醫(yī)學(xué)外文翻譯--利用獨立分量分析對自然連續(xù)音樂刺激下功能性磁共振（fmri）分解中關(guān)鍵性問題的研究（英文）

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4、aged to visit:http://www.elsevier.com/authorsrightsAuthor's personal copyF.Congetal./JournalofNeuroscienceMethods223 (2014) 74–84 75fMRIdataincludetheblockdesignandtheevent-relateddesign(Panetal.,2011).Fortheblockdes

5、ign,thecontrastoffMRIdatabetweenthestimulusonsetandthestimulusoffsetisanalyzed.Fortheevent-relatedone,thedesignmatrixcanbeusedforregressionduringwhichthetemporalcourseofavoxelandthecorrespondingspatialmaparelearned.Witht

6、hedevelopmentoffMRIresearch,somestudiesevenreportedfMRIdataobtainedduringarealisticexperiencewherethestimulusisnaturalistic,continuousandlong(Allurietal.,2012;Hassonetal.,2004;HaynesandRees,2006;Kauppietal.,2010;Kayetal.

7、,2008;SpiersandMaguire,2007).Suchnaturalisticbraindatacanprovidemuchricherbrainresponsesforresearchandittendstobedifficulttodirectlyobtaintheprecisecontrastordesignmatrixaccordingtotheexperimentaldesign.Inordertoprocessa

8、ndanalyzesuchnaturalisticbraindata,theinter-subjectcorrelation(ISC)(Hassonetal.,2004)hasbeenwidelyused.ISCisbasedonthecorrelationbetweentwotemporalcoursesoftwoparticipantsgiventhesamespatiallocation,i.e.,thevoxelwiththes

9、amecoordinates.Recently,basedonacousticalfeatureextractionalgorithmsusedinmusicinformationretrieval,musicalfeaturesofthemusicstimulushavebeenextractedandcorrelatedtothetemporalcourseofeachvoxelofthefMRIdata(Allurietal.,2

10、012).DuetothelargeamountofvoxelsinfMRIdata,thenum-berofmultiplecomparisonsinsuchcorrelationanalysesislargeaswell.Therefore,somestatisticalmethodsaretypicallyusedtoavoidthefalsealarm.Onestraightforwardmethodistoreducethen

11、umberoftimesofcorrelations.Forexample,whenindependentcomponentanalysis(ICA)isappliedtodecomposefMRI(McKeownetal.,1998),thenumberofICAcomponents(usuallylessthanhundreds)ismuchsmallerthanthenumberofvoxels(hundredsofthousan

12、ds).Thedatadrivendataprocessingmethods,likeICA,havebeenusedtoprocessnaturalisticbraindata(Malinenetal.,2007;Ylipaavalniemietal.,2009)andthesimilaritybetweenthetemporalcoursesofthestimulusandthetemporalcoursesofICAcompone

13、nts(i.e.,spatialmaps)wasexamined.We findthatsomekeyissuesinapplyingICAtodecomposenaturalisticbraindatahavenotbeenwelladdressedyet.Thisstudyisdevotedtoanalyzingeverystepfortheapplicationofthisadvancedmethod.ForICA,theFas

14、tICAalgorithm(Hyvärinen,1999)wasused.Since1998(McKeownetal.,1998)ICAhasbeenextensivelyusedforthefMRIdataprocessing.Fordifferentdefinitionsofsamplesandvariablesinthelineartransformmodel,theapplicationofICAcanbedivide

15、dintotemporalICAandspatialICA(McKeownetal.,1998;Erhardtetal.,2010;Calhounetal.,2001;Huetal.,2005;Leeetal.,2011).Intheformer,anindependentcomponentisatem-poralcourse.Forthelatter,anindependentcomponentisavoxelseries,which

16、canbeassembledintoaspatialmapoffMRI.GiventhetypicaldimensionsoffMRIdatasets,thespatialICAisusuallypre-ferredbothfortheplausibilityoftheunderlyingneurophysiologicalmodelandforcomputationalrequirements.Hence,thespatialICAi

17、schosenforthefMRIdataanalysisinthisstudy.Hereinafter,whenICAismentioned,itisreferredtospatialICA.ICAcanbefurtherdividedintoindividualICAforanindi-vidualdataset(e.g.,includingoneparticipant’sdata)andgroupICAfortheconcaten

18、ateddataset(e.g.,includingmultiplepar-ticipants’data)(Calhounetal.,2009).GroupICAcanbeevencategorizedasthetemporalconcatenationapproach(e.g.,multi-pleparticipants’dataareconcatenatedinthetimedomain)andthespatialone(e.g.,

19、multipleparticipants’dataareconcatenatedinthespatialdomain)(Calhounetal.,2009).Thetemporalandspatialapproachesallowexaminingindividualtemporalcoursesandindividualspatialmaps,respectively,andtheyprovidecom-monspatialmapsa

20、ndcommontemporalcoursesovermultipleparticipants,respectively.Actually,groupICArequiresadditionalassumptionsbesidesthoseneededbyindividualICA(Congetal.,2013).ItisunknownwhetherfMRIdataduringreal-worldexpe-riencescanmeetth

21、eadditionalassumptions.Consequently,bothindividualICAandgroupICAareappliedtodecomposethefMRIdataheretoexaminewhethersimilarfindingscanbeobtainedbybothmethods.NomatterwhichmeansofICAisapplied,itisverycriti-caltodeterminet

22、henumberofextractedcomponents.Modelorderselection(MOS)hasbeenappliedforthispurpose(Lietal.,2007)andtheinformationtheorybasedMOSalgorithmsareoftenused,forexample,Akaike’sinformationcriterion(AIC)(Akaike,1974),MinimumDista

23、nceLength(MDL)(Rissanen,1978),andKullback–Leiblerinformationcriterion(KIC)(Cavanaugh,1999).ThistypeofMOSalgorithmsassumesthedataareindependentlyandidenticallydistributedandthecollectedbraindatahavetoberesampledtosatisfyt

24、hisassumptionforMOS(Lietal.,2007).Inthisstudy,weexamineanotherrecentlydevelopedalgorithmcalledSORTE(Heetal.,2010)forMOSoffMRIdata.SORTEisveryeffi-cientinthecomputinganddoesnotrequiretheresamplingprocess(Heetal.,2010).Alt

25、houghMOShasbeenextensivelyusedforfMRIdata,therearefewexplicitmethodstovalidatewhethertheesti-mationofMOSisaccurateornotfortherealfMRIdata.Recently,asimulationstudyhasshownthatMOScannotpreciselyestimatethenumberofsourcesi

26、nthelineartransformmodelwhensignal-noise-ratio(SNR)islow(e.g.,lessthan0dB),andthatwhenSNRislowSORTEandMDLtendtooverestimateandunderestimatethetruenumberofsources,respectively(Congetal.,2012).Inthisstudy,SORTE,AIC,MDLandK

27、ICareperformedontheconvention-allypreprocessedfMRIdataandfurtherpreprocessed(byadigitalfilter)fMRIdatatoexaminetheirperformanceinestimatingthenumberofsourcesinfMRIdataofindividualparticipants.ForindividualICA,clusteringt

28、heextractedICAcomponentsoffMRIdataisusuallyappliedtofindthecommoncomponentsacrossdifferentparticipants,andthesimilaritymatrixbasedhierarchicalclusteringhasbeenoftenused(Calhounetal.,2009;Espositoetal.,2005).ThenumberofIC

29、Acomponents(n)isalwaysmuchsmallerthanthenumberofvoxels(p).InfMRIdata,pcanbehundredsofthousands.Fortheveryhigh-dimensionaldata,dimensionreduc-tiontendstobeperformedbeforemachinelearning,likeclusteringandclassification.Int

30、hisstudy,arecentlydevelopeddimensionreductionmethodcalleddiffusionmap(DM)(CoifmanandLafon,2006)isappliedtoreducethedimensionofthedatatobeclustered(i.e.,thenICAcomponentshere)frompto2,andthen,thedegreeofclosenessofthenICA

31、componentscanbevisualizedbythescatterplotofthetwodimensionaldata.Furthermore,thespectralclus-tering(Nadleretal.,2006)isusedtofindthecommoncomponentsacrossmultipleparticipantsinthisstudy.ForgroupICA,thetemporalconcatenati

32、onseemstooutperformthespatialconcatenation(Calhounetal.,2009).Indeed,thiscon-clusionisbasedongroupICAforfMRIdatamostlyintheblockorevent-relateddesigns.ItisunknownwhethertheconclusionisvalidforthefMRIdataduringreal-worlde

33、xperiences.Therefore,bothapproachesaretriedtodecomposethefMRIhere.Inordertoaddresstheissuesmentionedabove,fMRIdataofelevenmusiciansinafree-listeningexperiment(Allurietal.,2012)areusedinthisstudy.2.Method2.1.Datadescripti

34、on2.1.1.fMRIElevenhealthyparticipants(withnoneurological,hearingorpsychologicalproblems)withformalmusicaltrainingparticipatedinthestudy(meanage:23.2±3.7SD;5females).TheparticipantswerescannedwithfMRIwhilelisteningto