Statistical Parametric Mapping The Analysis of Functional Brain Images 1st Edition by Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny ISBN 9780080466507 0080466508

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ISBN 10: 0080466508
ISBN 13: 9780080466507
Author: Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny

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 proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain’s functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis.

Statistical Parametric Mapping The Analysis of Functional Brain Images 1st Edition Table of contents:

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

MULTIVARIATE ARMA MODELS

CONCLUSION

REFERENCES

Appendix 3 Expectation maximization

INTRODUCTION

RELATIONSHIP TO ReML

REFERENCES

Appendix 4 Variational Bayes under the Laplace approximation

INTRODUCTION

VARIATIONAL BAYES

VARIATIONAL BAYES FOR NON-LINEAR MODELS

EXPECTATION MAXIMIZATION FOR NON-LINEAR MODELS

RESTRICTED MAXIMUM LIKELIHOOD FOR LINEAR MODELS

RESTRICTED MAXIMUM LIKELIHOOD FOR HIERARCHICAL LINEAR MODELS

MODEL SELECTION WITH REML

REFERENCES

Appendix 5 Kalman filtering

INTRODUCTION

THE EXTENDED KALMAN FILTER

REFERENCES

Appendix 6 Random field theory

INTRODUCTION

THEORY

INTEGRAL GEOMETRY

RANDOM FIELDS

EXAMPLE

REFERENCES

Index

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Karl Friston,John Ashburner,Stefan Kiebel,Thomas Nichols,William Penny,Statistical Parametric Mapping