ISBN: 9780262354059 | 568 pp. | August 2019


Statistical Analysis of fMRI Data


Functional magnetic resonance imaging (fMRI), which allows researchers to observe neural activity in the human brain noninvasively, has revolutionized the scientific study of the mind. An fMRI experiment produces massive amounts of highly complex data for researchers to analyze. This book describes all aspects of experimental design and data analysis for fMRI experiments, covering every step—from preprocessing to advanced methods for assessing functional connectivity—as well as the most popular multivariate approaches. The goal is not to describe which buttons to push in the popular software packages but to help researchers understand the basic underlying logic, the assumptions, the strengths and weaknesses, and the appropriateness of each method.

The field of fMRI research has advanced dramatically in recent years, in both methodology and technology, and this second edition has been completely revised and updated. Six new chapters cover experimental design, functional connectivity analysis through the methods of psychophysiological interactions and beta-series regression, decoding using multi-voxel pattern analysis, dynamic causal modeling, and representational similarity analysis. Other chapters offer new material on recently discovered problems related to head movements, the multivariate GLM, meta-analysis, and other topics. All complex derivations now appear at the end of the relevant chapter to improve readability. A new appendix describes how to build a design matrix with effect coding for group analysis. As in the first edition, MATLAB code is provided with which readers can implement many of the methods described.

Table of Contents

  1. Preface to the Second Edition
  2. Preface to the First Edition
  3. List of Acronyms
  4. 1. Introduction
  5. 2. Data Formats
  6. 3. Modeling the BOLD Response
  7. 4. Experimental Designs
  8. 5. Preprocessing
  9. 6. The General Linear Model
  10. 7. The Multiple Comparisons Problem
  11. 8. Group Analyses
  12. 9. Functional Connectivity Analysis via Psychophysiological Interactions and Beta-Series Regression
  13. 10. Functional Connectivity Analysis via Granger Causality
  14. 11. Assessing Functional Connectivity via Coherence Analysis
  15. 12. Principal Component Analysis
  16. 13. Independent Component Analysis
  17. 14. Decoding via Multivoxel Pattern Analysis
  18. 15. Encoding Models
  19. 16. Dynamic Causal Modeling
  20. 17. Representational Similarity Analysis
  21. Appendix A. Matrix Algebra
  22. Appendix B. Multivariate Probability Distributions
  23. Appendix C. Building a Design Matrix for Group Analysis
  24. Notes
  25. References
  26. Index