Overview

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.

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Dr. Kendrick Kay is an Assistant Professor at the Center for Magnetic Resonance Research at the University of Minnesota. He received a BA in Philosophy from Harvard University in 2002, a PhD in Psychology from the University of California, Berkeley in 2009, and completed a postdoc at Stanford University in 2013. Research in his Computational Visual Neuroscience lab (http://cvnlab.net) focuses on understanding the computational principles by which the brain processes visual information, and lies at the intersection of cognitive neuroscience, functional magnetic resonance imaging methods, and computational modeling. The lab is highly collaborative, working with diverse groups around the world, with the goal of developing broad integrative insights into brain and behavior. Dr. Kay is a co-founder of the conference, Cognitive Computational Neuroscience, which seeks to bridge researchers across cognitive science, artificial intelligence, and neuroscience. Dr. Kay has published more than 50 scientific articles in top journals including Nature, Nature Neuroscience, Neuron, Nature Methods, eLife, PNAS, Current Biology, Journal of Neuroscience, and NeuroImage. Tools and resources (e.g., experiments, data, code) from the lab's research are made freely available, including the recently completed 7T fMRI Natural Scenes Dataset.

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