Statistical parametric mapping the analysis of funtional brain images

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 dev...

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Other Authors: Friston, K. J. (Editor), Ashburner, John (Editor), Kiebel, Stefan (Editor), Nichols, Thomas (Editor)
Format: eBook
Language:English
Published: Amsterdam Elsevier/Academic Press 2007, 2007
Edition:First edition
Subjects:
Online Access:
Collection: Elsevier ScienceDirect eBooks - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • Includes bibliographical references and index