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Research article summary (published 30 Jul 2003):

Multivariate autoregressive modeling of fMRI time series.

Full Abstract

We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.

 

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Author information

Author/s: Harrison, L (L); Penny, W D (WD); Friston, K (K);

Affiliation: Wellcome Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3BG, UK. lharris(-atsign-)fil.ion.ucl.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: NeuroImage (Neuroimage), published in United States. (Language: eng)

Reference: 2003-Aug; vol 19 (issue 4) : pp 1477-91

Dates: Created 2003/09/01; Completed 2003/10/23; Revised 2006/11/15;

PMID: 12948704, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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