Acknowledgements Part 1 Introduction 1 A short history of SPM 2 Statistical parametric mapping 3 Modelling brain responses Part 2 Computational anatomy 4 Rigid body registration 5 Non-linear registration 6 Segmentation 7 Voxel-Based Morphometry Part 3 General linear models 8 The general linear model 9 Contrasts and classical inference 10 Covariance components 11 Hierarchical models 12 Random effects analysis 13 Analysis of variance 14 Convolution models for fMRI 15 Efficient experimental design for fMRI 16 Hierarchical models for EEG and MEG Part 4 Classical inference 17 Parametric procedures 18 Random Field Theory 19 Topological inference 20 False Discovery Rate procedures 21 Non-parametric procedures Part 5 Bayesian inference 22 Empirical Bayes and hierarchical models 23 Posterior probability maps 24 Variational Bayes 25 Spatio-temporal models for fMRI 26 Spatio-temporal models for EEG Part 6 Biophysical models 27 Forward models for fMRI 28 Forward models for EEG 29 Bayesian inversion of EEG models 30 Bayesian inversion for induced responses 31 Neuronal models of ensemble dynamics 32 Neuronal models of energetics 33 Neuronal models of EEG and MEG 34 Bayesian inversion of dynamic models 35 Bayesian model selection and averaging Part 7 Connectivity 36 Functional integration 37 Functional connectivity: eigenimages and multivariate analyses 38 Effective Connectivity 39 Non-linear coupling and kernels 40 Multivariate autoregressive models 41 Dynamic Causal Models for fMRI 42 Dynamic Causal Models for EEG 43 Dynamic Causal Models and Bayesian selection Appendices Appendix 1 Linear models and inference Appendix 2 Dynamical systems Appendix 3 Expectation maximization Appendix 4 Variational Bayes under the Laplace approximation Appendix 5 Kalman filtering Appendix 6 Random field theory Index
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