Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook for 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.
Author(s): Russell A. Poldrack, Jeanette A. Mumford, Thomas E. Nichols
Edition: 1
Publisher: Cambridge University Press
Year: 2011
Language: English
Pages: 239
Tags: Медицинские дисциплины;Клинические методы диагностики, лабораторная диагностика;Магнитно-резонансная томография;
Title......Page 4
Copyright......Page 5
Contents......Page 6
Preface......Page 10
1.1 A brief overview of fMRI......Page 12
1.2 The emergence of cognitive neuroscience......Page 14
1.3 A brief history of fMRI analysis......Page 15
1.5 Software packages for fMRI analysis......Page 18
1.8 Prerequisites for fMRI analysis......Page 21
2.1 What is an image?......Page 24
2.2 Coordinate systems......Page 26
2.3 Spatial transformations......Page 28
2.4 Filtering and Fourier analysis......Page 42
3.3 Quality control techniques......Page 45
3.4 Distortion correction......Page 49
3.5 Slice timing correction......Page 52
3.6 Motion correction......Page 54
3.7 Spatial smoothing......Page 61
4.2 Anatomical variability......Page 64
4.3 Coordinate spaces for neuroimaging......Page 65
4.4.2 The MNI templates......Page 66
4.5.2 Brain extraction......Page 67
4.5.3 Tissue segmentation......Page 68
4.6 Processing streams for fMRI normalization......Page 69
4.7.2 Volume-based registration......Page 71
4.7.3 Computational anatomy......Page 72
4.8 Surface-based methods......Page 73
4.9 Choosing a spatial normalization method......Page 74
4.10 Quality control for spatial normalization......Page 76
4.12 Normalizing data from special populations......Page 77
5.1 The BOLD signal......Page 81
5.2.1 Characterizing the noise......Page 97
5.2.2 High-pass filtering......Page 99
5.2.3 Prewhitening......Page 101
5.3 Study design and modeling strategies......Page 103
6.1.1 Motivation......Page 111
6.1.2 Mixed effects modeling approach used in fMRI......Page 113
6.1.3 Fixed effects models......Page 115
6.2 Mean centering continuous covariates......Page 116
6.2.2 Multiple groups......Page 117
7.1 Basics of statistical inference......Page 121
7.2 Features of interest in images......Page 123
7.3 The multiple testing problem and solutions......Page 127
7.3.1.2 Random field theory......Page 128
7.3.1.4 Nonparametric approaches......Page 130
7.3.2 False discovery rate......Page 132
7.4 Combining inferences: masking and conjunctions......Page 134
7.6 Computing statistical power......Page 137
8.1 Introduction......Page 141
8.2.1 Seed voxel correlation: Between-subjects......Page 142
8.2.2 Seed voxel correlation: Within-subjects......Page 143
8.2.3 Beta-series correlation......Page 144
8.2.4 Psychophysiological interaction......Page 145
8.2.4.1 Creating the PPI regressor......Page 146
8.2.5 Multivariate decomposition......Page 147
8.2.5.1 Principal components analysis......Page 148
8.2.5.2 Independent components analysis......Page 149
8.2.5.3 Performing ICA/PCA on group data......Page 153
8.2.6 Partial least squares......Page 154
8.3 Effective connectivity......Page 155
8.4.1 Small world networks......Page 166
8.4.2 Modeling networks with resting-state fMRI data......Page 167
8.4.3 Preprocessing for connectivity analysis......Page 168
9.1.1 An overview of the machine learning approach......Page 171
9.1.1.2 Overfitting......Page 172
9.3 Data extraction......Page 174
9.4 Feature selection......Page 175
9.5.1 Feature selection/elimination......Page 176
9.5.2.1 Linear vs. nonlinear classifiers......Page 178
9.5.3 Which classifier is best?......Page 181
9.6 Characterizing the classifier......Page 182
10.1 Visualizing activation data......Page 184
10.2 Localizing activation......Page 187
10.2.1 The Talairach atlas......Page 188
10.2.2 Anatomical atlases......Page 189
10.3 Localizing and reporting activation......Page 190
10.4.2 Defining ROIs......Page 194
10.4.3.2 Extracting signals for ROI analysis......Page 196
10.4.3.3 Computing percent signal change......Page 197
10.4.3.4 Summarizing data within an ROI......Page 200
A.1 Estimating GLM parameters......Page 202
A.2 Hypothesis testing......Page 205
A.3 Correlation and heterogeneous variances......Page 206
A.4 Why "general'' linear model?
......Page 208
B.1 Computing for fMRI analysis......Page 212
B.2 Data organization......Page 213
B.3 Project management......Page 215
B.4 Scripting for data analysis......Page 216
C.1 Data storage......Page 219
C.2 File formats......Page 220
Bibliography......Page 222
Index......Page 236
