Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. This book covers the design, acquisition, and analysis of fMRI data.
About the Book
Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. In this book we provide an introduction to fMRI, including physics and acquisition, experimental design, analysis, and inferences about brain and mind. We discuss both theoretical considerations and practical aspects of conducting fMRI studies. The book is appropriate for readers who want to learn more about fMRI, and for researchers engaged in fMRI studies.
Author(s): Tor D. Wager and Martin A. Lindquist
Publisher: Leanpub
Year: 2015
Language: English
Pages: 139
Tags: fMRI, Neuroscience, Neuroimaging
Table of Contents
What's in this book?
About the Authors
Martin Lindquist
Tor Wager
Part 1: Motivation
Chapter 1 - Introduction
MRI, PET, and beyond: A quick tour
Principles
Chapter 2 - Why fMRI? Neuroimaging and the movement toward multidisciplinary science
Neuroimaging and the `common language' of the brain
Multiple roles, multiple fields: An example
Challenges and motivation for multidisciplinary science
Chapter 3 - Types of imaging: What PET and fMRI can measure
MRI: Multiple measures, multiple modalities
Structural MRI imaging
Functional imaging with fMRI and PET
Chapter 4 - Brain mapping: A conceptual overview
What is a brain map?
Fundamental assumptions and principles
Types of inference: What brain maps can and cannot tell us
Chapter 5 - Limitations in inferences from brain maps
Seven caveats in brain map inferences
A non-imaging example
Chapter 6 - How to lie with brain imaging
How to tell a story about the ``one brain region''
How to make your results look really strong
Overlapping processes: How to make two maps look the same
How to make two maps look really different
Conclusions
Part 2: Fundamentals: The origins of PET and fMRI signals in the brain
Chapter 7 - fMRI basics: Processing stages, terminology, and data structure
fMRI basics
Data structure in fMRI experiments
Conclusions
Chapter 8 - The MRI environment and human factors
MR basics and safety
Physical limitations on data collection
Chapter 9 - A head-to-head comparison of PET and MRI
Acquisition options and fidelity
Available signal types and their interpretability
Spatial and temporal resolution
Accessibility to a broad community
Conclusions
Chapter 10 - Fundamentals of MRI Physics
Chapter 11 - Physiological basis of fMRI signals
Chapter 12 - Constraints on fMRI spatial and temporal resolution
Spatial Limitations
Temporal Limitations
Part 3: Basics of fMRI signal processing and analysis
Chapter 14 - Experimental Design
Block designs
Event-related fMRI
Optimized experimental designs
Chapter 15 - Resting state, natural viewing, and non-experimental designs
Resting state
Natural viewing and non-experimental designs
Chapter 16 - Essentials of fMRI signal processing
BOLD signal
Noise and nuisance signal
Chapter 17 - Preprocessing
Reconstruction.
Slice-timing correction
Motion correction
Co-registration
Normalization
Spatial smoothing
Chapter 18 - The General Linear Model and Foundations of Analysis
Setting up the GLM
GLM Estimation
Chapter 19 - Conditions and Contrasts
Chapter 20 - Design Specification: Flexible Hemodynamics and Mis-modeling
Chapter 21 - Design Specification: Dealing with Artifacts and Noise
Chapter 22 - Group Analysis
Chapter 23 - Multiple Comparisons
FWE correction
FDR control
Chapter 24 - Assessing Brain Connectivity
Functional connectivity
Effective connectivity
Network analysis
Part 4: Predictive Mapping
Chapter 25 - Multivariate brain analysis: From maps to models
From univariate mapping to multivariate brain models
Basic criteria for a good model
Chapter 26 - Advantages of MVPA from a neuroscientific perspective
MPVA analysis choices: Spatial scope and flexibility
Sensitivity to neural topography
Sensitivity to distributed representations
Benefits in testing generalizability across individuals and studies
Resources and further reading
Other books about fMRI
