Alex Fornito; Andrew Zalesky; Edward Bullmore
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“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.

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 groups Assumes no prior knowledge beyond basic training in human MRI, and adopts a consistent format across chapters to facilitate learning and linking of different concepts “

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