Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior
Number of pages
SourceNeuroImage, 50, 1, (2010), pp. 150-161
Article / Letter to editor
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PI Group Predictive Brain
Information Retrieval and Information Systems
Donders Centre for Neuroscience
Subject180 000 Predictive Brain; Biophysics; Data Science
Bayesian logistic regression with a multivariate Laplace prior is introduced as a multivariate approach to the analysis of neuroimaging data. It is shown that, by rewriting the multivariate Laplace distribution as a scale mixture, we can incorporate spatio-temporal constraints which lead to smooth importance maps that facilitate subsequent interpretation. The posterior of interest is computed using an approximate inference method called expectation propagation and becomes feasible due to fast inversion of a sparse precision matrix. We illustrate the performance of the method on an fMRI dataset acquired while subjects were shown handwritten digits. The obtained models perform competitively in terms of predictive performance and give rise to interpretable importance maps. Estimation of the posterior of interest is shown to be feasible even for very large models with thousands of variables.
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