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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
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
Chapter 1: Benefits of fMRI: Versatility and an Open Community
Chapter 2: Mechanisms, Concepts, and Brain: A Cognitive Neuroscience Approach
Chapter 3: Imaging and Society
Chapter 4: Types of Imaging: fMRI Compared With Other Methods
Chapter 5: Working Together: Multidisciplinary Science
Part II: Brain Mapping
Chapter 6: What is a Brain Map? A Conceptual Overview of the Statistical Mapping Approach
Chapter 7: Forward and Reverse Inference
Chapter 8: Valid and Invalid Inferences: What We Can and Cannot Infer From Brain Maps
Chapter 9: Constrating Statistical Brain Mapping With Traditional Neuroradiology
Chapter 10: Why Imaging is Not Phrenology
Part III: MRI Environment and Fundamentals of MRI Signal
Chapter 11: The MRI Environment and Human Factors
Chapter 12: fMRI Basics: Processing Stages, Terminology, and Data Structure
Chapter 13: Fundamentals of MRI Physics
Chapter 14: Physiological Basis of fMRI Signals
Chapter 15: Constraints on fMRI Spatial and Temporal Resolution
Part IV: Fundamentals of fMRI Signal Processing and Analysis
Chapter 16: Artifacts and Noise in fMRI
Chapter 17: Image Preprocessing
Chapter 18: The Generals Linear Model and Foundations of Analysis
Chapter 19: GLM Design Specification
Chapter 20: Contrasts and Inference in the GLM
Chapter 21: Group Analysis
Chapter 22: Multiple Comparisons
Chapter 23: Localizing and Interpreting Neuroimaging Results
Chapter 24: Analysis Pipelines: Variations and Variability
Chapter 25: Neuroimaging Meta Analysis
Part V: Experimental Design
Chapter 26: Why do an Experiment? Experimental and Observational Designs
Chapter 27: Experimental Designs for Task fMRI
Chapter 28: Resting-state and Naturalistic Studies
Chapter 29: Effect Sizes, Statistical Power and Sample Size
Part VI: Brain Connectivity
Chapter 30: Introduction to Brain Connectivity
Chapter 31: Multivariate Decomposition Methods: Principal and Independent Components Analysis
Chapter 32: Graph Theory and Network Analysis
Chapter 33: Dynamic Connectivity
Chapter 34: Structural Equation and Path Models
Chapter 35: Dynamic Causal Models
Chapter 36: Granger Causal Models
Chapter 37: Multivariate Brain Alaysis: From Maps to Models
Part VII: Multivariate Brain Analysis
Chapter 38: Machine Learning Principles and Algorithms
Chapter 39: Training and Testing Predictive Models
Chapter 40: Applying Predictive Models to fMRI Data
Chapter 41: Biomarkers and Translational Neuroscience
Chapter 42: Artificial Intelligence and Neuroscience
References
| Erscheinungsjahr: | 2026 |
|---|---|
| Genre: | Biologie, Importe |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9780262045049 |
| ISBN-10: | 0262045044 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Lindquist, Martin A.
Wager, Tor |
| Hersteller: | MIT Press Ltd |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 177 x 230 x 33 mm |
| Von/Mit: | Martin A. Lindquist (u. a.) |
| Erscheinungsdatum: | 09.06.2026 |
| Gewicht: | 0,924 kg |
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