The field of neuroimaging with functional magnetic resonance imaging (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 fMRI, familiarizing the reader with the latest trends in neuroimaging, such as standardized data organization and preprocessing, advances in functional connectivity and machine learning, and current guidelines in data and code sharing. This includes advice about best practices in preprocessing, statistical modeling, QA checks, and some of the latest tools and concepts to be familiar with, including fMRIPrep, OpenNeuro.org, Open Science practices, and Jupyter notebooks Make educated choices about preprocessing, statistical modeling, and whether and how to use standardized data organization and analysis. Familiarize themselves with Open Science and the latest trends that are becoming norms, such as Jupyter notebooks and how to use platforms such as Neurodesk.org. Identify the most common pitfalls of neuroimaging analysis, including circular analysis, biased region of interest selection, and faulty inference of statistical tests, and how these pitfalls show up in different analysis scenarios. Learn about new developments in functional connectivity and machine learning analysis, including hyperalignment and dynamic connectivity. Make informed judgments about which statistical analysis and thresholds to use, especially for multiple comparisons, and to become a more nuanced user and interpreter of p-values, effect sizes, and plots of neuroimaging results.