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E-raamat: Statistical Parametric Mapping: The Analysis of Functional Brain Images

Edited by (Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK), Edited by , Edited by (Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London, UK), Edited by , Edited by
  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Apr-2011
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780080466507
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Apr-2011
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 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.
  • 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

Muu info

Describes the theoretical background behind Statistical Parametric Mapping and provides operational guidelines and technical details on data analysis.
Acknowledgements vii
Part 1 Introduction
A short history of SPM
3(7)
K. Friston
Statistical parametric mapping
10(22)
K. Friston
Modelling brain responses
32(17)
K. Friston
K. Stephan
Part 2 Computational anatomy
Rigid body registration
49(14)
J. Ashburner
K. Friston
Non-linear registration
63(18)
J. Ashburner
K. Friston
Segmentation
81(11)
J. Ashburner
K. Friston
Voxel-Based Morphometry
92(9)
J. Ashburner
K. Friston
Part 3 General linear models
The general linear model
101(25)
S.J. Kiebel
A.P. Holmes
Contrasts and classical inference
126(14)
J. Poline
F. Kherif
C. Pallier
W. Penny
Covariance components
140(8)
D. Glaser
K. Friston
Hierarchical models
148(8)
W. Penny
R. Henson
Random effects analysis
156(10)
W.D. Penny
A.J. Holmes
Analysis of variance
166(12)
W. Penny
R. Henson
Convolution models for fMRI
178(15)
R. Henson
K. Friston
Efficient experimental design for fMRI
193(18)
R. Henson
Hierarchical models for EEG and MEG
211(12)
S. Kiebel
J. Kilner
K. Friston
Part 4 Classical inference
Parametric procedures
223(9)
M. Brett
W. Penny
S. Kiebel
Random Field Theory
232(5)
K. Worsley
Topological inference
237(9)
K. Friston
False Discovery Rate procedures
246(7)
T. Nichols
Non-parametric procedures
253(22)
T. Nichols
A. Holmes
Part 5 Bayesian inference
Empirical Bayes and hierarchical models
275(20)
K. Friston
W. Penny
Posterior probability maps
295(8)
K. Friston
W. Penny
Variational Bayes
303(10)
W. Penny
S. Kiebel
K. Friston
Spatio-temporal models for fMRI
313(10)
W. Penny
G. Flandin
N. Trujillo-Barreto
Spatio-temporal models for EEG
323(16)
W. Penny
N. Trujillo-Barreto
E. Aubert
Part 6 Biophysical models
Forward models for fMRI
339(13)
K. Friston
D. Glaser
Forward models for EEG
352(15)
C. Phillips
J. Mattout
K. Friston
Bayesian inversion of EEG models
367(10)
J. Mattout
C. Phillips
J. Daunizeau
K. Friston
Bayesian inversion for induced responses
377(14)
J. Mattout
C. Phillips
J. Daunizeau
K. Friston
Neuronal models of ensemble dynamics
391(15)
L. Harrison
O. David
K. Friston
Neuronal models of energetics
406(8)
J. Kilner
O. David
K. Friston
Neuronal models of EEG and MEG
414(27)
O. David
L. Harrison
K. Friston
Bayesian inversion of dynamic models
441(13)
K. Friston
W. Penny
Bayesian model selection and averaging
454(17)
W.D. Penny
J. Mattout
N. Trujillo-Barreto
Part 7 Connectivity
Functional integration
471(21)
K. Friston
Functional connectivity: eigenimages and multivariate analyses
492(16)
K. Friston
C. Buchel
Effective Connectivity
508(14)
L. Harrison
K. Stephen
K. Friston
Non-linear coupling and kernels
522(12)
K. Friston
Multivariate autoregressive models
534(7)
W. Penny
L. Harrison
Dynamic Causal Models for fMRI
541(20)
K. Friston
Dynamic Causal Models for EEG
561(16)
K. Friston
S. Kiebel
M. Garrido
O. David
Dynamic Causal Models and Bayesian selection
577(48)
K. E. Stephan
W. D. Penny
Appendices
Appendix 1 Linear models and inference
589(3)
K. Friston
Appendix 2 Dynamical systems
592(11)
K. Friston
Appendix 3 Expectation maximization
603(3)
K. Friston
Appendix 4 Variational Bayes under the Laplace approximation
606(13)
K. Friston
J. Mattout
N. Trujillo-Barreto
J. Ashburner
W. Penny
Appendix 5 Kalman filtering
619(2)
K. Friston
W. Penny
Appendix 6 Random field theory
621(4)
K. Worsley
K. Friston
Index 625