Statistical Parametric Mapping: The Analysis of Functional Brain ImagesWilliam D. Penny, Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, Thomas E. Nichols Elsevier, 28.04.2011 - 656 Seiten 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.
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Inhalt
47 | |
Part 3 General linear models | 99 |
Part 4 Classical inference | 221 |
Part 5 Bayesian inference | 273 |
Part 6 Biophysical models | 337 |
Part 7 Connectivity | 469 |
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Statistical Parametric Mapping: The Analysis of Functional Brain Images William D. Penny,Karl J. Friston,John T. Ashburner Keine Leseprobe verfügbar - 2006 |
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activity algorithm analysis anatomical ANOVA approach approximation backward connections basis functions Bayes factors Bayesian inference bilinear changes chapter coefficients computed conditional constraints contrast convolution correlations corresponding cortex cortical coupling covariance components density described design matrix distribution dynamic causal modelling effective connectivity eigenimage equations error event-related evoked responses example experimental factor fMRI fMRI data frequency Friston KJ Gaussian haemodynamic response Hum Brain Mapp hyperparameters induced input interactions inverse kernels Laplace approximation likelihood linear model M/EEG mean modulation multivariate neural NeuroImage neuronal noise non-linear null hypothesis p-value panel parameter estimates permutation posterior potential prior random field regions regressors ReML scans shown in Figure shows signal simulated smooth spatial spatial normalization specific Statistical Parametric Mapping stimulus synaptic t-statistic temporal threshold time-series tion variables variance variational Bayes vector visual Volterra Volterra kernels voxels Worsley zero