Connectome Analysis: Characterization, Methods, and Analysis is a comprehensive companion for the analysis of brain networks, or connectomes. The book provides sources of constituent structural and functional MRI signals, network construction and practices for analysis, cutting-edge methods that address the latest challenges in neuroscience, and the fundamentals of network theory in the context of giving practical methods for building connectomes for analysis. Emphasis is placed on quality control of the individual analysis steps. Subsequent chapters discuss networks in neuroscience in clinical and general populations, including how findings are related to underlying neurophysiology and neuropsychology.
This book is aimed at students and early-career researchers in brain connectomics and neuroimaging who have a background in computer science, mathematics and physics, as well as more broadly to neuroscientists and psychologists who want to start incorporating connectomics into their research.
1. Introduction - p. Page xi 2. List of Illustrations - p. Page xiv 3. List of Tables - p. Page xv 4. Chapter 1 : Neurobiology and the connectome - p. Page xv 5. Chapter 2 : Structural network construction using diffusion MRI - p. Page 19 6. Chapter 3 : Functional network construction using functional MRI - p. Page 41 7. Chapter 4 : Network nodes in the brain - p. Page 67 8. Chapter 5 : Network measures and null models - p. Page 89 9. Chapter 6 : Hubs and rich clubs - p. Page 120 10. Chapter 7 : Community detection in network neuroscience - p. Page 141 11. Chapter 8 : Network comparisons and their applications in connectomics - p. Page 166 12. Chapter 9 : Beyond the shortest path—diffusion-based routing strategies - p. Page 189 13. Chapter 10 : Dynamic functional connectivity - p. Page 215 14. Chapter 11 : The synergy of structural and functional connectivity - p. Page 238 15. Chapter 12 : Machine learning in connectomics: from representation learning to model fitting - p. Page 262 16. Chapter 13 : Deep learning with connectomes - p. Page 283 17. Chapter 14 : Uncovering the genetics of the human connectome - p. Page 303 18. Chapter 15 : The developmental connectome - p. Page 327 19. Chapter 16 : Connectomics in aging and cognition - p. Page 360 20. Chapter 17 : Networks with lesions - p. Page 388 21. Chapter 18 : Clinical application of connectomics to disorders of consciousness - p. Page 404 22. Chapter 19 : Connectome analysis and psychiatric disorders - p. Page 418 23. Afterword: The road ahead for connectomics - p. Page 429 24. Index - p. Page 436
Connectome Analysis: Characterization, Methods, and Analysis is a comprehensive companion for the analysis of brain networks, or connectomes. The book provides sources of constituent structural and functional MRI signals, network construction and practices for analysis, cutting-edge methods that address the latest challenges in neuroscience, and the fundamentals of network theory in the context of giving practical methods for building connectomes for analysis. Emphasis is placed on quality control of the individual analysis steps. Subsequent chapters discuss networks in neuroscience in clinical and general populations, including how findings are related to underlying neurophysiology and neuropsychology.
This book is aimed at students and early-career researchers in brain connectomics and neuroimaging who have a background in computer science, mathematics and physics, as well as more broadly to neuroscientists and psychologists who want to start incorporating connectomics into their research.
1. Introduction - p. Page xi 2. List of Illustrations - p. Page xiv 3. List of Tables - p. Page xv 4. Chapter 1 : Neurobiology and the connectome - p. Page xv 5. Chapter 2 : Structural network construction using diffusion MRI - p. Page 19 6. Chapter 3 : Functional network construction using functional MRI - p. Page 41 7. Chapter 4 : Network nodes in the brain - p. Page 67 8. Chapter 5 : Network measures and null models - p. Page 89 9. Chapter 6 : Hubs and rich clubs - p. Page 120 10. Chapter 7 : Community detection in network neuroscience - p. Page 141 11. Chapter 8 : Network comparisons and their applications in connectomics - p. Page 166 12. Chapter 9 : Beyond the shortest path—diffusion-based routing strategies - p. Page 189 13. Chapter 10 : Dynamic functional connectivity - p. Page 215 14. Chapter 11 : The synergy of structural and functional connectivity - p. Page 238 15. Chapter 12 : Machine learning in connectomics: from representation learning to model fitting - p. Page 262 16. Chapter 13 : Deep learning with connectomes - p. Page 283 17. Chapter 14 : Uncovering the genetics of the human connectome - p. Page 303 18. Chapter 15 : The developmental connectome - p. Page 327 19. Chapter 16 : Connectomics in aging and cognition - p. Page 360 20. Chapter 17 : Networks with lesions - p. Page 388 21. Chapter 18 : Clinical application of connectomics to disorders of consciousness - p. Page 404 22. Chapter 19 : Connectome analysis and psychiatric disorders - p. Page 418 23. Afterword: The road ahead for connectomics - p. Page 429 24. Index - p. Page 436
