Statistical parametric mapping :: the analysis of funtional brain images /
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 underl...
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Weitere Verfasser: | , , , , |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Amsterdam ; Boston :
Elsevier/Academic Press,
2007.
|
Ausgabe: | First edition. |
Schlagworte: | |
Online-Zugang: | Volltext Volltext |
Zusammenfassung: | 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. |
Beschreibung: | 1 online resource (vii, 647 pages) : illustrations (some color) |
Bibliographie: | Includes bibliographical references and index. |
ISBN: | 9780123725608 0123725607 9780080466507 0080466508 9786610728992 6610728992 |
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250 | |a First edition. | ||
260 | |a Amsterdam ; |a Boston : |b Elsevier/Academic Press, |c 2007. | ||
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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. | ||
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. | |
504 | |a Includes bibliographical references and index. | ||
588 | 0 | |a Print version record. | |
650 | 0 | |a Brain mapping |x Statistical methods. | |
650 | 0 | |a Brain |x Imaging |x Statistical methods. | |
650 | 1 | 2 | |a Brain Mapping |x methods |
650 | 1 | 2 | |a Image Processing, Computer-Assisted |x methods |
650 | 2 | 2 | |a Magnetic Resonance Imaging |x methods |
650 | 2 | 2 | |a Models, Neurological |
650 | 2 | 2 | |a Models, Statistical |
650 | 6 | |a Cartographie cérébrale |x Méthodes statistiques. | |
650 | 6 | |a Cerveau |x Imagerie |x Méthodes statistiques. | |
650 | 7 | |a Online-Ressource |2 gnd | |
650 | 7 | |a Eletroencefalografia. |2 larpcal | |
650 | 7 | |a Hjärna. |2 sao | |
700 | 1 | |a Friston, K. J. |q (Karl J.), |e editor. |1 https://id.oclc.org/worldcat/entity/E39PBJdGQV9yP7HCMpd89JfDv3 | |
700 | 1 | |a Ashburner, John, |e editor. | |
700 | 1 | |a Kiebel, Stefan, |e editor. | |
700 | 1 | |a Nichols, Thomas, |e editor. | |
700 | 1 | |a Penny, William D., |e editor. | |
758 | |i has work: |a Statistical parametric mapping (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGYxmyHx8PpX64X4pqHFqP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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callnumber-label | RC386 |
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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. |
ctrlnum | (OCoLC)162574061 |
dewey-full | 616.8/04754 611.810222 |
dewey-hundreds | 600 - Technology (Applied sciences) |
dewey-ones | 616 - Diseases 611 - Human anatomy, cytology, histology |
dewey-raw | 616.8/04754 611.810222 |
dewey-search | 616.8/04754 611.810222 |
dewey-sort | 3616.8 44754 |
dewey-tens | 610 - Medicine and health |
discipline | Medizin |
edition | First edition. |
format | Electronic eBook |
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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. 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id | ZDB-4-EBA-ocn162574061 |
illustrated | Illustrated |
indexdate | 2024-11-27T13:16:06Z |
institution | BVB |
isbn | 9780123725608 0123725607 9780080466507 0080466508 9786610728992 6610728992 |
language | English |
oclc_num | 162574061 |
open_access_boolean | |
owner | MAIN DE-863 DE-BY-FWS |
owner_facet | MAIN DE-863 DE-BY-FWS |
physical | 1 online resource (vii, 647 pages) : illustrations (some color) |
psigel | ZDB-4-EBA |
publishDate | 2007 |
publishDateSearch | 2007 |
publishDateSort | 2007 |
publisher | Elsevier/Academic Press, |
record_format | marc |
spelling | Statistical parametric mapping : the analysis of funtional brain images / edited by Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny. First edition. Amsterdam ; Boston : Elsevier/Academic Press, 2007. 1 online resource (vii, 647 pages) : illustrations (some color) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file 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. 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. Includes bibliographical references and index. Print version record. Brain mapping Statistical methods. Brain Imaging Statistical methods. Brain Mapping methods Image Processing, Computer-Assisted methods Magnetic Resonance Imaging methods Models, Neurological Models, Statistical Cartographie cérébrale Méthodes statistiques. Cerveau Imagerie Méthodes statistiques. Online-Ressource gnd Eletroencefalografia. larpcal Hjärna. sao Friston, K. J. (Karl J.), editor. https://id.oclc.org/worldcat/entity/E39PBJdGQV9yP7HCMpd89JfDv3 Ashburner, John, editor. Kiebel, Stefan, editor. Nichols, Thomas, editor. Penny, William D., editor. has work: Statistical parametric mapping (Text) https://id.oclc.org/worldcat/entity/E39PCGYxmyHx8PpX64X4pqHFqP https://id.oclc.org/worldcat/ontology/hasWork Print version: Statistical parametric mapping. 1st ed. Amsterdam ; Boston : Elsevier/Academic Press, 2007 9780123725608 0123725607 (DLC) 2006933665 (OCoLC)104803228 FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780123725608 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=187303 Volltext |
spellingShingle | Statistical parametric mapping : the analysis of funtional brain images / 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. Brain mapping Statistical methods. Brain Imaging Statistical methods. Brain Mapping methods Image Processing, Computer-Assisted methods Magnetic Resonance Imaging methods Models, Neurological Models, Statistical Cartographie cérébrale Méthodes statistiques. Cerveau Imagerie Méthodes statistiques. Online-Ressource gnd Eletroencefalografia. larpcal Hjärna. sao |
title | Statistical parametric mapping : the analysis of funtional brain images / |
title_auth | Statistical parametric mapping : the analysis of funtional brain images / |
title_exact_search | Statistical parametric mapping : the analysis of funtional brain images / |
title_full | Statistical parametric mapping : the analysis of funtional brain images / edited by Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny. |
title_fullStr | Statistical parametric mapping : the analysis of funtional brain images / edited by Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny. |
title_full_unstemmed | Statistical parametric mapping : the analysis of funtional brain images / edited by Karl Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny. |
title_short | Statistical parametric mapping : |
title_sort | statistical parametric mapping the analysis of funtional brain images |
title_sub | the analysis of funtional brain images / |
topic | Brain mapping Statistical methods. Brain Imaging Statistical methods. Brain Mapping methods Image Processing, Computer-Assisted methods Magnetic Resonance Imaging methods Models, Neurological Models, Statistical Cartographie cérébrale Méthodes statistiques. Cerveau Imagerie Méthodes statistiques. Online-Ressource gnd Eletroencefalografia. larpcal Hjärna. sao |
topic_facet | Brain mapping Statistical methods. Brain Imaging Statistical methods. Brain Mapping methods Image Processing, Computer-Assisted methods Magnetic Resonance Imaging methods Models, Neurological Models, Statistical Cartographie cérébrale Méthodes statistiques. Cerveau Imagerie Méthodes statistiques. Online-Ressource Eletroencefalografia. Hjärna. |
url | https://www.sciencedirect.com/science/book/9780123725608 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=187303 |
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