Friston K., Ashburne J., Kiebel S., Nichols T. (eds.) Statistical Parametric Mapping: the Analysis of Functional Brain Images

N.-Y.: Academic Press, 2006. — 664 p. Коллективная монография, посвящённая проблеме визуализации функций мозга на основе современных технических средств и статистических методов. Состоит из разделов:
Введение, Вычислительная анатомия, Обобщённая линейная модель, Классические методы статистического оценивания, Байесовы оценки, Биофизические модели, Связность и приложений. Для разработчиков медицинской и биологической аппаратуры и методов исследования. Contents
Introduction
A short history of SPM.
Statistical parametric mapping.
Modelling brain responses.
Computational Anatomy
Rigid-body Registration.
Nonlinear Registration.
Segmentation.
Voxel-based Morphometry.
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.
Classical Inference
Parametric procedures for imaging.
Random Field Theory & inference.
Topological Inference.
False discovery rate procedures.
Non-parametric procedures.
Bayesian Inference
Empirical Bayes & hierarchical models.
Posterior probability maps.
Variational Bayes.
Spatiotemporal models for fMRI.
Spatiotemporal models for EEG.
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.
Connectivity
Functional integration.
Functional Connectivity.
Effective Connectivity.
Nonlinear coupling and Keels.
Multivariate autoregressive models.
Dynamic Causal Models for fMRI.
Dynamic Causal Models for EEG.
Dynamic Causal Models & Bayesian selection.
Appendices