Multivariate autoregressive modeling of fMRI time series.
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.
Harrison
L
L
Wellcome Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3BG, UK. lharris@fil.ion.ucl.ac.uk
Penny
W D
WD
Friston
K
K
D000465:Algorithms; D001143:Arousal / Q000502:physiology*; D001288:Attention / Q000502:physiology; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008432:Mathematical Computing; D009042:Motivation; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010296:Parietal Lobe / Q000502:physiology; D010364:Pattern Recognition, Visual / Q000502:physiology; D017397:Prefrontal Cortex / Q000502:physiology; D012044:Regression Analysis; D009435:Synaptic Transmission / Q000502:physiology