In this course we will explore the intersection of statistics and functional magnetic resonance imaging, or fMRI, which is a non-invasive technique for studying brain activity. We will discuss the analysis of fMRI data, from its acquisition to its use in locating brain activity, making inference about brain connectivity and predictions about psychological or disease states. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists.
Week 1 Module 1: Introduction to fMRIModule 2: Basic MR PhysicsModule 3: Image FormationModule 4 K-SpaceModule 5: fMRI Signal and Noise Week 2 Module 6: fMRI Data StructureModule 7: Experimental DesignModule 8: Pre-processing IModule 9: Pre-processing II Week 3Module 10: The General Linear ModelModule 11: GLM EstimationModule 12: Model Building IModule 13: Model Building IIModule 14: Noise ModelsModule 15: Inference Week 4 Module 16: Group-level Analysis IModule 17: Group-level Analysis IIModule 18: Multiple ComparisonsModule 19: FWER CorrectionModule 20: FDR CorrectionModule 21: More Multiple Comparisons Week 5Module 22: Brain ConnectivityModule 23: Functional ConnectivityModule 24: Multivariate Decomposition MethodsModule 25: Effective ConnectivityModule 26: Comments on Connectivity Week 6 Module 27: Multi-voxel Pattern AnalysisModule 28: Performing MVPA IModule 29: Performing MVPA IIModule 30: MVPA Example Module 31: Farewell