About this book
This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts.
The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters 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 providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.
Magnetic Reconance Imaging data formats BIDS standard preprocessing pipelines structural Magnetic Resonance Imaging functional Magnetic Resonance Imaging signal detection multiple comparisons resting state Magnetic Resonance Imaging functional connectivity diffusion models structural connectivity quantitative Magnetic Resonance Imaging Multi-Parameter Mapping structural adaptive smoothing NeuroConductor
- DOI https://doi.org/10.1007/978-3-030-29184-6
- Copyright Information Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-030-29182-2
- Online ISBN 978-3-030-29184-6
- Series Print ISSN 2197-5736
- Series Online ISSN 2197-5744
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