrid’recordings31,wireless'NeuralDust'recordings32,(deep)brainstimulation33,34,andmultimodalimaging12.Supportedbyagrowingcommunityofdeveloperscommittedtotheongoingpushtoimprovedataanalysismethods,wewillcoordinatewiththesenewelectrophysiologicalendeavorsandcontinuesharinganalysiscodewithothersoftwarepackages.CompatibilitywithFreeSurferTheprotocoliscompatiblewiththefreelyavailablesoftwarepackageFreeSurfer35.Althoughoptional,processingoftheanatomicalMRIwithFreeSurfer(Step6)offersseveraladvantagesforsubsequentanalysisanddatainterpretation.ProcessingtheMRIwithFreeSurferresultsinthecreationofacorticalmesh,consistingof


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approximatelyequallysizedtrianglesthatformatopologicalsphereforeachofthecerebralhemispheres.Thiscorticalmeshisparticularlyconvenientforananatomicallyrealisticrepresentationoftheelectrophysiologicaldataontheneocortex(e.g.,bottomcenterinFig.1).Asmoothedversionoftheextractedcorticalsurfacecanbeusedinthecompensationforelectrodedisplacementduetobrainshift(Step22).Moreover,FreeSurferautomaticallyregistersthesubject'sbraintoatemplatebrainonthebasisofitscorticalgyrificationpattern,anaspectofbrainstructurethatremainsdifficulttoaccuratelynormalizeusingvolume-basedregistrationtechniquesduetoitscomplexityandvariabilityacrosssubjects36,37.Ourprotocolusestheresultingsurfaceregistrationmapstolinkelectrodepositionstotheirtemplatehomologues(Step29).Finally,FreeSurfer-generatedatlasesareconvenientforrepresentationsofneuralandanatomicaldataforasinglesubject(Step52),sincetheyaredefinedinnativesubjectspace.Othersupportedatlasesaredefinedinstandardized(e.g.,MNI)spaceandrequiretheaddedstepoftransformingelectrodepositionstothatspace.HumanintracranialdataAnatomicalimages,typicallyMRIandCTscans,areusedaspartoftheepilepsydiagnosticandsurgicalprocedures.Apre-implantMRIshowstheanatomyoftheheadincludingthebrainandisusedtoidentifystructuralabnormalities.AnMRIisalsoinstrumentalinguidingSEEGelectrodeimplantationsubsequenttotheclinicaldecisiontorecordintracranially.Apost-implantCTshowshigh-intensityobjectssuchastheelectrodesandskullbutlacksdetailsofbrainanatomy.Toobtainknowledgeofanelectrode’slocationinrelationtothebrain’sanatomy,thetwoscanshavetobefused. Followingfusionofthepre-andpostoperativeanatomicalimages,electrodesthathavebeensurgicallyplacedonthecorticalsurfaceoccasionallyappear“buried”withinthecorticaltissue,sometimesmorethanacentimeterdeep38–43.Thiselectrodedisplacementistypicallydueto"brainshift",theinwardsinkingofthebrainpost-implantmostcommonlyobservedwithelectrocorticographicsurfacegridelectrodes.Thebrainshiftreflectstissuedisplacement,causedbytheelectrodesthemselves,andbysubduralfluidlossoraccumulation.Asnoted,thedisplacementismostpronounceddirectlybelowacraniotomyandisusuallyminimalforimplantssolelyinvolvingburrholes43.Itisimportanttoaccountforthisbrainshiftinordertoaccuratelyalignelectrodespecificsignalswiththelocalcorticalanatomy.Severallabshavedevelopedrealignmenttechniquestocompensateforelectrodedisplacementduetobrainshift,reducinglocalizationerrortounder3mmwhencomparedtointraoperativephotographs11,23–30.Ourprotocolcurrentlysupportstwoofthesetechniquestoprojectelectrodegridsbacktothecorticalsurfacewhileaccountingforagrid'sshapeandorientation23,30. Electrodelocalizationcanalsobedoneusingpost-implantMRIs,althoughthesearenotcommonlyacquiredinaclinicalsetting.Thesescansshowthebrainanatomyafterelectrodeimplantation,sobrainshiftisnotanissue.InaT1-weightedMRI,electrodesappeardark,duetothemagneticsusceptibilityartifact.Thisisgenerallynotanissueforrecordingswithdepthelectrodes(SEEG),wheretheelectrodesarevisibleasdarkvoidsinthehigherintensitybraintissue.Electrode


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gridsandstrips(ECoG),ontheotherhand,areplaceddirectlyonthecorticalsurface.Thiscomplicatestheiridentification,astheelectrodesaresurroundedbycerebralspinalfluid,whichalsoappearsdarkonaT1scan(butsee25,44–46forworkarounds).Dependingontheavailabilityofapost-implantMRIofsufficientqualitythatclearlyshowstheelectrodes,theCTpreprocessingandfusionSteps9-15maybeleftout,andelectrodelocalizationmaybedoneonthepost-implantMRI.However,ifthepost-implantMRIisofunsatisfactoryqualityregardingbrainanatomy,forinstanceduetoelectrodeinducedMRsignaldistortion,werecommendfusingthepost-implantMRIwiththepre-implantMRI,asifitwereapost-implantCT. Neuralrecordingsaretypicallypartoftheongoingclinicalmonitoringandcomeinvariousfileformats.Eachdatachannelrepresents,asafunctionoftime,theelectricpotentialdifference,obtainedwitheitherabipolarorreferentialelectrodescheme.Thatis,theelectrodesarepairwiselinkedorreferencedtoasingle,commonelectrodeduringacquisition.Thelattermontagehasthebenefitthattherecordingscanbeeasilyre-montagedtoamorepreferredschemeintheofflineanalysis47.Themarkersortriggersforstimulusonsettimesandresponsesaretypicallyrecordedsimultaneouslyinadedicatedchannel,allowingforprecisesynchronizationofexperimentalscenarioswiththeneuralrecording.OverviewoftheprocedureTheprotocolisgroundedintwoparallelbutinterrelatedworkflows,asshowninFigure1.Thefirstworkflowentailstheprocessingofanatomicaldata.Itsmainactivitiesconstitutethepreprocessingandfusionoftheanatomicalimages,andelectrodeplacement(Steps1-19).SecondaryactivitiesthatarealsodiscussedincludecorticalsurfaceextractionwithFreeSurfer,brainshiftcompensation,spatialnormalization,andanatomicallabeling(Steps6and20-33).Generally,theanatomicalworkflowaimstoobtainestimatesoftheelectrodelocationsinrelationtotheindividualandatlas-basedbrainanatomy,whichisaone-timeprocedureforeachsubject.Thesecondworkflowfocusesonimprovingthesignal-to-noiseratioandextractingtherelevantfeaturesfromtheelectrophysiologicaldata,whilepreparingforsubsequentanalyses.Itminimallyencompassesthepreprocessingoftheneuralrecordings,butmayalsoincludefollow-upactivitiessuchastime-frequencyandsingle-subjectorgroup-levelstatisticalanalysis(Steps34-45).Generally,thespecificsofthefunctionalworkflowdependultimatelyontheclinicalorresearchquestionathandandcontingenciesintheexperimentalparadigm. Thetwoworkflowsbecomeintrinsicallyconnectedforthefirsttimeduringtheelectrodeplacementactivity(Step17),whichofferstheopportunitytodirectlylinkanatomicallocationstoelectrodelabelscorrespondingtotheneuralrecordings.Thisactivityinvolvesaninteractiveelectrodeplacementtooldesignedforefficientyetpreciseidentificationofelectrodesineventhemostchallengingcases.Theintegrationofthetwoworkflowsculminatesinaninteractiveandanatomicallyinformeddataexplorationtoolandtheabilitytorepresentfunctionalandepileptiformneuralactivityoverlaidoncorticalandsubcorticalsurfacemodels,infigureorvideoformat(Steps46-56).


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ImplementationandadaptationoftheprocedureAllimplementationsrunonasingleuniversalplatform(MATLAB,exceptforFreeSurfer)tosupportrelativelyeasilyautomatedproceduresfordealingwithrepeatedanalyseswithinandacrosssubjects.Werecommendthattheuserconstructasinglescriptforasinglesubjectbyincrementallycopy-pastingcodefromthisprotocolintotheMATLABeditor(SupplementaryFile1),andevaluatingsegmentsofthatscriptintheMATLABcommandwindow.Oncethescriptproducessatisfactoryresults,itcanbeconvertedintoabatchanalysisbybreakingitintoseparatecomponents.Byloopingaroundtheseparatecomponentsforallsubjects,theentireanalysispipelineforallsubjectsinastudycaneasilybeexecutedandintermediateresultscanbesavedandevaluated. Thewholebatchcanbedocumentedandshared,orre-evaluatedwithdifferentparametersettingsasappropriate.Byvirtueofchangingsingleparametersatafunctioncall,onecanforinstancereadilyalternatebetweenvariousfusion,localization,projection,normalization,filtering,re-montaging,andspectralestimationalgorithmstoaccommodatedifferentcasesandsituations.Theoutputdatastructuresarekeptconsistentacrossthedifferentalgorithms,andtheparameterstotheusedalgorithmareappendedtoallowforaccesstothefulldataprovenanceatanyleveloftheanalysispipeline(Box1).ExperimentaldesignTheexampleiEEGdatasetwasacquiredattheMedicalCenteroftheUniversityofCalifornia,Irvine.TheOfficefortheProtectionofHumanSubjectsoftheUniversityofCalifornia,Berkeley,approvedthestudyandthesubjectgaveinformedconsent.Thedatasetincludesapre-implantMRI,apost-implantCT,apost-implantMRI,andneuralrecordingsfrom96ECoGand56SEEGelectrodesthatwereimplantedaspartofthepreparationfortheepilepsysurgery(seeMaterials).Theneuraldatawererecordedinthecontextofanexperimentthatrequiredthepatienttopressabuttonwiththerighthandwhenhearingatargettone.Theoriginaldataset(afterdefacingtheimagingdatawithft_defacevolume)andtheprocessedresultsareavailablefordownloadfromftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/tutorial/SubjectUCI29.zip.RawDICOMimagesandrecordingfilesarenotsharedtoprotectthesubject'sidentity. WechoosethisiEEGdatasetforthreereasons.First,itcontainsneuralrecordingsfrombothcorticalgrid(ECoG)andstereotacticallyinserteddepthelectrodes(SEEG),requiringstrategiesfordealingwitheachtypeaswellastheircombinationintheanalysis.Second,thepre-implantMRIisnotofthebestquality(acontrastagentwasused),electrodesofadjacentcorticalgridshaveseeminglymergedwithoneanotherinthepost-implantCT,andthereissignificantelectrodedisplacementduetoasubduralhygromacontributingtobrainshift.Theseissuesreflectrealworldchallengesinintracranialdataanalysis,allowingustodemonstratetheapplicationofourprotocoltonon-idealdata.Finally,theexperimentalparadigmissimpleenoughtoneednofurtherexplanation,yetrequiresperformingallthefundamentalstepsunderlyingtheanalysisofintracranialdatarecordedusingamorecomplexexperimentalparadigm19,48.Wedemonstratetheanalysisoftask-relatedhigh-frequency-bandactivity(~70to150


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Hz),aprominentneuralsignatureinintracranialdatathathasbeenassociatedwithneuronpopulationlevelfiringrate5,49–52.Manyothersupportedanalysessuchasevent-relatedpotentialanalysis,connectivityanalysis,andstatisticalanalysishavebeendescribedindetailelsewhere53–55.


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MATERIALSAnatomicalimages

• Pre-implantT1-weightedMRI(MagneticResonanceImage,Siemens3TTrioTim).

• Post-implantCT(ComputerizedTomography,PhilipsiCT256).• Post-implantT1-weightedMRI(MagneticResonanceImage,Siemens1.5T

Avanto).Thisscanisnotusedintheprocedurebutneverthelessincludedforcompleteness.

Neuralrecordings• 64-contactcorticalgridwithleftparietalcoverage(Integra,8x8layout,10

mminter-electrodespacing,labelshaveaLPGprefix)• 32-contactcorticalgridwithlefttemporalcoverage(Integra,4x8layout,10

mminter-electrodespacing,labelshaveaLTGprefix)• 8-contactlineardepthelectrodetargetingleftamygdala(Ad-Tech,5mm

inter-electrodespacing,labelshaveaLAMprefix)• 8-contactlineardepthelectrodetargetinglefthippocampushead(Ad-Tech,5

mminter-electrodespacing,labelshaveaLHHprefix)• 8-contactlineardepthelectrodetargetinglefthippocampustail(Ad-Tech,5

mminter-electrodespacing,labelshaveaLTHprefix)• 8-contactlineardepthelectrodetargetingrightamygdala(Ad-Tech,5mm

inter-electrodespacing,labelshaveaRAMprefix)• 8-contactlineardepthelectrodetargetingrighthippocampushead(Ad-Tech,

5mminter-electrodespacing,labelshaveaRHHprefix)• 8-contactlineardepthelectrodetargetingrighthippocampustail(Ad-Tech,5

mminter-electrodespacing,labelshaveaRTHprefix)• 8-contactlineardepthelectrodetargetingrightoccipitalcortex(Ad-Tech,5

mminter-electrodespacing,labelshaveaROCprefix)• AllneuralrecordingswereacquiredusingaNihonKohdenrecordingsystem

withaJE-120Aamplifier(NihonKohdenCorporation,Tokyo,Japan),analog-filteredabove0.01Hz,anddigitallysampledat5KHz

Software• MATLABenvironment(MathWorks,Natick,MA;installationandlicensing

throughhttp://www.mathworks.com)• FieldTriptoolbox(Box1,freelyavailableathttp://www.fieldtriptoolbox.org)• FreeSurfersoftwaresuiteforcorticalsurfaceextraction(optional;freely

availableathttp://www.freesurfer.net)Supportedanatomicaldataformats

• AFNI(*.head,*.brik)• Analyze(*.img,*.hdr)• ANT(*.mri)• DICOM(*.dcm,*.ima)• FreeSurfer(*.mgz,*.mgh)• MINC(*.mnc)• NIfTI(*.nii,*.nii.gz)


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Supportedelectrophysiologicaldataformats• Anywave(*.ah5)• BCI2000(*.dat)• BESA(*.besa)• Blackrock(*.nev,*.ns#)• CambridgeElectronicDesign(*.smr)• EuropeanDataFormat(*.edf)• GTec(*.mat,*.hdf5)• Micromed(*.trc)• Neuralynx(*.ncs,*.nse,*.nts,*.nst,*.ntt,*.nev)• Neuromag(*.fif)• Neuroscope(*.eeg,*.dat,*.xml)• NihonKohden(*.m00)• Plexon(*.ddt,*.nex,*.plx)• andvariousEEG,MEG,NIRS,andeye-trackerdataformats


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PROCEDURE1|SpecifythesubjectID.ThisIDwillbeusedinthefilenaming,inadditiontoinformationaboutthetypeofdata(e.g.,MRI,CT),thecoordinatesystemitisin(e.g.,ACPC,MNI),andtheprocess(es)thatwereappliedtoit(e.g.,fforfusion).Forexample,aCTscanthatisalignedtotheACPCcoordinatesystemandthathasjustbeenfusedwiththeanatomicalMRIiswrittenouttofileassubjID_CT_acpc_f.nii. subjID='SubjectUCI29';PreprocessingoftheanatomicalMRI,TIMING~2min2|ImporttheanatomicalMRIintotheMATLABworkspaceusingft_read_mri.TheMRIcomesintheformatofasinglefilewithan.imgor.niiextension,orafoldercontainingaseriesoffileswitha.dcmor.imaextension(DICOM;SupplementaryFile2mayaidinthesearchandvisualizationofaDICOMseries).

mri=ft_read_mri(<pathtoMRIfile>);

3|DeterminethenativeorientationoftheanatomicalMRI'sleft-rightaxisusingft_determine_coordsys(Box2andSupplementaryVideo1).CRITICALSTEPTocorrectlyfusetheMRIandCTscansatalaterstep,accuracyindemarcatingtherighthemispherelandmarkinthefollowingstepisimportantforavoidinganotherwisehardtodetectflipofthescan'sleftandrightorientation.4|AligntheanatomicalMRItotheACPCcoordinatesystem56,apreferredconventionfortheFreeSurferoperationoptionallyusedinalaterstep.Inthiscoordinatesystem,theorigin(coordinate[0,0,0])isattheanteriorcommissure(AC),theY-axisrunsalongthelinebetweentheanteriorandposteriorcommissure(PC),andtheZ-axisliesinthemidlinedividingthetwocerebralhemispheres.Specifytheanteriorandposteriorcommissure,aninterhemisphericlocationalongthemidlineatthetopofthebrain,andalocationinthebrain’srighthemisphere.Ifthescanwasfoundtohavealeft-to-rightorientationinthepreviousstep,therighthemisphereisidentifiedasthehemispherehavinglargervaluesalongtheleft-rightaxis.Viceversa,inaright-to-leftsystem,therighthemispherehassmallervaluesalongthataxisthanitsleftcounterpart(SupplementaryVideo2).

cfg=[];cfg.method='interactive';cfg.coordsys='acpc';mri_acpc=ft_volumerealign(cfg,mri);

5|WritethepreprocessedanatomicalMRIouttofile.

cfg=[];cfg.filename=[subjID'_MR_acpc'];cfg.filetype='nifti';cfg.parameter='anatomy';


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ft_volumewrite(cfg,mri_acpc);CorticalsurfaceextractionwithFreeSurfer(optional),TIMING~10hrs,automatic6|ExecuteFreeSurfer'srecon-allfunctionalityfromtheLinuxorMacOSterminal(WindowsviaVirtualBox),orfromtheMATLABcommandwindowasbelow.Thissetofcommandswillcreateafoldernamed‘freesurfer’inthesubjectdirectory,withsubdirectoriescontainingamultitudeofFreeSurfer-generatedfiles.

fshome=<pathtofreesurferhomedirectory>;subdir=<pathtosubjectdirectory>;mrfile=<pathtosubjectMR_acpc.nii>;system(['exportFREESURFER_HOME='fshome';'...

'source$FREESURFER_HOME/SetUpFreeSurfer.sh;'... 'mri_convert-c-oc000'mrfile''[subdir'/tmp.nii']';'... 'recon-all-i'[subdir'/tmp.nii']'-s''freesurfer''-sd'subdir'-all'])PAUSEPOINTFreeSurfer'sfullyautomatedsegmentationandcorticalextractionoftheanatomicalMRIcurrentlymaytakeup10hoursormore.Fortutorialpurposes,theexampledatasetcontainstheoutputfromFreeSurfer,afoldernamed'freesurfer',forcontinuationwiththeprotocol. 7|ImporttheextractedcorticalsurfacesintotheMATLABworkspaceandexaminetheirquality.Repeatthefollowingcodeusingrh.pialtovisualizethepialsurfaceoftherighthemisphere.

pial_lh=ft_read_headshape(<pathtofreesurfer/surf/lh.pial>);pial_lh.coordsys='acpc';ft_plot_mesh(pial_lh);lightinggouraud;camlight;

?TROUBLESHOOTING8|ImporttheFreeSurfer-processedMRIintotheMATLABworkspaceforthepurposeoffusingwiththeCTscanatalaterstep,andspecifythecoordinatesystemtowhichitwasalignedinStep4.

fsmri_acpc=ft_read_mri(<pathtofreesurfer/mri/T1.mgz>); fsmri_acpc.coordsys='acpc';PreprocessingoftheanatomicalCT,TIMING~2min9|ImporttheanatomicalCTintotheMATLABworkspace.SimilartotheMRI,theCTscancomesintheformatofasinglefilewithan.imgor.niiextension,orafolder


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containingaseriesoffileswitha.dcmor.imaextension(SupplementaryFile2mayaidinthesearchandvisualizationofaDICOMseries).

ct=ft_read_mri(<pathtoCTfile>);10|Incasethiscannotbedoneonthebasisofknowledgeofthelateralityofelectrodeimplantation,determinethenativeorientationoftheanatomicalCT'sleft-rightaxisusingft_determine_coordsys,similarlytohowitwasdonewiththeanatomicalMRIinStep3(Box2andSupplementaryVideo1).CRITICALSTEPTocorrectlyfusetheMRIandCTscansatalaterstep,accuracyindemarcatingtherightandleftpreauricularlandmarkinthefollowingstepisimportantforavoidinganotherwisehardtodetectflipofthescan'sleftandrightorientation.11|AligntheanatomicalCTtotheheadsurfacecoordinatesystembyspecifyingthenasion(attherootofthenose),leftandrightpreauricularpoints(justinfrontoftheearcanals),andaninterhemisphericlocationalongthemidlineatthetopofthebrain(SupplementaryVideo3).TheCTscanisinitiallyalignedtotheheadsurfacecoordinatesystem,giventhattheACPCcoordinatesystemusedfortheMRIreliesonneuroanatomicallandmarksthatarenotvisibleintheCT.

cfg=[];cfg.method='interactive';cfg.coordsys='ctf';ct_ctf=ft_volumerealign(cfg,ct);

12|AutomaticallyconverttheCT'scoordinatesystemintoanapproximationoftheACPCcoordinatesystem,thesamesystemtheanatomicalMRIwasalignedto.

ct_acpc=ft_convert_coordsys(ct_ctf,'acpc');FusionoftheCTwiththeMRI,TIMING~3min13|FusetheCTwiththeMRI,anecessarysteptolinktheelectrodelocationsintheanatomicalCTtotheircorrespondinglocationsintheanatomicalMRI57,58.Giventhatbothscansarefromthesamesubjectandtheircommondenominatoristheskull,arigidbodytransformationsufficesfortheiralignmentundernormalcircumstances(thedefaulttechniquewhenusingtheSPM-methodinFieldTrip).

cfg=[];cfg.method='spm';cfg.spmversion='spm12';cfg.coordsys='acpc';cfg.viewresult='yes';ct_acpc_f=ft_volumerealign(cfg,ct_acpc,fsmri_acpc);


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14|Carefullyexaminetheinteractivefigurethatisproducedafterthecoregistrationiscompleted,showingtheMRIandfusedCTsuperimposed.AsuccessfulfusionwillshowtightinterlockingofCT-positiveskull(inblue)andMRI-positivebrainandskintissue(inred).CRITICALSTEPAccuracyofthefusionoperationisimportantforcorrectlyplacingtheelectrodesinanatomicalcontextinafollowingstep.?TROUBLESHOOTING15|WritetheMRI-fusedanatomicalCTouttofile.

cfg=[];cfg.filename=[subjID'_CT_acpc_f'];cfg.filetype='nifti';cfg.parameter='anatomy';ft_volumewrite(cfg,ct_acpc_f);

Electrodeplacement,TIMING~15min16|Importtheheaderinformationfromtherecordingfile,ifpossible.Bygivingtheelectrodelabelsoriginatingfromtheheaderasinputtoft_electrodeplacementinthenextstep,thelabelswillappearasato-dolistduringtheinteractiveelectrodeplacementactivity.Asecondbenefitisthattheelectrodelocationscanbedirectlyassignedtolabelscollectedfromtherecordingfile,obviatingtheneedtosortandrenameelectrodestomatchtheelectrophysiologicaldata.

hdr=ft_read_header(<pathtorecordingfile>);

17|Localizetheelectrodesinthepost-implantCTwithft_electrodeplacement,showninFigure2.Clickinganelectrodelabelinthelistwilldirectlyassignthatlabeltothecurrentcrosshairlocation(SupplementaryVideo4).Severalin-appfeaturesfacilitateefficientyetprecisenavigationoftheanatomicalimage,suchasazoommode,amagnetoptionthattransportsthecrosshairtothenearestweightedmaximumwithsubvoxelaccuracy(orminimumincaseofapost-implantMRI),andaninteractivethree-dimensionalscatterfigurethatislinkedtothetwo-dimensionalvolumerepresentations.Furthermore,passingonthepre-implantMRI,fsmri_acpc,toft_electrodeplacementallowstogglingbetweenCTandMRIviewsfortheidentificationofspecificelectrodesbasedontheiranatomicallocation.Generally,electrode#1istheelectrodefarthestawayfromthecraniotomyorburrholeincaseofdepthsandsingle-rowstrips.Carefulnotestakenduringsurgeryandrecordingarecriticalfordeterminingthenumberingofgridandmulti-rowstripelectrodes.

cfg=[];cfg.channel=hdr.label;elec_acpc_f=ft_electrodeplacement(cfg,ct_acpc_f,fsmri_acpc);

18|Examinewhetherthevariablesinresultingelectrodestructureelec_acpc_fmatchtherecordingparameters,e.g.,thenumberofchannelsstoredinthelabel


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field.Theelectrodeandchannelpositionsarestoredintheelecposandchanposfields,respectively.Theelecposfieldcontainstheoriginalelectrodepositions.Withexceptionofpossiblebrainshiftcompensation,thisfieldisnotadjusted.Thechannelpositionsinthechanposfieldareinitiallyidenticaltotheelectrodepositionsbutmaybeupdatedtoaccommodateofflineadjustmentsinchannelcombinations,i.e.duringre-montaging.ForbipolariEEGdata,thebestconsideredchannelpositionisinbetweenthetwocorrespondingelectrodepositions.Thechanposfieldisusedforoverlayingtheneuraldataon(sub-)corticalmodelsduringdatavisualization.Thetrafieldisamatrixwiththeweightofeachelectrodeintoeachchannel,whichatthisstagemerelyisanidentitymatrixreflectingone-to-onemappingsbetweenelectrodesandchannels. elec_acpc_f= unit:'mm' coordsys:‘acpc’ label:{152x1cell}elecpos:[152x3double]chanpos:[152x3double]tra:[152x152double] cfg:[1x1struct]19|Savetheresultingelectrodeinformationtofile.

save([subjID'_elec_acpc_f.mat'],'elec_acpc_f');Brainshiftcompensation(optionalforcorticalgridsandstrips),TIMING~5min21|Incaseof"brainshift",adisplacementofbraintissueandelectrodespost-implant,realignmentofelectrodegridstothepreoperativecorticalsurfacemaybenecessary.Topreventelectrodesfrombeingincorrectlyplacedinthenearbycorticalsulciduringback-projection,createasmoothhullaroundthecorticalsurfacegeneratedbyFreeSurfer59.

cfg=[];cfg.method='cortexhull';

cfg.headshape=<pathtofreesurfer/surf/lh.pial>; cfg.fshome=<pathtofreesurferhomedirectory>;hull_lh=ft_prepare_mesh(cfg);21|Savethehulltofile.

save([subjID'_hull_lh.mat'],hull_lh);22|Projecttheelectrodegridstothesurfacehulloftheimplantedhemisphere.Giventhatdifferentgridscanmoveindependentlyfromoneanotherandthattheprojectionalgorithmspecifiedincfg.warpconsiderstheglobalelectrode


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configurationofagrid30,itisrecommendedtorealignelectrodegridsindividuallybyrunningseparaterealignmentproceduresforeachgrid.Here,werealigntheelectrodesoftheleftparietalgridfollowedbytheelectrodesofthelefttemporalgrid(LPGandLTGrespectively)andstoretheupdatedgridelectrodeinformationinanewvariabletogetherwiththeunalteredcoordinatesofthedepthelectrodes. elec_acpc_fr=elec_acpc_f; grids={'LPG*','LTG*'}; forg=1:numel(grids) cfg=[]; cfg.channel=grids{g}; cfg.keepchannel='yes'; cfg.elec=elec_acpc_fr; cfg.method='headshape'; cfg.headshape=hull_lh; cfg.warp='dykstra2012'; cfg.feedback='yes'; elec_acpc_fr=ft_electroderealign(cfg); end23|Visualizethecortexandelectrodestogetherandexaminewhethertheyshowexpectedbehavior(Fig.3).CRITICALSTEPAccuracyoftherealignmentoperationisimportantforcorrectlyplacingtheelectrodesinanatomicalcontextinafollowingstep.

ft_plot_mesh(pial_lh);ft_plot_sens(elec_acpc_fr);view([-5510]);materialdull;lightinggouraud;camlight;

?TROUBLESHOOTING24|Savetheupdatedelectrodeinformationtofile.

save([subjID'_elec_acpc_fr.mat'],'elec_acpc_fr');Volume-basedregistration(optional),TIMING~2min25|TogeneralizetheelectrodecoordinatestootherbrainsorMNI-basedneuroanatomicalatlasesinalaterstep,registerthesubject'sbraintothestandardMNIbrain.Thevolume-basedregistrationtechniqueconsiderstheoverallgeometryofthebrain60andcanbeusedforthespatialnormalizationofalltypesofelectrodes,whetherdepthoronthesurface.


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cfg=[];cfg.nonlinear='yes';cfg.spmversion='spm12';

fsmri_mni=ft_volumenormalise(cfg,fsmri_acpc);26|UsetheresultingdeformationparameterstoobtaintheelectrodepositionsinstandardMNIspace.

elec_mni_frv=elec_acpc_fr;elec_mni_frv.elecpos=ft_warp_apply(fsmri_mni.params,elec_acpc_fr.elecpos,

'individual2sn');elec_mni_frv.chanpos=ft_warp_apply(fsmri_mni.params,elec_acpc_fr.chanpos,

'individual2sn');elec_mni_frv.coordsys='mni';

27|VisualizethecorticalmeshextractedfromthestandardMNIbrainalongwiththespatiallynormalizedelectrodesandexaminewhethertheyshowexpectedbehavior(toprightinFig.4).CRITICALSTEPAccuracyofthespatialnormalizationstepisimportantforcorrectlyoverlayingtheelectrodepositionswithabrainatlasinafollowingstep.

load(<pathtofieldtrip/template/anatomy/surface_pial_left.mat>);ft_plot_mesh(mesh);ft_plot_sens(elec_mni_frv);view([-9020]);materialdull;lightinggouraud;camlight;

?TROUBLESHOOTING28|Savethenormalizedelectrodeinformationtofile.

save([subjID'_elec_mni_frv.mat'],'elec_mni_frv');Surface-basedregistration(optionalforsurfaceelectrodes),TIMING~2min29|Togeneralizetheelectrodecoordinatestootherbrainsinalaterstep,maptheelectrodesontoFreeSurfer'sfsaveragebrain.Thesurface-basedregistrationtechniquesolelyconsidersthecurvaturepatternsofthecortex35andthuscanbeusedforthespatialnormalizationofelectrodeslocatedonornearthecorticalsurface.Intheexamplecase,thispertainstoallelectrodesoftheleftparietalandtemporalgrids.

cfg=[];cfg.channel={'LPG*','LTG*'};


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cfg.elec=elec_acpc_fr;cfg.method='headshape';

cfg.headshape=<pathtofreesurfer/surf/lh.pial>;cfg.warp='fsaverage';cfg.fshome=<pathtofreesurferhomedirectory>;

elec_fsavg_frs=ft_electroderealign(cfg);30|VisualizeFreeSurfer'sfsaveragebrainalongwiththespatiallynormalizedelectrodesandexaminewhethertheyshowexpectedbehavior(bottomrightinFig.4).CRITICALSTEPAccuracyofthespatialnormalizationstepisimportantforcorrectlyoverlayingtheelectrodepositionswithabrainatlasinafollowingstep.

fspial_lh=ft_read_headshape(<pathtofshome/subjects/fsaverage/surf/lh.pial>);

fspial_lh.coordsys='fsaverage';ft_plot_mesh(fspial_lh);ft_plot_sens(elec_fsavg_frs);view([-9020]);materialdull;lightinggouraud;camlight;

31|Savethenormalizedelectrodeinformationtofile.

save([subjID'_elec_fsavg_frs.mat'],'elec_fsavg_frs');

Anatomicallabeling(optional),TIMING~1min32|FieldTripsupportslookinguptheanatomicalorfunctionallabelscorrespondingtotheelectrodesinanumberofatlases,includingtheAFNITalairachTournouxatlas61,theAALatlas62,theBrainWebdataset63,theJuBraincytoarchitectonicatlas64,theVTPMatlas65,andtheBrainnetomeatlas66,inadditiontothesubject-tailoredDesikan-KillianyandDestrieuxatlasesproducedbyFreeSurfer67,68.WithexceptionoftheaboveFreeSurfer-basedatlases,theseatlasesareinMNIcoordinatespaceandrequiretheelectrodestobespatiallynormalized(Step25).First,importanatlasofinterest,e.g.,theAALatlas,intotheMATLABworkspace.

atlas=ft_read_atlas(<pathtofieldtrip/template/atlas/aal/ROI_MNI_V4.nii>);33|Lookupthecorrespondinganatomicallabelofanelectrodeofinterest,e.g.,electrodeLHH2,targetingthelefthemisphere’shippocampus.SupplementaryFile3representsatoolthatautomaticallyoverlaysallchannelsinanelectrodestructurewithalloftheaboveatlasesandstorestheresultinganatomicallabelsinanexceltable(e.g.,SubjectUCI29_electable.xlsxinthezipfile).


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cfg=[]; cfg.roi =elec_mni_frv.chanpos(match_str(elec_mni_frv.label, 'LHH2'),:); cfg.atlas =atlas; cfg.inputcoord='mni'; cfg.output='label';

labels=ft_volumelookup(cfg,atlas);

[~,indx]=max(labels.count); labels.name(indx)

ans='ParaHippocampal_L'

?TROUBLESHOOTING

Preprocessingoftheneuralrecordings,TIMING~10min34|Definethetrials,thatis,thesegmentsofdatathatwillbeusedforfurtherprocessingandanalysis.Thisstepproducesamatrixcfg.trlcontainingforeachsegmentthebeginandendsampleintherecordingfile.Inthecaseoftheexampleprovidedintheshareddata,thesegmentsofinterestbegin400msbeforetoneonset,aremarkedwitha‘4’inthetriggerchannel,andend900msthereafter.

cfg=[];cfg.dataset=<pathtorecordingfile>;cfg.trialdef.eventtype=‘TRIGGER′;cfg.trialdef.eventvalue=4;cfg.trialdef.prestim=0.4;cfg.trialdef.poststim=0.9;cfg=ftdefinetrial(cfg);

35|ImportthedatasegmentsofinterestintotheMATLABworkspaceandfilterthedataforhigh-frequencyandpowerlinenoise(seethedocumentationofft_preprocessingforfilteringoptions).

cfg.demean ='yes';cfg.baselinewindow='all';cfg.lpfilter='yes';cfg.lpfreq=200;cfg.padding=2;cfg.padtype='data';cfg.bsfilter='yes';cfg.bsfiltord=3;cfg.bsfreq=[5961;119121;179181];data=ft_preprocessing(cfg);


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36|Examinewhetherthevariablesintheoutputdatastructurematchtherecordingandpreprocessingparameters,i.e.thesamplingrate(fsample),numberofrecordingchannels(label),andsegmentationintotheexperiment’stwenty-sixtrials(trial,andtheirrespectivetimeaxesintime).

data=label:{152x1cell}time:{1x26cell}trial:{1x26cell}fsample:5000

sampleinfo:[26x2double]cfg:[1x1struct]37|Addtheelecstructureoriginatingfromtheanatomicalworkflowandsavethepreprocessedelectrophysiologicaldatatofile.Theadvantageofaddingtheelectrodeinformationatthisstageisthatitwillbekeptconsistentwiththeneuraldatagoingforward,aswhenapplyingthesamemontageusedfortheneuralrecordingstothechannelpositions. data.elec=elec_acpc_fr;

save([subjID'_data.mat'],'data');38|Inspecttheneuralrecordingsusingft_databrowserandidentifychannelsorsegmentsofnon-interest,forinstancesegmentscontainingsignalartifactsor(inthiscase)epileptiformactivity.Markthebadsegmentsbydrawingaboxaroundthecorruptedsignal.Writedownthelabelsofbadchannels.CRITICALSTEPIdentifyingbadchannelsisimportantforavoidingthecontaminationofotherchannelsduringre-montaginginStep40. cfg=[]; cfg.viewmode='vertical'; cfg=ft_databrowser(cfg,data);39|Removeanybadsegmentsmarkedintheabovestep. data=ft_rejectartifact(cfg,data);40|Re-montagethecorticalgridstoacommonaveragereferenceinordertoremovenoisethatissharedacrossallchannels.Box3providesabackgroundonre-montaging.BadchannelsnotedinStep38canbeexcludedfromthisstepbyaddingthosechannelstocfg.channelwithaminusprefix.Thatis,cfg.channel={'LPG*','LTG*','-LPG1'}ifoneweretoexcludetheLPG1channelfromthelistofLPGandLTGchannels.

cfg=[];cfg.channel={'LPG*','LTG*'};


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cfg.reref ='yes';cfg.refchannel='all';reref_grids=ft_preprocessing(cfg,data);

41|Applyabipolarmontagetothedepthelectrodes.ThiscanbedoneinasimilarmannerasinStep40,butbyselectingsinglechannellabelsforcfg.channelandcfg.refchannel.Alternatively,createamoreelaborateschemewithcfg.montage(seethedocumentationofft_apply_montage).Here,wecombineforeachdepthelectrodeshaftthe8unipolarchannelsinto7bipolarchannels,usingtheweightsdefinedinthe7x8montage.trafield.Wealsocreatenewlabelsindicatingthebipolaroriginofthedata,e.g.,“RAM1-RAM2”,“RAM2-RAM3”,andsoon.NotethatbecauseweaddedtheelecstructuretothedatainStep37,thesamemontageisautomaticallyappliedtothechannelpositionsaswell,withtheresultingchanposfieldcontainingthemeanlocationsofallelectrodepairsthatcompriseabipolarchannel.

depths={'RAM*','RHH*','RTH*','ROC*','LAM*','LHH*','LTH*'};ford=1:numel(depths)cfg=[];cfg.channel=ft_channelselection(depths{d},data.label);

cfg.montage.labelold=cfg.channel; cfg.montage.labelnew=strcat(cfg.channel(1:end-1),'-',cfg.channel(2:end)); cfg.montage.tra=... [1-1000000 01-100000 001-10000 0001-1000 00001-100 000001-10 0000001-1]; cfg.updatesens='yes'; reref_depths{d}=ft_preprocessing(cfg,data);

end42|Combinethedatafrombothelectrodetypesintoonedatastructurefortheeaseoffurtherprocessing.

cfg=[];cfg.appendsens='yes';reref=ft_appenddata(cfg,reref_grids,reref_depths{:});

43|Savethere-referenceddatatofile.

save([subjID'_reref.mat'],reref);

Time-frequencyanalysis(optional),TIMING~2min


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44|Decomposethesignalintimeandfrequencybins.Theconfigurationoptionscfg.foiandcfg.toideterminethefrequenciesandtime-pointsofinterest,inthiscasefrom5to200Hzinstepsof5Hz,and300mspriortotoneonsetuntil800msthereafterinstepsof10ms.

cfg=[];cfg.method='mtmconvol';cfg.foi=5:5:200;cfg.toi=-.3:0.01:.8;cfg.t_ftimwin=ones(length(cfg.foi),1).*0.2;cfg.taper='hanning';cfg.output='pow';freq=ft_freqanalysis(cfg,reref);

45|Savethetime-frequencydatatofile.

save([subjID'_freq.mat'],'freq');Interactiveplotting,TIMING~3min46|Forananatomicallyinformedexplorationofthemultidimensionaloutcomeofananalysis,createalayoutbasedonthethree-dimensionalelectrodelocations.Thislayoutisasymbolicrepresentationinwhichthechannelsareprojectedonthetwo-dimensionalmediumofferedbypaperoracomputerscreen.Thelayoutiscomplementedbyanautomaticoutlineofthecorticalsheetthatisspecifiedincfg.headshape.Thecfg.boxchanneloptionallowsselectingchannelswhosetwo-dimensionaldistancesareusedtodeterminetheplottingboxsizesinthefollowingstep.

cfg=[];cfg.headshape=pial_lh;cfg.projection='orthographic';cfg.channel={'LPG*','LTG*'};cfg.viewpoint='left';cfg.mask='convex';cfg.boxchannel={'LTG30','LTG31'};lay=ft_prepare_layout(cfg,freq);

47|Expressthetime-frequencyrepresentationofneuralactivityateachchannelintermsoftherelativechangeinactivityfromabaselineinterval.

cfg=[];cfg.baseline=[-.3-.1];cfg.baselinetype='relchange';freq_blc=ft_freqbaseline(cfg,freq);


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48|Visualizethetime-frequencyrepresentationsoverlaidonthetwo-dimensionallayout.Thegeneratedfigureisinteractive,sothatselectingagroupofchannelswilllaunchanotherfigurerepresentingtheaveragetime-frequencyrepresentationoverthosechannels(Fig.5).Selectingacertainfrequencyandtimerangeinthattime-frequencyrepresentationwilllaunchyetanotherfigureshowingthetopographicaldistributionofactivityintheselectedinterval,andsoon(SupplementaryVideo5).

cfg=[];cfg.layout=lay;cfg.showoutline='yes';ft_multiplotTFR(cfg,freq_blc);

ECoGdatarepresentation,TIMING~1min49|Forananatomicallyrealisticrepresentationofcorticalactivity,overlayasurfacemodeloftheneocortexwiththespatialdistributionofthehighfrequency-bandactivity.First,extracthigh-frequency-bandactivityduringatimeintervalofinterest.

cfg=[];cfg.frequency=[70150];cfg.avgoverfreq='yes';cfg.latency=[00.8];cfg.avgovertime='yes';freq_sel=ft_selectdata(cfg,freq_blc);

50|Visualizethespatialdistributionofhigh-frequency-bandactivityonacorticalmeshofthesubject’sbrain.

cfg=[];cfg.funparameter='powspctrm';cfg.funcolorlim=[-.5.5];cfg.method='surface';cfg.interpmethod='sphere_weighteddistance';cfg.sphereradius=8;cfg.camlight='no';ft_sourceplot(cfg,freq_sel,pial_lh);view([-9020]);materialdull;lightinggouraud;camlight;

51|Addtheelectrodestothefigure(Fig.6).ByloopingaroundSteps49to51whilebreakingdownthetimeintervalofinterestspecifiedwithcfg.latencyinconsecutivesteps,itbecomesfeasibletoobservethespatiotemporaldynamicsofneuralactivityoccurringinrelationtoknownexperimentalstructureandbehavior


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(SupplementaryVideo6).Seehelpgetframeforcapturingandassemblingtime-lapsemovies.

ft_plot_sens(elec_acpc_fr);SEEGdatarepresentation,TIMING~2min52|Fordepthrecordings,createanintegratedrepresentationofneuralactivityandanatomybyinterpolatingneuraldatafromeachbipolarchannelinasphericalcloud,whichcanthenbeoverlaidonasurfacemeshofanydeepbrainstructure.First,createavolumetricmaskoftheregionsofinterest(ROI).Here,wegenerateamaskfortherighthippocampusandamygdalafromthecorticalparcellationandsubcorticalsegmentationproducedbyFreeSurfer. atlas=ft_read_atlas('freesurfer/mri/aparc+aseg.mgz'); atlas.coordsys='acpc'; cfg=[]; cfg.inputcoord='acpc'; cfg.atlas=atlas; cfg.roi={'Right-Hippocampus','Right-Amygdala'}; mask_rha=ft_volumelookup(cfg,atlas);53|Createatriangulatedandsmoothedsurfacemeshonthebasisofthevolumetricmasks.

seg=keepfields(atlas,{'dim','unit','coordsys','transform'});seg.brain=mask_rha;cfg=[];cfg.method='iso2mesh';cfg.radbound=2;cfg.maxsurf=0;cfg.tissue='brain';cfg.numvertices=1000;cfg.smooth=3;mesh_rha=ft_prepare_mesh(cfg,seg);

54|Identifythesubcorticalelectrodesofinterest.

cfg=[];cfg.channel={'RAM*','RTH*','RHH*'};freq_sel2=ft_selectdata(cfg,freq_sel);

55|Interpolatethehigh-frequency-bandactivityinthebipolarchannelsonasphericalcloudaroundthechannelpositions,whileoverlayingtheneuralactivitywiththeabovemesh.Byrepeatingthecurrentstepforneuraldatacorrespondingtoconsecutivetimeintervals,similarlytotheprocessoutlinedinStep51,itbecomesfeasibletocreatetime-lapsemoviesofthespatiotemporaldynamicsofdeep-brain


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activity(SupplementaryVideo7showsthespatiotemporalevolutionofepileptiformactivityinaseparatesubject). cfg=[]; cfg.funparameter='powspctrm'; cfg.funcolorlim=[-.5.5]; cfg.method='cloud'; cfg.slice='3d'; cfg.nslices=2; cfg.facealpha=.25; ft_sourceplot(cfg,freq_sel2,mesh_rha); view([12040]); lightinggouraud; camlight; 56|Tocreateamoredefinitiveimageoftheneuralactivityatparticularpositions,generatetwo-dimensionalslicesthroughthethree-dimensionalrepresentations.Thiscombinationprovidesthemostcompleteandintegratedrepresentationofneuralandanatomicaldata(Fig.7). cfg.slice='2d'; ft_sourceplot(cfg,freq_sel2,mesh_rha);


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TIMINGSteps2-5,PreprocessingoftheanatomicalMRI:~2minSteps6-8,CorticalsurfaceextractionwithFreeSurfer(optional):~10hrsSteps9-12,PreprocessingoftheanatomicalCT:~2minSteps13-15,FusionoftheCTwiththeMRI:~3minSteps16-19,Electrodeplacement:~15minSteps21-24,Brainshiftcompensation(optional):~5minSteps25-28,Volume-basedregistration(optional):~2minSteps29-31,Surface-basedregistration(optional):~2minSteps32-33,Anatomicallabeling(optional):~1minSteps34-43,Preprocessingoftheneuralrecordings:~10minSteps44-45,Time-frequencyanalysis(optional):~2minSteps46-48,Interactiveplotting:~3minSteps49-51,ECoGdatarepresentation:~1minSteps52-56,SEEGdatarepresentation:~2minBox2,Coordinatesystemdetermination:~1min


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ANTICIPATEDRESULTSUponcompletionoftheprotocol,oneshouldobtainanintegratedrepresentationofneuralandanatomicaldata.Theexactresultsdependultimatelyontheclinicalorresearchquestionathand,contingenciesintheexperimentalparadigm,anddecisionsmadeduringtheexecutionoftheprotocol.Wedemonstratedtheanalysisofspatiotemporalneuraldynamicsoccurringinrelationtoknownexperimentalstructureandrelativelysimplebehavior,namelythepressingofabuttonwiththerighthandwhenhearingatargettone(Fig.5-7,SupplementaryVideo6).However,withsmalladaptationsoftheprotocolitisfeasibletotrackthespatiotemporalevolutionofepileptiformactivitywithhighprecision(SupplementaryVideo7),ortoperformgroup-levelinvestigationsoffine-graineddecision-relatedneuraldynamicsinhumanorbitofrontalcortex48.Aprecisefusionoftheanatomicalimageswiththeelectrophysiologicaldataiskeytoreproducibleanalysesandfindings.Hence,itisimportanttoexaminetheoutcomeofanycriticalstep,aswehavedoneinthisprotocol(e.g.,Fig.3and4).


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AcknowledgmentsTheauthorsthankthepatientforparticipationandChristopherR.Holdgraf,VinithaRangarajan,ColinW.Hoy,JuliaKam,LudovicBellier,RandolphHelfrich,RichardJimenez,EddenGerber,AlejandroBlenkmann,JamieLubell,andMichaelPereiraforfruitfuldiscussions.TheauthorsarealsogratefultothepresentandformerFieldTripcoredevelopersaswellasthegreaterFieldTripcommunityforcontributingcode,documentationandexpertisethathavemadethisprotocolpossible.A.S.wassupportedbyRubicongrant#446-14-007fromNWOandMarieSklodowska-CurieGlobalFellowship#658868fromtheEuropeanUnion;R.vd.M.byR01#MH095984-03S1fromNIMH;J-M.S.byVIDI#864-14-011fromNWO,R.T.K.byNINDSR37NS21135,andR.O.byH2020-MSCA-ITN-2014fromtheECMarieCurieActions.AuthorcontributionsA.S.,S.M.G,R.v.d.M.,J-M.S.,andR.O.developedtheprotocol.G.P.contributedthealgorithmforbrainshiftcompensation.J.J.L. providedaccessandguidanceinthedataacquisition.A.S.,S.M.G,J-M.S.,R.T.K,andR.O.wrotethepaper,andallotherauthorsprovidedsubstantialeditorialrevisions.CompetingfinancialinterestsTheauthorsdeclarethattheyhavenocompetingfinancialinterests.


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TABLE1|Troubleshootingtable.Step Problem Possiblereason Solution7 Unsatisfactory

qualityofcorticalsurfaces

InsufficientqualityoftheMRI

Repeatstep6onanotherMRIormanuallycorrectthetopologicaldefects(seeFreeSurferwebsite)

14 SeveremisalignmentofCTandMRI

FailureoftheautomaticCTconversioninstep12

DirectlyaligntotheACPCsysteminstep11byvirtueofeducatedguessesofthecommissurelocations

ImperfectalignmentofCTandMRI

Aleft-rightflipofeitherMRIorCT

Re-examinethenativeorientationsoftheMRIandCTinsteps3and10,andredothepreprocessingoftheaffectedscan

ImperfectalignmentofCTandMRI

MRIandCTcontaindifferentheadanatomies

Repeatstep13withadifferentcostfunction(typehelpft_volumerealign)

17 Electrodeshardtoidentifyinthe2Dorthoplot

Corticalgridorientationnotalignedwithanyofthe2Dplanes

Identifyelectrodesinthe3Dscatterfigure(tickthescattercheckbox)

23 Severedeformationoftheelectrodegrid

Incorrectpairingofneighboringelectrodesinspace

Repeatstep22withalternatesettings(typehelpft_electroderealign)

27 Unsatisfactoryqualityofthevolume-basedregistration

InsufficientqualityoftheMRI

Repeatstep25withanalternatecostfunctionortemplateversion(typehelpft_volumenormalise)

33 Noanatomicallabelfound

Nooverlapofelectrodepositionwithanyanatomicalmask

Increasethesearchradiusaroundtheelectrodebyincreasingcfg.maxqueryrange(typehelpft_volumelookup)


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Figure1|Overviewoftheprocedure.Theprotocolisgroundedintwoparallelbutinterrelatedworkflows.Theanatomicalworkflowminimallyconsistsofthepreprocessingandfusionoftheanatomicalimagesandelectrodeplacement.Thefunctionalworkflowencompassesthepreprocessingoftheneuralrecordings,butmayalsoincludefollow-upactivitiessuchasevent-relatedaveraging,time-frequencyandstatisticalanalysis.Theelectrodeplacementactivityofferstheopportunitytodirectlylinkanatomicallocationstoelectrodelabelscorrespondingtotheneuralrecordings,allowingforanearlyseamlessintegrationofthetwoworkflowstofacilitateanatomicallyinformeddataexplorationandvisualization.Figure2|Interactiveelectrodeplacement.ClickinganelectrodelabelinthemainpanelontheleftwilldirectlyassignthatlabeltothecurrentcrosshairpositionintheCTscan.SeveralfeaturesfacilitateprecisenavigationoftheanatomicalCT,suchasazoommode,amagnetoptionthattransportsthecrosshairtothenearestweightedmaximum(orminimumincaseofapost-implantMRI),andtheinteractivethree-dimensionalscatterfigureshownontheright.Figure3|Brainshiftcompensation.Insomepatients,compensationforelectrodedisplacementduetobrainshiftafterimplantationmaybenecessary.Inthisparticularcase,asubduralhygromaatthetopofthebraincausedsevereelectrodedisplacementinadirectionoppositetothemorecommonlyobservedinwardshift(left).Realigningelectrodegridstothecorticalsurfacecancompensateforelectrodedisplacementduetobrainshift(right).Thethinblacklinesindicateeachelectrode’spathfromitslocalizedoriginonthelefttoitsprojectedlocationontheright.Figure4|Spatialnormalization.Ontheleftaretheelectrodesontheindividualcorticalsheet.ThetoprightshowstheelectrodesonthestandardMNIbrainaftervolume-basedregistration.ThebottomrightshowstheelectrodesonFreeSurfer'sfsaveragebrainaftersurface-basedregistration.Comparedtovolume-basedregistration,withsurface-basedregistrationtheoriginalgridgeometryisnolongerpreservedaselectrodesaremovedfromonebraintoanotheraccordingtothecurvaturepatternofthecortex.Figure5|Interactiveplotting.FastbrowsingthroughvariousanatomicallyinformedrepresentationsoftheneuraldatacanhelpaddressthemultidimensionalityofintracranialEEGdata.Figure6|ECoGdatarepresentation.Task-inducedhigh-frequency-bandactivityrelativetoabaselineinterval,plottedonacorticalsurfacemeshofthesubject’sbrain.Figure7|SEEGdatarepresentation.


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Task-inducedhigh-frequency-bandactivityrelativetoabaselineinterval,plottedaspointcloudsaroundatriangulatedmeshofthesubject’samygdalaandhippocampusintherighthemisphere.Thetwo-dimensionalplanesontherightcorrespondtotheslicesintheimageontheleft.


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Box1|GettingstartedwithFieldTripAllcodeoftheprotocolisdirectlyintegratedwith,andfreelyavailablethroughFieldTrip53.ThisMATLAB-basedopensourcetoolboxoffersadvancedanalysismethodsforelectrophysiologicaldata,suchasevent-relatedaveraging,frequencyandtime-frequencyanalysis,sourcemodeling(forEEGandMEG),connectivityanalysis,classification,real-timedataprocessing,and(non)parametricstatisticalinference.TheimplementationasatoolboxallowsuserstoperformelaborateandstructuredanalysesoflargedatasetsusingtheMATLABcommandlineandbatchscripting.Tutorialdocumentation,answerstofrequentlyaskedquestions,andexamplecodeareavailableonlineasawiki:http://www.fieldtriptoolbox.org.Thetoolbox’sinfrastructureallowsusersanddeveloperstorelativelyeasilyextendthefunctionalityandimplementnewalgorithms.Overthepastdecade,theFieldTriptoolboxhasgrowntoanestimated5000users.

TogetstartedwithFieldTrip,downloadthemostrecentversionfromitshomepageorGitHub,andsetupyourMATLABpath.

addpath<pathtofieldtriphomedirectory>ft_defaults

FieldTripfunctionalities,recognizablebyanftprefix,typicallyhaveasingleoutputargumentandoneortwoinputarguments,thefirstinputargumentbeingconfigurationstructurecfg.

cfg=[];cfg.hpfilter=‘yes’;

cfg.hpfreq=1;data_filt=ft_preprocessing(cfg,data);

Here,inputdataisprocessedbyft_preprocessingaccordingtoparametersspecifiedinthecfgfields,inthiscaseapplyinga1Hzhigh-passfilter.Eachfunction’soptionalparametersareavailableintherespectivefunction’sheader(typehelpfunctionname)andexamplesaregivenonthewiki. Thecfgstructurethatholdstheparameterstothealgorithmatthepresentlevelisautomaticallyappendedtotheoutputdatastructure,i.e.data_filt.cfg.Configurationstructuresusedatpreviouslevelsarekeptindata_filt.cfg.previous,data_filt.cfg.previous.previous,andsoon.Thisnestingofpreviousconfigurationsallowsforaccesstothefulldataprovenanceatanyleveloftheanalysispipeline(seeft_analysispipelineforvisualizingthepipelineasaflowchart).


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Box2|Coordinatesystemdetermination,TIMING:~1minCoordinatesystemsdefinetheorientationandunitsoftheX-,Y-,andZ-axesofananatomicalvolumeinadditiontoanoriginpointalongthebrain’smidline(e.g.,anteriorcommissure).HereweprovideaguidelinefordeterminingthenativecoordinatesystemoftheMRIandCTscansand,inparticular,whethertheyhavealeft-to-rightoraright-to-leftorientation.Knowledgeoftheorientationoftheleft-rightaxisofthescan'snativecoordinatesystemprovidesthenecessarycontextfordemarcatingtherighthemispherelandmarkinthesucceedingalignmentstep.Althoughtheinterpretationofposterior-anteriorandinferior-superioraxesisstraightforwardfromaxial,coronal,orsagittalslicesofthebrain,differentiatingleftandrightrequiresathree-dimensionalcontext.Toaccomplishthis,werecommendusingft_determine_coordsys,whichdepictsananatomicalvolumeasthreeintersecting,orthogonalslicesandlabelstheX-,Y-,andZ-axes.Thisallowsdeterminingwhichofthesethreeaxesrepresentstheleft-rightaxisand,importantly,whetherthataxishasaleft-to-rightoraright-to-leftorientation(SupplementaryVideo1).1.VisualizethecoordinatesystemoftheMRIorCT:ft_determine_coordsys(mri)2.Determinewhichofthethreeaxes,X,Y,orZ,runsthroughoralongtheleft-rightaxisofthesubject'shead.Thisaxisistheleft-rightaxisforthisanatomicalvolume.3.Determinetheorientationoftheleft-rightaxis.Ifthevaluesontheleft-rightaxisincreasetotheright(indicatedbya+sign),thenthescanhasaleft-to-Rightorientation.Ifthevaluesontheleft-rightaxisincreasetotheleft,thenthescanhasaright-to-Leftorientation.4.Writedowntheorientationofthescan'sleft-rightaxis.


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Box3|Re-montagingTherecordedelectrophysiologicalsignalsareamixtureofsignal-of-interestandnoise,bothneuralandnon-neural.Themainobjectiveofthepreprocessingoftheneuralrecordingsistoimprovethesignal-to-noiseratioofthedatawhileoptimallypreparingitforfollow-upanalysis.Re-montagingtoadifferentreferencingscheme,alsoknownasamontage,mayaidintheremovalofnoisethatissharedacrossmultiplechannels.Thecommonaveragere-referencingtechnique,forinstance,involvestakingtheaveragepotentialfromallchannelsandsubtractingthisglobalnoiseestimatefromthepotentialineachchannel47,69–72.Wedemonstratedhowtoapplythistechniquetothecorticalgridelectrodesinourexamplecase. Depthelectrodes,locatedinsidethebrainandusingdifferentlysizedandshapedcontactpoints,haveadifferentsensitivitydistributionandcapturedifferenttypesofactivityandlevelsofnoise2.Thereiscurrentlynoconsensusonthepreferredmontagefordepth-electroderecordingsand,thus,whatelectrodestouseasreferences73–76.Whitemattersignalsmaynotbeassilentasonewouldintuitivelyexpect,andbipolarsignals,despitebeingrelativelyclean,missoutonactivitythathadthesameamplitudeonthetwoconsecutiveelectrodespriortotheircombination5,77.Differentoptionsmayneedtobetestedandevaluatedpercase,takingintoaccountthepurposeofanyfollow-upanalysis71.Forinstance,see55foradiscussionofconnectivityanalysisinrelationtothereferencingscheme.


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SupplementaryinformationSupplementaryFile1.Start-to-endimplementationoftheanatomicalandfunctional

workflows,SubjectUCI29.mSupplementaryFile2.AutomaticDICOMseriessearchandvisualizationtool,

search_dicomseries.mSupplementaryFile3.Automaticelectrodelabelingtool,generate_electable.mSupplementaryVideo1.PreprocessingoftheanatomicalMRIpart1SupplementaryVideo2.PreprocessingoftheanatomicalMRIpart2SupplementaryVideo3.PreprocessingoftheanatomicalCTSupplementaryVideo4.ElectrodeplacementSupplementaryVideo5.InteractiveplottingSupplementaryVideo6.Spatiotemporaldynamicsoftask-modulatedhigh-

frequency-bandactivityatsurfaceelectrodesoverlaidonleftparietalandtemporalcortex.Itcanbeobservedthatprocessingoccursinthetemporallobeathearingthetargettonefollowedbythesensorimotorsystemcontralateraltothehandusedforthebuttonpress.Warmandcoldcolorsrepresentincreasesanddecreasesinhigh-frequency-bandpower,respectively.

SupplementaryVideo7.Spatiotemporaldynamicsofepileptiformactivityrecorded

fromdepthelectrodestargetingbilateralhippocampusandamygdala.Itcanbeobservedthatthe(interictal)epileptiformdischargesfirstoccurinthelefthippocampusandamygdalaandthenspreadtotheirrighthemispherehomologuesduringthisparticularepisode.Warmandcoldcolorsrepresentpositiveandnegativedeflectionsinsignalamplitude,respectively.Thesizeofthepointcloudsindicatesthesignal'samplitude.


arjen stolk

https://youtu.be/KvmilLY9MiE

arjen stolk

https://youtu.be/K9rwGWr-ZnA

arjen stolk

https://youtu.be/0r-0mFWCSWI

arjen stolk

https://youtu.be/31aUH711GUk

arjen stolk

https://youtu.be/k7tvP87bnN4

arjen stolk

https://youtu.be/PTpIve6yiBM

arjen stolk

https://youtu.be/7YYumAjLFik

https://doi.org/10.1101/230912


Preprocessing ofthe anatomical CT

(~2 mins)

Preprocessing ofthe anatomical MRI

(~2 mins)

AN

ATOM

ICA

L WO

RK

FLOW

FUN

CTIO

NA

L WO

RK

FLOW

Electrode placement(~20 mins)

Preprocessing ofthe neural recordings

Time-frequencyanalysis

Statistical analysis

FreeSurfer(optional, ~10 hours,

automatic)

time

chan

nels

time

frequ

enci

es

time

t-sta

tistic

N

LR

R P A

ZZ

Fusion of the CT withthe MRI

(~3 mins, automatic)

VISUALIZATION


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Brain shiftcompensation


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Volume-basedregistration

Surface-basedregistration


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50%-50%


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50%-50%


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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Start-to-end MATLAB implementation of the protocol%% Appendix S1 of Stolk, Griffin et al., Integrated analysis of% anatomical and electrophysiological human intracranial data%% data available at: ftp://ftp.fieldtriptoolbox.org/pub/fieldtrip/ ...% tutorial/SubjectUCI29.zip%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

subjID = 'SubjectUCI29';

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% preprocessing of the anatomical MRI%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%mri = ft_read_mri([subjID '_MR_acpc.nii']); % we used the dcm series

ft_determine_coordsys(mri);

cfg = [];cfg.method = 'interactive';cfg.coordsys = 'acpc';mri_acpc = ft_volumerealign(cfg, mri);

cfg = [];cfg.filename = [subjID '_MR_acpc'];cfg.filetype = 'nifti';cfg.parameter = 'anatomy';ft_volumewrite(cfg, mri_acpc);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FreeSurfer%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fshome = '/Applications/freesurfer';subdir = pwd; % present working directorymrfile = [subdir filesep subjID '_MR_acpc.nii'];system(['export FREESURFER_HOME=' fshome '; ' ... 'source $FREESURFER_HOME/SetUpFreeSurfer.sh; ' ... 'mri_convert -c -oc 0 0 0 ' mrfile ' ' [subdir '/tmp.nii'] '; ' ... 'recon-all -i ' [subdir '/tmp.nii'] ' -s ' 'freesurfer' ' -sd ' ... subdir ' -all'])

fsmri_acpc = ft_read_mri('freesurfer/mri/T1.mgz');fsmri_acpc.coordsys = 'acpc';

pial_lh = ft_read_headshape('freesurfer/surf/lh.pial');pial_lh.coordsys = 'acpc';

ft_plot_mesh(pial_lh);material dull; lighting gouraud; camlight;

1


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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% preprocessing of the anatomical CT%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ct = ft_read_mri([subjID '_CT_acpc_f.nii']); % we used the dcm series

cfg = [];cfg.method = 'interactive';cfg.coordsys = 'ctf';ct_ctf = ft_volumerealign(cfg, ct);

ct_acpc = ft_convert_coordsys(ct_ctf, 'acpc');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% fusion of the CT with the MRI%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.method = 'spm';cfg.spmversion = 'spm12';cfg.coordsys = 'acpc';cfg.viewresult = 'yes';ct_acpc_f = ft_volumerealign(cfg, ct_acpc, fsmri_acpc);

cfg = [];cfg.filename = [subjID '_CT_acpc_f'];cfg.filetype = 'nifti';cfg.parameter = 'anatomy';ft_volumewrite(cfg, ct_acpc_f);

print([subjID '_CT_acpc_f.png'], '-dpng');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% electrode placement%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%load([subjID '_hdr.mat']); % we used ft_read_header

cfg = [];cfg.channel = hdr.label;elec_acpc_f = ft_electrodeplacement(cfg, ct_acpc_f, fsmri_acpc);

save([subjID '_elec_acpc_f.mat'], 'elec_acpc_f');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% brain shift compensation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.method = 'cortexhull';cfg.headshape = 'freesurfer/surf/lh.pial';cfg.fshome = '/Applications/freesurfer';hull_lh = ft_prepare_mesh(cfg);

save([subjID '_hull_lh.mat'], 'mesh');

elec_acpc_fr = elec_acpc_f;grids = {'LPG*', 'LTG*'};

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for g = 1:numel(grids) cfg = []; cfg.channel = grids{g}; cfg.keepchannel = 'yes'; cfg.elec = elec_acpc_fr; cfg.method = 'headshape'; cfg.headshape = hull_lh; cfg.warp = 'dykstra2012'; cfg.feedback = 'yes'; elec_acpc_fr = ft_electroderealign(cfg);end

ft_plot_mesh(pial_lh);ft_plot_sens(elec_acpc_fr);view([-55 10]); material dull; lighting gouraud; camlight

save([subjID '_elec_acpc_fr.mat'], 'elec_acpc_fr');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% volume-based registration%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%[ftver, ftpath] = ft_version;cfg = [];cfg.nonlinear = 'yes';cfg.spmversion = 'spm12';fsmri_mni = ft_volumenormalise(cfg, fsmri_acpc);

elec_mni_frv = elec_acpc_fr;elec_mni_frv.elecpos = ft_warp_apply(fsmri_mni.params, ... elec_acpc_fr.elecpos, 'individual2sn');elec_mni_frv.chanpos = ft_warp_apply(fsmri_mni.params, ... elec_acpc_fr.chanpos, 'individual2sn');elec_mni_frv.coordsys = 'mni';

save([subjID '_elec_mni_frv.mat'], 'elec_mni_frv');

load([ftpath filesep 'template/anatomy/surface_pial_left.mat']);ft_plot_mesh(mesh);ft_plot_sens(elec_mni_frv);view([-90 20]); material dull; lighting gouraud; camlight;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% surface-based registration%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.channel = {'LPG*', 'LTG*'};cfg.elec = elec_acpc_fr;cfg.method = 'headshape';cfg.headshape = 'freesurfer/surf/lh.pial';cfg.warp = 'fsaverage';cfg.fshome = '/Applications/freesurfer';elec_fsavg_frs = ft_electroderealign(cfg);

save([subjID '_elec_fsavg_frs.mat'], 'elec_fsavg_frs');

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fspial_lh = ft_read_headshape( ... '/Applications/freesurfer/subjects/fsaverage/surf/lh.pial');fspial_lh.coordsys = 'fsaverage';ft_plot_mesh(fspial_lh);ft_plot_sens(elec_fsavg_frs);view([-90 20]); material dull; lighting gouraud; camlight;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% anatomical labeling%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%atlas = ft_read_atlas([ftpath filesep ... 'template/atlas/aal/ROI_MNI_V4.nii']);

cfg = [];cfg.roi = elec_mni_frv.chanpos( ... match_str(elec_mni_frv.label, 'LHH2'),:);cfg.atlas = atlas;cfg.inputcoord = 'mni';cfg.output = 'label';labels = ft_volumelookup(cfg, atlas);

[~, indx] = max(labels.count);labels.name(indx)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% data inspection and artifact rejection%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%load([subjID '_data.mat'], 'data'); % we used ft_preprocessing

cfg = [];cfg.viewmode = 'vertical';cfg = ft_databrowser(cfg, data);

data = ft_rejectartifact(cfg, data);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% re-referencing%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.channel = {'LPG*', 'LTG*'};cfg.reref = 'yes';cfg.refchannel = 'all';reref_grids = ft_preprocessing(cfg, data);

depths = {'RAM*', 'RHH*', 'RTH*', 'ROC*', 'LAM*', 'LHH*', 'LTH*'};for d = 1:numel(depths) cfg = []; cfg.channel = ft_channelselection(depths{d}, data.label); cfg.montage.labelold = cfg.channel; cfg.montage.labelnew = strcat(cfg.channel(1:end-1),'-', ... cfg.channel(2:end)); cfg.montage.tra = ... [1 -1 0 0 0 0 0 0

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0 1 -1 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 1 -1]; cfg.updatesens = 'yes'; reref_depths{d} = ft_preprocessing(cfg, data);end

cfg = [];cfg.appendsens = 'yes';reref = ft_appenddata(cfg, reref_grids, reref_depths{:});

save([subjID '_reref.mat'], 'reref');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% time-frequency analysis%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.method = 'mtmconvol';cfg.foi = 5:5:200;cfg.toi = -.3:0.01:.8;cfg.t_ftimwin = ones(length(cfg.foi),1).*0.2;cfg.taper = 'hanning';cfg.output = 'pow';freq = ft_freqanalysis(cfg, reref);

save([subjID '_freq.mat'], 'freq');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% interactive plotting%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.headshape = pial_lh;cfg.projection = 'orthographic';cfg.channel = {'LPG*', 'LTG*'};cfg.viewpoint = 'left';cfg.mask = 'convex';cfg.boxchannel = {'LTG30', 'LTG31'};lay = ft_prepare_layout(cfg, freq);

cfg = [];cfg.baseline = [-.3 -.1];cfg.baselinetype = 'relchange';freq_blc = ft_freqbaseline(cfg, freq);

cfg = [];cfg.layout = lay;cfg.showoutline = 'yes';ft_multiplotTFR(cfg, freq_blc);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ECoG data representation

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cfg = [];cfg.frequency = [70 150];cfg.avgoverfreq = 'yes';cfg.latency = [0 0.8];cfg.avgovertime = 'yes';freq_sel = ft_selectdata(cfg, freq_blc);

cfg = [];cfg.funparameter = 'powspctrm';cfg.funcolorlim = [-.5 .5];cfg.method = 'surface';cfg.interpmethod = 'sphere_weighteddistance';cfg.sphereradius = 8;cfg.camlight = 'no';ft_sourceplot(cfg, freq_sel, pial_lh);view([-90 20]); material dull; lighting gouraud; camlight;

ft_plot_sens(elec_acpc_fr);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SEEG data representation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%atlas = ft_read_atlas('freesurfer/mri/aparc+aseg.mgz');atlas.coordsys = 'acpc';cfg = [];cfg.inputcoord = 'acpc';cfg.atlas = atlas;cfg.roi = {'Right-Hippocampus', 'Right-Amygdala'};mask_rha = ft_volumelookup(cfg, atlas);

seg = keepfields(atlas, {'dim', 'unit','coordsys','transform'});seg.brain = mask_rha;cfg = [];cfg.method = 'iso2mesh';cfg.numvertices = 10000;cfg.radbound = 2;cfg.maxsurf = 0;cfg.tissue = 'brain';cfg.smooth = 3;mesh_rha = ft_prepare_mesh(cfg, seg);

cfg = [];cfg.channel = {'RAM*', 'RTH*', 'RHH*'};freq_sel2 = ft_selectdata(cfg, freq_sel);

cfg = [];cfg.funparameter = 'powspctrm';cfg.funcolorlim = [-.5 .5];cfg.method = 'cloud';cfg.slice = '3d';cfg.nslices = 2;cfg.facealpha = .25;ft_sourceplot(cfg, freq_sel2, mesh_rha);

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view([120 40]); lighting gouraud; camlight;

cfg.slice = '2d';ft_sourceplot(cfg, freq_sel2, mesh_rha);

Published with MATLAB® R2017b

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function search_dicomseries(directory)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SEARCH_DICOMSERIES searches and visualizes all DICOM files (.DCM)% in a directory and its subdirectories. This can be instrumental for% selecting the best quality scan for follow-up analysis in case there% are multiple.%% Use as:% search_dicomseries(directory)%% Ensure FieldTrip is correcty added to the MATLAB path:% addpath <path to fieldtrip home directory>% ft_defaults%% This function is part of Stolk, Griffin et al., Integrated analysis% of anatomical and electrophysiological human intracranial data%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

threshold = 50; % minimum number of DICOM files required for plotting

list = dir(directory);for l = 1:numel(list) % list loop

if (strcmp(list(l).name, '.') || strcmp(list(l).name, '..') || ... strcmp(list(l).name, '.DS_Store')) % ignore '.' and '..' cases continue; % skip this 'file' end

full_directory = fullfile(directory, list(l).name); if isequal(list(l).isdir, 1) % recurse down search_dicomseries(full_directory); elseif isequal(list(l).isdir, 0) && numel(list)-2 > threshold % plot try fprintf(['>> plotting ' full_directory ' <<\n']); mri = ft_read_mri(full_directory); ft_sourceplot([], mri); title([full_directory ', ' num2str(numel(list)-2) ' dicoms']); drawnow clear mri catch fprintf(['>> could not plot ' full_directory ' <<\n']); end return; % use only one DICOM from each directory end

end % end of list loop


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function generate_electable(filename, varargin)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% GENERATE_ELECTABLE writes an electrode anatomy and annotation table%% Use as:% generate_electable(filename, ...)% where filename has an .xlsx file extension,%% and at least one of the following sets of key-value pairs is% specified:% elec_mni = electrode structure, with positions in MNI space%% elec_nat = electrode structure, with positions in native space% fsdir = string, path to freesurfer directory for the subject% (e.g. 'SubjectUCI29/freesurfer')%% Ensure FieldTrip is correcty added to the MATLAB path:% addpath <path to fieldtrip home directory>% ft_defaults%% On Mac and Linux, the freely available xlwrite plugin is needed,% hosted at: http://www.mathworks.com/matlabcentral/fileexchange/38591% xldir = string, path to xlwrite dir (e.g. 'MATLAB/xlwrite')%% This function is part of Stolk, Griffin et al., Integrated analysis% of anatomical and electrophysiological human intracranial data%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% get the optional input argumentselec_mni = ft_getopt(varargin, 'elec_mni');elec_nat = ft_getopt(varargin, 'elec_nat');fsdir = ft_getopt(varargin, 'fsdir');xldir = ft_getopt(varargin, 'xldir');

if isunix % on mac and linux % add java-based xlwrite to overcome windows-only xlswrite addpath(xldir); javaaddpath([xldir '/poi_library/poi-3.8-20120326.jar']); javaaddpath([xldir '/poi_library/poi-ooxml-3.8-20120326.jar']); javaaddpath([xldir ... '/poi_library/poi-ooxml-schemas-3.8-20120326.jar']); javaaddpath([xldir '/poi_library/xmlbeans-2.3.0.jar']); javaaddpath([xldir '/poi_library/dom4j-1.6.1.jar']); javaaddpath([xldir '/poi_library/stax-api-1.0.1.jar']);end

% prepare the atlases and elec structureatlas = {};name = {};elec = [];

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if ~isempty(elec_mni) % mni-based atlases [~, ftpath] = ft_version; atlas{end+1} = ft_read_atlas([ftpath ... '/template/atlas/afni/TTatlas+tlrc.HEAD']); % AFNI name{end+1} = 'AFNI'; atlas{end+1} = ft_read_atlas([ftpath ... '/template/atlas/aal/ROI_MNI_V4.nii']); % AAL name{end+1} = 'AAL'; brainweb = load([ftpath ... '/template/atlas/brainweb/brainweb_discrete.mat']); atlas{end+1} = brainweb.atlas; clear brainweb; % BrainWeb name{end+1} = 'BrainWeb'; atlas{end+1} = ft_read_atlas([ftpath ... '/template/atlas/spm_anatomy/AllAreas_v18_MPM']); % JuBrain name{end+1} = 'JuBrain'; load([ftpath '/template/atlas/vtpm/vtpm.mat']); atlas{end+1} = vtpm; % VTPM name{end+1} = 'VTPM'; atlas{end+1} = ft_read_atlas([ftpath ... % Brainnetome '/template/atlas/brainnetome/BNA_MPM_thr25_1.25mm.nii']); name{end+1} = 'Brainnetome'; elec = elec_mni;endif ~isempty(elec_nat) && ~isempty(fsdir) % freesurfer-based atlases atlas{end+1} = ft_read_atlas([fsdir ... '/mri/aparc+aseg.mgz']); % Desikan-Killiany (+volumetric) atlas{end}.coordsys = 'mni'; name{end+1} = 'Desikan-Killiany'; atlas{end+1} = ft_read_atlas([fsdir ... '/mri/aparc.a2009s+aseg.mgz']); % Destrieux (+volumetric) atlas{end}.coordsys = 'mni'; name{end+1} = 'Destrieux'; if isempty(elec) % elec_mni not present elec = elec_nat; end elec.elecpos_fs = elec_nat.elecpos;end

% generate the tabletable = {'Electrode','Coordinates','Discard','Epileptic', ... 'Out of Brain','Notes','Loc Meeting',name{:}};for e = 1:numel(elec.label) % electrode loop table{e+1,1} = elec.label{e}; % Electrode table{e+1,2} = num2str(elec.elecpos(e,:)); % Coordinates table{e+1,3} = 0; % Discard table{e+1,4} = 0; % Epileptic table{e+1,5} = 0; % Out of Brain table{e+1,6} = ''; % Notes table{e+1,7} = ''; % Localization Meeting

for a = 1:numel(atlas) % atlas loop fprintf(['>> electrode ' elec.label{e} ', ' table{1,7+a} ... ' atlas <<\n']) cfg = [];

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if strcmp(name{a}, 'Desikan-Killiany') || ... strcmp(name{a}, 'Destrieux') % freesurfer-based atlases cfg.roi = elec.elecpos_fs(e,:); % from elec_nat else cfg.roi = elec.elecpos(e,:); % from elec_mni end cfg.atlas = atlas{a}; cfg.inputcoord = 'mni'; cfg.output = 'label'; cfg.maxqueryrange = 5; labels = ft_volumelookup(cfg, atlas{a}); [~, indx] = max(labels.count); table{e+1,7+a} = char(labels.name(indx)); % anatomical label clear labels indx end % end of atlas loopend % end of electrode loop

% write to excel fileif isunix xlwrite(filename, table);else xlswrite(filename, table);end


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