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Bayesian fMRI time series analysis with spatial priors.

William D Penny·Nelson J Trujillo-Barreto·Karl J Friston
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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.

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Bayes TheoremBrainBrain MappingFaceHumansMagnetic Resonance ImagingModels, NeurologicalModels, TheoreticalMultivariate AnalysisNormal DistributionRegression AnalysisReproducibility of ResultsSensitivity and SpecificityVisual PerceptionJournal ArticleResearch Support, Non-U.S. Gov't

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William D Penny, Nelson J Trujillo-Barreto, Karl J Friston. 2005-01-15. Bayesian fMRI time series analysis with spatial priors.. https://doi.org/10.1016/j.neuroimage.2004.08.034

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