UID:
almafu_9960074062102883
Umfang:
1 online resource (xvii, 476 pages) :
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illustrations (some color), portraits
Ausgabe:
First edition.
ISBN:
9780124081185
,
0124081185
Serie:
Gale eBooks
Inhalt:
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.
Anmerkung:
Description based upon print version of record.
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Front Cover; Fundamentals of Brain Network Analysis; Copyright; Contents; Author Biographies; Foreword; Acknowledgments; Chapter 1: An Introduction to Brain Networks; 1.1. Graphs as Models for Complex Systems; 1.1.1. A Brief History of Graph Theory; 1.1.2. Space, Time, and Topology; 1.2. Graph Theory and the Brain; 1.2.1. The Neuron Theory and Connectivity at the Microscale; 1.2.2. Clinicopathological Correlations and Connectivity at the Macroscale; 1.2.3. The Dawn of Connectomics; 1.2.4. Neuroimaging and Human Connectomics; 1.2.5. Back to Basics: From Macro to Meso and Micro Connectomics
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1.3. Are graph theory and connectomics useful?1.4. Summary; Chapter 2: Nodes and Edges; 2.1. Microscale Connectomics; 2.1.1. Structural Connectivity at the Microscale; 2.1.2. Functional Connectivity at the Microscale; 2.2. Mesoscale Connectomics; 2.2.1. Structural Connectivity at the Mesoscale; 2.2.2. Functional Connectivity at the Mesoscale; 2.3. Macroscale Connectomics; 2.3.1. Defining Nodes at the Macroscale; 2.3.2. Structural Connectivity at the Macroscale; 2.3.3. Functional Connectivity at the Macroscale; 2.4. Summary; Chapter 3: Connectivity Matrices and Brain Graphs
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3.1. The Connectivity Matrix3.1.1. Diagonal and Off-Diagonal Elements; 3.1.2. Directionality; 3.1.3. Connectivity Weights; 3.1.4. Sparse Matrices; 3.2. The Adjacency Matrix; 3.2.1. Thresholding; 3.2.2. Binarization; 3.2.3. Network Density and Weight; 3.3. Network Visualization; 3.3.1. Visualizing the Adjacency Matrix; 3.3.2. Visualizing Brain Graphs; 3.4. What Type of Network Is a Connectome?; 3.5. Summary; Chapter 4: Node Degree and Strength; 4.1. Measures of Node Connectivity; 4.1.1. Node Degree; 4.1.2. Node Strength; 4.1.3. Node Degree and Network Density; 4.2. Degree Distributions
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4.2.1. Single-Scale Distributions4.2.2. Scale-Free Distributions; 4.2.3. Broad-Scale Distributions; 4.3. Weight Distributions; 4.3.1. The Lognormal Distribution; 4.4. Summary; Chapter 5: Centrality and Hubs; 5.1. Centrality; 5.1.1. Degree-Based Measures of Centrality; 5.1.2. Closeness Centrality; 5.1.3. Betweenness Centrality; 5.1.4. Delta Centrality; 5.1.5. Characterizing Centrality in Brain Networks; 5.2. Identifying Hub Nodes; 5.2.1. Classifying Hubs Based on Degree and Centrality; 5.2.2. Consensus Classification of Hubs; 5.2.3. Module-Based Role Classification; 5.3. Summary
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Chapter 6: Components, Cores, and Clubs6.1. Connected Components; 6.1.1. Components in Undirected Networks; 6.1.2. Components in Directed Networks; 6.1.3. Percolation and Robustness; 6.1.4. Components and Group Differences in Networks; 6.2. Core-Periphery Organization; 6.2.1. Maximal Cliques; 6.2.2. k-Cores and s-Cores; 6.2.3. Model-Based Decomposition; 6.2.4. Knotty Centrality; 6.2.5. Bow-Tie Structure; 6.3. Rich Clubs; 6.3.1. The Unweighted Rich-Club Coefficient; 6.3.2. The Weighted Rich-Club Coefficient; 6.3.3. Rich-Clubs in Brain Networks; 6.3.4. Assortativity; 6.4. Summary
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Chapter 7: Paths, Diffusion, and Navigation
Weitere Ausg.:
ISBN 9780124079083
Weitere Ausg.:
ISBN 0124079083
Sprache:
Englisch
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