東華大學圖書館 |

Statistical parametric mapping = the analysis of funtional brain images /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical parametric mapping/ edited by Karl Friston ... [et al.].{me_controlnum}
其他題名:	the analysis of funtional brain images /
其他作者:	Friston, K. J.
出版者:	Amsterdam ;Elsevier/Academic Press, : 2007.,
面頁冊數:	vii, 647 p. :ill. (some col.) ;29 cm.
內容註:	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.
標題:	Brain mapping - Statistical methods. -
電子資源:	http://www.sciencedirect.com/science/book/9780123725608An electronic book accessible through the World Wide Web; click for information
ISBN:	9780123725608

Statistical parametric mapping = the analysis of funtional brain images /
Statistical parametric mappingthe analysis of funtional brain images /[electronic resource] :edited by Karl Friston ... [et al.].{me_controlnum} - 1st ed. - Amsterdam ;Elsevier/Academic Press,2007. - vii, 647 p. :ill. (some col.) ;29 cm.

Includes bibliographical references and index.

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.

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.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2007.

Mode of access: World Wide Web.

ISBN: 9780123725608

Source: 116398:116496Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

1362682
Brain mapping
--Statistical methods.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: RC386.6.B7 / S73 2007eb

Dewey Class. No.: 616.8/04754

National Library of Medicine Call No.: 2007 C-278

Statistical parametric mapping = the analysis of funtional brain images /
LDR:05942cmm 2200373Ia 4500 001 1033166
003 OCoLC
005 20110614113944.0
006 m d
007 cr cn|||||||||
008 120420s2007 ne a ob 001 0 eng d
020 $a 9780123725608
020 $a 0123725607
029 1 $a NZ1 $b 11778501
035 $a (OCoLC)162574061
035 $a ocn162574061
037 $a 116398:116496 $b Elsevier Science & Technology $n http://www.sciencedirect.com
040 $a OPELS $c OPELS $d OCLCG
049 $a TEFA
050 1 4 $a RC386.6.B7 $b S73 2007eb
060 1 4 $a 2007 C-278
060 1 4 $a WL 26.5 $b S797 2007
082 0 4 $a 616.8/04754 $2 22
082 0 4 $a 611.810222 $2 22
245 0 0 $a Statistical parametric mapping $h [electronic resource] : $b the analysis of funtional brain images / $c edited by Karl Friston ... [et al.].{me_controlnum}
250 $a 1st ed.
260 $a Amsterdam ; $a Boston : $b Elsevier/Academic Press, $c 2007.
300 $a vii, 647 p. : $b ill. (some col.) ; $c 29 cm.
504 $a Includes bibliographical references and index.
505 0 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Amsterdam : $c Elsevier Science & Technology, $d 2007. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Title from title screen (viewed on Aug. 2, 2007). $n Access may be restricted to users at subscribing institutions.
650 0 $a Brain mapping $x Statistical methods. $3 1362682
650 0 $a Brain $x Imaging $x Statistical methods. $3 1362683
650 1 2 $a Brain Mapping $x methods. $3 827745
650 1 2 $a Image Processing, Computer-Assisted $x methods. $3 816671
650 2 2 $a Magnetic Resonance Imaging $x methods. $3 841806
650 2 2 $a Models, Neurological. $3 610860
650 2 2 $a Models, Statistical. $3 716279
655 7 $a Electronic books. $2 lcsh $3 542853
700 1 $a Friston, K. J. $q (Karl J.) $3 1362681
710 2 $a ScienceDirect (Online service) $3 848416
776 1 $c Original $z 9780123725608 $z 0123725607 $w (DLC) 2006933665 $w (OCoLC)104803228
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780123725608 $z An electronic book accessible through the World Wide Web; click for information
994 $a C0 $b TEF