Acknowledgements |
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vii | |
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3 | (7) |
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Statistical parametric mapping |
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10 | (22) |
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Modelling brain responses |
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32 | (17) |
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Part 2 Computational anatomy |
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49 | (14) |
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63 | (18) |
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81 | (11) |
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92 | (9) |
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Part 3 General linear models |
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101 | (25) |
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Contrasts and classical inference |
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126 | (14) |
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140 | (8) |
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148 | (8) |
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156 | (10) |
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166 | (12) |
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Convolution models for fMRI |
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178 | (15) |
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Efficient experimental design for fMRI |
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193 | (18) |
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Hierarchical models for EEG and MEG |
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211 | (12) |
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Part 4 Classical inference |
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223 | (9) |
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232 | (5) |
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237 | (9) |
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False Discovery Rate procedures |
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246 | (7) |
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Non-parametric procedures |
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253 | (22) |
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Part 5 Bayesian inference |
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Empirical Bayes and hierarchical models |
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275 | (20) |
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Posterior probability maps |
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295 | (8) |
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303 | (10) |
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Spatio-temporal models for fMRI |
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313 | (10) |
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Spatio-temporal models for EEG |
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323 | (16) |
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Part 6 Biophysical models |
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339 | (13) |
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352 | (15) |
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Bayesian inversion of EEG models |
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367 | (10) |
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Bayesian inversion for induced responses |
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377 | (14) |
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Neuronal models of ensemble dynamics |
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391 | (15) |
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Neuronal models of energetics |
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406 | (8) |
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Neuronal models of EEG and MEG |
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414 | (27) |
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Bayesian inversion of dynamic models |
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441 | (13) |
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Bayesian model selection and averaging |
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454 | (17) |
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471 | (21) |
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Functional connectivity: eigenimages and multivariate analyses |
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492 | (16) |
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508 | (14) |
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Non-linear coupling and kernels |
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522 | (12) |
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Multivariate autoregressive models |
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534 | (7) |
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Dynamic Causal Models for fMRI |
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541 | (20) |
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Dynamic Causal Models for EEG |
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561 | (16) |
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Dynamic Causal Models and Bayesian selection |
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577 | (48) |
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Appendix 1 Linear models and inference |
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589 | (3) |
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Appendix 2 Dynamical systems |
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592 | (11) |
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Appendix 3 Expectation maximization |
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603 | (3) |
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Appendix 4 Variational Bayes under the Laplace approximation |
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606 | (13) |
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Appendix 5 Kalman filtering |
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619 | (2) |
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Appendix 6 Random field theory |
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621 | (4) |
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Index |
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625 | |