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Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources

Author(s): Gershman, Samuel J.; Blei, David M.; Norman, Kenneth A.; Sederberg, Per B.

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Abstract: This paper extends earlier work on spatial modeling of fMRI data to the temporal domain, providing a framework for analyzing high temporal resolution brain imaging modalities such as electroencapholography (EEG). The central idea is to decompose brain imaging data into a covariate-dependent superposition of functions defined over continuous time and space (what we refer to as topographic latent sources). The continuous formulation allows us to parametrically model spatiotemporally localized activations. To make group-level inferences, we elaborate the model hierarchically by sharing sources across subjects. We describe a variational algorithm for parameter estimation that scales efficiently to large data sets. Applied to three EEG data sets, we find that the model produces good predictive performance and reproduces a number of classic findings. Our results suggest that topographic latent sources serve as an effective hypothesis space for interpreting spatiotemporal brain imaging data.
Publication Date: Sep-2014
Electronic Publication Date: Sep-2014
Citation: Gershman, Samuel J, Blei, David M, Norman, Kenneth A, Sederberg, Per B. (2014). Decomposing spatiotemporal brain patterns into topographic latent sources. NeuroImage, 98 (91 - 102. doi:10.1016/j.neuroimage.2014.04.055
DOI: doi:10.1016/j.neuroimage.2014.04.055
ISSN: 1053-8119
Pages: 91 - 102
Type of Material: Journal Article
Journal/Proceeding Title: NeuroImage
Version: Author's manuscript

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