Connectomics: Applications to Neuroimaging

Description

Connectomics: Applications to Neuroimaging is unique in presenting the frontier of neuro-applications using brain connectomics techniques. The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer’s, epilepsy, stroke, autism, Parkinson’s, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment. With this book the reader will learn: Basic mathematical principles underlying connectomicsHow connectomics is applied to a wide range of neuro-applicationsWhat is the future direction of connectomics techniques. This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics, or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods. Features: Combines connectomics methods with relevant and interesting neuro-applicationsCovers most of the hot topics in neuroscience and clinical areasAppeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience, and medical researchers interested in the technical methods of connectomics Combines connectomics methods with relevant and interesting neuro-applicationsPresents information that will appeal to researchers in a wide range of disciplines, including computer science, engineering, data science, mathematics, computational physics, computational neuroscience, and more Includes a mathematics primer that formulates connectomics from an applied point-of-view, thus avoiding difficult to understand theoretical perspectiveLists publicly available neuro-imaging datasets that can be used to construct structural and functional connectomes Cover Connectomics Applications to Neuroimaging Copyright Contributors 1 Autism spectrum disorders: Unbiased functional connectomics provide new insights into a multifaceted neurodevelopmental di ... Introduction Functional Connectomics as a Window Into ASD An Unbiased Bayesian Framework for Functional Connectomics Multisite Network Analysis of Autism Experimental Setup Network-Based Differences in ASD Toward Characterizing Patient Heterogeneity Experimental Setup Network Dysfunction Linked to ASD Severity Concluding Remarks References 2 Insights into cognition from network science analyses of human brain functional connectivity: Working memory as a test case Introduction Working Memory Tasks for Studying Working Memory Insights From Network Science Analyses of Human Brain Functional Connectivity Resting State Data Resting State Functional Connectivity and Working Memory Modularity Functional Connectivity During Working Memory Task Performance Conclusions References Further Reading 3 Overlapping and dynamic networks of the emotional brain Brain Networks Are Overlapping Network Modularity Node Taxonomy: Hubs and Bridges Brain Networks Are Dynamic Evolution of Network Organization Across Time Fluid Network Identity Across Time Conclusions References Further Reading 4 he uniqueness of the individual functional connectome Introduction Identification Procedure FC Identification: Early Work and Results in Adult Subjects Results in Adolescents and Applications to Disease Common Anatomical Themes Factors Affecting the Detection of Individual Differences Identification in Rest, Task, and Naturalistic Scans Identification Relevance to Behavior Conclusions and Outlook References 5 Dysfunctional brain network organization in neurodevelopmental disorders Attention Deficit Hyperactivity Disorder Autism Spectrum Disorder Integration and Segregation as a Framework for Understanding Neurodevelopmental Disorders: Next Steps References 6 Addiction: Informing drug abuse interventions with brain networks Seed-Based Functional Connectivity Independent Component Analysis Graph Theory Discussion and Future Directions Acknowledgments References 7 Connectivity and dysconnectivity: A brief history of functional connectivity research in schizophrenia and future directions Introduction Schizophrenia Connectivity Structural Connectivity Functional Connectivity Graph Theory Technical foundations Findings in schizophrenia Strengths and weaknesses ROI-/Seed-Based Functional Connectivity Technical foundations Findings in schizophrenia Strengths and weaknesses Independent Component Analysis Technical foundations—Static functional network connectivity Static Functional Network Connectivity Findings in schizophrenia Dynamic functional network connectivity Technical foundations—Dynamic FNC Findings in schizophrenia Strengths and weaknesses Functional connectivity in the clinical high-risk population Summary Future Directions References 8 Genetics of brain networks and connectivity Motivation for Genetic Studies of the Brain’s Structural and Functional Connectivity To What Extent Are Brain Variations Influenced by Genetics? A History of Heritability With Twin and Family Studies Altered Brain Connectivity in Neurogenetic Disorders and Genetic Deletions and Duplications Huntington’s Disease Fragile X Syndrome 22q11.2 Deletion Syndrome Williams Syndrome Prader-Willi Syndrome Turner Syndrome Down Syndrome Digging Deeper—Searching for the Effect of Single Nucleotide Polymorphisms Genome-Wide Searches and Boosting Statistical Power to Address Small Effect Sizes Gene Expression Networks Translating Findings to the Clinic Altered Connectivity in Traumatic Brain Injury The Interaction Between Genetics and TBI Future Directions Acknowledgments References 9 Characterizing dynamic functional connectivity using data-driven approaches and its application in the diagnosis of alzhei ... Introduction Dynamic Functional Connectivity Alzheimer’s Disease Constructing Robust Static Functional Connectivity Data-Driven Approach to Measure Functional Connectivity Data-Driven Approach to Characterize Dynamic Functional Connectivities Statistic Model to Capture Functional Connectivity Variations Optimization of Tensor Statistic Model Obtain Compact Representation by the Learned Tensor Statistic Model Application of Tensor Statistic Model in AD Diagnosis Conclusion References 10 Toward a more integrative cognitive neuroscience of episodic memory Introduction Univariate Activation Analyses Bivariate and Seed-Based Functional Connectivity Analyses Multivariate Network Analyses Conclusions and Future Directions References Further Reading Index B C D E F G H I K L M N O P R S T U V W Back Cover