Principles of fMRI
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Principles of fMRI

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. 

About the Authors

Martin A. Lindquist
Martin A. Lindquist

Dr. Lindquist is a Professor of Biostatistics at Johns Hopkins University. He received his Ph.D. in Statistics from Rutgers University in 2001, and served as an Assistant and Associate Professor at Columbia University from 2003-2012. His research focuses on mathematical and statistical problems relating to functional Magnetic Resonance Imaging (fMRI). Dr. Lindquist is actively involved in developing new analysis methods to enhance our ability to understand brain function using human 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

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