Computational and Network Modeling of Neuroimaging Data

Kendrick Kay

• Publisher: Academic Press
• Year: 2024
• Print ISBN:
  9780443134807
• eBook ISBN:
  9780443134814
• Topic: Biological Sciences
• Subtopic: Diagnostics Imaging
• Index: Biological Sciences

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. - 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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : Statistical modeling: Harnessing uncertainty and variation in neuroimaging data
• Chapter 2 : Sensory modeling: Understanding computation in sensory systems through image-computable models
• Chapter 3 : Cognitive modeling: Joint models use cognitive theory to understand brain activations
• Chapter 4 : Network modeling: The explanatory power of activity flow models of brain function
• Chapter 5 : Biophysical modeling: An approach for understanding the physiological fingerprint of the BOLD fMRI signal
• Chapter 6 : Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging
• Chapter 7 : Dynamic brain network models: How interactions in the structural connectome shape brain dynamics
• Chapter 8 : Neural graph modeling
• Chapter 9 : Machine learning and neuroimaging: Understanding the human brain in health and disease
• Chapter 10 : Decoding models: From brain representation to machine interfaces
• Chapter 11 : Normative modeling for clinical neuroscience
• Index
• A