Modern fMRI

1. Introduction: A brief history of neuroimaging and functional magnetic resonance imaging<br>2. Acquisition parameters and your experiment: The intersection of scanning protocols, experimental<br>design, and statistical power<br>3. Choosing your functional magnetic resonance imaging analysis software: An introduction to the<br>big three (SPM, FSL, and AFNI), recent packages to be familiar with, and the advantages of each<br>4. Choosing your programming language: Unix, MATLAB, Python, and the rise of Jupyter Notebooks<br>5. Standardized data organization and preprocessing: The history and uses of BIDS, fMRIPREP, and an<br>introduction to Neurodesk.org<br>6. Statistical modeling and correcting for multiple comparisons: The mass univariate approach, recent developments, and what might work best for you<br>7. Region of interest analysis: The many ways to select and analyze a region, and the strengths of each approach<br>8. Pitfalls of fMRI analysis: Circular analyses, biased ROIs, logical fallacies, and how to avoid them<br>9. New developments in functional connectivity: Dynamic connectivity, graph theory, and the connectome<br>10. New developments in machine learning: Representational similarity analysis, hyperalignment, and their applications<br>11. Open science: An overview of preregistration, data sharing, and current guidelines<br>12. Open-access databases, meta-analysis, and reproducibility<br>13. Bringing it all together: Summarizing the main points of this book<br>14. Where do we go from here? The future of neuroimaging analysis<br>Appendix A: Review of papers that question fMRI findings—What to learn from them, and how to keep<br>them in perspective<br>Appendix B: AI and neuroimaging analysis–How Generative AI can inform the preprocessing and analysis of fMRI data