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. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Highlighting the many emerging points of contact between neuroscience and network science, the book serves to introduce network theory to neuroscientists and neuroscience to those working on theoretical network models.
The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies.
For those new to the field of resting state fMRI, the large variety of approaches to functional connectivity analysis is highly confusing.
2.14 (A) MATLAB Figure Window produced with program wilsoneuler.m with different integration time steps Δt. (b) Results of the program wilson.m that solves the same model with a higher-order ODE solver. two results in the spike train ...
Network scaling effects in graph analytic studies of human resting-state fMRI data. Frontiers in Systems Neuroscience, 4, 1–16. Fornito, A., Zalesky, A., and Bullmore, E. 2016. Fundamentals of Brain Network Analysis. Academic Press.
A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years.
As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, ...
In this book, Olaf Sporns surveys current efforts to chart these connections—to map the human connectome.
This text takes a distinctive, commonsense approach to help newcomers easily learn the basics of how the brain functions when we learn, act, feel, speak and socialize.