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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, knowing what decisions to make is a difficult task. 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.
