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Statistical Analysis of fMRI Data [Hardcover]

by F. Gregory Ashby
5.0 out of 5 stars  See all reviews (3 customer reviews)

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Book Description

March 11, 2011 0262015048 978-0262015042 1

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; researchers face significant challenges in analyzing the data they collect. This book offers an overview of the most widely used statistical methods of analyzing fMRI data. Every step is covered, from preprocessing to advanced methods for assessing functional connectivity. The goal is not to describe which buttons to push in the popular software packages but to help readers understand the basic underlying logic, the assumptions, the strengths and weaknesses, and the appropriateness of each method.

The book covers all of the important current topics in fMRI data analysis, including the relation of the fMRI BOLD (blood oxygen-level dependent) response to neural activation; basic analyses done in virtually every fMRI article -- preprocessing, constructing statistical parametrical maps using the general linear model, solving the multiple comparison problem, and group analyses; the most popular methods for assessing functional connectivity -- coherence analysis and Granger causality; two widely used multivariate approaches, principal components analysis and independent component analysis; and a brief survey of other current fMRI methods. The necessary mathematics is explained at a conceptual level, but in enough detail to allow mathematically sophisticated readers to gain more than a purely conceptual understanding. The book also includes short examples of Matlab code that implement many of the methods described; an appendix offers an introduction to basic Matlab matrix algebra commands (as well as a tutorial on matrix algebra). A second appendix introduces multivariate probability distributions.


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Editorial Reviews

Review

This book covers all major fMRI analyses with a level of mathematical depth that is appropriate for cognitive and brain scientists with some background in statistics. The author has the rare ability of explaining complex issues using intuitive and engaging prose. The perfect balance between conceptual intelligibility and mathematical rigor makes this an ideal textbook for undergraduate and graduate courses.

(Roberto Cabeza, Professor of Psychology and Neuroscience, Duke University)

Understanding the complexities associated with the generation of brain function images is essential, but this information is often difficult to obtain. This lovely new book by Ashby is a major step in meeting that need. I recommend it with enthusiasm to new initiates to imaging as well as seasoned veterans.

(Marcus Raichle, Professor of Radiology, Neurology, Neurobiology, and Biomedical Engineering at Washington University in St Louis)

About the Author

F. Gregory Ashby is Professor and Chair in the Department of Psychology and former Director of the Brain Imaging Center at the University of California, Santa Barbara.

Product Details

  • Hardcover: 352 pages
  • Publisher: The MIT Press; 1 edition (March 11, 2011)
  • Language: English
  • ISBN-10: 0262015048
  • ISBN-13: 978-0262015042
  • Product Dimensions: 9.3 x 6.9 x 0.8 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #102,114 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews
11 of 12 people found the following review helpful
5.0 out of 5 stars Must read reference for serious students of fMRI May 31, 2011
Format:Hardcover|Verified Purchase
One of the most challenging aspects of learning and teaching the non-trivial statistical methodology associated with the analysis of fMRI data is a widespread tendency (in textbooks and papers) not to write down the underlying mathematical ideas precisely and clearly.

In an effort to improve my own understanding of the field, and to train my post-docs and PhD students, I have carefully looked at every related textbook in the field. This book is on a class of its own and is a MUST read for every serious student of fMRI.

The book carries the reader through a precise but highly readable analysis of the statistical issues associated with analyzing fMRI data, from basic issues such as preprocessing, to an outstanding four chapter discussion of 'connectivity methods' and 'multivariate approaches'. To top it all, the book includes multiple snipets of MATLAB code that greatly add to the reader's understanding of the subject.

Overall, the book is an example of how methods should be disseminated in this highly technical area to an area that spans a wide range of mathematical backgrounds.

Everybody in my lab is getting a copy of this!

Antonio Rangel
Prof. of Neuroscience & Economics
Caltech
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6 of 6 people found the following review helpful
Format:Hardcover
The books succeeds in elucidates the complex world of fMRI statistics. The book guides a reader throughout the process of fMRI data analysis with clear, practical examples. It is also possible to read select chapters on particular topics of interest. Additionally, there are many specific tips and tricks to improve the quality of fMRI studies and subsequent analysis. The value (i.e., price for information) is extraordinary. Thus, making it an appropriate textbook choice for a graduate level course in fMRI data analysis. As additional value, there is Matlab code on his personal website, [...], to facilitate a deeper understanding of the concepts. Overall, this book is an invaluable asset to the field.
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1 of 1 people found the following review helpful
5.0 out of 5 stars Very good book for fMRI course November 17, 2012
By Kourosh
Format:Hardcover|Verified Purchase
I bought the book because I was auditing fMRI course at MIT. It's good book for starting to work with fMRI data without getting too much into physical details.
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