Connectomics: Methods, Mathematical Models and Applications is unique in combining a broad introduction to methods in connectomics with neuro- applications. In Part 1 the book explains the importance of connectomics using brain connectivity maps and then outlines the historical advancements in connectivity analysis, showing how these are related to imaging modalities such as diffusion and functional MRI. It then summarizes connectivity analysis approaches that apply different mathematical modeling techniques, such as linear models with regularization, deep-learning models, and graph models.
In part 2 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, traumatic brain injury, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain finger-print applications, speech-language assessments, cognitive assessment, as well as how the connectome can be used to predict demographic information (such as age, gender, or race).
With this book the reader will learn:
- The historical development of connectomics together with state-of-the-art methods
- Basic mathematical principles underlying connectomics.
- How connectomics is applied to a wide range of neuro-applications.
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
- 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
- Includes a mathematics primer that formulates connectomics from an applied point-of-view, avoiding a difficult to understand theoretical perspective
- An introduction to popular machine learning techniques that enable diagnostic values to connectomics
Information on publically available software tools that are used to construct, analyze, and visualize connectome data
- Lists of publically available neuro-imaging datasets that can be used to construct structural and functional connectomes
Researchers and graduate students in computer science, data science, computational neuroscience, computational physics or mathematics who need an understanding of how computational models derived from brain connectivity data are being used in clinical applications
2. Image Processing and the Connectome
3. Connectome analysis using graph techniques
4. Machine learning techniques
5. Machine learning software toolsets
6. Connectome software toolsets
7. Autism Spectrum Disorders: Functional connectomics and social-communication networks in Autism.
8. Stroke and epilepsy: Post-stroke aphasia severity and the disorganization of structural networks
9. Cognition: Insights into Cognition from Functional Connectivity
10. Emotion: Dynamic networks in the emotional brain
11. Fingerprinting: Connectome Based Predictive Modeling
12. Neurodevelopment disorders: A network perspective on neurodevelopmental disorders
13. Addiction: Informing drug abuse interventions with brain networks
14. Parkinson's Disease: Brain Networks in Parkinson's Disease
15. Mental Illness: Aberrant whole-brain networks in schizophrenia
16. Traumatic Brain Injury: Connectome-scale assessment of connectivity in mild traumatic brain injury at the acute stage
17. Genetic Analysis: Genetic analysis of structural brain connectivity
18. Alzheimer's disease: Hyper-graph inference framework for computer assisted diagnosis of Alzheimer's disease
19. Towards a Science of Episodic Memory Network Function
- No. of pages:
- © Academic Press 2019
- 1st September 2018
- Academic Press
- Paperback ISBN:
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.
Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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.
College of Charleston, South Carolina, USA
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.
Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
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.
The Medical University of South Carolina, Charleston, SC, USA