Statistical Parametric Mapping: The Analysis of Functional Brain Images

Statistical Parametric Mapping: The Analysis of Functional Brain Images

4.08 (12 ratings by Goodreads)
Edited by  , Edited by  , Edited by  , Edited by  , Edited by 

List price: US$165.00

Currently unavailable

We can notify you when this item is back in stock

Add to wishlist

AbeBooks may have this title (opens in new window).

Try AbeBooks

Description

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
show more

Product details

  • Paperback | 656 pages
  • English
  • 1493300954
  • 9781493300952

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.
show more

Rating details

12 ratings
4.08 out of 5 stars
5 50% (6)
4 17% (2)
3 25% (3)
2 8% (1)
1 0% (0)
Book ratings by Goodreads
Goodreads is the world's largest site for readers with over 50 million reviews. We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X