Modern fMRI

Nyhet

Practical Lessons and Insights

Häftad, Engelska, 2026

1 669 kr

Kommande

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.

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 using Jupyter notebooks to analyze data, interacting with Github websites to store and download code, and how to use containers such as Docker and Neurodesk.org;
Learn 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 good judgements of 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.

Produktinformation

Utgivningsdatum2026-07-01
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
SerieNeuroimaging Methods and Applications
Antal sidor250
FörlagElsevier Science
ISBN9780443405822

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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.

1. Introduction: A Brief History of fMRI, from the 1990s to the Present2. Acquisition Parameters and Your Experiment: The Intersection of Scanning Protocols, Experimental Design, and Statistical Power3. 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 Each4. Determining which Programming Language to Use: Unix, Matlab, Python, and the Rise of Jupyter Notebooks5. Standardized Data Organization and Preprocessing: The History and Uses of BIDS, fMRIPREP, and introduction to Neurodesk.org6. Region of Interest analyses revisited: The Many Ways to Select and Analyze a Region, and the Strengths of Each Approach7. Pitfalls of fMRI Analysis: Circularity, Biased Analyses, and Insidious Artifacts - How You Can Best Protect Yourself and Your Data8. Statistical Modeling and Correcting for Multiple Comparisons: The History of Univariate Analysis, a Deep Dive into Recent Developments, and What Might Work Best for You9. New Developments in Functional Connectivity: Dynamic Connectivity, Clinical Applications, and the Connectome as a Neural Fingerprint10. New Developments in Machine Learning: Hyperalignment, Hybrid Hyperalignment, and Clinical Uses of Representational Similarity Analysis11. 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 Field12. 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 Book14. Where do we go from here? The Future of Neuroimaging Analysis15. 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 Perspective16. Appendix B: AI and Neuroimaging Analysis – How Tools such as ChatGPT and Other Large Language Models Can Inform Preprocessing and Analysis of fMRI Data