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Proceedings Paper

Bayesian fMRI data analysis with sparse spatial basis function priors
Author(s): Guillaume Flandin; William D Penny
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Paper 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.

Paper Details

Date Published: 20 September 2007
PDF: 9 pages
Proc. SPIE 6701, Wavelets XII, 67010X (20 September 2007); doi: 10.1117/12.734494
Show Author Affiliations
Guillaume Flandin, NeuroSpin, CEA (France)
Institute d'Imagerie Neurofonctionnelle, IFR (France)
Wellcome Trust Ctr. for Neuroimaging (United Kingdom)
William D Penny, Wellcome Trust Ctr. for Neuroimaging (United Kingdom)

Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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