Bayesian fMRI data analysis with sparse spatial basis function priors

Guillaume Flandin; William D Penny

doi:10.1117/12.734494

20 September 2007 Bayesian fMRI data analysis with sparse spatial basis function priors

Guillaume Flandin, William D Penny

Proceedings Volume 6701, Wavelets XII; 67010X (2007) https://doi.org/10.1117/12.734494
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

This article presents a statistical framework to analyse brain functional Magnetic Resonance Imaging (fMRI) data. A particular emphasis is made on spatial correlation, which, contrary to the usual preprocessing step of spatial smoothing, is now part of the probabilistic model. The characterisation of regionally specific effects is done via the General Linear Model (GLM) using Posterior Probability Maps (PPMs). The spatial regularisation is defined over regression coefficients by specifying a spatial prior using Sparse Spatial Basis Functions (SSBFs), such as Wavelets. These are embedded in a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The inversion of the model is done using Variational Bayes. We present results on synthetic data and on data from an event-related fMRI experiment. We conclude that SSBFs allow for spatial variations in signal smoothness, provide an increased sensitivity and are more computationally efficient than previously presented work.

Citation Download Citation

Guillaume Flandin and William D Penny "Bayesian fMRI data analysis with sparse spatial basis function priors", Proc. SPIE 6701, Wavelets XII, 67010X (20 September 2007); https://doi.org/10.1117/12.734494

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