This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory-centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students. INDICE: 1. Statistical preliminary; 2. Brain network nodes and edges; 3. Graph theory; 4. Correlation networks; 5. Big brain network data; 6. Network simulations; 7. Persistent homology; 8. Diffusion on graphs; 9. Sparse networks; 10. Brain network distances; 11. Combinatorial inference for networks; 12. Series expansion of connectivity matrices; 13. Dynamic network models.
- ISBN: 978-1-107-18486-2
- Editorial: Cambridge University Press
- Encuadernacion: Cartoné
- Páginas: 338
- Fecha Publicación: 27/06/2019
- Nº Volúmenes: 1
- Idioma: Inglés