NDLI: Conditionally Gaussian Hypermodels for Cerebral Source Localization

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Conditionally Gaussian Hypermodels for Cerebral Source Localization

Content Provider	Society for Industrial and Applied Mathematics (SIAM)
Author	Somersalo, Erkki Hakula, Harri Pursiainen, Sampsa Calvetti, Daniela
Copyright Year	2009
Abstract	Bayesian modeling and analysis of the magnetoencephalography and electroencephalography modalities provide a flexible framework for introducing prior information complementary to the measured data. This prior information is often qualitative in nature, making the translation of the available information into a computational model a challenging task. We propose a generalized gamma family of hyperpriors which allows the impressed currents to be focal and we advocate a fast and efficient iterative algorithm, the iterative alternating sequential algorithm for computing maximum a posteriori (MAP) estimates. Furthermore, we show that for particular choices of the scalar parameters specifying the hyperprior, the algorithm effectively approximates popular regularization strategies such as the minimum current estimate and the minimum support estimate. The connection between priorconditioning and adaptive regularization methods is also pointed out. The posterior densities are explored by means of a Markov chain Monte Carlo strategy suitable for this family of hypermodels. The computed experiments suggest that the known preference of regularization methods for superficial sources over deep sources is a property of the MAP estimators only, and that estimation of the posterior mean in the hierarchical model is better adapted for localizing deep sources.
Starting Page	879
Ending Page	909
Page Count	31
File Format	PDF
DOI	10.1137/080723995
e-ISSN	19364954
Journal	SIAM Journal on Imaging Sciences (SJISBI)
Issue Number	3
Volume Number	2
Language	English
Publisher	Society for Industrial and Applied Mathematics
Publisher Date	2009-09-09
Access Restriction	Subscribed
Subject Keyword	Biomedical imaging and signal processing Monte Carlo methods finite element methods Markov chain Monte Carlo Finite element methods electroencephalography/magnetoencephalography
Content Type	Text
Resource Type	Article

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