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• W1991237518 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." @default.
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• W1991237518 date "2005-01-01" @default.
• W1991237518 modified "2023-10-06" @default.
• W1991237518 title "Bayesian fMRI time series analysis with spatial priors" @default.
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• W1991237518 doi "https://doi.org/10.1016/j.neuroimage.2004.08.034" @default.
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