Intro
Preface to the Second Edition
Preface to First Edition
Contents
Acronyms
1 Introduction
2 Magnetic Resonance Imaging in a Nutshell
2.1 The Principles of Magnetic Resonance Imaging
2.1.1 The Zeeman effect for Atomic Nuclei
2.1.2 Macroscopic Magnetization Vector
2.1.3 Spin Excitation and Relaxation
2.1.4 Spatial Localization and Pulse Sequences
2.1.5 MR Image Formation and Parallel Imaging
2.2 Special MR Imaging Modalities
2.2.1 Functional Magnetic Resonance Imaging (fMRI)
2.2.2 Diffusion Weighted Magnetic Resonance Imaging(dMRI)

2.2.3 Multi-parameter Mapping (MPM)
2.2.4 Inversion Recovery Magnetic Resonance Imaging (IR-MRI)
3 Medical Imaging Data Formats
3.1 DICOM Format
3.2 ANALYZE and NIfTI format
3.3 The BIDS Standard for Neuroimaging Data
4 Functional Magnetic Resonance Imaging
4.1 Prerequisites for Running the Code in This Chapter
4.2 Pre-processing fMRI Data
4.2.1 Example Data
Functional MRI Data on Visual Object Recognition (ds000105)
Multi-subject and Multi-modal Neuroimaging Dataset on Face Processing (ds000117)
Multi-modal Longitudinal Study of a Single Subject (ds000031)

4.2.2 Slice Time Correction
4.2.3 Motion Correction
4.2.4 Registration
4.2.5 Normalization
4.2.6 Brain Mask
4.2.7 Brain Tissue Segmentation
4.2.8 Using Brain Atlas Information
4.2.9 Spatial Smoothing
4.3 The General Linear Model (GLM) for fMRI
4.3.1 Modeling the BOLD Signal
4.3.2 The Linear Model
4.3.3 Simulated fMRI Data
4.4 Signal Detection in Single-Subject Experiments
4.4.1 Voxelwise Signal Detection and the Multiple Comparison Problem
4.4.2 Bonferroni Correction
4.4.3 Random Field Theory
4.4.4 False Discovery Rate (FDR)

4.4.5 Cluster Thresholds
4.4.6 Permutation Tests
4.5 Adaptive Smoothing in fMRI
4.5.1 Analyzing fMRI Experiments with Structural Adaptive Smoothing Procedures
4.5.2 Structural Adaptive Segmentation in fMRI
4.6 Other Approaches for fMRI Analysis Using R
4.6.1 Multivariate fMRI Analysis
4.6.2 Independent Component Analysis (ICA)
4.7 Functional Connectivity for Resting-State fMRI
5 Diffusion-Weighted Imaging
5.1 Prerequisites
5.2 Diffusion-Weighted MRI Data
5.2.1 The Diffusion Equation and MRI
5.2.2 Example Data
5.2.3 Data Pre-processing

5.2.4 Reading Pre-processed Data
5.2.5 Basic Data Properties
5.2.6 Definition of a Brain Mask
5.2.7 Characterization of Noise in Diffusion-Weighted MRI
5.3 Modeling Diffusion-Weighted MRI Data
5.3.1 The Apparent Diffusion Coefficient (ADC)
5.3.2 Diffusion Tensor Imaging (DTI)
5.3.3 Diffusion Kurtosis Imaging (DKI)
5.3.4 The Orientation Distribution Function
5.3.5 Tensor Mixture Models
5.4 Smoothing Diffusion-Weighted Data
5.4.1 Effects of Gaussian Filtering
5.4.2 Multi-shell Position-Orientation Adaptive Smoothing (msPOAS)
5.5 Fiber Tracking Methods