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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.

Key Features
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



Table of Contents
Author Biographies
Foreword
Acknowledgments
Chapter 1: An Introduction to Brain Networks
Abstract
1.1 Graphs as Models for Complex Systems
1.2 Graph Theory and the Brain
1.3 Are graph theory and connectomics useful?
1.4 Summary
Chapter 2: Nodes and Edges
Abstract
2.1 Microscale Connectomics
2.2 Mesoscale Connectomics
2.3 Macroscale Connectomics
2.4 Summary
Chapter 3: Connectivity Matrices and Brain Graphs
Abstract
3.1 The Connectivity Matrix
3.2 The Adjacency Matrix
3.3 Network Visualization
3.4 What Type of Network Is a Connectome?
3.5 Summary
Chapter 4: Node Degree and Strength
Abstract
4.1 Measures of Node Connectivity
4.2 Degree Distributions
4.3 Weight Distributions
4.4 Summary
Chapter 5: Centrality and Hubs
Abstract
5.1 Centrality
5.2 Identifying Hub Nodes
5.3 Summary
Chapter 6: Components, Cores, and Clubs
Abstract
6.1 Connected Components
6.2 Core-Periphery Organization
6.3 Rich Clubs
6.4 Summary
Chapter 7: Paths, Diffusion, and Navigation
Abstract
7.1 Walks, Trails, Paths, and Cycles
7.2 Shortest Path Routing
7.3 Diffusion Processes
7.4 Navigation and Other Models of Neural Communication
7.5 Summary
Chapter 8: Motifs, Small Worlds, and Network Economy
Abstract
8.1 Network Motifs
8.2 Clustering, Degeneracy, and Small Worlds
8.3 Network Economy
8.4 Summary
Chapter 9: Modularity
Abstract
9.1 Defining Modules
9.2 Node Roles
9.3 Comparing and Aggregating Network Partitions