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Connectomics: Applications to Neuroimaging [Mīkstie vāki]

Edited by (College of Charleston, South Carolina, USA), Edited by , Edited by (The Medical University of South Carolina, Charleston, SC, USA), Edited by (Assistant Professor of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, USA)
  • Formāts: Paperback / softback, 233 pages, height x width: 235x191 mm, weight: 630 g
  • Sērija : The MICCAI Society book Series
  • Izdošanas datums: 12-Sep-2018
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128138386
  • ISBN-13: 9780128138380
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  • Cena: 130,13 €
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Connectomics: Applications to NeuroimagingPalielināt
  • Formāts: Paperback / softback, 233 pages, height x width: 235x191 mm, weight: 630 g
  • Sērija : The MICCAI Society book Series
  • Izdošanas datums: 12-Sep-2018
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128138386
  • ISBN-13: 9780128138380
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780128138380.html
Keywords:
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 (Alzheimers, epilepsy, stroke, autism, Parkinsons, 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.

Features:





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
Contributors ix
Chapter 1 Autism Spectrum Disorders: Unbiased Functional Connectomics Provide New Insights into a Multifaceted Neurodevelopmental Disorder
1(20)
Archana Venkataraman
Introduction
1(2)
Functional Connectomics as a Window Into ASD
3(1)
An Unbiased Bayesian Framework for Functional Connectomics
4(3)
Multisite Network Analysis of Autism
7(6)
Experimental Setup
7(2)
Network-Based Differences in ASD
9(4)
Toward Characterizing Patient Heterogeneity
13(4)
Experimental Setup
14(1)
Network Dysfunction Linked to ASD Severity
15(2)
Concluding Remarks
17(2)
References
19(2)
Chapter 2 Insights Into Cognition from Network Science Analyses of Human Brain Functional Connectivity: Working Memory as a Test Case
21(22)
Dale Dagenbach
Introduction
27(10)
Working Memory
28(2)
Tasks for Studying Working Memory
30(1)
Insights From Network Science Analyses of Human Brain Functional Connectivity Resting State Data
31(1)
Resting State Functional Connectivity and Working Memory
32(1)
Functional Connectivity During Working Memory Task Performance
33(4)
Conclusions
37(2)
References
39(2)
Further Reading
41(2)
Chapter 3 Overlapping and Dynamic Networks of the Emotional Brain
43(20)
Luiz Pessoa
Brain Networks are Overlapping
44(8)
Network Modularity
47(1)
Node Taxonomy: Hubs and Bridges
48(4)
Brain Networks are Dynamic
52(6)
Evolution of Network Organization Across Time
52(4)
Fluid Network Identity Across Time
56(2)
Conclusions
58(1)
References
59(2)
Further Reading
61(2)
Chapter 4 The Uniqueness of the Individual Functional Connectome
63(20)
Corey Horien
Dustin Scheinost
R. Todd Constable
Introduction
63(11)
Identification Procedure
64(1)
FC Identification: Early Work and Results in Adult Subjects
65(1)
Results in Adolescents and Applications to Disease
66(2)
Common Anatomical Themes
68(1)
Factors Affecting the Detection of Individual Differences
69(2)
Identification in Rest, Task, and Naturalistic Scans
71(2)
Identification Relevance to Behavior
73(1)
Conclusions and Outlook
74(1)
References
75(8)
Chapter 5 Dysfunctional Brain Network Organization in Neurodevelopmental Disorders
83(18)
Teague R. Henry
Jessica R. Cohen
Attention Deficit Hyperactivity Disorder
87(3)
Autism Spectrum Disorder
90(4)
Integration and Segregation as a Framework for Understanding Neurodevelopmental Disorders: Next Steps
94(2)
References
96(5)
Chapter 6 Addiction: Informing Drug Abuse Interventions with Brain Networks
101(22)
Vaughn R. Steele
Xiaoyu Ding
Thomas J. Ross
Seed-Based Functional Connectivity
105(1)
Independent Component Analysis
106(3)
Graph Theory
109(3)
Discussion and Future Directions
112(3)
Acknowledgments
115(1)
References
115(8)
Chapter 7 Connectivity and Dysconnectivity: A Brief History of Functional Connectivity Research in Schizophrenia and Future Directions
123(32)
Eva Mennigen
Barnaly Rashid
Vince D. Calhoun
Introduction
123(1)
Schizophrenia
124(2)
Connectivity
126(1)
Structural Connectivity
126(1)
Functional Connectivity
127(15)
Graph Theory
129(3)
ROI-/Seed-Based Functional Connectivity
132(2)
Independent Component Analysis
134(1)
Static Functional Network Connectivity
135(7)
Summary
142(2)
Future Directions
144(1)
References
145(10)
Chapter 8 Genetics of Brain Networks and Connectivity
155(26)
Emily L. Dennis
Paul M. Thompson
Neda Jahanshad
Motivation for Genetic Studies of the Brain's Structural and Functional Connectivity
155(1)
To What Extent are Brain Variations Influenced by Genetics? A History of Heritability With Twin and Family Studies
156(2)
Altered Brain Connectivity in Neurogenetic Disorders and Genetic Deletions and Duplications
158(3)
Huntington's Disease
158(1)
Fragile X Syndrome
159(1)
22q11.2 Deletion Syndrome
159(1)
Williams Syndrome
160(1)
Prader-Willi Syndrome
160(1)
Turner Syndrome
161(1)
Down Syndrome
161(1)
Digging Deeper---Searching for the Effect of Single Nucleotide Polymorphisms
161(1)
Genome-Wide Searches and Boosting Statistical Power to Address Small Effect Sizes
162(2)
Gene Expression Networks
164(1)
Translating Findings to the Clinic
164(3)
Altered Connectivity in Traumatic Brain Injury
165(1)
The Interaction Between Genetics and TBI
166(1)
Future Directions
167(1)
Acknowledgments
168(1)
References
168(13)
Chapter 9 Characterizing Dynamic Functional Connectivity Using Data-Driven Approaches and its Application in the Diagnosis of Alzheimer's Disease igi
181(18)
Yingying Zhu
Xiaofeng Zhu
Minjeong Kim
Daniel Kaufer
Paul J. Laurienti
Guorong Wu
Introduction
182(4)
Dynamic Functional Connectivity
182(3)
Alzheimer's Disease
185(1)
Constructing Robust Static Functional Connectivity
186(9)
Data-Driven Approach to Measure Functional Connectivity
186(1)
Data-Driven Approach to Characterize Dynamic Functional Connectivities
187(1)
Statistic Model to Capture Functional Connectivity Variations
188(1)
Optimization of Tensor Statistic Model
189(1)
Obtain Compact Representation by the Learned Tensor Statistic Model
190(1)
Application of Tensor Statistic Model in AD Diagnosis
191(4)
Conclusion
195(1)
References
195(4)
Chapter 10 Toward a more Integrative Cognitive Neuroscience of Episodic Memory
199(20)
Matthew L. Stanley
Benjamin R. Geib
Simon W. Davis
Introduction
199(4)
Univariate Activation Analyses
203(2)
Bivariate and Seed-Based Functional Connectivity Analyses
205(6)
Multivariate Network Analyses
211(3)
Conclusions and Future Directions
214(1)
References
215(3)
Further Reading
218(1)
Index 219
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. Munsells 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. 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. Wus 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. 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. 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.