Week 1-Overview
Module 1: Introduction and Ground Rules
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Syllabus
Principles of fMRI Book
Module 2: Goals of fMRI Analysis
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Module 3: fMRI Data Structure
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Module 4.1: Psychological Inference Part 1
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Module 4.2: Psychological Inference Part 2
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Week 1-Acquisition and Reconstruction
Module 5: Basic Understanding of MR Physics
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Module 6: Forming an Image
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Module 7: K Space
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Week 2-Physiology, Signal, and Noise
Module 8: Signal, Noise, and Bold Physiology
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Module 9: fMRI Artifacts and Types of Noise
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Module 10.1: Spatial and Temporal Resolution of Bold Part 1
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Module 10.2: Spatial and Temporal Resolution of Bold Part 2
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Week 2-Experimental Design
Module 11: Experimental Design
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Module 12.1: Kinds of Designs Part 1
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Module 12.2: Kinds of Designs Part 2
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Week 2-Prepocessing
Module 13: Pre-Processing of fMRI Data
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Module 14: Pre-Processing (continued)
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Week 3-Statistical Analysis: The General Linear Model
Module 15: General Linear Model
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Module 16: Applying GLM to fMRI Data
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Module 17: Details of Building GLM Models
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Module 18: Linear Basis Sets
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Module 19: Filtering & Nuisance Covariates
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Week 3-Inference and Group Analysis
Module 20: GLM Estimation
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Module 21: Noise Models - AR Models
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Module 22: Inference - Contrasts and T-tests
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Week 4-Group Analysis
Module 23: Group-level Analysis I
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Module 24: Group-level Analysis II
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Module 25: Group-level Analysis III
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Week 4-Multiple Comparisons
Module 26: Multiple Comparison Problem in fMRI
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Module 27: FWER Correction
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Module 28: FDR Correction
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Module 29: Pitfalls and Multiple Comparisons
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