Fundamentals of Brain Network Analysis

by Alex Fornito, Andrew Zalesky, Edward Bullmore

"Fundamentals of Human Imaging Connectomics" is the first book to provide an accessible, practical, and comprehensive introduction to imaging connectomics for researchers of any background. Written by experts in all areas of the field, this book contains nontechnical, conceptual, and instructive discussion of each of the core principles of imaging connectomics. Featuring intuitive diagrams, graphical illustrations of key concepts, step-by-step explanations of mathematical formulae, and recommendations for best practices, "Fundamentals of Human Imaging Connectomics" is an indispensable guide for researchers studying the human connectome.
The only volume to offer a step-by-step introduction to connectomics suitable for both researchers and students. Provides a general overview, discussion of various issues involved in using neuroimaging to build a connectomic map, the main measures used to analyze connectomic data, an intro to advanced topics in the field, and discussion of as yet unresolved issues and future directions. Helps readers determine how they can best use fMRI/DTI data to make a brain network, how they can analyze that network using graph theory, and how they can compare/interpret their findings across different groupsAssumes no prior knowledge beyond basic training in human MRI, and adopts a consistent format across chapters to facilitate learning and linking of different concepts

Genres: Neuroscience, Nonfiction, Textbooks

494 pages, Hardcover

First published November 15, 2015

Reviews

[Kayson Fakhar · Rating 4 out of 5]

This is a very nice book that covers all the way from general concepts to the detailed technical issues that you have to address. Definitely recommend if you want to get into network neuroscience.

[Marinho Lopes · Rating 4 out of 5]

This is a well-written book about network theory applied to brain data. Overall, I liked the way the concepts were described and the examples provided. However, I would say that the structure of the book is questionable (though I recognize that any structure could probably look bad, given how intermingled the subject is). Furthermore, both chapter 2 (about node and connection definition) and chapter 11 (statistical methods in network inference) make it clear that the whole literature about the subject is at best very questionable, which makes most of the examples presented throughout the book very questionable as well. The authors are careful with their words, but sometimes it sounds that it is just way too much speculation. Taking this into account, I would say that is missing a discussion about the importance of constructing ground truth models to validate all these methods. I think the book also misses one last chapter summing up the state of the art and the authors' opinion about the future of the field. 

After reading this book, I am once again very concerned about brain connectomics: it seems very likely that many people may be applying all kinds of different measures to their data until something "statistically significant" comes up by chance. Unfortunately, most research groups deal with small datasets, which imply that they cannot divide their data into training and testing datasets... Are therefore reported hypothesis being thought at the beginning of the studies or invented after "results" have been found?...
Nevertheless, the book is interesting and provides a good summary of the tools for brain network analysis. 

[Zaki · Rating 4 out of 5]

Solid text that provides a strong background for structural and functional network analysis without overwhelming the learner with computational formulae. Earlier chapters are beginner friendly for those who have a background in linear algebra and more complicated topics are presented in later chapters in an easy-to-follow way.

[Carter · Rating 1 out of 5]

Overrated. This is basically, a graph theory book, with some standard "heuristics"/"metrics", which tend to vary, depending on the application area; some are in common with social networks. A graph theory book, would be more useful.

[Oldfish Yu · Rating 5 out of 5]

Please read this book, if you want to conduct brain network analysis!

[Eduarda Zampieri · Rating 5 out of 5]

a great summary for the field and whoever wants to start working with network neuroscience. Really important for my research at the moment.