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Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework by Anders H. Andersen; Don M. Gash; Malcolm J. Avison is a Medicine article available to read on EtoBox.

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Principal component analysis (PCA) is one of several structure-seeking multivariate statistical techniques, exploratory as well as inferential, that have been proposed recently for the characterization and detection of activation in both PET and fMRI time series data. In particular, PCA is data driven and does not assume that the neural or hemodynamic response reaches some steady state, nor does it involve correlation with any pre-defined or exogenous experimental design template. In this paper, we present a generalized linear systems framework for PCA based on the singular value decomposition (SVD) model for representation of spatiotemporal fMRI data sets. Statistical inference procedures for PCA, including point and interval estimation will be introduced without the constraint of explicit hypotheses about specific task-dependent effects. The principal eigenvectors capture both the spatial and temporal aspects of fMRI data in a progressive fashion; they are inherently matched to unique and uncorrelated features and are ranked in order of the amount of variance explained. PCA also acts as a variation reduction technique, relegating most of the random noise to the trailing component

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Author

Anders H. Andersen; Don M. Gash; Malcolm J. Avison

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0730-725X)

Published

1999

Language

EN

Field

Medicine (Health Sciences)