Modern fMRI

Practical Lessons and Insights

Andrew Jahn

• Publisher: Academic Press
• Year: 2026
• eBook ISBN:
  9780443405839
• Topic: Sciences biologiques
• Subtopic: Biotechnologie

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.

Table of contents

• Cover image
• Neuroimaging Methods and Applications Series
• Title page
• Copyright
• Dedication
• Contents
• Author Biography
• Preface
• Acknowledgments
• CHAPTER 1 Introduction: A brief history of neuroimaging and functional magnetic resonance imaging
• CHAPTER 2 Acquisition parameters and your experiment: The intersection of scanning protocols, experimental design, and statistical power
• CHAPTER 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
• CHAPTER 4 Choosing your programming language: Unix, MATLAB, Python, and the rise of Jupyter Notebooks
• CHAPTER 5 Standardized data organization and preprocessing: The history and uses of BIDS, fMRIPrep, and an introduction to Neurodesk
• CHAPTER 6 Statistical modeling and correcting for multiple comparisons: The mass univariate approach, recent developments, and what might work best for you
• CHAPTER 7 Region of interest analysis: The many ways to select and analyze a region, and the strengths of each approach
• CHAPTER 8 Pitfalls of fMRI analysis: Circular analyses, biased ROIs, logical fallacies, and how to avoid them
• CHAPTER 9 New developments in functional connectivity: Dynamic connectivity, graph theory, and the connectome
• CHAPTER 10 New developments in machine learning: Representational similarity analysis, hyperalignment, and their applications
• CHAPTER 11 Open science: An overview of preregistration, data sharing, and current guidelines
• CHAPTER 12 Open-access databases, meta-analysis, and reproducibility
• CHAPTER 13 Bringing it all together: Summarizing the main points of this book
• CHAPTER 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
• Glossary
• Index