Local landmark alignment for high-resolution fMRI group studies: Toward a fine cortical investigation of hand movements in human

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ContentslistsavailableatSciVerseScienceDirect

Journal

of

Neuroscience

Methods

j o ur na l h o me p a g e :w w w . e l s e v i e r . c o m / l o c a t e / j n e u m e t h

Basic

Neuroscience

Local

landmark

alignment

for

high-resolution

fMRI

group

studies:

Toward

a

ﬁne

cortical

investigation

of

hand

movements

in

human

F. Pizzagalli

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_G.

_Auzias

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_,

_C.

_Delon-Martin

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_,

_M.

_Dojat

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a_INSERM_U836_(GIN),_Grenoble_F-38700,_France b_Université_Joseph_Fourier,_Grenoble,_France c_LSIS_Lab,_UMR_CNRS_6168,_Marseille_F-13000,_France

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•Weproposethecombinationofhigh-resolutionfMRIwithnon-linearregistrationtechniqueinvolvingexplicitanatomicalconstraintforindividual cortexregionalignment.

•Preciselocalanatomicallandmarkalignmentimprovesfunctionalclusterdetectionandlocalizationatgrouplevel. •Thequalityoftheregistrationobtainedisassessedbybothstructuralandfunctionalindexes.

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Articlehistory:

Received19October2012

Receivedinrevisedform10May2013 Accepted12May2013 Keywords: Realignment Diffeomorphicregistration FunctionalMRI StructuralMRI Jacobian Sulci Brain

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Convergingevidenceconclusivelydemonstratestherobustrelationshipbetweenanatomicallandmarks andunderlyingfunctionalorganizationinprimarycorticalregions.Inconsequence,aprecisealignment acrosssubjectsofsuchspecificindividuallandmarksshouldimprovetheoverlapofthecorresponding functionalareasandthusthedetectionofactiveclustersatthegrouplevel.Inanefforttodefinea dedi-catedprocessingpipelineforafinenon-invasiveexplorationofthemotorcortexinhuman,weevaluated fourrecentnon-linearregistrationmethodsbasedonanatomicalandfunctionalindexes.Weused high-resolutionfunctionalMRIdatatofinelyrevealtheimpactoftheregistrationonthecorticalassignment ofthedetectedclusters.Ourresultsfirstdemonstratethatthequalityofregistrationstronglyaffectsthe statisticalsignificanceandtheassignmentofactivatedclusterstospecificanatomicalregions,herein theprimarymotorarea.Ourresultsalsoillustratethebiasinducedbythechosenreferencetemplateon thedetectedclusters.TheanalysisoftheJacobianofthedeformationfieldinformsusabouthoweach methoddeformstheanatomicalstructuresandfunctionalmaps.Themethodologywepropose, combin-inghighresolutionfMRIandnon-linearregistrationmethod,allowsarobustnon-invasiveexploration ofthemotorcortex.

1. Introduction

Group analysis of neuroimaging data requires registering anatomicalandfunctionalindividualdatainacommonspace.This spatialregistrationshouldensureaprecisealignmentofindividual corticalgraymatterribbonsandideallyofunderlyingfunctional areas.Forprimarycorticalregions,convergingevidence conclu-sivelydemonstratestherobustrelationshipbetweenanatomical landmarksand underlyingfunctionalorganization (Fischl etal., 2008).Inconsequencewhensucharelationshipexists,aprecise alignment across subjects of individual anatomical landmarks

∗ Correspondingauthorat:GrenobleInstitutdesNeurosciences,INSERMU836, BatimentEdmondJSafra,CheminFortunéFerrini,38700LaTronche,France. Tel.:+33456520601;fax:+33452560599.

E-mailaddresses:Michel.Dojat@ujf-grenoble.fr,mdojat@gmail.com(M.Dojat).

shouldimprovetheoverlapofthecorrespondingfunctionalareas andthusthedetectionofactiveclustersatthegrouplevel.Inthis paper, we considertheprimary motor cortex M1 that exhibits a clear relationship with a constant anatomical landmark, the centralsulcus(Fischletal.,2008).Weinvestigatewhetherafine alignmentofindividualcentralsulcusimprovesfunctional align-mentatthegrouplevel.AnimportantpartofM1isdevotedtothe controlofhandmovements.Ourcurrentknowledgeofthehand movement control relies mainly on electrophysiological recor-dingsinmonkeys(Humphrey,1986;Kakeietal.,1999;Rathelot and Strick,2006)and inhumans,onpre-surgicalinvestigations (Penfield,1935)orpostmortemcytoarchitectonicstudies(Geyer etal.,1996).Indeed,humanM1appearstobegrosslysomatotopic butitsfinefunctionalorganizationremainspoorlyknown(Geyer etal.,1996;SanesandSchieber,2001).Neuroimagingappearsas theidealnon-invasivetooltoprogressinourunderstandingofthe corticalrepresentationofhandmovementinhumans.However,

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allnon-invasivefunctionalstudiesofhumanM1proposedtodate havebeenlimitedbytheconsiderableinter-subjectvariabilityin subject’sresponse,degreeofsomatotopyandtheexactlocalization oftheirmotormaps(Kleinschmidtetal.,1997;Meieretal.,2008).In healthysubjects,differentfMRIstudiesofthecortical representa-tionoffingersrevealedhighlyoverlappingmaps,togetherwithan arrangementoffinger-specifichotspotsthatfollowsasomatotopic organization(Sanesetal.,1995;Raoetal.,1995;Kleinschmidtetal., 1997;IndovinaandSanes,2001;Beisteineretal.,2001;Dechent andFrahm,2003).Morerecently,Meierandcollaborators(Meier etal.,2008)foundanoverlappingsomatotopyinhumanforwhich therepresentationsofthedifferentbodypartsareintermingled, anda complexorganizationfor thearm andhand,withfingers emphasizedinacoreregionsurroundedbyadorsalandventral representationofthearm.Inthesameline,Strotheretal.reporteda doublerepresentationofthewristandelbow(Strotheretal.,2012). Ourgoalwiththepresent studywastodefinea specificimage processingpipelinetoinvestigatefinelyhandcorticalmovement organizationinM1inhealthyandpathologicalsubjects.

Structurally,theprimarymotorM1andsomatosensoryS1 cor-ticeslieonthetwobanksofthecentralsulcus(CS),respectively anteriorlyandposteriorly.The segmentof theprecentralgyrus shapedlikeaninvertedomegaorepsilonintheaxialplaneand likeahookinthesagittalplane,isareliablelandmarkfor identify-ingtheprecentralgyrusundernormalandpathologicalconditions. Itisgenerallyreferredasthehand-knobstructure andisagood predictorofthehandrepresentationinM1attheindividuallevel (Yousryetal.,1997).However,itpresentsaconsiderablevariability inshapeanddorso-ventralpositionalongtheCSbetweensubjects (Cauloetal.,2007;Sunetal.,2011).Functionally,the investiga-tionofM1withfunctionalMagneticResonanceImaging(fMRI)is limitedbytwofactors:

1.InsufﬁcientspatialresolutionoffMRI.Atacquisition,thetypical spatialresolution(3mm_×3mm_×3mm)leadstoa mixingof functionalBOLDresponseswithinM1subregionsandbetween M1andsomatosensorycortexS1.

2.Poorcross-participantalignmentofhandmotorarea.Thisleads totheunderestimationorthefailuretodetectfunctional acti-vationinM1.Moreover,spatialalignmentandspatialﬁltering, performedpriortostatisticalanalysistoreinforcetheoverlap betweenindividualareas,introducepersethefunctional blur-ringandthelossofapreciselocalizationoffMRIsignals.This clearlyimpedestheseparationofpotentiallydistinctactivated clustersfoundinM1.

Wehypothesizedthatthecombinationofaperfectalignment ofallindividualhand-knobsfromstructuralMRimageswithhigh resolutionfunctional MR images, insuringthe mappingofeach functionalvoxeltoasinglegyruslocation,couldimproveatthe populationlevel functionalareasoverlap,increasingthe signiﬁ-canceofstatisticalparametricmaps.

Given the high tortuosity of the cortical ribbon, Hyde and co-workers(Hydeetal.,2001)foundthatanisotropicspatial res-olutionof1.5mmwasoptimaltodetectrobustcorticalactivation followingaﬁnger-tappingparadigm.Thisspatialresolutioncanbe achievedbydecreasingplanethicknessandincreasingthein-plane acquisitionmatrixusingmulti-shotEPIsequenceswhilerestricting theacquiredvolumetoencompassthemotorarea.Forspatial align-ment,itiswellestablishedthatlinearregistrationisinadequate foraligningcorticalstructuressuchassulci.Severalstudieshave comparednon-linear brainimageregistrationalgorithms work-ingonthewholebrain(Hellieretal.,2003;Kleinetal.,2009),on speciﬁcregions(YassaandStark,2009), orusingvolumeversus surface-basedapproaches(Kleinetal.,2010).Inallthesestudies asetofquantitativemeasureswasusedtocomparethedeformed

structuralMRsourceimagestothetarget.Theeffectsofthe regis-trationonbrainactivationdetectionwereinvestigated,tothebest ofourknowledge,mainlyinauditorycortex(Viceicetal.,2009; Tahmasebietal.,2009,2012).Inthispaper,inthecontextofmotor cortexstudies,we explore theeffectsof non-linearregistration methodsonthedetectionofstatisticallysigniﬁcantactivated clus-tersandontheirlocalization.Theuseofhigh-resolutionfunctional MRimagesallowstoﬁnelyexploretheimpactoftheregistration onthecorticalassignmentofthedetectedclusters.Weselected fourrecentregistrationmethodsasrepresentativeofthecurrent state-of-the-art.

First,weconsideredDARTEL(Ashburner,2007)as representa-tiveofstandard diffeomorphicmethods wherethedeformation constraints,estimatedgloballyonthewholebrainscan,alignthe corticalvalleysandcrestsbutwithoutguaranteethatsulciof identi-calanatomicaldenominationwouldbeproperlyalignedaltogether. Then,weconsideredDISCO+DARTEL(Auziasetal.,2011)as repre-sentativeofaclassofmethodswhereexplicitsulcallandmarksare usedtoconstraintthe3Ddeformation.Finally,weconsidered Dif-feomorphicDemons(Vercauterenetal.,2009)asrepresentative ofdiffeomorphicmethodswheredeformationscanbeestimated locallytoalignpredeﬁnedthree-dimensionalregionofinterests (ROIs), heresurrounding thehand-knob region.Data werealso processedusingtheSPM8normalizationprocedure1_widely_used_in

theneurosciencecommunity.Toassesstheaccuracyofanatomical andfunctionalalignmentsprovidedbyeachregistrationmethod weconsideredcomplementarymeasuresandaquantitative analy-sisofthedeformationﬁeldcomputedandappliedtohighresolution functionaldatasetscomingfromthirteenhealthycontrolsubjects. Ourresultsdemonstratetheclearimprovement providedby registrationmethods,DARTELandDISCO+DARTEL,allowing large-but-controlled deformations on the quality of inter-individual brainstructurealignmentandonthedetectionandlocalizationof activatedclustersinM1areaatthepopulationlevel.

2. Materialsandmethods

2.1. Dataacquisition

Thirteen right-handed healthy subjects (mean 27.5 y.o.), as assessedbytheEdinburghinventory(Oldﬁeld,1971),withoutpast orcurrent braindiseaseand nodetectedcognitivedeﬁcitwere involvedinthisstudy.Allsubjectsgavewritteninformedconsent toparticipateinthestudy,whichwasapprovedbyour institu-tionalreviewboard.Weacquiredhigh-resolutionstructuralimages (1mm×1mm×1mm)onaBruker3TMedspecS300wholebody scannerequippedwithabirdcageheadcoilusingaT1-weighted 3DMP-RAGE optimizedsequence (Deichmannetal.,2000).For eachsubjectweacquired176sagittalpartitionsintwosegments withanimagematrixof256×112(read×phase).Furtherimaging sequenceparameterswere: TR/TE/TI:16/4.96/903ms,excitation pulseangle:8deg,acquisitionmatrix:176×224×256(x,y,z),fast phaseencodinginantero-posteriordirection(112stepsperRAGE train, 2 segments), slow phase encoding in left-right direction, isotropicnominalresolution=1mm,BW=130Hz/pixel,readoutin caudo-cranialdirection,numberofaverages=1andtotal measure-menttime=14min40s.

BOLDfunctionalimageswereacquiredwiththehighspatial res-olutionof1.5mm×1.5mm×1.5mmusinga4shotEPIsequence (pershot:TR/TE:1500/30ms,ﬂipangle:77deg,acquisitionmatrix: 72×16;2 stacks of 15 adjacent contiguous slices of thickness 1.5mm, the ﬁrst stack encompassing the hand portion of the

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primarymotorcortexandthesecondonetheupperpartofthe cere-bellum).Thetotalk-spacematrix(72×64)wasdividedin4parts, onepershot.For eachshotaquarterofeachslicewasacquired (72×16).Thefollowingsliceorderwasused:1stsliceofstack1, 1stsliceofstack2,2ndsliceofstack1,2ndsliceofstack2,etc.Thus, eachslicewassampledatfourdifferenttimepoints1.5sapart, pro-vidingatemporalresolutionof6s.Functionaldataacquiredinthe cerebellumregionareoutofthescopeofthepresentpaper. 2.2. fMRIexperiments

Becauseof the highcomplexity of themotor command, we chosetostudysimplebasicmovements,namelyrepetitive flex-ionandrepetitiveextension,performedseparatelywitheitherthe thumbor thefingersorthewristduringa simple block-design fMRIprotocol.Thesetaskswereperformedunilaterallywiththe dominant(right)andwiththenon-dominant(left)hand.Thetotal numberofbasicmovementsequals12(2movementdirections×3 handparts×2hands).For each basicmovement,subjectswere instructedtoperforman alternationof aself-paced‘movement andrelax’duringeachactivationblockduration.Themovements wereregisteredforeachsubjectusingasetofflexiblesensorsof flexion/extension(FlexpointSensorSystems,Inc.,Utah)fixedon4 jointsofeachhandwithHypafix®_,_to_insure_a_close_contact_between

thesensorsandtheskin.Additionally,thisﬁxationmethodexertsa restoringforcethathelpsthehandjointstopassivelyreturntotheir initialpositionduringtherelaxingtimefollowingeachﬂexionor extension.Flexionandextensionsignalswererecordedonlineata frequencyof200HzusingtheLabViewsoftwarepackage(National Instruments®₎_and_displayed_on_a_screen_in_the_acquisition_room

throughouttheexperiment,sothatthetaskscouldbecontrolledin real-timebytheexperimenters.

Eachsubjectunderwentablock-designfMRIprotocol.Inorder not to mix all the twelve movements (2 directions×3 hand parts×2hands),wepresentedthestimuliinfourdifferent func-tional runsfor respectively flexion or extension withtheright hand or the left hand. The order was randomized among the subjects.Each run begins with thepresentation of the type of stimuli(forinstance:‘Righthandflexion’),then12s-restand18 s-activationblocksalternatedandthiscyclewasrepeated9times. Threeactivation blocks wereperformed respectivelywith each handpart,thumb,fingersandwrist,inapseudorandomorderto avoidpossiblemotorpreparation.Priortotheirinstallationwithin theMRscanner,thesubjectsweretrainedtoensurethatthetasks wereproperlyexecuted. Thisshorttrainingperiod avoidedany learningeffect.WeusedthePresentation®_software

(Neurobeha-vorialSystems,Inc.)todisplayinstructionsback-projectedusing avideo-projector(Epson7250M,EpsonInc.,LongBeach,CA)ona translucentscreenpositionedattherearofthemagnet.Subjects viewedthisscreenviaamirrorﬁxedontheheadcoil.

2.3. Structuralimageprocessing

Individualstructuralimageswereﬁrstlysegmentedand debi-ased using uniﬁed segmentation approach as implemented in SPM82₍_Ashburner_and_Friston,₂₀₀₅₎_in_considering_two_brain

tis-sues(GMandWM)andfournonbraintissues(CSF,largeveins, scalpandmeningia).WealsousedtheBrainVISA3 _segmentation

pipelinetoautomaticallyextractandidentifythecorticalsulciand inparticulartheCSineachindividualbrain.Then,thevolumeswere registeredbetweensubjectsfollowingfourdifferentstrategiesand software using their own default parameters values as brieﬂy

2_‘New_Segment’_SPM_function. 3_{http://www.brainvisa.info}

described hereafter: (1) SPM8, (2) DARTEL, (3) DISCO+DARTEL (DiDa)and(4)DiffeomorphicDemons(DDe).Eachstrategy pro-videsasubject-relateddeformationﬁeldappliedtofunctionaldata (seeSection2.4).

2.3.1. SPM8

SPM8registration(spatialnormalization)isembeddedwithin aprobabilisticframeworkthatcombinedimageregistration, tis-sueclassificationandbiasfieldcorrection(AshburnerandFriston, 2005).Tissueprobabilitymaps areusedtoassist the classifica-tion.Thisallowsregistrationtoa standardspacetobeincluded withinthesame generativemodel. Thepriorsused representa modifiedversionoftheICBMtissueprobabilisticatlas.Estimating themodel parametersalternates among classification,bias cor-rection, and registrationsteps. The non-linear registration part usesalow-dimensionalapproach(comparedtoDARTEL),which parameterizesthedeformationsbyalinearcombinationofabout athousandcosinestransformbases.WeusedtheMNItemplateas thetargetforregistration.

2.3.2. DARTEL

DARTEL(Ashburner,2007)registrationoptimizestheoverlapof grayandwhitemattertissuemasksbetweensubjectsthrougha largedeformationmethodwheredeformationsareparameterized basedona stationaryvelocityﬁeld.Toavoidthebiasrelatedto thearbitraryselectionofasinglebrainasaregistrationtemplate, thealgorithmembedstheconstructionofanaverageimage tem-plate.Ititeratestoregisterallimagestotheiraveragetemplateand computeanewaveragetemplateateachstep.Theinitialsmooth templatebecomesgraduallysharpereachtimeitisre-computed, resulting inan iterativecoarse-to-ﬁne registrationscheme.The methodhasbeenalreadyappliedindifferentvoxel-based mor-phometrystudieswithgoodperformances.

2.3.3. DISCO+DARTEL(DiDa)

The DiffeomorphicSulcal-based Cortical(DISCO) registration explicitlyforces thealignment ofsulci inaniterativeapproach. Individualsulciarefirstsegmentedandmodeledasweightedsets ofpoints.Anempiricaltemplateisdefinedastheunionoftheentire setofsulcalpointsthroughthegroupofsubjects.Foreachsulcal label,thecorrespondingsulcallandmark inthetemplate corre-spondstotheunionofallpointsassociatedtothislabelforeach subject.Diffeomorphictransformationofeachindividualdataonto theempiricaltemplateisthenproceededinthegeneralframework oftheLargeDeformationDiffeomorphicMetricMappingtheory. Improvedmaskoverlapandreductionofsulcaldispersioncanbe reachedthroughthesequentialcombinationofDISCOand DAR-TEL(DiDa),withDARTELbeinginitializedusingDISCOsoutcome (Auziasetal.,2011).Inthisstudy,becausewewereinterestedin corticalactivationslocatedinpre-andpost-centralgyri,onlythe centralsulcuswasconsidered.Forthis verystablesulcus, auto-maticdetectionofmisidentificationaspresentedinAuziasetal. (2011) didnotdetectany error.Neither manualnor automatic correctionwasappliedforpossiblecentralsulcalmisalignments. The deformations resulting from the different methods were appliedtothesamesulcithatservedasconstraintinDISCOand DISCO+DARTEL.

2.3.4. Diffeomorphicdemons(DDe)

DiffeomorphicDemonsisan efﬁcientalgorithm forthe non-parametric registration of multiple dimensional images based on the sum of squared difference in voxels intensity between

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imagesimplementedin MedINRIA toolbox4 ₍_Vercauteren_et _al.,

2009).Itadaptstheoptimizationprocedureontheentirespaceof displacementfieldsproposedinThirion(1998)tothespaceof dif-feomorphictransformations.SimilarlytoDARTEL,DDereliesona stationaryvelocityvectorfieldbuttheobjectivefunctiontobe min-imizedandtheoptimizationstrategyaredifferent.Thismethodhas beenshownoutperformingothermethodswhenappliedlocally inanROIinthemedialtemporallobe(YassaandStark,2009).In thepresentwork,weappliedDDeinacommonROIinthemotor cortexregionconstructedasfollows.Wefirstisolatedthevoxels correspondingtothecentralsulcusofeach subjectusing Brain-VISA.Wethen linearlyregisteredusingSPM8thesevoxels in a commonreferential,theMNIspace.Wemanuallydefined individ-ualROIssurroundingtheprimarysomato-sensorycorticalareaand encompassingthehand-knobstructureoneachindividual struc-turalimageregisteredintheMNIspace.Wecomputedacommon ROIastheunionofallindividualROIsfollowedbymorphological operationsofclosinganddilatation.Thisprocedurewasrepeated foreachhemisphereandresultedintwoROIspersubject(oneper hemisphere).Using individual(inverse)transformation parame-ters,thetwocommonROIswerethenputbackineachindividual referential.DDewasthenappliedtorealigneachindividualROI toachosenreferencesubject.Toassesstheinfluenceofthe refer-enceimageonthealignmentquality,weusedtworeferences,R1 (DDe-R1)andR2(DDe-R2).

2.4. Functionalimageprocessing

For each subject, the functional images were corrected for motionandregisteredtothecorrespondingstructuralimage(rigid transformation) using SMP8. Individual data analysis was per-formedinthesubject’sreferentialusingaclassicalGeneralLinear Model with a regressor for each condition of interest (flexion or extension with fingers, thumb or wrist for each hand) and sixmotion parametersas regressorsof non-interest, convolved withthe canonical hemodynamic response function. Individual statisticalcontrastimageswerecomputedandthen,usingeach registration strategy, registered to the common referential by applying the corresponding deformation field. A slight spatial smoothing wasapplied using a Gaussian kernel with a FWHM equaltothesizeofthespatialresolutionofthefunctionalimages (1.5mm×1.5mm×1.5mm)inordertofittotheGaussian field hypothesis.Randomeffectsstatisticalgroupanalysiswasfurther performedandT-mapsforeachtaskandeachregistrationmethod werecomputed.

2.5. Assessmentofregistrationperformances

Theperformancesofthefourregistrationmethodswere evalu-atedbasedonthecomputationofindexesthatquantitativelyassess bothinter-individualanatomicalandfunctionalalignments.Forthe former,weconsideredboththegray-matteroverlapbetween sub-jectsincomputingtheJaccardoverlapdistance,andthedispersion oftheCSsincomputingtheHausdorffdistance.TheJacobianwas alsocalculatedtoassesstheregularityofthecorresponding defor-mationfield.Forthefunctionalalignment,wereasonedthatifit isimprovedbyaregistrationmethodthenmoresignificant vox-elsshouldbedetectedandcorrectlypositionedintheM1cortex. Inordertofocusonourregionofinterest,themotorregion,and allowforthecomparisonbetweenglobal(DARTEL,DISCO+DARTEL andSPM8)andlocal(DDe)approaches,werestrictedtheanalysis ofregistrationperformancestothecommonROIdefinedforDDe.

4 _{http://www-sop.inria.fr/asclepios/software/MedINRIA/}

2.5.1. Assessmentofaccuracyofanatomicalalignment

Foreachmethod,weappliedthecomputeddeformationﬁeldto theindividualgraymatterimagesandtotheindividualstructural images.Wecomputedforeachpairofsubjects(S1_,_S2_):

1.ThefuzzyJaccardmeasure,JO,quantiﬁestheoverlapbetween thegreymatterprobabilitymapsacrosstwosubjects(Auzias etal.,2011;Crumetal.,2005).Itiscomputedbasedonthegrey matterprobabilityvalueGMiforeachvoxeliasfollows:

JO(S1_,_S2₎₌

voxels,imin(GM1i,GM2i)

voxels,imax(GM1i,GM2i)

whereGM1 _and_GM2 _are_the_gray_matter_maps_for_S1 _and_S2

respectivelyinthecommonROI.

2.TheHausdorffdistance,H,betweentheCS.Hisanindicatorof thespatialdispersionbetweentwodatasets.Itcanbeobtained bygeneratingthesetofdistancesfromeachpointin oneset tothenearestpointintheotherset(minimalvalue),andthen takingthelargestofallthesedistances(maximalvalue).This iscomputedforeachsetinturnandlastlythelargestdistance iskept.Here,eachpointiandjrefersrespectivelytothevoxels correspondingtothesulci(CS)ofsubjectsS1_and_S2_._The_number

ofpointsforS1_and_S2_can_be_different._This_measure_is_in_our

contextanindicatorofthespatialdispersionofthecentralsulci afteralignment.

H(S1_,_S2₎₌_max(max i minj (||CS

1

i −CS2j||),max_j min_i (||CS1j −CS2i||))

ThehigherJOvalueandthelowerHvalue,thebetterthe accu-racyoftheregistrationmethodis.

3.Jacobiancomputation

Thedeformationfieldproducedreflectsthequalityofthe reg-istrationmethod(Leow etal.,2007).Indeed,tworegistration methods can achieve similarGM overlaps but witha differ-entamountofdilatationsandcompressions.Unnecessarytissue stretchingorcompressionshouldbeavoided.Amethodthat gen-eratesfewdeformationswhileprovidinganaccuratealignment isalwayspreferable.TheJacobianofthedeformationfieldwas computedtoassesstheregularityofthefieldandtheamount ofdilatationorcompressionforeachvoxelofthecommonROI forthethreediffeomorphicmethodsandforeachsubject.The JacobiancomputationwasstraightforwardforDARTELandDDe methods.AsDiDaisasequentialcombinationofDISCOand DAR-TELwecomputedthefollowingJacobianmeasureforeachvoxel

v

,

JacobianDI+DA(

v

)=JacobianDISCO(

v

)∗JacobianDARTEL(

v

)

Thespatialdistributionofhighcompressionandhighdilatation ratesintheROIandamong subjectsillustratestheregularity oftheﬁeldaposterioriandhowthecorrespondingregistration methodhasdeformedtheanatomicalstructures.

2.5.2. Assessmentofaccuracyoffunctionalalignment

Becausenogroundtruthisavailabletocomparethe statisti-calparametricmapsobtainedusingeachregistrationmethod,we consideredseparatelytwoaspectsatthepopulationlevel:the acti-vationdetectability(t-values)andthelocalizationofactivation.For each registrationmethodwecomputed,for eachtaskand each hand,thenumberofdetectedvoxelsaboveapredeﬁned thresh-old.Basedonourhypothesisthatpreciseinter-subjectcentralsulci alignmentresultsinprecisealignmentofcorrespondingactivated functionalregions,themoreprecisetheregistration,thehigher

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Fig.1.Hand-knobregion.Left:ThecommonROI(seeSection2.3.3)surroundingthehand-knobisdelineatedbytheredcontouroneachhemisphereofonesubject.Thetop rowshowsforthissubject,theROIforeachhemisphereafterdeformationwitheachregistrationmethod.Onthebottomrow:themeanimagerestrictedtothecommonROI afterregistrationofthe13subjectsforeachhemisphereandeachmethod.L,left;R,right.

thenumberofstatisticalsignificantactivatedvoxelsis.Wefurther testedwhetherthefourmethodshadsignificanteffecton activa-tiondetectabilitywhateverthetask.WecomputedusingStatistica 8(StatSoft©),a 2-way(method,hemisphere)repeated-measure ANOVAwiththe total number of detectedvoxels as measures. A post-hoc Tukey–Kramertest wasfurthercomputed toassess whichmethodsperformedsignificantlybetterintermsof activa-tiondetectability.Forlocalization,wesuperposedthesignificantly activatedvoxels ontothecorrespondingmeanstructuralimage. Themajorpartofactivation shouldbeprecisely locatedinM1. Additionally,wequantifiedthespreadofindividualsubjectpeak activitycoordinatesbyidentifyingbeforeregistrationacluster(size ≥5voxels)statisticallysignificantandvisuallylocalizedatasimilar positionineachindividualforeachtaskandhand.Afterregistration usingSPM8,DARTELandDiDa,wecomputedadispersionindex,for eachtaskandhand,astheEuclideandistancebetweeneach indi-vidualpeakcoordinatesandtheircentroid(meancoordinate).We computeda2-way(method,task)repeated-measureANOVAwith Euclideandistancesasmeasuresandapost-hocTukey–Kramertest wascalculatedassessingwhichmethodsperformedsignificantly betterintermsofdispersionreduction.Therationaleisthatbetter theregistration,lowerthefinaldispersionis.

3. Results

ThecommonROIconsideredfortheanalysisissuperposedin redononeindividualstructuralimageinFig.1.Fig.1showsfor agivensubject(upperrow)thedeformedROIafterapplicationof eachregistrationmethod.Themeanstructural,restrictedtothe commonROI,obtainedafterregistrationofthe13subjectsusing eachmethodisdisplayed(lowerrow).Theblurringobtainedusing SPM8clearlyindicatesapoorrealignment.Non-linear deforma-tionsintroducedinSPM8areclearlytoolimitedtoreachasatisfying registrationofallbrainsfortheconsideredpopulation.Themean deformedROIissharperforthethreediffeomorphicmethods.For DDe,themeanshapeisveryclosetothechosenreferenceimage (notshown).Fig.2displaysthesetof13deformedcentralsulciafter registration.Itillustrateshowthefourmethodsdeformlocallythe individualleftandrightCS.Notethepersistentlargedispersionof sulciafterrealignmentwithSPM8andDDe.Asexpected,wenote thestronginﬂuenceofthereferenceusedwithDDe(seeDDE-R1 vs.DDe-R2).

3.1. Anatomicalalignmentaccuracy 3.1.1. JaccardoverlapandHausdorffdistance

Fig.3showsthedistributionacrossallpairsofsubjectsofthe fuzzyJaccardoverlapforgraymatterwithintheROIandHausdorff distancebetweenCS.Thesequantitativemeasuresconﬁrmaclear improvementofthegraymatteroverlapwhenusingdiffeomorphic

methodscomparedtoSPM8.BothDARTELandDiDamethods pro-videdalowHausdorffmeandistanceandagoodoverlapoftheGM aroundthehand-knob.ConcerningDemons,inspiteofthe obten-tionofarelativelyhighoverlapscoreandtheapparentpreservation ofthehand-knobstructureshape,asshowedonthemeanimages (Fig.1),theHausdorffdistancesacrossCSremainrelativelyhigh comparedtoDARTELandDiDa.Wenotedforsomesubjects(6)a localmatchingbetweencentralsulcusandprecentralor postcen-tralsulci.Thissuggeststhatalocalminimumintheenergyterm wasachievedasaconsequenceofthelargedeformationstolerance withoutinsertingexplicitanatomicalconstraints.These quantita-tivemeasuresalsoconﬁrmtheinﬂuenceofthechosenreference ontheaccuracyofthemethod.

3.1.2. Deformationﬁeldsfordiffeomorphicmethods

Fig.4aillustratestheconsistencyofthespatialdeformations acrossthesubjects.ForeachvoxelinthecommonROI,wecounted the number of subjectssustaining relatively largecompression (J<0.6)orlargeexpansion(J>1.4).Fig.4bdepictsthehistogram oftheJacobianvaluesacrossallvoxelsinthecommonROIforthe 13subjectsandforeachhemisphere.

AllJacobianvaluesarepositive,anecessaryconditionfora trans-formationtobediffeomorphic.However,theirdistributionvaries signiﬁcantlydependingonthemethod.Thehistogramsand spa-tialdistributionmapsshowclearlythatDDegeneratesverystrong deformations(especially compressions) distributed through the entireROI.Thisisanexpectedoutcomeofthepreviouslyobserved mismatchbetweenthecentralsulcusandpre-and post-central sulci.ComparedtoDARTEL,morevoxelssustainedadeformation withDiDa,withsimilarcompressionsandmoredilatations.

3.2. Functionalalignmentaccuracy

Figs.5 and6illustratethelocalization ofthemainactivated clusters derived from group analysis for extension and ﬂexion respectively(p≤0.0001,voxelleveluncorrectedwithno thresh-oldingonclustersize),superimposedonthecorrespondingmean structuralimagecomputedforeachmethodandrestrictedtothe commonROI.WithSPM8,activatedspotscannotbeidentiﬁedas belongingtoM1orS1becauseofthepooralignment.WithDARTEL andDiDa,inallconditions,wecouldeasilyidentifyseveralspots alongthehand-knobregionwhicharecoherentwiththecurrent knowledgeaboutcorticalrepresentationofhandmovement(Meier etal.,2008).ResultsprovidedbyDDe-R1seemcoherentwiththe literatureforthelefthand(seeespeciallyFig.5)butincoherentfor therighthandwhereactivationfociareclearlyfoundbothalong centralandmostlyalongpostcentralsulci.Theactivationpattern obtainedwithDDe(Figs.5and6)ishighlydependentonthechosen reference(DDeR1versusDDeR2).

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Fig.2. Thedeformedleft(left)andright(right)sulci(13subjects)afterrealignmentbyeachmethod.

Figs.7and8comparethehistogramsoftheT-valuemapsfor extensionandﬂexion respectivelyforeachtaskandeach hemi-sphere.ThesehistogramsconﬁrmthatthegoodalignmentofCS obtainedwithDiDaandtoalessextentwithDARTELisassociated withahighernumberofactivatedvoxelscomparedtoSPM8and DDe.

Jointly with the bad registration of CSs, DDe provides low T-values for the left and right hand ﬂexion in spite of a relatively good overlap of gray-matter in the left hemi-sphere (Fig. 3). The bias induced by the choice of the reference subject between DDe-R1 and DDe-R2 appears clearly.

Fig.3.BoxplotofHausdorffdistances(upperrow)andfuzzyJaccardoverlapforgraymatter(bottomrow)obtainedforthefourmethods.Leftcolumn:lefthemisphere,right column:righthemisphere.

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Fig.4.Deformationﬁeldsfordiffeomorphicmethods.(a)Thespatialdistributionmapsofthedeformationsacrossthesubjects.Theyreportforeachvoxelofthecommon ROIthenumberofsubjectssustaininghighcompressions(J<0.6,top)andhighdilatations(J>1.4,bottom)followingDARTEL,DiDa,DDeR1andDDeR2registration.These valuesaresuperimposedonthecorrespondingmeanstructuralimagecomputedforeachmethodandrestrictedtothecommonROI.(b)HistogramsofJacobianvaluesacross everyvoxelinthecommonROIandacrossallthesubjectsforthelefthemisphere(left)andrighthemisphere(right).

Fig.9clearlyshowsthereductionofthespreadofindividual subjectpeakactivitywithDARTELandDiDacomparedtoSPM8. Thereisahighlysigniﬁcanteffectofthemethodonthedispersion (F2,10=12)andahighlysigniﬁcantdifferencebetweenSPM8and

DARTEL(p≤0.001)orDiDa(p≤0.0003).Nodifferencewas signiﬁ-cantbetweenDARTELandDiDa.

Fig.10showsthetotalnumberofstatisticallysigniﬁcantvoxels detectedatthegrouplevelacrossalltasksforeachhemisphereand foreachmethod.Thereisahighlysigniﬁcanteffectofthemethod ontheactivationdetectability(F4,20=16,partialeta-squared2p=

0.76,p≤5×10−6_),_and_a_small_but_signiﬁcant_effect_of_the

hemi-sphere(F=7,2

p=0.59,p≤4×10−2)andasigniﬁcantinteraction

betweenhemisphereandmethod(F=19,2

p=0.79,p≤10−6).For

thelefthemisphere,differencesbetweenthemethodsintermof activationdetectabilityarehighlysigniﬁcantbutforSPM8versus DDE-R1orDDE-R2andforDDE-R1versusDDE-R2.Fortheright hemisphere,thedifferencesbetweenmethodsarelesssigniﬁcant exceptforDiDaversusDDe-R2.

4. Discussion

Inanefforttoimprovethequalityoftheresultsprovidedat thepopulationlevelusingfMRIdata,weinvestigatedthe combina-tionofstate-of-the-artregistrationmethodswithhighresolution functionalMRdataimagingthemotorcortexregion.Theuseof high-resolutionfunctionalMRimagesallowedtofinelyrevealthe impactoftheregistrationonthecorticalassignmentofthedetected clusters.Ourstudyclearlyshowsthatthealignmentquality pro-videdbynon-linearmethodshasadirectandstrongimpactonthe detectionofactivatedclusters,i.e.improvementofthestatistical significanceofthedetectedclusters,andontheirlocalization,i.e. improvementoftheaccuracyoftheclusterspositionand reduc-tionoftheirspatialdispersion.Thegainweobtainedinfunctional activationdetection confirmsourinitialhypothesis thata good alignmentofindividualcentralsulcishouldimprovethemapping ofindividualfunctionalM1regions.Sucharelationshipbetweenan anatomicallandmarkandafunctionalareaiscertainlylessclearin

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Fig.5.Activationmapsobtainedforextensiontasksforlefthand(top,righthemisphere)andrighthand(bottom,lefthemisphere)foreachmethod.Threecontiguous transverseslicesaredisplayedtoshowthelargestclustersaroundtheright(resp.left)centralsulcusforeachmethod.Mapsaresuperimposedonthecorrespondingmean structuralimagecomputedforeachmethodandrestrictedtothecommonROI.t≥4.0,p≤0.0001voxelleveluncorrectedwithnothresholdingontheclustersize.

nonprimarycorticalareasasobservedforinstancebyTahmasebi etal.(2012)forearlystagesversuscognitivelyhigherlevelsof audi-toryprocessing.Thegeneralizationofourresultsisthenlimited becausespatiallocationoffunctionalareamayvarygreatlyacross subjectswithinacorticalarea(FrostandGoebel,2012)anddue totheuseofpossibleindividualstrategyforhigh-leveltasks.Note thatdespitetheimprovementinspatialresolutionandfunctional alignment,wefailedtoseparatethedifferentialactivationresulting fromdifferentmovements.

4.1. Methodologicalissues

Ourstudyemphasizesthattheaccuracyofregistrationmethod shouldbeassessedviacomplementaryquantitativeindexesthat measuretheaccuracyoftheanatomicalalignmentandthe func-tional overlap. In accordance to Hellieret al. (2003) and Klein etal.(2009),anatomicalmeasuresshowedthatmethodswithhigh

degreeoffreedomcanincreaseoverlapofcorticalribbons(Fig.3, bottom).However,thisdoesnotnecessarilyimprovethealignment ofcentralsulci(CS)(Figs.2and3,up).Amongthe4registration methodsevaluatedhere,onlyDARTELandDiDapresentbothalow HausdorffdistanceforCSandahighoverlapforgraymatter.These goodperformances(Fig.3)highlightthefactthatDARTELachieves agoodalignmentofCS,asalreadyobservedinKleinetal.(2009). Theanatomicalconstraintonsulcialignment broughtbyDISCO doesnotclearlyimprovetheanatomicalalignmentmeasures.With thetwo methods that reduced distances betweenCS, i.e. DAR-TELandDiDa,thefunctionalT-valuedistributions(Figs.7and8), the activation detection (Fig. 10) and the clusters localization (Figs.5and6)wereclearlyimprovedascomparedtoSPM8andDDe. Afterrealignment, theindividualcluster coordinates dispersion wasreducedusingDARTEL,DiDaorDDecomparedtoSPM8(Fig.9) butnosigniﬁcantdifferencewasfoundbetweenDDe,DiDaand DARTEL.

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Fig.6.Activationmapsobtainedforﬂexiontasksforlefthand(top,righthemisphere)andrighthand(bottom,lefthemisphere)foreachmethod.Threecontiguoustransverse slicesaredisplayedtoshowthelargestclustersaroundtheright(resp.left)centralsulcusforeachmethod.Mapsaresuperimposedonthecorrespondingmeanstructural imagecomputedforeachmethodandrestrictedtothecommonROI.t≥4.0,p≤0.0001voxelleveluncorrectedwithnothresholdingontheclustersize.

Indeed, the slight improvement in CS realignment obtained with DiDa has a significant positive effect on group statistics (Figs. 7 and 8). This confirms that any change in deformation hasanimpactonfunctionalgroupanalysis,asreportedinViceic etal. (2009)or Tahmasebietal. (2009, 2012)for auditory cor-tex.NotethateffectivesmoothnessasprovidedbySPM8wasnot statisticallydifferentbetweentheregistrationmethodsused (aver-agevolumetricresolution=3.2,=0.2).InViceicetal.(2009),the authorscomparedtheirlocallandmarkbasedregistrationmethod –developedspecificallyforthesupratemporalplaneandusing sulcidelimitingHeschl’sgyrus–tostandardglobalaffineand non-rigidmethods.Theyshowedtheimportanceoflocalconstraints theyintroducedontheaccuracyof thealignment ofindividual Heschl’sregions.However,thestandardspatialsmoothing(6-mm fullwidthathalfmaximum)theyusedlimitedtheaccuracyofthe assignmentofactivatedclusterstopreciseanatomicalregions.In

Tahmasebietal.(2009)theeffectofspatialsmoothingwas inves-tigatedinauditorycortex,showingthatsmoothingdegradesthe localizationoftheactivationandincreasesthemagnitudeofthe peakactivationfor allstudiedmethods.Weuseda highspatial resolutionaftersmoothingequalto1.5mm×1.5mm×1.5mm.As expected,alargerresolution(3mm×3mm×3mm),after smooth-ing, comparable to image resolution (3.3mm_×3.3mm_×4mm) usedinTahmasebietal.(2009)improvedthenumberofactivated voxels(see Supplementarydata,Figs.13and 14)buthampered theseparation ofdifferentfociofactivation(seeSupplementary data, Figs.11 and 12). In this case, thedifference inactivation detectabilitybetweenDARTELandDiscoisnomoresigniﬁcant(see Supplementarydata,Fig.15).

Our studyprovidesadditionaldata abouttheeffectof regis-trationwiththeanalysisoftheJacobianofthedeformations.The spatialdistributionofthedeformation(Fig.4a)indicatesclearly

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Fig.7.Inﬂuenceofregistrationtechniquesongroup-averagedfunctionalmapsforextension.ThezoomedpartemphasizesT-valuesgreaterthan7(p≤10−5_)._Right_hemisphere (rightcolumn)andlefthemisphere(leftcolumn).

that in theabsence of explicit anatomical constraints, such as gray-andwhite-matteroverlapforDARTELandsulcalconstraints forDiDa,DDe computedunsatisfactory deformationsdespiteof arelativelygoodmeasureofgray-matteroverlap.DARTEL defor-mationsremainrelativelysmooth(Fig.4b)whileassociatedwith goodanatomicalandfunctionalmeasures.Thissuggeststhat DAR-TELprovidesdeformations thatrespect theanatomo-functional organizationofthecortex.DiDageneratedstrongerdeformations comparedtoDARTEL.Theadditionalcompressionsweremainly locatedbetweentheleft andright centralsulci, indicating that

theconstraintsfromthetwocentralsulcicompetedinthisregion (Fig.4b).Indeed,unlikeDDe,DiDaandDARTELwerenotrestricted tothecommonROIandcoveredtheentirebrainvolumesuchthat theconstraintsbetweenhemispherescouldinteract.More inter-estingly,voxelscorrespondingtorelativelyhighdilatationswere distributedonbothbanksofthecentralsulciandespeciallyaround thehand-knob structure. This suggeststhat DiDa didintegrate theinformationfromtheshapeofthecentralsulcuswhile regis-teringthesurroundingcorticesbetweensubjects.Thissupports theimprovementobservedwithfunctionalmeasurescomparedto

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Fig.8.Inﬂuenceofregistrationtechniquesongroup-averagedfunctionalmapsforﬂexion.ThezoomedpartemphasizesT-valuesgreaterthan7(p≤10−5_)._Right_hemisphere (rightcolumn)andlefthemisphere(leftcolumn).

DARTEL.Inaddition,ourresultswithDDealsoillustratethebias inducedbythechosenreferencesubject.Thisbiasislikelytooccur withanyregistrationstrategyrelyingonthearbitrarychoiceofa reference.InTahmasebietal.(2009),theauthorsconcludedthat HAMMER(ShenandDavatzikos,2002)outperformedDARTELin theauditorycortexbutthesensitivityoftheformertothechosen referencewasnotreported.AnextensionofDDe(orHAMMER)to iterativelycomputeameanimageasatemplate,similarlyto DAR-TELorDiDa,couldbeconsideredforthemethodimprovement. DARTELandDiDausedthesameiterativelycomputedtemplate andwecanruleoutthepossibleinﬂuenceofthetemplateonthe

observeddifferences.Ourresultsclearlyindicatethesuperiorityof methodsthatiterativelybuiltagroup-speciﬁctemplatecompared totheuseofapredeﬁnedreferencetemplate.Adrawbackoftheuse ofsuchtemplatesisthatcomparisonsofactivatedclustersposition betweenliteratureislessstraightforward.

Ourstudycouldbeinterestinglyextendedusingefﬁcient non-linearmethodssuchasIRTK,surface-basedregistrationmethods (e.g.FreeSurferFischletal.,1999;Yeoetal.,2009)orrecent combi-nationsofvolumeandsurfaceregistration(Postelnicuetal.,2009; Duetal.,2011).Indeed,surfacebasedanalysisisattractive espe-ciallyforfunctionaldataanalysisbecauseitrespectstheessentially

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Fig.9.Dispersionofindividualsubjectpeakactivitycoordinates.Foreachmethodonestatisticallysignificantclusterwasselectedinindividualsrespectivelyforrighthand extension(RHext,n=10),lefthandextension(LHext,n=11),righthandflexion(RHflex,n=13)andrighthandflexion(LHflex,n=13).Weshowthemeanandstandard deviationofEuclidiandistancebetweeneachindividualpeakcoordinatesandtheircentroid(meancoordinate)afterregistrationwithSPM8,DARTEL,DiDa,DDeR1and DDeR2.

Fig.10.Activationdetectability.Foreachmethod,weshowthemeanandstandarddeviationofthenumberofvoxelssigniﬁcantlyactivatedatthegrouplevel(t≥4.0, p≤0.0001voxelleveluncorrected)acrossthetasksforeachhemisphere.Thepvaluescorrespondingtosigniﬁcantdifferencesbetweenmethodsareindicatedasfollows:* p≤0.05,***p≤0.001.

2D-structureofthecorticalribbonfacilitatingforinstancethe affec-tationofactivationtotherightsideofthebankofasulcus.However, forregistrationpurpose,surface-basedandvolume-basedmethods seemtoperformwithsimilaraccuracies(Kleinetal.,2010).Finally, theuseofaspeciﬁcEPItemplatesuchasproposedinHuangetal. (2010)wouldnotallowreachingtheaccuratelocalizationinmotor cortexwesearchfor.

Toconclude,ourstudydemonstratesthatcombininghigh res-olutionfMRI withaccuraterealignmentof thecentralsulcusof individualsprovidedbyDARTELorDiDaopensthewaytonon inva-sivefunctionalexplorationofthehumanhandmotorcortexatthe populationlevel.Exploringcorticalrepresentationofsimplehand movementsatthehealthy populationlevelgives areferenceto studymechanismsofbrainplasticityunderpathologicalconditions orafterrehabilitationtrainingfollowinghandsurgery.

Funding

This work received ﬁnancial support from Cluster Handi-cap Vieillissement Neurosciences of the Région Rhône-Alpes, France (grant number RA0000R175) and from PHRC régional

TTPCB(TransfertTendineux,PlasticitéCérébraleetBiomécanique), France. F.P. was supported by a PhD grant from Ministère de la Recherche. G.A. was funded by the Agence Nationale de la Recherche(ANR-09-BLAN-0038-01,‘Brainmorph’).

Acknowledgements

Wethankthetechnicalstaffatthe3Tscannerfacilityincluding LaurentLamalle,OlivierMontigonandIrèneTroprèsfromtheSFR RMNBiomédicaleetNeurosciences.

AppendixA. Supplementarydata

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.jneumeth.2013.05.005.

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