Computational and Network Modeling of Neuroimaging Data - Edition 1 - Edited by Kendrick Kay Elsevier Inspection Copies

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Computational and Network Modeling of Neuroimaging Data,

Edition 1

Edited by Kendrick Kay

Publication Date: 18 Jun 2024

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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.

Key Features

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

About the author

Edited by Kendrick Kay, Assistant Professor , Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA

Statistical modeling: Harnessing uncertainty and variation in neuroimaging data
Sensory modeling: Understanding computation in 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: Multicompartment biophysical models for brain tissue microstructure imaging
Dynamic brain network models: How interactions in the structural connectome shape brain dynamics
Neural graph modelling
Machine learning and neuroimaging: Understanding the human brain in health and disease
Decoding models: From brain representation to machine interfaces
Normative modeling for clinical neuroscience