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. 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.
Table of Contents
- Statistical modeling: harnessing uncertainty and variation in neuroimaging data
- Sensory modeling Understanding sensory systems through image computable models
- Cognitive modeling: Joint Models Use Cognitive Theory to Understand Brain Activations
- Network modeling: The explanatory power of activity flow models of brain function
- Biophysical modeling: an approach for understanding the physiological fingerprint of the BOLD fMRI signal
- Biophysical modeling: multi-compartment biophysical models for brain tissue microstructure imaging
- Dynamic brain network models: how interactions in the structural connectome shape brain dynamics
- Neural graph modeling: a framework for modeling dynamic cognitive function in the brain
- Machine learning and neuroimaging: understanding the human brain in health and disease
- Decoding models: From brain representation to machine interfaces
- Normative modeling: a framework for characterizing individual variation in clinical neuroscience
