Fundamentals of Brain Network Analysis

by Alex Fornito, Andrew Zalesky, Edward Bullmore

Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain

Genres: Neuroscience, Nonfiction, Textbooks

476 pages, Kindle Edition

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.