Statistical parametric mapping : the analysis of funtional brain images /

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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 underl...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Friston, K. J. (Karl J.) (Editor ), Ashburner, John (Editor ), Kiebel, Stefan (Editor ), Nichols, Thomas (Editor ), Penny, William D. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Elsevier/Academic Press, 2007.
Edición:First edition.
Temas:
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.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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