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  • ID: 4482968
  • Book
  • 233 Pages
  • Elsevier Science and Technology
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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 connectomics- How connectomics is applied to a wide range of neuro-applications- What 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.


- Combines connectomics methods with relevant and interesting neuro-applications- Covers most of the hot topics in neuroscience and clinical areas- Appeals 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-applications- Presents 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 perspective- Lists publicly available neuro-imaging datasets that can be used to construct structural and functional connectomes

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1. Autism Spectrum Disorders: Unbiased Functional Connectomics Provide New Insights into a Multifaceted Neurodevelopmental Disorder
2. Insights Into Cognition from Network Science Analyses of Human Brain Functional Connectivity:Working Memory as a Test Case
3. Overlapping and Dynamic Networks of the Emotional Brain
4. The Uniqueness of the Individual Functional Connectome
5. Dysfunctional Brain Network Organization in Neurodevelopmental Disorders
6. Addiction: Informing Drug Abuse Interventions with Brain Networks
7. Connectivity and Dysconnectivity: A Brief History of Functional Connectivity Research in Schizophrenia and Future Directions
8. Genetics of Brain Networks and Connectivity
9. Characterizing Dynamic Functional Connectivity Using Data-Driven Approaches and its Application in the Diagnosis of Alzheimer's Disease
10. Toward a more Integrative Cognitive Neuroscience of Episodic Memory
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Munsell, Brent C.
Brent C. Munsell is an Assistant Professor in the Department of Computer Science at the College of Charleston, US. He received a Ph.D. degree in Computer Science and Engineering from the University of South Carolina, a Masters degree in Electrical Engineering from Clemson University, and a B.S. degree in Electrical Engineering from Michigan State University. Dr. Munsell's research aims to develop computational tools that draw inferences from biomedical imaging data, particular in the context of brain connectivity and network analysis. He is interested in medical image analysis, machine learning, and computer vision. Dr. Munsell has published papers in several top journals such as Nature, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging, International Journal of Computer Vision and NeuroImage, and is actively working on structural and functional connectivity research projects that will allow clinicians to diagnose children who may have an Autism spectrum disorder before the age of two years old.
Wu, Guorong
Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition. He has published more than 100 papers in the international journals and conferences. Dr. Wu is actively in the development of medical image processing software to facilitate the scientific research on neuroscience and radiology therapy.
Bonilha, Leonardo
Dr Leonardo Bonilha is a neurologist and clinical researcher, working within neurophysiology, epilepsy, language problems and stroke. His research focuses on understanding structural and functional network adaptations to brain injury, particularly regarding language impairments (aphasia) after stroke and its recovery. He also studies neuronal networks associated with epilepsy and its response to treatment. His main research tools focus around Structural and functional MRI, neurophysiology (scalp and intracranial EEG) as well as behavioral language treatments for language.
Laurienti, Paul
Paul Laurienti completed his MD and PhD training at the University of Texas Medical Branch at Galveston in 1999. He completed a research fellowship at Wake Forest School of Medicine and became an assistant professor in the Department of Radiology in 2002. He has since achieved the level of tenured full professor and has published over 100 peer-reviewed manuscripts. He is the Director of the Laboratory for Complex Brain Networks and leads an interdisciplinary group of scientists. They use functional and structural brain imaging combined with network science to study the brain as an integrated system. His current research focuses on methodological development and the application of network methods to neuroscientific questions.
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