Handbook of Functional MRI Data Analysis / Russell A. Poldrack, Jeanette A. Mumford, Thomas E. Nichols.
Using minimal jargon, this book provides a comprehensive and practical introduction to the methods used for fMRI data analysis.
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Full Text (via ProQuest) |
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| Format: | eBook |
| Language: | English |
| Published: |
Cambridge :
Cambridge University Press,
2011.
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Table of Contents:
- Cover; Title; Copyright; Contents; Preface; 1 Introduction; 1.1 A brief overview of fMRI; 1.2 The emergence of cognitive neuroscience; 1.3 A brief history of fMRI analysis; 1.4 Major components of fMRI analysis; 1.5 Software packages for fMRI analysis; 1.6 Choosing a software package; 1.7 Overview of processing streams; 1.8 Prerequisites for fMRI analysis; 2 Image processing basics; 2.1 What is an image?; 2.2 Coordinate systems; 2.3 Spatial transformations; 2.4 Filtering and Fourier analysis; 3 Preprocessing fMRI data; 3.1 Introduction; 3.2 An overview of fMRI preprocessing.
- 3.3 Quality control techniques3.4 Distortion correction; 3.5 Slice timing correction; 3.6 Motion correction; 3.7 Spatial smoothing; 4 Spatial normalization; 4.1 Introduction; 4.2 Anatomical variability; 4.3 Coordinate spaces for neuroimaging; 4.4 Atlases and templates; 4.4.1 The Talairach atlas; 4.4.2 The MNI templates; 4.5 Preprocessing of anatomical images; 4.5.1 Bias field correction; 4.5.2 Brain extraction; 4.5.3 Tissue segmentation; 4.6 Processing streams for fMRI normalization; 4.7 Spatial normalization methods; 4.7.1 Landmark-based methods; 4.7.2 Volume-based registration.
- 4.7.3 Computational anatomy4.8 Surface-based methods; 4.9 Choosing a spatial normalization method; 4.10 Quality control for spatial normalization; 4.11 Troubleshooting normalization problems; 4.12 Normalizing data from special populations; 5 Statistical modeling: Single subject analysis; 5.1 The BOLD signal; 5.2 The BOLD noise; 5.2.1 Characterizing the noise; 5.2.2 High-pass filtering; 5.2.3 Prewhitening; 5.2.4 Precoloring; 5.3 Study design and modeling strategies; 6 Statistical modeling: Group analysis; 6.1 The mixed effects model; 6.1.1 Motivation.
- 6.1.2 Mixed effects modeling approach used in fMRI6.1.3 Fixed effects models; 6.2 Mean centering continuous covariates; 6.2.1 Single group; 6.2.2 Multiple groups; 7 Statistical inference on images; 7.1 Basics of statistical inference; 7.2 Features of interest in images; 7.3 The multiple testing problem and solutions; 7.3.1 Familywise error rate; 7.3.1.1 Bonferroni correction; 7.3.1.2 Random field theory; 7.3.1.3 Parametric simulations; 7.3.1.4 Nonparametric approaches; 7.3.2 False discovery rate; 7.3.3 Inference example; 7.4 Combining inferences: masking and conjunctions.
- 7.5 Use of region of interest masks7.6 Computing statistical power; 8 Modeling brain connectivity; 8.1 Introduction; 8.2 Functional connectivity; 8.2.1 Seed voxel correlation: Between-subjects; 8.2.2 Seed voxel correlation: Within-subjects; 8.2.2.1 Avoiding activation-induced correlations; 8.2.3 Beta-series correlation; 8.2.4 Psychophysiological interaction; 8.2.4.1 Creating the PPI regressor; 8.2.4.2 Potential problems with PPI; 8.2.5 Multivariate decomposition; 8.2.5.1 Principal components analysis; 8.2.5.2 Independent components analysis; 8.2.5.3 Performing ICA/PCA on group data.