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II. Advanced Data Analysis

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FMRI Data Analysis: II. Advanced Data AnalysisAdvanced Data AnalysesBig ConceptSlide 4Slide 5Data-Driven AnalysesFunctional ConnectivitySlide 8Slide 9CausalityGranger CausalitySlide 12Slide 13Slide 14Intersubject CommonalitiesSlide 16Slide 17Independent Components Analysis (ICA)Slide 19Slide 20Slide 21MELODIC (FSL’s version of ICA)Examples of MELODIC ICA outputLimitations of ICAPredicting Behavior and ThoughtsSlide 26Real-Time fMRISimple Correlations(Logistic) Regression for BehaviorKey Steps of Pattern ClassificationSlide 31Slide 32Slide 33Slide 34Slide 35Data-Driven Analyses: Beautiful or Seductive?FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityFMRI Data Analysis:II. Advanced Data AnalysisFMRI Graduate Course (NBIO 381, PSY 362)Dr. Scott Huettel, Course DirectorFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityAdvanced Data Analyses•Complex modeling•Analyses of Connectivity–Functional Connectivity Analysis–Causality analysis–Across-subjects regularities•Independent Components Analysis•Prediction–Real-time analyses–Correlation techniques–Support Vector MachinesFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityBig Concept•Your analysis model should not determined by your stimuli.•It should be determined by your hypothesis about the underlying cognitive processes.You can construct and test an arbitrarily complex model, if that model is justified by the brain processes.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityDaw and colleagues (2006) used a “four-arm bandit” gambling task. In this task, subjects sometimes exploit a winning arm, and sometimes explore to learn about new arms.They not only did analyses based on how much subjects won (top row), but also on how predictable was the subject’s decision (bottom row), which reflects how much reward the subject expected.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversitySuppose that our experimental was based on the game show “Deal or No Deal”. How could we model the subject’s cognition?FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityData-Driven Analyses•Broadly considered, they examine the data to identify coherent patterns.•Complement hypothesis-driven analyses (e.g., GLM)•The primary challenge: interpretationFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityFunctional ConnectivitySeed voxel in “b”. Colormap shows voxels with r > 0.35.Biswal et al. (1995)Active Task Resting State!FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityLet’s just pick a voxel in the posterior cingulate and look at its connectivity.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityResting-state connectivity (positive) for the posterior cingulate cortex (PCC, arrow).Resting-state connectivity (negative) for lateral prefrontal cortex.PCC correlation is similar during active task and resting state.Greicius et al. (2003)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityCausalityFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityGranger Causality“The basic "Granger Causality" definition is quite simple. Suppose that we have three terms, Xt, Yt, and Wt, and that we first attempt to forecast Xt+1 using past terms of Yt and Wt. We then try to forecast Xt+1 using past terms of Xt, Yt, and Wt. If the second forecast is found to be more successful, according to standard cost functions, then the past of Y appears to contain information helping in forecasting Xt+1 that is not in past Xt or Wt. … Thus, Yt would "Granger cause" Xt+1 if (a) Yt occurs before Xt+1 ; and (b) it contains information useful in forecasting Xt+1 that is not found in a group of other appropriate variables.”- Clive Granger, 2003 Nobel Laureate in Economics.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityDo changes in the exports of a country (Granger) cause changes in that country’s gross domestic product?That is, does export activity lead economic growth?Konya (2006)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityRoebroeck et al. (2005)Simulated Activity (LFP)FxyFyxFx,yInfluence Delayed by <100msFMRI gives information about correct causality (blue), but also introduces spurious simultaneous influence (red).FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityRoebroeck et al. (2005)Red = SourceGreen = InputsBlue = TargetsFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityIntersubject CommonalitiesHasson et al. (2004)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityHasson et al. (2004)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityHasson et al. (2004)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityIndependent Components Analysis (ICA)McKeown, et al. (1998)Assumption: The observed data is the sum of a set of inputs which have been mixed together in an unknown fashion.The goal of ICA is to discover both the inputs and how they were mixed.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityPrincipal Components Analysis (PCA) finds a set of components that are uncorrelated. The first principal component gives the direction of maximal variance in the data.Value of Component 1Value of Component 1The assumption of temporal non-correlation can be violated by some forms of structure in the data.The assumption of spatial non-correlation is violated when a given voxel contributes to more than one process.FMRI – Week 10 – Analysis II Scott Huettel, Duke University“The brain is not orthogonal!”Cf. Makeig, others.FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityMcKeown et al, (2003)Visual CortexHeartbeatBreathingBreathingHead Motion?Vascular Oscillations?FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityMELODIC (FSL’s version of ICA)FMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityExamples of MELODIC ICA outputABCDReplace the words “Timecourse” with “Component”; remove “TR = 2 s” from each plotFMRI – Week 10 – Analysis II Scott Huettel, Duke UniversityLimitations of ICA•Cannot test hypotheses•Provides no criterion for significance•Relies on interpretations drawn by


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