Full Description
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.
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, this book helps readers understand what decisions to make.
Contents
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
