A comprehensive, accessible treatment of fMRI neuroimaging, covering conceptual foundations, technical details, and contemporary applications. The growing prevalence of neuroimaging, particularly fMRI, poses a challenge. As an inherently technical enterprise, it requires a synthesis of biological, computational, statistical, and psychological expertise that spans many fields. Providing a much-needed comprehensive treatment of the subject designed for practitioners working with neuroimaging data as well as non-specialists, this textbook introduces the fundamentals of how fMRI works and the principles that underlie it. Tor Wager and Martin Lindquist begin with a broad conceptual overview of what neuroimaging can and cannot say about the mind and behavior before moving to in-depth coverage of technical and mathematical detail. Topics include MRI physics and data acquisition, physiology, experimental design, preprocessing, general linear models, effect sizes and power, brain connectivity, multivariate statistical models, graph theory and network analysis, causal models and directed connectivity, predictive models and biomarkers, and machine learning and deep learning applications of artificial intelligence. Gives readers the conceptual tools to evaluate neuroimaging findings - Details standard statistical methods, advanced analytic techniques, and cutting-edge methods using machine learning and AI - Covers fundamental topics in physics, data acquisition, and experimental design - Has applications in psychology, neuroscience, economics, medicine, statistics, engineering, law, political science, journalism, and marketing Tor D. Wager is the Diana L. Taylor Distinguished Professor in Neuroscience at Dartmouth College and Director of Dartmouth’s Cognitive and Affective Neuroscience laboratory, the Dartmouth Brain Imaging Center, and the Dartmouth Center for Cognitive Neuroscience. He has taught over forty fMRI training courses. Martin A. Lindquist is Professor of Biostatistics at Johns Hopkins University and a recipient of the Organization for Human Brain Mapping's Education in Neuroimaging Award for teaching statistical issues to the neuroimaging community and the development of online classes that have taught fMRI methods to more than 100,000 students worldwide.