Fundamentals of Brain Network Analysis

"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. 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"--OCLC. "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.Builds a conceptual understanding on the minimum of prior knowledge by a series of chapters that lead from basic to more advanced technical issuesExtensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analysis of nervous systemsComprehensively covers graph theoretical analysis of structural and functional brain networks from microscopic to macroscopic scales using examples based on a wide variety of experimental methods in neuroscienceDetailed text boxes and glossary provide in-depth background to key concepts and terminologyDesigned to inform and empower scientists with all levels of experience, and from any specialist background, wanting to use the modern methods of network science to analyse the organization of the brain

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

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 Content: Front Matter,Copyright,Author Biographies,Foreword,AcknowledgmentsEntitled to full textChapter 1 - An Introduction to Brain Networks, Pages 1-35 Chapter 2 - Nodes and Edges, Pages 37-88 Chapter 3 - Connectivity Matrices and Brain Graphs, Pages 89-113 Chapter 4 - Node Degree and Strength, Pages 115-136 Chapter 5 - Centrality and Hubs, Pages 137-161 Chapter 6 - Components, Cores, and Clubs, Pages 163-206 Chapter 7 - Paths, Diffusion, and Navigation, Pages 207-255 Chapter 8 - Motifs, Small Worlds, and Network Economy, Pages 257-301 Chapter 9 - Modularity, Pages 303-354 Chapter 10 - Null Models, Pages 355-381 Chapter 11 - Statistical Connectomics, Pages 383-419 Glossary, Pages 421-432 References, Pages 433-472 Index, Pages 473-476 The field of imaging connectomics is technically challenging and highly multi-disciplinary, creating the need for a "how to" connectomics reference. This book offers just that: an accessible and comprehensive introduction to the fundamental principles and practices of imaging connectomics This book provides an introduction to neural connectomics. It explains fundamental concepts with detailed examples of their application to neuroscience. It is suitable for use as a reference for both researchers and students aiming to gain familiarity with the field