Modern fMRI: Practical Lessons and Insights provides an up-to-date, holistic overview of the field of functional magnetic resonance imaging (fMRI), familiarizing the reader with the latest trends in neuroimaging such as standardized data organization and preprocessing, hierarchical data analysis, and advances in data and code sharing. Importantly, the book gives practical advice on best practices in preprocessing, statistical modeling, QA checks, and on some of the latest tools and concepts to be familiar with, including fMRIPREP, OpenNeuro.org, Open Science practices, and Jupyter notebooks.

The field of neuroimaging with fMRI is developing at a rapid pace, with a seemingly endless number of software packages, statistical methods, and different ways to organize and analyze neuroimaging data. Among such a wide variety of options, and with so many seemingly conflicting pieces of advice on the “correct” way of analyzing neuroimaging data, this book helps readers understand what decisions to make.

• Covers reprocessing, statistical modeling, and how to use standardized data organization and analysis
• Familiarizes readers with Open Science and the latest trends that are becoming norms, such as using Jupyter notebooks to analyze data, interacting with Github websites to store and download code, and how to use containers such as Docker and Neurodesk.org
• Presents the most common pitfalls of neuroimaging analysis, including circular analysis, biased region of interest selection, faulty inference of statistical tests, and how these pitfalls show up in different analysis scenarios
• Illustrates new developments in functional connectivity and machine learning analysis, including hyperalignment and dynamic connectivity