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Bayesian Source Localization with the Multivariate Laplace Prior
Marcel Van Gerven · Botond Cseke · Robert Oostenveld · Tom Heskes

Wed Dec 09 07:00 PM -- 11:59 PM (PST) @

We introduce a novel multivariate Laplace (MVL) distribution as a sparsity promoting prior for Bayesian source localization that allows the specification of constraints between and within sources. We represent the MVL distribution as a scale mixture that induces a coupling between source variances instead of their means. Approximation of the posterior marginals using expectation propagation is shown to be very efficient due to properties of the scale mixture representation. The computational bottleneck amounts to computing the diagonal elements of a sparse matrix inverse. Our approach is illustrated using a mismatch negativity paradigm for which MEG data and a structural MRI have been acquired. We show that spatial coupling leads to sources which are active over larger cortical areas as compared with an uncoupled prior.

Author Information

Marcel Van Gerven (Radboud University)
Botond Cseke (Radboud University Nijmegen)
Robert Oostenveld
Tom Heskes (Radboud University Nijmegen)

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