@article{1482391,
      recid = {1482391},
      author = {Polzehl, Jörg. and Tabelow, Karsten.},
      title = {Magnetic resonance brain imaging : modelling and data  analysis using R /},
      publisher = {Springer,},
      address = {Cham :},
      pages = {1 online resource (268 p.).},
      year = {2023},
      note = {5.6 Structural Connectivity},
      abstract = {This book discusses modelling 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 main chapters then  cover four common MR imaging modalities and their data  modeling and analysis problems: functional MRI, diffusion  MRI, Multi-Parameter Mapping and Inversion Recovery MRI.  The book concludes with extended Appendices on details of  the utilize 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: The books  R-code and intermediate results are available for  reproducibility of the examples.},
      url = {http://library.usi.edu/record/1482391},
      doi = {https://doi.org/10.1007/978-3-031-38949-8},
}