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Computational and Network Modeling of Neuroimaging Data
- 1st Edition - June 17, 2024
- Editor: Kendrick Kay
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 4 8 0 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 4 8 1 - 4
Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfull… Read more
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Request a sales quoteComputational 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. As neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired, this book gives an accessible foundation to the field of computational neuroimaging, suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging.
It is widely recognized that effective interpretation and extraction of information from complex data requires quantitative modeling. However, modeling the brain 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. This book takes a critical step towards synthesizing and integrating across different modeling approaches.
- Provides an authoritative and comprehensive overview of major modeling approaches to neuroimaging data
- Written by experts, the book's chapters use a common structure to introduce, motivate, and describe a specific modeling approach used in neuroimaging
- Gives insights into the similarities and differences across different modeling approaches
- Analyses details of outstanding research challenges in the field
- Cover image
- Title page
- Table of Contents
- Front Matter
- Copyright
- Contributors
- Preface
- Chapter 1 Statistical modeling: Harnessing uncertainty and variation in neuroimaging data
- Abstract
- Introduction to statistical modeling
- Examples of successful statistical models
- Assumptions of statistical models
- Building, testing, interpreting statistical models
- Pitfalls in statistical modeling
- Open challenges and future directions
- Take-home points
- References
- Chapter 2 Sensory modeling: Understanding computation in sensory systems through image-computable models
- Abstract
- Introduction to image-computable models
- Examples of successful image-computable models
- Assumptions of image-computable models
- Building, testing, interpreting image-computable models
- Pitfalls in image-computable modeling
- Open challenges and future directions
- Take-home points
- References
- Chapter 3 Cognitive modeling: Joint models use cognitive theory to understand brain activations
- Abstract
- Introduction to joint modeling
- Examples of successful joint models
- Assumptions of joint models
- Building, testing, interpreting joint models
- Pitfalls of joint modeling
- Open challenges and future directions
- Take-home points
- References
- Chapter 4 Network modeling: The explanatory power of activity flow models of brain function
- Abstract
- Acknowledgments
- Introduction to activity flow modeling
- Examples of successful activity flow models
- Assumptions of activity flow models
- Building, testing, interpreting activity flow models
- Pitfalls of activity flow models
- How activity flow modeling relates to other approaches
- Open challenges and future directions
- Take-home points
- Appendix: How activity flow modeling relates to additional other approaches
- References
- Glossary
- Chapter 5 Biophysical modeling: An approach for understanding the physiological fingerprint of the BOLD fMRI signal
- Abstract
- Introduction to biophysical modeling
- Examples of successful biophysical models
- Assumptions of biophysical models
- Building, testing, interpreting biophysical models
- Pitfalls in biophysical modeling
- Open challenges and future directions
- Take-home points
- References
- Chapter 6 Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging
- Abstract
- Introduction to biophysical tissue models
- Assumptions of biophysical tissue models
- Building, testing, interpreting biophysical tissue models
- Examples of successful multicompartment biophysical tissue models
- Limitations of multicompartment biophysical tissue modeling
- Open challenges and future directions
- Take-home points
- Appendix A: Water diffusion
- Appendix B: Multicompartment biophysical model functions
- Appendix C: Biophysical model implementation libraries
- References
- Glossary
- Chapter 7 Dynamic brain network models: How interactions in the structural connectome shape brain dynamics
- Abstract
- Introduction to dynamic brain network models
- Examples of dynamic brain network models
- Assumptions of brain network models
- Building, testing and interpreting dynamic brain network models
- Pitfalls of brain network modeling
- Open challenges and future directions
- Take-home points
- References
- Chapter 8 Neural graph modeling
- Abstract
- Introduction to neural graph modeling
- Examples of successful neural graph models
- Assumptions of neural graph models
- Building, testing, interpreting neural graph models
- Pitfalls of neural graph models
- Open challenges and future directions
- Take-home points
- References
- Chapter 9 Machine learning and neuroimaging: Understanding the human brain in health and disease
- Abstract
- Introduction to machine learning in neuroimaging
- Examples of successful machine learning in neuroimaging
- Assumptions of machine learning models
- Building, testing, interpreting machine learning models
- Pitfalls/considerations of machine learning models
- Open challenges and future directions
- Take-home points
- References
- Chapter 10 Decoding models: From brain representation to machine interfaces
- Abstract
- Introduction to decoding models
- Examples of successful decoding models
- Assumptions of decoding models
- Building, testing, interpreting decoding models
- Pitfalls of decoding modeling
- Open challenges and future directions
- Take-home points
- Further resources
- References
- Chapter 11 Normative modeling for clinical neuroscience
- Abstract
- Introduction to normative modeling
- Examples of successful normative modeling
- Assumptions of normative modeling
- Building, testing, interpreting normative models
- Pitfalls and limitations of normative modeling
- Open challenges and future directions
- Take-home points
- References
- Index
- No. of pages: 354
- Language: English
- Edition: 1
- Published: June 17, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443134807
- eBook ISBN: 9780443134814
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