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Harrison, L, Penny, W D 
ORCID: https://orcid.org/0000-0001-9064-1191 and Friston, K
(2003)
Multivariate autoregressive modeling of fMRI time series.
NeuroImage, 19 (4).
pp. 1477-1491.
ISSN 1053-8119
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | algorithms,arousal,attention,brain mapping,cerebral cortex,ocular fixation,humans,linear models,magnetic resonance imaging,mathematical computing,motivation,multivariate analysis,nerve net,neural networks (computer),nonlinear dynamics,parietal lobe,visual pattern recognition,prefrontal cortex,regression analysis,synaptic transmission |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 23 Aug 2017 05:04 |
| Last Modified: | 20 Apr 2023 00:33 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64634 |
| DOI: | 10.1016/S1053-8119(03)00160-5 |
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