Statistical Parametric Mapping: The Analysis of Functional Brain Images

Edited by

  • William Penny, Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
  • Karl Friston, Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
  • John Ashburner, Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
  • Stefan Kiebel
  • Thomas Nichols

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.
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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.


Book information

  • Published: November 2006
  • ISBN: 978-0-12-372560-8

Table of Contents

INTRODUCTION A short history of SPM. Statistical parametric mapping. Modelling brain responses. SECTION 1: COMPUTATIONAL ANATOMYRigid-body Registration. Nonlinear Registration. Segmentation. Voxel-based Morphometry. SECTION 2: GENERAL LINEAR MODELSThe General Linear Model. Contrasts & Classical Inference. Covariance Components. Hierarchical models. Random Effects Analysis. Analysis of variance. Convolution models for fMRI. Efficient Experimental Design for fMRI. Hierarchical models for EEG/MEG. SECTION 3: CLASSICAL INFERENCEParametric procedures for imaging. Random Field Theory & inference. Topological Inference. False discovery rate procedures. Non-parametric procedures. SECTION 4: BAYESIAN INFERENCEEmpirical Bayes & hierarchical models. Posterior probability maps. Variational Bayes. Spatiotemporal models for fMRI. Spatiotemporal models for EEG. SECTION 5: BIOPHYSICAL MODELSForward models for fMRI. Forward models for EEG and MEG. Bayesian inversion of EEG models. Bayesian inversion for induced responses. Neuronal models of ensemble dynamics. Neuronal models of energetics. Neuronal models of EEG and MEG. Bayesian inversion of dynamic models Bayesian model selection & averaging. SECTION 6: CONNECTIVITYFunctional integration. Functional Connectivity. Effective Connectivity. Nonlinear coupling and Kernels. Multivariate autoregressive models. Dynamic Causal Models for fMRI. Dynamic Causal Models for EEG. Dynamic Causal Models & Bayesian selection.APPENDICESLinear models and inference. Dynamical systems. Expectation maximisation. Variational Bayes under the Laplace approximation. Kalman Filtering. Random Field Theory.