
Contents
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Introduction Introduction
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A Brief History of Imaging Methods and Analyses A Brief History of Imaging Methods and Analyses
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Early Analytic Approaches Based on the General Linear Model Early Analytic Approaches Based on the General Linear Model
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Statistical Parametric Mapping Statistical Parametric Mapping
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Positron Emission Tomography and Electroencephalography Positron Emission Tomography and Electroencephalography
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Functional Magnetic Resonance Imaging Functional Magnetic Resonance Imaging
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Spatial Normalization and Topological Inference Spatial Normalization and Topological Inference
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Statistical Parametric Mapping—A Closer Look Statistical Parametric Mapping—A Closer Look
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Sequential Steps—Image Acquisition to Analysis Sequential Steps—Image Acquisition to Analysis
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Analytic Methods Analytic Methods
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General Linear Modeling—Foundational Issues in Neuroimaging General Linear Modeling—Foundational Issues in Neuroimaging
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Model Basics Model Basics
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Equation 9.1. General Linear Equation Equation 9.1. General Linear Equation
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Equation 9.2. Matrix Formulation of the General Linear Equation Equation 9.2. Matrix Formulation of the General Linear Equation
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Equation 9.3. Matrix Notation of the General Linear Equation Equation 9.3. Matrix Notation of the General Linear Equation
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Parameter Estimation Parameter Estimation
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Analytic Models and Designs Analytic Models and Designs
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Positron Emission Tomography Positron Emission Tomography
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Equation 9.5. Equation for Global Normalization of Raw Activation Data Equation 9.5. Equation for Global Normalization of Raw Activation Data
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Equation 9.6. Analysis of Covariance Model for Regional and Global Effects Equation 9.6. Analysis of Covariance Model for Regional and Global Effects
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Equation 9.7. Analysis of Covariance Model for Regional and Global Effects Equation 9.7. Analysis of Covariance Model for Regional and Global Effects
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fMRI Models fMRI Models
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Equation 9.8. Linear Time Series Model for fMRI Equation 9.8. Linear Time Series Model for fMRI
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Equation 9.9. Linear Time Series Model for fMRI Expressed in Matrix Form Equation 9.9. Linear Time Series Model for fMRI Expressed in Matrix Form
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Modeling Serial Correlation via the Error Covariance Matrix Modeling Serial Correlation via the Error Covariance Matrix
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Time Series General Linear Model at the Voxel Level Time Series General Linear Model at the Voxel Level
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Equation 9.10. Linear Model At The Level of One Voxel with Modified Error Term Equation 9.10. Linear Model At The Level of One Voxel with Modified Error Term
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Equation 9.11. Pooled Correlation as a Mixture of Variance Components Equation 9.11. Pooled Correlation as a Mixture of Variance Components
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Equation 9.12. Voxel-Level Autoregressive-1 Plus White Noise Equation Equation 9.12. Voxel-Level Autoregressive-1 Plus White Noise Equation
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Equation 9.13. Voxel-Level Error Covariance Matrix Equation 9.13. Voxel-Level Error Covariance Matrix
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Multilevel Models—the Hierarchical Linear Model Multilevel Models—the Hierarchical Linear Model
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Expectation Maximization Expectation Maximization
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Bayesian Methods of Analysis Bayesian Methods of Analysis
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Bayesian Probability and Inference Bayesian Probability and Inference
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Equation 9.14. Relationship Between Bayesian and Direct Probability Equation 9.14. Relationship Between Bayesian and Direct Probability
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Equation 9.15 bayes’ theorem Equation 9.15 bayes’ theorem
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Limitations of Classic Frequentist Probability in Imaging Analytics Limitations of Classic Frequentist Probability in Imaging Analytics
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The Bayesian Hierarchical Model The Bayesian Hierarchical Model
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Equation 9.16. Bayes’ Theorem Equation 9.16. Bayes’ Theorem
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Equation 9.17. Two-Level Hierarchal Linear Model with Known Covariance Components Equation 9.17. Two-Level Hierarchal Linear Model with Known Covariance Components
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Equation 9.18. Equation for Posterior Parameters Using Bayes’ Theorem Equation 9.18. Equation for Posterior Parameters Using Bayes’ Theorem
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Equation 9.19. Two-Level Hierarchal Linear Model Equation 9.19. Two-Level Hierarchal Linear Model
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Spatial and Temporal Generative Bayesian Models for Functional Magnetic Resonance Imaging Spatial and Temporal Generative Bayesian Models for Functional Magnetic Resonance Imaging
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Analytic Methods for Functional and Effective Connectivity Analytic Methods for Functional and Effective Connectivity
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Functional and Effective Connectivity of Brain Regions Functional and Effective Connectivity of Brain Regions
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Functional Connectivity Methods Functional Connectivity Methods
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Effective Connectivity Effective Connectivity
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Dynamic Causal Models Dynamic Causal Models
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Multivariate Autoregressive Models Multivariate Autoregressive Models
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Modeling of Effective and Functional Connectivity With Structural Equation Modeling Modeling of Effective and Functional Connectivity With Structural Equation Modeling
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Conclusion Conclusion
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Future Directions Future Directions
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Author Note Author Note
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References References
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9 Analysis of Imaging Data
Get accessLarry R. Price, College of Education and College of Science, Texas State University-San Marcos
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Published:01 October 2013
Cite
Abstract
A brief history of imaging neuroscience is presented followed by an introduction to data acquisition using positron emission tomography (PET)and functional magnetic resonance imaging (fMRI). Next, statistical parametric mapping is introduced in conjunction with random field theory as being fundamental to identifying sites of neural activation. The general linear model is discussed as being foundational for all imaging analyses. Finally, methods for studying functional and effective connectivity such as eigenimage analysis, partial least squares, multivariate autoregressive models, structural equation models, and dynamic causal models are reviewed in light of deterministic and stochastic analytic approaches.
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