Front Cover -- Statistical Parametric Mapping -- Copyright Page -- Table of Contents -- Acknowledgements -- Part 1 Introduction -- Chapter 1 A short history of SPM -- INTRODUCTION -- THE PET YEARS -- THE fMRI YEARS -- THE MEG-EEG YEARS -- REFERENCES -- Chapter 2 Statistical parametric mapping -- INTRODUCTION -- SPATIAL TRANSFORMS AND COMPUTATIONAL ANATOMY -- STATISTICAL PARAMETRIC MAPPING AND THE GENERAL LINEAR MODEL -- TOPOLOGICAL INFERENCE AND THE THEORY OF RANDOM FIELDS -- EXPERIMENTAL AND MODEL DESIGN -- INFERENCE IN HIERARCHICAL MODELS -- CONCLUSION -- REFERENCES -- Chapter 3 Modelling brain responses -- INTRODUCTION -- ANATOMICAL MODELS -- STATISTICAL MODELS -- MODELS OF FUNCTIONAL INTEGRATION -- CONCLUSION -- REFERENCES -- Part 2 Computational anatomy -- Chapter 4 Rigid Body Registration -- INTRODUCTION -- RE-SAMPLING IMAGES -- RIGID BODY TRANSFORMATIONS -- WITHIN-MODALITY RIGID REGISTRATION -- BETWEEN-MODALITY RIGID REGISTRATION -- REFERENCES -- Chapter 5 Non-linear Registration -- INTRODUCTION -- OBJECTIVE FUNCTIONS -- LARGE DEFORMATION APPROACHES -- ESTIMATING THE MAPPINGS -- SPATIAL NORMALIZATION IN THE SPM SOFTWARE -- EVALUATION STRATEGIES -- REFERENCES -- Chapter 6 Segmentation -- INTRODUCTION -- THE OBJECTIVE FUNCTION -- OPTIMIZATION -- REFERENCES -- Chapter 7 Voxel-Based Morphometry -- INTRODUCTION -- PREPARING THE DATA -- STATISTICAL MODELLING AND INFERENCE -- REFERENCES -- Part 3 General linear models -- Chapter 8 The General Linear Model -- INTRODUCTION -- THE GENERAL LINEAR MODEL -- INFERENCE -- PET AND BASIC MODELS -- fMRI MODELS -- APPENDIX 8.1 THE AUTOREGRESSIVE MODEL OF ORDER 1 PLUS WHITE NOISE -- APPENDIX 8.2 THE SATTERTHWAITE APPROXIMATION -- REFERENCES -- Chapter 9 Contrasts and Classical Inference -- INTRODUCTION -- CONSTRUCTING MODELS What should be included in the model? -- CONSTRUCTING AND TESTING CONTRASTS.

CONSTRUCTING AND TESTING F-CONTRASTS -- CORRELATION BETWEEN REGRESSORS -- DESIGN COMPLEXITY -- SUMMARY -- APPENDIX 9.1 NOTATION -- APPENDIX 9.2 SUBSPACES -- APPENDIX 9.3 ORTHOGONAL PROJECTION -- REFERENCES -- Chaper 10 Covariance Components -- INTRODUCTION -- SOME MATHEMATICAL EQUIVALENCES -- ESTIMATING COVARIANCE COMPONENTS -- CONCLUSION -- REFERENCES -- Chapter 11 Hierarchical Models -- INTRODUCTION -- TWO-LEVEL MODELS -- PARAMETRIC EMPIRICAL BAYES -- NUMERICAL EXAMPLE -- BELIEF PROPAGATION -- DISCUSSION -- REFERENCES -- Chapter 12 Random Effects Analysis -- INTRODUCTION -- RANDOM EFFECTS ANALYSIS -- FIXED EFFECTS ANALYSIS -- PARAMETRIC EMPIRICAL BAYES -- PET DATA EXAMPLE -- fMRI DATA EXAMPLE -- DISCUSSION -- APPENDIX 12.1 EXPECTATIONS AND TRANSFORMATIONS -- REFERENCES -- Chapter 13 Analysis of Variance -- INTRODUCTION -- ONE-WAY BETWEEN-SUBJECT ANOVA -- ONE-WAY WITHIN-SUBJECT ANOVA -- TWO-WAY WITHIN-SUBJECT ANOVAs -- GENERALIZATION TO M-WAY ANOVAs -- fMRI BASIS FUNCTIONS -- DISCUSSION -- APPENDIX 13.1 THE KRONECKER PRODUCT -- APPENDIX 13.2 WITHIN-SUBJECT MODELS -- REFERENCES -- Chapter 14 Convolution Models for fMRI -- INTRODUCTION -- THE HAEMODYNAMIC RESPONSE FUNCTION (HRF) -- TEMPORAL BASIS FUNCTIONS -- TEMPORAL FILTERING AND AUTOCORRELATION -- NON-LINEAR CONVOLUTION MODELS -- A WORKED EXAMPLE -- REFERENCES -- Chapter 15 Efficient Experimental Design for fMRI -- INTRODUCTION -- TAXONOMY OF EXPERIMENTAL DESIGN -- EVENT-RELATED fMRI, AND RANDOMIZED VERSUS BLOCKED DESIGNS -- EFFICIENCY AND OPTIMIZATION OF fMRI DESIGNS -- COMMON QUESTIONS What is the minimum number of events I need? -- REFERENCES -- Chapter 16 Hierarchical models for EEG and MEG -- INTRODUCTION -- SPATIAL MODELS -- TEMPORAL MODELS -- HYPOTHESIS TESTING WITH HIERARCHICAL MODELS -- SUMMARY -- REFERENCES -- Part 4 Classical inference -- Chapter 17 Parametric procedures -- INTRODUCTION.

THE BONFERRONI CORRECTION -- RANDOM FIELD THEORY -- DISCUSSION -- REFERENCES -- Chapter 18 Random Field Theory -- INTRODUCTION -- THE MAXIMUM TEST STATISTIC -- THE MAXIMUM SPATIAL EXTENT OF THE TEST STATISTIC -- SEARCHING IN SMALL REGIONS -- ESTIMATING THE FWHM -- FALSE DISCOVERY RATE -- CONCLUSION -- REFERENCES -- Chapter 19 Topological Inference -- INTRODUCTION -- TOPOLOGICAL INFERENCE -- THEORY AND DISTRIBUTIONAL APPROXIMATIONS -- POWER ANALYSES -- SUMMARY -- REFERENCES -- Chapter 20 False Discovery Rate procedures -- INTRODUCTION -- MULTIPLE TESTING DEFINITIONS -- FDR METHODS -- EXAMPLES AND DEMONSTRATIONS -- CONCLUSION -- REFERENCES -- Chapter 21 Non-parametric procedures -- INTRODUCTION -- PERMUTATION TESTS -- WORKED EXAMPLES -- CONCLUSIONS -- REFERENCES -- Part 5 Bayesian inference -- Chapter 22 Empirical Bayes and hierarchical models -- INTRODUCTION -- THEORETICAL BACKGROUND -- EM AND COVARIANCE COMPONENT ESTIMATION -- REFERENCES -- Chapter 23 Posterior probability maps -- INTRODUCTION -- THEORY -- EMPIRICAL DEMONSTRATIONS -- CONCLUSION -- REFERENCES -- Chapter 24 Variational Bayes -- INTRODUCTION -- THEORY -- EXAMPLES -- DISCUSSION -- APPENDIX 24.1 -- REFERENCES -- Chapter 25 Spatio-temporal models for fMRI -- INTRODUCTION -- THEORY -- RESULTS -- DISCUSSION -- APPENDIX 25.1 -- REFERENCES -- Chapter 26 Spatio-temporal models for EEG -- INTRODUCTION -- THEORY -- PCA -- RESULTS -- DISCUSSION -- REFERENCES -- Part 6 Biophysical models -- Chapter 27 Forward models for fMRI -- INTRODUCTION -- NON-LINEAR EVOKED RESPONSES -- THE HAEMODYNAMIC MODEL -- KERNEL ESTIMATION -- RESULTS AND DISCUSSION -- DISCUSSION -- CONCLUSION -- REFERENCES -- Chapter 28 Forward models for EEG -- INTRODUCTION -- ANALYTICAL FORMULATION Maxwell's equations -- NUMERICAL SOLUTION OF THE BEM EQUATION -- ANALYTIC SOLUTION OF THE BEM EQUATION -- DISCUSSION -- REFERENCES.

Chapter 29 Bayesian inversion of EEG models -- INTRODUCTION -- THE BAYESIAN FORMULATION OF CLASSICAL REGULARIZATION -- A HIERARCHICAL OR PARAMETRIC EMPIRICAL BAYES APPROACH -- RESTRICTED MAXIMUM LIKELIHOOD -- APPLICATION TO SYNTHETIC MEG DATA -- APPLICATION TO SYNTHETIC EEG DATA -- CONCLUSION -- APPENDIX 29.1 THE L-CURVE APPROACH -- REFERENCES -- Chapter 30 Bayesian inversion for induced responses -- INTRODUCTION -- THE BASIC ReML APPROACH TO DISTRIBUTED SOURCE RECONSTRUCTION -- A TEMPORALLY INFORMED SCHEME -- ESTIMATING RESPONSE ENERGY -- AVERAGING OVER TRIALS -- SOME EXAMPLES -- DISCUSSION -- REFERENCES -- Chapter 31 Neuronal models of ensemble dynamics -- INTRODUCTION -- THEORY -- ILLUSTRATIVE APPLICATIONS -- CONCLUSION -- APPENDIX 31.1 NUMERICAL SOLUTION OF FOKKER-PLANCK EQUATION -- REFERENCES -- Chapter 32 Neuronal models of energetics -- INTRODUCTION -- EEG AND fMRI INTEGRATION -- A HEURISTIC FOR EEG-fMRI INTEGRATION -- EMPIRICAL EVIDENCE -- SUMMARY -- REFERENCES -- Chapter 33 Neuronal models of EEG and MEG -- INTRODUCTION -- NEURAL-MASS MODELS -- MODELLING CORTICAL SOURCES -- HIERARCHICAL MODELS OF CORTICAL NETWORKS -- MECHANISMS OF ERP GENERATION -- PHASE-RESETTING AND THE ERP -- ONGOING AND EVENT-RELATED ACTIVITY -- INDUCED RESPONSES AND ERPs -- DISCUSSION -- CONCLUSION -- REFERENCES -- Chapter 34 Bayesian inversion of dynamic models -- INTRODUCTION -- A HAEMODYNAMIC MODEL -- PRIORS -- SYSTEM IDENTIFICATION -- EMPIRICAL ILLUSTRATIONS -- CONCLUSION -- REFERENCES -- Chapter 35 Bayesian model selection and averaging -- INTRODUCTION -- CONDITIONAL PARAMETER INFERENCE -- MODEL INFERENCE -- MODEL AVERAGING -- DYNAMIC CAUSAL MODELS -- SOURCE RECONSTRUCTION -- MULTIPLE CONSTRAINTS -- MODEL AVERAGING -- DISCUSSION -- REFERENCES -- Part 7 Connectivity -- Chapter 36 Functional integration -- INTRODUCTION -- FUNCTIONAL SPECIALIZATION AND INTEGRATION.

LEARNING AND INFERENCE IN THE BRAIN -- IMPLICATIONS FOR CORTICAL INFRASTRUCTURE AND PLASTICITY -- ASSESSING FUNCTIONAL ARCHITECTURES WITH BRAIN IMAGING -- FUNCTIONAL INTEGRATION AND NEUROPSYCHOLOGY -- CONCLUSION -- REFERENCES -- Chapter 37 Functional connectivity: eigenimages and multivariate analyses -- INTRODUCTION -- EIGENIMAGES, MULTIDIMENSIONAL SCALING AND OTHER DEVICES -- NON-LINEAR PRINCIPAL AND INDEPENDENT COMPONENT ANALYSIS (PCA AND ICA) -- MANCOVA AND CANONICAL IMAGE ANALYSES -- REFERENCES -- Chapter 38 Effective Connectivity -- INTRODUCTION -- IDENTIFICATION OF DYNAMIC SYSTEMS -- STATIC MODELS -- DYNAMIC MODELS -- CONCLUSION -- REFERENCES -- Chapter 39 Non-linear coupling and kernels -- INTRODUCTION -- NEURONAL TRANSIENTS -- NEURONAL CODES -- EVIDENCE FOR NON-LINEAR COUPLING -- THE NEURAL BASIS OF NON-LINEAR COUPLING -- CONCLUSION -- REFERENCES -- Chapter 40 Multivariate autoregressive models -- INTRODUCTION -- THEORY -- APPLICATION -- DISCUSSION -- APPENDIX 40.1 -- REFERENCES -- Chapter 41 Dynamic Causal Models for fMRI -- INTRODUCTION -- THEORY -- FACE VALIDITY - SIMULATIONS -- PREDICTIVE VALIDITY - AN ANALYSIS OF SINGLE WORD PROCESSING -- CONSTRUCT VALIDITY - AN ANALYSIS OF ATTENTIONAL EFFECTS ON CONNECTIONS -- CONCLUSION -- REFERENCES -- Chapter 42 Dynamic causal models for EEG -- INTRODUCTION -- THEORY -- BAYESIAN INFERENCE AND MODEL COMPARISON -- EMPIRICAL STUDIES -- CONCLUSION -- SUMMARY -- APPENDIX -- REFERENCES -- Chapter 43 Dynamic Causal Models and Bayesian selection -- INTRODUCTION -- INTER-HEMISPHERIC INTEGRATION IN THE VENTRAL STREAM -- DISCUSSION -- REFERENCES -- Appendices -- Appendix 1 Linear models and inference -- INTRODUCTION -- INFORMATION THEORY AND DEPENDENCY -- OTHER PERSPECTIVES -- SUMMARY -- REFERENCES -- Appendix 2 Dynamical systems -- INTRODUCTION -- EFFECTIVE CONNECTIVITY -- INPUT-OUTPUT MODELS.

INPUT-STATE-OUTPUT MODELS.