Handbook of Functional MRI Data Analysis

Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.

Preface	p. ix
Introduction	p. 1
A brief overview of fMRI	p. 1
The emergence of cognitive neuroscience	p. 3
A brief history of fMRI analysis	p. 4
Major components of fMRI analysis	p. 7
Software packages for fMRI analysis	p. 7
Choosing a software package	p. 10
Overview of processing streams	p. 10
Prerequisites for fMRI analysis	p. 10
Image processing basics	p. 13
What is an image?	p. 13
Coordinate systems	p. 15
Spatial transformations	p. 17
Filtering and Fourier analysis	p. 31
Preprocessing fMRI data	p. 34
Introduction	p. 34
An overview of fMRI preprocessing	p. 34
Quality control techniques	p. 34
Distortion correction	p. 38
Slice timing correction	p. 41
Motion correction	p. 43
Spatial smoothing	p. 50
Spatial normalization	p. 53
Introduction	p. 53
Anatomical variability	p. 53
Coordinate spaces for neuroimaging	p. 54
Atlases and templates	p. 55
Preprocessing of anatomical images	p. 56
Processing streams for fMRI normalization	p. 58
Spatial normalization methods	p. 60
Surface-based methods	p. 62
Choosing a spatial normalization method	p. 63
Quality control for spatial normalization	p. 65
Troubleshooting normalization problems	p. 66
Normalizing data from special populations	p. 66
Statistical modeling: Single subject analysis	p. 70
The BOLD signal	p. 70
The BOLD noise	p. 86
Study design and modeling strategies	p. 92
Statistical modeling: Group analysis	p. 100
The mixed effects model	p. 100
Mean centering continuous covariates	p. 105
Statistical inference on images	p. 110
Basics of statistical inference	p. 110
Features of interest in images	p. 112
The multiple testing problem and solutions	p. 116
Combining inferences: masking and conjunctions	p. 123
Use of region of interest masks	p. 126
Computing statistical power	p. 126
Modeling brain connectivity	p. 130
Introduction	p. 130
Functional connectivity	p. 131
Effective connectivity	p. 144
Network analysis and graph theory	p. 155
Multivoxel pattern analysis and machine learning	p. 160
Introduction to pattern classification	p. 160
Applying classifiers to fMRI data	p. 163
Data extraction	p. 163
Feature selection	p. 164
Training and testing the classifier	p. 165
Characterizing the classifier	p. 171
Visualizing, localizing, and reporting fMRI data	p. 173
Visualizing activation data	p. 173
Localizing activation	p. 176
Localizing and reporting activation	p. 179
Region of interest analysis	p. 183
Review of the General Linear Model	p. 191
Estimating GLM parameters	p. 191
Hypothesis testing	p. 194
Correlation and heterogeneous variances	p. 195
Why "general" linear model?	p. 197
Data organization and management	p. 201
Computing for fMRI analysis	p. 201
Data organization	p. 202
Project management	p. 204
Scripting for data analysis	p. 205
Image formats	p. 208
Data storage	p. 208
File formats	p. 209
Bibliography	p. 211
Index	p. 225
Table of Contents provided by Ingram. All Rights Reserved.

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Handbook of Functional MRI Data Analysis

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