Table of contents Tor D. Wager
Martin A. Lindquist
The book at a glance ¶ Elements of Functional Magnetic Resonance Imaging — Tor D. Wager & Martin A. Lindquist.
Seven parts, forty-two chapters. Every chapter has a companion tutorial page here.
Part 1 · Motivation ¶ Chapter Tutorial 1 Benefits of fMRI: Versatility and an Open Community Open → 2 Mechanisms and Concepts: Brain and Cognitive Neuroscience Open → 3 Imaging and Society Open → 4 Types of Neuroimaging Open → 5 Working Together and Multidisciplinary Science Open →
Part 2 · Brain Mapping ¶ Chapter Tutorial 6 Inferences About Mind, Brain, and Behavior: The Statistical Brain Mapping Approach Open → 7 Forward and Reverse Inference Open → 8 Valid and Invalid Inferences Open → 9 Contrasting Statistical Mapping with Traditional Neuroradiology Open → 10 Why Imaging Is Not Phrenology Open →
Part 3 · MRI Environment and MRI Signal ¶ Chapter Tutorial 11 MRI and Human Factors Open → 12 fMRI Basics and Terminology Open → 13 Fundamental MRI Physics Open → 14 BOLD Physiology Open → 15 Spatial and Temporal Resolution Open →
Part 4 · Fundamentals of fMRI Signal Processing and Analysis ¶ Chapter Tutorial 16 Artifacts and Noise in fMRI Open → 17 Image Preprocessing Open → 18 The General Linear Model and Foundations of Analysis Open → 19 GLM Design Specification Open → 20 Contrasts and Inference with the GLM Open → 21 Group Analysis Open → 22 Multiple Comparisons Open → 23 Localizing and Interpreting Results Open → 24 Analysis Pipelines: Variations and Variability Open → 25 Neuroimaging Meta-Analysis Open →
Part 5 · Experimental Design ¶ Chapter Tutorial 26 Experiments, Observation, and Causality Open → 27 Experimental Design and Task fMRI Open → 28 Resting-State and Ecological Designs Open → 29 Statistical Power and Sample Size Open →
Part 6 · Brain Connectivity ¶ Chapter Tutorial 30 Introduction to Brain Connectivity Open → 31 Multivariate Decomposition: PCA and ICA Open → 32 Network Analysis Open → 33 Dynamic Connectivity Open → 34 Structural Equation and Path Models Open → 35 Dynamic Causal Models Open → 36 Granger Causal Models Open →
Part 7 · Predictive Modeling ¶ Chapter Tutorial 37 Multivariate Brain Analysis: From Maps to Models Open → 38 Machine Learning Principles and Algorithms Open → 39 Training and Testing Predictive Models Open → 40 Applying Predictive Models to fMRI Data Open → 41 Biomarkers and Translational Neuroscience Open → 42 AI and Neuroscience Open →
The book: Elements of Functional Magnetic Resonance Imaging — Wager & Lindquist, MIT Press