Bayesian fMRI time series analysis with spatial priors

Penny, William D, Trujillo-Barreto, Nelson J and Friston, Karl J (2005) Bayesian fMRI time series analysis with spatial priors. NeuroImage, 24 (2). pp. 350-362. ISSN 1053-8119

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Abstract

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.

Item Type: Article
Uncontrolled Keywords: bayes theorem,brain,brain mapping,face,humans,magnetic resonance imaging,neurological models,theoretical models,multivariate analysis,normal distribution,regression analysis,reproducibility of results,sensitivity and specificity,visual perception
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 22 Aug 2017 06:35
Last Modified: 25 Jul 2019 00:08
URI: https://ueaeprints.uea.ac.uk/id/eprint/64620
DOI: 10.1016/j.neuroimage.2004.08.034

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