Handbook of Functional MRI Data Analysis

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2011-08-22
  • Publisher: Cambridge University Press
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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

Prefacep. ix
Introductionp. 1
A brief overview of fMRIp. 1
The emergence of cognitive neurosciencep. 3
A brief history of fMRI analysisp. 4
Major components of fMRI analysisp. 7
Software packages for fMRI analysisp. 7
Choosing a software packagep. 10
Overview of processing streamsp. 10
Prerequisites for fMRI analysisp. 10
Image processing basicsp. 13
What is an image?p. 13
Coordinate systemsp. 15
Spatial transformationsp. 17
Filtering and Fourier analysisp. 31
Preprocessing fMRI datap. 34
Introductionp. 34
An overview of fMRI preprocessingp. 34
Quality control techniquesp. 34
Distortion correctionp. 38
Slice timing correctionp. 41
Motion correctionp. 43
Spatial smoothingp. 50
Spatial normalizationp. 53
Introductionp. 53
Anatomical variabilityp. 53
Coordinate spaces for neuroimagingp. 54
Atlases and templatesp. 55
Preprocessing of anatomical imagesp. 56
Processing streams for fMRI normalizationp. 58
Spatial normalization methodsp. 60
Surface-based methodsp. 62
Choosing a spatial normalization methodp. 63
Quality control for spatial normalizationp. 65
Troubleshooting normalization problemsp. 66
Normalizing data from special populationsp. 66
Statistical modeling: Single subject analysisp. 70
The BOLD signalp. 70
The BOLD noisep. 86
Study design and modeling strategiesp. 92
Statistical modeling: Group analysisp. 100
The mixed effects modelp. 100
Mean centering continuous covariatesp. 105
Statistical inference on imagesp. 110
Basics of statistical inferencep. 110
Features of interest in imagesp. 112
The multiple testing problem and solutionsp. 116
Combining inferences: masking and conjunctionsp. 123
Use of region of interest masksp. 126
Computing statistical powerp. 126
Modeling brain connectivityp. 130
Introductionp. 130
Functional connectivityp. 131
Effective connectivityp. 144
Network analysis and graph theoryp. 155
Multivoxel pattern analysis and machine learningp. 160
Introduction to pattern classificationp. 160
Applying classifiers to fMRI datap. 163
Data extractionp. 163
Feature selectionp. 164
Training and testing the classifierp. 165
Characterizing the classifierp. 171
Visualizing, localizing, and reporting fMRI datap. 173
Visualizing activation datap. 173
Localizing activationp. 176
Localizing and reporting activationp. 179
Region of interest analysisp. 183
Review of the General Linear Modelp. 191
Estimating GLM parametersp. 191
Hypothesis testingp. 194
Correlation and heterogeneous variancesp. 195
Why "general" linear model?p. 197
Data organization and managementp. 201
Computing for fMRI analysisp. 201
Data organizationp. 202
Project managementp. 204
Scripting for data analysisp. 205
Image formatsp. 208
Data storagep. 208
File formatsp. 209
Bibliographyp. 211
Indexp. 225
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