Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data.
This book gives an accessible foundation to the field of computational and network modeling of neuroimaging data and is suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging.
1. Computational and Network Modeling of Neuroimaging Data - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page vi 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : Statistical modeling: Harnessing uncertainty and variation in neuroimaging data - p. Page ix 11. Chapter 2 : Sensory modeling: Understanding computation in sensory systems through image-computable models - p. Page 25 12. Chapter 3 : Cognitive modeling: Joint models use cognitive theory to understand brain activations - p. Page 47 13. Chapter 4 : Network modeling: The explanatory power of activity flow models of brain function - p. Page 77 14. Chapter 5 : Biophysical modeling: An approach for understanding the physiological fingerprint of the BOLD fMRI signal - p. Page 111 15. Chapter 6 : Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging - p. Page 147 16. Chapter 7 : Dynamic brain network models: How interactions in the structural connectome shape brain dynamics - p. Page 195 17. Chapter 8 : Neural graph modeling - p. Page 219 18. Chapter 9 : Machine learning and neuroimaging: Understanding the human brain in health and disease - p. Page 256 19. Chapter 10 : Decoding models: From brain representation to machine interfaces - p. Page 278 20. Chapter 11 : Normative modeling for clinical neuroscience - p. Page 303 21. Index - p. Page 324
Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data.
This book gives an accessible foundation to the field of computational and network modeling of neuroimaging data and is suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging.
1. Computational and Network Modeling of Neuroimaging Data - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page vi 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : Statistical modeling: Harnessing uncertainty and variation in neuroimaging data - p. Page ix 11. Chapter 2 : Sensory modeling: Understanding computation in sensory systems through image-computable models - p. Page 25 12. Chapter 3 : Cognitive modeling: Joint models use cognitive theory to understand brain activations - p. Page 47 13. Chapter 4 : Network modeling: The explanatory power of activity flow models of brain function - p. Page 77 14. Chapter 5 : Biophysical modeling: An approach for understanding the physiological fingerprint of the BOLD fMRI signal - p. Page 111 15. Chapter 6 : Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging - p. Page 147 16. Chapter 7 : Dynamic brain network models: How interactions in the structural connectome shape brain dynamics - p. Page 195 17. Chapter 8 : Neural graph modeling - p. Page 219 18. Chapter 9 : Machine learning and neuroimaging: Understanding the human brain in health and disease - p. Page 256 19. Chapter 10 : Decoding models: From brain representation to machine interfaces - p. Page 278 20. Chapter 11 : Normative modeling for clinical neuroscience - p. Page 303 21. Index - p. Page 324
