Full Description
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, 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 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.
Contents
1. Introduction: A Brief History of fMRI, from the 1990s to the Present
2. Acquisition Parameters and Your Experiment: The Intersection of Scanning Protocols, Experimental Design, and Statistical Power
3. How to Choose which Package to use?: An Introduction to the Big Three (AFNI, FSL, and SPM), Recent Packages to be Familiar with, and the Advantages of Each
4. Determining which Programming Language to Use: Unix, Matlab, Python, and the Rise of Jupyter Notebooks
5. Standardized Data Organization and Preprocessing: The History and Uses of BIDS, fMRIPREP, and introduction to Neurodesk.org
6. Region of Interest analyses revisited: The Many Ways to Select and Analyze a Region, and the Strengths of Each Approach
7. Pitfalls of fMRI Analysis: Circularity, Biased Analyses, and Insidious Artifacts - How You Can Best Protect Yourself and Your Data
8. Statistical Modeling and Correcting for Multiple Comparisons: The History of Univariate Analysis, a Deep Dive into Recent Developments, and What Might Work Best for You
9. New Developments in Functional Connectivity: Dynamic Connectivity, Clinical Applications, and the Connectome as a Neural Fingerprint
10. New Developments in Machine Learning: Hyperalignment, Hybrid Hyperalignment, and Clinical Uses of Representational Similarity Analysis
11. What is Open Science?: A Look into the Latest Trend of Pre-Registering Your Study, How to Share Data and Results, and What This Means for the Field
12. Large Databases, Open-Access Databases, and their Uses: Can Big Data and Meta-Analyses Ameliorate the Problems of 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: Vul, Bennet Eklund, Marek, and the NARPS Paper: What to Learn from Them, and How to Keep Them in Perspective
16. Appendix B: AI and Neuroimaging Analysis - How Tools such as ChatGPT and Other Large Language Models Can Inform Preprocessing and Analysis of fMRI Data
