Connectome Analysis

Characterization, Methods, and Analysis

1st Edition - June 28, 2023
Editors: Markus D. Schirmer, Tomoki Arichi, Ai Wern Chung
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 2 8 0 - 7
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 2 8 1 - 4

Connectome Analysis: Characterization, Methods, and Analysis is a comprehensive companion for the analysis of brain networks, or connectomes. The book provides sources of constitue… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
List of contributors
About the editors
Preface
Acknowledgments
Introduction
Section I: Fundamentals of connectomics
Chapter 1. Neurobiology and the connectome
Abstract
1.1 Introduction
1.2 A brief history of what preceded the connectome era
1.3 Microscale neurobiology
1.4 Mesoscale columns and circuits
1.5 Assembling the brain from microscale to macroscale connectivity
1.6 Conclusion—what can we answer with the connectome?
Dos and don’ts
References
Chapter 2. Structural network construction using diffusion MRI
Abstract
2.1 Introduction
2.2 Diffusion-weighted MRI
2.3 Image processing
2.4 Connectome construction
2.5 Conclusion
Dos and don’ts
Acknowledgments
References
Chapter 3. Functional network construction using functional MRI
Abstract
3.1 Introduction
3.2 Biology and physics of the Blood Oxygen Level Dependent signal
3.3 BOLD contrast fMRI acquisition
3.4 fMRI preprocessing
3.5 Functional network matrix construction
3.6 Conclusion
Dos and don’ts
Additional resources
Acknowledgments
References
Chapter 4. Network nodes in the brain
Abstract
4.1 What is a network node?
4.2 How have nodes been defined previously in human brain networks?
4.3 Strengths and limitations of current node definitions
4.4 What is the ideal definition of a brain node?
4.5 Conclusion
Dos and don’ts
References
Chapter 5. Network measures and null models
Abstract
5.1 Introduction
5.2 Background
5.3 Network measures
5.4 Network topology and null models
5.5 Network measures and null models
5.6 Future directions
5.7 Conclusion
Dos and don'ts
References
Chapter 6. Hubs and rich clubs
Abstract
6.1 Introduction
6.2 Network theoretical principles of hubs and rich clubs
6.3 Hubs and rich clubs in brain networks
6.4 Future directions
6.5 Conclusions
Dos and don’ts
Additional resources
References
Chapter 7. Community detection in network neuroscience
Abstract
7.1 Introduction
7.2 Background
7.3 Community detection: methods
7.4 Limitations
7.5 Future directions and alternative approaches
7.6 Outlook and conclusion
Dos and don’ts
Additional resources
Acknowledgments
References
Chapter 8. Network comparisons and their applications in connectomics
Abstract
8.1 Introduction
8.2 Preliminaries
8.3 Graph theory for population studies
8.4 Mechanistic network models
8.5 Data-driven approaches
8.6 Recommendations and warnings
8.7 Future directions and conclusion
Dos and don’ts
References
Section II: Advanced concepts and methods
Chapter 9. Beyond the shortest path—diffusion-based routing strategies
Abstract
9.1 Introduction
9.2 Methods
9.3 Limitations
9.4 Future directions and conclusion
Dos and don’ts
References
Chapter 10. Dynamic functional connectivity
Abstract
10.1 Introduction
10.2 Background
10.3 Methods
10.4 Limitations and considerations
10.5 Future directions and conclusion
Dos and don’ts
Additional resources
References
Chapter 11. The synergy of structural and functional connectivity
Abstract
11.1 Introduction
11.2 Background
11.3 Methods
11.4 Limitations, practical choices, and recommendations
11.5 Conclusion
Dos and don’ts
Acknowledgments
References
Chapter 12. Machine learning in connectomics: from representation learning to model fitting
Abstract
12.1 Introduction
12.2 Background
12.3 Methods
12.4 Limitations
12.5 Recommendations
12.6 Future directions and conclusions
Dos and don’ts
References
Chapter 13. Deep learning with connectomes
Abstract
13.1 Introduction
13.2 Background
13.3 Model training
13.4 Limitations
13.5 Recommendations and warnings
13.6 Future directions
13.7 Conclusions
Dos and don'ts
References
Chapter 14. Uncovering the genetics of the human connectome
Abstract
14.1 Introduction
14.2 Heritability analyses
14.3 Association analyses
14.4 Transcriptional analyses
14.5 Conclusions and future directions
Dos and don’ts
Additional resources
References
Section III: Applications in the human brain
Chapter 15. The developmental connectome
Abstract
15.1 Introduction
15.2 A brief description of the connectome
15.3 Development of the functional connectome
15.4 Development of the structural connectome
15.5 Coupling between structural and functional connectomes
15.6 Methodological considerations
15.7 Conclusions and future directions
Dos and don’ts
Additional resources
Acknowledgment
References
Chapter 16. Connectomics in aging and cognition
Abstract
16.1 Introduction
16.2 Connectomic changes with aging and association with cognition
16.3 Questions of interest in aging
16.4 Methodological challenges in aging connectome construction and analysis
16.5 Going further—how to inform connectomics with other clinical data
16.6 Summary, caveats, and future directions in aging connectome research
16.7 Conclusions
Dos and don’ts
Additional resources
References
Chapter 17. Networks with lesions
Abstract
17.1 Introduction
17.2 Background
17.3 Methods
17.4 Limitations
17.5 Future directions and conclusion
Dos and don’ts
References
Chapter 18. Clinical application of connectomics to disorders of consciousness
Abstract
18.1 Introduction
18.2 Background
18.3 Methods and limitations
18.4 Future directions and conclusion
Dos and don’ts
References
Chapter 19. Connectome analysis and psychiatric disorders
Abstract
19.1 Studying the brain in psychiatric disorders
19.2 Advantages of connectomic approaches
19.3 Psychiatric disorders as pathological processes of global network organization
19.4 Psychiatric disorders as pathological processes within a network
19.5 Summary and recommendations
Dos and don’ts
Additional resources
References
Afterword: The road ahead for connectomics
1 From brain structure to function
2 Network science
3 Clinical and translational connectomics
4 Network neuroscience
Index

Markus D. Schirmer

Dr. Markus D. Schirmer, PhD, is an instructor of investigation at Harvard Medical School (HMS) at the J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital (MGH), Boston. After his diploma in theoretical physics at Aachen University, he completed his PhD in biomedical engineering at King’s College London, where he focused on methodological connectome research in premature babies. In 2016 he started his postdoctoral fellowship at MGH/HMS, using connectomics and methodological approaches to study patients with stroke through clinical imaging data available in the emergency department. He continued his work as a Marie Sklodowska-Curie Global Fellow in 2017 at the German Center for Neurodegenerative Diseases in Bonn, Germany, MGH/HMS, and Massachusetts Institute of Technology, and while leading his own research group in the field of computational neuroradiology at the University Hospital Bonn (202022). Dr. Schirmer applies his methodological background combined with a focus on medical image analysis across the life span to improve our understanding of neurological diseases and the health outcomes, with a focus on bridging the gap between computational and clinical research.

Affiliations and expertise

German Centre for Neurodegenerative Disease, Bonn, Germany

Tomoki Arichi

Dr. Tomoki Arichi, MBChB, FRCPCH, FHEA, PhD, is a reader in perinatal imaging at the Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences at King’s College London; a group leader in the MRC Centre for Neurodevelopmental Disorders; and a visiting researcher in the Department of Bioengineering, Imperial College London. His research focuses on the development and application of advanced MRI techniques for studying the developing human brain. He is also a practicing clinician at the Evelina London Children’s Hospital, where his work as a consultant in pediatric Neurodisability focuses on the early identification and resulting management of the disabilities associated with perinatal brain injury and neurodevelopmental disorders. He graduated in medicine and surgery from the University of Birmingham in 2001 and obtained his PhD in imaging sciences as an MRC Chain Florey fellow at Imperial College London in 2012.

Affiliations and expertise

Centre for the Developing Brain, King’s College London, London, United Kingdom

Ai Wern Chung

Dr. Ai Wern Chung, PhD, is an instructor of pediatrics at Harvard Medical School and an associate scientific researcher at the Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital. Her PhD in medical physics and neuroimaging was awarded from the Great Ormond Street Institute of Child Health, University College London, in 2014. She has held several neuroimaging research posts in hospital-affiliated institutes. These include the Stroke and Dementia Neuroimaging Unit at St. George’s University of London, and the Biostatistics Group at the Centre for the Developing Brain, King’s College London. Dr. Chung is a recipient of a joint American Heart Association and Children’s Heart Foundation Fellowship, and her research interest is in connectome markers of altered brain organization in relation to injury or pathology such as concussion, or congenital heart disease, towards improving well-being and outcomes throughout the life span.

Affiliations and expertise

Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States

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Connectome Analysis

Characterization, Methods, and Analysis

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Resources

Markus D. Schirmer

Tomoki Arichi

Ai Wern Chung