Magnetic Resonance Brain Imaging
Modeling and Data Analysis Using R

About the book

This book is about modeling and analysis of Magnetic Resonance Imaging (MRI) data of the human brain. For the data processing pipelines we rely on R, the software environment for statistical computing and graphics. The book is intended for readers from two communities: Statisticians, who are interested in neuroimaging and look for an introduction to the acquired data and typical scientific problems in the field and neuroimaging students, who want to learn about the statistical modeling and analysis of MRI data. Being a practical introduction the book focuses on those problems in data analysis for which implementations within R are available. By providing full worked-out examples the book thus serves as a tutorial for MRI analysis with R, from which the reader can derive its own data processing scripts.

The book starts with a short introduction into MRI. The next chapter considers the process of reading and writing common neuroimaging data formats to and from the R session. The three main chapters then cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended Appendices on details of the utilized non-parametric statistics and on resources for R and MRI data.

The book also addresses the issues of reproducibility and topics like data organization and description, open data and open science. It completely relies on a dynamic report generation with knitr: It uses neuroimaging data publicly available on data repositories, the PDF was created executing the R code in the chunks and then running LaTeX. Thus, almost all figures, numbers, and results are generated while producing the PDF from the sources.

On this website we present the code and examples from the book.