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| STATISTICAL PARAMETRIC MAPPING: THE ANALYSIS OF FUNCTIONAL BRAIN IMAGES
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Edited By
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
William Penny, Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK
Description
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.
Audience
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.
Contents
INTRODUCTION
A short history of SPM.
Statistical parametric mapping.
Modelling brain responses.
SECTION 1: COMPUTATIONAL
ANATOMY
Rigid-body Registration.
Nonlinear Registration.
Segmentation.
Voxel-based Morphometry.
SECTION 2: GENERAL LINEAR
MODELS
The 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 INFERENCE
Parametric procedures for imaging.
Random Field Theory & inference.
Topological
Inference.
False discovery rate procedures.
Non-parametric procedures.
SECTION 4: BAYESIAN INFERENCE
Empirical
Bayes & hierarchical models.
Posterior probability maps.
Variational Bayes.
Spatiotemporal models for fMRI.
Spatiotemporal
models for EEG.
SECTION 5: BIOPHYSICAL MODELS
Forward 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: CONNECTIVITY
Functional
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.
APPENDICES
Linear models and inference.
Dynamical systems.
Expectation maximisation.
Variational Bayes under the Laplace approximation.
Kalman Filtering.
Random Field Theory.
| Bibliographic details |
Hardbound, 656 pages, publication date: NOV-2006
ISBN-13: 978-0-12-372560-8
ISBN-10: 0-12-372560-7
Imprint: ACADEMIC PRESS
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| Price and Ordering |
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USD 130
GBP 58.80
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095/945
Last update: 3 Oct 2009
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