Variational Bayesian inference for fMRI time series.

Will Penny; Stefan Kiebel; Karl Friston

doi:10.1016/s1053-8119(03)00071-5

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PubMed · 12880802

Variational Bayesian inference for fMRI time series.

Will Penny·Stefan Kiebel·Karl 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 with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an event-related fMRI experiment.

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Algorithms Bayes Theorem Brain Mapping Face Factor Analysis, Statistical Humans Linear Models Magnetic Resonance Imaging Models, Statistical Time Factors Journal Article Research Support, Non-U.S. Gov't

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Will Penny, Stefan Kiebel, Karl Friston. 2003. Variational Bayesian inference for fMRI time series.. https://doi.org/10.1016/s1053-8119(03)00071-5

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