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
Table of Contents
1. Introduction: A brief history of neuroimaging and functional magnetic resonance imaging2. Acquisition parameters and your experiment: The intersection of scanning protocols, experimental
design, and statistical power
3. Choosing your functional magnetic resonance imaging analysis software: An introduction to the
big three (SPM, FSL, and AFNI), recent packages to be familiar with, and the advantages of each
4. Choosing your programming language: Unix, MATLAB, Python, and the rise of Jupyter Notebooks
5. Standardized data organization and preprocessing: The history and uses of BIDS, fMRIPREP, and an
introduction to Neurodesk.org
6. Statistical modeling and correcting for multiple comparisons: The mass univariate approach, recent developments, and what might work best for you
7. Region of interest analysis: The many ways to select and analyze a region, and the strengths of each approach
8. Pitfalls of fMRI analysis: Circular analyses, biased ROIs, logical fallacies, and how to avoid them
9. New developments in functional connectivity: Dynamic connectivity, graph theory, and the connectome
10. New developments in machine learning: Representational similarity analysis, hyperalignment, and their applications
11. Open science: An overview of preregistration, data sharing, and current guidelines
12. Open-access databases, meta-analysis, and reproducibility
13. Bringing it all together: Summarizing the main points of this book
14. Where do we go from here? The future of neuroimaging analysis
Appendix A: Review of papers that question fMRI findings-What to learn from them, and how to keep
them in perspective
Appendix B: AI and neuroimaging analysis-How Generative AI can inform the preprocessing and analysis of fMRI data
