preprocessing fmri data - usc neuroscience graduate … · pre‐preprocessing • scanner output:...

PreprocessingfMRIData

LeiLiewUSCNeuroimagingWorkshop

08.11.2011

WhyPreprocess?

•  Dataisnoisy!•  Subjectsmove!

•  ThingschangeovertheJmeofyourexperiment(moreifit’slongandboring)!

•  Thereagiantbulldozersandexcavators10feetawayfromyourscanner!Wait,that’snotnormal..?

•  Etcetcetc…

WhyPreprocess?•  PreprocessingaUemptstoincreaseSignal(e.g.,BOLDcontrast)toNoise(variancefrommovement,subject,scannerarJfacts,otheruncontrollables)raJo

•  HelpsyoumeetassumpJonssoyoucandostatsonyourdata

PreprocessingResources

•  Fullcreditforalltheinfoandmostoftheslides(andnicepictures)goesto:

•  FSLtutorials‐– hUp://www.fmrib.ox.ac.uk/fslcourse/

•  UCLANITPSummerCourse(SeeMonJ7/12Preprocessing)– hUp://www.brainmapping.org/NITP/Summer2011.php

PreprocessingSteps

•  Pre‐Preprocessing–  DICOMtransformaJon,ImagereconstrucJon,BET

•  MoJoncorrecJon•  Slice‐JmingcorrecJon

•  SpaJalfiltering•  Temporalfiltering•  GlobalintensitynormalizaJon

•  RegistraJon/Normalizing(technicallypost‐preprocessing)

Pre‐Preprocessing

•  Scanneroutput:DICOMS•  Analysisprograminput:Analyze(SPM),NiFTi(FSL,SPM),BrainVoyager

•  SPM,BrainVoyagerhavebuilt‐infuncJons

•  FSL–useMRICRON’sdcm2niiconvert– Cansetuppreferencesondcm2nii.inifile– Putalldcmsinonefolder

– dcm2nii–b/ApplicaJons/mricronmac/dcm2nii.inifirscile.dcm

Pre‐Preprocessing

•  ImageregistraJon– Lookatyourdata!UseFSLvideomode.

– OsirixViewer(oranyotherDICOMviewer):•  hUp://www.osirix‐viewer.com/

•  BrainextracJon– MostlyinFSL,useBETtoolbox

PreprocessingSteps





MoJonCorrecJon

•  “Pleasebevery,very,verysJll….”•  Movementthat’s1%ofvoxelsizecaninducea1%signalchange,whichissomeJmesgreaterthanyouractualBOLDsignal!(saytheFSLgods)

•  Also,yourvoxelisnolongerthesamevoxel…

MoJonCorrecJonFromMon),UCLANITP2011

MoJonCorrecJon

•  Chooseavolumetoregisteralltheothervolumesto(e.g.,first,middle,last,mean,standardspace;inFSLitsmiddle)

•  Withinsubject:use6DOFrigid

•  X,y,z,roll,pitch,yaw•  Realignstoreferencetominimizevariance

MoJonCorrecJon

•  Output(fromFSL):

•  Suddenspikes?MoJon>1voxelsize?Or.5?

•  Modelthese6regressors!

MoJonCorrecJon•  Results(fromFSL):

MoJonCorrecJon

•  Othernotes:– Canpreventwithtraining,makesubjectsfeelrelaxed(saying“don’tmove!”usuallymakesthemmorenevous!)

– GivethemaminutetogetseUledandcomfortable– Makesuretaskisn’tcorrelatedw/moJon

– ToomuchmoJon?Throwoutrun/subject..

PreprocessingSteps





Slice‐TimingCorrecJon•  Scanners(likeourSiemens3T)mayacquireslicesinaninterleavedfashion(e.g.,0,2,4..1,3,5)toavoidcontaminaJngneighboringslice

•  Cancorrectandputslicesbackinorder•  Currentconsensusisnoneed(FSL,MonJ)–  It’sanotherinterpolaJon(theless,thebeUer)–  Itdoesn’thelpthatmuch,couldmakethingsworse

–  Instead,addTEMPORALDERIVATIVEtomodel

Slice‐TimingCorrecJonFromMon),UCLANITP2011

Slice‐TimingCorrecJon

PreprocessingSteps





SpaJalFiltering(Smoothing)

•  Averagesonevoxel’svalueswithitsneighbors•  Minimum“smoothness”neededifusingGaussianrandomfieldtheory(FSL)

•  PROS:canincreaseSNRbydecreasingvariance

•  CONS:mayreducesignalifsmallacJvaJons–  Ifyouexpectthis,useasmallerkernel

– AlsoreducesspaJalresoluJon


•  GaussianFullWidthHalfMaximum(FWHM)kernel(fromFSLtutorial)


•  Onaverage,akernelof5mmisadequate– Picksomethingabout2.5xFWHM

– Cangoupto10‐15mmifexpecJnglargeacJvaJons

– Ornotuse(otheropJonsforthresholding)

PreprocessingSteps





TemporalFiltering•  Temporalnoise:– Temporaldriofromscanner;physiologicalcycles(cardiac,repiratory)

•  Thesecanmaskyouractualsignal!

•  Highpassfilterletshighfrequenciesthrough•  Lowpassfilterletslowfrequenciesthrough(removeshigh‐frequencyfluctuaJons)butcanremovesignal,espifevent‐related.

•  Usuallyjustusehighpass–FSLusesprewhiteningtoavoidlow‐pass

TemporalFiltering

•  Usethehigh‐passfiltertoremovelow‐frequency(e.g.,long,slow)noise(fromFSL):

TemporalFiltering

•  GenerallyaHPFilterof100sisadquate– Changeifyourmodelisdifferent(egverylongevent)

•  SPM:modelslowdriostocatchvariance;usescosinebasisset

•  FSL:removeslowdriosandthenconvolvessignalwithgaussian‐weightedrunningline

TemporalFilteringFromMon),UCLANITP2011

PreprocessingSteps





GlobalIntensityNormalizaJon•  FromMonJ2011slides:•  GOOD:•  Between‐sessions(grandmeanscaling)soyouarecomparingallrunsbyasinglefactor(canbearbitrary)centeredonthesamemean

•  BAD:•  Within‐session(globalscaling),forceseachVOLUMEtohavesamemeanintensity,that’ssilly

GlobalIntensityNormalizaJon•  Don’tdoglobalscaling!

PreprocessingSteps




•  **ICADenoising•  RegistraJon/Normalizing(technicallypost‐preprocessing)

ICADenoising

•  Manuallyremovenoisecomponents

•  FeedrawdataintoMELODIC(FSL;MulJvariateExploratoryLinearOpJmisedDecomposiJonintoIndependentComponents)

•  Modelfree–pullsoutallthe“components”•  CanmanuallygothroughcomponentsandidenJfynoise![demo]

•  SeeKelly2010JNeurosciMethodsfordetailsonwhattoremove

•  EspeciallygoodifresJngstatedata;oroddmovements!Badifnoiserelatestosignal…

RegistraJon/NormalizaJonFromMon),UCLANITP2011

RegistraJon

•  Allowscomparingbetweenindividualsbymappingalltoatemplatebrain(fromMonJ2011slides)

•  Rigidbody:–  6DOF–3rotaJons,3translaJons,typicallywithinsubj–  7DOF–addglobalscaling,goodforfunctoanatomical

•  Affine:–  12DOF–3rotaJons,3translaJons,3scalings,3skewings–  SubjecttotemplaterotaJon

•  Non‐linear:–  RepresentedbyadeformaJonfield(notamatrix)

RegistraJonFromMon),UCLANITP2011

RegistraJon

•  Re:Non‐lineartransformaJons

•  SeeFSLspecificallyonregistraJonandMonJformoredetailedinformaJon(FSLFLIRT,FNIRT)

•  SPMDARTELisalsogood(soIhear)•  LotsofopJonsoverall!

RegistraJon

•  Whenregistering,youhavetointerpolateagain–whattodowithnewemptyspaces?–  Nearestneighbor?Trilinear(4neighbors)?Sinc(10)?

•  FSL:analyzesinnaJvespace,thennormalizes•  SPM:normalizesfirst,thenGLM,decreasesvariance&SD

•  Canaccountforsomedifferencesinresults!

QuesJons/Discussion?

[email protected]

ThankstoPreprocessingResources

•  Fullcreditforalltheinfoandmostoftheslides(andnicepictures)goesto:

•  FSLtutorials‐– hUp://www.fmrib.ox.ac.uk/fslcourse/

•  UCLANITPSummerCourse(SeeMonJ7/12Preprocessing)– hUp://www.brainmapping.org/NITP/Summer2011.php

preprocessing fmri data - usc neuroscience graduate … · pre‐preprocessing • scanner output:...

Documents