Handbook of Functional MRI Data Analysis
by Russell A. Poldrack , Jeanette A. Mumford , Thomas E. NicholsEdition: 1st
Format: Hardcover
Pub. Date: 2011-08-22
Publisher(s): Cambridge University Press
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Summary
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.
Table of Contents
| 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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