Sparse Spatio-temporal Inference of Electromagnetic Brain Sources

Carsten Stahlhut, Hagai T. Attias, David Wipf, Lars K. Hansen, Srikantan S. Nagarajan

AbstractThe electromagnetic brain activity measured via MEG (or EEG) can be interpreted as arising from a collection of current dipoles or sources located throughout the cortex. Because the number of candidate locations for these sources is much larger than the number of sensors, source reconstruction involves solving an inverse problem that is severely underdetermined. Bayesian graphical models provide a powerful means of incorporating prior assumptions that narrow the solution space and lead to tractable posterior distributions over the unknown sources given the observed data. In particular, this paper develops a hierarchical, spatio-temporal Bayesian model that accommodates the principled computation of sparse spatial and smooth temporal M/EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms. The underlying methodology relies on the notion of automatic relevance determination (ARD) to express the unknown sources via a small collection of spatio-temporal basis functions. Experiments with several data sets provide evidence that the proposed model leads to improved source estimates. The underlying methodology is also well-suited for estimation problems that arise from other brain imaging modalities such as functional or diffusion weighted MRI.
Keywordsunderdetermined inverse problems, M/EEG source reconstruction, probabilistic graphical models, variational Bayes, ARD
TypeConference paper [With referee]
ConferenceLecture Notes in Computer Science: International Workshop on Machine Learning in Medical Imaging (MLMI)
EditorsFei Wang, Pingkun Yan and Kenji Suzuki and Dinggang Shen
Year2010    Month September    Vol. 6357    pp. 157-164
PublisherSpringer Berlin / Heidelberg
Publication link
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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