Statistical Parametric Mapping: The Analysis of Functional Brain Images

Statistical Parametric Mapping: The Analysis of Functional Brain Images

1st Edition - October 6, 2006

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  • Editors: William Penny, Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols
  • Hardcover ISBN: 9780123725608
  • eBook ISBN: 9780080466507

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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.

Key Features

  • An essential reference and companion for users of the SPM software
  • Provides a complete description of the concepts and procedures entailed by the analysis of brain images
  • Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data
  • Stands as a compendium of all the advances in neuroimaging data analysis over the past decade
  • Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes
  • Structured treatment of data analysis issues that links different modalities and models
  • Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible


Scientists actively involved in neuroimaging research and the analysis of data, as well as students at a masters and doctoral level studying cognitive neuroscience and brain imaging.

Table of Contents

  • Acknowledgements

    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


    Linear models and inference

    Dynamical systems

    Expectation maximization

    Variational Bayes under the Laplace approximation

    Kalman filtering

    Random field theory


    Color Plates

Product details

  • No. of pages: 688
  • Language: English
  • Copyright: © Academic Press 2006
  • Published: October 6, 2006
  • Imprint: Academic Press
  • Hardcover ISBN: 9780123725608
  • eBook ISBN: 9780080466507

About the Editors

William Penny

Affiliations and Expertise

Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK

Karl Friston

Affiliations and Expertise

Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK

John Ashburner

Affiliations and Expertise

Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK

Stefan Kiebel

Thomas Nichols

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