spm fmri数据分析总述-全面但省略了细节

SPM 0、预处理的 2、Realignment头动校 3、Coregister配 4、Segment分 5、Normalize空间标准 6、Smooth平 二、分 2、ModelEstimate模型估 3、ContrastsandPrinting 4、Multipleconditionsfilefor1stleveldesign 三、群体分析2ndlevel Specify Model Overlay 四、fMRIdataqualitycontrol 1、Motivations-whywedothis 2、Termsusedinquality 3、Common 4、QualityControl(QC) SPMSPM，即统计参数图，也是这个软件的最终输出，它是由英国伦敦大学的Friston教安装：有必要说一下SPM实际不是一个独立的软件，它相当于一个用 ，整个spm8文件夹到 下。然后运行，在其主窗口选择File->setpath->Addwith 中执行命令>>spmfmri。这样出现了spm8的操作界面（如下的窗口为输入窗口(inputwindow)，右侧大窗口为树形结构窗口或图形窗口(TreeBuildingWindoworthegraphicswindow)。一 SPM如何使a、SPM的三大batchSPMscriptfMRIdata。二 Orderof filestohdrfilesandiiasempaig，即liceingSaiprocessing，包括gn、Nrli和Smooth。具Specify1stlevel(singlesubject)组分析——使用Specify2ndlevel(groupysisInordertobetterunderstanddifferentfMRIdataysissteps,twodifferentviewsonthefour-dimensional(3xspaceand1xtime)datasetsarehelpful.Inonei,the4Ddataiscceuzeasasequenceofucinavolumes(3Dimages).Thisviewisveryusefultounderstandspatialysissteps(seefigureabove,toppart).Duringmotioncorrection,forexample,eachfunctionalvolumeofarunisalignedtoaselectedreferencevolumebyadjustingrotationandtranslationparameters.Thesecondefocusesoncoursesofindidulvoxels(seefigurebbottomprThissecondviewhelpstounderstandthosepreprocessingandstatisticalprocedures,whichprocesstimecoursesofindividualvoxels.MoststandardstatisticalysisproceduresincludingtheGeneralLinearModel(GLM)operateinthisway.InaGLMysis,forexample,thedataisprocessed"voxel-wise"(univariate)byfittingamodeltothetimecourseofeachvoxelindependentlyorfortheaveragesignaltimecourseinaregion-of-interest.Formultivariatedataysisapproaches,consultthechaptersmulti-voxelpatternysis, independentcompoment (ICA)andthehelpcomingwiththeGrangercausality(effectiveconnectivity)第三部分引自/bvqx/doc/UsersGuide/MVPA/MultiVoxelPatternysisMVPA.html，这是一个非常好的介绍fmri基本知识的，系统而且直白，强烈推荐。 功能像，data3D文件夹中存放转换后的结构像, 选择NIfTI，可用SPM输入面板中的 Import模块转换，也可以采用专门的转换软件，如MRIcovert。然后进行数据预处理，预处理结束后到安装 spm*.ps文件，其中包含了空间校正和标准化的信息，然后进行建模分析。据）和.hdr（矩阵数据；data3D文件夹中只有一个.img和.hdr文件。DeleteSliceNormalize：①NormalizebyusingEPItemplatesEPI模版来进行空间②NormalizebyusingT1imageunifiedsegmentationT1像来进行空间标准化，这样的话需要用到T1normalizeT1像进行CoregisterSegmentcoregisteredandsegmentedT1像来进行空间标准化。另外，如果以后希望把功能激活图像叠加到结构像上，那像也需要做一次空间标准化。Parametersfilesnormalizesegment中生成的空间标准化参数文件（批处理中选择Subj→MNI。Imagestowrite选择在SliceTiming用来校正1个volume中层与层之间获取()时间的差异，对事件相关设计的实验尤为重要。我们在按钮窗口中的预处理面板中点击“SliceTiming”，将出现一 选择你要处理的数据，如文件夹data中的所有数据NumberofSlices：我们输入每祯图像的层数，如“32”（可以在（注意：西南大学的西门子机器默认总层数为偶数从第二TR的那个图层，即时间上何标记，一般采用默认设置。默认为a（interleaved）、、、、、、、realignsliceSlicetiming；spmafni2、RealignmentfMRI实验中尤为明显。这一步就是把一个实验序列中的每一帧图像都头动范围（CheckRealign：平动≤2.0mmand旋转≤2.0degree[严老师观点Files”，用spm文件选择器选择刚做完时间校准的全部图像（a*.img。据，如果头动超过1voxel（功能图像扫描矩阵一般是64*64，则体素的大小为小二乘法(leastsquaresapproach)原理和含6个参数(刚体模型)的空间变换，对从一个被试个volume，也可选择比较有代表性的volume(更明智的选择)，例如选择磁场相对稳定的第4个volume。校正信息(头动信息)将在结果窗口(GraphicsWindow)显示。每个Session图形显示。如下图：translation图表示被试头部在X，Y，Z三个方向的平移，分别用蓝，转角度，分别以毫米和度为单位。采用SPM8，头动信息和空间标准化的图形文件将以spm_“data”.ps的形式保存于的工作 年月日处理的数据，则将以spm_2009Apr30.ps文件存于的work 像保存成.jpg格式：File->SaveAs->.jpg在中输入命令：b=load(‘rp_af*.txt’); %---[载入头动数据文件]c=max(abs(b));%--[取b值的绝对值的最大值，表示找出每列的最大值] 3、Coregister (modality)有效，对于同一被试的不同成像方法（功能像/结构像）所得图像,volume对齐，对功能像与结构Coregister里面的（Estimate）呢？因为我们相信对于hdr文件存有一个矩阵，而这个矩阵就包含了功能像的信息。只需要ReferenceImage---[选择头动校正后生成的mean*.img文件]说明：SourceimageReferenceimagemean开头的功能像里估计，估计结束后就可以将旋转矩阵写入到精度更高的3D文件当中，最后做4、Segment.Cleanupanypartitions---LightAffineRegularisation---[选择欧洲人或东亚脑模板]如：ICBMspaceEuropean5、NormalizecoregistersegmentT1选中“data”——“newsbject”，在data下新出现的“subject”选项中作如下设有刚进行完校准的文件“ra*.img”，“templateimage”我们选择“EPI.nii”，其余采用Normalise:Normalise:Data/newsubject/ParameterFile---[参数文件，选择3D文件夹下segment后的文Boundingbox---[默认的偏小，可以改为-90-126-729090108] Voxelsizes—[改为333]（不能为111Normalise:WriteData:newBoundingbox---[默认的偏小，可以改为-90-126-729090108]Voxelsizes—[改为336、SmoothwFWHM”,将fMRIdataqualitycontrolQC)detectionandrejectionArtifactdetection:timeseries,motion,Artifactrejection:scanomissions,covariates,interpolations,deweighting对于预处理某些步骤和参数的选择**NotesSlicetimingand如果是Blockslicetiming。如果是事件相关设计，则需要做；b.后做realignment；如是顺序采样，则先做realignment后做slicetiming；)MicrotimeresolutionreferstohowmanyintervalstodividetheTRinto.Thedefaultis16,butyoucouldalsochoosethenumberofslices,forexample.Themicrotimeonsetreferstothetimetoreferenceallyoureventonsetsto.ThisisaTEMPORALselection.Ifyouchoose16forthemicrotimeresolutionand8forthemicrotimeonsetyouarereferencingallyoureventonsetsto1/2theTR.Youshouldmakesuretheslicetimingchoicesandthemicrotimechoicescorrespondtooneanother.SoifyouslicetimecorrectyourtimeseriestothesliceacquiredatmidTRyoushouldadjustyourmicrotimeonsettobeat1/2TR.广义线性模型（generalizedlinearmodelGLM)是简单最小二乘回归（OLSOLS进行GLM建模时，需要指定分布类型和连接函数。信息更加突出。为了达到这个目的，SPM采用一般线性模型（GLM）对到的信号进行统计分通俗的说，GLM基于这样一种假设：每个像素上的实验数据（用Y来表示，同一任务的时间数学表达式为：Y=β*X+ε，其中ε得到的β。SPM得到的脑功能激图实际上就是根据对参数β的统计推断而得到的。 𝜀2最β系数做tF根据t或F值以及相应的阈值，可以得到该阈值对应置信度的脑功能激活图。由于它是对模型的参数做统计分析而得到的，所以被称为统计参数图。与阈值对应的t或F值记为SPM{t}，SPM{F}。SPM还提供了与t或F值及它们相应的自由度所对应置信度的{0，1}正态分布的参数值TimingTimingdurations的时间按TR的倍数计算；如选seconds，则以秒为单位计算。?Microtimeresolution16TR]?MicrotimeonsetERslicetimingreferenceslicereferenceslice251313SPMData&Designonsettime。Onsets代表任务刺激启动的扫描数（如1:14:70，1TR开始，每14TR为一个周期，共70TROnsets中输入数组conditionfiles以避免输入每个run参数的苦恼。【DurationsER0、blockUnitsMultiModuleER设计选第二项，block其他参数默认。设置完毕后点击绿三角运行。这样将会在开始选择的中生成文spm.mat。spm总的按钮窗口中的Review按钮来检查。点击Review按钮之后，会弹出一个窗口。在窗口中有一个design菜单，菜单中有这样几个子选项：Designmatrix会把设计矩阵进行图形化展示，这里最好要掌握一点矩阵的Exploresession1–active（runactiverest两种刺激）会展示一个叫做“exploringdesignmatrix”的窗格。总共会展示三幅activeregressoractivecondition。但是这不同于condition化的展示，在这里将和hrf做了卷积。以及这activeregressor的谱密度图，还包括一个hrf图形。过小的filer给过滤掉了。具体可以参考SPMmanual中相关章节。阵，用以展示不同regressor之间的正交性。 spmGLM已经完成一大半。下面所要做的就是估计和推断beta参数，beta参数指的是不同regressor对最后测量得到的信号所产生的effect了多大的权重影响多大。然后，我们基于betat统计量，进行推注意事项：DesignSpecificationFIR,Fourier ysis,canonicalhrf,empiricalhrfScaling:HPFHPFCutoff:InSPM2andhigheredition,SPMnolongertakesintoaccountyourdesignwhengivingyouaHPFdefaultcutoff.Rather,italwaysdefaultsto128secondsor0.01Hz,basedontheobservationthattheamplitudeasafunctionoffrequency,forasubjectatresthasa"1/f+whitenoise"form,inwhichamplitudehaseffectivelyplateauedforfrequenciesaboveapproxima y0.01Hz(theinflectionpointofthe"1/f"and"white"noisecomponents).Soit’sagoodideatolookatthepowerspectraofyourdesignsothatyoucanbesurethatyou’renotfilteringoutdataofinterest.Belowisanexampleoftwo yesdonewiththe.01hzvs.02hz.在上图中，用0.01HZ为标准就有结果，用0.02HZ就没有结果**NotesonSingleSessionModels。fmrisession里会有runrunGLMsessionrun共同建立一个GLM模型；同理，如果一个被试前后测做了一个fMRI认知任务，一阶分析是前后测单独建立一个GLM模型还是放到一起？Temporaldiscontinuitiesofthedataacrosssessions.Temporalfilteringoperationsshouldnotjumpacrosssessionborderssoifyouuseasingle-sessionapproachyouwouldneedtomanuallymodifythefilteringoperators(SPM.xX.K)totakeintoaccountthesesessionborders.Session-specificcovariates.Session-specificcovariateswouldneedtobedefinedmanuallyifusingasingle-sessionapproach.Inparticular,thestandardmainsessioneffectsbutalsoofthesession-specificmotionparameters,bothofwhichshouldbeenteredmanuallyassession-specificeffectsinthecontextofa"single-session"ysis.Simplyconcatenatingthemotionparametersdoesnotaccountforpossiblesessionxmotioninteractions(e.g.duetosubjectmovementacrosssessions),andthiscannegativelyimpactontheysissensitivity(forbothfirst-andsecond-levelyses).Similarlyitwouldnotbeadvisabletodisregardmainsessioneffects(thatcanbeintroducedbybetween-sessionmovements,ormovementbysusceptibilityscalingto modatepossiblebetween-sessionscalingdifferencesintherawBOLDsignal.Thiseffectisdifficulttomanuallyincorporateintoasingle-sessionapproach,whichassumesnoscalingdifferencesacrosssessions.Againpossiblescalingdifferencesacrosssessionswouldaddtotheresidualvarianceinsingle-sessionysisaffectingsensitivity.Noiseestimates.TypicallySPMusesasession-specificestimationofnoisemodels(SPM.W),sothatboththeglobalnoisehyperparametersandthevoxel-specificnoisescalingparametersareestimatedseparayforeachsession.Usingasingle-sessionapproachassumesthatthenoisemodelsareidenticalacrosssessions(unlessmanuallydefiningtheSPM.xVistructureto modatesession-specificnoisemodelsintoasingle-sessionapproach)FirstLevelModels:Typicallyfirst-levelmodelstestaneffectofinterestmodelingsessionsasfixed-effects,andtestingthesizeoftheeffectsofinterestagainsttheresidualBOLDsignalvariance.Inasingle-sessionapproachbetween-sessiondifferencesinactivation(effectsofinterest)wouldaddtotheresidualvariance(typicallydecreasingsensitivity).Notethatthisisnotequivalenttomodelingsessionsasrandomeffects(forthisyouwouldneedtouseamixed-effectsmodel,asinFristonetal2004),ratheritisequivalenttomodelingsessionsasfixed-effectsbutsimplynotincludinganysessionbyeffects-of-interestinteractionsintothemodel.Inaddition,thebetaestimatesobtainedfromasingle-session-designcorrespondtoacontrastweightingthedifferentsessionsinan"optimal"way(onebasedoneachsessioncontributiontoeacheffect,forunbalancedacross-session**NotesonusingexplicitysisUsingan ysisSetglobaldefaults;defaults.mask.thresh=-beforerunningthefMRI-model-specificationstep.Simplydefininganexplicitmaskdoesnotskipthethrehsold-maskingstepinspm,soyouhavetosettheabovevariableto-infinorder lspmnottousethrehsold-masking(andusetheexplicitmaskinstead).即可。估计完成后，在结果生成文件中可以看到一系列的beat_0001.hdr和beat_0001.img文件，分别代表设计矩阵中的第一列、第二列·一般设计矩阵中有两个行contrast赋值为1，然后再在组分析中进行方差分析，其实也可以直接把每个条件对应的beta文件用copy语句到对应的条件folder下面再进行组水平的方差分析，道理contrastconcopy再分析。,参数设置框。在这里，把在一中生成的SPM.mat选进来就好了。这时，就会弹出contrastmanager框。首先，contrast是为了简化多变量分析，多变量假设检验的法。Contrast其实是一个权重向量，通过contrast给条件赋值为1或-1可以突出某个变量的效应effect，赋值为0可以忽略某个变量的效应effect。中的常数covariant是不考虑的。那么在分析中应该如何设置contrast呢？I mendfirstspecifyingat-contrastforeachconditionindividuallythenusingafactorialdesignatthegrouplevel.Inaddition,ifyouwanttodogroupcomparisonsenterthespecificcontrastdesired(cond1vscond2)attheindividuallevelaswell.Definenewcontrast中进行contrast的设置。设置后，重新返回到contrastmanager框中。Titleforcomparisonactiverest（具体情况具体分析pvalueadjustmenttocontrol:[FWE/none].选择FEW（分析选none）pvalue(family-wiseerror)具体的p值设置为默认值0.05。接下来，会让你设定最小cluster的大小：就代表最终激活图中，各个cluster中至少包含v素>> 两个参数文件：con0002.hdr/img,conumintensity么骨头啊，组织什么的，全部都忽略掉。其实，在spm中最大强度投影指的就是玻璃脑。beta向量中的元在spm里，当你有许多被试需要处理，在一阶分析的时候你不得不着逐个输入每runconditionsessionsessionrun的每个conditionnames，onsets以及durations。但是有一个方法能够减少你在这个环节里的工作量建立每个runmulticonditionfile。 工作里的其他变量那么输入names={‘view-neg’,‘view-neutral’};双击它，打开便看到这个（注意，这里的变量是横着排成一行的，有几个条几列如果有三个变量，第三个变量为response那么你就输入{‘view-neg’,’view-onsets定义为cell类型的数据；现在定义第二个条件view-neutralonsets值双击点开one,你会发现有两列数据（注意，要是两列，而不是两行，列数要跟你的条件数对应，如果有三个条件，那么这里最终就是三列）4.4）输入此时双 durations，就看到两列数据，每列对应于一个条件4.5）保存save。检查无误之后，在 这时你就能够在左侧看到新生成的文件了 因为每个run你只有两个条件，尽管run1的条件和run2的条件不同，但仍然每runonsets里两列，duration有两列，names有一行两说明：（1）0controlcontrast1-extentthreshold：范围的阈值，定义多少个连在一起有意义的体素数目才不认为有可能是噪声。这个数值的选择一般要结合选定的Psmooth中FWHM值来Overlaysslice2Dsectors3D激活图，文件选择经标准化后的3D文件，以wms开头；也可选Renderspm8工具箱中的MSU插件（toolbox）来获得每一个激活区三、群体分析2ndlevelTraditionallyPETstudiesareyzedatthefirstlevel.However,fMRISecond-levelysesareusuallyemployedwhenyouwanttomakeaninferenceaboutgroupdifferencesgivensomewithin-subjectreplications.So,组分析是基于随机效应(RandomEffectsysis)的分析，考虑被试间的差Ifyouonlyhaveonescanperconditionpersubject(e.gFDG-PET),asecond-levelysisisnotappropriate.Thepointofasecond-levelysisistoincorporatetheappropriatemixofwithin-subjectandbetween-subjectvarianceestimates.Withonescan/condition/subject,thereisnotusuallyavalid"within-subject"varianceestimate,soyoushouldsticktothefirstlevelofysis.fMRIstudieslendthemselvesverynicelytoaRandomEffectsysis,sincethefirststepofysisistypicallytocondensealargenumberofscansfortwoormoreconditionsdowntoasinglecon*.imgforeachconditionpair(e.g.activation-neutral).注注释RandomandFixedEffects具体内容数据准备：Thepreparationistofeedaseriesofcontrastimages(con*.img,notthespmT*.img)resultingfromthefirst-levelysisofanumberofindividualsubjectsintoasecondlevelofysiswithinSPM.Acontrastimageisreferredtoasa"con*.img"becausewithinSPM,thevariouscontrastimagesarenamedaccordingtotheirspecificnumberedorderwithintheSPMcontrastmanagerforaparticularysis.Forinstance,if3contrastsweredefined,youwouldhavethefilesnamedcon001.img,con002.img,andcon003.img.这里需要注意的是，每个被试每种条件的con*.img文件应该按照实验条件进行归类整1con_0001.img可能指的Specify点击Specify2nd-level按钮，出现Factorialdesignspecification框Design:Choosefromtheappropriate ysisyouwanttorunandfollowtheinstructionsbelow.iv)One-SampleT-test:contrastsselectedshouldbeacomparisonatthe1stlevel(ex:fearful-left>fearful-right)Selecttheappropriatecon_000*filesfromtheindividualsubjectSelectoutputTwo-samplet-test:Thisdesignisforbetween-subjectscomparisons.Contrastsselectedshouldbeacomparisonatthe1stlevel(ex:fearful-left>fearful-right)Groupscans:Selecttheappropriatecon_000*filesfromtheindividualsubjectBesuretoenterthefilesforgrp1andthenforfilesforgrp2 SelectoutputPairedt-test:Thisdesignisforwith-ingroupcomparisons(time1vstime2).Contrastsselectedshouldbeacomparisonatthe1stlevel(ex:fearful-left>fearful-right)Pairs:selectthecontrastofinterestforanindividualattime1&timeThenumberofpairsshouldmatchthenumberofSelectoutputMultipleRegression:ThisstepallowsyoutorunaOne-samplet-testwithaSelecttheappropriatecon_000*filesfromtheindividualsubjectCovariates:(a)enter1valuepersubject(b)MultiplecovariatescanbeSelectoutputdirectoryFullFactorial:contrastsselectedshouldnotbeacomparisonatthe1stlevelbutratherrepresentasinglecondition(ex:fearful-left)Factors:NewFactor(a)Enterthenumberoffactors(b)Nameeachfactor(valence/location)(i)Canbeusedtospecifyaconditionorgroup(c)Enterthenumberoflevelsforeachfactor(2x3or3x4)SpecifyCells:NewCell(a)Enterthelevelofthecellasavector(ex:21)(b)Selecttheappropriatecon_000*filesfromtheindividualsubjectfoldersSelectoutputModelSelectSPM.mati)Dependency:fMRImodelspecification:Contrastsalsoneedtobespecifiedfor2ndlevel(group) ysissimilarto1stlevel(individualsubject)contrasts.Refertothedesignmatrixtodeterminecolumnassignment.Specifycontrastname:cond1-cond2orgrp1cond1-Specifycontrast:1- Specifycontrastname:cond1-Specifycontrast:thesamerulesapplyasatthesubjectlevel:(1)Specifyanumberforeachcondition/column(2)Eachsideshouldaddto1/-1(ex..333.333.333-.5-.5)ViewingResultsInthelatestversionofSPM(SPM8)viewingdifferentcontrastsandchangingthresholdsismoreuserfriendlyandonecaneasilyswitchbackandforth.Forresultsyouwillmainlybeusingthelower-leftwindowtonavigateandthegraphicswindowtoSelectSelectcontrastofApply(1) (2) (3)Corrected:FWE(Family-WiseError)defaultsettopUncorrected:NonedefaultsettoVoxels:k=minimumnumberofvoxelspersignificantcluster(a)Thiswillvarydependingonregionofinterest(visualcortexvsamygdala)SwitchingtoanewcontrastorYouwillnowseeaContrastsdrop-downonthelower-leftOptions:(1)Contrasts:previouscontrast,nextcontrast,listofcontraststopickfrom,newcontrast(2)Threshold:FWE/p<.05/0voxels,uncorrected/p<.001/0voxels,specifythresholdSignificanceTablea)WholeBraini)Thiswilloutputallthesignificantclusterswithcoordinates,FEW,FDR&uncorrectedsignificancelevels,and#ofvoxelspercluster.ii)Clickingoncoordinateswillmovecursoringlassbrainb)Currentcluster:givesinformationforsub-clusters,usefulforlargeclustersc)PrintTextTable:rightclicktableinthegraphicswindowi)Outputinwindowii)Copy&pastetoexcelOverlayOverlayoptionsallowyoutoviewthedataonaxialslices(slices),2Dimage(section),or3Dimage(render).Templatefilesarefoundinthemainspm8folderwhichislistedatthetopofthefileselectiondrop-down.GlassAtthetopyouwillalwaysseeaglassbrainofthesignificantactivity.Tonavigatedragtheredarrowtothedesiredlocation.Thiswillmoveinsyncwithsection&renderoverlays.spm8/canonical/orindividualT1simplyclickaroundtheimagetonavigatetheimage24iii)previoussection:afterswitchingcontrastsusethistoquicklyusethepreviouslyusedrendertemplate*ii)rotatewithmouseiii)right-clickoptions(1)inflate:smoothsoutgyri(2)(3)hemispheres(components)(4)saveas:giffpreviousrender:afterswitchingcontrastsusethistoquicklyusethepreviouslyusedrendertemplateContrastEstimates%&90%ConfidenceSelectcontrast:effectsofFittedResponses:Plotsadjusteddata&fittedresponsesacrossTy“Y”in windowwillgivefittedresponsedata,“y:givesadjusted1、Motivations-whywedothisBOLDeffectsofinterestaresmall,sotemporalstabilityduringfunctionalacquisitionisimportant.Inordertoaccuraymeasuresuchsmallsignalchanges,anMRsystemmusthaveintrinsicimagetimeseriesfluctuationlevelsmuchlowerthantheseexpectedsignalchanges.Qualitychecksallowyoutoassessifyourdataareworthbeingyzed.[adaptedfromNYUCBI'sDataQuality2、Termsusedinquality“signal”=averagevoxel“Temporalfluctuationnoise”iscalculatedbyasecond-orderpolynomialdetrendingtoremovetheslowdrift.Then,afluctuationimageiscalculatedasthetemporalstandarddeviationoftheresidualvarianceofeachvoxelafterthedetrending(subtractingthefitlinefromdata).AnSFNRimagecanbecreatedbydividingthesignalimagebythefluctuationimage.AveragingacrossvoxelsgivesanSFNRsummaryvalue.(2)Onecanalsocalculatedriftbysubtractingthe umfitvaluefromtheminimumfitvalueanddividingthatbythemeansignalintensity.Thereareseveralcontributionstothetemporalintensityfluctuationsatanygivenfunctionalactivityelectronicsnoise(usuallywhitenoisefromphysiologicalchanges(respiration&heartscannernoise(B0driftduetoheating,Themainsourceoftemporalvariationofaverageintensitywillcomefromscannernoise&physioactivity(whichcanberecordedandsubtracted).Sil-to-osRatio(SNR):theratioofthemean“signal”(averagevoxelintensityacrosstime),to“noise”(thestandarddeviation(acrossspace)ofbackgroundaveragedacrosstime)(1)Sil-to-GhostRatio(SGR):Comparesthesignalatthecenterofthebrain/objecttothemeansignalinthebackground(outsidethebrain)forasignaltoumghostratio,orthemeansignaltothemeanghostsignal(outsidethebrain).(1)Visualizing'ghosts'andSeeWeisskoff(3)forexplanationoftheRDCmeasureofscannerstability-basicallyameasureofthesizeoftheROIwherestatisticalindependenceofthevoxelsislost.Itassumesscannerinstabilitycausesincreasesinthevoxelcorrelationwitheachother.Withnoinstabilities,thestandarddeviationofanROItimeseriesdividedbythemeanscalesinverselydependingonthesquarerootofthenumberofvoxels.However,usuallyasthenumberofvoxelsincreasethereductioninratiobetweenthestandarddeviationandmeanplateaus(2)WhydoaFFTofdatatodetectnoise?Ideally,themagnitudeofthedatatransformedintofrequencyshouldbelowandrelativelyuniform.Periodicnoisewouldshowupasaeinthefrequencyspace,andparticularlylargeescanindicatethattheremaybeproblemswiththescannerorothersourcesofmechanicalnoise.(2)ANoteones:Ifanelectronicmalfunctionoccursduringdataacquisition,a“e”mayappearinthedata.Atypical,“clean”ewilllastaveryshorttime,producingaverylocalizedsignalincreaseinthedata.WhenonetransformsthesedataintoanMRIimage,theewillbetransformedintoaperiodicnoise,withaspatialfrequencygivenbythek-valuewheretheeappeared.Akeyfacthereisthatthelocalizednoiseink-spaceistransformedintospatialnoisedistributedallovertheimagespace,withawelldefinedpattern.Visualizea“y”Useful(1)Simmons,A.,Moore,E.,&Williams,S.C.R.(1999).andShewhartCharting.JournalofBoneandMineralResearch,1278(January),1274-1278.(2)Friedman,L.,&Glover,G.H.(2006).ReportonamulticenterfMRIqualityassuranceprotocol.Journalofmagneticresonanceimaging:JMRI,23(6),827-39.:10.1002/jmri.20583(3)Weisskoff,R.M.(1996).SimplemeasurementofscannerstabilityforfunctionalNMRimagingofactivationinthebrain.Magneticresonanceinmedicine:officialjournaloftheSocietyofMagneticResonanceinMedicine/SocietyofMagneticResonanceinMedicine,36(4),643-5.3、Common*Susceptiblity-induceddistortions:Throughandin-planedistortions/blurring,causedbymagneticfieldinhomogenity,causesdifferentdephasingofprotonsThelocalizednoiseinkspace esanartifactwiththatspatialfrequencyinthefmriimage.Kspaceartifactsarenotalwayseasytosee,andcansometimesbedetectedbetterbylookingatthedifferenceimages.Picture*Transientgradientartifacts:A einimageintensitycausedbygradientinstabilityorspin-historyeffectsduetointeractionswithheadmovementMitigatebymodelingbadimagesasdummyregressors,modelmovementparameterhsn:Wraparoundinimagescausedbyacquisitionerrors(badshim)ornotlargeenoughfieldofviewPicturefrom4、QualityControl(QC)Thislistcoversmanyoftheissuesthatshouldbecheckedpriortoandduringysis.It'snotacompletelist,butitdoesincludemanyimportantitems.InfoontheCANlabqualitycontroldatabaseishere:canlabfmriqcdatabase（还没有开放，尚不能用）SNR-signaltonoise-singleimage(contrasttonoise,dropout/susceptibility;gray/whitemattercontrastinstructurals)SNR-temporal(e.g.,mapsofmean/std.deviationacrossghosting-imagewrap-imageintensity-highenoughtoavoidinformationloss?(e.g.,distortions-spatialdistortionsines-gradientartifactsandbaddrift-largesignaldriftovertime(lookatFFTorperiodicnoise-low-frequencynoisestreaks/striinimages(RFroomskullstriporsegmentationfailures(missingcoregistration(anatomicatofunctionalnormalization(warok?badmovementestimates(reasonable?toomuchcolinearityandpredictorvarianceinmask(whichvoxelsareimageregistration(consistentacrossallimagesincontrastscaling(consistentacrossallindicatorsoftask-correlatedartifacts:globalshift,largestdacrossoutliers(veryunusual "globalnull"conjunction ysis"inSPM,currentlythisisusedwhenselectingmultiplecontrasts,andthenwhenpromptedselecting"globalnull".ThismethodtestswhetherSOME(oneormore)oftheeffectsaresignificant(similartoanF-test,onlyone-sided).The"globalnull"namereferstothenullhypothesisbeingthatALLoftheeffectsarezero.Thisformofconjunctionrequiresorthogonalcontrastsforproperstatistics(ifthecontrastsarenotorthogonalanorthogonalsetofcontrastsspanningthesamesubspacearecreatedbyspmbeforeperformingtheyses).AsNicholspointedout,thisisnottrulyatestoftheconjunction(understoo