Handbook of Functional MRI Data Analysis

ISBN-10: 0521517664

ISBN-13: 9780521517669

Edition: 2011

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Book details

List price: $150.00
Copyright year: 2011
Publisher: Cambridge University Press
Publication date: 8/22/2011
Binding: Hardcover
Pages: 238
Size: 7.25" wide x 10.25" long x 1.00" tall
Weight: 1.738

Dr Russell A. Poldrack is the Director of the Imaging Research Center and Professor of Psychology and Neurobiology at the University of Texas, Austin. He has published more than 100 articles in the field of cognitive neuroscience, in journals including Science, Nature, Neuron, Nature Neuroscience and PNAS. He is well known for his writings on how neuroimaging can be used to make inferences about psychological function, as well as for his research using fMRI and other imaging techniques to understand the brain systems that support learning and memory, decision making and executive function.

Dr Jeanette A. Mumford is a Research Assistant Professor in the Department of Psychology at the University of Texas, Austin. Trained in biostatistics, her research has focused on the development and characterisation of new methods for statistical modeling and analysis of fMRI data. Her work has examined the impact of different group modeling strategies and developed new tools for modeling network structure in resting-state fMRI data. She is the developer of the fmriPower software package, which provides power analysis tools for fMRI data.

Preface
Introduction
A brief overview of fMRI
The emergence of cognitive neuroscience
A brief history of fMRI analysis
Major components of fMRI analysis
Software packages for fMRI analysis
Choosing a software package
Overview of processing streams
Prerequisites for fMRI analysis
Image processing basics
What is an image?
Coordinate systems
Spatial transformations
Filtering and Fourier analysis
Preprocessing fMRI data
Introduction
An overview of fMRI preprocessing
Quality control techniques
Distortion correction
Slice timing correction
Motion correction
Spatial smoothing
Spatial normalization
Introduction
Anatomical variability
Coordinate spaces for neuroimaging
Atlases and templates
Preprocessing of anatomical images
Processing streams for fMRI normalization
Spatial normalization methods
Surface-based methods
Choosing a spatial normalization method
Quality control for spatial normalization
Troubleshooting normalization problems
Normalizing data from special populations
Statistical modeling: Single subject analysis
The BOLD signal
The BOLD noise
Study design and modeling strategies
Statistical modeling: Group analysis
The mixed effects model
Mean centering continuous covariates
Statistical inference on images
Basics of statistical inference
Features of interest in images
The multiple testing problem and solutions
Combining inferences: masking and conjunctions
Use of region of interest masks
Computing statistical power
Modeling brain connectivity
Introduction
Functional connectivity
Effective connectivity
Network analysis and graph theory
Multivoxel pattern analysis and machine learning
Introduction to pattern classification
Applying classifiers to fMRI data
Data extraction
Feature selection
Training and testing the classifier
Characterizing the classifier
Visualizing, localizing, and reporting fMRI data
Visualizing activation data
Localizing activation
Localizing and reporting activation
Region of interest analysis
Review of the General Linear Model
Estimating GLM parameters
Hypothesis testing
Correlation and heterogeneous variances
Why "general" linear model?
Data organization and management
Computing for fMRI analysis
Data organization
Project management
Scripting for data analysis
Image formats
Data storage
File formats
Bibliography
Index
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