Functional And Structural Brain Network Construction Representation And Application

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. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology 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

An integrative overview of network approaches to neuroscience explores the origins of brain complexity and the link between brain structure and function. Over the last decade, the study of complex networks has expanded across diverse scientific fields. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems ranging from cells to ecosystems. In Networks of the Brain, Olaf Sporns describes how the integrative nature of brain function can be illuminated from a complex network perspective. 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. Sporns emphasizes how networks connect levels of organization in the brain and how they link structure to function, offering an informal and nonmathematical treatment of the subject. Networks of the Brain provides a synthesis of the sciences of complex networks and the brain that will be an essential foundation for future research.

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. Provides practical recommendations on how to construct, assess and analyze brain networks Gives an understanding of all the technical methods for connectome analysis Presents the basic network theoretical principles typically used in neuroscience Covers the latest tools and data repositories that are freely available for the reader to carry out connectomic analyses

‘Connectivity and Functional Specialization in the Brain’ is a topic that describes nerve cells in terms of their anatomical and functional connections. The term connectome refers to a comprehensive map of neural connections, like a wiring diagram of an organism’s nervous system. Connectomics, the study of connectomes, can be applied to individual neurons and their synaptic connections, as well as to connections between neuronal populations or to functional and structural connectivity of different brain regions. This book addresses neural connectivity at these various scales in health and disease. The chapters review novel findings related to neuroanatomy and cell biology, neurophysiology, neural plasticity, changes of connectivity in neurological disorders, and sensory system connectivity. The book provides the reader with an overview of the current state-of-the-art of research of neural connectivity and focuses on the most important evidence-based developments in this area. Individual chapters focus on recent advances in specific areas of neural connectivity and in different brain regions. All chapters represent recent contributions to the rapidly developing field of neural connectivity.

The present book illustrates the theoretical aspects of several methodologies related to the possibility of i) enhancing the poor spatial information of the electroencephalographic (EEG) activity on the scalp and giving a measure of the electrical activity on the cortical surface. ii) estimating the directional influences between any given pair of channels in a multivariate dataset. iii) modeling the brain networks as graphs. The possible applications are discussed in three different experimental designs regarding i) the study of pathological conditions during a motor task, ii) the study of memory processes during a cognitive task iii) the study of the instantaneous dynamics throughout the evolution of a motor task in physiological conditions. The main outcome from all those studies indicates clearly that the performance of cognitive and motor tasks as well as the presence of neural diseases can affect the brain network topology. This evidence gives the power of reflecting cerebral "states" or "traits" to the mathematical indexes derived from the graph theory. In particular, the observed structural changes could critically depend on patterns of synchronization and desynchronization - i.e. the dynamic binding of neural assemblies - as also suggested by a wide range of previous electrophysiological studies. Moreover, the fact that these patterns occur at multiple frequencies support the evidence that brain functional networks contain multiple frequency channels along which information is transmitted. The graph theoretical approach represents an effective means to evaluate the functional connectivity patterns obtained from scalp EEG signals. The possibility to describe the complex brain networks sub-serving different functions in humans by means of "numbers" is a promising tool toward the generation of a better understanding of the brain functions. Table of Contents: Introduction / Brain Functional Connectivity / Graph Theory / High-Resolution EEG / Cortical Networks in Spinal Cord Injured Patients / Cortical Networks During a Lifelike Memory Task / Application to Time-varying Cortical Networks / Conclusions

Current research in computational neuroscience puts great emphasis on the computation and analysis of the functional connectivity of the brain. The methodological developments presented in this work are concerned with a group-specific comprehensive analysis of networks that represent functional interaction patterns. Four application studies are presented, in which functional brain network samples of different clinical background were analyzed in different ways, using combinations of established approaches and own methodological developments. Study I is concerned with a sample-specific decomposition of the functional brain networks of depressed subjects and healthy controls into small functionally important and recurring subnetworks (motifs) using own developments. Study II investigates whether lithium treatment effects are reflected in the functional brain networks of HIV-positive subjects with diagnosed cognitive impairment. For it, microscopic and macroscopic structural properties were analyzed. Study III explores spatially highly resolved functional brain networks with regard to a functional segmentation given by identified module (community) structure. Also, ground truth networks with known module structure were generated using own methodological developments. They formed the basis of a comprehensive simulation study that quantified module structure quality and preservation in order to evaluate the effects of a novel approach for the identification of connectivity (lsGCI). Study IV tracks the time-evolution of module structure and introduces a newly developed own approach for the determination of edge weight thresholds based on multicriteria optimization. The methodological challenges that underly these different topological analyses, but also the various opportunities to gain an improved understanding of neural information processing among brain areas were highlighted by this work and the presented results.

One of the key challenges in the analyses of the human connectome is the development of a systematic framework for representing and evaluating generative models. Network generative models go beyond summary statistics and attempt to identify principles which can account for the complex patterns of network interconnections. In this project, a theoretical framework for generative modeling is developed to formally hypothesize and test organizational principles in human functional brain networks using fMRI data. The framework is based on a Hidden Markov Random Field, a probabilistic graphical model with latent variables, which provides a natural structure to make an explicit distinction between the abstract functional brain networks and the observable fMRI BOLD connectivity matrices. The framework conceptualizes whole-brain functional network topology as probabilistic constraint satisfaction, and allows representation of high dimensional connectivity matrices using low dimensional probability models where model parameters are interpretable as brain network topology constraints. To explicitly illustrate the use of the framework, a small number of hypotheses compiled from the theoretical and empirical literature are mathematically instantiated and tested using resting-state fMRI data. The empirical studies provide further evidence to support two hypothesized principles of functional brain organization; a wiring cost rule where the probability of functional connectivity between brain areas decreases with physical distance, and a common neighbors rule where the probability of functional connectivity between brain areas increases with the number of shared neighbors. Overall, the preliminary empirical studies are encouraging and warrant further development and application of the theoretical framework.

Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring structural and functional connectivity in the brain. Part three provides an overview of the analysis techniques currently available and highlights new developments. Part four introduces the application and translation of the concepts of brain connectivity to behavior, cognition and the clinical domain.

Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease.