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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

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