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
Table of Contents
Part 1: IntroductionChapter 1: A short history of SPMChapter 2: Statistical parametric mappingChapter 3: Modelling brain responsesPart 2: Computational anatomyChapter 4: Rigid Body RegistrationChapter 5: Non-linear RegistrationChapter 6: SegmentationChapter 7: Voxel-Based MorphometryPart 3: General linear modelsChapter 8: The General Linear ModelChapter 9: Contrasts and Classical InferenceChapter 10: Covariance ComponentsChapter 11: Hierarchical ModelsChapter 12: Random Effects AnalysisChapter 13: Analysis of VarianceChapter 14: Convolution Models for fMRIChapter 15: Efficient Experimental Design for fMRIChapter 16: Hierarchical models for EEG and MEGPart 4: Classical inferenceChapter 17: Parametric proceduresChapter 18: Random Field TheoryChapter 19: Topological InferenceChapter 20: False Discovery Rate proceduresChapter 21: Non-parametric proceduresPart 5: Bayesian inferenceChapter 22: Empirical Bayes and hierarchical modelsChapter 23: Posterior probability mapsChapter 24: Variational BayesChapter 25: Spatio-temporal models for fMRIChapter 26: Spatio-temporal models for EEGPart 6: Biophysical modelsChapter 27: Forward models for fMRIChapter 28: Forward models for EEGChapter 29: Bayesian inversion of EEG modelsChapter 30: Bayesian inversion for induced responsesChapter 31: Neuronal models of ensemble dynamicsChapter 32: Neuronal models of energeticsChapter 33: Neuronal models of EEG and MEGChapter 34: Bayesian inversion of dynamic modelsChapter 35: Bayesian model selection and averagingPart 7: ConnectivityChapter 36: Functional integrationChapter 37: Functional connectivity: eigenimages and multivariate analysesChapter 38: Effective ConnectivityChapter 39: Non-linear coupling and kernelsChapter 40: Multivariate autoregressive modelsChapter 41: Dynamic Causal Models for fMRIChapter 42: Dynamic causal models for EEGChapter 43: Dynamic Causal Models and Bayesian selectionAppendicesLinear models and inferenceDynamical systemsExpectation maximizationVariational Bayes under the Laplace approximationKalman filteringRandom field theoryIndexColor Plates
