Modern fMRI
Practical Lessons and Insights
- 1st Edition - July 1, 2026
- Latest edition
- Author: Andrew Jahn
- Language: English
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… Read more
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 analysing neuroimaging data, knowing what decisions to make is a difficult task.
Modern fMRI: Practical Lessons and Insights provide 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, 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.
Modern fMRI: Practical Lessons and Insights provide 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, 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.
With this book the reader will be able to:
- 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.
Undergraduate and graduate students in biomedical engineering, neuroscience and psychology taking a course on neuroimaging using fMRI, early career researchers carrying out neuroimaging using fMRI and veterans of the field who are curious about the latest trends in neuroimaging
1. Introduction: A Brief History of Neuroimaging and fMRI
2. Acquisition Parameters and Your Experiment: The Intersection of Scanning Protocols, Experimental Design, and Statistical Power
3. Choosing Your fMRI 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 Neurodesk
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: Different 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 Pre-Registration, 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
15. Appendix A: Review of Papers that Question fMRI Findings - What to Learn from Them, and How to Keep Them in Perspective
16. Appendix B: AI and Neuroimaging Analysis – How Generative AI Can Inform the Preprocessing and Analysis of fMRI Data
2. Acquisition Parameters and Your Experiment: The Intersection of Scanning Protocols, Experimental Design, and Statistical Power
3. Choosing Your fMRI 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 Neurodesk
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: Different 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 Pre-Registration, 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
15. Appendix A: Review of Papers that Question fMRI Findings - What to Learn from Them, and How to Keep Them in Perspective
16. Appendix B: AI and Neuroimaging Analysis – How Generative AI Can Inform the Preprocessing and Analysis of fMRI Data
- Edition: 1
- Latest edition
- Published: July 1, 2026
- Language: English
AJ
Andrew Jahn
Dr. Andrew Jahn is a research scientist in the Department of Radiology at the University of Michigan. He is the creator of Andy’s Brain Blog and its associated YouTube channel, online resources that host tutorials and videos about neuroimaging analysis from start to finish in all the major software packages. He continues to produce training materials and teach workshops about neuroimaging analysis, functional connectivity, machine learning, and other topics related to cognitive neuroscience.
Affiliations and expertise
University of Michigan, Ann Arbor, MI, USA