Fundamentals of brain network analysis:
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
Amsterdam ; Boston ; Heidelberg
Elsevier, Academic Press
[2016]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xvii, 476 Seiten Illustrationen, Diagramme |
| ISBN: | 9780124079083 |
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MARC
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| 245 | 1 | 0 | |a Fundamentals of brain network analysis |c Alex Fornito (Monash University, Australia), Andrew Zalesky (The University of Melbourne, Australia), Edward T. Bullmore (University of Cambridge, United Kingdom) |
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Record in the Search Index
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| adam_text | Contents
AUTHOR BIOGRAPHIES.............................. ....................xi
FOREWORD........................................ ...................... xiii
ACKNOWLEDGMENTS......................................................xvii
CHAPTER 1 An Introduction to Brain Networks............................1
1.1 Graphs as Models for Complex Systems................4
1.1.1 A Brief History of Graph Theory................5
1.1.2 Space, Time, and Topology.....................11
1.2 Graph Theory and the Brain.........................13
1.2.1 The Neuron Theory and Connectivity at the
Microscale.....................................13
1.2.2 Clinicopathological Correlations and Connectivity
at the Macroscale..............................15
1.2.3 The Dawn of Connectomics......................18
1.2.4 Neuroimaging and Human Connectomics...........22
1.2.5 Back to Basics: From Macro to Meso and Micro
Connectomics...................................30
1.3 Are Graph Theory and Connectomics Useful?..........33
1.4 Summary............................................34
CHAPTER 2 Nodes and Edges..............................................37
2.1 Microscale Connectomics............................42
2.1.1 Structural Connectivity at the Microscale.....42
2.1.2 Functional Connectivity at the Microscale.....48
2.2 Mesoscale Connectomics.............................56
2.2.1 Structural Connectivity at the Mesoscale......56
2.2.2 Functional Connectivity at the Mesoscale......66
2.3 Macroscale Connectomics............................70
2.3.1 Defining Nodes at the Macroscale..............71
2.3.2 Structural Connectivity at the Macroscale.....77
2.3.3 Functional Connectivity at the Macroscale.....82
2.4 Summary............................................87
Contents
CHAPTER 3 Connectivity Matrices and Brain Graphs.............................89
3.1 The Connectivity Matrix....................................89
3.1.1 Diagonal and Off-Diagonal Elements...................90
3.1.2 Directionality......................................91
3.1.3 Connectivity Weights................................93
3.1.4 Sparse Matrices.....................................94
3.2 The Adjacency Matrix.....................................-95
3.2.1 Thresholding.........................................95
3.2.2 Binarization.........................................97
3.2.3 Network Density and Weight...........................98
3.3 Network Visualization....................................101
3.3.1 Visualizing the Adjacency Matrix....................102
3.3.2 Visualizing Brain Graphs...........................104
3.4 What Type of Network Is a Connectome?....................108
3.5 Summary..................................................113
CHAPTER 4 Node Degree and Strength.....................................115
4.1 Measures of Node Connectivity............................116
4.1.1 Node Degree.........................................117
4.1.2 Node Strength......................................118
4.1.3 Node Degree and Network Density....................120
4.2 Degree Distributions.....................................121
4.2.1 Single-Scale Distributions..........................122
4.2.2 Scale-Free Distributions.........................125
4.2.3 Broad-Scale Distributions........................129
4.3 Weight Distributions.....................................132
4.3.1 The Lognormal Distribution..........................132
4.4 Summary..................................................136
CHAPTER 5 Centrality and Hubs..........................................137
5.1 Centrality...............................................137
5.1.1 Degree-Based Measures of Centrality.................139
5.1.2 Closeness Centrality...............................147
5.1.3 Betweenness Centrality.............................150
5.1.4 Delta Centrality...................................151
5.1.5 Characterizing Centrality in Brain Networks........152
5.2 Identifying Hub Nodes...................................156
5.2.1 Classifying Hubs Based on Degree and Centrality...156
5.2.2 Consensus Classification of Hubs...................157
5.2.3 Module-Based Role Classification...................160
5.3 Summary.................................................161
CHAPTER 6 Components, Cores, and Clubs................................163
6.1 Connected Components...................................164
6.1.1 Components in Undirected Networks................167
6.1.2 Components in Directed Networks..................168
6.1.3 Percolation and Robustness.......................172
6.1.4 Components and Group Differences in Networks....177
6.2 Core-Periphery Organization...........................179
6.2.1 Maximal Cliques..................................180
6.2.2 k-Cores and s-Cores..............................180
6.2.3 Model-Based Decomposition........................185
6.2.4 Knotty Centrality................................189
6.2.5 Bow-Tie Structure................................193
6.3 Rich Clubs............................................194
6.3.1 The Unweighted Rich-Club Coefficient............195
6.3.2 The Weighted Rich-Club Coefficient..............198
6.3.3 Rich-Clubs in Brain Networks.....................200
6.3.4 Assortativity.................................. 204
6.4 Summary................................................206
CHAPTER 7 Paths, Diffusion, and Navigation............................207
7.1 Walks, Trails, Paths, and Cycles.......................210
7.1.1 Shortest Paths..................................211
7.1.2 Finding Shortest Paths...........................214
7.1.3 Shortest Paths and Negative Edges................217
7.2 Shortest Path Routing..................................223
7.2.1 Characteristic Path Length.......................223
7.2.2 Global and Nodal Efficiency......................225
7.3 Diffusion Processes....................................228
7.3.1 Search Information and Path Transitivity.........230
7.3.2 Measures of Diffusion Efficiency.................234
7.3.3 Communicability..................................244
7.4 Navigation and Other Models of Neural Communication...245
7.4.1 Navigation of Small-World Networks...............246
7.4.2 Internet and Computer Analogies..................250
7.5 Summary................................................254
CHAPTER 8 Motifs, Small Worlds, and Network Economy...................257
8.1 Network Motifs.........................................257
8.1.1 Node Motifs......................................259
8.1.2 Path Motifs......................................265
8.2 Clustering, Degeneracy, and Small Worlds...............268
8.2.1 The Clustering Coefficient.......................269
Contents
8.2.2 Redundancy, Degeneracy, and Structurât
Equivalence......................................272
8.2.3 Small Worlds....................................281
8.3 Network Economy......................................287
8.3.1 Wiring Cost Optimization in Nervous Systems.....288
8.3.2 Cost-Efficiency Trade-Offs......................291
8.3.3 Rentian Scaling.................................296
8.4 Summary..............................................301
CHAPTER 9 Modularity......................................................303
9.1 Defining Modules.....................................307
9.1.1 Agglomerative and Divisive Clustering...........308
9.1.2 Quantifying Modularity..........................311
9.1.3 Maximizing Modularity...........................316
9.2 Node Roles...........................................325
9.2.1 Cartographic Classification of Nodes............325
9.2.2 Node Roles in Brain Networks....................331
9.3 Comparing and Aggregating Network Partitions.........334
9.3.1 Comparing Two Partitions........................335
9.3.2 Comparing Populations of Partitions.............339
9.3.3 Consensus Clustering............................340
9.4 Dynamic Modularity...................................346
9.4.1 Multilayer Modularity...........................347
9.4.2 Dynamic Modularity of Brain Networks............351
9.5 Summary..............................................353
CHAPTER 10 Null Models....................................................355
10.1 Generative Null Models..............................357
10.1.1 Erdôs-Rényi Networks...........................361
10.1.2 Watts-Strogatz Networks........................363
10.1.3 Barabasi-Albert Networks.......................364
10.2 Null Networks from Rewiring Connections.............367
10.2.1 Maslov-Sneppen Rewiring........................367
10.2.2 Rewiring Connections in Weighted and Signed
Networks.......................................369
10.2.3 Lattice Null Networks..........................371
10.2.4 Spatial Embedding..............................372
10.3 Functional Connectivity Networks....................375
10.3.1 Null models for Correlation-Based Networks.....377
10.4 Summary............................................380
Contents
CHAPTER 11 Statistical Connectomics.......................................383
11.1 Matrix Thresholding.................................384
11.1.1 Global Thresholding............................384
11.1.2 Network Fragmentation..........................388
11.1.3 Local Thresholding............................388
11.1.4 Normalization with Reference Networks.........391
11.1.5 Integration and Area Under the Curve..........392
11.1.6 Multiresolution Thresholding..................393
11.2 Statistical Inference on Brain Networks.............396
11.2.1 Global Testing.................................397
11.2.2 Mass Univariate Testing........................397
11.2.3 Strong Control of Familywise Errors............398
11.2.4 Multiple Comparisons Under Dependence.........401
11.2.5 The False Discovery Rate......................403
11.2.6 The Network-Based Statistic...................404
11.2.7 Spatial Pairwise Clustering...................408
11.2.8 The Screening-Filtering Method................411
11.2.9 Sum of Powered Score..........................412
11.3 Multivariate Approaches.............................413
11.3.1 Support Vector Machines........................414
11.3.2 Other Multivariate Approaches..................417
11.4 Summary.............................................418
GLOSSARY..................................................................421
REFERENCES............................................................... 433
INDEX................................................................... 473
|
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| spelling | Fornito, Alex Verfasser (DE-588)1104143488 aut Fundamentals of brain network analysis Alex Fornito (Monash University, Australia), Andrew Zalesky (The University of Melbourne, Australia), Edward T. Bullmore (University of Cambridge, United Kingdom) Amsterdam ; Boston ; Heidelberg Elsevier, Academic Press [2016] © 2016 xvii, 476 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Gehirn (DE-588)4019752-9 gnd rswk-swf Neurobiologie (DE-588)4041871-6 gnd rswk-swf Nervennetz (DE-588)4041638-0 gnd rswk-swf Nervennetz (DE-588)4041638-0 s Neurobiologie (DE-588)4041871-6 s Gehirn (DE-588)4019752-9 s DE-604 Zalesky, Andrew Verfasser (DE-588)1104143828 aut Bullmore, Edward T. Verfasser (DE-588)1011283468 aut Erscheint auch als Online-Ausgabe 978-0-12-408118-5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029173395&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Fundamentals of brain network analysis |
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| title_full | Fundamentals of brain network analysis Alex Fornito (Monash University, Australia), Andrew Zalesky (The University of Melbourne, Australia), Edward T. Bullmore (University of Cambridge, United Kingdom) |
| title_fullStr | Fundamentals of brain network analysis Alex Fornito (Monash University, Australia), Andrew Zalesky (The University of Melbourne, Australia), Edward T. Bullmore (University of Cambridge, United Kingdom) |
| title_full_unstemmed | Fundamentals of brain network analysis Alex Fornito (Monash University, Australia), Andrew Zalesky (The University of Melbourne, Australia), Edward T. Bullmore (University of Cambridge, United Kingdom) |
| title_short | Fundamentals of brain network analysis |
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