Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources

By Samuel J. Gershman, David M. Blei, Kenneth A. Norman, Per B. Sederberg. 2014

14 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Pediatrics, Perinatology and Child Health

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Jon D. McAuliffe

Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “…(Wool-rich et al. 2004), spatial models (Sato et al. 2004; Zumer et al. 2007; Kiebel et al. 2008; Wipf and Nagarajan 2009; Lashkari et al. 2012; Nathoo et al. 2014), brain-computer interfaces (Sykacek, Roberts, and Stokes 2004), and factor models (Manning et al. 2014; Gershman et al. 2014).”