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Statistical Analysis of fMRI Data

Johns Hopkins University via Coursera

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Overview

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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.

Syllabus

Week 1 
Module 1: Introduction to fMRI
Module 2: Basic MR Physics
Module 3: Image Formation
Module 4 K-Space
Module 5: fMRI Signal and Noise

Week 2 
Module 6: fMRI Data Structure
Module 7: Experimental Design
Module 8: Pre-processing I
Module 9: Pre-processing II

Week 3
Module 10: The General Linear Model
Module 11: GLM Estimation
Module 12: Model Building I
Module 13: Model Building II
Module 14: Noise Models
Module 15: Inference

Week 4 
Module 16: Group-level Analysis I
Module 17: Group-level Analysis II
Module 18: Multiple Comparisons
Module 19: FWER Correction
Module 20: FDR Correction
Module 21: More Multiple Comparisons

Week 5
Module 22: Brain Connectivity
Module 23: Functional Connectivity
Module 24: Multivariate Decomposition Methods
Module 25: Effective Connectivity
Module 26: Comments on Connectivity

Week 6 
Module 27: Multi-voxel Pattern Analysis
Module 28: Performing MVPA I
Module 29: Performing MVPA II
Module 30: MVPA Example 
Module 31: Farewell

Taught by

Martin Lindquist

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