Cover Image

Statistical parametric mapping : the analysis of funtional brain images /

Other Authors: Nichols, Thomas,
Format: Book
Language:English
Published: Amsterdam ; Boston : Elsevier/Academic Press, 2007.
Edition:First edition.
Subjects:
Brain mapping > Statistical methods.
Brain > Imaging > Statistical methods.
Brain Mapping > methods.
Image Processing, Computer-Assisted > methods.
Magnetic Resonance Imaging > methods.
Models, Neurological.
Models, Statistical.
Eletroencefalografia.
Online-Publikation.
Online Access:http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=187303
Table of 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.

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