
Statistical Parametric Mapping: The Analysis of Functional Brain Images
- 1st Edition - December 9, 2006
- Editors: William D. Penny, Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, Thomas E. Nichols
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 9 3 3 - 0 0 9 5 - 2
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 7 2 5 6 0 - 8
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 4 6 6 5 0 - 7
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure… Read more

- An essential reference and companion for users of the SPM software
- Provides a complete description of the concepts and procedures entailed by the analysis of brain images
- Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data
- Stands as a compendium of all the advances in neuroimaging data analysis over the past decade
- Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes
- Structured treatment of data analysis issues that links different modalities and models
- Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
Acknowledgements
Part 1: Introduction
Chapter 1: A short history of SPM
Chapter 2: Statistical parametric mapping
Chapter 3: Modelling brain responses
Part 2: Computational anatomy
Chapter 4: Rigid Body Registration
Chapter 5: Non-linear Registration
Chapter 6: Segmentation
Chapter 7: Voxel-Based Morphometry
Part 3: General linear models
Chapter 8: The General Linear Model
Chapter 9: Contrasts and Classical Inference
Chapter 10: Covariance Components
Chapter 11: Hierarchical Models
Chapter 12: Random Effects Analysis
Chapter 13: Analysis of Variance
Chapter 14: Convolution Models for fMRI
Chapter 15: Efficient Experimental Design for fMRI
Chapter 16: Hierarchical models for EEG and MEG
Part 4: Classical inference
Chapter 17: Parametric procedures
Chapter 18: Random Field Theory
Chapter 19: Topological Inference
Chapter 20: False Discovery Rate procedures
Chapter 21: Non-parametric procedures
Part 5: Bayesian inference
Chapter 22: Empirical Bayes and hierarchical models
Chapter 23: Posterior probability maps
Chapter 24: Variational Bayes
Chapter 25: Spatio-temporal models for fMRI
Chapter 26: Spatio-temporal models for EEG
Part 6: Biophysical models
Chapter 27: Forward models for fMRI
Chapter 28: Forward models for EEG
Chapter 29: Bayesian inversion of EEG models
Chapter 30: Bayesian inversion for induced responses
Chapter 31: Neuronal models of ensemble dynamics
Chapter 32: Neuronal models of energetics
Chapter 33: Neuronal models of EEG and MEG
Chapter 34: Bayesian inversion of dynamic models
Chapter 35: Bayesian model selection and averaging
Part 7: Connectivity
Chapter 36: Functional integration
Chapter 37: Functional connectivity: eigenimages and multivariate analyses
Chapter 38: Effective Connectivity
Chapter 39: Non-linear coupling and kernels
Chapter 40: Multivariate autoregressive models
Chapter 41: Dynamic Causal Models for fMRI
Chapter 42: Dynamic causal models for EEG
Chapter 43: Dynamic Causal Models and Bayesian selection
Appendices
Linear models and inference
Dynamical systems
Expectation maximization
Variational Bayes under the Laplace approximation
Kalman filtering
Random field theory
Index
Color Plates
- Edition: 1
- Published: December 9, 2006
- Language: English
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William D. Penny
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Karl J. Friston
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