Książki

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. "...this text promises to be an essential title on the bookshelf of the intellectually curious neuroscientist. And for those whose curiosity is never satiated, the book motivates new empirical work to address as yet unanswered questions..." --Brain

...a thorough and didactic presentation of the tools available to research scientists wishing to engage in the emgerging field of network neuroscience...this text promises to be an essential title on the bookshelf of the intellectually curious neuroscientist...as with any good book, one turns the final page wishing there were more. --Prof Danielle S Basset, University of Pennsylvania, BRAIN: A Journal of Neurology

Fundamentals of Brain Network Analysis

1. An introduction to brain networks 2. Nodes and edges 3. Connectivity matrices and brain graphs 4. Connectivity degree and strength 5. Centrality and hubs 6. Components, cores and clubs 7. Paths, efficiency and diffusion 8. Motifs, small worlds and network economy 9. Modularity 10. Null models 11. Statistical connectomics