Connectome Analysis

Characterization, Methods, and Analysis

1st Edition - June 28, 2023

Editors: Markus D. Schirmer, Tomoki Arichi, Ai Wern Chung

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… 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

Markus is a Marie-Curie Fellow PhD at the German Centre for Neurodegenerative Disease Bonn, Germany, Harvard Medical School, and Massachusetts General Hospital. He received his M.Sc. in Theoretical Physics from Aachen University in Germany and his Ph.D. in Brain Connectivity from King’s College in London. In the Rost Research Lab, Markus applies his theoretical background combined with his interest in neuroimage analysis across the life-span to further research to improve our understanding of stroke and the associated outcome for patients. In his current work, Markus promotes the use of clinical magnetic resonance images in large scale analyses. He is investigating different outcomes in stroke patients, aiming to understand and utilize the concept that some brains are seemingly more resilient to insults. In the future, his goal is the facilitation of translational research to the point where theoretical neuroimage analysis can be used to understand individual differences in patients. This will help support medical decision making and personalize treatment options for patients in order to improve their long-term outcome.

Affiliations and expertise

Marie-Curie Fellow, German Centre for Neurodegenerative Disease Bonn, Germany

Tomoki Arichi

Dr Tomoki Arichi MBChB FRCPCH PhDMBChB FRCPCH PhD is a MRC Clinician Scientist and Clinical Senior Lecturer in the Centre for the Developing Brain, King's College London. He received his PhD from Imperial College London in 2012, following the award of a Chain-Florey Fellowship from the MRC Clinical Sciences Centre. The work of his thesis focused on the optimisation of functional MRI techniques for studying activity in the newborn brain. He was appointed as an MRC Clinician Scientist in March 2017. Dr Arichi also holds an honorary position as a Consultant in Paediatric Neurodisability in the Evelina London Children's Hospital. His clinical work is focused on the early identification and resulting management of the disabilities associated with perinatal brain injury. His current work aims to apply non-invasive imaging techniques (EEG, functional MRI and simultaneous EEG-fMRI, motion-tracking methods) to characterise the development of functional activity in the human brain, during fetal and preterm life and following brain injury. This is particularly focused on understanding how early somatosensory and motor processing relates to brain development and behaviour. He is also aiming to gain a deeper understanding of the underlying biophysics of the fMRI signal in the newborn brain. He also holds a visiting position in the Human Robotics group at Imperial College London, where they are developing novel tools for use in the MRI scanner and automated rehabilitative strategies for young infants who have suffered brain injury.

Affiliations and expertise

MRC Clinician Scientist and Clinical Senior Lecturer, Centre for the Developing Brain, King's College London, UK

Ai Wern Chung

Ai Wern Chung PhD is a Research Fellow in the Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA.

Affiliations and expertise

Research Fellow, Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA