Bayesian fMRI data analysis with sparse spatial basis function priors

Flandin, Guillaume and Penny, William D (2007) Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage, 34 (3). pp. 1108-1125. ISSN 1053-8119

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Abstract

In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.

Item Type: Article
Uncontrolled Keywords: algorithms,artificial intelligence,bayes theorem,brain,brain mapping,computer simulation,evoked potentials,humans,image enhancement,computer-assisted image interpretation,magnetic resonance imaging,neurological models,statistical models,automated pattern recognition,evaluation studies
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 22 Aug 2017 06:35
Last Modified: 22 Apr 2020 05:23
URI: https://ueaeprints.uea.ac.uk/id/eprint/64617
DOI: 10.1016/j.neuroimage.2006.10.005

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